TMI_GKP_demo.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "collapsed_sections": [
        "cEIxcQbUugas"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "premium"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Environment Setup"
      ],
      "metadata": {
        "id": "3sHpfiACsqBd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To interact with this notebook, you should first clone our [GitHub repo](https://github.com/choH/lottery_regulated_grouped_kernel_pruning) into the folder you setup for syncing with Google Drive (or simply upload our repo's folder to your Google Drive). By default, this syncing folder should be `My Drive` — if this is not the case, you need to change the `cd` line.\n",
        "\n",
        "(and yes, we are using an ancient version of `sk-learn` as we utilize [`ndanielsen/Same-Size-K-Means`](https://github.com/ndanielsen/Same-Size-K-Means) for equal-size $k$-means.)"
      ],
      "metadata": {
        "id": "c6kwF3EoiKL4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "\n",
        "!pip install numpy==1.19\n",
        "!pip install scikit-learn==0.20.1\n",
        "\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "zAQeBJTd8gD8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XFu2uubeKYvM",
        "outputId": "e5b412a7-0046-4308-a7b0-b5848fc98449"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Demo: Complete Task Walkthrough on ResNet-20"
      ],
      "metadata": {
        "id": "Irj4ndPlsvp5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To demo the usage of this codebase, let's do a complete walkthrough on a CIFAR ResNet-20. We'd first initialize and *save* an untrained model: `baseline/resnet20_cifar10_init.pt`."
      ],
      "metadata": {
        "id": "FRHAXs2dpql8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import models.resnet\n",
        "\n",
        "init_model_save_dir = '/content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet20_cifar10_init.pt'\n",
        "model = models.resnet.resnet20()\n",
        "print(model)\n",
        "\n",
        "torch.save(model, init_model_save_dir)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4H-H7BYg-8-M",
        "outputId": "4c8ded40-4775-4c31-88a2-b2de14036b5d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CifarResNet : Depth : 20 , Layers for each block : 3\n",
            "CifarResNet(\n",
            "  (conv_1_3x3): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "  (bn_1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "  (stage_1): Sequential(\n",
            "    (0): ResNetBasicblock(\n",
            "      (conv_a): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_a): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv_b): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_b): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (1): ResNetBasicblock(\n",
            "      (conv_a): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_a): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv_b): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_b): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (2): ResNetBasicblock(\n",
            "      (conv_a): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_a): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv_b): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_b): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (stage_2): Sequential(\n",
            "    (0): ResNetBasicblock(\n",
            "      (conv_a): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn_a): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv_b): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_b): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (downsample): DownsampleA(\n",
            "        (avg): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
            "      )\n",
            "    )\n",
            "    (1): ResNetBasicblock(\n",
            "      (conv_a): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_a): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv_b): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_b): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (2): ResNetBasicblock(\n",
            "      (conv_a): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_a): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv_b): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_b): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (stage_3): Sequential(\n",
            "    (0): ResNetBasicblock(\n",
            "      (conv_a): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn_a): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv_b): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_b): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (downsample): DownsampleA(\n",
            "        (avg): AvgPool2d(kernel_size=1, stride=2, padding=0)\n",
            "      )\n",
            "    )\n",
            "    (1): ResNetBasicblock(\n",
            "      (conv_a): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_a): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv_b): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_b): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "    (2): ResNetBasicblock(\n",
            "      (conv_a): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_a): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      (conv_b): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "      (bn_b): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    )\n",
            "  )\n",
            "  (avgpool): AvgPool2d(kernel_size=8, stride=8, padding=0)\n",
            "  (classifier): Linear(in_features=64, out_features=10, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With the untrained model saved, we'd supply it to the `main.py` with a setting file of `resnet_train.json` and `--task` set to `train`. This combination will train the model at `--model_dir` according to the setting specified in `resnet_train.json`; which has the correct training setting used for reported experiments in our paper, yet saving its snapshots along the way."
      ],
      "metadata": {
        "id": "S_HTixvAp5pQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python main.py \\\n",
        "--exp_desc resnet20_cifar10_baseline_train \\\n",
        "--setting_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/settings/resnet_train.json \\\n",
        "--model_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet20_cifar10_init.pt \\\n",
        "--output_folder_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_baseline_train \\\n",
        "--baseline True \\\n",
        "--task train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KBNg0RE8oPu3",
        "outputId": "4f38ef14-7572-4fd1-90fc-45e02218a551"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-11-24 00:17:44,884 | INFO : Experiment resnet20_cifar10_baseline_train starts at 2022-11-24T00:17:44.880115.\n",
            "2022-11-24 00:17:44,885 | INFO : Parsed setting file from /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/settings/resnet_train.json...\n",
            "2022-11-24 00:17:44,912 | INFO : ==> Preparing dataset: cifar10\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "2022-11-24 00:17:47,218 | INFO : ==> Building baseline model: /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet20_cifar10_init.pt (dataset: cifar10)\n",
            "2022-11-24 00:17:48,115 | INFO : Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
            "2022-11-24 00:17:48,115 | INFO : NumExpr defaulting to 8 threads.\n",
            "2022-11-24 00:17:49,978 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet20_cifar10_init.pt loaded (<class 'models.resnet.CifarResNet'>)\n",
            "2022-11-24 00:17:53,514 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet20_cifar10_init.pt <class 'models.resnet.CifarResNet'> has a baseline of 10.0.\n",
            "2022-11-24 00:17:53,514 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet20_cifar10_init.pt <class 'models.resnet.CifarResNet'> is already grouped or task is test or train (task: train False).\n",
            "2022-11-24 00:17:53,515 | INFO : ==> Starting task: train for 300 epochs; multi_prune_flag: False)\n",
            "2022-11-24 00:17:54,922 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet20_cifar10_init.pt has a baseline of 10.0.\n",
            "2022-11-24 00:18:08,856 | INFO : Epoch #0\n",
            "2022-11-24 00:18:08,857 | INFO : \t train_acc: 39.32; train_loss: 1.131; test_loss: 137.587; lr: [0.1]\n",
            "2022-11-24 00:18:08,857 | INFO : \t test_acc: 50.16 (best: 50.16 from #0)\n",
            "2022-11-24 00:18:22,691 | INFO : Epoch #1\n",
            "2022-11-24 00:18:22,692 | INFO : \t train_acc: 59.084; train_loss: 0.927; test_loss: 122.788; lr: [0.1]\n",
            "2022-11-24 00:18:22,692 | INFO : \t test_acc: 56.9 (best: 56.9 from #1)\n",
            "2022-11-24 00:18:36,669 | INFO : Epoch #2\n",
            "2022-11-24 00:18:36,669 | INFO : \t train_acc: 67.892; train_loss: 0.759; test_loss: 131.315; lr: [0.1]\n",
            "2022-11-24 00:18:36,670 | INFO : \t test_acc: 60.28 (best: 60.28 from #2)\n",
            "2022-11-24 00:18:50,764 | INFO : Epoch #3\n",
            "2022-11-24 00:18:50,765 | INFO : \t train_acc: 71.652; train_loss: 1.048; test_loss: 106.840; lr: [0.1]\n",
            "2022-11-24 00:18:50,765 | INFO : \t test_acc: 66.81 (best: 66.81 from #3)\n",
            "2022-11-24 00:19:04,257 | INFO : Epoch #4\n",
            "2022-11-24 00:19:04,257 | INFO : \t train_acc: 73.524; train_loss: 1.025; test_loss: 87.377; lr: [0.1]\n",
            "2022-11-24 00:19:04,258 | INFO : \t test_acc: 71.04 (best: 71.04 from #4)\n",
            "2022-11-24 00:19:17,687 | INFO : Epoch #5\n",
            "2022-11-24 00:19:17,688 | INFO : \t train_acc: 74.73; train_loss: 1.282; test_loss: 89.259; lr: [0.1]\n",
            "2022-11-24 00:19:17,688 | INFO : \t test_acc: 71.44 (best: 71.44 from #5)\n",
            "2022-11-24 00:19:31,104 | INFO : Epoch #6\n",
            "2022-11-24 00:19:31,105 | INFO : \t train_acc: 75.656; train_loss: 0.531; test_loss: 103.648; lr: [0.1]\n",
            "2022-11-24 00:19:31,105 | INFO : \t test_acc: 68.86 (best: 71.44 from #5)\n",
            "2022-11-24 00:19:44,557 | INFO : Epoch #7\n",
            "2022-11-24 00:19:44,557 | INFO : \t train_acc: 76.242; train_loss: 0.567; test_loss: 69.144; lr: [0.1]\n",
            "2022-11-24 00:19:44,557 | INFO : \t test_acc: 76.06 (best: 76.06 from #7)\n",
            "2022-11-24 00:19:58,316 | INFO : Epoch #8\n",
            "2022-11-24 00:19:58,317 | INFO : \t train_acc: 76.884; train_loss: 0.920; test_loss: 78.380; lr: [0.1]\n",
            "2022-11-24 00:19:58,317 | INFO : \t test_acc: 74.54 (best: 76.06 from #7)\n",
            "2022-11-24 00:20:11,802 | INFO : Epoch #9\n",
            "2022-11-24 00:20:11,803 | INFO : \t train_acc: 76.734; train_loss: 0.711; test_loss: 78.746; lr: [0.1]\n",
            "2022-11-24 00:20:11,803 | INFO : \t test_acc: 73.22 (best: 76.06 from #7)\n",
            "2022-11-24 00:20:25,549 | INFO : Epoch #10\n",
            "2022-11-24 00:20:25,550 | INFO : \t train_acc: 77.338; train_loss: 1.410; test_loss: 113.548; lr: [0.1]\n",
            "2022-11-24 00:20:25,550 | INFO : \t test_acc: 63.17 (best: 76.06 from #7)\n",
            "2022-11-24 00:20:38,902 | INFO : Epoch #11\n",
            "2022-11-24 00:20:38,903 | INFO : \t train_acc: 77.732; train_loss: 0.569; test_loss: 85.347; lr: [0.1]\n",
            "2022-11-24 00:20:38,903 | INFO : \t test_acc: 72.33 (best: 76.06 from #7)\n",
            "2022-11-24 00:20:53,265 | INFO : Epoch #12\n",
            "2022-11-24 00:20:53,266 | INFO : \t train_acc: 77.804; train_loss: 0.861; test_loss: 78.972; lr: [0.1]\n",
            "2022-11-24 00:20:53,266 | INFO : \t test_acc: 73.71 (best: 76.06 from #7)\n",
            "2022-11-24 00:21:06,921 | INFO : Epoch #13\n",
            "2022-11-24 00:21:06,921 | INFO : \t train_acc: 78.23; train_loss: 1.068; test_loss: 98.891; lr: [0.1]\n",
            "2022-11-24 00:21:06,921 | INFO : \t test_acc: 68.96 (best: 76.06 from #7)\n",
            "2022-11-24 00:21:20,422 | INFO : Epoch #14\n",
            "2022-11-24 00:21:20,422 | INFO : \t train_acc: 78.092; train_loss: 0.893; test_loss: 94.071; lr: [0.1]\n",
            "2022-11-24 00:21:20,423 | INFO : \t test_acc: 70.07 (best: 76.06 from #7)\n",
            "2022-11-24 00:21:34,169 | INFO : Epoch #15\n",
            "2022-11-24 00:21:34,170 | INFO : \t train_acc: 78.408; train_loss: 0.762; test_loss: 76.119; lr: [0.1]\n",
            "2022-11-24 00:21:34,170 | INFO : \t test_acc: 75.06 (best: 76.06 from #7)\n",
            "2022-11-24 00:21:47,750 | INFO : Epoch #16\n",
            "2022-11-24 00:21:47,751 | INFO : \t train_acc: 78.822; train_loss: 0.789; test_loss: 104.248; lr: [0.1]\n",
            "2022-11-24 00:21:47,751 | INFO : \t test_acc: 68.46 (best: 76.06 from #7)\n",
            "2022-11-24 00:22:01,343 | INFO : Epoch #17\n",
            "2022-11-24 00:22:01,343 | INFO : \t train_acc: 78.816; train_loss: 0.398; test_loss: 71.892; lr: [0.1]\n",
            "2022-11-24 00:22:01,344 | INFO : \t test_acc: 75.51 (best: 76.06 from #7)\n",
            "2022-11-24 00:22:14,908 | INFO : Epoch #18\n",
            "2022-11-24 00:22:14,909 | INFO : \t train_acc: 79.002; train_loss: 0.470; test_loss: 66.654; lr: [0.1]\n",
            "2022-11-24 00:22:14,909 | INFO : \t test_acc: 77.39 (best: 77.39 from #18)\n",
            "2022-11-24 00:22:28,910 | INFO : Epoch #19\n",
            "2022-11-24 00:22:28,910 | INFO : \t train_acc: 78.96; train_loss: 0.455; test_loss: 110.003; lr: [0.1]\n",
            "2022-11-24 00:22:28,910 | INFO : \t test_acc: 67.65 (best: 77.39 from #18)\n",
            "2022-11-24 00:22:42,940 | INFO : Epoch #20\n",
            "2022-11-24 00:22:42,940 | INFO : \t train_acc: 79.196; train_loss: 0.519; test_loss: 64.128; lr: [0.1]\n",
            "2022-11-24 00:22:42,940 | INFO : \t test_acc: 78.28 (best: 78.28 from #20)\n",
            "2022-11-24 00:22:56,503 | INFO : Epoch #21\n",
            "2022-11-24 00:22:56,503 | INFO : \t train_acc: 79.132; train_loss: 0.638; test_loss: 61.717; lr: [0.1]\n",
            "2022-11-24 00:22:56,503 | INFO : \t test_acc: 78.86 (best: 78.86 from #21)\n",
            "2022-11-24 00:23:10,278 | INFO : Epoch #22\n",
            "2022-11-24 00:23:10,278 | INFO : \t train_acc: 79.026; train_loss: 0.364; test_loss: 70.379; lr: [0.1]\n",
            "2022-11-24 00:23:10,279 | INFO : \t test_acc: 76.53 (best: 78.86 from #21)\n",
            "2022-11-24 00:23:24,013 | INFO : Epoch #23\n",
            "2022-11-24 00:23:24,014 | INFO : \t train_acc: 79.494; train_loss: 0.901; test_loss: 95.709; lr: [0.1]\n",
            "2022-11-24 00:23:24,014 | INFO : \t test_acc: 70.7 (best: 78.86 from #21)\n",
            "2022-11-24 00:23:37,525 | INFO : Epoch #24\n",
            "2022-11-24 00:23:37,526 | INFO : \t train_acc: 79.302; train_loss: 0.769; test_loss: 82.156; lr: [0.1]\n",
            "2022-11-24 00:23:37,526 | INFO : \t test_acc: 73.27 (best: 78.86 from #21)\n",
            "2022-11-24 00:23:51,005 | INFO : Epoch #25\n",
            "2022-11-24 00:23:51,005 | INFO : \t train_acc: 79.388; train_loss: 0.826; test_loss: 65.024; lr: [0.1]\n",
            "2022-11-24 00:23:51,006 | INFO : \t test_acc: 77.79 (best: 78.86 from #21)\n",
            "2022-11-24 00:24:04,570 | INFO : Epoch #26\n",
            "2022-11-24 00:24:04,571 | INFO : \t train_acc: 79.442; train_loss: 0.254; test_loss: 76.443; lr: [0.1]\n",
            "2022-11-24 00:24:04,571 | INFO : \t test_acc: 74.11 (best: 78.86 from #21)\n",
            "2022-11-24 00:24:17,989 | INFO : Epoch #27\n",
            "2022-11-24 00:24:17,990 | INFO : \t train_acc: 79.544; train_loss: 0.916; test_loss: 79.490; lr: [0.1]\n",
            "2022-11-24 00:24:17,990 | INFO : \t test_acc: 74.5 (best: 78.86 from #21)\n",
            "2022-11-24 00:24:32,043 | INFO : Epoch #28\n",
            "2022-11-24 00:24:32,043 | INFO : \t train_acc: 79.35; train_loss: 0.835; test_loss: 87.445; lr: [0.1]\n",
            "2022-11-24 00:24:32,043 | INFO : \t test_acc: 70.65 (best: 78.86 from #21)\n",
            "2022-11-24 00:24:45,777 | INFO : Epoch #29\n",
            "2022-11-24 00:24:45,777 | INFO : \t train_acc: 79.306; train_loss: 0.599; test_loss: 94.578; lr: [0.1]\n",
            "2022-11-24 00:24:45,778 | INFO : \t test_acc: 68.64 (best: 78.86 from #21)\n",
            "2022-11-24 00:24:59,246 | INFO : Epoch #30\n",
            "2022-11-24 00:24:59,247 | INFO : \t train_acc: 79.902; train_loss: 0.665; test_loss: 63.748; lr: [0.1]\n",
            "2022-11-24 00:24:59,247 | INFO : \t test_acc: 78.07 (best: 78.86 from #21)\n",
            "2022-11-24 00:25:12,644 | INFO : Epoch #31\n",
            "2022-11-24 00:25:12,644 | INFO : \t train_acc: 79.566; train_loss: 0.547; test_loss: 71.530; lr: [0.1]\n",
            "2022-11-24 00:25:12,645 | INFO : \t test_acc: 75.89 (best: 78.86 from #21)\n",
            "2022-11-24 00:25:25,839 | INFO : Epoch #32\n",
            "2022-11-24 00:25:25,839 | INFO : \t train_acc: 79.854; train_loss: 0.452; test_loss: 104.197; lr: [0.1]\n",
            "2022-11-24 00:25:25,840 | INFO : \t test_acc: 67.47 (best: 78.86 from #21)\n",
            "2022-11-24 00:25:39,107 | INFO : Epoch #33\n",
            "2022-11-24 00:25:39,108 | INFO : \t train_acc: 79.464; train_loss: 1.162; test_loss: 74.631; lr: [0.1]\n",
            "2022-11-24 00:25:39,108 | INFO : \t test_acc: 75.14 (best: 78.86 from #21)\n",
            "2022-11-24 00:25:52,404 | INFO : Epoch #34\n",
            "2022-11-24 00:25:52,404 | INFO : \t train_acc: 79.698; train_loss: 1.184; test_loss: 63.789; lr: [0.1]\n",
            "2022-11-24 00:25:52,404 | INFO : \t test_acc: 78.03 (best: 78.86 from #21)\n",
            "2022-11-24 00:26:05,988 | INFO : Epoch #35\n",
            "2022-11-24 00:26:05,989 | INFO : \t train_acc: 79.804; train_loss: 0.966; test_loss: 91.405; lr: [0.1]\n",
            "2022-11-24 00:26:05,989 | INFO : \t test_acc: 72.05 (best: 78.86 from #21)\n",
            "2022-11-24 00:26:19,861 | INFO : Epoch #36\n",
            "2022-11-24 00:26:19,861 | INFO : \t train_acc: 79.734; train_loss: 1.135; test_loss: 70.492; lr: [0.1]\n",
            "2022-11-24 00:26:19,861 | INFO : \t test_acc: 75.96 (best: 78.86 from #21)\n",
            "2022-11-24 00:26:33,354 | INFO : Epoch #37\n",
            "2022-11-24 00:26:33,355 | INFO : \t train_acc: 79.882; train_loss: 0.406; test_loss: 90.480; lr: [0.1]\n",
            "2022-11-24 00:26:33,355 | INFO : \t test_acc: 71.45 (best: 78.86 from #21)\n",
            "2022-11-24 00:26:46,638 | INFO : Epoch #38\n",
            "2022-11-24 00:26:46,638 | INFO : \t train_acc: 80.026; train_loss: 0.843; test_loss: 70.670; lr: [0.1]\n",
            "2022-11-24 00:26:46,638 | INFO : \t test_acc: 76.83 (best: 78.86 from #21)\n",
            "2022-11-24 00:27:00,549 | INFO : Epoch #39\n",
            "2022-11-24 00:27:00,550 | INFO : \t train_acc: 79.948; train_loss: 0.385; test_loss: 74.791; lr: [0.1]\n",
            "2022-11-24 00:27:00,550 | INFO : \t test_acc: 74.84 (best: 78.86 from #21)\n",
            "2022-11-24 00:27:14,163 | INFO : Epoch #40\n",
            "2022-11-24 00:27:14,164 | INFO : \t train_acc: 79.902; train_loss: 0.955; test_loss: 78.893; lr: [0.1]\n",
            "2022-11-24 00:27:14,164 | INFO : \t test_acc: 74.04 (best: 78.86 from #21)\n",
            "2022-11-24 00:27:27,616 | INFO : Epoch #41\n",
            "2022-11-24 00:27:27,617 | INFO : \t train_acc: 79.962; train_loss: 0.759; test_loss: 87.947; lr: [0.1]\n",
            "2022-11-24 00:27:27,617 | INFO : \t test_acc: 73.17 (best: 78.86 from #21)\n",
            "2022-11-24 00:27:41,193 | INFO : Epoch #42\n",
            "2022-11-24 00:27:41,193 | INFO : \t train_acc: 80.244; train_loss: 0.813; test_loss: 74.619; lr: [0.1]\n",
            "2022-11-24 00:27:41,193 | INFO : \t test_acc: 75.29 (best: 78.86 from #21)\n",
            "2022-11-24 00:27:54,838 | INFO : Epoch #43\n",
            "2022-11-24 00:27:54,838 | INFO : \t train_acc: 80.02; train_loss: 0.396; test_loss: 63.276; lr: [0.1]\n",
            "2022-11-24 00:27:54,838 | INFO : \t test_acc: 77.55 (best: 78.86 from #21)\n",
            "2022-11-24 00:28:08,760 | INFO : Epoch #44\n",
            "2022-11-24 00:28:08,760 | INFO : \t train_acc: 80.318; train_loss: 0.876; test_loss: 71.756; lr: [0.1]\n",
            "2022-11-24 00:28:08,761 | INFO : \t test_acc: 75.9 (best: 78.86 from #21)\n",
            "2022-11-24 00:28:22,832 | INFO : Epoch #45\n",
            "2022-11-24 00:28:22,832 | INFO : \t train_acc: 79.752; train_loss: 0.995; test_loss: 65.196; lr: [0.1]\n",
            "2022-11-24 00:28:22,832 | INFO : \t test_acc: 78.07 (best: 78.86 from #21)\n",
            "2022-11-24 00:28:36,521 | INFO : Epoch #46\n",
            "2022-11-24 00:28:36,521 | INFO : \t train_acc: 79.86; train_loss: 0.459; test_loss: 67.354; lr: [0.1]\n",
            "2022-11-24 00:28:36,522 | INFO : \t test_acc: 77.39 (best: 78.86 from #21)\n",
            "2022-11-24 00:28:49,866 | INFO : Epoch #47\n",
            "2022-11-24 00:28:49,866 | INFO : \t train_acc: 80.07; train_loss: 0.547; test_loss: 127.375; lr: [0.1]\n",
            "2022-11-24 00:28:49,867 | INFO : \t test_acc: 65.8 (best: 78.86 from #21)\n",
            "2022-11-24 00:29:03,394 | INFO : Epoch #48\n",
            "2022-11-24 00:29:03,395 | INFO : \t train_acc: 79.818; train_loss: 0.631; test_loss: 72.508; lr: [0.1]\n",
            "2022-11-24 00:29:03,395 | INFO : \t test_acc: 76.4 (best: 78.86 from #21)\n",
            "2022-11-24 00:29:16,766 | INFO : Epoch #49\n",
            "2022-11-24 00:29:16,766 | INFO : \t train_acc: 80.27; train_loss: 0.571; test_loss: 62.823; lr: [0.1]\n",
            "2022-11-24 00:29:16,766 | INFO : \t test_acc: 78.7 (best: 78.86 from #21)\n",
            "2022-11-24 00:29:30,432 | INFO : Epoch #50\n",
            "2022-11-24 00:29:30,432 | INFO : \t train_acc: 80.28; train_loss: 0.824; test_loss: 79.080; lr: [0.1]\n",
            "2022-11-24 00:29:30,433 | INFO : \t test_acc: 74.86 (best: 78.86 from #21)\n",
            "2022-11-24 00:29:44,152 | INFO : Epoch #51\n",
            "2022-11-24 00:29:44,153 | INFO : \t train_acc: 80.006; train_loss: 1.110; test_loss: 76.073; lr: [0.1]\n",
            "2022-11-24 00:29:44,153 | INFO : \t test_acc: 74.86 (best: 78.86 from #21)\n",
            "2022-11-24 00:29:58,721 | INFO : Epoch #52\n",
            "2022-11-24 00:29:58,721 | INFO : \t train_acc: 80.18; train_loss: 0.414; test_loss: 93.916; lr: [0.1]\n",
            "2022-11-24 00:29:58,721 | INFO : \t test_acc: 70.0 (best: 78.86 from #21)\n",
            "2022-11-24 00:30:11,895 | INFO : Epoch #53\n",
            "2022-11-24 00:30:11,896 | INFO : \t train_acc: 80.432; train_loss: 0.266; test_loss: 66.255; lr: [0.1]\n",
            "2022-11-24 00:30:11,896 | INFO : \t test_acc: 77.32 (best: 78.86 from #21)\n",
            "2022-11-24 00:30:25,360 | INFO : Epoch #54\n",
            "2022-11-24 00:30:25,361 | INFO : \t train_acc: 80.166; train_loss: 0.984; test_loss: 79.745; lr: [0.1]\n",
            "2022-11-24 00:30:25,361 | INFO : \t test_acc: 72.9 (best: 78.86 from #21)\n",
            "2022-11-24 00:30:38,949 | INFO : Epoch #55\n",
            "2022-11-24 00:30:38,950 | INFO : \t train_acc: 80.33; train_loss: 0.489; test_loss: 69.633; lr: [0.1]\n",
            "2022-11-24 00:30:38,950 | INFO : \t test_acc: 76.74 (best: 78.86 from #21)\n",
            "2022-11-24 00:30:52,529 | INFO : Epoch #56\n",
            "2022-11-24 00:30:52,529 | INFO : \t train_acc: 80.284; train_loss: 0.265; test_loss: 68.374; lr: [0.1]\n",
            "2022-11-24 00:30:52,529 | INFO : \t test_acc: 77.23 (best: 78.86 from #21)\n",
            "2022-11-24 00:31:06,255 | INFO : Epoch #57\n",
            "2022-11-24 00:31:06,255 | INFO : \t train_acc: 80.308; train_loss: 1.938; test_loss: 101.719; lr: [0.1]\n",
            "2022-11-24 00:31:06,255 | INFO : \t test_acc: 69.31 (best: 78.86 from #21)\n",
            "2022-11-24 00:31:20,206 | INFO : Epoch #58\n",
            "2022-11-24 00:31:20,206 | INFO : \t train_acc: 79.944; train_loss: 0.447; test_loss: 82.987; lr: [0.1]\n",
            "2022-11-24 00:31:20,207 | INFO : \t test_acc: 73.7 (best: 78.86 from #21)\n",
            "2022-11-24 00:31:33,530 | INFO : Epoch #59\n",
            "2022-11-24 00:31:33,531 | INFO : \t train_acc: 80.448; train_loss: 0.655; test_loss: 66.998; lr: [0.1]\n",
            "2022-11-24 00:31:33,531 | INFO : \t test_acc: 77.74 (best: 78.86 from #21)\n",
            "2022-11-24 00:31:46,885 | INFO : Epoch #60\n",
            "2022-11-24 00:31:46,885 | INFO : \t train_acc: 80.328; train_loss: 0.762; test_loss: 61.331; lr: [0.1]\n",
            "2022-11-24 00:31:46,885 | INFO : \t test_acc: 79.21 (best: 79.21 from #60)\n",
            "2022-11-24 00:32:00,198 | INFO : Epoch #61\n",
            "2022-11-24 00:32:00,198 | INFO : \t train_acc: 80.408; train_loss: 0.653; test_loss: 85.468; lr: [0.1]\n",
            "2022-11-24 00:32:00,198 | INFO : \t test_acc: 73.07 (best: 79.21 from #60)\n",
            "2022-11-24 00:32:13,543 | INFO : Epoch #62\n",
            "2022-11-24 00:32:13,543 | INFO : \t train_acc: 80.096; train_loss: 0.183; test_loss: 72.635; lr: [0.1]\n",
            "2022-11-24 00:32:13,544 | INFO : \t test_acc: 76.28 (best: 79.21 from #60)\n",
            "2022-11-24 00:32:27,287 | INFO : Epoch #63\n",
            "2022-11-24 00:32:27,288 | INFO : \t train_acc: 80.176; train_loss: 0.735; test_loss: 100.698; lr: [0.1]\n",
            "2022-11-24 00:32:27,288 | INFO : \t test_acc: 68.93 (best: 79.21 from #60)\n",
            "2022-11-24 00:32:40,708 | INFO : Epoch #64\n",
            "2022-11-24 00:32:40,709 | INFO : \t train_acc: 80.228; train_loss: 0.708; test_loss: 69.041; lr: [0.1]\n",
            "2022-11-24 00:32:40,709 | INFO : \t test_acc: 76.75 (best: 79.21 from #60)\n",
            "2022-11-24 00:32:54,399 | INFO : Epoch #65\n",
            "2022-11-24 00:32:54,399 | INFO : \t train_acc: 80.068; train_loss: 0.833; test_loss: 85.944; lr: [0.1]\n",
            "2022-11-24 00:32:54,399 | INFO : \t test_acc: 72.83 (best: 79.21 from #60)\n",
            "2022-11-24 00:33:07,647 | INFO : Epoch #66\n",
            "2022-11-24 00:33:07,647 | INFO : \t train_acc: 80.306; train_loss: 0.474; test_loss: 81.195; lr: [0.1]\n",
            "2022-11-24 00:33:07,647 | INFO : \t test_acc: 73.46 (best: 79.21 from #60)\n",
            "2022-11-24 00:33:21,416 | INFO : Epoch #67\n",
            "2022-11-24 00:33:21,416 | INFO : \t train_acc: 80.176; train_loss: 0.635; test_loss: 63.228; lr: [0.1]\n",
            "2022-11-24 00:33:21,416 | INFO : \t test_acc: 78.46 (best: 79.21 from #60)\n",
            "2022-11-24 00:33:34,838 | INFO : Epoch #68\n",
            "2022-11-24 00:33:34,839 | INFO : \t train_acc: 80.064; train_loss: 0.467; test_loss: 68.213; lr: [0.1]\n",
            "2022-11-24 00:33:34,839 | INFO : \t test_acc: 77.59 (best: 79.21 from #60)\n",
            "2022-11-24 00:33:48,413 | INFO : Epoch #69\n",
            "2022-11-24 00:33:48,413 | INFO : \t train_acc: 80.358; train_loss: 0.502; test_loss: 74.737; lr: [0.1]\n",
            "2022-11-24 00:33:48,413 | INFO : \t test_acc: 75.66 (best: 79.21 from #60)\n",
            "2022-11-24 00:34:01,892 | INFO : Epoch #70\n",
            "2022-11-24 00:34:01,893 | INFO : \t train_acc: 80.036; train_loss: 0.498; test_loss: 98.050; lr: [0.1]\n",
            "2022-11-24 00:34:01,893 | INFO : \t test_acc: 72.02 (best: 79.21 from #60)\n",
            "2022-11-24 00:34:15,317 | INFO : Epoch #71\n",
            "2022-11-24 00:34:15,317 | INFO : \t train_acc: 80.172; train_loss: 0.578; test_loss: 62.745; lr: [0.1]\n",
            "2022-11-24 00:34:15,317 | INFO : \t test_acc: 79.34 (best: 79.34 from #71)\n",
            "2022-11-24 00:34:28,786 | INFO : Epoch #72\n",
            "2022-11-24 00:34:28,787 | INFO : \t train_acc: 80.464; train_loss: 0.269; test_loss: 60.522; lr: [0.1]\n",
            "2022-11-24 00:34:28,787 | INFO : \t test_acc: 79.85 (best: 79.85 from #72)\n",
            "2022-11-24 00:34:42,307 | INFO : Epoch #73\n",
            "2022-11-24 00:34:42,308 | INFO : \t train_acc: 80.318; train_loss: 0.555; test_loss: 66.456; lr: [0.1]\n",
            "2022-11-24 00:34:42,308 | INFO : \t test_acc: 77.65 (best: 79.85 from #72)\n",
            "2022-11-24 00:34:55,786 | INFO : Epoch #74\n",
            "2022-11-24 00:34:55,786 | INFO : \t train_acc: 80.412; train_loss: 0.925; test_loss: 80.949; lr: [0.1]\n",
            "2022-11-24 00:34:55,787 | INFO : \t test_acc: 73.47 (best: 79.85 from #72)\n",
            "2022-11-24 00:35:09,311 | INFO : Epoch #75\n",
            "2022-11-24 00:35:09,312 | INFO : \t train_acc: 80.192; train_loss: 0.482; test_loss: 87.361; lr: [0.1]\n",
            "2022-11-24 00:35:09,312 | INFO : \t test_acc: 71.3 (best: 79.85 from #72)\n",
            "2022-11-24 00:35:23,028 | INFO : Epoch #76\n",
            "2022-11-24 00:35:23,028 | INFO : \t train_acc: 80.298; train_loss: 0.320; test_loss: 84.786; lr: [0.1]\n",
            "2022-11-24 00:35:23,028 | INFO : \t test_acc: 72.15 (best: 79.85 from #72)\n",
            "2022-11-24 00:35:36,713 | INFO : Epoch #77\n",
            "2022-11-24 00:35:36,713 | INFO : \t train_acc: 80.168; train_loss: 0.513; test_loss: 72.813; lr: [0.1]\n",
            "2022-11-24 00:35:36,714 | INFO : \t test_acc: 75.61 (best: 79.85 from #72)\n",
            "2022-11-24 00:35:50,295 | INFO : Epoch #78\n",
            "2022-11-24 00:35:50,295 | INFO : \t train_acc: 80.35; train_loss: 0.261; test_loss: 69.694; lr: [0.1]\n",
            "2022-11-24 00:35:50,295 | INFO : \t test_acc: 76.79 (best: 79.85 from #72)\n",
            "2022-11-24 00:36:03,789 | INFO : Epoch #79\n",
            "2022-11-24 00:36:03,790 | INFO : \t train_acc: 80.38; train_loss: 0.804; test_loss: 81.731; lr: [0.1]\n",
            "2022-11-24 00:36:03,790 | INFO : \t test_acc: 72.01 (best: 79.85 from #72)\n",
            "2022-11-24 00:36:17,205 | INFO : Epoch #80\n",
            "2022-11-24 00:36:17,205 | INFO : \t train_acc: 80.402; train_loss: 0.734; test_loss: 87.358; lr: [0.1]\n",
            "2022-11-24 00:36:17,205 | INFO : \t test_acc: 71.7 (best: 79.85 from #72)\n",
            "2022-11-24 00:36:31,436 | INFO : Epoch #81\n",
            "2022-11-24 00:36:31,437 | INFO : \t train_acc: 80.352; train_loss: 0.542; test_loss: 71.274; lr: [0.1]\n",
            "2022-11-24 00:36:31,437 | INFO : \t test_acc: 75.97 (best: 79.85 from #72)\n",
            "2022-11-24 00:36:44,775 | INFO : Epoch #82\n",
            "2022-11-24 00:36:44,775 | INFO : \t train_acc: 80.466; train_loss: 0.745; test_loss: 66.957; lr: [0.1]\n",
            "2022-11-24 00:36:44,775 | INFO : \t test_acc: 77.76 (best: 79.85 from #72)\n",
            "2022-11-24 00:36:59,065 | INFO : Epoch #83\n",
            "2022-11-24 00:36:59,065 | INFO : \t train_acc: 80.574; train_loss: 0.834; test_loss: 76.886; lr: [0.1]\n",
            "2022-11-24 00:36:59,066 | INFO : \t test_acc: 74.24 (best: 79.85 from #72)\n",
            "2022-11-24 00:37:12,330 | INFO : Epoch #84\n",
            "2022-11-24 00:37:12,330 | INFO : \t train_acc: 80.446; train_loss: 0.529; test_loss: 113.890; lr: [0.1]\n",
            "2022-11-24 00:37:12,331 | INFO : \t test_acc: 66.25 (best: 79.85 from #72)\n",
            "2022-11-24 00:37:25,563 | INFO : Epoch #85\n",
            "2022-11-24 00:37:25,563 | INFO : \t train_acc: 80.428; train_loss: 0.616; test_loss: 88.113; lr: [0.1]\n",
            "2022-11-24 00:37:25,563 | INFO : \t test_acc: 70.29 (best: 79.85 from #72)\n",
            "2022-11-24 00:37:38,979 | INFO : Epoch #86\n",
            "2022-11-24 00:37:38,979 | INFO : \t train_acc: 80.546; train_loss: 0.831; test_loss: 80.300; lr: [0.1]\n",
            "2022-11-24 00:37:38,979 | INFO : \t test_acc: 73.51 (best: 79.85 from #72)\n",
            "2022-11-24 00:37:52,235 | INFO : Epoch #87\n",
            "2022-11-24 00:37:52,236 | INFO : \t train_acc: 80.37; train_loss: 0.837; test_loss: 69.072; lr: [0.1]\n",
            "2022-11-24 00:37:52,236 | INFO : \t test_acc: 77.03 (best: 79.85 from #72)\n",
            "2022-11-24 00:38:05,701 | INFO : Epoch #88\n",
            "2022-11-24 00:38:05,702 | INFO : \t train_acc: 80.034; train_loss: 0.628; test_loss: 96.106; lr: [0.1]\n",
            "2022-11-24 00:38:05,702 | INFO : \t test_acc: 71.26 (best: 79.85 from #72)\n",
            "2022-11-24 00:38:18,985 | INFO : Epoch #89\n",
            "2022-11-24 00:38:18,986 | INFO : \t train_acc: 80.338; train_loss: 0.459; test_loss: 88.453; lr: [0.1]\n",
            "2022-11-24 00:38:18,986 | INFO : \t test_acc: 72.22 (best: 79.85 from #72)\n",
            "2022-11-24 00:38:32,348 | INFO : Epoch #90\n",
            "2022-11-24 00:38:32,349 | INFO : \t train_acc: 80.504; train_loss: 1.314; test_loss: 79.834; lr: [0.1]\n",
            "2022-11-24 00:38:32,349 | INFO : \t test_acc: 74.19 (best: 79.85 from #72)\n",
            "2022-11-24 00:38:46,322 | INFO : Epoch #91\n",
            "2022-11-24 00:38:46,323 | INFO : \t train_acc: 80.282; train_loss: 0.365; test_loss: 88.840; lr: [0.1]\n",
            "2022-11-24 00:38:46,323 | INFO : \t test_acc: 71.55 (best: 79.85 from #72)\n",
            "2022-11-24 00:39:00,060 | INFO : Epoch #92\n",
            "2022-11-24 00:39:00,060 | INFO : \t train_acc: 80.46; train_loss: 1.013; test_loss: 61.682; lr: [0.1]\n",
            "2022-11-24 00:39:00,061 | INFO : \t test_acc: 79.19 (best: 79.85 from #72)\n",
            "2022-11-24 00:39:13,473 | INFO : Epoch #93\n",
            "2022-11-24 00:39:13,473 | INFO : \t train_acc: 80.13; train_loss: 0.571; test_loss: 61.550; lr: [0.1]\n",
            "2022-11-24 00:39:13,474 | INFO : \t test_acc: 79.08 (best: 79.85 from #72)\n",
            "2022-11-24 00:39:26,813 | INFO : Epoch #94\n",
            "2022-11-24 00:39:26,813 | INFO : \t train_acc: 80.432; train_loss: 0.734; test_loss: 92.065; lr: [0.1]\n",
            "2022-11-24 00:39:26,814 | INFO : \t test_acc: 70.81 (best: 79.85 from #72)\n",
            "2022-11-24 00:39:40,646 | INFO : Epoch #95\n",
            "2022-11-24 00:39:40,647 | INFO : \t train_acc: 80.574; train_loss: 0.440; test_loss: 59.060; lr: [0.1]\n",
            "2022-11-24 00:39:40,647 | INFO : \t test_acc: 80.17 (best: 80.17 from #95)\n",
            "2022-11-24 00:39:54,323 | INFO : Epoch #96\n",
            "2022-11-24 00:39:54,324 | INFO : \t train_acc: 80.486; train_loss: 0.474; test_loss: 103.455; lr: [0.1]\n",
            "2022-11-24 00:39:54,324 | INFO : \t test_acc: 68.3 (best: 80.17 from #95)\n",
            "2022-11-24 00:40:08,091 | INFO : Epoch #97\n",
            "2022-11-24 00:40:08,092 | INFO : \t train_acc: 80.116; train_loss: 0.636; test_loss: 83.456; lr: [0.1]\n",
            "2022-11-24 00:40:08,092 | INFO : \t test_acc: 73.52 (best: 80.17 from #95)\n",
            "2022-11-24 00:40:22,054 | INFO : Epoch #98\n",
            "2022-11-24 00:40:22,055 | INFO : \t train_acc: 80.49; train_loss: 1.029; test_loss: 83.230; lr: [0.1]\n",
            "2022-11-24 00:40:22,055 | INFO : \t test_acc: 73.43 (best: 80.17 from #95)\n",
            "2022-11-24 00:40:36,298 | INFO : Epoch #99\n",
            "2022-11-24 00:40:36,299 | INFO : \t train_acc: 80.622; train_loss: 0.575; test_loss: 73.985; lr: [0.1]\n",
            "2022-11-24 00:40:36,299 | INFO : \t test_acc: 75.91 (best: 80.17 from #95)\n",
            "2022-11-24 00:40:49,580 | INFO : Epoch #100\n",
            "2022-11-24 00:40:49,580 | INFO : \t train_acc: 87.456; train_loss: 0.418; test_loss: 35.148; lr: [0.010000000000000002]\n",
            "2022-11-24 00:40:49,580 | INFO : \t test_acc: 87.75 (best: 87.75 from #100)\n",
            "2022-11-24 00:41:03,308 | INFO : Epoch #101\n",
            "2022-11-24 00:41:03,308 | INFO : \t train_acc: 89.118; train_loss: 0.204; test_loss: 34.544; lr: [0.010000000000000002]\n",
            "2022-11-24 00:41:03,308 | INFO : \t test_acc: 88.39 (best: 88.39 from #101)\n",
            "2022-11-24 00:41:16,762 | INFO : Epoch #102\n",
            "2022-11-24 00:41:16,763 | INFO : \t train_acc: 89.838; train_loss: 0.287; test_loss: 32.473; lr: [0.010000000000000002]\n",
            "2022-11-24 00:41:16,763 | INFO : \t test_acc: 89.01 (best: 89.01 from #102)\n",
            "2022-11-24 00:41:30,133 | INFO : Epoch #103\n",
            "2022-11-24 00:41:30,134 | INFO : \t train_acc: 90.518; train_loss: 0.143; test_loss: 32.057; lr: [0.010000000000000002]\n",
            "2022-11-24 00:41:30,134 | INFO : \t test_acc: 89.29 (best: 89.29 from #103)\n",
            "2022-11-24 00:41:43,612 | INFO : Epoch #104\n",
            "2022-11-24 00:41:43,612 | INFO : \t train_acc: 90.566; train_loss: 0.390; test_loss: 32.531; lr: [0.010000000000000002]\n",
            "2022-11-24 00:41:43,612 | INFO : \t test_acc: 88.67 (best: 89.29 from #103)\n",
            "2022-11-24 00:41:56,975 | INFO : Epoch #105\n",
            "2022-11-24 00:41:56,975 | INFO : \t train_acc: 90.848; train_loss: 0.012; test_loss: 32.715; lr: [0.010000000000000002]\n",
            "2022-11-24 00:41:56,976 | INFO : \t test_acc: 88.84 (best: 89.29 from #103)\n",
            "2022-11-24 00:42:10,282 | INFO : Epoch #106\n",
            "2022-11-24 00:42:10,283 | INFO : \t train_acc: 91.264; train_loss: 0.124; test_loss: 32.017; lr: [0.010000000000000002]\n",
            "2022-11-24 00:42:10,283 | INFO : \t test_acc: 89.14 (best: 89.29 from #103)\n",
            "2022-11-24 00:42:24,239 | INFO : Epoch #107\n",
            "2022-11-24 00:42:24,240 | INFO : \t train_acc: 91.318; train_loss: 0.213; test_loss: 31.627; lr: [0.010000000000000002]\n",
            "2022-11-24 00:42:24,240 | INFO : \t test_acc: 89.39 (best: 89.39 from #107)\n",
            "2022-11-24 00:42:37,908 | INFO : Epoch #108\n",
            "2022-11-24 00:42:37,909 | INFO : \t train_acc: 91.414; train_loss: 0.720; test_loss: 33.942; lr: [0.010000000000000002]\n",
            "2022-11-24 00:42:37,909 | INFO : \t test_acc: 88.36 (best: 89.39 from #107)\n",
            "2022-11-24 00:42:51,323 | INFO : Epoch #109\n",
            "2022-11-24 00:42:51,324 | INFO : \t train_acc: 91.758; train_loss: 0.182; test_loss: 34.929; lr: [0.010000000000000002]\n",
            "2022-11-24 00:42:51,324 | INFO : \t test_acc: 88.26 (best: 89.39 from #107)\n",
            "2022-11-24 00:43:05,126 | INFO : Epoch #110\n",
            "2022-11-24 00:43:05,126 | INFO : \t train_acc: 91.702; train_loss: 0.618; test_loss: 31.022; lr: [0.010000000000000002]\n",
            "2022-11-24 00:43:05,126 | INFO : \t test_acc: 89.29 (best: 89.39 from #107)\n",
            "2022-11-24 00:43:18,595 | INFO : Epoch #111\n",
            "2022-11-24 00:43:18,595 | INFO : \t train_acc: 91.582; train_loss: 0.154; test_loss: 34.648; lr: [0.010000000000000002]\n",
            "2022-11-24 00:43:18,596 | INFO : \t test_acc: 88.36 (best: 89.39 from #107)\n",
            "2022-11-24 00:43:31,901 | INFO : Epoch #112\n",
            "2022-11-24 00:43:31,902 | INFO : \t train_acc: 91.702; train_loss: 0.458; test_loss: 36.900; lr: [0.010000000000000002]\n",
            "2022-11-24 00:43:31,902 | INFO : \t test_acc: 87.59 (best: 89.39 from #107)\n",
            "2022-11-24 00:43:45,371 | INFO : Epoch #113\n",
            "2022-11-24 00:43:45,372 | INFO : \t train_acc: 91.798; train_loss: 0.721; test_loss: 33.991; lr: [0.010000000000000002]\n",
            "2022-11-24 00:43:45,372 | INFO : \t test_acc: 88.99 (best: 89.39 from #107)\n",
            "2022-11-24 00:43:59,565 | INFO : Epoch #114\n",
            "2022-11-24 00:43:59,565 | INFO : \t train_acc: 91.576; train_loss: 0.769; test_loss: 32.519; lr: [0.010000000000000002]\n",
            "2022-11-24 00:43:59,565 | INFO : \t test_acc: 89.02 (best: 89.39 from #107)\n",
            "2022-11-24 00:44:13,564 | INFO : Epoch #115\n",
            "2022-11-24 00:44:13,565 | INFO : \t train_acc: 91.888; train_loss: 0.332; test_loss: 42.120; lr: [0.010000000000000002]\n",
            "2022-11-24 00:44:13,565 | INFO : \t test_acc: 86.82 (best: 89.39 from #107)\n",
            "2022-11-24 00:44:27,302 | INFO : Epoch #116\n",
            "2022-11-24 00:44:27,302 | INFO : \t train_acc: 91.722; train_loss: 0.121; test_loss: 32.240; lr: [0.010000000000000002]\n",
            "2022-11-24 00:44:27,302 | INFO : \t test_acc: 89.55 (best: 89.55 from #116)\n",
            "2022-11-24 00:44:40,823 | INFO : Epoch #117\n",
            "2022-11-24 00:44:40,823 | INFO : \t train_acc: 91.652; train_loss: 0.307; test_loss: 35.751; lr: [0.010000000000000002]\n",
            "2022-11-24 00:44:40,823 | INFO : \t test_acc: 88.2 (best: 89.55 from #116)\n",
            "2022-11-24 00:44:54,280 | INFO : Epoch #118\n",
            "2022-11-24 00:44:54,280 | INFO : \t train_acc: 91.6; train_loss: 0.512; test_loss: 36.870; lr: [0.010000000000000002]\n",
            "2022-11-24 00:44:54,281 | INFO : \t test_acc: 87.73 (best: 89.55 from #116)\n",
            "2022-11-24 00:45:07,918 | INFO : Epoch #119\n",
            "2022-11-24 00:45:07,919 | INFO : \t train_acc: 91.66; train_loss: 0.480; test_loss: 38.819; lr: [0.010000000000000002]\n",
            "2022-11-24 00:45:07,919 | INFO : \t test_acc: 87.36 (best: 89.55 from #116)\n",
            "2022-11-24 00:45:21,466 | INFO : Epoch #120\n",
            "2022-11-24 00:45:21,467 | INFO : \t train_acc: 91.682; train_loss: 0.055; test_loss: 36.674; lr: [0.010000000000000002]\n",
            "2022-11-24 00:45:21,467 | INFO : \t test_acc: 87.83 (best: 89.55 from #116)\n",
            "2022-11-24 00:45:35,010 | INFO : Epoch #121\n",
            "2022-11-24 00:45:35,010 | INFO : \t train_acc: 91.596; train_loss: 0.223; test_loss: 35.577; lr: [0.010000000000000002]\n",
            "2022-11-24 00:45:35,011 | INFO : \t test_acc: 88.19 (best: 89.55 from #116)\n",
            "2022-11-24 00:45:48,959 | INFO : Epoch #122\n",
            "2022-11-24 00:45:48,960 | INFO : \t train_acc: 91.824; train_loss: 0.641; test_loss: 37.302; lr: [0.010000000000000002]\n",
            "2022-11-24 00:45:48,960 | INFO : \t test_acc: 87.71 (best: 89.55 from #116)\n",
            "2022-11-24 00:46:02,347 | INFO : Epoch #123\n",
            "2022-11-24 00:46:02,347 | INFO : \t train_acc: 91.702; train_loss: 0.260; test_loss: 35.819; lr: [0.010000000000000002]\n",
            "2022-11-24 00:46:02,348 | INFO : \t test_acc: 87.97 (best: 89.55 from #116)\n",
            "2022-11-24 00:46:15,714 | INFO : Epoch #124\n",
            "2022-11-24 00:46:15,714 | INFO : \t train_acc: 91.63; train_loss: 0.680; test_loss: 38.811; lr: [0.010000000000000002]\n",
            "2022-11-24 00:46:15,714 | INFO : \t test_acc: 87.49 (best: 89.55 from #116)\n",
            "2022-11-24 00:46:29,172 | INFO : Epoch #125\n",
            "2022-11-24 00:46:29,173 | INFO : \t train_acc: 91.656; train_loss: 0.198; test_loss: 34.300; lr: [0.010000000000000002]\n",
            "2022-11-24 00:46:29,173 | INFO : \t test_acc: 88.32 (best: 89.55 from #116)\n",
            "2022-11-24 00:46:42,404 | INFO : Epoch #126\n",
            "2022-11-24 00:46:42,405 | INFO : \t train_acc: 91.73; train_loss: 0.477; test_loss: 34.450; lr: [0.010000000000000002]\n",
            "2022-11-24 00:46:42,405 | INFO : \t test_acc: 88.8 (best: 89.55 from #116)\n",
            "2022-11-24 00:46:55,830 | INFO : Epoch #127\n",
            "2022-11-24 00:46:55,831 | INFO : \t train_acc: 91.826; train_loss: 0.382; test_loss: 37.593; lr: [0.010000000000000002]\n",
            "2022-11-24 00:46:55,831 | INFO : \t test_acc: 87.47 (best: 89.55 from #116)\n",
            "2022-11-24 00:47:09,194 | INFO : Epoch #128\n",
            "2022-11-24 00:47:09,195 | INFO : \t train_acc: 91.984; train_loss: 0.123; test_loss: 44.350; lr: [0.010000000000000002]\n",
            "2022-11-24 00:47:09,195 | INFO : \t test_acc: 86.14 (best: 89.55 from #116)\n",
            "2022-11-24 00:47:22,534 | INFO : Epoch #129\n",
            "2022-11-24 00:47:22,534 | INFO : \t train_acc: 91.714; train_loss: 0.230; test_loss: 38.459; lr: [0.010000000000000002]\n",
            "2022-11-24 00:47:22,535 | INFO : \t test_acc: 87.57 (best: 89.55 from #116)\n",
            "2022-11-24 00:47:35,918 | INFO : Epoch #130\n",
            "2022-11-24 00:47:35,919 | INFO : \t train_acc: 91.826; train_loss: 0.548; test_loss: 37.765; lr: [0.010000000000000002]\n",
            "2022-11-24 00:47:35,919 | INFO : \t test_acc: 87.57 (best: 89.55 from #116)\n",
            "2022-11-24 00:47:49,255 | INFO : Epoch #131\n",
            "2022-11-24 00:47:49,255 | INFO : \t train_acc: 91.776; train_loss: 0.478; test_loss: 33.089; lr: [0.010000000000000002]\n",
            "2022-11-24 00:47:49,255 | INFO : \t test_acc: 89.21 (best: 89.55 from #116)\n",
            "2022-11-24 00:48:02,632 | INFO : Epoch #132\n",
            "2022-11-24 00:48:02,633 | INFO : \t train_acc: 92.04; train_loss: 0.251; test_loss: 34.578; lr: [0.010000000000000002]\n",
            "2022-11-24 00:48:02,633 | INFO : \t test_acc: 88.71 (best: 89.55 from #116)\n",
            "2022-11-24 00:48:15,912 | INFO : Epoch #133\n",
            "2022-11-24 00:48:15,912 | INFO : \t train_acc: 92.126; train_loss: 0.276; test_loss: 32.886; lr: [0.010000000000000002]\n",
            "2022-11-24 00:48:15,912 | INFO : \t test_acc: 89.4 (best: 89.55 from #116)\n",
            "2022-11-24 00:48:29,625 | INFO : Epoch #134\n",
            "2022-11-24 00:48:29,626 | INFO : \t train_acc: 91.92; train_loss: 0.711; test_loss: 35.407; lr: [0.010000000000000002]\n",
            "2022-11-24 00:48:29,626 | INFO : \t test_acc: 88.56 (best: 89.55 from #116)\n",
            "2022-11-24 00:48:43,142 | INFO : Epoch #135\n",
            "2022-11-24 00:48:43,143 | INFO : \t train_acc: 91.632; train_loss: 0.131; test_loss: 34.484; lr: [0.010000000000000002]\n",
            "2022-11-24 00:48:43,143 | INFO : \t test_acc: 89.22 (best: 89.55 from #116)\n",
            "2022-11-24 00:48:56,455 | INFO : Epoch #136\n",
            "2022-11-24 00:48:56,455 | INFO : \t train_acc: 92.3; train_loss: 0.245; test_loss: 38.098; lr: [0.010000000000000002]\n",
            "2022-11-24 00:48:56,455 | INFO : \t test_acc: 88.24 (best: 89.55 from #116)\n",
            "2022-11-24 00:49:10,125 | INFO : Epoch #137\n",
            "2022-11-24 00:49:10,126 | INFO : \t train_acc: 91.976; train_loss: 0.137; test_loss: 41.923; lr: [0.010000000000000002]\n",
            "2022-11-24 00:49:10,126 | INFO : \t test_acc: 86.77 (best: 89.55 from #116)\n",
            "2022-11-24 00:49:23,442 | INFO : Epoch #138\n",
            "2022-11-24 00:49:23,442 | INFO : \t train_acc: 92.14; train_loss: 0.058; test_loss: 32.733; lr: [0.010000000000000002]\n",
            "2022-11-24 00:49:23,443 | INFO : \t test_acc: 89.05 (best: 89.55 from #116)\n",
            "2022-11-24 00:49:37,320 | INFO : Epoch #139\n",
            "2022-11-24 00:49:37,321 | INFO : \t train_acc: 92.286; train_loss: 0.499; test_loss: 35.863; lr: [0.010000000000000002]\n",
            "2022-11-24 00:49:37,321 | INFO : \t test_acc: 88.05 (best: 89.55 from #116)\n",
            "2022-11-24 00:49:50,638 | INFO : Epoch #140\n",
            "2022-11-24 00:49:50,638 | INFO : \t train_acc: 92.208; train_loss: 0.894; test_loss: 36.366; lr: [0.010000000000000002]\n",
            "2022-11-24 00:49:50,638 | INFO : \t test_acc: 88.08 (best: 89.55 from #116)\n",
            "2022-11-24 00:50:04,076 | INFO : Epoch #141\n",
            "2022-11-24 00:50:04,077 | INFO : \t train_acc: 91.854; train_loss: 0.332; test_loss: 38.204; lr: [0.010000000000000002]\n",
            "2022-11-24 00:50:04,077 | INFO : \t test_acc: 87.66 (best: 89.55 from #116)\n",
            "2022-11-24 00:50:17,622 | INFO : Epoch #142\n",
            "2022-11-24 00:50:17,623 | INFO : \t train_acc: 92.002; train_loss: 0.528; test_loss: 35.390; lr: [0.010000000000000002]\n",
            "2022-11-24 00:50:17,623 | INFO : \t test_acc: 88.21 (best: 89.55 from #116)\n",
            "2022-11-24 00:50:30,829 | INFO : Epoch #143\n",
            "2022-11-24 00:50:30,829 | INFO : \t train_acc: 92.216; train_loss: 0.494; test_loss: 36.266; lr: [0.010000000000000002]\n",
            "2022-11-24 00:50:30,830 | INFO : \t test_acc: 88.46 (best: 89.55 from #116)\n",
            "2022-11-24 00:50:44,408 | INFO : Epoch #144\n",
            "2022-11-24 00:50:44,408 | INFO : \t train_acc: 92.316; train_loss: 0.179; test_loss: 36.795; lr: [0.010000000000000002]\n",
            "2022-11-24 00:50:44,408 | INFO : \t test_acc: 88.45 (best: 89.55 from #116)\n",
            "2022-11-24 00:50:57,810 | INFO : Epoch #145\n",
            "2022-11-24 00:50:57,811 | INFO : \t train_acc: 92.176; train_loss: 0.357; test_loss: 38.949; lr: [0.010000000000000002]\n",
            "2022-11-24 00:50:57,811 | INFO : \t test_acc: 87.81 (best: 89.55 from #116)\n",
            "2022-11-24 00:51:11,687 | INFO : Epoch #146\n",
            "2022-11-24 00:51:11,688 | INFO : \t train_acc: 92.402; train_loss: 0.481; test_loss: 36.798; lr: [0.010000000000000002]\n",
            "2022-11-24 00:51:11,688 | INFO : \t test_acc: 88.26 (best: 89.55 from #116)\n",
            "2022-11-24 00:51:25,324 | INFO : Epoch #147\n",
            "2022-11-24 00:51:25,325 | INFO : \t train_acc: 92.268; train_loss: 0.066; test_loss: 38.884; lr: [0.010000000000000002]\n",
            "2022-11-24 00:51:25,325 | INFO : \t test_acc: 87.32 (best: 89.55 from #116)\n",
            "2022-11-24 00:51:38,682 | INFO : Epoch #148\n",
            "2022-11-24 00:51:38,682 | INFO : \t train_acc: 92.44; train_loss: 0.427; test_loss: 35.697; lr: [0.010000000000000002]\n",
            "2022-11-24 00:51:38,683 | INFO : \t test_acc: 88.64 (best: 89.55 from #116)\n",
            "2022-11-24 00:51:52,250 | INFO : Epoch #149\n",
            "2022-11-24 00:51:52,251 | INFO : \t train_acc: 92.462; train_loss: 0.243; test_loss: 36.989; lr: [0.010000000000000002]\n",
            "2022-11-24 00:51:52,251 | INFO : \t test_acc: 87.86 (best: 89.55 from #116)\n",
            "2022-11-24 00:52:05,537 | INFO : Epoch #150\n",
            "2022-11-24 00:52:05,538 | INFO : \t train_acc: 92.216; train_loss: 0.072; test_loss: 38.327; lr: [0.010000000000000002]\n",
            "2022-11-24 00:52:05,538 | INFO : \t test_acc: 87.55 (best: 89.55 from #116)\n",
            "2022-11-24 00:52:19,002 | INFO : Epoch #151\n",
            "2022-11-24 00:52:19,003 | INFO : \t train_acc: 92.246; train_loss: 0.386; test_loss: 37.703; lr: [0.010000000000000002]\n",
            "2022-11-24 00:52:19,003 | INFO : \t test_acc: 87.87 (best: 89.55 from #116)\n",
            "2022-11-24 00:52:32,471 | INFO : Epoch #152\n",
            "2022-11-24 00:52:32,471 | INFO : \t train_acc: 92.55; train_loss: 0.094; test_loss: 39.783; lr: [0.010000000000000002]\n",
            "2022-11-24 00:52:32,472 | INFO : \t test_acc: 87.23 (best: 89.55 from #116)\n",
            "2022-11-24 00:52:45,784 | INFO : Epoch #153\n",
            "2022-11-24 00:52:45,785 | INFO : \t train_acc: 92.33; train_loss: 0.115; test_loss: 33.206; lr: [0.010000000000000002]\n",
            "2022-11-24 00:52:45,785 | INFO : \t test_acc: 89.33 (best: 89.55 from #116)\n",
            "2022-11-24 00:52:59,258 | INFO : Epoch #154\n",
            "2022-11-24 00:52:59,258 | INFO : \t train_acc: 92.336; train_loss: 0.358; test_loss: 34.153; lr: [0.010000000000000002]\n",
            "2022-11-24 00:52:59,258 | INFO : \t test_acc: 89.15 (best: 89.55 from #116)\n",
            "2022-11-24 00:53:12,682 | INFO : Epoch #155\n",
            "2022-11-24 00:53:12,683 | INFO : \t train_acc: 92.378; train_loss: 0.443; test_loss: 36.536; lr: [0.010000000000000002]\n",
            "2022-11-24 00:53:12,683 | INFO : \t test_acc: 88.1 (best: 89.55 from #116)\n",
            "2022-11-24 00:53:26,682 | INFO : Epoch #156\n",
            "2022-11-24 00:53:26,683 | INFO : \t train_acc: 92.566; train_loss: 0.640; test_loss: 35.939; lr: [0.010000000000000002]\n",
            "2022-11-24 00:53:26,683 | INFO : \t test_acc: 88.33 (best: 89.55 from #116)\n",
            "2022-11-24 00:53:39,994 | INFO : Epoch #157\n",
            "2022-11-24 00:53:39,995 | INFO : \t train_acc: 92.3; train_loss: 0.153; test_loss: 37.239; lr: [0.010000000000000002]\n",
            "2022-11-24 00:53:39,995 | INFO : \t test_acc: 87.8 (best: 89.55 from #116)\n",
            "2022-11-24 00:53:53,358 | INFO : Epoch #158\n",
            "2022-11-24 00:53:53,359 | INFO : \t train_acc: 92.536; train_loss: 0.397; test_loss: 34.447; lr: [0.010000000000000002]\n",
            "2022-11-24 00:53:53,359 | INFO : \t test_acc: 88.64 (best: 89.55 from #116)\n",
            "2022-11-24 00:54:06,815 | INFO : Epoch #159\n",
            "2022-11-24 00:54:06,816 | INFO : \t train_acc: 92.354; train_loss: 0.081; test_loss: 33.659; lr: [0.010000000000000002]\n",
            "2022-11-24 00:54:06,816 | INFO : \t test_acc: 88.74 (best: 89.55 from #116)\n",
            "2022-11-24 00:54:20,194 | INFO : Epoch #160\n",
            "2022-11-24 00:54:20,195 | INFO : \t train_acc: 92.564; train_loss: 0.600; test_loss: 35.915; lr: [0.010000000000000002]\n",
            "2022-11-24 00:54:20,195 | INFO : \t test_acc: 88.09 (best: 89.55 from #116)\n",
            "2022-11-24 00:54:33,885 | INFO : Epoch #161\n",
            "2022-11-24 00:54:33,885 | INFO : \t train_acc: 92.324; train_loss: 0.026; test_loss: 33.612; lr: [0.010000000000000002]\n",
            "2022-11-24 00:54:33,885 | INFO : \t test_acc: 88.82 (best: 89.55 from #116)\n",
            "2022-11-24 00:54:47,267 | INFO : Epoch #162\n",
            "2022-11-24 00:54:47,268 | INFO : \t train_acc: 92.38; train_loss: 0.015; test_loss: 36.395; lr: [0.010000000000000002]\n",
            "2022-11-24 00:54:47,268 | INFO : \t test_acc: 88.38 (best: 89.55 from #116)\n",
            "2022-11-24 00:55:00,675 | INFO : Epoch #163\n",
            "2022-11-24 00:55:00,676 | INFO : \t train_acc: 92.632; train_loss: 0.384; test_loss: 32.733; lr: [0.010000000000000002]\n",
            "2022-11-24 00:55:00,676 | INFO : \t test_acc: 89.05 (best: 89.55 from #116)\n",
            "2022-11-24 00:55:13,955 | INFO : Epoch #164\n",
            "2022-11-24 00:55:13,956 | INFO : \t train_acc: 92.68; train_loss: 0.092; test_loss: 34.999; lr: [0.010000000000000002]\n",
            "2022-11-24 00:55:13,956 | INFO : \t test_acc: 88.68 (best: 89.55 from #116)\n",
            "2022-11-24 00:55:27,204 | INFO : Epoch #165\n",
            "2022-11-24 00:55:27,205 | INFO : \t train_acc: 92.544; train_loss: 0.283; test_loss: 36.550; lr: [0.010000000000000002]\n",
            "2022-11-24 00:55:27,205 | INFO : \t test_acc: 87.99 (best: 89.55 from #116)\n",
            "2022-11-24 00:55:40,621 | INFO : Epoch #166\n",
            "2022-11-24 00:55:40,622 | INFO : \t train_acc: 92.46; train_loss: 0.439; test_loss: 42.759; lr: [0.010000000000000002]\n",
            "2022-11-24 00:55:40,622 | INFO : \t test_acc: 87.11 (best: 89.55 from #116)\n",
            "2022-11-24 00:55:54,102 | INFO : Epoch #167\n",
            "2022-11-24 00:55:54,103 | INFO : \t train_acc: 92.72; train_loss: 0.416; test_loss: 34.281; lr: [0.010000000000000002]\n",
            "2022-11-24 00:55:54,103 | INFO : \t test_acc: 88.79 (best: 89.55 from #116)\n",
            "2022-11-24 00:56:07,403 | INFO : Epoch #168\n",
            "2022-11-24 00:56:07,403 | INFO : \t train_acc: 92.684; train_loss: 0.366; test_loss: 31.939; lr: [0.010000000000000002]\n",
            "2022-11-24 00:56:07,403 | INFO : \t test_acc: 89.47 (best: 89.55 from #116)\n",
            "2022-11-24 00:56:20,704 | INFO : Epoch #169\n",
            "2022-11-24 00:56:20,705 | INFO : \t train_acc: 92.742; train_loss: 0.253; test_loss: 38.546; lr: [0.010000000000000002]\n",
            "2022-11-24 00:56:20,705 | INFO : \t test_acc: 87.8 (best: 89.55 from #116)\n",
            "2022-11-24 00:56:34,400 | INFO : Epoch #170\n",
            "2022-11-24 00:56:34,401 | INFO : \t train_acc: 92.692; train_loss: 0.216; test_loss: 36.903; lr: [0.010000000000000002]\n",
            "2022-11-24 00:56:34,401 | INFO : \t test_acc: 88.08 (best: 89.55 from #116)\n",
            "2022-11-24 00:56:48,221 | INFO : Epoch #171\n",
            "2022-11-24 00:56:48,221 | INFO : \t train_acc: 92.698; train_loss: 0.307; test_loss: 37.681; lr: [0.010000000000000002]\n",
            "2022-11-24 00:56:48,222 | INFO : \t test_acc: 88.21 (best: 89.55 from #116)\n",
            "2022-11-24 00:57:01,912 | INFO : Epoch #172\n",
            "2022-11-24 00:57:01,912 | INFO : \t train_acc: 92.752; train_loss: 0.277; test_loss: 33.634; lr: [0.010000000000000002]\n",
            "2022-11-24 00:57:01,912 | INFO : \t test_acc: 89.16 (best: 89.55 from #116)\n",
            "2022-11-24 00:57:15,394 | INFO : Epoch #173\n",
            "2022-11-24 00:57:15,394 | INFO : \t train_acc: 92.68; train_loss: 0.318; test_loss: 44.996; lr: [0.010000000000000002]\n",
            "2022-11-24 00:57:15,394 | INFO : \t test_acc: 85.32 (best: 89.55 from #116)\n",
            "2022-11-24 00:57:28,939 | INFO : Epoch #174\n",
            "2022-11-24 00:57:28,939 | INFO : \t train_acc: 92.596; train_loss: 0.137; test_loss: 34.723; lr: [0.010000000000000002]\n",
            "2022-11-24 00:57:28,939 | INFO : \t test_acc: 88.67 (best: 89.55 from #116)\n",
            "2022-11-24 00:57:42,625 | INFO : Epoch #175\n",
            "2022-11-24 00:57:42,626 | INFO : \t train_acc: 92.66; train_loss: 0.306; test_loss: 41.729; lr: [0.010000000000000002]\n",
            "2022-11-24 00:57:42,626 | INFO : \t test_acc: 86.72 (best: 89.55 from #116)\n",
            "2022-11-24 00:57:55,979 | INFO : Epoch #176\n",
            "2022-11-24 00:57:55,980 | INFO : \t train_acc: 92.814; train_loss: 0.301; test_loss: 34.303; lr: [0.010000000000000002]\n",
            "2022-11-24 00:57:55,980 | INFO : \t test_acc: 88.98 (best: 89.55 from #116)\n",
            "2022-11-24 00:58:09,339 | INFO : Epoch #177\n",
            "2022-11-24 00:58:09,340 | INFO : \t train_acc: 92.61; train_loss: 0.374; test_loss: 36.841; lr: [0.010000000000000002]\n",
            "2022-11-24 00:58:09,340 | INFO : \t test_acc: 88.16 (best: 89.55 from #116)\n",
            "2022-11-24 00:58:22,659 | INFO : Epoch #178\n",
            "2022-11-24 00:58:22,660 | INFO : \t train_acc: 92.636; train_loss: 0.070; test_loss: 37.909; lr: [0.010000000000000002]\n",
            "2022-11-24 00:58:22,660 | INFO : \t test_acc: 88.09 (best: 89.55 from #116)\n",
            "2022-11-24 00:58:36,373 | INFO : Epoch #179\n",
            "2022-11-24 00:58:36,373 | INFO : \t train_acc: 92.72; train_loss: 0.079; test_loss: 32.294; lr: [0.010000000000000002]\n",
            "2022-11-24 00:58:36,373 | INFO : \t test_acc: 89.61 (best: 89.61 from #179)\n",
            "2022-11-24 00:58:49,842 | INFO : Epoch #180\n",
            "2022-11-24 00:58:49,843 | INFO : \t train_acc: 92.818; train_loss: 0.216; test_loss: 44.203; lr: [0.010000000000000002]\n",
            "2022-11-24 00:58:49,843 | INFO : \t test_acc: 86.25 (best: 89.61 from #179)\n",
            "2022-11-24 00:59:03,866 | INFO : Epoch #181\n",
            "2022-11-24 00:59:03,867 | INFO : \t train_acc: 92.774; train_loss: 0.134; test_loss: 33.034; lr: [0.010000000000000002]\n",
            "2022-11-24 00:59:03,867 | INFO : \t test_acc: 89.11 (best: 89.61 from #179)\n",
            "2022-11-24 00:59:17,167 | INFO : Epoch #182\n",
            "2022-11-24 00:59:17,167 | INFO : \t train_acc: 92.92; train_loss: 0.492; test_loss: 38.012; lr: [0.010000000000000002]\n",
            "2022-11-24 00:59:17,167 | INFO : \t test_acc: 87.82 (best: 89.61 from #179)\n",
            "2022-11-24 00:59:30,728 | INFO : Epoch #183\n",
            "2022-11-24 00:59:30,729 | INFO : \t train_acc: 92.644; train_loss: 0.231; test_loss: 39.136; lr: [0.010000000000000002]\n",
            "2022-11-24 00:59:30,729 | INFO : \t test_acc: 87.63 (best: 89.61 from #179)\n",
            "2022-11-24 00:59:44,241 | INFO : Epoch #184\n",
            "2022-11-24 00:59:44,241 | INFO : \t train_acc: 92.946; train_loss: 0.084; test_loss: 35.370; lr: [0.010000000000000002]\n",
            "2022-11-24 00:59:44,241 | INFO : \t test_acc: 88.7 (best: 89.61 from #179)\n",
            "2022-11-24 00:59:57,900 | INFO : Epoch #185\n",
            "2022-11-24 00:59:57,900 | INFO : \t train_acc: 92.626; train_loss: 0.351; test_loss: 43.853; lr: [0.010000000000000002]\n",
            "2022-11-24 00:59:57,900 | INFO : \t test_acc: 85.84 (best: 89.61 from #179)\n",
            "2022-11-24 01:00:11,267 | INFO : Epoch #186\n",
            "2022-11-24 01:00:11,268 | INFO : \t train_acc: 92.962; train_loss: 0.257; test_loss: 33.219; lr: [0.010000000000000002]\n",
            "2022-11-24 01:00:11,268 | INFO : \t test_acc: 88.98 (best: 89.61 from #179)\n",
            "2022-11-24 01:00:24,848 | INFO : Epoch #187\n",
            "2022-11-24 01:00:24,848 | INFO : \t train_acc: 92.59; train_loss: 0.202; test_loss: 37.048; lr: [0.010000000000000002]\n",
            "2022-11-24 01:00:24,849 | INFO : \t test_acc: 87.93 (best: 89.61 from #179)\n",
            "2022-11-24 01:00:38,558 | INFO : Epoch #188\n",
            "2022-11-24 01:00:38,559 | INFO : \t train_acc: 92.794; train_loss: 0.130; test_loss: 35.703; lr: [0.010000000000000002]\n",
            "2022-11-24 01:00:38,559 | INFO : \t test_acc: 88.15 (best: 89.61 from #179)\n",
            "2022-11-24 01:00:52,673 | INFO : Epoch #189\n",
            "2022-11-24 01:00:52,674 | INFO : \t train_acc: 92.798; train_loss: 0.267; test_loss: 35.812; lr: [0.010000000000000002]\n",
            "2022-11-24 01:00:52,674 | INFO : \t test_acc: 88.53 (best: 89.61 from #179)\n",
            "2022-11-24 01:01:05,969 | INFO : Epoch #190\n",
            "2022-11-24 01:01:05,969 | INFO : \t train_acc: 92.822; train_loss: 0.243; test_loss: 35.639; lr: [0.010000000000000002]\n",
            "2022-11-24 01:01:05,969 | INFO : \t test_acc: 88.64 (best: 89.61 from #179)\n",
            "2022-11-24 01:01:19,348 | INFO : Epoch #191\n",
            "2022-11-24 01:01:19,349 | INFO : \t train_acc: 92.876; train_loss: 0.235; test_loss: 35.615; lr: [0.010000000000000002]\n",
            "2022-11-24 01:01:19,349 | INFO : \t test_acc: 88.78 (best: 89.61 from #179)\n",
            "2022-11-24 01:01:33,039 | INFO : Epoch #192\n",
            "2022-11-24 01:01:33,040 | INFO : \t train_acc: 93.018; train_loss: 0.272; test_loss: 37.323; lr: [0.010000000000000002]\n",
            "2022-11-24 01:01:33,040 | INFO : \t test_acc: 87.82 (best: 89.61 from #179)\n",
            "2022-11-24 01:01:46,586 | INFO : Epoch #193\n",
            "2022-11-24 01:01:46,587 | INFO : \t train_acc: 92.766; train_loss: 0.224; test_loss: 35.772; lr: [0.010000000000000002]\n",
            "2022-11-24 01:01:46,587 | INFO : \t test_acc: 88.53 (best: 89.61 from #179)\n",
            "2022-11-24 01:02:00,092 | INFO : Epoch #194\n",
            "2022-11-24 01:02:00,092 | INFO : \t train_acc: 92.826; train_loss: 0.250; test_loss: 37.273; lr: [0.010000000000000002]\n",
            "2022-11-24 01:02:00,092 | INFO : \t test_acc: 88.24 (best: 89.61 from #179)\n",
            "2022-11-24 01:02:13,390 | INFO : Epoch #195\n",
            "2022-11-24 01:02:13,391 | INFO : \t train_acc: 92.826; train_loss: 0.145; test_loss: 38.213; lr: [0.010000000000000002]\n",
            "2022-11-24 01:02:13,391 | INFO : \t test_acc: 87.64 (best: 89.61 from #179)\n",
            "2022-11-24 01:02:27,257 | INFO : Epoch #196\n",
            "2022-11-24 01:02:27,257 | INFO : \t train_acc: 92.91; train_loss: 0.214; test_loss: 42.137; lr: [0.010000000000000002]\n",
            "2022-11-24 01:02:27,258 | INFO : \t test_acc: 86.48 (best: 89.61 from #179)\n",
            "2022-11-24 01:02:40,526 | INFO : Epoch #197\n",
            "2022-11-24 01:02:40,526 | INFO : \t train_acc: 92.948; train_loss: 0.156; test_loss: 37.699; lr: [0.010000000000000002]\n",
            "2022-11-24 01:02:40,526 | INFO : \t test_acc: 87.93 (best: 89.61 from #179)\n",
            "2022-11-24 01:02:53,864 | INFO : Epoch #198\n",
            "2022-11-24 01:02:53,865 | INFO : \t train_acc: 92.714; train_loss: 0.080; test_loss: 33.456; lr: [0.010000000000000002]\n",
            "2022-11-24 01:02:53,865 | INFO : \t test_acc: 89.35 (best: 89.61 from #179)\n",
            "2022-11-24 01:03:07,644 | INFO : Epoch #199\n",
            "2022-11-24 01:03:07,645 | INFO : \t train_acc: 92.952; train_loss: 0.510; test_loss: 34.156; lr: [0.010000000000000002]\n",
            "2022-11-24 01:03:07,645 | INFO : \t test_acc: 88.81 (best: 89.61 from #179)\n",
            "2022-11-24 01:03:21,144 | INFO : Epoch #200\n",
            "2022-11-24 01:03:21,144 | INFO : \t train_acc: 95.428; train_loss: 0.359; test_loss: 25.583; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:03:21,145 | INFO : \t test_acc: 91.81 (best: 91.81 from #200)\n",
            "2022-11-24 01:03:34,410 | INFO : Epoch #201\n",
            "2022-11-24 01:03:34,411 | INFO : \t train_acc: 96.502; train_loss: 0.064; test_loss: 24.931; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:03:34,411 | INFO : \t test_acc: 91.99 (best: 91.99 from #201)\n",
            "2022-11-24 01:03:47,933 | INFO : Epoch #202\n",
            "2022-11-24 01:03:47,933 | INFO : \t train_acc: 96.882; train_loss: 0.303; test_loss: 24.897; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:03:47,933 | INFO : \t test_acc: 92.16 (best: 92.16 from #202)\n",
            "2022-11-24 01:04:01,443 | INFO : Epoch #203\n",
            "2022-11-24 01:04:01,443 | INFO : \t train_acc: 96.852; train_loss: 0.102; test_loss: 24.776; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:04:01,443 | INFO : \t test_acc: 92.12 (best: 92.16 from #202)\n",
            "2022-11-24 01:04:15,242 | INFO : Epoch #204\n",
            "2022-11-24 01:04:15,242 | INFO : \t train_acc: 97.198; train_loss: 0.007; test_loss: 24.752; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:04:15,242 | INFO : \t test_acc: 92.2 (best: 92.2 from #204)\n",
            "2022-11-24 01:04:29,015 | INFO : Epoch #205\n",
            "2022-11-24 01:04:29,015 | INFO : \t train_acc: 97.38; train_loss: 0.305; test_loss: 25.138; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:04:29,016 | INFO : \t test_acc: 92.06 (best: 92.2 from #204)\n",
            "2022-11-24 01:04:42,383 | INFO : Epoch #206\n",
            "2022-11-24 01:04:42,383 | INFO : \t train_acc: 97.348; train_loss: 0.011; test_loss: 24.961; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:04:42,384 | INFO : \t test_acc: 92.08 (best: 92.2 from #204)\n",
            "2022-11-24 01:04:55,965 | INFO : Epoch #207\n",
            "2022-11-24 01:04:55,965 | INFO : \t train_acc: 97.532; train_loss: 0.207; test_loss: 24.739; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:04:55,965 | INFO : \t test_acc: 92.22 (best: 92.22 from #207)\n",
            "2022-11-24 01:05:09,394 | INFO : Epoch #208\n",
            "2022-11-24 01:05:09,394 | INFO : \t train_acc: 97.64; train_loss: 0.199; test_loss: 25.157; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:05:09,395 | INFO : \t test_acc: 92.43 (best: 92.43 from #208)\n",
            "2022-11-24 01:05:22,807 | INFO : Epoch #209\n",
            "2022-11-24 01:05:22,808 | INFO : \t train_acc: 97.828; train_loss: 0.249; test_loss: 24.857; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:05:22,808 | INFO : \t test_acc: 92.28 (best: 92.43 from #208)\n",
            "2022-11-24 01:05:36,098 | INFO : Epoch #210\n",
            "2022-11-24 01:05:36,099 | INFO : \t train_acc: 97.822; train_loss: 0.409; test_loss: 25.376; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:05:36,099 | INFO : \t test_acc: 92.18 (best: 92.43 from #208)\n",
            "2022-11-24 01:05:49,833 | INFO : Epoch #211\n",
            "2022-11-24 01:05:49,834 | INFO : \t train_acc: 97.87; train_loss: 0.050; test_loss: 25.393; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:05:49,834 | INFO : \t test_acc: 92.08 (best: 92.43 from #208)\n",
            "2022-11-24 01:06:03,326 | INFO : Epoch #212\n",
            "2022-11-24 01:06:03,326 | INFO : \t train_acc: 97.912; train_loss: 0.028; test_loss: 25.382; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:06:03,326 | INFO : \t test_acc: 92.3 (best: 92.43 from #208)\n",
            "2022-11-24 01:06:16,747 | INFO : Epoch #213\n",
            "2022-11-24 01:06:16,747 | INFO : \t train_acc: 98.016; train_loss: 0.006; test_loss: 25.640; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:06:16,747 | INFO : \t test_acc: 92.02 (best: 92.43 from #208)\n",
            "2022-11-24 01:06:30,236 | INFO : Epoch #214\n",
            "2022-11-24 01:06:30,237 | INFO : \t train_acc: 98.004; train_loss: 0.011; test_loss: 25.699; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:06:30,237 | INFO : \t test_acc: 92.21 (best: 92.43 from #208)\n",
            "2022-11-24 01:06:43,936 | INFO : Epoch #215\n",
            "2022-11-24 01:06:43,937 | INFO : \t train_acc: 98.158; train_loss: 0.181; test_loss: 26.076; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:06:43,937 | INFO : \t test_acc: 92.34 (best: 92.43 from #208)\n",
            "2022-11-24 01:06:57,407 | INFO : Epoch #216\n",
            "2022-11-24 01:06:57,408 | INFO : \t train_acc: 97.986; train_loss: 0.040; test_loss: 26.213; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:06:57,408 | INFO : \t test_acc: 92.17 (best: 92.43 from #208)\n",
            "2022-11-24 01:07:10,773 | INFO : Epoch #217\n",
            "2022-11-24 01:07:10,774 | INFO : \t train_acc: 98.18; train_loss: 0.067; test_loss: 25.962; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:07:10,774 | INFO : \t test_acc: 92.17 (best: 92.43 from #208)\n",
            "2022-11-24 01:07:24,567 | INFO : Epoch #218\n",
            "2022-11-24 01:07:24,567 | INFO : \t train_acc: 98.17; train_loss: 0.221; test_loss: 26.175; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:07:24,567 | INFO : \t test_acc: 92.26 (best: 92.43 from #208)\n",
            "2022-11-24 01:07:38,513 | INFO : Epoch #219\n",
            "2022-11-24 01:07:38,514 | INFO : \t train_acc: 98.164; train_loss: 0.114; test_loss: 26.364; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:07:38,514 | INFO : \t test_acc: 92.2 (best: 92.43 from #208)\n",
            "2022-11-24 01:07:52,495 | INFO : Epoch #220\n",
            "2022-11-24 01:07:52,496 | INFO : \t train_acc: 98.32; train_loss: 0.007; test_loss: 26.029; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:07:52,496 | INFO : \t test_acc: 92.15 (best: 92.43 from #208)\n",
            "2022-11-24 01:08:05,830 | INFO : Epoch #221\n",
            "2022-11-24 01:08:05,830 | INFO : \t train_acc: 98.368; train_loss: 0.023; test_loss: 26.478; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:08:05,830 | INFO : \t test_acc: 92.2 (best: 92.43 from #208)\n",
            "2022-11-24 01:08:19,238 | INFO : Epoch #222\n",
            "2022-11-24 01:08:19,238 | INFO : \t train_acc: 98.208; train_loss: 0.215; test_loss: 26.589; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:08:19,238 | INFO : \t test_acc: 92.24 (best: 92.43 from #208)\n",
            "2022-11-24 01:08:33,196 | INFO : Epoch #223\n",
            "2022-11-24 01:08:33,196 | INFO : \t train_acc: 98.436; train_loss: 0.141; test_loss: 26.563; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:08:33,196 | INFO : \t test_acc: 92.04 (best: 92.43 from #208)\n",
            "2022-11-24 01:08:46,834 | INFO : Epoch #224\n",
            "2022-11-24 01:08:46,834 | INFO : \t train_acc: 98.492; train_loss: 0.017; test_loss: 26.671; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:08:46,835 | INFO : \t test_acc: 92.14 (best: 92.43 from #208)\n",
            "2022-11-24 01:09:00,260 | INFO : Epoch #225\n",
            "2022-11-24 01:09:00,260 | INFO : \t train_acc: 98.522; train_loss: 0.011; test_loss: 26.553; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:09:00,261 | INFO : \t test_acc: 92.14 (best: 92.43 from #208)\n",
            "2022-11-24 01:09:13,818 | INFO : Epoch #226\n",
            "2022-11-24 01:09:13,818 | INFO : \t train_acc: 98.532; train_loss: 0.087; test_loss: 26.778; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:09:13,818 | INFO : \t test_acc: 92.22 (best: 92.43 from #208)\n",
            "2022-11-24 01:09:27,170 | INFO : Epoch #227\n",
            "2022-11-24 01:09:27,170 | INFO : \t train_acc: 98.536; train_loss: 0.024; test_loss: 26.612; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:09:27,171 | INFO : \t test_acc: 92.12 (best: 92.43 from #208)\n",
            "2022-11-24 01:09:40,414 | INFO : Epoch #228\n",
            "2022-11-24 01:09:40,415 | INFO : \t train_acc: 98.502; train_loss: 0.073; test_loss: 26.669; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:09:40,415 | INFO : \t test_acc: 92.2 (best: 92.43 from #208)\n",
            "2022-11-24 01:09:53,650 | INFO : Epoch #229\n",
            "2022-11-24 01:09:53,650 | INFO : \t train_acc: 98.638; train_loss: 0.011; test_loss: 26.692; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:09:53,651 | INFO : \t test_acc: 92.29 (best: 92.43 from #208)\n",
            "2022-11-24 01:10:06,989 | INFO : Epoch #230\n",
            "2022-11-24 01:10:06,990 | INFO : \t train_acc: 98.638; train_loss: 0.039; test_loss: 26.812; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:10:06,990 | INFO : \t test_acc: 92.17 (best: 92.43 from #208)\n",
            "2022-11-24 01:10:20,534 | INFO : Epoch #231\n",
            "2022-11-24 01:10:20,534 | INFO : \t train_acc: 98.638; train_loss: 0.291; test_loss: 27.996; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:10:20,535 | INFO : \t test_acc: 92.02 (best: 92.43 from #208)\n",
            "2022-11-24 01:10:33,864 | INFO : Epoch #232\n",
            "2022-11-24 01:10:33,865 | INFO : \t train_acc: 98.63; train_loss: 0.140; test_loss: 27.420; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:10:33,865 | INFO : \t test_acc: 92.1 (best: 92.43 from #208)\n",
            "2022-11-24 01:10:47,523 | INFO : Epoch #233\n",
            "2022-11-24 01:10:47,524 | INFO : \t train_acc: 98.618; train_loss: 0.285; test_loss: 27.405; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:10:47,524 | INFO : \t test_acc: 92.11 (best: 92.43 from #208)\n",
            "2022-11-24 01:11:00,919 | INFO : Epoch #234\n",
            "2022-11-24 01:11:00,919 | INFO : \t train_acc: 98.658; train_loss: 0.086; test_loss: 27.435; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:11:00,919 | INFO : \t test_acc: 92.07 (best: 92.43 from #208)\n",
            "2022-11-24 01:11:14,319 | INFO : Epoch #235\n",
            "2022-11-24 01:11:14,319 | INFO : \t train_acc: 98.732; train_loss: 0.158; test_loss: 27.305; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:11:14,319 | INFO : \t test_acc: 92.25 (best: 92.43 from #208)\n",
            "2022-11-24 01:11:27,966 | INFO : Epoch #236\n",
            "2022-11-24 01:11:27,966 | INFO : \t train_acc: 98.742; train_loss: 0.029; test_loss: 27.692; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:11:27,966 | INFO : \t test_acc: 92.1 (best: 92.43 from #208)\n",
            "2022-11-24 01:11:41,300 | INFO : Epoch #237\n",
            "2022-11-24 01:11:41,301 | INFO : \t train_acc: 98.764; train_loss: 0.089; test_loss: 27.861; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:11:41,301 | INFO : \t test_acc: 92.29 (best: 92.43 from #208)\n",
            "2022-11-24 01:11:54,601 | INFO : Epoch #238\n",
            "2022-11-24 01:11:54,601 | INFO : \t train_acc: 98.762; train_loss: 0.024; test_loss: 27.395; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:11:54,602 | INFO : \t test_acc: 92.21 (best: 92.43 from #208)\n",
            "2022-11-24 01:12:08,183 | INFO : Epoch #239\n",
            "2022-11-24 01:12:08,184 | INFO : \t train_acc: 98.824; train_loss: 0.132; test_loss: 27.355; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:12:08,184 | INFO : \t test_acc: 92.31 (best: 92.43 from #208)\n",
            "2022-11-24 01:12:21,487 | INFO : Epoch #240\n",
            "2022-11-24 01:12:21,487 | INFO : \t train_acc: 98.824; train_loss: 0.054; test_loss: 27.727; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:12:21,487 | INFO : \t test_acc: 92.25 (best: 92.43 from #208)\n",
            "2022-11-24 01:12:35,015 | INFO : Epoch #241\n",
            "2022-11-24 01:12:35,015 | INFO : \t train_acc: 98.854; train_loss: 0.009; test_loss: 28.072; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:12:35,015 | INFO : \t test_acc: 92.23 (best: 92.43 from #208)\n",
            "2022-11-24 01:12:48,294 | INFO : Epoch #242\n",
            "2022-11-24 01:12:48,295 | INFO : \t train_acc: 98.87; train_loss: 0.039; test_loss: 28.016; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:12:48,295 | INFO : \t test_acc: 92.21 (best: 92.43 from #208)\n",
            "2022-11-24 01:13:01,666 | INFO : Epoch #243\n",
            "2022-11-24 01:13:01,667 | INFO : \t train_acc: 98.822; train_loss: 0.032; test_loss: 28.386; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:13:01,667 | INFO : \t test_acc: 92.21 (best: 92.43 from #208)\n",
            "2022-11-24 01:13:15,392 | INFO : Epoch #244\n",
            "2022-11-24 01:13:15,393 | INFO : \t train_acc: 98.932; train_loss: 0.101; test_loss: 27.923; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:13:15,393 | INFO : \t test_acc: 92.1 (best: 92.43 from #208)\n",
            "2022-11-24 01:13:28,882 | INFO : Epoch #245\n",
            "2022-11-24 01:13:28,883 | INFO : \t train_acc: 98.778; train_loss: 0.023; test_loss: 27.652; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:13:28,883 | INFO : \t test_acc: 92.19 (best: 92.43 from #208)\n",
            "2022-11-24 01:13:42,271 | INFO : Epoch #246\n",
            "2022-11-24 01:13:42,271 | INFO : \t train_acc: 98.88; train_loss: 0.151; test_loss: 27.986; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:13:42,271 | INFO : \t test_acc: 92.12 (best: 92.43 from #208)\n",
            "2022-11-24 01:13:55,714 | INFO : Epoch #247\n",
            "2022-11-24 01:13:55,714 | INFO : \t train_acc: 98.854; train_loss: 0.335; test_loss: 27.776; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:13:55,714 | INFO : \t test_acc: 92.29 (best: 92.43 from #208)\n",
            "2022-11-24 01:14:09,383 | INFO : Epoch #248\n",
            "2022-11-24 01:14:09,384 | INFO : \t train_acc: 98.89; train_loss: 0.219; test_loss: 28.546; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:14:09,384 | INFO : \t test_acc: 92.17 (best: 92.43 from #208)\n",
            "2022-11-24 01:14:23,073 | INFO : Epoch #249\n",
            "2022-11-24 01:14:23,073 | INFO : \t train_acc: 98.822; train_loss: 0.016; test_loss: 28.236; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:14:23,073 | INFO : \t test_acc: 92.1 (best: 92.43 from #208)\n",
            "2022-11-24 01:14:36,371 | INFO : Epoch #250\n",
            "2022-11-24 01:14:36,371 | INFO : \t train_acc: 98.958; train_loss: 0.217; test_loss: 28.954; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:14:36,371 | INFO : \t test_acc: 92.11 (best: 92.43 from #208)\n",
            "2022-11-24 01:14:49,687 | INFO : Epoch #251\n",
            "2022-11-24 01:14:49,687 | INFO : \t train_acc: 98.938; train_loss: 0.082; test_loss: 28.518; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:14:49,687 | INFO : \t test_acc: 92.09 (best: 92.43 from #208)\n",
            "2022-11-24 01:15:03,300 | INFO : Epoch #252\n",
            "2022-11-24 01:15:03,301 | INFO : \t train_acc: 98.906; train_loss: 0.069; test_loss: 28.911; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:15:03,301 | INFO : \t test_acc: 92.17 (best: 92.43 from #208)\n",
            "2022-11-24 01:15:17,063 | INFO : Epoch #253\n",
            "2022-11-24 01:15:17,064 | INFO : \t train_acc: 98.926; train_loss: 0.030; test_loss: 28.976; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:15:17,064 | INFO : \t test_acc: 92.06 (best: 92.43 from #208)\n",
            "2022-11-24 01:15:30,380 | INFO : Epoch #254\n",
            "2022-11-24 01:15:30,381 | INFO : \t train_acc: 98.97; train_loss: 0.092; test_loss: 28.222; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:15:30,381 | INFO : \t test_acc: 92.3 (best: 92.43 from #208)\n",
            "2022-11-24 01:15:43,604 | INFO : Epoch #255\n",
            "2022-11-24 01:15:43,605 | INFO : \t train_acc: 98.978; train_loss: 0.002; test_loss: 29.396; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:15:43,605 | INFO : \t test_acc: 91.92 (best: 92.43 from #208)\n",
            "2022-11-24 01:15:57,139 | INFO : Epoch #256\n",
            "2022-11-24 01:15:57,140 | INFO : \t train_acc: 99.028; train_loss: 0.029; test_loss: 28.511; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:15:57,140 | INFO : \t test_acc: 92.01 (best: 92.43 from #208)\n",
            "2022-11-24 01:16:10,453 | INFO : Epoch #257\n",
            "2022-11-24 01:16:10,453 | INFO : \t train_acc: 99.054; train_loss: 0.054; test_loss: 28.451; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:16:10,454 | INFO : \t test_acc: 92.05 (best: 92.43 from #208)\n",
            "2022-11-24 01:16:24,006 | INFO : Epoch #258\n",
            "2022-11-24 01:16:24,006 | INFO : \t train_acc: 99.024; train_loss: 0.027; test_loss: 29.079; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:16:24,006 | INFO : \t test_acc: 92.11 (best: 92.43 from #208)\n",
            "2022-11-24 01:16:37,544 | INFO : Epoch #259\n",
            "2022-11-24 01:16:37,545 | INFO : \t train_acc: 99.034; train_loss: 0.077; test_loss: 29.512; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:16:37,545 | INFO : \t test_acc: 91.96 (best: 92.43 from #208)\n",
            "2022-11-24 01:16:50,903 | INFO : Epoch #260\n",
            "2022-11-24 01:16:50,904 | INFO : \t train_acc: 99.092; train_loss: 0.012; test_loss: 29.367; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:16:50,904 | INFO : \t test_acc: 92.0 (best: 92.43 from #208)\n",
            "2022-11-24 01:17:04,324 | INFO : Epoch #261\n",
            "2022-11-24 01:17:04,324 | INFO : \t train_acc: 99.006; train_loss: 0.027; test_loss: 28.490; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:17:04,324 | INFO : \t test_acc: 92.37 (best: 92.43 from #208)\n",
            "2022-11-24 01:17:17,722 | INFO : Epoch #262\n",
            "2022-11-24 01:17:17,723 | INFO : \t train_acc: 98.994; train_loss: 0.104; test_loss: 28.921; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:17:17,723 | INFO : \t test_acc: 92.01 (best: 92.43 from #208)\n",
            "2022-11-24 01:17:31,185 | INFO : Epoch #263\n",
            "2022-11-24 01:17:31,185 | INFO : \t train_acc: 98.986; train_loss: 0.015; test_loss: 29.067; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:17:31,185 | INFO : \t test_acc: 92.07 (best: 92.43 from #208)\n",
            "2022-11-24 01:17:44,564 | INFO : Epoch #264\n",
            "2022-11-24 01:17:44,564 | INFO : \t train_acc: 99.07; train_loss: 0.364; test_loss: 29.727; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:17:44,564 | INFO : \t test_acc: 92.17 (best: 92.43 from #208)\n",
            "2022-11-24 01:17:57,917 | INFO : Epoch #265\n",
            "2022-11-24 01:17:57,917 | INFO : \t train_acc: 99.056; train_loss: 0.018; test_loss: 28.287; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:17:57,917 | INFO : \t test_acc: 92.5 (best: 92.5 from #265)\n",
            "2022-11-24 01:18:11,202 | INFO : Epoch #266\n",
            "2022-11-24 01:18:11,202 | INFO : \t train_acc: 99.02; train_loss: 0.019; test_loss: 28.854; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:18:11,203 | INFO : \t test_acc: 92.21 (best: 92.5 from #265)\n",
            "2022-11-24 01:18:24,830 | INFO : Epoch #267\n",
            "2022-11-24 01:18:24,830 | INFO : \t train_acc: 99.096; train_loss: 0.036; test_loss: 29.048; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:18:24,830 | INFO : \t test_acc: 92.14 (best: 92.5 from #265)\n",
            "2022-11-24 01:18:38,091 | INFO : Epoch #268\n",
            "2022-11-24 01:18:38,091 | INFO : \t train_acc: 99.08; train_loss: 0.197; test_loss: 29.567; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:18:38,091 | INFO : \t test_acc: 92.16 (best: 92.5 from #265)\n",
            "2022-11-24 01:18:51,802 | INFO : Epoch #269\n",
            "2022-11-24 01:18:51,803 | INFO : \t train_acc: 99.01; train_loss: 0.056; test_loss: 28.755; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:18:51,803 | INFO : \t test_acc: 92.02 (best: 92.5 from #265)\n",
            "2022-11-24 01:19:05,107 | INFO : Epoch #270\n",
            "2022-11-24 01:19:05,108 | INFO : \t train_acc: 99.056; train_loss: 0.154; test_loss: 28.909; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:19:05,108 | INFO : \t test_acc: 92.27 (best: 92.5 from #265)\n",
            "2022-11-24 01:19:18,696 | INFO : Epoch #271\n",
            "2022-11-24 01:19:18,697 | INFO : \t train_acc: 99.102; train_loss: 0.010; test_loss: 28.977; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:19:18,697 | INFO : \t test_acc: 92.2 (best: 92.5 from #265)\n",
            "2022-11-24 01:19:32,454 | INFO : Epoch #272\n",
            "2022-11-24 01:19:32,455 | INFO : \t train_acc: 99.118; train_loss: 0.112; test_loss: 29.024; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:19:32,455 | INFO : \t test_acc: 92.2 (best: 92.5 from #265)\n",
            "2022-11-24 01:19:45,852 | INFO : Epoch #273\n",
            "2022-11-24 01:19:45,852 | INFO : \t train_acc: 99.064; train_loss: 0.002; test_loss: 29.534; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:19:45,852 | INFO : \t test_acc: 92.25 (best: 92.5 from #265)\n",
            "2022-11-24 01:19:59,465 | INFO : Epoch #274\n",
            "2022-11-24 01:19:59,466 | INFO : \t train_acc: 99.094; train_loss: 0.147; test_loss: 29.203; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:19:59,466 | INFO : \t test_acc: 91.91 (best: 92.5 from #265)\n",
            "2022-11-24 01:20:13,000 | INFO : Epoch #275\n",
            "2022-11-24 01:20:13,000 | INFO : \t train_acc: 99.072; train_loss: 0.195; test_loss: 30.079; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:20:13,001 | INFO : \t test_acc: 91.78 (best: 92.5 from #265)\n",
            "2022-11-24 01:20:26,350 | INFO : Epoch #276\n",
            "2022-11-24 01:20:26,350 | INFO : \t train_acc: 99.122; train_loss: 0.072; test_loss: 30.220; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:20:26,350 | INFO : \t test_acc: 91.8 (best: 92.5 from #265)\n",
            "2022-11-24 01:20:40,359 | INFO : Epoch #277\n",
            "2022-11-24 01:20:40,360 | INFO : \t train_acc: 99.126; train_loss: 0.057; test_loss: 29.388; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:20:40,360 | INFO : \t test_acc: 91.98 (best: 92.5 from #265)\n",
            "2022-11-24 01:20:53,888 | INFO : Epoch #278\n",
            "2022-11-24 01:20:53,888 | INFO : \t train_acc: 99.14; train_loss: 0.505; test_loss: 29.803; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:20:53,888 | INFO : \t test_acc: 92.0 (best: 92.5 from #265)\n",
            "2022-11-24 01:21:07,228 | INFO : Epoch #279\n",
            "2022-11-24 01:21:07,229 | INFO : \t train_acc: 99.128; train_loss: 0.080; test_loss: 29.186; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:21:07,229 | INFO : \t test_acc: 91.88 (best: 92.5 from #265)\n",
            "2022-11-24 01:21:20,625 | INFO : Epoch #280\n",
            "2022-11-24 01:21:20,626 | INFO : \t train_acc: 99.106; train_loss: 0.048; test_loss: 30.534; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:21:20,626 | INFO : \t test_acc: 91.78 (best: 92.5 from #265)\n",
            "2022-11-24 01:21:34,113 | INFO : Epoch #281\n",
            "2022-11-24 01:21:34,114 | INFO : \t train_acc: 99.044; train_loss: 0.013; test_loss: 29.822; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:21:34,114 | INFO : \t test_acc: 91.86 (best: 92.5 from #265)\n",
            "2022-11-24 01:21:47,608 | INFO : Epoch #282\n",
            "2022-11-24 01:21:47,608 | INFO : \t train_acc: 99.122; train_loss: 0.017; test_loss: 29.753; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:21:47,609 | INFO : \t test_acc: 92.07 (best: 92.5 from #265)\n",
            "2022-11-24 01:22:00,997 | INFO : Epoch #283\n",
            "2022-11-24 01:22:00,997 | INFO : \t train_acc: 99.138; train_loss: 0.014; test_loss: 28.664; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:22:00,998 | INFO : \t test_acc: 92.15 (best: 92.5 from #265)\n",
            "2022-11-24 01:22:14,504 | INFO : Epoch #284\n",
            "2022-11-24 01:22:14,505 | INFO : \t train_acc: 99.202; train_loss: 0.636; test_loss: 30.051; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:22:14,505 | INFO : \t test_acc: 92.06 (best: 92.5 from #265)\n",
            "2022-11-24 01:22:28,545 | INFO : Epoch #285\n",
            "2022-11-24 01:22:28,546 | INFO : \t train_acc: 99.146; train_loss: 0.026; test_loss: 29.503; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:22:28,546 | INFO : \t test_acc: 92.12 (best: 92.5 from #265)\n",
            "2022-11-24 01:22:42,195 | INFO : Epoch #286\n",
            "2022-11-24 01:22:42,196 | INFO : \t train_acc: 99.18; train_loss: 0.019; test_loss: 29.050; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:22:42,196 | INFO : \t test_acc: 92.34 (best: 92.5 from #265)\n",
            "2022-11-24 01:22:55,692 | INFO : Epoch #287\n",
            "2022-11-24 01:22:55,692 | INFO : \t train_acc: 99.114; train_loss: 0.003; test_loss: 29.955; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:22:55,693 | INFO : \t test_acc: 92.09 (best: 92.5 from #265)\n",
            "2022-11-24 01:23:09,214 | INFO : Epoch #288\n",
            "2022-11-24 01:23:09,214 | INFO : \t train_acc: 99.132; train_loss: 0.007; test_loss: 29.699; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:23:09,214 | INFO : \t test_acc: 92.0 (best: 92.5 from #265)\n",
            "2022-11-24 01:23:22,762 | INFO : Epoch #289\n",
            "2022-11-24 01:23:22,762 | INFO : \t train_acc: 99.17; train_loss: 0.006; test_loss: 29.915; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:23:22,762 | INFO : \t test_acc: 92.2 (best: 92.5 from #265)\n",
            "2022-11-24 01:23:36,083 | INFO : Epoch #290\n",
            "2022-11-24 01:23:36,083 | INFO : \t train_acc: 99.136; train_loss: 0.450; test_loss: 30.623; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:23:36,084 | INFO : \t test_acc: 91.76 (best: 92.5 from #265)\n",
            "2022-11-24 01:23:49,555 | INFO : Epoch #291\n",
            "2022-11-24 01:23:49,555 | INFO : \t train_acc: 99.084; train_loss: 0.102; test_loss: 30.240; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:23:49,556 | INFO : \t test_acc: 92.12 (best: 92.5 from #265)\n",
            "2022-11-24 01:24:02,840 | INFO : Epoch #292\n",
            "2022-11-24 01:24:02,841 | INFO : \t train_acc: 99.07; train_loss: 0.013; test_loss: 33.097; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:24:02,841 | INFO : \t test_acc: 91.33 (best: 92.5 from #265)\n",
            "2022-11-24 01:24:16,133 | INFO : Epoch #293\n",
            "2022-11-24 01:24:16,134 | INFO : \t train_acc: 99.206; train_loss: 0.211; test_loss: 30.406; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:24:16,134 | INFO : \t test_acc: 92.14 (best: 92.5 from #265)\n",
            "2022-11-24 01:24:29,487 | INFO : Epoch #294\n",
            "2022-11-24 01:24:29,487 | INFO : \t train_acc: 99.184; train_loss: 0.046; test_loss: 31.028; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:24:29,488 | INFO : \t test_acc: 91.91 (best: 92.5 from #265)\n",
            "2022-11-24 01:24:42,836 | INFO : Epoch #295\n",
            "2022-11-24 01:24:42,836 | INFO : \t train_acc: 99.068; train_loss: 0.024; test_loss: 30.562; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:24:42,836 | INFO : \t test_acc: 91.9 (best: 92.5 from #265)\n",
            "2022-11-24 01:24:56,174 | INFO : Epoch #296\n",
            "2022-11-24 01:24:56,175 | INFO : \t train_acc: 99.176; train_loss: 0.020; test_loss: 29.963; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:24:56,175 | INFO : \t test_acc: 92.07 (best: 92.5 from #265)\n",
            "2022-11-24 01:25:09,593 | INFO : Epoch #297\n",
            "2022-11-24 01:25:09,593 | INFO : \t train_acc: 99.196; train_loss: 0.075; test_loss: 30.436; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:25:09,593 | INFO : \t test_acc: 91.8 (best: 92.5 from #265)\n",
            "2022-11-24 01:25:23,034 | INFO : Epoch #298\n",
            "2022-11-24 01:25:23,035 | INFO : \t train_acc: 99.066; train_loss: 0.152; test_loss: 31.365; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:25:23,035 | INFO : \t test_acc: 91.81 (best: 92.5 from #265)\n",
            "2022-11-24 01:25:36,476 | INFO : Epoch #299\n",
            "2022-11-24 01:25:36,476 | INFO : \t train_acc: 99.138; train_loss: 0.059; test_loss: 29.762; lr: [0.0010000000000000002]\n",
            "2022-11-24 01:25:36,476 | INFO : \t test_acc: 92.06 (best: 92.5 from #265)\n",
            "2022-11-24 01:25:36,493 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet20_cifar10_init.pt finetuned 10.0 --> 92.5.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With a trained baseline model and its snapshots obtained, we may now prune and finetune the trained model. Note in this case, we set `--task` to `finetune` and supply the `main.py` with a setting file that includes pruning/fine-tuning specifications (`resnet20_cifar10_prune_and_finetune.json`) and a folder dir that includes all the corresponding snapshot files (at `--snapsho_folder_dir`)."
      ],
      "metadata": {
        "id": "gGaF-dcbqWPg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python main.py \\\n",
        "--exp_desc resnet20_cifar10_demo_exp \\\n",
        "--setting_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/settings/resnet20_cifar10_prune_and_finetune.json \\\n",
        "--model_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_train/trained \\\n",
        "--snapshot_folder_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_baseline_train/checkpoints \\\n",
        "--output_folder_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_demo_exp \\\n",
        "--baseline True \\\n",
        "--task finetune"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xDP1pqSjpe-Q",
        "outputId": "8057dda6-8fd0-4047-892e-ede69a2b7e7b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-11-24 01:33:00,250 | INFO : Experiment resnet20_cifar10_demo_exp starts at 2022-11-24T01:33:00.246175.\n",
            "2022-11-24 01:33:00,250 | INFO : Parsed setting file from /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/settings/resnet20_cifar10_prune_and_finetune.json...\n",
            "2022-11-24 01:33:00,278 | INFO : ==> Preparing dataset: cifar10\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "2022-11-24 01:33:02,597 | INFO : ==> Building baseline model: /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_train/trained (dataset: cifar10)\n",
            "2022-11-24 01:33:03,483 | INFO : Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
            "2022-11-24 01:33:03,483 | INFO : NumExpr defaulting to 8 threads.\n",
            "2022-11-24 01:33:05,339 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_train/trained loaded (<class 'models.resnet.CifarResNet'>)\n",
            "2022-11-24 01:33:08,867 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_train/trained <class 'models.resnet.CifarResNet'> has a baseline of 92.57.\n",
            "2022-11-24 01:33:08,878 | INFO : pruning_strategy: greedy (tickets magnitute increase); n_clusters: 8; eval_pruned_kernel_relationship: True; cost_balancer: auto; eval_kept_kernel_ratio: 0.2\n",
            "2022-11-24 01:34:26,356 | INFO : Layer 2-0-0; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 22, 'K-PCA': 14});\tSequence: {'K-PCA': 6, 'DBSCAN': 18});\n",
            "2022-11-24 01:35:47,383 | INFO : Layer 2-0-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 36});\tSequence: {'K-PCA': 35});\n",
            "2022-11-24 01:37:07,167 | INFO : Layer 2-1-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 20, 'DBSCAN': 13, 'Spectral': 3});\tSequence: {'K-PCA': 4, 'DBSCAN': 2});\n",
            "2022-11-24 01:38:29,479 | INFO : Layer 2-1-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 31, 'DBSCAN': 5});\tSequence: {'K-PCA': 20, 'DBSCAN': 2});\n",
            "2022-11-24 01:39:51,692 | INFO : Layer 2-2-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 36});\tSequence: {'K-PCA': 35});\n",
            "2022-11-24 01:41:09,484 | INFO : Layer 2-2-2; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 22, 'DBSCAN': 13, 'K-PCA': 1});\tSequence: {'DBSCAN': 3, 'Spectral': 4});\n",
            "2022-11-24 01:43:10,258 | INFO : Layer 3-0-0; Shape (32, 16, 3, 3) -> (32, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 17, 'Spectral': 11, 'K-PCA': 8});\tSequence: {'DBSCAN': 6, 'Spectral': 4, 'K-PCA': 2});\n",
            "2022-11-24 01:46:23,357 | INFO : Layer 3-0-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 24, 'K-PCA': 7, 'DBSCAN': 5});\tSequence: {'Spectral': 6, 'K-PCA': 1});\n",
            "2022-11-24 01:48:47,659 | INFO : Layer 3-1-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 19, 'Spectral': 13, 'DBSCAN': 4});\tSequence: {'Spectral': 2, 'K-PCA': 4, 'DBSCAN': 1});\n",
            "2022-11-24 01:50:47,830 | INFO : Layer 3-1-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 21, 'Spectral': 14, 'DBSCAN': 1});\tSequence: {'K-PCA': 6, 'Spectral': 4});\n",
            "2022-11-24 01:53:18,705 | INFO : Layer 3-2-0; Shape (32, 32, 3, 3) -> (32, 288); Method: DBSCAN; Occurrence: ({'DBSCAN': 14, 'Spectral': 13, 'K-PCA': 9});\tSequence: {'Spectral': 2, 'K-PCA': 3, 'DBSCAN': 3});\n",
            "2022-11-24 01:55:49,293 | INFO : Layer 3-2-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 25, 'DBSCAN': 7, 'K-PCA': 4});\tSequence: {'Spectral': 15, 'DBSCAN': 2});\n",
            "2022-11-24 02:02:33,642 | INFO : Layer 4-0-0; Shape (64, 32, 3, 3) -> (64, 288); Method: Spectral; Occurrence: ({'Spectral': 14, 'K-PCA': 14, 'DBSCAN': 8});\tSequence: {'Spectral': 5, 'K-PCA': 4, 'DBSCAN': 2});\n",
            "2022-11-24 02:09:09,494 | INFO : Layer 4-0-2; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 28, 'K-PCA': 8});\tSequence: {'DBSCAN': 11, 'K-PCA': 2});\n",
            "2022-11-24 02:13:06,182 | INFO : Layer 4-1-0; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 23, 'K-PCA': 7, 'Spectral': 6});\tSequence: {'DBSCAN': 4, 'K-PCA': 1});\n",
            "2022-11-24 02:24:49,635 | INFO : Layer 4-1-2; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 28, 'Spectral': 6, 'K-PCA': 2});\tSequence: {'DBSCAN': 9, 'Spectral': 1});\n",
            "2022-11-24 02:32:20,465 | INFO : Layer 4-2-0; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 25, 'DBSCAN': 7, 'K-PCA': 4});\tSequence: {'Spectral': 4, 'DBSCAN': 1});\n",
            "2022-11-24 02:45:54,086 | INFO : Layer 4-2-2; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 23, 'Spectral': 13});\tSequence: {'Spectral': 3, 'DBSCAN': 6});\n",
            "2022-11-24 02:45:55,977 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_train/trained <class 'prune_cifar.ModifiedResNet'> now pruned.\n",
            "GroupResNet pruning rate: 0.4375; kernel_gcd: 16\n",
            "2022-11-24 02:45:56,010 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_train/trained <class 'group_cifar.GroupResNet'> now grouped.\n",
            "2022-11-24 02:45:56,012 | INFO : ==> Starting task: finetune for 300 epochs; multi_prune_flag: False)\n",
            "2022-11-24 02:45:57,213 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_train/trained (type: <class 'group_cifar.GroupResNet'>) pruned and grouped (grouped baseline: 19.23); now to finetune with 300 epochs.\n",
            "\n",
            "2022-11-24 02:45:58,484 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_train/trained has a baseline of 19.23.\n",
            "2022-11-24 02:46:15,892 | INFO : Epoch #0\n",
            "2022-11-24 02:46:15,893 | INFO : \t train_acc: 85.73; train_loss: 0.366; test_loss: 43.731; lr: [0.01]\n",
            "2022-11-24 02:46:15,893 | INFO : \t test_acc: 85.51 (best: 85.51 from #0)\n",
            "2022-11-24 02:46:33,561 | INFO : Epoch #1\n",
            "2022-11-24 02:46:33,561 | INFO : \t train_acc: 88.254; train_loss: 0.324; test_loss: 41.632; lr: [0.01]\n",
            "2022-11-24 02:46:33,562 | INFO : \t test_acc: 86.45 (best: 86.45 from #1)\n",
            "2022-11-24 02:46:50,672 | INFO : Epoch #2\n",
            "2022-11-24 02:46:50,673 | INFO : \t train_acc: 88.844; train_loss: 0.403; test_loss: 40.766; lr: [0.01]\n",
            "2022-11-24 02:46:50,673 | INFO : \t test_acc: 86.46 (best: 86.46 from #2)\n",
            "2022-11-24 02:47:08,125 | INFO : Epoch #3\n",
            "2022-11-24 02:47:08,126 | INFO : \t train_acc: 89.53; train_loss: 0.337; test_loss: 39.699; lr: [0.01]\n",
            "2022-11-24 02:47:08,126 | INFO : \t test_acc: 86.33 (best: 86.46 from #2)\n",
            "2022-11-24 02:47:25,529 | INFO : Epoch #4\n",
            "2022-11-24 02:47:25,530 | INFO : \t train_acc: 89.814; train_loss: 0.367; test_loss: 41.544; lr: [0.01]\n",
            "2022-11-24 02:47:25,530 | INFO : \t test_acc: 86.47 (best: 86.47 from #4)\n",
            "2022-11-24 02:47:43,365 | INFO : Epoch #5\n",
            "2022-11-24 02:47:43,365 | INFO : \t train_acc: 89.894; train_loss: 0.643; test_loss: 44.692; lr: [0.01]\n",
            "2022-11-24 02:47:43,365 | INFO : \t test_acc: 85.26 (best: 86.47 from #4)\n",
            "2022-11-24 02:48:00,559 | INFO : Epoch #6\n",
            "2022-11-24 02:48:00,559 | INFO : \t train_acc: 90.044; train_loss: 0.381; test_loss: 36.130; lr: [0.01]\n",
            "2022-11-24 02:48:00,559 | INFO : \t test_acc: 87.94 (best: 87.94 from #6)\n",
            "2022-11-24 02:48:18,111 | INFO : Epoch #7\n",
            "2022-11-24 02:48:18,112 | INFO : \t train_acc: 90.248; train_loss: 0.052; test_loss: 45.407; lr: [0.01]\n",
            "2022-11-24 02:48:18,112 | INFO : \t test_acc: 85.18 (best: 87.94 from #6)\n",
            "2022-11-24 02:48:35,809 | INFO : Epoch #8\n",
            "2022-11-24 02:48:35,810 | INFO : \t train_acc: 90.48; train_loss: 0.319; test_loss: 34.958; lr: [0.01]\n",
            "2022-11-24 02:48:35,810 | INFO : \t test_acc: 88.57 (best: 88.57 from #8)\n",
            "2022-11-24 02:48:53,197 | INFO : Epoch #9\n",
            "2022-11-24 02:48:53,197 | INFO : \t train_acc: 90.294; train_loss: 0.331; test_loss: 36.764; lr: [0.01]\n",
            "2022-11-24 02:48:53,197 | INFO : \t test_acc: 87.9 (best: 88.57 from #8)\n",
            "2022-11-24 02:49:10,549 | INFO : Epoch #10\n",
            "2022-11-24 02:49:10,549 | INFO : \t train_acc: 90.584; train_loss: 0.457; test_loss: 42.198; lr: [0.01]\n",
            "2022-11-24 02:49:10,549 | INFO : \t test_acc: 86.0 (best: 88.57 from #8)\n",
            "2022-11-24 02:49:27,777 | INFO : Epoch #11\n",
            "2022-11-24 02:49:27,777 | INFO : \t train_acc: 90.706; train_loss: 0.489; test_loss: 38.763; lr: [0.01]\n",
            "2022-11-24 02:49:27,777 | INFO : \t test_acc: 86.97 (best: 88.57 from #8)\n",
            "2022-11-24 02:49:45,086 | INFO : Epoch #12\n",
            "2022-11-24 02:49:45,086 | INFO : \t train_acc: 90.484; train_loss: 0.325; test_loss: 39.111; lr: [0.01]\n",
            "2022-11-24 02:49:45,087 | INFO : \t test_acc: 87.16 (best: 88.57 from #8)\n",
            "2022-11-24 02:50:02,479 | INFO : Epoch #13\n",
            "2022-11-24 02:50:02,480 | INFO : \t train_acc: 90.96; train_loss: 0.276; test_loss: 36.948; lr: [0.01]\n",
            "2022-11-24 02:50:02,480 | INFO : \t test_acc: 87.79 (best: 88.57 from #8)\n",
            "2022-11-24 02:50:19,943 | INFO : Epoch #14\n",
            "2022-11-24 02:50:19,943 | INFO : \t train_acc: 91.028; train_loss: 0.122; test_loss: 34.540; lr: [0.01]\n",
            "2022-11-24 02:50:19,943 | INFO : \t test_acc: 88.22 (best: 88.57 from #8)\n",
            "2022-11-24 02:50:37,274 | INFO : Epoch #15\n",
            "2022-11-24 02:50:37,275 | INFO : \t train_acc: 90.842; train_loss: 0.225; test_loss: 53.480; lr: [0.01]\n",
            "2022-11-24 02:50:37,275 | INFO : \t test_acc: 83.13 (best: 88.57 from #8)\n",
            "2022-11-24 02:50:54,393 | INFO : Epoch #16\n",
            "2022-11-24 02:50:54,393 | INFO : \t train_acc: 91.048; train_loss: 0.568; test_loss: 35.641; lr: [0.01]\n",
            "2022-11-24 02:50:54,394 | INFO : \t test_acc: 88.37 (best: 88.57 from #8)\n",
            "2022-11-24 02:51:11,935 | INFO : Epoch #17\n",
            "2022-11-24 02:51:11,935 | INFO : \t train_acc: 90.856; train_loss: 0.032; test_loss: 43.025; lr: [0.01]\n",
            "2022-11-24 02:51:11,935 | INFO : \t test_acc: 85.78 (best: 88.57 from #8)\n",
            "2022-11-24 02:51:29,157 | INFO : Epoch #18\n",
            "2022-11-24 02:51:29,157 | INFO : \t train_acc: 90.876; train_loss: 0.207; test_loss: 42.408; lr: [0.01]\n",
            "2022-11-24 02:51:29,157 | INFO : \t test_acc: 86.03 (best: 88.57 from #8)\n",
            "2022-11-24 02:51:46,738 | INFO : Epoch #19\n",
            "2022-11-24 02:51:46,738 | INFO : \t train_acc: 91.01; train_loss: 1.070; test_loss: 39.278; lr: [0.01]\n",
            "2022-11-24 02:51:46,738 | INFO : \t test_acc: 87.1 (best: 88.57 from #8)\n",
            "2022-11-24 02:52:04,444 | INFO : Epoch #20\n",
            "2022-11-24 02:52:04,445 | INFO : \t train_acc: 91.104; train_loss: 0.225; test_loss: 39.248; lr: [0.01]\n",
            "2022-11-24 02:52:04,445 | INFO : \t test_acc: 87.21 (best: 88.57 from #8)\n",
            "2022-11-24 02:52:22,377 | INFO : Epoch #21\n",
            "2022-11-24 02:52:22,377 | INFO : \t train_acc: 91.036; train_loss: 0.670; test_loss: 37.933; lr: [0.01]\n",
            "2022-11-24 02:52:22,377 | INFO : \t test_acc: 87.58 (best: 88.57 from #8)\n",
            "2022-11-24 02:52:40,161 | INFO : Epoch #22\n",
            "2022-11-24 02:52:40,161 | INFO : \t train_acc: 91.1; train_loss: 0.653; test_loss: 37.206; lr: [0.01]\n",
            "2022-11-24 02:52:40,161 | INFO : \t test_acc: 87.6 (best: 88.57 from #8)\n",
            "2022-11-24 02:52:57,283 | INFO : Epoch #23\n",
            "2022-11-24 02:52:57,284 | INFO : \t train_acc: 91.168; train_loss: 0.552; test_loss: 38.404; lr: [0.01]\n",
            "2022-11-24 02:52:57,284 | INFO : \t test_acc: 87.02 (best: 88.57 from #8)\n",
            "2022-11-24 02:53:14,408 | INFO : Epoch #24\n",
            "2022-11-24 02:53:14,409 | INFO : \t train_acc: 91.144; train_loss: 0.274; test_loss: 36.679; lr: [0.01]\n",
            "2022-11-24 02:53:14,409 | INFO : \t test_acc: 87.83 (best: 88.57 from #8)\n",
            "2022-11-24 02:53:31,758 | INFO : Epoch #25\n",
            "2022-11-24 02:53:31,759 | INFO : \t train_acc: 91.242; train_loss: 0.359; test_loss: 33.077; lr: [0.01]\n",
            "2022-11-24 02:53:31,759 | INFO : \t test_acc: 88.81 (best: 88.81 from #25)\n",
            "2022-11-24 02:53:49,253 | INFO : Epoch #26\n",
            "2022-11-24 02:53:49,254 | INFO : \t train_acc: 91.11; train_loss: 0.140; test_loss: 36.104; lr: [0.01]\n",
            "2022-11-24 02:53:49,254 | INFO : \t test_acc: 88.17 (best: 88.81 from #25)\n",
            "2022-11-24 02:54:07,701 | INFO : Epoch #27\n",
            "2022-11-24 02:54:07,702 | INFO : \t train_acc: 91.278; train_loss: 0.223; test_loss: 39.967; lr: [0.01]\n",
            "2022-11-24 02:54:07,702 | INFO : \t test_acc: 87.29 (best: 88.81 from #25)\n",
            "2022-11-24 02:54:25,573 | INFO : Epoch #28\n",
            "2022-11-24 02:54:25,573 | INFO : \t train_acc: 91.242; train_loss: 0.379; test_loss: 35.428; lr: [0.01]\n",
            "2022-11-24 02:54:25,573 | INFO : \t test_acc: 88.21 (best: 88.81 from #25)\n",
            "2022-11-24 02:54:43,632 | INFO : Epoch #29\n",
            "2022-11-24 02:54:43,633 | INFO : \t train_acc: 91.146; train_loss: 0.318; test_loss: 39.705; lr: [0.01]\n",
            "2022-11-24 02:54:43,633 | INFO : \t test_acc: 87.09 (best: 88.81 from #25)\n",
            "2022-11-24 02:55:01,161 | INFO : Epoch #30\n",
            "2022-11-24 02:55:01,162 | INFO : \t train_acc: 91.296; train_loss: 0.217; test_loss: 33.166; lr: [0.01]\n",
            "2022-11-24 02:55:01,162 | INFO : \t test_acc: 89.05 (best: 89.05 from #30)\n",
            "2022-11-24 02:55:18,352 | INFO : Epoch #31\n",
            "2022-11-24 02:55:18,352 | INFO : \t train_acc: 91.184; train_loss: 0.949; test_loss: 38.109; lr: [0.01]\n",
            "2022-11-24 02:55:18,352 | INFO : \t test_acc: 87.31 (best: 89.05 from #30)\n",
            "2022-11-24 02:55:35,806 | INFO : Epoch #32\n",
            "2022-11-24 02:55:35,807 | INFO : \t train_acc: 91.292; train_loss: 0.297; test_loss: 43.048; lr: [0.01]\n",
            "2022-11-24 02:55:35,807 | INFO : \t test_acc: 86.16 (best: 89.05 from #30)\n",
            "2022-11-24 02:55:53,426 | INFO : Epoch #33\n",
            "2022-11-24 02:55:53,426 | INFO : \t train_acc: 91.47; train_loss: 0.431; test_loss: 42.383; lr: [0.01]\n",
            "2022-11-24 02:55:53,426 | INFO : \t test_acc: 86.12 (best: 89.05 from #30)\n",
            "2022-11-24 02:56:10,840 | INFO : Epoch #34\n",
            "2022-11-24 02:56:10,840 | INFO : \t train_acc: 91.126; train_loss: 0.262; test_loss: 41.399; lr: [0.01]\n",
            "2022-11-24 02:56:10,840 | INFO : \t test_acc: 86.81 (best: 89.05 from #30)\n",
            "2022-11-24 02:56:28,110 | INFO : Epoch #35\n",
            "2022-11-24 02:56:28,111 | INFO : \t train_acc: 91.336; train_loss: 0.243; test_loss: 42.077; lr: [0.01]\n",
            "2022-11-24 02:56:28,111 | INFO : \t test_acc: 86.78 (best: 89.05 from #30)\n",
            "2022-11-24 02:56:45,993 | INFO : Epoch #36\n",
            "2022-11-24 02:56:45,994 | INFO : \t train_acc: 91.272; train_loss: 0.181; test_loss: 39.740; lr: [0.01]\n",
            "2022-11-24 02:56:45,994 | INFO : \t test_acc: 86.78 (best: 89.05 from #30)\n",
            "2022-11-24 02:57:03,283 | INFO : Epoch #37\n",
            "2022-11-24 02:57:03,283 | INFO : \t train_acc: 91.368; train_loss: 0.421; test_loss: 38.188; lr: [0.01]\n",
            "2022-11-24 02:57:03,283 | INFO : \t test_acc: 87.45 (best: 89.05 from #30)\n",
            "2022-11-24 02:57:20,317 | INFO : Epoch #38\n",
            "2022-11-24 02:57:20,318 | INFO : \t train_acc: 91.402; train_loss: 0.202; test_loss: 38.583; lr: [0.01]\n",
            "2022-11-24 02:57:20,318 | INFO : \t test_acc: 87.08 (best: 89.05 from #30)\n",
            "2022-11-24 02:57:37,407 | INFO : Epoch #39\n",
            "2022-11-24 02:57:37,408 | INFO : \t train_acc: 91.328; train_loss: 0.172; test_loss: 37.356; lr: [0.01]\n",
            "2022-11-24 02:57:37,408 | INFO : \t test_acc: 87.41 (best: 89.05 from #30)\n",
            "2022-11-24 02:57:54,911 | INFO : Epoch #40\n",
            "2022-11-24 02:57:54,912 | INFO : \t train_acc: 91.504; train_loss: 0.300; test_loss: 39.347; lr: [0.01]\n",
            "2022-11-24 02:57:54,912 | INFO : \t test_acc: 87.2 (best: 89.05 from #30)\n",
            "2022-11-24 02:58:12,456 | INFO : Epoch #41\n",
            "2022-11-24 02:58:12,456 | INFO : \t train_acc: 91.358; train_loss: 0.040; test_loss: 38.974; lr: [0.01]\n",
            "2022-11-24 02:58:12,457 | INFO : \t test_acc: 87.45 (best: 89.05 from #30)\n",
            "2022-11-24 02:58:30,480 | INFO : Epoch #42\n",
            "2022-11-24 02:58:30,480 | INFO : \t train_acc: 91.574; train_loss: 0.394; test_loss: 36.009; lr: [0.01]\n",
            "2022-11-24 02:58:30,480 | INFO : \t test_acc: 88.16 (best: 89.05 from #30)\n",
            "2022-11-24 02:58:48,267 | INFO : Epoch #43\n",
            "2022-11-24 02:58:48,268 | INFO : \t train_acc: 91.368; train_loss: 0.169; test_loss: 37.604; lr: [0.01]\n",
            "2022-11-24 02:58:48,268 | INFO : \t test_acc: 87.67 (best: 89.05 from #30)\n",
            "2022-11-24 02:59:06,411 | INFO : Epoch #44\n",
            "2022-11-24 02:59:06,412 | INFO : \t train_acc: 91.446; train_loss: 0.875; test_loss: 40.692; lr: [0.01]\n",
            "2022-11-24 02:59:06,412 | INFO : \t test_acc: 86.59 (best: 89.05 from #30)\n",
            "2022-11-24 02:59:23,957 | INFO : Epoch #45\n",
            "2022-11-24 02:59:23,958 | INFO : \t train_acc: 91.536; train_loss: 0.218; test_loss: 36.436; lr: [0.01]\n",
            "2022-11-24 02:59:23,958 | INFO : \t test_acc: 88.15 (best: 89.05 from #30)\n",
            "2022-11-24 02:59:41,550 | INFO : Epoch #46\n",
            "2022-11-24 02:59:41,551 | INFO : \t train_acc: 91.288; train_loss: 0.166; test_loss: 38.266; lr: [0.01]\n",
            "2022-11-24 02:59:41,551 | INFO : \t test_acc: 87.59 (best: 89.05 from #30)\n",
            "2022-11-24 02:59:59,390 | INFO : Epoch #47\n",
            "2022-11-24 02:59:59,391 | INFO : \t train_acc: 91.398; train_loss: 0.373; test_loss: 40.136; lr: [0.01]\n",
            "2022-11-24 02:59:59,391 | INFO : \t test_acc: 87.27 (best: 89.05 from #30)\n",
            "2022-11-24 03:00:16,752 | INFO : Epoch #48\n",
            "2022-11-24 03:00:16,753 | INFO : \t train_acc: 91.402; train_loss: 0.271; test_loss: 43.102; lr: [0.01]\n",
            "2022-11-24 03:00:16,753 | INFO : \t test_acc: 86.18 (best: 89.05 from #30)\n",
            "2022-11-24 03:00:34,626 | INFO : Epoch #49\n",
            "2022-11-24 03:00:34,627 | INFO : \t train_acc: 91.544; train_loss: 0.319; test_loss: 33.315; lr: [0.01]\n",
            "2022-11-24 03:00:34,627 | INFO : \t test_acc: 88.7 (best: 89.05 from #30)\n",
            "2022-11-24 03:00:52,017 | INFO : Epoch #50\n",
            "2022-11-24 03:00:52,018 | INFO : \t train_acc: 91.51; train_loss: 0.248; test_loss: 38.881; lr: [0.01]\n",
            "2022-11-24 03:00:52,018 | INFO : \t test_acc: 87.28 (best: 89.05 from #30)\n",
            "2022-11-24 03:01:09,241 | INFO : Epoch #51\n",
            "2022-11-24 03:01:09,242 | INFO : \t train_acc: 91.47; train_loss: 0.290; test_loss: 36.593; lr: [0.01]\n",
            "2022-11-24 03:01:09,242 | INFO : \t test_acc: 88.03 (best: 89.05 from #30)\n",
            "2022-11-24 03:01:26,700 | INFO : Epoch #52\n",
            "2022-11-24 03:01:26,701 | INFO : \t train_acc: 91.582; train_loss: 0.053; test_loss: 38.789; lr: [0.01]\n",
            "2022-11-24 03:01:26,701 | INFO : \t test_acc: 87.16 (best: 89.05 from #30)\n",
            "2022-11-24 03:01:44,170 | INFO : Epoch #53\n",
            "2022-11-24 03:01:44,171 | INFO : \t train_acc: 91.408; train_loss: 0.283; test_loss: 38.713; lr: [0.01]\n",
            "2022-11-24 03:01:44,171 | INFO : \t test_acc: 87.59 (best: 89.05 from #30)\n",
            "2022-11-24 03:02:01,626 | INFO : Epoch #54\n",
            "2022-11-24 03:02:01,626 | INFO : \t train_acc: 91.584; train_loss: 0.612; test_loss: 36.889; lr: [0.01]\n",
            "2022-11-24 03:02:01,627 | INFO : \t test_acc: 87.56 (best: 89.05 from #30)\n",
            "2022-11-24 03:02:19,117 | INFO : Epoch #55\n",
            "2022-11-24 03:02:19,118 | INFO : \t train_acc: 91.74; train_loss: 0.185; test_loss: 35.721; lr: [0.01]\n",
            "2022-11-24 03:02:19,118 | INFO : \t test_acc: 87.93 (best: 89.05 from #30)\n",
            "2022-11-24 03:02:36,609 | INFO : Epoch #56\n",
            "2022-11-24 03:02:36,609 | INFO : \t train_acc: 91.54; train_loss: 0.828; test_loss: 41.373; lr: [0.01]\n",
            "2022-11-24 03:02:36,610 | INFO : \t test_acc: 87.16 (best: 89.05 from #30)\n",
            "2022-11-24 03:02:53,893 | INFO : Epoch #57\n",
            "2022-11-24 03:02:53,893 | INFO : \t train_acc: 91.496; train_loss: 0.683; test_loss: 41.134; lr: [0.01]\n",
            "2022-11-24 03:02:53,893 | INFO : \t test_acc: 87.32 (best: 89.05 from #30)\n",
            "2022-11-24 03:03:11,259 | INFO : Epoch #58\n",
            "2022-11-24 03:03:11,259 | INFO : \t train_acc: 91.404; train_loss: 0.447; test_loss: 43.964; lr: [0.01]\n",
            "2022-11-24 03:03:11,259 | INFO : \t test_acc: 86.12 (best: 89.05 from #30)\n",
            "2022-11-24 03:03:28,926 | INFO : Epoch #59\n",
            "2022-11-24 03:03:28,927 | INFO : \t train_acc: 91.588; train_loss: 0.240; test_loss: 36.558; lr: [0.01]\n",
            "2022-11-24 03:03:28,927 | INFO : \t test_acc: 88.13 (best: 89.05 from #30)\n",
            "2022-11-24 03:03:46,161 | INFO : Epoch #60\n",
            "2022-11-24 03:03:46,162 | INFO : \t train_acc: 91.51; train_loss: 0.330; test_loss: 36.242; lr: [0.01]\n",
            "2022-11-24 03:03:46,162 | INFO : \t test_acc: 88.43 (best: 89.05 from #30)\n",
            "2022-11-24 03:04:04,000 | INFO : Epoch #61\n",
            "2022-11-24 03:04:04,001 | INFO : \t train_acc: 91.714; train_loss: 0.207; test_loss: 40.338; lr: [0.01]\n",
            "2022-11-24 03:04:04,001 | INFO : \t test_acc: 86.43 (best: 89.05 from #30)\n",
            "2022-11-24 03:04:21,588 | INFO : Epoch #62\n",
            "2022-11-24 03:04:21,589 | INFO : \t train_acc: 91.856; train_loss: 0.646; test_loss: 39.199; lr: [0.01]\n",
            "2022-11-24 03:04:21,589 | INFO : \t test_acc: 87.13 (best: 89.05 from #30)\n",
            "2022-11-24 03:04:39,137 | INFO : Epoch #63\n",
            "2022-11-24 03:04:39,138 | INFO : \t train_acc: 91.51; train_loss: 0.525; test_loss: 36.864; lr: [0.01]\n",
            "2022-11-24 03:04:39,138 | INFO : \t test_acc: 88.17 (best: 89.05 from #30)\n",
            "2022-11-24 03:04:57,300 | INFO : Epoch #64\n",
            "2022-11-24 03:04:57,300 | INFO : \t train_acc: 91.566; train_loss: 0.784; test_loss: 40.838; lr: [0.01]\n",
            "2022-11-24 03:04:57,301 | INFO : \t test_acc: 87.07 (best: 89.05 from #30)\n",
            "2022-11-24 03:05:15,145 | INFO : Epoch #65\n",
            "2022-11-24 03:05:15,146 | INFO : \t train_acc: 91.58; train_loss: 0.282; test_loss: 37.502; lr: [0.01]\n",
            "2022-11-24 03:05:15,146 | INFO : \t test_acc: 87.64 (best: 89.05 from #30)\n",
            "2022-11-24 03:05:32,258 | INFO : Epoch #66\n",
            "2022-11-24 03:05:32,258 | INFO : \t train_acc: 91.73; train_loss: 0.096; test_loss: 38.584; lr: [0.01]\n",
            "2022-11-24 03:05:32,258 | INFO : \t test_acc: 87.52 (best: 89.05 from #30)\n",
            "2022-11-24 03:05:50,153 | INFO : Epoch #67\n",
            "2022-11-24 03:05:50,154 | INFO : \t train_acc: 91.682; train_loss: 0.451; test_loss: 37.124; lr: [0.01]\n",
            "2022-11-24 03:05:50,154 | INFO : \t test_acc: 87.77 (best: 89.05 from #30)\n",
            "2022-11-24 03:06:07,680 | INFO : Epoch #68\n",
            "2022-11-24 03:06:07,681 | INFO : \t train_acc: 91.608; train_loss: 0.231; test_loss: 35.921; lr: [0.01]\n",
            "2022-11-24 03:06:07,681 | INFO : \t test_acc: 88.31 (best: 89.05 from #30)\n",
            "2022-11-24 03:06:24,802 | INFO : Epoch #69\n",
            "2022-11-24 03:06:24,803 | INFO : \t train_acc: 91.668; train_loss: 0.366; test_loss: 38.948; lr: [0.01]\n",
            "2022-11-24 03:06:24,803 | INFO : \t test_acc: 87.49 (best: 89.05 from #30)\n",
            "2022-11-24 03:06:42,029 | INFO : Epoch #70\n",
            "2022-11-24 03:06:42,030 | INFO : \t train_acc: 91.73; train_loss: 0.109; test_loss: 36.846; lr: [0.01]\n",
            "2022-11-24 03:06:42,030 | INFO : \t test_acc: 87.88 (best: 89.05 from #30)\n",
            "2022-11-24 03:06:59,840 | INFO : Epoch #71\n",
            "2022-11-24 03:06:59,840 | INFO : \t train_acc: 91.772; train_loss: 0.178; test_loss: 45.699; lr: [0.01]\n",
            "2022-11-24 03:06:59,840 | INFO : \t test_acc: 85.98 (best: 89.05 from #30)\n",
            "2022-11-24 03:07:17,062 | INFO : Epoch #72\n",
            "2022-11-24 03:07:17,062 | INFO : \t train_acc: 91.758; train_loss: 0.167; test_loss: 35.722; lr: [0.01]\n",
            "2022-11-24 03:07:17,063 | INFO : \t test_acc: 88.51 (best: 89.05 from #30)\n",
            "2022-11-24 03:07:34,534 | INFO : Epoch #73\n",
            "2022-11-24 03:07:34,534 | INFO : \t train_acc: 91.592; train_loss: 0.382; test_loss: 37.835; lr: [0.01]\n",
            "2022-11-24 03:07:34,535 | INFO : \t test_acc: 87.64 (best: 89.05 from #30)\n",
            "2022-11-24 03:07:51,942 | INFO : Epoch #74\n",
            "2022-11-24 03:07:51,942 | INFO : \t train_acc: 91.756; train_loss: 0.368; test_loss: 39.515; lr: [0.01]\n",
            "2022-11-24 03:07:51,943 | INFO : \t test_acc: 86.67 (best: 89.05 from #30)\n",
            "2022-11-24 03:08:09,263 | INFO : Epoch #75\n",
            "2022-11-24 03:08:09,263 | INFO : \t train_acc: 91.748; train_loss: 0.423; test_loss: 36.111; lr: [0.01]\n",
            "2022-11-24 03:08:09,263 | INFO : \t test_acc: 88.07 (best: 89.05 from #30)\n",
            "2022-11-24 03:08:26,597 | INFO : Epoch #76\n",
            "2022-11-24 03:08:26,598 | INFO : \t train_acc: 91.854; train_loss: 0.466; test_loss: 42.205; lr: [0.01]\n",
            "2022-11-24 03:08:26,598 | INFO : \t test_acc: 86.74 (best: 89.05 from #30)\n",
            "2022-11-24 03:08:44,421 | INFO : Epoch #77\n",
            "2022-11-24 03:08:44,421 | INFO : \t train_acc: 91.614; train_loss: 0.275; test_loss: 37.993; lr: [0.01]\n",
            "2022-11-24 03:08:44,421 | INFO : \t test_acc: 87.64 (best: 89.05 from #30)\n",
            "2022-11-24 03:09:02,077 | INFO : Epoch #78\n",
            "2022-11-24 03:09:02,078 | INFO : \t train_acc: 91.524; train_loss: 0.077; test_loss: 34.603; lr: [0.01]\n",
            "2022-11-24 03:09:02,078 | INFO : \t test_acc: 88.58 (best: 89.05 from #30)\n",
            "2022-11-24 03:09:19,312 | INFO : Epoch #79\n",
            "2022-11-24 03:09:19,312 | INFO : \t train_acc: 91.638; train_loss: 0.382; test_loss: 38.083; lr: [0.01]\n",
            "2022-11-24 03:09:19,312 | INFO : \t test_acc: 87.78 (best: 89.05 from #30)\n",
            "2022-11-24 03:09:36,461 | INFO : Epoch #80\n",
            "2022-11-24 03:09:36,462 | INFO : \t train_acc: 91.676; train_loss: 0.503; test_loss: 35.255; lr: [0.01]\n",
            "2022-11-24 03:09:36,462 | INFO : \t test_acc: 88.34 (best: 89.05 from #30)\n",
            "2022-11-24 03:09:53,599 | INFO : Epoch #81\n",
            "2022-11-24 03:09:53,599 | INFO : \t train_acc: 91.582; train_loss: 0.063; test_loss: 38.092; lr: [0.01]\n",
            "2022-11-24 03:09:53,599 | INFO : \t test_acc: 87.21 (best: 89.05 from #30)\n",
            "2022-11-24 03:10:11,133 | INFO : Epoch #82\n",
            "2022-11-24 03:10:11,133 | INFO : \t train_acc: 91.614; train_loss: 0.737; test_loss: 39.217; lr: [0.01]\n",
            "2022-11-24 03:10:11,133 | INFO : \t test_acc: 87.5 (best: 89.05 from #30)\n",
            "2022-11-24 03:10:28,994 | INFO : Epoch #83\n",
            "2022-11-24 03:10:28,994 | INFO : \t train_acc: 91.73; train_loss: 0.057; test_loss: 36.846; lr: [0.01]\n",
            "2022-11-24 03:10:28,995 | INFO : \t test_acc: 87.71 (best: 89.05 from #30)\n",
            "2022-11-24 03:10:46,948 | INFO : Epoch #84\n",
            "2022-11-24 03:10:46,949 | INFO : \t train_acc: 91.896; train_loss: 0.144; test_loss: 36.370; lr: [0.01]\n",
            "2022-11-24 03:10:46,949 | INFO : \t test_acc: 88.16 (best: 89.05 from #30)\n",
            "2022-11-24 03:11:04,793 | INFO : Epoch #85\n",
            "2022-11-24 03:11:04,793 | INFO : \t train_acc: 91.698; train_loss: 0.110; test_loss: 35.653; lr: [0.01]\n",
            "2022-11-24 03:11:04,793 | INFO : \t test_acc: 88.15 (best: 89.05 from #30)\n",
            "2022-11-24 03:11:22,015 | INFO : Epoch #86\n",
            "2022-11-24 03:11:22,015 | INFO : \t train_acc: 91.862; train_loss: 0.234; test_loss: 41.467; lr: [0.01]\n",
            "2022-11-24 03:11:22,015 | INFO : \t test_acc: 86.74 (best: 89.05 from #30)\n",
            "2022-11-24 03:11:39,466 | INFO : Epoch #87\n",
            "2022-11-24 03:11:39,467 | INFO : \t train_acc: 91.806; train_loss: 0.327; test_loss: 36.023; lr: [0.01]\n",
            "2022-11-24 03:11:39,467 | INFO : \t test_acc: 88.22 (best: 89.05 from #30)\n",
            "2022-11-24 03:11:56,761 | INFO : Epoch #88\n",
            "2022-11-24 03:11:56,761 | INFO : \t train_acc: 91.508; train_loss: 0.263; test_loss: 37.719; lr: [0.01]\n",
            "2022-11-24 03:11:56,762 | INFO : \t test_acc: 87.83 (best: 89.05 from #30)\n",
            "2022-11-24 03:12:13,962 | INFO : Epoch #89\n",
            "2022-11-24 03:12:13,963 | INFO : \t train_acc: 91.776; train_loss: 0.448; test_loss: 36.720; lr: [0.01]\n",
            "2022-11-24 03:12:13,963 | INFO : \t test_acc: 87.94 (best: 89.05 from #30)\n",
            "2022-11-24 03:12:31,735 | INFO : Epoch #90\n",
            "2022-11-24 03:12:31,735 | INFO : \t train_acc: 91.604; train_loss: 0.058; test_loss: 37.715; lr: [0.01]\n",
            "2022-11-24 03:12:31,736 | INFO : \t test_acc: 87.97 (best: 89.05 from #30)\n",
            "2022-11-24 03:12:50,047 | INFO : Epoch #91\n",
            "2022-11-24 03:12:50,048 | INFO : \t train_acc: 91.624; train_loss: 0.154; test_loss: 40.672; lr: [0.01]\n",
            "2022-11-24 03:12:50,048 | INFO : \t test_acc: 86.64 (best: 89.05 from #30)\n",
            "2022-11-24 03:13:07,179 | INFO : Epoch #92\n",
            "2022-11-24 03:13:07,180 | INFO : \t train_acc: 91.892; train_loss: 0.520; test_loss: 40.551; lr: [0.01]\n",
            "2022-11-24 03:13:07,180 | INFO : \t test_acc: 86.98 (best: 89.05 from #30)\n",
            "2022-11-24 03:13:24,655 | INFO : Epoch #93\n",
            "2022-11-24 03:13:24,655 | INFO : \t train_acc: 91.764; train_loss: 0.395; test_loss: 38.519; lr: [0.01]\n",
            "2022-11-24 03:13:24,656 | INFO : \t test_acc: 87.35 (best: 89.05 from #30)\n",
            "2022-11-24 03:13:42,502 | INFO : Epoch #94\n",
            "2022-11-24 03:13:42,503 | INFO : \t train_acc: 91.898; train_loss: 0.479; test_loss: 39.314; lr: [0.01]\n",
            "2022-11-24 03:13:42,503 | INFO : \t test_acc: 87.89 (best: 89.05 from #30)\n",
            "2022-11-24 03:13:59,784 | INFO : Epoch #95\n",
            "2022-11-24 03:13:59,785 | INFO : \t train_acc: 91.624; train_loss: 0.330; test_loss: 47.689; lr: [0.01]\n",
            "2022-11-24 03:13:59,785 | INFO : \t test_acc: 85.21 (best: 89.05 from #30)\n",
            "2022-11-24 03:14:17,423 | INFO : Epoch #96\n",
            "2022-11-24 03:14:17,424 | INFO : \t train_acc: 91.774; train_loss: 0.152; test_loss: 35.942; lr: [0.01]\n",
            "2022-11-24 03:14:17,424 | INFO : \t test_acc: 88.5 (best: 89.05 from #30)\n",
            "2022-11-24 03:14:34,721 | INFO : Epoch #97\n",
            "2022-11-24 03:14:34,721 | INFO : \t train_acc: 91.838; train_loss: 0.196; test_loss: 36.109; lr: [0.01]\n",
            "2022-11-24 03:14:34,721 | INFO : \t test_acc: 88.09 (best: 89.05 from #30)\n",
            "2022-11-24 03:14:52,406 | INFO : Epoch #98\n",
            "2022-11-24 03:14:52,407 | INFO : \t train_acc: 91.706; train_loss: 0.151; test_loss: 34.651; lr: [0.01]\n",
            "2022-11-24 03:14:52,407 | INFO : \t test_acc: 88.58 (best: 89.05 from #30)\n",
            "2022-11-24 03:15:09,876 | INFO : Epoch #99\n",
            "2022-11-24 03:15:09,876 | INFO : \t train_acc: 91.84; train_loss: 0.128; test_loss: 39.799; lr: [0.01]\n",
            "2022-11-24 03:15:09,876 | INFO : \t test_acc: 86.88 (best: 89.05 from #30)\n",
            "2022-11-24 03:15:28,303 | INFO : Epoch #100\n",
            "2022-11-24 03:15:28,304 | INFO : \t train_acc: 94.392; train_loss: 0.240; test_loss: 26.722; lr: [0.001]\n",
            "2022-11-24 03:15:28,304 | INFO : \t test_acc: 91.06 (best: 91.06 from #100)\n",
            "2022-11-24 03:15:45,459 | INFO : Epoch #101\n",
            "2022-11-24 03:15:45,459 | INFO : \t train_acc: 95.362; train_loss: 0.105; test_loss: 26.433; lr: [0.001]\n",
            "2022-11-24 03:15:45,460 | INFO : \t test_acc: 91.33 (best: 91.33 from #101)\n",
            "2022-11-24 03:16:03,904 | INFO : Epoch #102\n",
            "2022-11-24 03:16:03,904 | INFO : \t train_acc: 95.67; train_loss: 0.153; test_loss: 26.793; lr: [0.001]\n",
            "2022-11-24 03:16:03,904 | INFO : \t test_acc: 91.31 (best: 91.33 from #101)\n",
            "2022-11-24 03:16:21,448 | INFO : Epoch #103\n",
            "2022-11-24 03:16:21,449 | INFO : \t train_acc: 96.018; train_loss: 0.170; test_loss: 26.294; lr: [0.001]\n",
            "2022-11-24 03:16:21,449 | INFO : \t test_acc: 91.47 (best: 91.47 from #103)\n",
            "2022-11-24 03:16:39,116 | INFO : Epoch #104\n",
            "2022-11-24 03:16:39,116 | INFO : \t train_acc: 96.04; train_loss: 0.092; test_loss: 26.433; lr: [0.001]\n",
            "2022-11-24 03:16:39,116 | INFO : \t test_acc: 91.34 (best: 91.47 from #103)\n",
            "2022-11-24 03:16:56,271 | INFO : Epoch #105\n",
            "2022-11-24 03:16:56,272 | INFO : \t train_acc: 96.3; train_loss: 0.129; test_loss: 26.102; lr: [0.001]\n",
            "2022-11-24 03:16:56,272 | INFO : \t test_acc: 91.37 (best: 91.47 from #103)\n",
            "2022-11-24 03:17:13,497 | INFO : Epoch #106\n",
            "2022-11-24 03:17:13,497 | INFO : \t train_acc: 96.37; train_loss: 0.300; test_loss: 26.245; lr: [0.001]\n",
            "2022-11-24 03:17:13,498 | INFO : \t test_acc: 91.55 (best: 91.55 from #106)\n",
            "2022-11-24 03:17:31,117 | INFO : Epoch #107\n",
            "2022-11-24 03:17:31,118 | INFO : \t train_acc: 96.426; train_loss: 0.442; test_loss: 26.142; lr: [0.001]\n",
            "2022-11-24 03:17:31,118 | INFO : \t test_acc: 91.72 (best: 91.72 from #107)\n",
            "2022-11-24 03:17:48,360 | INFO : Epoch #108\n",
            "2022-11-24 03:17:48,360 | INFO : \t train_acc: 96.656; train_loss: 0.041; test_loss: 26.646; lr: [0.001]\n",
            "2022-11-24 03:17:48,361 | INFO : \t test_acc: 91.38 (best: 91.72 from #107)\n",
            "2022-11-24 03:18:06,261 | INFO : Epoch #109\n",
            "2022-11-24 03:18:06,262 | INFO : \t train_acc: 96.636; train_loss: 0.096; test_loss: 26.359; lr: [0.001]\n",
            "2022-11-24 03:18:06,262 | INFO : \t test_acc: 91.64 (best: 91.72 from #107)\n",
            "2022-11-24 03:18:24,254 | INFO : Epoch #110\n",
            "2022-11-24 03:18:24,255 | INFO : \t train_acc: 96.7; train_loss: 0.089; test_loss: 26.326; lr: [0.001]\n",
            "2022-11-24 03:18:24,255 | INFO : \t test_acc: 91.5 (best: 91.72 from #107)\n",
            "2022-11-24 03:18:42,429 | INFO : Epoch #111\n",
            "2022-11-24 03:18:42,429 | INFO : \t train_acc: 96.74; train_loss: 0.295; test_loss: 26.539; lr: [0.001]\n",
            "2022-11-24 03:18:42,430 | INFO : \t test_acc: 91.65 (best: 91.72 from #107)\n",
            "2022-11-24 03:18:59,507 | INFO : Epoch #112\n",
            "2022-11-24 03:18:59,508 | INFO : \t train_acc: 96.702; train_loss: 0.048; test_loss: 26.558; lr: [0.001]\n",
            "2022-11-24 03:18:59,508 | INFO : \t test_acc: 91.44 (best: 91.72 from #107)\n",
            "2022-11-24 03:19:17,229 | INFO : Epoch #113\n",
            "2022-11-24 03:19:17,230 | INFO : \t train_acc: 96.698; train_loss: 0.334; test_loss: 26.822; lr: [0.001]\n",
            "2022-11-24 03:19:17,230 | INFO : \t test_acc: 91.59 (best: 91.72 from #107)\n",
            "2022-11-24 03:19:34,941 | INFO : Epoch #114\n",
            "2022-11-24 03:19:34,942 | INFO : \t train_acc: 96.824; train_loss: 0.031; test_loss: 26.979; lr: [0.001]\n",
            "2022-11-24 03:19:34,942 | INFO : \t test_acc: 91.41 (best: 91.72 from #107)\n",
            "2022-11-24 03:19:52,330 | INFO : Epoch #115\n",
            "2022-11-24 03:19:52,330 | INFO : \t train_acc: 96.846; train_loss: 0.548; test_loss: 27.364; lr: [0.001]\n",
            "2022-11-24 03:19:52,330 | INFO : \t test_acc: 91.47 (best: 91.72 from #107)\n",
            "2022-11-24 03:20:09,658 | INFO : Epoch #116\n",
            "2022-11-24 03:20:09,659 | INFO : \t train_acc: 96.942; train_loss: 0.026; test_loss: 27.183; lr: [0.001]\n",
            "2022-11-24 03:20:09,659 | INFO : \t test_acc: 91.66 (best: 91.72 from #107)\n",
            "2022-11-24 03:20:27,057 | INFO : Epoch #117\n",
            "2022-11-24 03:20:27,057 | INFO : \t train_acc: 97.032; train_loss: 0.147; test_loss: 27.209; lr: [0.001]\n",
            "2022-11-24 03:20:27,058 | INFO : \t test_acc: 91.46 (best: 91.72 from #107)\n",
            "2022-11-24 03:20:44,884 | INFO : Epoch #118\n",
            "2022-11-24 03:20:44,885 | INFO : \t train_acc: 97.154; train_loss: 0.138; test_loss: 27.301; lr: [0.001]\n",
            "2022-11-24 03:20:44,885 | INFO : \t test_acc: 91.63 (best: 91.72 from #107)\n",
            "2022-11-24 03:21:02,510 | INFO : Epoch #119\n",
            "2022-11-24 03:21:02,511 | INFO : \t train_acc: 97.014; train_loss: 0.077; test_loss: 27.217; lr: [0.001]\n",
            "2022-11-24 03:21:02,511 | INFO : \t test_acc: 91.71 (best: 91.72 from #107)\n",
            "2022-11-24 03:21:20,341 | INFO : Epoch #120\n",
            "2022-11-24 03:21:20,341 | INFO : \t train_acc: 97.014; train_loss: 0.159; test_loss: 27.593; lr: [0.001]\n",
            "2022-11-24 03:21:20,342 | INFO : \t test_acc: 91.49 (best: 91.72 from #107)\n",
            "2022-11-24 03:21:37,832 | INFO : Epoch #121\n",
            "2022-11-24 03:21:37,833 | INFO : \t train_acc: 97.338; train_loss: 0.142; test_loss: 27.988; lr: [0.001]\n",
            "2022-11-24 03:21:37,833 | INFO : \t test_acc: 91.45 (best: 91.72 from #107)\n",
            "2022-11-24 03:21:55,630 | INFO : Epoch #122\n",
            "2022-11-24 03:21:55,630 | INFO : \t train_acc: 97.206; train_loss: 0.080; test_loss: 27.794; lr: [0.001]\n",
            "2022-11-24 03:21:55,630 | INFO : \t test_acc: 91.73 (best: 91.73 from #122)\n",
            "2022-11-24 03:22:13,160 | INFO : Epoch #123\n",
            "2022-11-24 03:22:13,161 | INFO : \t train_acc: 97.32; train_loss: 0.189; test_loss: 27.642; lr: [0.001]\n",
            "2022-11-24 03:22:13,161 | INFO : \t test_acc: 91.67 (best: 91.73 from #122)\n",
            "2022-11-24 03:22:31,315 | INFO : Epoch #124\n",
            "2022-11-24 03:22:31,316 | INFO : \t train_acc: 97.312; train_loss: 0.124; test_loss: 27.393; lr: [0.001]\n",
            "2022-11-24 03:22:31,316 | INFO : \t test_acc: 91.62 (best: 91.73 from #122)\n",
            "2022-11-24 03:22:49,590 | INFO : Epoch #125\n",
            "2022-11-24 03:22:49,591 | INFO : \t train_acc: 97.348; train_loss: 0.099; test_loss: 28.366; lr: [0.001]\n",
            "2022-11-24 03:22:49,591 | INFO : \t test_acc: 91.32 (best: 91.73 from #122)\n",
            "2022-11-24 03:23:07,243 | INFO : Epoch #126\n",
            "2022-11-24 03:23:07,243 | INFO : \t train_acc: 97.288; train_loss: 0.231; test_loss: 28.053; lr: [0.001]\n",
            "2022-11-24 03:23:07,243 | INFO : \t test_acc: 91.37 (best: 91.73 from #122)\n",
            "2022-11-24 03:23:24,850 | INFO : Epoch #127\n",
            "2022-11-24 03:23:24,850 | INFO : \t train_acc: 97.43; train_loss: 0.049; test_loss: 27.826; lr: [0.001]\n",
            "2022-11-24 03:23:24,851 | INFO : \t test_acc: 91.29 (best: 91.73 from #122)\n",
            "2022-11-24 03:23:42,668 | INFO : Epoch #128\n",
            "2022-11-24 03:23:42,669 | INFO : \t train_acc: 97.4; train_loss: 0.098; test_loss: 28.050; lr: [0.001]\n",
            "2022-11-24 03:23:42,669 | INFO : \t test_acc: 91.51 (best: 91.73 from #122)\n",
            "2022-11-24 03:24:00,451 | INFO : Epoch #129\n",
            "2022-11-24 03:24:00,451 | INFO : \t train_acc: 97.426; train_loss: 0.192; test_loss: 27.867; lr: [0.001]\n",
            "2022-11-24 03:24:00,452 | INFO : \t test_acc: 91.3 (best: 91.73 from #122)\n",
            "2022-11-24 03:24:18,091 | INFO : Epoch #130\n",
            "2022-11-24 03:24:18,091 | INFO : \t train_acc: 97.394; train_loss: 0.127; test_loss: 28.214; lr: [0.001]\n",
            "2022-11-24 03:24:18,091 | INFO : \t test_acc: 91.41 (best: 91.73 from #122)\n",
            "2022-11-24 03:24:36,276 | INFO : Epoch #131\n",
            "2022-11-24 03:24:36,277 | INFO : \t train_acc: 97.552; train_loss: 0.161; test_loss: 27.819; lr: [0.001]\n",
            "2022-11-24 03:24:36,277 | INFO : \t test_acc: 91.43 (best: 91.73 from #122)\n",
            "2022-11-24 03:24:54,228 | INFO : Epoch #132\n",
            "2022-11-24 03:24:54,228 | INFO : \t train_acc: 97.336; train_loss: 0.241; test_loss: 27.778; lr: [0.001]\n",
            "2022-11-24 03:24:54,229 | INFO : \t test_acc: 91.59 (best: 91.73 from #122)\n",
            "2022-11-24 03:25:11,872 | INFO : Epoch #133\n",
            "2022-11-24 03:25:11,873 | INFO : \t train_acc: 97.404; train_loss: 0.083; test_loss: 28.786; lr: [0.001]\n",
            "2022-11-24 03:25:11,873 | INFO : \t test_acc: 91.57 (best: 91.73 from #122)\n",
            "2022-11-24 03:25:29,674 | INFO : Epoch #134\n",
            "2022-11-24 03:25:29,674 | INFO : \t train_acc: 97.494; train_loss: 0.096; test_loss: 28.843; lr: [0.001]\n",
            "2022-11-24 03:25:29,674 | INFO : \t test_acc: 91.35 (best: 91.73 from #122)\n",
            "2022-11-24 03:25:47,550 | INFO : Epoch #135\n",
            "2022-11-24 03:25:47,551 | INFO : \t train_acc: 97.58; train_loss: 0.060; test_loss: 28.642; lr: [0.001]\n",
            "2022-11-24 03:25:47,551 | INFO : \t test_acc: 91.55 (best: 91.73 from #122)\n",
            "2022-11-24 03:26:05,332 | INFO : Epoch #136\n",
            "2022-11-24 03:26:05,332 | INFO : \t train_acc: 97.606; train_loss: 0.145; test_loss: 29.194; lr: [0.001]\n",
            "2022-11-24 03:26:05,332 | INFO : \t test_acc: 91.38 (best: 91.73 from #122)\n",
            "2022-11-24 03:26:22,707 | INFO : Epoch #137\n",
            "2022-11-24 03:26:22,708 | INFO : \t train_acc: 97.56; train_loss: 0.128; test_loss: 28.968; lr: [0.001]\n",
            "2022-11-24 03:26:22,708 | INFO : \t test_acc: 91.26 (best: 91.73 from #122)\n",
            "2022-11-24 03:26:40,046 | INFO : Epoch #138\n",
            "2022-11-24 03:26:40,047 | INFO : \t train_acc: 97.476; train_loss: 0.028; test_loss: 29.289; lr: [0.001]\n",
            "2022-11-24 03:26:40,047 | INFO : \t test_acc: 91.43 (best: 91.73 from #122)\n",
            "2022-11-24 03:26:57,772 | INFO : Epoch #139\n",
            "2022-11-24 03:26:57,772 | INFO : \t train_acc: 97.58; train_loss: 0.201; test_loss: 29.453; lr: [0.001]\n",
            "2022-11-24 03:26:57,773 | INFO : \t test_acc: 91.29 (best: 91.73 from #122)\n",
            "2022-11-24 03:27:15,357 | INFO : Epoch #140\n",
            "2022-11-24 03:27:15,358 | INFO : \t train_acc: 97.624; train_loss: 0.174; test_loss: 29.478; lr: [0.001]\n",
            "2022-11-24 03:27:15,358 | INFO : \t test_acc: 91.29 (best: 91.73 from #122)\n",
            "2022-11-24 03:27:32,994 | INFO : Epoch #141\n",
            "2022-11-24 03:27:32,995 | INFO : \t train_acc: 97.542; train_loss: 0.434; test_loss: 29.943; lr: [0.001]\n",
            "2022-11-24 03:27:32,995 | INFO : \t test_acc: 90.91 (best: 91.73 from #122)\n",
            "2022-11-24 03:27:50,659 | INFO : Epoch #142\n",
            "2022-11-24 03:27:50,659 | INFO : \t train_acc: 97.686; train_loss: 0.169; test_loss: 29.009; lr: [0.001]\n",
            "2022-11-24 03:27:50,659 | INFO : \t test_acc: 91.27 (best: 91.73 from #122)\n",
            "2022-11-24 03:28:08,880 | INFO : Epoch #143\n",
            "2022-11-24 03:28:08,880 | INFO : \t train_acc: 97.76; train_loss: 0.034; test_loss: 29.262; lr: [0.001]\n",
            "2022-11-24 03:28:08,880 | INFO : \t test_acc: 91.42 (best: 91.73 from #122)\n",
            "2022-11-24 03:28:26,762 | INFO : Epoch #144\n",
            "2022-11-24 03:28:26,762 | INFO : \t train_acc: 97.752; train_loss: 0.124; test_loss: 28.990; lr: [0.001]\n",
            "2022-11-24 03:28:26,763 | INFO : \t test_acc: 91.24 (best: 91.73 from #122)\n",
            "2022-11-24 03:28:44,993 | INFO : Epoch #145\n",
            "2022-11-24 03:28:44,993 | INFO : \t train_acc: 97.642; train_loss: 0.555; test_loss: 29.302; lr: [0.001]\n",
            "2022-11-24 03:28:44,993 | INFO : \t test_acc: 91.39 (best: 91.73 from #122)\n",
            "2022-11-24 03:29:03,350 | INFO : Epoch #146\n",
            "2022-11-24 03:29:03,350 | INFO : \t train_acc: 97.74; train_loss: 0.076; test_loss: 30.172; lr: [0.001]\n",
            "2022-11-24 03:29:03,350 | INFO : \t test_acc: 91.21 (best: 91.73 from #122)\n",
            "2022-11-24 03:29:21,197 | INFO : Epoch #147\n",
            "2022-11-24 03:29:21,197 | INFO : \t train_acc: 97.792; train_loss: 0.018; test_loss: 28.953; lr: [0.001]\n",
            "2022-11-24 03:29:21,198 | INFO : \t test_acc: 91.47 (best: 91.73 from #122)\n",
            "2022-11-24 03:29:38,437 | INFO : Epoch #148\n",
            "2022-11-24 03:29:38,437 | INFO : \t train_acc: 97.784; train_loss: 0.041; test_loss: 29.872; lr: [0.001]\n",
            "2022-11-24 03:29:38,437 | INFO : \t test_acc: 91.15 (best: 91.73 from #122)\n",
            "2022-11-24 03:29:55,941 | INFO : Epoch #149\n",
            "2022-11-24 03:29:55,941 | INFO : \t train_acc: 97.778; train_loss: 0.074; test_loss: 29.866; lr: [0.001]\n",
            "2022-11-24 03:29:55,942 | INFO : \t test_acc: 91.31 (best: 91.73 from #122)\n",
            "2022-11-24 03:30:13,726 | INFO : Epoch #150\n",
            "2022-11-24 03:30:13,727 | INFO : \t train_acc: 97.728; train_loss: 0.141; test_loss: 29.317; lr: [0.001]\n",
            "2022-11-24 03:30:13,727 | INFO : \t test_acc: 91.34 (best: 91.73 from #122)\n",
            "2022-11-24 03:30:31,743 | INFO : Epoch #151\n",
            "2022-11-24 03:30:31,744 | INFO : \t train_acc: 97.858; train_loss: 0.024; test_loss: 29.692; lr: [0.001]\n",
            "2022-11-24 03:30:31,744 | INFO : \t test_acc: 91.39 (best: 91.73 from #122)\n",
            "2022-11-24 03:30:49,167 | INFO : Epoch #152\n",
            "2022-11-24 03:30:49,167 | INFO : \t train_acc: 97.726; train_loss: 0.025; test_loss: 30.406; lr: [0.001]\n",
            "2022-11-24 03:30:49,167 | INFO : \t test_acc: 91.27 (best: 91.73 from #122)\n",
            "2022-11-24 03:31:06,360 | INFO : Epoch #153\n",
            "2022-11-24 03:31:06,361 | INFO : \t train_acc: 97.77; train_loss: 0.557; test_loss: 29.793; lr: [0.001]\n",
            "2022-11-24 03:31:06,361 | INFO : \t test_acc: 91.16 (best: 91.73 from #122)\n",
            "2022-11-24 03:31:24,070 | INFO : Epoch #154\n",
            "2022-11-24 03:31:24,071 | INFO : \t train_acc: 97.744; train_loss: 0.563; test_loss: 29.935; lr: [0.001]\n",
            "2022-11-24 03:31:24,071 | INFO : \t test_acc: 91.33 (best: 91.73 from #122)\n",
            "2022-11-24 03:31:41,671 | INFO : Epoch #155\n",
            "2022-11-24 03:31:41,671 | INFO : \t train_acc: 97.822; train_loss: 0.067; test_loss: 29.813; lr: [0.001]\n",
            "2022-11-24 03:31:41,672 | INFO : \t test_acc: 91.34 (best: 91.73 from #122)\n",
            "2022-11-24 03:31:59,567 | INFO : Epoch #156\n",
            "2022-11-24 03:31:59,567 | INFO : \t train_acc: 97.704; train_loss: 0.350; test_loss: 30.072; lr: [0.001]\n",
            "2022-11-24 03:31:59,567 | INFO : \t test_acc: 91.25 (best: 91.73 from #122)\n",
            "2022-11-24 03:32:16,890 | INFO : Epoch #157\n",
            "2022-11-24 03:32:16,891 | INFO : \t train_acc: 97.752; train_loss: 0.245; test_loss: 30.101; lr: [0.001]\n",
            "2022-11-24 03:32:16,891 | INFO : \t test_acc: 91.2 (best: 91.73 from #122)\n",
            "2022-11-24 03:32:34,532 | INFO : Epoch #158\n",
            "2022-11-24 03:32:34,533 | INFO : \t train_acc: 97.856; train_loss: 0.022; test_loss: 30.782; lr: [0.001]\n",
            "2022-11-24 03:32:34,533 | INFO : \t test_acc: 91.03 (best: 91.73 from #122)\n",
            "2022-11-24 03:32:52,165 | INFO : Epoch #159\n",
            "2022-11-24 03:32:52,166 | INFO : \t train_acc: 97.98; train_loss: 0.375; test_loss: 30.934; lr: [0.001]\n",
            "2022-11-24 03:32:52,166 | INFO : \t test_acc: 91.03 (best: 91.73 from #122)\n",
            "2022-11-24 03:33:09,641 | INFO : Epoch #160\n",
            "2022-11-24 03:33:09,642 | INFO : \t train_acc: 97.836; train_loss: 0.282; test_loss: 30.229; lr: [0.001]\n",
            "2022-11-24 03:33:09,642 | INFO : \t test_acc: 91.43 (best: 91.73 from #122)\n",
            "2022-11-24 03:33:26,875 | INFO : Epoch #161\n",
            "2022-11-24 03:33:26,875 | INFO : \t train_acc: 97.864; train_loss: 0.048; test_loss: 30.376; lr: [0.001]\n",
            "2022-11-24 03:33:26,875 | INFO : \t test_acc: 91.35 (best: 91.73 from #122)\n",
            "2022-11-24 03:33:44,406 | INFO : Epoch #162\n",
            "2022-11-24 03:33:44,406 | INFO : \t train_acc: 97.808; train_loss: 0.297; test_loss: 31.381; lr: [0.001]\n",
            "2022-11-24 03:33:44,406 | INFO : \t test_acc: 90.95 (best: 91.73 from #122)\n",
            "2022-11-24 03:34:01,865 | INFO : Epoch #163\n",
            "2022-11-24 03:34:01,865 | INFO : \t train_acc: 97.774; train_loss: 0.099; test_loss: 31.261; lr: [0.001]\n",
            "2022-11-24 03:34:01,865 | INFO : \t test_acc: 91.09 (best: 91.73 from #122)\n",
            "2022-11-24 03:34:20,194 | INFO : Epoch #164\n",
            "2022-11-24 03:34:20,194 | INFO : \t train_acc: 97.932; train_loss: 0.275; test_loss: 30.520; lr: [0.001]\n",
            "2022-11-24 03:34:20,194 | INFO : \t test_acc: 91.12 (best: 91.73 from #122)\n",
            "2022-11-24 03:34:38,131 | INFO : Epoch #165\n",
            "2022-11-24 03:34:38,132 | INFO : \t train_acc: 98.054; train_loss: 0.258; test_loss: 31.507; lr: [0.001]\n",
            "2022-11-24 03:34:38,132 | INFO : \t test_acc: 91.01 (best: 91.73 from #122)\n",
            "2022-11-24 03:34:55,922 | INFO : Epoch #166\n",
            "2022-11-24 03:34:55,923 | INFO : \t train_acc: 97.858; train_loss: 0.371; test_loss: 31.521; lr: [0.001]\n",
            "2022-11-24 03:34:55,923 | INFO : \t test_acc: 90.98 (best: 91.73 from #122)\n",
            "2022-11-24 03:35:14,128 | INFO : Epoch #167\n",
            "2022-11-24 03:35:14,129 | INFO : \t train_acc: 97.834; train_loss: 0.676; test_loss: 30.981; lr: [0.001]\n",
            "2022-11-24 03:35:14,129 | INFO : \t test_acc: 91.24 (best: 91.73 from #122)\n",
            "2022-11-24 03:35:32,419 | INFO : Epoch #168\n",
            "2022-11-24 03:35:32,419 | INFO : \t train_acc: 97.874; train_loss: 0.380; test_loss: 31.681; lr: [0.001]\n",
            "2022-11-24 03:35:32,420 | INFO : \t test_acc: 90.91 (best: 91.73 from #122)\n",
            "2022-11-24 03:35:50,646 | INFO : Epoch #169\n",
            "2022-11-24 03:35:50,647 | INFO : \t train_acc: 97.894; train_loss: 0.064; test_loss: 30.401; lr: [0.001]\n",
            "2022-11-24 03:35:50,647 | INFO : \t test_acc: 91.15 (best: 91.73 from #122)\n",
            "2022-11-24 03:36:08,142 | INFO : Epoch #170\n",
            "2022-11-24 03:36:08,142 | INFO : \t train_acc: 97.846; train_loss: 0.066; test_loss: 30.660; lr: [0.001]\n",
            "2022-11-24 03:36:08,142 | INFO : \t test_acc: 91.2 (best: 91.73 from #122)\n",
            "2022-11-24 03:36:25,585 | INFO : Epoch #171\n",
            "2022-11-24 03:36:25,585 | INFO : \t train_acc: 97.864; train_loss: 0.155; test_loss: 30.037; lr: [0.001]\n",
            "2022-11-24 03:36:25,586 | INFO : \t test_acc: 91.5 (best: 91.73 from #122)\n",
            "2022-11-24 03:36:43,225 | INFO : Epoch #172\n",
            "2022-11-24 03:36:43,226 | INFO : \t train_acc: 97.946; train_loss: 0.023; test_loss: 31.452; lr: [0.001]\n",
            "2022-11-24 03:36:43,226 | INFO : \t test_acc: 90.83 (best: 91.73 from #122)\n",
            "2022-11-24 03:37:00,797 | INFO : Epoch #173\n",
            "2022-11-24 03:37:00,798 | INFO : \t train_acc: 97.878; train_loss: 0.137; test_loss: 31.441; lr: [0.001]\n",
            "2022-11-24 03:37:00,798 | INFO : \t test_acc: 91.09 (best: 91.73 from #122)\n",
            "2022-11-24 03:37:18,159 | INFO : Epoch #174\n",
            "2022-11-24 03:37:18,160 | INFO : \t train_acc: 97.902; train_loss: 0.095; test_loss: 31.999; lr: [0.001]\n",
            "2022-11-24 03:37:18,160 | INFO : \t test_acc: 91.01 (best: 91.73 from #122)\n",
            "2022-11-24 03:37:36,154 | INFO : Epoch #175\n",
            "2022-11-24 03:37:36,155 | INFO : \t train_acc: 97.988; train_loss: 0.330; test_loss: 30.886; lr: [0.001]\n",
            "2022-11-24 03:37:36,155 | INFO : \t test_acc: 91.26 (best: 91.73 from #122)\n",
            "2022-11-24 03:37:53,386 | INFO : Epoch #176\n",
            "2022-11-24 03:37:53,387 | INFO : \t train_acc: 98.034; train_loss: 0.017; test_loss: 32.865; lr: [0.001]\n",
            "2022-11-24 03:37:53,387 | INFO : \t test_acc: 90.83 (best: 91.73 from #122)\n",
            "2022-11-24 03:38:12,040 | INFO : Epoch #177\n",
            "2022-11-24 03:38:12,040 | INFO : \t train_acc: 97.994; train_loss: 0.094; test_loss: 32.555; lr: [0.001]\n",
            "2022-11-24 03:38:12,040 | INFO : \t test_acc: 90.89 (best: 91.73 from #122)\n",
            "2022-11-24 03:38:30,536 | INFO : Epoch #178\n",
            "2022-11-24 03:38:30,536 | INFO : \t train_acc: 97.986; train_loss: 0.284; test_loss: 31.851; lr: [0.001]\n",
            "2022-11-24 03:38:30,536 | INFO : \t test_acc: 91.02 (best: 91.73 from #122)\n",
            "2022-11-24 03:38:48,574 | INFO : Epoch #179\n",
            "2022-11-24 03:38:48,574 | INFO : \t train_acc: 97.974; train_loss: 0.240; test_loss: 31.700; lr: [0.001]\n",
            "2022-11-24 03:38:48,574 | INFO : \t test_acc: 91.2 (best: 91.73 from #122)\n",
            "2022-11-24 03:39:06,358 | INFO : Epoch #180\n",
            "2022-11-24 03:39:06,359 | INFO : \t train_acc: 97.986; train_loss: 0.326; test_loss: 31.433; lr: [0.001]\n",
            "2022-11-24 03:39:06,359 | INFO : \t test_acc: 91.43 (best: 91.73 from #122)\n",
            "2022-11-24 03:39:23,852 | INFO : Epoch #181\n",
            "2022-11-24 03:39:23,853 | INFO : \t train_acc: 97.87; train_loss: 0.042; test_loss: 30.672; lr: [0.001]\n",
            "2022-11-24 03:39:23,853 | INFO : \t test_acc: 91.18 (best: 91.73 from #122)\n",
            "2022-11-24 03:39:41,924 | INFO : Epoch #182\n",
            "2022-11-24 03:39:41,924 | INFO : \t train_acc: 98.074; train_loss: 0.012; test_loss: 32.095; lr: [0.001]\n",
            "2022-11-24 03:39:41,924 | INFO : \t test_acc: 91.02 (best: 91.73 from #122)\n",
            "2022-11-24 03:39:59,630 | INFO : Epoch #183\n",
            "2022-11-24 03:39:59,631 | INFO : \t train_acc: 97.994; train_loss: 0.106; test_loss: 31.074; lr: [0.001]\n",
            "2022-11-24 03:39:59,631 | INFO : \t test_acc: 91.17 (best: 91.73 from #122)\n",
            "2022-11-24 03:40:17,448 | INFO : Epoch #184\n",
            "2022-11-24 03:40:17,448 | INFO : \t train_acc: 97.966; train_loss: 0.132; test_loss: 31.434; lr: [0.001]\n",
            "2022-11-24 03:40:17,448 | INFO : \t test_acc: 91.04 (best: 91.73 from #122)\n",
            "2022-11-24 03:40:34,774 | INFO : Epoch #185\n",
            "2022-11-24 03:40:34,774 | INFO : \t train_acc: 98.036; train_loss: 0.028; test_loss: 31.438; lr: [0.001]\n",
            "2022-11-24 03:40:34,775 | INFO : \t test_acc: 91.1 (best: 91.73 from #122)\n",
            "2022-11-24 03:40:52,241 | INFO : Epoch #186\n",
            "2022-11-24 03:40:52,242 | INFO : \t train_acc: 98.088; train_loss: 0.038; test_loss: 32.200; lr: [0.001]\n",
            "2022-11-24 03:40:52,242 | INFO : \t test_acc: 90.86 (best: 91.73 from #122)\n",
            "2022-11-24 03:41:10,001 | INFO : Epoch #187\n",
            "2022-11-24 03:41:10,002 | INFO : \t train_acc: 98.024; train_loss: 0.083; test_loss: 32.139; lr: [0.001]\n",
            "2022-11-24 03:41:10,002 | INFO : \t test_acc: 91.26 (best: 91.73 from #122)\n",
            "2022-11-24 03:41:27,342 | INFO : Epoch #188\n",
            "2022-11-24 03:41:27,342 | INFO : \t train_acc: 98.006; train_loss: 0.145; test_loss: 31.368; lr: [0.001]\n",
            "2022-11-24 03:41:27,342 | INFO : \t test_acc: 90.95 (best: 91.73 from #122)\n",
            "2022-11-24 03:41:44,686 | INFO : Epoch #189\n",
            "2022-11-24 03:41:44,687 | INFO : \t train_acc: 97.94; train_loss: 0.072; test_loss: 32.132; lr: [0.001]\n",
            "2022-11-24 03:41:44,687 | INFO : \t test_acc: 90.82 (best: 91.73 from #122)\n",
            "2022-11-24 03:42:02,436 | INFO : Epoch #190\n",
            "2022-11-24 03:42:02,436 | INFO : \t train_acc: 97.95; train_loss: 0.048; test_loss: 32.666; lr: [0.001]\n",
            "2022-11-24 03:42:02,436 | INFO : \t test_acc: 90.57 (best: 91.73 from #122)\n",
            "2022-11-24 03:42:20,597 | INFO : Epoch #191\n",
            "2022-11-24 03:42:20,598 | INFO : \t train_acc: 97.864; train_loss: 0.023; test_loss: 32.064; lr: [0.001]\n",
            "2022-11-24 03:42:20,598 | INFO : \t test_acc: 91.1 (best: 91.73 from #122)\n",
            "2022-11-24 03:42:37,892 | INFO : Epoch #192\n",
            "2022-11-24 03:42:37,892 | INFO : \t train_acc: 98.002; train_loss: 0.036; test_loss: 32.918; lr: [0.001]\n",
            "2022-11-24 03:42:37,892 | INFO : \t test_acc: 90.85 (best: 91.73 from #122)\n",
            "2022-11-24 03:42:55,632 | INFO : Epoch #193\n",
            "2022-11-24 03:42:55,632 | INFO : \t train_acc: 98.124; train_loss: 0.220; test_loss: 32.308; lr: [0.001]\n",
            "2022-11-24 03:42:55,632 | INFO : \t test_acc: 91.27 (best: 91.73 from #122)\n",
            "2022-11-24 03:43:13,317 | INFO : Epoch #194\n",
            "2022-11-24 03:43:13,317 | INFO : \t train_acc: 98.052; train_loss: 0.202; test_loss: 31.985; lr: [0.001]\n",
            "2022-11-24 03:43:13,317 | INFO : \t test_acc: 91.16 (best: 91.73 from #122)\n",
            "2022-11-24 03:43:31,171 | INFO : Epoch #195\n",
            "2022-11-24 03:43:31,172 | INFO : \t train_acc: 98.072; train_loss: 0.071; test_loss: 31.696; lr: [0.001]\n",
            "2022-11-24 03:43:31,172 | INFO : \t test_acc: 91.08 (best: 91.73 from #122)\n",
            "2022-11-24 03:43:48,654 | INFO : Epoch #196\n",
            "2022-11-24 03:43:48,654 | INFO : \t train_acc: 98.024; train_loss: 0.048; test_loss: 32.787; lr: [0.001]\n",
            "2022-11-24 03:43:48,654 | INFO : \t test_acc: 90.95 (best: 91.73 from #122)\n",
            "2022-11-24 03:44:06,320 | INFO : Epoch #197\n",
            "2022-11-24 03:44:06,320 | INFO : \t train_acc: 98.03; train_loss: 0.020; test_loss: 33.110; lr: [0.001]\n",
            "2022-11-24 03:44:06,320 | INFO : \t test_acc: 90.7 (best: 91.73 from #122)\n",
            "2022-11-24 03:44:24,061 | INFO : Epoch #198\n",
            "2022-11-24 03:44:24,062 | INFO : \t train_acc: 98.038; train_loss: 0.017; test_loss: 32.163; lr: [0.001]\n",
            "2022-11-24 03:44:24,062 | INFO : \t test_acc: 90.98 (best: 91.73 from #122)\n",
            "2022-11-24 03:44:41,894 | INFO : Epoch #199\n",
            "2022-11-24 03:44:41,894 | INFO : \t train_acc: 97.974; train_loss: 0.127; test_loss: 32.553; lr: [0.001]\n",
            "2022-11-24 03:44:41,894 | INFO : \t test_acc: 90.97 (best: 91.73 from #122)\n",
            "2022-11-24 03:45:00,056 | INFO : Epoch #200\n",
            "2022-11-24 03:45:00,057 | INFO : \t train_acc: 98.424; train_loss: 0.044; test_loss: 30.686; lr: [0.0001]\n",
            "2022-11-24 03:45:00,057 | INFO : \t test_acc: 91.4 (best: 91.73 from #122)\n",
            "2022-11-24 03:45:17,986 | INFO : Epoch #201\n",
            "2022-11-24 03:45:17,987 | INFO : \t train_acc: 98.53; train_loss: 0.101; test_loss: 30.730; lr: [0.0001]\n",
            "2022-11-24 03:45:17,987 | INFO : \t test_acc: 91.35 (best: 91.73 from #122)\n",
            "2022-11-24 03:45:35,920 | INFO : Epoch #202\n",
            "2022-11-24 03:45:35,921 | INFO : \t train_acc: 98.598; train_loss: 0.044; test_loss: 30.923; lr: [0.0001]\n",
            "2022-11-24 03:45:35,921 | INFO : \t test_acc: 91.29 (best: 91.73 from #122)\n",
            "2022-11-24 03:45:53,563 | INFO : Epoch #203\n",
            "2022-11-24 03:45:53,563 | INFO : \t train_acc: 98.704; train_loss: 0.330; test_loss: 30.923; lr: [0.0001]\n",
            "2022-11-24 03:45:53,563 | INFO : \t test_acc: 91.43 (best: 91.73 from #122)\n",
            "2022-11-24 03:46:11,300 | INFO : Epoch #204\n",
            "2022-11-24 03:46:11,301 | INFO : \t train_acc: 98.732; train_loss: 0.024; test_loss: 30.912; lr: [0.0001]\n",
            "2022-11-24 03:46:11,301 | INFO : \t test_acc: 91.47 (best: 91.73 from #122)\n",
            "2022-11-24 03:46:28,671 | INFO : Epoch #205\n",
            "2022-11-24 03:46:28,672 | INFO : \t train_acc: 98.756; train_loss: 0.082; test_loss: 31.074; lr: [0.0001]\n",
            "2022-11-24 03:46:28,672 | INFO : \t test_acc: 91.48 (best: 91.73 from #122)\n",
            "2022-11-24 03:46:45,871 | INFO : Epoch #206\n",
            "2022-11-24 03:46:45,871 | INFO : \t train_acc: 98.806; train_loss: 0.054; test_loss: 30.702; lr: [0.0001]\n",
            "2022-11-24 03:46:45,871 | INFO : \t test_acc: 91.52 (best: 91.73 from #122)\n",
            "2022-11-24 03:47:03,508 | INFO : Epoch #207\n",
            "2022-11-24 03:47:03,508 | INFO : \t train_acc: 98.79; train_loss: 0.034; test_loss: 30.932; lr: [0.0001]\n",
            "2022-11-24 03:47:03,509 | INFO : \t test_acc: 91.54 (best: 91.73 from #122)\n",
            "2022-11-24 03:47:21,384 | INFO : Epoch #208\n",
            "2022-11-24 03:47:21,384 | INFO : \t train_acc: 98.802; train_loss: 0.024; test_loss: 30.785; lr: [0.0001]\n",
            "2022-11-24 03:47:21,384 | INFO : \t test_acc: 91.49 (best: 91.73 from #122)\n",
            "2022-11-24 03:47:39,361 | INFO : Epoch #209\n",
            "2022-11-24 03:47:39,361 | INFO : \t train_acc: 98.794; train_loss: 0.219; test_loss: 30.948; lr: [0.0001]\n",
            "2022-11-24 03:47:39,361 | INFO : \t test_acc: 91.43 (best: 91.73 from #122)\n",
            "2022-11-24 03:47:57,845 | INFO : Epoch #210\n",
            "2022-11-24 03:47:57,845 | INFO : \t train_acc: 98.894; train_loss: 0.051; test_loss: 31.308; lr: [0.0001]\n",
            "2022-11-24 03:47:57,845 | INFO : \t test_acc: 91.42 (best: 91.73 from #122)\n",
            "2022-11-24 03:48:15,102 | INFO : Epoch #211\n",
            "2022-11-24 03:48:15,103 | INFO : \t train_acc: 98.842; train_loss: 0.129; test_loss: 30.799; lr: [0.0001]\n",
            "2022-11-24 03:48:15,103 | INFO : \t test_acc: 91.57 (best: 91.73 from #122)\n",
            "2022-11-24 03:48:32,477 | INFO : Epoch #212\n",
            "2022-11-24 03:48:32,478 | INFO : \t train_acc: 98.878; train_loss: 0.039; test_loss: 30.762; lr: [0.0001]\n",
            "2022-11-24 03:48:32,478 | INFO : \t test_acc: 91.59 (best: 91.73 from #122)\n",
            "2022-11-24 03:48:49,950 | INFO : Epoch #213\n",
            "2022-11-24 03:48:49,950 | INFO : \t train_acc: 98.892; train_loss: 0.062; test_loss: 30.921; lr: [0.0001]\n",
            "2022-11-24 03:48:49,950 | INFO : \t test_acc: 91.58 (best: 91.73 from #122)\n",
            "2022-11-24 03:49:07,588 | INFO : Epoch #214\n",
            "2022-11-24 03:49:07,589 | INFO : \t train_acc: 98.856; train_loss: 0.077; test_loss: 30.728; lr: [0.0001]\n",
            "2022-11-24 03:49:07,589 | INFO : \t test_acc: 91.52 (best: 91.73 from #122)\n",
            "2022-11-24 03:49:25,319 | INFO : Epoch #215\n",
            "2022-11-24 03:49:25,319 | INFO : \t train_acc: 98.788; train_loss: 0.052; test_loss: 30.858; lr: [0.0001]\n",
            "2022-11-24 03:49:25,319 | INFO : \t test_acc: 91.47 (best: 91.73 from #122)\n",
            "2022-11-24 03:49:43,637 | INFO : Epoch #216\n",
            "2022-11-24 03:49:43,638 | INFO : \t train_acc: 98.874; train_loss: 0.113; test_loss: 31.090; lr: [0.0001]\n",
            "2022-11-24 03:49:43,638 | INFO : \t test_acc: 91.45 (best: 91.73 from #122)\n",
            "2022-11-24 03:50:01,653 | INFO : Epoch #217\n",
            "2022-11-24 03:50:01,653 | INFO : \t train_acc: 98.922; train_loss: 0.084; test_loss: 30.892; lr: [0.0001]\n",
            "2022-11-24 03:50:01,653 | INFO : \t test_acc: 91.6 (best: 91.73 from #122)\n",
            "2022-11-24 03:50:19,431 | INFO : Epoch #218\n",
            "2022-11-24 03:50:19,432 | INFO : \t train_acc: 98.91; train_loss: 0.082; test_loss: 30.643; lr: [0.0001]\n",
            "2022-11-24 03:50:19,432 | INFO : \t test_acc: 91.63 (best: 91.73 from #122)\n",
            "2022-11-24 03:50:36,750 | INFO : Epoch #219\n",
            "2022-11-24 03:50:36,751 | INFO : \t train_acc: 98.926; train_loss: 0.163; test_loss: 30.903; lr: [0.0001]\n",
            "2022-11-24 03:50:36,751 | INFO : \t test_acc: 91.54 (best: 91.73 from #122)\n",
            "2022-11-24 03:50:54,690 | INFO : Epoch #220\n",
            "2022-11-24 03:50:54,690 | INFO : \t train_acc: 98.908; train_loss: 0.361; test_loss: 31.111; lr: [0.0001]\n",
            "2022-11-24 03:50:54,690 | INFO : \t test_acc: 91.45 (best: 91.73 from #122)\n",
            "2022-11-24 03:51:12,312 | INFO : Epoch #221\n",
            "2022-11-24 03:51:12,313 | INFO : \t train_acc: 98.944; train_loss: 0.046; test_loss: 30.741; lr: [0.0001]\n",
            "2022-11-24 03:51:12,313 | INFO : \t test_acc: 91.57 (best: 91.73 from #122)\n",
            "2022-11-24 03:51:30,584 | INFO : Epoch #222\n",
            "2022-11-24 03:51:30,584 | INFO : \t train_acc: 99.01; train_loss: 0.030; test_loss: 31.155; lr: [0.0001]\n",
            "2022-11-24 03:51:30,585 | INFO : \t test_acc: 91.64 (best: 91.73 from #122)\n",
            "2022-11-24 03:51:48,601 | INFO : Epoch #223\n",
            "2022-11-24 03:51:48,601 | INFO : \t train_acc: 98.93; train_loss: 0.031; test_loss: 31.008; lr: [0.0001]\n",
            "2022-11-24 03:51:48,602 | INFO : \t test_acc: 91.59 (best: 91.73 from #122)\n",
            "2022-11-24 03:52:05,979 | INFO : Epoch #224\n",
            "2022-11-24 03:52:05,979 | INFO : \t train_acc: 98.974; train_loss: 0.209; test_loss: 30.747; lr: [0.0001]\n",
            "2022-11-24 03:52:05,979 | INFO : \t test_acc: 91.51 (best: 91.73 from #122)\n",
            "2022-11-24 03:52:24,134 | INFO : Epoch #225\n",
            "2022-11-24 03:52:24,135 | INFO : \t train_acc: 99.014; train_loss: 0.103; test_loss: 31.141; lr: [0.0001]\n",
            "2022-11-24 03:52:24,135 | INFO : \t test_acc: 91.45 (best: 91.73 from #122)\n",
            "2022-11-24 03:52:41,606 | INFO : Epoch #226\n",
            "2022-11-24 03:52:41,606 | INFO : \t train_acc: 98.918; train_loss: 0.141; test_loss: 30.941; lr: [0.0001]\n",
            "2022-11-24 03:52:41,606 | INFO : \t test_acc: 91.54 (best: 91.73 from #122)\n",
            "2022-11-24 03:52:59,477 | INFO : Epoch #227\n",
            "2022-11-24 03:52:59,477 | INFO : \t train_acc: 99.014; train_loss: 0.179; test_loss: 30.847; lr: [0.0001]\n",
            "2022-11-24 03:52:59,478 | INFO : \t test_acc: 91.66 (best: 91.73 from #122)\n",
            "2022-11-24 03:53:17,469 | INFO : Epoch #228\n",
            "2022-11-24 03:53:17,469 | INFO : \t train_acc: 98.988; train_loss: 0.027; test_loss: 30.750; lr: [0.0001]\n",
            "2022-11-24 03:53:17,469 | INFO : \t test_acc: 91.51 (best: 91.73 from #122)\n",
            "2022-11-24 03:53:34,722 | INFO : Epoch #229\n",
            "2022-11-24 03:53:34,723 | INFO : \t train_acc: 98.882; train_loss: 0.018; test_loss: 30.806; lr: [0.0001]\n",
            "2022-11-24 03:53:34,723 | INFO : \t test_acc: 91.53 (best: 91.73 from #122)\n",
            "2022-11-24 03:53:52,732 | INFO : Epoch #230\n",
            "2022-11-24 03:53:52,733 | INFO : \t train_acc: 98.952; train_loss: 0.074; test_loss: 31.550; lr: [0.0001]\n",
            "2022-11-24 03:53:52,733 | INFO : \t test_acc: 91.29 (best: 91.73 from #122)\n",
            "2022-11-24 03:54:10,240 | INFO : Epoch #231\n",
            "2022-11-24 03:54:10,240 | INFO : \t train_acc: 98.95; train_loss: 0.004; test_loss: 31.477; lr: [0.0001]\n",
            "2022-11-24 03:54:10,240 | INFO : \t test_acc: 91.64 (best: 91.73 from #122)\n",
            "2022-11-24 03:54:27,709 | INFO : Epoch #232\n",
            "2022-11-24 03:54:27,710 | INFO : \t train_acc: 99.022; train_loss: 0.017; test_loss: 30.909; lr: [0.0001]\n",
            "2022-11-24 03:54:27,710 | INFO : \t test_acc: 91.61 (best: 91.73 from #122)\n",
            "2022-11-24 03:54:45,840 | INFO : Epoch #233\n",
            "2022-11-24 03:54:45,840 | INFO : \t train_acc: 98.878; train_loss: 0.216; test_loss: 30.942; lr: [0.0001]\n",
            "2022-11-24 03:54:45,840 | INFO : \t test_acc: 91.45 (best: 91.73 from #122)\n",
            "2022-11-24 03:55:03,367 | INFO : Epoch #234\n",
            "2022-11-24 03:55:03,367 | INFO : \t train_acc: 98.914; train_loss: 0.445; test_loss: 31.374; lr: [0.0001]\n",
            "2022-11-24 03:55:03,368 | INFO : \t test_acc: 91.55 (best: 91.73 from #122)\n",
            "2022-11-24 03:55:21,820 | INFO : Epoch #235\n",
            "2022-11-24 03:55:21,820 | INFO : \t train_acc: 98.922; train_loss: 0.098; test_loss: 30.892; lr: [0.0001]\n",
            "2022-11-24 03:55:21,821 | INFO : \t test_acc: 91.5 (best: 91.73 from #122)\n",
            "2022-11-24 03:55:39,704 | INFO : Epoch #236\n",
            "2022-11-24 03:55:39,704 | INFO : \t train_acc: 98.954; train_loss: 0.055; test_loss: 31.232; lr: [0.0001]\n",
            "2022-11-24 03:55:39,705 | INFO : \t test_acc: 91.6 (best: 91.73 from #122)\n",
            "2022-11-24 03:55:57,308 | INFO : Epoch #237\n",
            "2022-11-24 03:55:57,308 | INFO : \t train_acc: 98.984; train_loss: 0.157; test_loss: 31.004; lr: [0.0001]\n",
            "2022-11-24 03:55:57,309 | INFO : \t test_acc: 91.53 (best: 91.73 from #122)\n",
            "2022-11-24 03:56:14,893 | INFO : Epoch #238\n",
            "2022-11-24 03:56:14,893 | INFO : \t train_acc: 98.996; train_loss: 0.031; test_loss: 30.575; lr: [0.0001]\n",
            "2022-11-24 03:56:14,893 | INFO : \t test_acc: 91.5 (best: 91.73 from #122)\n",
            "2022-11-24 03:56:32,219 | INFO : Epoch #239\n",
            "2022-11-24 03:56:32,219 | INFO : \t train_acc: 99.046; train_loss: 0.087; test_loss: 31.119; lr: [0.0001]\n",
            "2022-11-24 03:56:32,220 | INFO : \t test_acc: 91.53 (best: 91.73 from #122)\n",
            "2022-11-24 03:56:50,133 | INFO : Epoch #240\n",
            "2022-11-24 03:56:50,133 | INFO : \t train_acc: 98.984; train_loss: 0.019; test_loss: 31.110; lr: [0.0001]\n",
            "2022-11-24 03:56:50,133 | INFO : \t test_acc: 91.53 (best: 91.73 from #122)\n",
            "2022-11-24 03:57:08,239 | INFO : Epoch #241\n",
            "2022-11-24 03:57:08,240 | INFO : \t train_acc: 99.008; train_loss: 0.105; test_loss: 30.854; lr: [0.0001]\n",
            "2022-11-24 03:57:08,240 | INFO : \t test_acc: 91.52 (best: 91.73 from #122)\n",
            "2022-11-24 03:57:25,941 | INFO : Epoch #242\n",
            "2022-11-24 03:57:25,942 | INFO : \t train_acc: 99.008; train_loss: 0.017; test_loss: 30.749; lr: [0.0001]\n",
            "2022-11-24 03:57:25,942 | INFO : \t test_acc: 91.58 (best: 91.73 from #122)\n",
            "2022-11-24 03:57:43,630 | INFO : Epoch #243\n",
            "2022-11-24 03:57:43,631 | INFO : \t train_acc: 99.026; train_loss: 0.004; test_loss: 31.287; lr: [0.0001]\n",
            "2022-11-24 03:57:43,631 | INFO : \t test_acc: 91.48 (best: 91.73 from #122)\n",
            "2022-11-24 03:58:01,131 | INFO : Epoch #244\n",
            "2022-11-24 03:58:01,131 | INFO : \t train_acc: 98.998; train_loss: 0.066; test_loss: 30.912; lr: [0.0001]\n",
            "2022-11-24 03:58:01,131 | INFO : \t test_acc: 91.52 (best: 91.73 from #122)\n",
            "2022-11-24 03:58:19,703 | INFO : Epoch #245\n",
            "2022-11-24 03:58:19,703 | INFO : \t train_acc: 99.05; train_loss: 0.034; test_loss: 30.740; lr: [0.0001]\n",
            "2022-11-24 03:58:19,703 | INFO : \t test_acc: 91.35 (best: 91.73 from #122)\n",
            "2022-11-24 03:58:37,371 | INFO : Epoch #246\n",
            "2022-11-24 03:58:37,372 | INFO : \t train_acc: 98.944; train_loss: 0.084; test_loss: 31.116; lr: [0.0001]\n",
            "2022-11-24 03:58:37,372 | INFO : \t test_acc: 91.5 (best: 91.73 from #122)\n",
            "2022-11-24 03:58:55,168 | INFO : Epoch #247\n",
            "2022-11-24 03:58:55,168 | INFO : \t train_acc: 99.038; train_loss: 0.265; test_loss: 31.593; lr: [0.0001]\n",
            "2022-11-24 03:58:55,168 | INFO : \t test_acc: 91.54 (best: 91.73 from #122)\n",
            "2022-11-24 03:59:12,933 | INFO : Epoch #248\n",
            "2022-11-24 03:59:12,933 | INFO : \t train_acc: 99.038; train_loss: 0.070; test_loss: 31.492; lr: [0.0001]\n",
            "2022-11-24 03:59:12,933 | INFO : \t test_acc: 91.57 (best: 91.73 from #122)\n",
            "2022-11-24 03:59:30,693 | INFO : Epoch #249\n",
            "2022-11-24 03:59:30,694 | INFO : \t train_acc: 99.018; train_loss: 0.155; test_loss: 31.488; lr: [0.0001]\n",
            "2022-11-24 03:59:30,694 | INFO : \t test_acc: 91.47 (best: 91.73 from #122)\n",
            "2022-11-24 03:59:48,342 | INFO : Epoch #250\n",
            "2022-11-24 03:59:48,342 | INFO : \t train_acc: 99.078; train_loss: 0.010; test_loss: 31.163; lr: [0.0001]\n",
            "2022-11-24 03:59:48,342 | INFO : \t test_acc: 91.42 (best: 91.73 from #122)\n",
            "2022-11-24 04:00:05,681 | INFO : Epoch #251\n",
            "2022-11-24 04:00:05,681 | INFO : \t train_acc: 99.026; train_loss: 0.295; test_loss: 31.348; lr: [0.0001]\n",
            "2022-11-24 04:00:05,682 | INFO : \t test_acc: 91.39 (best: 91.73 from #122)\n",
            "2022-11-24 04:00:23,586 | INFO : Epoch #252\n",
            "2022-11-24 04:00:23,586 | INFO : \t train_acc: 99.012; train_loss: 0.087; test_loss: 31.394; lr: [0.0001]\n",
            "2022-11-24 04:00:23,586 | INFO : \t test_acc: 91.52 (best: 91.73 from #122)\n",
            "2022-11-24 04:00:41,646 | INFO : Epoch #253\n",
            "2022-11-24 04:00:41,647 | INFO : \t train_acc: 99.066; train_loss: 0.034; test_loss: 30.894; lr: [0.0001]\n",
            "2022-11-24 04:00:41,647 | INFO : \t test_acc: 91.6 (best: 91.73 from #122)\n",
            "2022-11-24 04:00:59,228 | INFO : Epoch #254\n",
            "2022-11-24 04:00:59,228 | INFO : \t train_acc: 99.026; train_loss: 0.155; test_loss: 31.307; lr: [0.0001]\n",
            "2022-11-24 04:00:59,228 | INFO : \t test_acc: 91.39 (best: 91.73 from #122)\n",
            "2022-11-24 04:01:16,892 | INFO : Epoch #255\n",
            "2022-11-24 04:01:16,892 | INFO : \t train_acc: 99.09; train_loss: 0.029; test_loss: 31.366; lr: [0.0001]\n",
            "2022-11-24 04:01:16,892 | INFO : \t test_acc: 91.43 (best: 91.73 from #122)\n",
            "2022-11-24 04:01:35,411 | INFO : Epoch #256\n",
            "2022-11-24 04:01:35,412 | INFO : \t train_acc: 99.036; train_loss: 0.209; test_loss: 31.615; lr: [0.0001]\n",
            "2022-11-24 04:01:35,412 | INFO : \t test_acc: 91.39 (best: 91.73 from #122)\n",
            "2022-11-24 04:01:53,063 | INFO : Epoch #257\n",
            "2022-11-24 04:01:53,063 | INFO : \t train_acc: 99.094; train_loss: 0.072; test_loss: 32.052; lr: [0.0001]\n",
            "2022-11-24 04:01:53,063 | INFO : \t test_acc: 91.28 (best: 91.73 from #122)\n",
            "2022-11-24 04:02:10,692 | INFO : Epoch #258\n",
            "2022-11-24 04:02:10,693 | INFO : \t train_acc: 99.092; train_loss: 0.327; test_loss: 31.833; lr: [0.0001]\n",
            "2022-11-24 04:02:10,693 | INFO : \t test_acc: 91.46 (best: 91.73 from #122)\n",
            "2022-11-24 04:02:28,364 | INFO : Epoch #259\n",
            "2022-11-24 04:02:28,364 | INFO : \t train_acc: 99.084; train_loss: 0.249; test_loss: 31.436; lr: [0.0001]\n",
            "2022-11-24 04:02:28,365 | INFO : \t test_acc: 91.49 (best: 91.73 from #122)\n",
            "2022-11-24 04:02:45,512 | INFO : Epoch #260\n",
            "2022-11-24 04:02:45,513 | INFO : \t train_acc: 99.054; train_loss: 0.074; test_loss: 31.660; lr: [0.0001]\n",
            "2022-11-24 04:02:45,513 | INFO : \t test_acc: 91.47 (best: 91.73 from #122)\n",
            "2022-11-24 04:03:03,017 | INFO : Epoch #261\n",
            "2022-11-24 04:03:03,017 | INFO : \t train_acc: 99.006; train_loss: 0.046; test_loss: 32.041; lr: [0.0001]\n",
            "2022-11-24 04:03:03,017 | INFO : \t test_acc: 91.44 (best: 91.73 from #122)\n",
            "2022-11-24 04:03:21,263 | INFO : Epoch #262\n",
            "2022-11-24 04:03:21,264 | INFO : \t train_acc: 99.056; train_loss: 0.172; test_loss: 31.772; lr: [0.0001]\n",
            "2022-11-24 04:03:21,264 | INFO : \t test_acc: 91.29 (best: 91.73 from #122)\n",
            "2022-11-24 04:03:38,411 | INFO : Epoch #263\n",
            "2022-11-24 04:03:38,411 | INFO : \t train_acc: 99.182; train_loss: 0.077; test_loss: 31.324; lr: [0.0001]\n",
            "2022-11-24 04:03:38,411 | INFO : \t test_acc: 91.4 (best: 91.73 from #122)\n",
            "2022-11-24 04:03:55,764 | INFO : Epoch #264\n",
            "2022-11-24 04:03:55,764 | INFO : \t train_acc: 99.016; train_loss: 0.044; test_loss: 31.228; lr: [0.0001]\n",
            "2022-11-24 04:03:55,764 | INFO : \t test_acc: 91.41 (best: 91.73 from #122)\n",
            "2022-11-24 04:04:13,872 | INFO : Epoch #265\n",
            "2022-11-24 04:04:13,872 | INFO : \t train_acc: 99.008; train_loss: 0.056; test_loss: 31.504; lr: [0.0001]\n",
            "2022-11-24 04:04:13,872 | INFO : \t test_acc: 91.51 (best: 91.73 from #122)\n",
            "2022-11-24 04:04:32,295 | INFO : Epoch #266\n",
            "2022-11-24 04:04:32,296 | INFO : \t train_acc: 99.078; train_loss: 0.198; test_loss: 31.689; lr: [0.0001]\n",
            "2022-11-24 04:04:32,296 | INFO : \t test_acc: 91.35 (best: 91.73 from #122)\n",
            "2022-11-24 04:04:49,554 | INFO : Epoch #267\n",
            "2022-11-24 04:04:49,554 | INFO : \t train_acc: 99.076; train_loss: 0.011; test_loss: 31.456; lr: [0.0001]\n",
            "2022-11-24 04:04:49,554 | INFO : \t test_acc: 91.37 (best: 91.73 from #122)\n",
            "2022-11-24 04:05:07,165 | INFO : Epoch #268\n",
            "2022-11-24 04:05:07,166 | INFO : \t train_acc: 99.11; train_loss: 0.109; test_loss: 30.876; lr: [0.0001]\n",
            "2022-11-24 04:05:07,166 | INFO : \t test_acc: 91.56 (best: 91.73 from #122)\n",
            "2022-11-24 04:05:24,372 | INFO : Epoch #269\n",
            "2022-11-24 04:05:24,373 | INFO : \t train_acc: 98.998; train_loss: 0.071; test_loss: 31.263; lr: [0.0001]\n",
            "2022-11-24 04:05:24,373 | INFO : \t test_acc: 91.6 (best: 91.73 from #122)\n",
            "2022-11-24 04:05:41,530 | INFO : Epoch #270\n",
            "2022-11-24 04:05:41,530 | INFO : \t train_acc: 99.102; train_loss: 0.164; test_loss: 31.336; lr: [0.0001]\n",
            "2022-11-24 04:05:41,530 | INFO : \t test_acc: 91.33 (best: 91.73 from #122)\n",
            "2022-11-24 04:06:00,166 | INFO : Epoch #271\n",
            "2022-11-24 04:06:00,166 | INFO : \t train_acc: 99.014; train_loss: 0.037; test_loss: 31.442; lr: [0.0001]\n",
            "2022-11-24 04:06:00,166 | INFO : \t test_acc: 91.53 (best: 91.73 from #122)\n",
            "2022-11-24 04:06:17,378 | INFO : Epoch #272\n",
            "2022-11-24 04:06:17,379 | INFO : \t train_acc: 99.078; train_loss: 0.123; test_loss: 31.545; lr: [0.0001]\n",
            "2022-11-24 04:06:17,379 | INFO : \t test_acc: 91.51 (best: 91.73 from #122)\n",
            "2022-11-24 04:06:34,576 | INFO : Epoch #273\n",
            "2022-11-24 04:06:34,577 | INFO : \t train_acc: 99.054; train_loss: 0.008; test_loss: 31.649; lr: [0.0001]\n",
            "2022-11-24 04:06:34,577 | INFO : \t test_acc: 91.37 (best: 91.73 from #122)\n",
            "2022-11-24 04:06:51,902 | INFO : Epoch #274\n",
            "2022-11-24 04:06:51,902 | INFO : \t train_acc: 99.116; train_loss: 0.030; test_loss: 31.517; lr: [0.0001]\n",
            "2022-11-24 04:06:51,902 | INFO : \t test_acc: 91.45 (best: 91.73 from #122)\n",
            "2022-11-24 04:07:09,101 | INFO : Epoch #275\n",
            "2022-11-24 04:07:09,102 | INFO : \t train_acc: 99.032; train_loss: 0.104; test_loss: 31.634; lr: [0.0001]\n",
            "2022-11-24 04:07:09,102 | INFO : \t test_acc: 91.36 (best: 91.73 from #122)\n",
            "2022-11-24 04:07:28,318 | INFO : Epoch #276\n",
            "2022-11-24 04:07:28,318 | INFO : \t train_acc: 99.086; train_loss: 0.158; test_loss: 31.567; lr: [0.0001]\n",
            "2022-11-24 04:07:28,318 | INFO : \t test_acc: 91.34 (best: 91.73 from #122)\n",
            "2022-11-24 04:07:46,083 | INFO : Epoch #277\n",
            "2022-11-24 04:07:46,084 | INFO : \t train_acc: 99.108; train_loss: 0.050; test_loss: 31.303; lr: [0.0001]\n",
            "2022-11-24 04:07:46,084 | INFO : \t test_acc: 91.54 (best: 91.73 from #122)\n",
            "2022-11-24 04:08:03,780 | INFO : Epoch #278\n",
            "2022-11-24 04:08:03,780 | INFO : \t train_acc: 99.09; train_loss: 0.190; test_loss: 31.888; lr: [0.0001]\n",
            "2022-11-24 04:08:03,780 | INFO : \t test_acc: 91.44 (best: 91.73 from #122)\n",
            "2022-11-24 04:08:20,879 | INFO : Epoch #279\n",
            "2022-11-24 04:08:20,879 | INFO : \t train_acc: 99.198; train_loss: 0.004; test_loss: 31.763; lr: [0.0001]\n",
            "2022-11-24 04:08:20,879 | INFO : \t test_acc: 91.33 (best: 91.73 from #122)\n",
            "2022-11-24 04:08:38,764 | INFO : Epoch #280\n",
            "2022-11-24 04:08:38,765 | INFO : \t train_acc: 99.036; train_loss: 0.051; test_loss: 31.309; lr: [0.0001]\n",
            "2022-11-24 04:08:38,765 | INFO : \t test_acc: 91.39 (best: 91.73 from #122)\n",
            "2022-11-24 04:08:56,273 | INFO : Epoch #281\n",
            "2022-11-24 04:08:56,273 | INFO : \t train_acc: 99.072; train_loss: 0.028; test_loss: 31.463; lr: [0.0001]\n",
            "2022-11-24 04:08:56,273 | INFO : \t test_acc: 91.46 (best: 91.73 from #122)\n",
            "2022-11-24 04:09:14,007 | INFO : Epoch #282\n",
            "2022-11-24 04:09:14,008 | INFO : \t train_acc: 99.118; train_loss: 0.032; test_loss: 31.728; lr: [0.0001]\n",
            "2022-11-24 04:09:14,008 | INFO : \t test_acc: 91.39 (best: 91.73 from #122)\n",
            "2022-11-24 04:09:31,501 | INFO : Epoch #283\n",
            "2022-11-24 04:09:31,501 | INFO : \t train_acc: 99.112; train_loss: 0.050; test_loss: 32.023; lr: [0.0001]\n",
            "2022-11-24 04:09:31,501 | INFO : \t test_acc: 91.36 (best: 91.73 from #122)\n",
            "2022-11-24 04:09:49,017 | INFO : Epoch #284\n",
            "2022-11-24 04:09:49,018 | INFO : \t train_acc: 99.05; train_loss: 0.130; test_loss: 31.782; lr: [0.0001]\n",
            "2022-11-24 04:09:49,018 | INFO : \t test_acc: 91.46 (best: 91.73 from #122)\n",
            "2022-11-24 04:10:06,963 | INFO : Epoch #285\n",
            "2022-11-24 04:10:06,963 | INFO : \t train_acc: 99.156; train_loss: 0.220; test_loss: 31.887; lr: [0.0001]\n",
            "2022-11-24 04:10:06,964 | INFO : \t test_acc: 91.41 (best: 91.73 from #122)\n",
            "2022-11-24 04:10:25,120 | INFO : Epoch #286\n",
            "2022-11-24 04:10:25,121 | INFO : \t train_acc: 99.158; train_loss: 0.062; test_loss: 31.379; lr: [0.0001]\n",
            "2022-11-24 04:10:25,121 | INFO : \t test_acc: 91.49 (best: 91.73 from #122)\n",
            "2022-11-24 04:10:42,505 | INFO : Epoch #287\n",
            "2022-11-24 04:10:42,505 | INFO : \t train_acc: 99.03; train_loss: 0.012; test_loss: 31.677; lr: [0.0001]\n",
            "2022-11-24 04:10:42,506 | INFO : \t test_acc: 91.45 (best: 91.73 from #122)\n",
            "2022-11-24 04:10:59,896 | INFO : Epoch #288\n",
            "2022-11-24 04:10:59,896 | INFO : \t train_acc: 99.156; train_loss: 0.008; test_loss: 31.359; lr: [0.0001]\n",
            "2022-11-24 04:10:59,897 | INFO : \t test_acc: 91.58 (best: 91.73 from #122)\n",
            "2022-11-24 04:11:17,021 | INFO : Epoch #289\n",
            "2022-11-24 04:11:17,021 | INFO : \t train_acc: 99.114; train_loss: 0.042; test_loss: 32.249; lr: [0.0001]\n",
            "2022-11-24 04:11:17,021 | INFO : \t test_acc: 91.51 (best: 91.73 from #122)\n",
            "2022-11-24 04:11:34,485 | INFO : Epoch #290\n",
            "2022-11-24 04:11:34,486 | INFO : \t train_acc: 99.138; train_loss: 0.030; test_loss: 31.794; lr: [0.0001]\n",
            "2022-11-24 04:11:34,486 | INFO : \t test_acc: 91.5 (best: 91.73 from #122)\n",
            "2022-11-24 04:11:51,732 | INFO : Epoch #291\n",
            "2022-11-24 04:11:51,732 | INFO : \t train_acc: 99.13; train_loss: 0.046; test_loss: 31.636; lr: [0.0001]\n",
            "2022-11-24 04:11:51,732 | INFO : \t test_acc: 91.4 (best: 91.73 from #122)\n",
            "2022-11-24 04:12:09,177 | INFO : Epoch #292\n",
            "2022-11-24 04:12:09,178 | INFO : \t train_acc: 99.048; train_loss: 0.024; test_loss: 31.496; lr: [0.0001]\n",
            "2022-11-24 04:12:09,178 | INFO : \t test_acc: 91.42 (best: 91.73 from #122)\n",
            "2022-11-24 04:12:27,015 | INFO : Epoch #293\n",
            "2022-11-24 04:12:27,016 | INFO : \t train_acc: 99.082; train_loss: 0.053; test_loss: 31.621; lr: [0.0001]\n",
            "2022-11-24 04:12:27,016 | INFO : \t test_acc: 91.55 (best: 91.73 from #122)\n",
            "2022-11-24 04:12:44,297 | INFO : Epoch #294\n",
            "2022-11-24 04:12:44,297 | INFO : \t train_acc: 99.126; train_loss: 0.048; test_loss: 31.888; lr: [0.0001]\n",
            "2022-11-24 04:12:44,297 | INFO : \t test_acc: 91.35 (best: 91.73 from #122)\n",
            "2022-11-24 04:13:01,661 | INFO : Epoch #295\n",
            "2022-11-24 04:13:01,661 | INFO : \t train_acc: 99.166; train_loss: 0.209; test_loss: 31.919; lr: [0.0001]\n",
            "2022-11-24 04:13:01,661 | INFO : \t test_acc: 91.42 (best: 91.73 from #122)\n",
            "2022-11-24 04:13:19,308 | INFO : Epoch #296\n",
            "2022-11-24 04:13:19,309 | INFO : \t train_acc: 99.1; train_loss: 0.008; test_loss: 31.679; lr: [0.0001]\n",
            "2022-11-24 04:13:19,309 | INFO : \t test_acc: 91.47 (best: 91.73 from #122)\n",
            "2022-11-24 04:13:36,459 | INFO : Epoch #297\n",
            "2022-11-24 04:13:36,459 | INFO : \t train_acc: 99.178; train_loss: 0.197; test_loss: 31.713; lr: [0.0001]\n",
            "2022-11-24 04:13:36,459 | INFO : \t test_acc: 91.53 (best: 91.73 from #122)\n",
            "2022-11-24 04:13:54,361 | INFO : Epoch #298\n",
            "2022-11-24 04:13:54,362 | INFO : \t train_acc: 99.186; train_loss: 0.283; test_loss: 32.061; lr: [0.0001]\n",
            "2022-11-24 04:13:54,362 | INFO : \t test_acc: 91.48 (best: 91.73 from #122)\n",
            "2022-11-24 04:14:13,386 | INFO : Epoch #299\n",
            "2022-11-24 04:14:13,386 | INFO : \t train_acc: 99.182; train_loss: 0.075; test_loss: 31.312; lr: [0.0001]\n",
            "2022-11-24 04:14:13,386 | INFO : \t test_acc: 91.55 (best: 91.73 from #122)\n",
            "2022-11-24 04:14:13,447 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_train/trained finetuned 19.23 --> 91.73.\n",
            "2022-11-24 04:14:13,448 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_train/trained pruned, grouped, and finetuned: 92.57 --> 19.23 --> 91.73.\n",
            "\n",
            "2022-11-24 04:14:13,452 | INFO : setting\n",
            "2022-11-24 04:14:13,454 | INFO : Saving setting file to /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet20_cifar10_demo_exp/setting.json...\n",
            "2022-11-24 04:14:13,458 | INFO : Experiment resnet20_cifar10_demo_exp (task: finetune) ends at 2022-11-24T04:14:13.448352; result: 92.57 --> 91.73.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you are here, you have now completed a full task cycle of TMI-GKP. Congratulations — we thank you for your attention.\n",
        "\n",
        "---\n",
        "\n",
        "To give a relatively deeper dive of our code base, the basic logic is the `main.py` file will take a baseline model, the baseline model's training snapshot folder for TMI evaluation, and a pruning setting file. You will also need to specify an output folder, where upon completion of this `--task finetune` experiment, you should find:\n",
        "\n",
        "* `baseline`: Copy of the baseline model\n",
        "* `pruned`: The pruned model (with clustering information to extract a `cluster.json` as described in Section A.3.2 of the paper).\n",
        "* `grouped`: The grouped model. This is basically `pruned` but in grouped convolution architecture (with sparsity removed). All non-inference-related portions are also removed (e.g., the clustering info).\n",
        "* `finetuned`: The finetuned model trained upon `grouped`.\n",
        "* `setting.json`: Copy of the assigned setting file, but with more information stored (e.g., configuration assigned via `argparse`, log of acc/loss, etc.). Should you want to rerun this experiment, you should pass the original setting file (in this case, `settings/resnet20_cifar10_prune_and_finetune.json`) but not this one.\n",
        "* `experiment.log`: Experiment printouts registered by the logger. This will also print to the terminal for easier monitoring.\n",
        "* `/checkpoints/` folder: Saved checkpoints during the finetuning process.\n",
        "\n",
        "---\n",
        "\n",
        "Please do not hesitate to open an issue for questions or discussions. Being my undergraduate work, I'd be first to admit — and there's no denying with the complete lack of docs and comments — this is not the cleanest codebase you'd work with, but I keep a close monitor of this repo, yet I put in serious efforts to help people. In fact, this demo update is inspired by the discussion with [@DavidYanAnDe](https://github.com/DavidYanAnDe) in [Issue #2](https://github.com/choH/lottery_regulated_grouped_kernel_pruning/issues/2), I'd thank him for help me improve the reproducibility and usability of the codebase.\n",
        "\n",
        "**We the authors believe *Grouped Kernel Pruning* is a promising direction for future structured pruning methods, as it makes sense to pursue a higher degree of pruning freedom yet retain those densely structured properties** (empirical results also suggest a one-shot adoption of GKP is often better than most iterative filter pruning methods with much higher retraining/finetuning budget). We'd hope more people from the pruning community may explore it in lieu of the already crowded filter/channel pruning realms.\n",
        "\n",
        "Good day :)"
      ],
      "metadata": {
        "id": "oWmDjrMvrDhW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Experiment Showcase on Reported ResNet-32/56/110\n",
        "\n",
        "The following three experiments are ResNet-32, ResNet-56, and ResNet-110 on CIFAR-10. You are able to replicate the exact settings of these experiment by using their respective setting file. However, you will have to obtain a trained baseline model with training snapshots first (they are too large to store in the repo). These are provided here for reference/demonstration purposes.\n",
        "\n",
        "The ResNet-32 and ResNet-56 experiments below are actually the reported ones in Table 2 of our paper. For ResNet-110, Colab unfortunately stalled on us so the experiment was interapted. Since we are too lazy to \"baby\" another run on Colab, we have supplemented the `experiment.log` and setting file of the reported experiment (`94.26 pruned + 0.64 delta = 94.90 finetuned acc.`) should you want to checkout or replicate."
      ],
      "metadata": {
        "id": "cEIxcQbUugas"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python main.py \\\n",
        "--exp_desc resnet32_cifar10_demo  \\\n",
        "--setting_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/settings/resnet32_cifar10_reported.json \\\n",
        "--model_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet32_cifar10_baseline \\\n",
        "--snapshot_folder_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/snapshots/resnet32/ \\\n",
        "--output_folder_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet32_cifar10_demo_run \\\n",
        "--baseline True \\\n",
        "--task finetune"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDo4dNRxMDfK",
        "outputId": "7a89499d-c38a-4bdb-953a-de684ff7b614"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-03-11 04:35:35,895 | INFO : Experiment resnet32_cifar10_demo starts at 2022-03-11T04:35:35.891115.\n",
            "2022-03-11 04:35:35,895 | INFO : Parsed setting file from /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/settings/resnet32_cifar10_reported.json...\n",
            "2022-03-11 04:35:35,910 | INFO : ==> Preparing dataset: cifar10\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "2022-03-11 04:35:38,134 | INFO : ==> Building baseline model: /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet32_cifar10_baseline (dataset: cifar10)\n",
            "2022-03-11 04:35:41,554 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet32_cifar10_baseline loaded (<class 'models.resnet.CifarResNet'>)\n",
            "2022-03-11 04:35:42,556 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet32_cifar10_baseline <class 'models.resnet.CifarResNet'> has a baseline of 92.82.\n",
            "2022-03-11 04:35:42,570 | INFO : pruning_strategy: greedy (tickets magnitute increase); n_clusters: 8; eval_pruned_kernel_relationship: True; cost_balancer: 1; eval_kept_kernel_ratio: 0.2\n",
            "2022-03-11 04:36:59,268 | INFO : Layer 2-0-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 36});\tSequence: {'K-PCA': 35});\n",
            "2022-03-11 04:38:16,586 | INFO : Layer 2-0-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 32, 'DBSCAN': 3, 'Spectral': 1});\tSequence: {'DBSCAN': 1, 'K-PCA': 19});\n",
            "2022-03-11 04:39:35,966 | INFO : Layer 2-1-0; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 20, 'K-PCA': 15, 'Spectral': 1});\tSequence: {'K-PCA': 4, 'DBSCAN': 11});\n",
            "2022-03-11 04:40:49,594 | INFO : Layer 2-1-2; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 24, 'K-PCA': 12});\tSequence: {'Spectral': 7, 'K-PCA': 2});\n",
            "2022-03-11 04:42:08,611 | INFO : Layer 2-2-0; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 20, 'K-PCA': 13, 'Spectral': 3});\tSequence: {'K-PCA': 4, 'DBSCAN': 16});\n",
            "2022-03-11 04:43:25,425 | INFO : Layer 2-2-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 20, 'DBSCAN': 8, 'Spectral': 8});\tSequence: {'Spectral': 1, 'DBSCAN': 1, 'K-PCA': 10});\n",
            "2022-03-11 04:44:44,710 | INFO : Layer 2-3-0; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 28, 'K-PCA': 4, 'Spectral': 4});\tSequence: {'DBSCAN': 18, 'K-PCA': 1, 'Spectral': 3});\n",
            "2022-03-11 04:46:04,287 | INFO : Layer 2-3-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 21, 'DBSCAN': 10, 'Spectral': 5});\tSequence: {'K-PCA': 7, 'Spectral': 1, 'DBSCAN': 6});\n",
            "2022-03-11 04:47:20,984 | INFO : Layer 2-4-0; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 18, 'DBSCAN': 12, 'K-PCA': 6});\tSequence: {'Spectral': 3, 'K-PCA': 2, 'DBSCAN': 3});\n",
            "2022-03-11 04:48:39,286 | INFO : Layer 2-4-2; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 15, 'K-PCA': 11, 'DBSCAN': 10});\tSequence: {'Spectral': 7, 'K-PCA': 2, 'DBSCAN': 1});\n",
            "2022-03-11 04:50:39,126 | INFO : Layer 3-0-0; Shape (32, 16, 3, 3) -> (32, 144); Method: K-PCA; Occurrence: ({'K-PCA': 22, 'DBSCAN': 13, 'Spectral': 1});\tSequence: {'DBSCAN': 1, 'K-PCA': 9});\n",
            "2022-03-11 04:53:36,430 | INFO : Layer 3-0-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 23, 'DBSCAN': 10, 'Spectral': 3});\tSequence: {'K-PCA': 10, 'Spectral': 1, 'DBSCAN': 3});\n",
            "2022-03-11 04:55:33,607 | INFO : Layer 3-1-0; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 28, 'DBSCAN': 7, 'K-PCA': 1});\tSequence: {'DBSCAN': 1, 'Spectral': 11});\n",
            "2022-03-11 04:57:52,568 | INFO : Layer 3-1-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 18, 'K-PCA': 13, 'DBSCAN': 5});\tSequence: {'Spectral': 8, 'K-PCA': 3});\n",
            "2022-03-11 04:59:49,411 | INFO : Layer 3-2-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 35, 'Spectral': 1});\tSequence: {'K-PCA': 32});\n",
            "2022-03-11 05:01:44,631 | INFO : Layer 3-2-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 25, 'K-PCA': 11});\tSequence: {'Spectral': 11, 'K-PCA': 7});\n",
            "2022-03-11 05:04:01,551 | INFO : Layer 3-3-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 23, 'DBSCAN': 13});\tSequence: {'DBSCAN': 5, 'K-PCA': 14});\n",
            "2022-03-11 05:05:54,063 | INFO : Layer 3-3-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 15, 'DBSCAN': 11, 'Spectral': 10});\tSequence: {'DBSCAN': 3, 'Spectral': 4, 'K-PCA': 5});\n",
            "2022-03-11 05:07:50,230 | INFO : Layer 3-4-0; Shape (32, 32, 3, 3) -> (32, 288); Method: DBSCAN; Occurrence: ({'DBSCAN': 23, 'Spectral': 8, 'K-PCA': 5});\tSequence: {'K-PCA': 4, 'Spectral': 1, 'DBSCAN': 11});\n",
            "2022-03-11 05:09:43,160 | INFO : Layer 3-4-2; Shape (32, 32, 3, 3) -> (32, 288); Method: DBSCAN; Occurrence: ({'DBSCAN': 16, 'Spectral': 12, 'K-PCA': 8});\tSequence: {'DBSCAN': 5, 'K-PCA': 1, 'Spectral': 2});\n",
            "2022-03-11 05:13:11,564 | INFO : Layer 4-0-0; Shape (64, 32, 3, 3) -> (64, 288); Method: K-PCA; Occurrence: ({'K-PCA': 17, 'DBSCAN': 16, 'Spectral': 3});\tSequence: {'DBSCAN': 6, 'K-PCA': 6});\n",
            "2022-03-11 05:16:50,036 | INFO : Layer 4-0-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 24, 'Spectral': 8, 'DBSCAN': 4});\tSequence: {'K-PCA': 9, 'Spectral': 3});\n",
            "2022-03-11 05:20:29,245 | INFO : Layer 4-1-0; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 23, 'DBSCAN': 13});\tSequence: {'DBSCAN': 2, 'K-PCA': 8});\n",
            "2022-03-11 05:24:11,609 | INFO : Layer 4-1-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 32, 'Spectral': 3, 'DBSCAN': 1});\tSequence: {'K-PCA': 26, 'Spectral': 1});\n",
            "2022-03-11 05:28:46,157 | INFO : Layer 4-2-0; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 14, 'DBSCAN': 14, 'K-PCA': 8});\tSequence: {'DBSCAN': 2, 'Spectral': 3, 'K-PCA': 1});\n",
            "2022-03-11 05:34:53,262 | INFO : Layer 4-2-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 35, 'DBSCAN': 1});\tSequence: {'K-PCA': 21});\n",
            "2022-03-11 05:39:26,801 | INFO : Layer 4-3-0; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 31, 'DBSCAN': 5});\tSequence: {'Spectral': 18, 'DBSCAN': 1});\n",
            "2022-03-11 05:43:01,649 | INFO : Layer 4-3-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 15, 'Spectral': 13, 'DBSCAN': 8});\tSequence: {'K-PCA': 14, 'Spectral': 7, 'DBSCAN': 4});\n",
            "2022-03-11 05:51:38,292 | INFO : Layer 4-4-0; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 29, 'K-PCA': 7});\tSequence: {'DBSCAN': 13, 'K-PCA': 2});\n",
            "2022-03-11 05:55:18,859 | INFO : Layer 4-4-2; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 36});\tSequence: {'Spectral': 35});\n",
            "2022-03-11 05:55:20,621 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet32_cifar10_baseline <class 'prune_cifar.ModifiedResNet'> now pruned.\n",
            "GroupResNet pruning rate: 0.4375; kernel_gcd: 16\n",
            "2022-03-11 05:55:20,667 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet32_cifar10_baseline <class 'group_cifar.GroupResNet'> now grouped.\n",
            "2022-03-11 05:55:20,668 | INFO : ==> Starting task: finetune for 300 epochs; multi_prune_flag: False)\n",
            "2022-03-11 05:55:23,549 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet32_cifar10_baseline (type: <class 'group_cifar.GroupResNet'>) pruned and grouped (grouped baseline: 18.1); now to finetune with 300 epochs.\n",
            "\n",
            "2022-03-11 05:55:26,401 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet32_cifar10_baseline has a baseline of 18.1.\n",
            "2022-03-11 05:56:17,017 | INFO : Epoch #0\n",
            "2022-03-11 05:56:17,017 | INFO : \t train_acc: 78.464; train_loss: 0.799; test_loss: 61.490; lr: [0.01]\n",
            "2022-03-11 05:56:17,017 | INFO : \t test_acc: 80.0 (best: 80.0 from #0)\n",
            "2022-03-11 05:57:07,638 | INFO : Epoch #1\n",
            "2022-03-11 05:57:07,639 | INFO : \t train_acc: 84.764; train_loss: 0.702; test_loss: 53.570; lr: [0.01]\n",
            "2022-03-11 05:57:07,639 | INFO : \t test_acc: 82.6 (best: 82.6 from #1)\n",
            "2022-03-11 05:57:58,285 | INFO : Epoch #2\n",
            "2022-03-11 05:57:58,286 | INFO : \t train_acc: 86.468; train_loss: 0.267; test_loss: 49.663; lr: [0.01]\n",
            "2022-03-11 05:57:58,286 | INFO : \t test_acc: 83.62 (best: 83.62 from #2)\n",
            "2022-03-11 05:58:48,945 | INFO : Epoch #3\n",
            "2022-03-11 05:58:48,946 | INFO : \t train_acc: 87.75; train_loss: 0.165; test_loss: 48.205; lr: [0.01]\n",
            "2022-03-11 05:58:48,946 | INFO : \t test_acc: 84.25 (best: 84.25 from #3)\n",
            "2022-03-11 05:59:39,640 | INFO : Epoch #4\n",
            "2022-03-11 05:59:39,640 | INFO : \t train_acc: 88.158; train_loss: 0.699; test_loss: 47.816; lr: [0.01]\n",
            "2022-03-11 05:59:39,640 | INFO : \t test_acc: 84.44 (best: 84.44 from #4)\n",
            "2022-03-11 06:00:30,249 | INFO : Epoch #5\n",
            "2022-03-11 06:00:30,249 | INFO : \t train_acc: 88.846; train_loss: 0.265; test_loss: 39.450; lr: [0.01]\n",
            "2022-03-11 06:00:30,249 | INFO : \t test_acc: 86.67 (best: 86.67 from #5)\n",
            "2022-03-11 06:01:20,938 | INFO : Epoch #6\n",
            "2022-03-11 06:01:20,939 | INFO : \t train_acc: 89.162; train_loss: 0.074; test_loss: 41.954; lr: [0.01]\n",
            "2022-03-11 06:01:20,939 | INFO : \t test_acc: 85.98 (best: 86.67 from #5)\n",
            "2022-03-11 06:02:11,543 | INFO : Epoch #7\n",
            "2022-03-11 06:02:11,543 | INFO : \t train_acc: 89.658; train_loss: 0.350; test_loss: 53.025; lr: [0.01]\n",
            "2022-03-11 06:02:11,543 | INFO : \t test_acc: 83.29 (best: 86.67 from #5)\n",
            "2022-03-11 06:03:02,159 | INFO : Epoch #8\n",
            "2022-03-11 06:03:02,159 | INFO : \t train_acc: 89.902; train_loss: 0.317; test_loss: 47.631; lr: [0.01]\n",
            "2022-03-11 06:03:02,160 | INFO : \t test_acc: 85.03 (best: 86.67 from #5)\n",
            "2022-03-11 06:03:52,799 | INFO : Epoch #9\n",
            "2022-03-11 06:03:52,799 | INFO : \t train_acc: 90.166; train_loss: 0.064; test_loss: 48.408; lr: [0.01]\n",
            "2022-03-11 06:03:52,799 | INFO : \t test_acc: 84.5 (best: 86.67 from #5)\n",
            "2022-03-11 06:04:43,495 | INFO : Epoch #10\n",
            "2022-03-11 06:04:43,495 | INFO : \t train_acc: 90.27; train_loss: 0.238; test_loss: 44.185; lr: [0.01]\n",
            "2022-03-11 06:04:43,495 | INFO : \t test_acc: 85.23 (best: 86.67 from #5)\n",
            "2022-03-11 06:05:34,159 | INFO : Epoch #11\n",
            "2022-03-11 06:05:34,159 | INFO : \t train_acc: 90.532; train_loss: 0.728; test_loss: 40.437; lr: [0.01]\n",
            "2022-03-11 06:05:34,159 | INFO : \t test_acc: 87.27 (best: 87.27 from #11)\n",
            "2022-03-11 06:06:24,861 | INFO : Epoch #12\n",
            "2022-03-11 06:06:24,861 | INFO : \t train_acc: 90.644; train_loss: 0.674; test_loss: 45.295; lr: [0.01]\n",
            "2022-03-11 06:06:24,861 | INFO : \t test_acc: 85.52 (best: 87.27 from #11)\n",
            "2022-03-11 06:07:15,551 | INFO : Epoch #13\n",
            "2022-03-11 06:07:15,551 | INFO : \t train_acc: 90.756; train_loss: 0.534; test_loss: 43.326; lr: [0.01]\n",
            "2022-03-11 06:07:15,551 | INFO : \t test_acc: 86.0 (best: 87.27 from #11)\n",
            "2022-03-11 06:08:06,214 | INFO : Epoch #14\n",
            "2022-03-11 06:08:06,215 | INFO : \t train_acc: 90.872; train_loss: 0.351; test_loss: 45.137; lr: [0.01]\n",
            "2022-03-11 06:08:06,215 | INFO : \t test_acc: 85.25 (best: 87.27 from #11)\n",
            "2022-03-11 06:08:56,833 | INFO : Epoch #15\n",
            "2022-03-11 06:08:56,834 | INFO : \t train_acc: 90.822; train_loss: 0.588; test_loss: 38.893; lr: [0.01]\n",
            "2022-03-11 06:08:56,834 | INFO : \t test_acc: 87.57 (best: 87.57 from #15)\n",
            "2022-03-11 06:09:47,531 | INFO : Epoch #16\n",
            "2022-03-11 06:09:47,531 | INFO : \t train_acc: 90.936; train_loss: 0.070; test_loss: 44.518; lr: [0.01]\n",
            "2022-03-11 06:09:47,531 | INFO : \t test_acc: 85.76 (best: 87.57 from #15)\n",
            "2022-03-11 06:10:38,221 | INFO : Epoch #17\n",
            "2022-03-11 06:10:38,221 | INFO : \t train_acc: 91.278; train_loss: 0.334; test_loss: 41.558; lr: [0.01]\n",
            "2022-03-11 06:10:38,221 | INFO : \t test_acc: 86.85 (best: 87.57 from #15)\n",
            "2022-03-11 06:11:28,943 | INFO : Epoch #18\n",
            "2022-03-11 06:11:28,943 | INFO : \t train_acc: 91.258; train_loss: 0.109; test_loss: 36.548; lr: [0.01]\n",
            "2022-03-11 06:11:28,944 | INFO : \t test_acc: 88.35 (best: 88.35 from #18)\n",
            "2022-03-11 06:12:19,753 | INFO : Epoch #19\n",
            "2022-03-11 06:12:19,753 | INFO : \t train_acc: 91.272; train_loss: 0.145; test_loss: 41.568; lr: [0.01]\n",
            "2022-03-11 06:12:19,753 | INFO : \t test_acc: 86.38 (best: 88.35 from #18)\n",
            "2022-03-11 06:13:10,486 | INFO : Epoch #20\n",
            "2022-03-11 06:13:10,486 | INFO : \t train_acc: 91.438; train_loss: 0.471; test_loss: 38.015; lr: [0.01]\n",
            "2022-03-11 06:13:10,487 | INFO : \t test_acc: 87.58 (best: 88.35 from #18)\n",
            "2022-03-11 06:14:01,119 | INFO : Epoch #21\n",
            "2022-03-11 06:14:01,120 | INFO : \t train_acc: 91.43; train_loss: 0.311; test_loss: 39.775; lr: [0.01]\n",
            "2022-03-11 06:14:01,120 | INFO : \t test_acc: 87.38 (best: 88.35 from #18)\n",
            "2022-03-11 06:14:51,802 | INFO : Epoch #22\n",
            "2022-03-11 06:14:51,803 | INFO : \t train_acc: 91.538; train_loss: 0.134; test_loss: 38.193; lr: [0.01]\n",
            "2022-03-11 06:14:51,803 | INFO : \t test_acc: 87.68 (best: 88.35 from #18)\n",
            "2022-03-11 06:15:42,470 | INFO : Epoch #23\n",
            "2022-03-11 06:15:42,470 | INFO : \t train_acc: 91.652; train_loss: 0.136; test_loss: 39.104; lr: [0.01]\n",
            "2022-03-11 06:15:42,471 | INFO : \t test_acc: 87.13 (best: 88.35 from #18)\n",
            "2022-03-11 06:16:33,163 | INFO : Epoch #24\n",
            "2022-03-11 06:16:33,164 | INFO : \t train_acc: 91.598; train_loss: 0.163; test_loss: 44.806; lr: [0.01]\n",
            "2022-03-11 06:16:33,164 | INFO : \t test_acc: 85.49 (best: 88.35 from #18)\n",
            "2022-03-11 06:17:23,876 | INFO : Epoch #25\n",
            "2022-03-11 06:17:23,876 | INFO : \t train_acc: 91.694; train_loss: 0.725; test_loss: 39.097; lr: [0.01]\n",
            "2022-03-11 06:17:23,876 | INFO : \t test_acc: 87.53 (best: 88.35 from #18)\n",
            "2022-03-11 06:18:14,574 | INFO : Epoch #26\n",
            "2022-03-11 06:18:14,575 | INFO : \t train_acc: 91.742; train_loss: 0.359; test_loss: 42.785; lr: [0.01]\n",
            "2022-03-11 06:18:14,575 | INFO : \t test_acc: 86.28 (best: 88.35 from #18)\n",
            "2022-03-11 06:19:05,248 | INFO : Epoch #27\n",
            "2022-03-11 06:19:05,248 | INFO : \t train_acc: 91.652; train_loss: 0.776; test_loss: 37.904; lr: [0.01]\n",
            "2022-03-11 06:19:05,248 | INFO : \t test_acc: 87.9 (best: 88.35 from #18)\n",
            "2022-03-11 06:19:55,937 | INFO : Epoch #28\n",
            "2022-03-11 06:19:55,938 | INFO : \t train_acc: 91.75; train_loss: 0.267; test_loss: 39.930; lr: [0.01]\n",
            "2022-03-11 06:19:55,938 | INFO : \t test_acc: 87.04 (best: 88.35 from #18)\n",
            "2022-03-11 06:20:46,584 | INFO : Epoch #29\n",
            "2022-03-11 06:20:46,585 | INFO : \t train_acc: 92.072; train_loss: 0.332; test_loss: 41.898; lr: [0.01]\n",
            "2022-03-11 06:20:46,585 | INFO : \t test_acc: 87.28 (best: 88.35 from #18)\n",
            "2022-03-11 06:21:37,329 | INFO : Epoch #30\n",
            "2022-03-11 06:21:37,330 | INFO : \t train_acc: 91.844; train_loss: 0.475; test_loss: 40.804; lr: [0.01]\n",
            "2022-03-11 06:21:37,330 | INFO : \t test_acc: 86.75 (best: 88.35 from #18)\n",
            "2022-03-11 06:22:28,033 | INFO : Epoch #31\n",
            "2022-03-11 06:22:28,034 | INFO : \t train_acc: 91.88; train_loss: 0.542; test_loss: 44.457; lr: [0.01]\n",
            "2022-03-11 06:22:28,034 | INFO : \t test_acc: 86.14 (best: 88.35 from #18)\n",
            "2022-03-11 06:23:18,688 | INFO : Epoch #32\n",
            "2022-03-11 06:23:18,688 | INFO : \t train_acc: 91.984; train_loss: 0.192; test_loss: 36.618; lr: [0.01]\n",
            "2022-03-11 06:23:18,688 | INFO : \t test_acc: 88.25 (best: 88.35 from #18)\n",
            "2022-03-11 06:24:09,339 | INFO : Epoch #33\n",
            "2022-03-11 06:24:09,340 | INFO : \t train_acc: 91.876; train_loss: 0.379; test_loss: 41.703; lr: [0.01]\n",
            "2022-03-11 06:24:09,340 | INFO : \t test_acc: 86.82 (best: 88.35 from #18)\n",
            "2022-03-11 06:25:00,052 | INFO : Epoch #34\n",
            "2022-03-11 06:25:00,052 | INFO : \t train_acc: 91.842; train_loss: 0.514; test_loss: 42.896; lr: [0.01]\n",
            "2022-03-11 06:25:00,052 | INFO : \t test_acc: 86.89 (best: 88.35 from #18)\n",
            "2022-03-11 06:25:50,719 | INFO : Epoch #35\n",
            "2022-03-11 06:25:50,719 | INFO : \t train_acc: 91.86; train_loss: 0.386; test_loss: 37.539; lr: [0.01]\n",
            "2022-03-11 06:25:50,719 | INFO : \t test_acc: 87.41 (best: 88.35 from #18)\n",
            "2022-03-11 06:26:41,388 | INFO : Epoch #36\n",
            "2022-03-11 06:26:41,388 | INFO : \t train_acc: 92.288; train_loss: 0.421; test_loss: 38.802; lr: [0.01]\n",
            "2022-03-11 06:26:41,389 | INFO : \t test_acc: 87.56 (best: 88.35 from #18)\n",
            "2022-03-11 06:27:32,080 | INFO : Epoch #37\n",
            "2022-03-11 06:27:32,080 | INFO : \t train_acc: 92.138; train_loss: 0.109; test_loss: 34.821; lr: [0.01]\n",
            "2022-03-11 06:27:32,080 | INFO : \t test_acc: 88.4 (best: 88.4 from #37)\n",
            "2022-03-11 06:28:22,787 | INFO : Epoch #38\n",
            "2022-03-11 06:28:22,787 | INFO : \t train_acc: 92.28; train_loss: 0.062; test_loss: 41.681; lr: [0.01]\n",
            "2022-03-11 06:28:22,788 | INFO : \t test_acc: 86.78 (best: 88.4 from #37)\n",
            "2022-03-11 06:29:13,441 | INFO : Epoch #39\n",
            "2022-03-11 06:29:13,441 | INFO : \t train_acc: 92.238; train_loss: 0.369; test_loss: 49.314; lr: [0.01]\n",
            "2022-03-11 06:29:13,441 | INFO : \t test_acc: 85.58 (best: 88.4 from #37)\n",
            "2022-03-11 06:30:04,202 | INFO : Epoch #40\n",
            "2022-03-11 06:30:04,203 | INFO : \t train_acc: 92.192; train_loss: 0.592; test_loss: 37.890; lr: [0.01]\n",
            "2022-03-11 06:30:04,203 | INFO : \t test_acc: 88.08 (best: 88.4 from #37)\n",
            "2022-03-11 06:30:54,862 | INFO : Epoch #41\n",
            "2022-03-11 06:30:54,863 | INFO : \t train_acc: 92.386; train_loss: 0.242; test_loss: 40.086; lr: [0.01]\n",
            "2022-03-11 06:30:54,863 | INFO : \t test_acc: 87.45 (best: 88.4 from #37)\n",
            "2022-03-11 06:31:45,528 | INFO : Epoch #42\n",
            "2022-03-11 06:31:45,529 | INFO : \t train_acc: 92.094; train_loss: 0.593; test_loss: 38.892; lr: [0.01]\n",
            "2022-03-11 06:31:45,529 | INFO : \t test_acc: 87.5 (best: 88.4 from #37)\n",
            "2022-03-11 06:32:36,219 | INFO : Epoch #43\n",
            "2022-03-11 06:32:36,219 | INFO : \t train_acc: 92.222; train_loss: 0.275; test_loss: 33.874; lr: [0.01]\n",
            "2022-03-11 06:32:36,220 | INFO : \t test_acc: 89.23 (best: 89.23 from #43)\n",
            "2022-03-11 06:33:26,936 | INFO : Epoch #44\n",
            "2022-03-11 06:33:26,936 | INFO : \t train_acc: 92.292; train_loss: 0.122; test_loss: 36.289; lr: [0.01]\n",
            "2022-03-11 06:33:26,936 | INFO : \t test_acc: 87.93 (best: 89.23 from #43)\n",
            "2022-03-11 06:34:17,672 | INFO : Epoch #45\n",
            "2022-03-11 06:34:17,672 | INFO : \t train_acc: 92.174; train_loss: 0.153; test_loss: 46.530; lr: [0.01]\n",
            "2022-03-11 06:34:17,672 | INFO : \t test_acc: 86.14 (best: 89.23 from #43)\n",
            "2022-03-11 06:35:08,413 | INFO : Epoch #46\n",
            "2022-03-11 06:35:08,414 | INFO : \t train_acc: 92.33; train_loss: 0.566; test_loss: 36.619; lr: [0.01]\n",
            "2022-03-11 06:35:08,414 | INFO : \t test_acc: 88.44 (best: 89.23 from #43)\n",
            "2022-03-11 06:35:59,152 | INFO : Epoch #47\n",
            "2022-03-11 06:35:59,153 | INFO : \t train_acc: 92.388; train_loss: 0.129; test_loss: 39.238; lr: [0.01]\n",
            "2022-03-11 06:35:59,153 | INFO : \t test_acc: 87.47 (best: 89.23 from #43)\n",
            "2022-03-11 06:36:49,929 | INFO : Epoch #48\n",
            "2022-03-11 06:36:49,929 | INFO : \t train_acc: 92.25; train_loss: 0.417; test_loss: 46.782; lr: [0.01]\n",
            "2022-03-11 06:36:49,929 | INFO : \t test_acc: 86.14 (best: 89.23 from #43)\n",
            "2022-03-11 06:37:40,621 | INFO : Epoch #49\n",
            "2022-03-11 06:37:40,621 | INFO : \t train_acc: 92.202; train_loss: 0.073; test_loss: 42.861; lr: [0.01]\n",
            "2022-03-11 06:37:40,621 | INFO : \t test_acc: 86.59 (best: 89.23 from #43)\n",
            "2022-03-11 06:38:31,313 | INFO : Epoch #50\n",
            "2022-03-11 06:38:31,313 | INFO : \t train_acc: 92.496; train_loss: 0.048; test_loss: 45.884; lr: [0.01]\n",
            "2022-03-11 06:38:31,313 | INFO : \t test_acc: 86.27 (best: 89.23 from #43)\n",
            "2022-03-11 06:39:22,022 | INFO : Epoch #51\n",
            "2022-03-11 06:39:22,022 | INFO : \t train_acc: 92.578; train_loss: 0.544; test_loss: 41.254; lr: [0.01]\n",
            "2022-03-11 06:39:22,023 | INFO : \t test_acc: 87.06 (best: 89.23 from #43)\n",
            "2022-03-11 06:40:12,658 | INFO : Epoch #52\n",
            "2022-03-11 06:40:12,658 | INFO : \t train_acc: 92.252; train_loss: 0.593; test_loss: 36.802; lr: [0.01]\n",
            "2022-03-11 06:40:12,658 | INFO : \t test_acc: 88.01 (best: 89.23 from #43)\n",
            "2022-03-11 06:41:03,341 | INFO : Epoch #53\n",
            "2022-03-11 06:41:03,342 | INFO : \t train_acc: 92.288; train_loss: 0.376; test_loss: 42.735; lr: [0.01]\n",
            "2022-03-11 06:41:03,342 | INFO : \t test_acc: 86.69 (best: 89.23 from #43)\n",
            "2022-03-11 06:41:54,062 | INFO : Epoch #54\n",
            "2022-03-11 06:41:54,062 | INFO : \t train_acc: 92.488; train_loss: 0.196; test_loss: 37.011; lr: [0.01]\n",
            "2022-03-11 06:41:54,062 | INFO : \t test_acc: 88.27 (best: 89.23 from #43)\n",
            "2022-03-11 06:42:44,782 | INFO : Epoch #55\n",
            "2022-03-11 06:42:44,782 | INFO : \t train_acc: 92.464; train_loss: 0.403; test_loss: 37.056; lr: [0.01]\n",
            "2022-03-11 06:42:44,782 | INFO : \t test_acc: 88.03 (best: 89.23 from #43)\n",
            "2022-03-11 06:43:35,604 | INFO : Epoch #56\n",
            "2022-03-11 06:43:35,604 | INFO : \t train_acc: 92.524; train_loss: 0.067; test_loss: 42.523; lr: [0.01]\n",
            "2022-03-11 06:43:35,605 | INFO : \t test_acc: 86.28 (best: 89.23 from #43)\n",
            "2022-03-11 06:44:26,460 | INFO : Epoch #57\n",
            "2022-03-11 06:44:26,460 | INFO : \t train_acc: 92.416; train_loss: 0.651; test_loss: 35.056; lr: [0.01]\n",
            "2022-03-11 06:44:26,461 | INFO : \t test_acc: 88.95 (best: 89.23 from #43)\n",
            "2022-03-11 06:45:17,333 | INFO : Epoch #58\n",
            "2022-03-11 06:45:17,334 | INFO : \t train_acc: 92.494; train_loss: 0.193; test_loss: 46.258; lr: [0.01]\n",
            "2022-03-11 06:45:17,334 | INFO : \t test_acc: 86.26 (best: 89.23 from #43)\n",
            "2022-03-11 06:46:08,201 | INFO : Epoch #59\n",
            "2022-03-11 06:46:08,201 | INFO : \t train_acc: 92.51; train_loss: 0.324; test_loss: 34.453; lr: [0.01]\n",
            "2022-03-11 06:46:08,202 | INFO : \t test_acc: 88.85 (best: 89.23 from #43)\n",
            "2022-03-11 06:46:59,051 | INFO : Epoch #60\n",
            "2022-03-11 06:46:59,052 | INFO : \t train_acc: 92.24; train_loss: 0.542; test_loss: 41.713; lr: [0.01]\n",
            "2022-03-11 06:46:59,052 | INFO : \t test_acc: 87.17 (best: 89.23 from #43)\n",
            "2022-03-11 06:47:49,737 | INFO : Epoch #61\n",
            "2022-03-11 06:47:49,737 | INFO : \t train_acc: 92.378; train_loss: 0.408; test_loss: 34.537; lr: [0.01]\n",
            "2022-03-11 06:47:49,737 | INFO : \t test_acc: 88.6 (best: 89.23 from #43)\n",
            "2022-03-11 06:48:40,448 | INFO : Epoch #62\n",
            "2022-03-11 06:48:40,448 | INFO : \t train_acc: 92.404; train_loss: 0.140; test_loss: 39.529; lr: [0.01]\n",
            "2022-03-11 06:48:40,449 | INFO : \t test_acc: 87.59 (best: 89.23 from #43)\n",
            "2022-03-11 06:49:31,165 | INFO : Epoch #63\n",
            "2022-03-11 06:49:31,165 | INFO : \t train_acc: 92.466; train_loss: 0.452; test_loss: 50.124; lr: [0.01]\n",
            "2022-03-11 06:49:31,165 | INFO : \t test_acc: 84.66 (best: 89.23 from #43)\n",
            "2022-03-11 06:50:21,837 | INFO : Epoch #64\n",
            "2022-03-11 06:50:21,837 | INFO : \t train_acc: 92.708; train_loss: 0.272; test_loss: 39.868; lr: [0.01]\n",
            "2022-03-11 06:50:21,837 | INFO : \t test_acc: 87.37 (best: 89.23 from #43)\n",
            "2022-03-11 06:51:12,527 | INFO : Epoch #65\n",
            "2022-03-11 06:51:12,528 | INFO : \t train_acc: 92.514; train_loss: 0.344; test_loss: 37.203; lr: [0.01]\n",
            "2022-03-11 06:51:12,528 | INFO : \t test_acc: 87.95 (best: 89.23 from #43)\n",
            "2022-03-11 06:52:03,215 | INFO : Epoch #66\n",
            "2022-03-11 06:52:03,216 | INFO : \t train_acc: 92.576; train_loss: 0.568; test_loss: 37.449; lr: [0.01]\n",
            "2022-03-11 06:52:03,216 | INFO : \t test_acc: 88.25 (best: 89.23 from #43)\n",
            "2022-03-11 06:52:53,906 | INFO : Epoch #67\n",
            "2022-03-11 06:52:53,906 | INFO : \t train_acc: 92.582; train_loss: 0.124; test_loss: 40.229; lr: [0.01]\n",
            "2022-03-11 06:52:53,906 | INFO : \t test_acc: 87.41 (best: 89.23 from #43)\n",
            "2022-03-11 06:53:44,648 | INFO : Epoch #68\n",
            "2022-03-11 06:53:44,648 | INFO : \t train_acc: 92.46; train_loss: 0.366; test_loss: 45.249; lr: [0.01]\n",
            "2022-03-11 06:53:44,649 | INFO : \t test_acc: 86.03 (best: 89.23 from #43)\n",
            "2022-03-11 06:54:35,347 | INFO : Epoch #69\n",
            "2022-03-11 06:54:35,347 | INFO : \t train_acc: 92.748; train_loss: 0.537; test_loss: 41.663; lr: [0.01]\n",
            "2022-03-11 06:54:35,347 | INFO : \t test_acc: 87.6 (best: 89.23 from #43)\n",
            "2022-03-11 06:55:26,053 | INFO : Epoch #70\n",
            "2022-03-11 06:55:26,054 | INFO : \t train_acc: 92.592; train_loss: 0.298; test_loss: 36.208; lr: [0.01]\n",
            "2022-03-11 06:55:26,054 | INFO : \t test_acc: 88.63 (best: 89.23 from #43)\n",
            "2022-03-11 06:56:16,735 | INFO : Epoch #71\n",
            "2022-03-11 06:56:16,736 | INFO : \t train_acc: 92.694; train_loss: 0.129; test_loss: 46.029; lr: [0.01]\n",
            "2022-03-11 06:56:16,736 | INFO : \t test_acc: 86.01 (best: 89.23 from #43)\n",
            "2022-03-11 06:57:07,398 | INFO : Epoch #72\n",
            "2022-03-11 06:57:07,398 | INFO : \t train_acc: 92.664; train_loss: 0.602; test_loss: 34.443; lr: [0.01]\n",
            "2022-03-11 06:57:07,398 | INFO : \t test_acc: 89.31 (best: 89.31 from #72)\n",
            "2022-03-11 06:57:58,130 | INFO : Epoch #73\n",
            "2022-03-11 06:57:58,131 | INFO : \t train_acc: 92.736; train_loss: 0.028; test_loss: 47.281; lr: [0.01]\n",
            "2022-03-11 06:57:58,131 | INFO : \t test_acc: 85.37 (best: 89.31 from #72)\n",
            "2022-03-11 06:58:48,828 | INFO : Epoch #74\n",
            "2022-03-11 06:58:48,828 | INFO : \t train_acc: 92.762; train_loss: 0.193; test_loss: 36.846; lr: [0.01]\n",
            "2022-03-11 06:58:48,828 | INFO : \t test_acc: 88.29 (best: 89.31 from #72)\n",
            "2022-03-11 06:59:39,577 | INFO : Epoch #75\n",
            "2022-03-11 06:59:39,577 | INFO : \t train_acc: 92.544; train_loss: 0.169; test_loss: 40.561; lr: [0.01]\n",
            "2022-03-11 06:59:39,578 | INFO : \t test_acc: 86.97 (best: 89.31 from #72)\n",
            "2022-03-11 07:00:30,245 | INFO : Epoch #76\n",
            "2022-03-11 07:00:30,245 | INFO : \t train_acc: 92.696; train_loss: 0.440; test_loss: 36.580; lr: [0.01]\n",
            "2022-03-11 07:00:30,245 | INFO : \t test_acc: 88.38 (best: 89.31 from #72)\n",
            "2022-03-11 07:01:20,993 | INFO : Epoch #77\n",
            "2022-03-11 07:01:20,994 | INFO : \t train_acc: 92.618; train_loss: 0.237; test_loss: 32.979; lr: [0.01]\n",
            "2022-03-11 07:01:20,994 | INFO : \t test_acc: 89.39 (best: 89.39 from #77)\n",
            "2022-03-11 07:02:11,703 | INFO : Epoch #78\n",
            "2022-03-11 07:02:11,703 | INFO : \t train_acc: 92.846; train_loss: 0.321; test_loss: 38.816; lr: [0.01]\n",
            "2022-03-11 07:02:11,703 | INFO : \t test_acc: 88.21 (best: 89.39 from #77)\n",
            "2022-03-11 07:03:02,428 | INFO : Epoch #79\n",
            "2022-03-11 07:03:02,429 | INFO : \t train_acc: 92.766; train_loss: 0.227; test_loss: 40.300; lr: [0.01]\n",
            "2022-03-11 07:03:02,429 | INFO : \t test_acc: 87.46 (best: 89.39 from #77)\n",
            "2022-03-11 07:03:53,192 | INFO : Epoch #80\n",
            "2022-03-11 07:03:53,192 | INFO : \t train_acc: 92.634; train_loss: 0.094; test_loss: 39.847; lr: [0.01]\n",
            "2022-03-11 07:03:53,192 | INFO : \t test_acc: 87.76 (best: 89.39 from #77)\n",
            "2022-03-11 07:04:43,927 | INFO : Epoch #81\n",
            "2022-03-11 07:04:43,927 | INFO : \t train_acc: 92.724; train_loss: 0.438; test_loss: 38.910; lr: [0.01]\n",
            "2022-03-11 07:04:43,927 | INFO : \t test_acc: 88.03 (best: 89.39 from #77)\n",
            "2022-03-11 07:05:34,630 | INFO : Epoch #82\n",
            "2022-03-11 07:05:34,630 | INFO : \t train_acc: 92.672; train_loss: 0.096; test_loss: 34.956; lr: [0.01]\n",
            "2022-03-11 07:05:34,630 | INFO : \t test_acc: 89.27 (best: 89.39 from #77)\n",
            "2022-03-11 07:06:25,469 | INFO : Epoch #83\n",
            "2022-03-11 07:06:25,469 | INFO : \t train_acc: 92.424; train_loss: 0.269; test_loss: 54.645; lr: [0.01]\n",
            "2022-03-11 07:06:25,470 | INFO : \t test_acc: 84.03 (best: 89.39 from #77)\n",
            "2022-03-11 07:07:16,113 | INFO : Epoch #84\n",
            "2022-03-11 07:07:16,113 | INFO : \t train_acc: 92.616; train_loss: 0.480; test_loss: 33.509; lr: [0.01]\n",
            "2022-03-11 07:07:16,113 | INFO : \t test_acc: 88.72 (best: 89.39 from #77)\n",
            "2022-03-11 07:08:06,905 | INFO : Epoch #85\n",
            "2022-03-11 07:08:06,905 | INFO : \t train_acc: 92.37; train_loss: 0.137; test_loss: 43.076; lr: [0.01]\n",
            "2022-03-11 07:08:06,905 | INFO : \t test_acc: 86.71 (best: 89.39 from #77)\n",
            "2022-03-11 07:08:57,648 | INFO : Epoch #86\n",
            "2022-03-11 07:08:57,648 | INFO : \t train_acc: 92.71; train_loss: 0.445; test_loss: 36.226; lr: [0.01]\n",
            "2022-03-11 07:08:57,649 | INFO : \t test_acc: 88.45 (best: 89.39 from #77)\n",
            "2022-03-11 07:09:48,331 | INFO : Epoch #87\n",
            "2022-03-11 07:09:48,331 | INFO : \t train_acc: 92.656; train_loss: 0.213; test_loss: 45.131; lr: [0.01]\n",
            "2022-03-11 07:09:48,332 | INFO : \t test_acc: 86.54 (best: 89.39 from #77)\n",
            "2022-03-11 07:10:39,006 | INFO : Epoch #88\n",
            "2022-03-11 07:10:39,007 | INFO : \t train_acc: 92.922; train_loss: 0.134; test_loss: 39.788; lr: [0.01]\n",
            "2022-03-11 07:10:39,007 | INFO : \t test_acc: 87.43 (best: 89.39 from #77)\n",
            "2022-03-11 07:11:29,721 | INFO : Epoch #89\n",
            "2022-03-11 07:11:29,722 | INFO : \t train_acc: 92.876; train_loss: 0.209; test_loss: 38.068; lr: [0.01]\n",
            "2022-03-11 07:11:29,722 | INFO : \t test_acc: 88.17 (best: 89.39 from #77)\n",
            "2022-03-11 07:12:20,486 | INFO : Epoch #90\n",
            "2022-03-11 07:12:20,486 | INFO : \t train_acc: 92.768; train_loss: 0.169; test_loss: 43.005; lr: [0.01]\n",
            "2022-03-11 07:12:20,487 | INFO : \t test_acc: 86.66 (best: 89.39 from #77)\n",
            "2022-03-11 07:13:11,219 | INFO : Epoch #91\n",
            "2022-03-11 07:13:11,219 | INFO : \t train_acc: 92.68; train_loss: 0.162; test_loss: 38.027; lr: [0.01]\n",
            "2022-03-11 07:13:11,220 | INFO : \t test_acc: 88.09 (best: 89.39 from #77)\n",
            "2022-03-11 07:14:01,965 | INFO : Epoch #92\n",
            "2022-03-11 07:14:01,965 | INFO : \t train_acc: 92.794; train_loss: 0.420; test_loss: 39.086; lr: [0.01]\n",
            "2022-03-11 07:14:01,966 | INFO : \t test_acc: 87.79 (best: 89.39 from #77)\n",
            "2022-03-11 07:14:52,679 | INFO : Epoch #93\n",
            "2022-03-11 07:14:52,680 | INFO : \t train_acc: 92.808; train_loss: 0.283; test_loss: 42.502; lr: [0.01]\n",
            "2022-03-11 07:14:52,680 | INFO : \t test_acc: 86.86 (best: 89.39 from #77)\n",
            "2022-03-11 07:15:43,354 | INFO : Epoch #94\n",
            "2022-03-11 07:15:43,355 | INFO : \t train_acc: 92.868; train_loss: 0.607; test_loss: 33.891; lr: [0.01]\n",
            "2022-03-11 07:15:43,355 | INFO : \t test_acc: 88.84 (best: 89.39 from #77)\n",
            "2022-03-11 07:16:34,015 | INFO : Epoch #95\n",
            "2022-03-11 07:16:34,015 | INFO : \t train_acc: 93.04; train_loss: 0.372; test_loss: 35.394; lr: [0.01]\n",
            "2022-03-11 07:16:34,015 | INFO : \t test_acc: 88.96 (best: 89.39 from #77)\n",
            "2022-03-11 07:17:24,736 | INFO : Epoch #96\n",
            "2022-03-11 07:17:24,737 | INFO : \t train_acc: 92.908; train_loss: 0.421; test_loss: 39.333; lr: [0.01]\n",
            "2022-03-11 07:17:24,737 | INFO : \t test_acc: 87.88 (best: 89.39 from #77)\n",
            "2022-03-11 07:18:15,414 | INFO : Epoch #97\n",
            "2022-03-11 07:18:15,414 | INFO : \t train_acc: 92.83; train_loss: 0.405; test_loss: 37.781; lr: [0.01]\n",
            "2022-03-11 07:18:15,415 | INFO : \t test_acc: 88.1 (best: 89.39 from #77)\n",
            "2022-03-11 07:19:06,143 | INFO : Epoch #98\n",
            "2022-03-11 07:19:06,144 | INFO : \t train_acc: 92.782; train_loss: 0.170; test_loss: 37.326; lr: [0.01]\n",
            "2022-03-11 07:19:06,144 | INFO : \t test_acc: 88.39 (best: 89.39 from #77)\n",
            "2022-03-11 07:19:56,871 | INFO : Epoch #99\n",
            "2022-03-11 07:19:56,872 | INFO : \t train_acc: 92.948; train_loss: 0.079; test_loss: 40.122; lr: [0.01]\n",
            "2022-03-11 07:19:56,872 | INFO : \t test_acc: 87.96 (best: 89.39 from #77)\n",
            "2022-03-11 07:20:47,587 | INFO : Epoch #100\n",
            "2022-03-11 07:20:47,587 | INFO : \t train_acc: 95.876; train_loss: 0.223; test_loss: 25.098; lr: [0.001]\n",
            "2022-03-11 07:20:47,587 | INFO : \t test_acc: 92.02 (best: 92.02 from #100)\n",
            "2022-03-11 07:21:38,368 | INFO : Epoch #101\n",
            "2022-03-11 07:21:38,369 | INFO : \t train_acc: 96.812; train_loss: 0.168; test_loss: 24.245; lr: [0.001]\n",
            "2022-03-11 07:21:38,369 | INFO : \t test_acc: 92.28 (best: 92.28 from #101)\n",
            "2022-03-11 07:22:29,090 | INFO : Epoch #102\n",
            "2022-03-11 07:22:29,090 | INFO : \t train_acc: 97.072; train_loss: 0.356; test_loss: 23.971; lr: [0.001]\n",
            "2022-03-11 07:22:29,091 | INFO : \t test_acc: 92.32 (best: 92.32 from #102)\n",
            "2022-03-11 07:23:19,794 | INFO : Epoch #103\n",
            "2022-03-11 07:23:19,795 | INFO : \t train_acc: 97.48; train_loss: 0.072; test_loss: 24.378; lr: [0.001]\n",
            "2022-03-11 07:23:19,795 | INFO : \t test_acc: 92.38 (best: 92.38 from #103)\n",
            "2022-03-11 07:24:10,550 | INFO : Epoch #104\n",
            "2022-03-11 07:24:10,550 | INFO : \t train_acc: 97.53; train_loss: 0.404; test_loss: 23.917; lr: [0.001]\n",
            "2022-03-11 07:24:10,550 | INFO : \t test_acc: 92.61 (best: 92.61 from #104)\n",
            "2022-03-11 07:25:01,261 | INFO : Epoch #105\n",
            "2022-03-11 07:25:01,261 | INFO : \t train_acc: 97.59; train_loss: 0.029; test_loss: 24.069; lr: [0.001]\n",
            "2022-03-11 07:25:01,261 | INFO : \t test_acc: 92.54 (best: 92.61 from #104)\n",
            "2022-03-11 07:25:51,962 | INFO : Epoch #106\n",
            "2022-03-11 07:25:51,963 | INFO : \t train_acc: 97.756; train_loss: 0.117; test_loss: 24.048; lr: [0.001]\n",
            "2022-03-11 07:25:51,963 | INFO : \t test_acc: 92.61 (best: 92.61 from #106)\n",
            "2022-03-11 07:26:42,684 | INFO : Epoch #107\n",
            "2022-03-11 07:26:42,684 | INFO : \t train_acc: 97.902; train_loss: 0.013; test_loss: 24.036; lr: [0.001]\n",
            "2022-03-11 07:26:42,685 | INFO : \t test_acc: 92.58 (best: 92.61 from #106)\n",
            "2022-03-11 07:27:33,368 | INFO : Epoch #108\n",
            "2022-03-11 07:27:33,368 | INFO : \t train_acc: 97.868; train_loss: 0.038; test_loss: 24.342; lr: [0.001]\n",
            "2022-03-11 07:27:33,368 | INFO : \t test_acc: 92.69 (best: 92.69 from #108)\n",
            "2022-03-11 07:28:24,098 | INFO : Epoch #109\n",
            "2022-03-11 07:28:24,099 | INFO : \t train_acc: 98.076; train_loss: 0.067; test_loss: 24.624; lr: [0.001]\n",
            "2022-03-11 07:28:24,099 | INFO : \t test_acc: 92.47 (best: 92.69 from #108)\n",
            "2022-03-11 07:29:14,858 | INFO : Epoch #110\n",
            "2022-03-11 07:29:14,859 | INFO : \t train_acc: 98.026; train_loss: 0.751; test_loss: 24.820; lr: [0.001]\n",
            "2022-03-11 07:29:14,859 | INFO : \t test_acc: 92.71 (best: 92.71 from #110)\n",
            "2022-03-11 07:30:05,623 | INFO : Epoch #111\n",
            "2022-03-11 07:30:05,623 | INFO : \t train_acc: 98.128; train_loss: 0.043; test_loss: 24.328; lr: [0.001]\n",
            "2022-03-11 07:30:05,623 | INFO : \t test_acc: 92.83 (best: 92.83 from #111)\n",
            "2022-03-11 07:30:56,389 | INFO : Epoch #112\n",
            "2022-03-11 07:30:56,390 | INFO : \t train_acc: 98.226; train_loss: 0.115; test_loss: 24.452; lr: [0.001]\n",
            "2022-03-11 07:30:56,390 | INFO : \t test_acc: 92.8 (best: 92.83 from #111)\n",
            "2022-03-11 07:31:47,128 | INFO : Epoch #113\n",
            "2022-03-11 07:31:47,129 | INFO : \t train_acc: 98.314; train_loss: 0.014; test_loss: 24.792; lr: [0.001]\n",
            "2022-03-11 07:31:47,129 | INFO : \t test_acc: 92.78 (best: 92.83 from #111)\n",
            "2022-03-11 07:32:37,862 | INFO : Epoch #114\n",
            "2022-03-11 07:32:37,862 | INFO : \t train_acc: 98.324; train_loss: 0.149; test_loss: 25.101; lr: [0.001]\n",
            "2022-03-11 07:32:37,862 | INFO : \t test_acc: 92.98 (best: 92.98 from #114)\n",
            "2022-03-11 07:33:28,658 | INFO : Epoch #115\n",
            "2022-03-11 07:33:28,658 | INFO : \t train_acc: 98.384; train_loss: 0.095; test_loss: 25.095; lr: [0.001]\n",
            "2022-03-11 07:33:28,658 | INFO : \t test_acc: 92.79 (best: 92.98 from #114)\n",
            "2022-03-11 07:34:19,363 | INFO : Epoch #116\n",
            "2022-03-11 07:34:19,364 | INFO : \t train_acc: 98.48; train_loss: 0.248; test_loss: 25.200; lr: [0.001]\n",
            "2022-03-11 07:34:19,364 | INFO : \t test_acc: 92.89 (best: 92.98 from #114)\n",
            "2022-03-11 07:35:10,076 | INFO : Epoch #117\n",
            "2022-03-11 07:35:10,077 | INFO : \t train_acc: 98.372; train_loss: 0.082; test_loss: 25.185; lr: [0.001]\n",
            "2022-03-11 07:35:10,077 | INFO : \t test_acc: 92.8 (best: 92.98 from #114)\n",
            "2022-03-11 07:36:00,791 | INFO : Epoch #118\n",
            "2022-03-11 07:36:00,792 | INFO : \t train_acc: 98.348; train_loss: 0.009; test_loss: 25.351; lr: [0.001]\n",
            "2022-03-11 07:36:00,792 | INFO : \t test_acc: 92.83 (best: 92.98 from #114)\n",
            "2022-03-11 07:36:51,537 | INFO : Epoch #119\n",
            "2022-03-11 07:36:51,538 | INFO : \t train_acc: 98.542; train_loss: 0.020; test_loss: 25.823; lr: [0.001]\n",
            "2022-03-11 07:36:51,538 | INFO : \t test_acc: 92.8 (best: 92.98 from #114)\n",
            "2022-03-11 07:37:42,237 | INFO : Epoch #120\n",
            "2022-03-11 07:37:42,238 | INFO : \t train_acc: 98.576; train_loss: 0.175; test_loss: 25.505; lr: [0.001]\n",
            "2022-03-11 07:37:42,238 | INFO : \t test_acc: 92.89 (best: 92.98 from #114)\n",
            "2022-03-11 07:38:32,994 | INFO : Epoch #121\n",
            "2022-03-11 07:38:32,994 | INFO : \t train_acc: 98.656; train_loss: 0.285; test_loss: 25.549; lr: [0.001]\n",
            "2022-03-11 07:38:32,994 | INFO : \t test_acc: 92.74 (best: 92.98 from #114)\n",
            "2022-03-11 07:39:23,831 | INFO : Epoch #122\n",
            "2022-03-11 07:39:23,832 | INFO : \t train_acc: 98.554; train_loss: 0.050; test_loss: 26.153; lr: [0.001]\n",
            "2022-03-11 07:39:23,832 | INFO : \t test_acc: 92.73 (best: 92.98 from #114)\n",
            "2022-03-11 07:40:14,660 | INFO : Epoch #123\n",
            "2022-03-11 07:40:14,660 | INFO : \t train_acc: 98.642; train_loss: 0.086; test_loss: 25.704; lr: [0.001]\n",
            "2022-03-11 07:40:14,661 | INFO : \t test_acc: 92.96 (best: 92.98 from #114)\n",
            "2022-03-11 07:41:05,451 | INFO : Epoch #124\n",
            "2022-03-11 07:41:05,452 | INFO : \t train_acc: 98.632; train_loss: 0.041; test_loss: 26.228; lr: [0.001]\n",
            "2022-03-11 07:41:05,452 | INFO : \t test_acc: 92.84 (best: 92.98 from #114)\n",
            "2022-03-11 07:41:56,185 | INFO : Epoch #125\n",
            "2022-03-11 07:41:56,185 | INFO : \t train_acc: 98.734; train_loss: 0.109; test_loss: 26.286; lr: [0.001]\n",
            "2022-03-11 07:41:56,185 | INFO : \t test_acc: 92.78 (best: 92.98 from #114)\n",
            "2022-03-11 07:42:46,896 | INFO : Epoch #126\n",
            "2022-03-11 07:42:46,896 | INFO : \t train_acc: 98.77; train_loss: 0.025; test_loss: 25.689; lr: [0.001]\n",
            "2022-03-11 07:42:46,897 | INFO : \t test_acc: 92.87 (best: 92.98 from #114)\n",
            "2022-03-11 07:43:37,540 | INFO : Epoch #127\n",
            "2022-03-11 07:43:37,540 | INFO : \t train_acc: 98.742; train_loss: 0.077; test_loss: 26.745; lr: [0.001]\n",
            "2022-03-11 07:43:37,540 | INFO : \t test_acc: 92.49 (best: 92.98 from #114)\n",
            "2022-03-11 07:44:28,264 | INFO : Epoch #128\n",
            "2022-03-11 07:44:28,264 | INFO : \t train_acc: 98.77; train_loss: 0.197; test_loss: 26.226; lr: [0.001]\n",
            "2022-03-11 07:44:28,265 | INFO : \t test_acc: 92.99 (best: 92.99 from #128)\n",
            "2022-03-11 07:45:19,051 | INFO : Epoch #129\n",
            "2022-03-11 07:45:19,051 | INFO : \t train_acc: 98.778; train_loss: 0.310; test_loss: 27.178; lr: [0.001]\n",
            "2022-03-11 07:45:19,051 | INFO : \t test_acc: 92.61 (best: 92.99 from #128)\n",
            "2022-03-11 07:46:09,883 | INFO : Epoch #130\n",
            "2022-03-11 07:46:09,884 | INFO : \t train_acc: 98.864; train_loss: 0.196; test_loss: 27.338; lr: [0.001]\n",
            "2022-03-11 07:46:09,884 | INFO : \t test_acc: 92.79 (best: 92.99 from #128)\n",
            "2022-03-11 07:47:00,575 | INFO : Epoch #131\n",
            "2022-03-11 07:47:00,576 | INFO : \t train_acc: 98.926; train_loss: 0.235; test_loss: 28.045; lr: [0.001]\n",
            "2022-03-11 07:47:00,576 | INFO : \t test_acc: 92.49 (best: 92.99 from #128)\n",
            "2022-03-11 07:47:51,256 | INFO : Epoch #132\n",
            "2022-03-11 07:47:51,257 | INFO : \t train_acc: 98.922; train_loss: 0.612; test_loss: 27.479; lr: [0.001]\n",
            "2022-03-11 07:47:51,257 | INFO : \t test_acc: 92.62 (best: 92.99 from #128)\n",
            "2022-03-11 07:48:41,944 | INFO : Epoch #133\n",
            "2022-03-11 07:48:41,944 | INFO : \t train_acc: 98.858; train_loss: 0.004; test_loss: 26.835; lr: [0.001]\n",
            "2022-03-11 07:48:41,944 | INFO : \t test_acc: 92.68 (best: 92.99 from #128)\n",
            "2022-03-11 07:49:32,753 | INFO : Epoch #134\n",
            "2022-03-11 07:49:32,753 | INFO : \t train_acc: 98.958; train_loss: 0.019; test_loss: 27.484; lr: [0.001]\n",
            "2022-03-11 07:49:32,753 | INFO : \t test_acc: 92.57 (best: 92.99 from #128)\n",
            "2022-03-11 07:50:23,529 | INFO : Epoch #135\n",
            "2022-03-11 07:50:23,529 | INFO : \t train_acc: 98.942; train_loss: 0.029; test_loss: 27.644; lr: [0.001]\n",
            "2022-03-11 07:50:23,529 | INFO : \t test_acc: 92.77 (best: 92.99 from #128)\n",
            "2022-03-11 07:51:14,220 | INFO : Epoch #136\n",
            "2022-03-11 07:51:14,221 | INFO : \t train_acc: 98.884; train_loss: 0.065; test_loss: 27.704; lr: [0.001]\n",
            "2022-03-11 07:51:14,221 | INFO : \t test_acc: 92.6 (best: 92.99 from #128)\n",
            "2022-03-11 07:52:04,955 | INFO : Epoch #137\n",
            "2022-03-11 07:52:04,956 | INFO : \t train_acc: 98.91; train_loss: 0.025; test_loss: 27.844; lr: [0.001]\n",
            "2022-03-11 07:52:04,956 | INFO : \t test_acc: 92.48 (best: 92.99 from #128)\n",
            "2022-03-11 07:52:55,710 | INFO : Epoch #138\n",
            "2022-03-11 07:52:55,710 | INFO : \t train_acc: 99.008; train_loss: 0.016; test_loss: 28.298; lr: [0.001]\n",
            "2022-03-11 07:52:55,710 | INFO : \t test_acc: 92.74 (best: 92.99 from #128)\n",
            "2022-03-11 07:53:46,487 | INFO : Epoch #139\n",
            "2022-03-11 07:53:46,487 | INFO : \t train_acc: 99.006; train_loss: 0.006; test_loss: 28.351; lr: [0.001]\n",
            "2022-03-11 07:53:46,487 | INFO : \t test_acc: 92.78 (best: 92.99 from #128)\n",
            "2022-03-11 07:54:37,251 | INFO : Epoch #140\n",
            "2022-03-11 07:54:37,252 | INFO : \t train_acc: 99.012; train_loss: 0.147; test_loss: 27.693; lr: [0.001]\n",
            "2022-03-11 07:54:37,252 | INFO : \t test_acc: 92.76 (best: 92.99 from #128)\n",
            "2022-03-11 07:55:28,047 | INFO : Epoch #141\n",
            "2022-03-11 07:55:28,047 | INFO : \t train_acc: 99.018; train_loss: 0.006; test_loss: 27.919; lr: [0.001]\n",
            "2022-03-11 07:55:28,047 | INFO : \t test_acc: 92.63 (best: 92.99 from #128)\n",
            "2022-03-11 07:56:18,701 | INFO : Epoch #142\n",
            "2022-03-11 07:56:18,702 | INFO : \t train_acc: 98.942; train_loss: 0.017; test_loss: 28.842; lr: [0.001]\n",
            "2022-03-11 07:56:18,702 | INFO : \t test_acc: 92.49 (best: 92.99 from #128)\n",
            "2022-03-11 07:57:09,439 | INFO : Epoch #143\n",
            "2022-03-11 07:57:09,439 | INFO : \t train_acc: 99.016; train_loss: 0.018; test_loss: 28.506; lr: [0.001]\n",
            "2022-03-11 07:57:09,439 | INFO : \t test_acc: 92.61 (best: 92.99 from #128)\n",
            "2022-03-11 07:58:00,185 | INFO : Epoch #144\n",
            "2022-03-11 07:58:00,186 | INFO : \t train_acc: 99.054; train_loss: 0.281; test_loss: 28.440; lr: [0.001]\n",
            "2022-03-11 07:58:00,186 | INFO : \t test_acc: 92.68 (best: 92.99 from #128)\n",
            "2022-03-11 07:58:50,866 | INFO : Epoch #145\n",
            "2022-03-11 07:58:50,867 | INFO : \t train_acc: 99.078; train_loss: 0.027; test_loss: 28.762; lr: [0.001]\n",
            "2022-03-11 07:58:50,867 | INFO : \t test_acc: 92.63 (best: 92.99 from #128)\n",
            "2022-03-11 07:59:41,595 | INFO : Epoch #146\n",
            "2022-03-11 07:59:41,596 | INFO : \t train_acc: 99.076; train_loss: 0.018; test_loss: 29.452; lr: [0.001]\n",
            "2022-03-11 07:59:41,596 | INFO : \t test_acc: 92.65 (best: 92.99 from #128)\n",
            "2022-03-11 08:00:32,366 | INFO : Epoch #147\n",
            "2022-03-11 08:00:32,367 | INFO : \t train_acc: 99.17; train_loss: 0.002; test_loss: 28.879; lr: [0.001]\n",
            "2022-03-11 08:00:32,367 | INFO : \t test_acc: 92.71 (best: 92.99 from #128)\n",
            "2022-03-11 08:01:23,143 | INFO : Epoch #148\n",
            "2022-03-11 08:01:23,144 | INFO : \t train_acc: 99.032; train_loss: 0.031; test_loss: 29.111; lr: [0.001]\n",
            "2022-03-11 08:01:23,144 | INFO : \t test_acc: 92.66 (best: 92.99 from #128)\n",
            "2022-03-11 08:02:13,870 | INFO : Epoch #149\n",
            "2022-03-11 08:02:13,870 | INFO : \t train_acc: 99.176; train_loss: 0.104; test_loss: 28.551; lr: [0.001]\n",
            "2022-03-11 08:02:13,870 | INFO : \t test_acc: 92.8 (best: 92.99 from #128)\n",
            "2022-03-11 08:03:04,690 | INFO : Epoch #150\n",
            "2022-03-11 08:03:04,691 | INFO : \t train_acc: 99.028; train_loss: 0.175; test_loss: 29.254; lr: [0.001]\n",
            "2022-03-11 08:03:04,691 | INFO : \t test_acc: 92.55 (best: 92.99 from #128)\n",
            "2022-03-11 08:03:55,472 | INFO : Epoch #151\n",
            "2022-03-11 08:03:55,472 | INFO : \t train_acc: 99.048; train_loss: 0.631; test_loss: 28.726; lr: [0.001]\n",
            "2022-03-11 08:03:55,472 | INFO : \t test_acc: 92.76 (best: 92.99 from #128)\n",
            "2022-03-11 08:04:46,195 | INFO : Epoch #152\n",
            "2022-03-11 08:04:46,195 | INFO : \t train_acc: 99.112; train_loss: 0.017; test_loss: 29.137; lr: [0.001]\n",
            "2022-03-11 08:04:46,195 | INFO : \t test_acc: 92.5 (best: 92.99 from #128)\n",
            "2022-03-11 08:05:36,872 | INFO : Epoch #153\n",
            "2022-03-11 08:05:36,873 | INFO : \t train_acc: 99.11; train_loss: 0.097; test_loss: 29.914; lr: [0.001]\n",
            "2022-03-11 08:05:36,873 | INFO : \t test_acc: 92.65 (best: 92.99 from #128)\n",
            "2022-03-11 08:06:27,580 | INFO : Epoch #154\n",
            "2022-03-11 08:06:27,580 | INFO : \t train_acc: 99.204; train_loss: 0.216; test_loss: 29.050; lr: [0.001]\n",
            "2022-03-11 08:06:27,581 | INFO : \t test_acc: 92.75 (best: 92.99 from #128)\n",
            "2022-03-11 08:07:18,366 | INFO : Epoch #155\n",
            "2022-03-11 08:07:18,367 | INFO : \t train_acc: 99.16; train_loss: 0.368; test_loss: 29.264; lr: [0.001]\n",
            "2022-03-11 08:07:18,367 | INFO : \t test_acc: 92.64 (best: 92.99 from #128)\n",
            "2022-03-11 08:08:09,092 | INFO : Epoch #156\n",
            "2022-03-11 08:08:09,093 | INFO : \t train_acc: 99.142; train_loss: 0.086; test_loss: 28.782; lr: [0.001]\n",
            "2022-03-11 08:08:09,093 | INFO : \t test_acc: 92.57 (best: 92.99 from #128)\n",
            "2022-03-11 08:08:59,764 | INFO : Epoch #157\n",
            "2022-03-11 08:08:59,764 | INFO : \t train_acc: 99.18; train_loss: 0.026; test_loss: 29.940; lr: [0.001]\n",
            "2022-03-11 08:08:59,764 | INFO : \t test_acc: 92.53 (best: 92.99 from #128)\n",
            "2022-03-11 08:09:50,451 | INFO : Epoch #158\n",
            "2022-03-11 08:09:50,451 | INFO : \t train_acc: 99.132; train_loss: 0.100; test_loss: 29.702; lr: [0.001]\n",
            "2022-03-11 08:09:50,452 | INFO : \t test_acc: 92.66 (best: 92.99 from #128)\n",
            "2022-03-11 08:10:41,170 | INFO : Epoch #159\n",
            "2022-03-11 08:10:41,170 | INFO : \t train_acc: 99.086; train_loss: 0.296; test_loss: 29.679; lr: [0.001]\n",
            "2022-03-11 08:10:41,170 | INFO : \t test_acc: 92.43 (best: 92.99 from #128)\n",
            "2022-03-11 08:11:31,928 | INFO : Epoch #160\n",
            "2022-03-11 08:11:31,928 | INFO : \t train_acc: 99.11; train_loss: 0.092; test_loss: 29.709; lr: [0.001]\n",
            "2022-03-11 08:11:31,928 | INFO : \t test_acc: 92.6 (best: 92.99 from #128)\n",
            "2022-03-11 08:12:22,701 | INFO : Epoch #161\n",
            "2022-03-11 08:12:22,701 | INFO : \t train_acc: 99.16; train_loss: 0.014; test_loss: 30.953; lr: [0.001]\n",
            "2022-03-11 08:12:22,701 | INFO : \t test_acc: 92.38 (best: 92.99 from #128)\n",
            "2022-03-11 08:13:13,411 | INFO : Epoch #162\n",
            "2022-03-11 08:13:13,412 | INFO : \t train_acc: 99.066; train_loss: 0.034; test_loss: 30.976; lr: [0.001]\n",
            "2022-03-11 08:13:13,412 | INFO : \t test_acc: 92.45 (best: 92.99 from #128)\n",
            "2022-03-11 08:14:04,117 | INFO : Epoch #163\n",
            "2022-03-11 08:14:04,117 | INFO : \t train_acc: 99.046; train_loss: 0.008; test_loss: 30.540; lr: [0.001]\n",
            "2022-03-11 08:14:04,118 | INFO : \t test_acc: 92.35 (best: 92.99 from #128)\n",
            "2022-03-11 08:14:54,824 | INFO : Epoch #164\n",
            "2022-03-11 08:14:54,824 | INFO : \t train_acc: 99.178; train_loss: 0.005; test_loss: 29.468; lr: [0.001]\n",
            "2022-03-11 08:14:54,824 | INFO : \t test_acc: 92.54 (best: 92.99 from #128)\n",
            "2022-03-11 08:15:45,546 | INFO : Epoch #165\n",
            "2022-03-11 08:15:45,547 | INFO : \t train_acc: 99.142; train_loss: 0.039; test_loss: 29.644; lr: [0.001]\n",
            "2022-03-11 08:15:45,547 | INFO : \t test_acc: 92.73 (best: 92.99 from #128)\n",
            "2022-03-11 08:16:36,357 | INFO : Epoch #166\n",
            "2022-03-11 08:16:36,357 | INFO : \t train_acc: 99.16; train_loss: 0.077; test_loss: 30.042; lr: [0.001]\n",
            "2022-03-11 08:16:36,357 | INFO : \t test_acc: 92.53 (best: 92.99 from #128)\n",
            "2022-03-11 08:17:27,190 | INFO : Epoch #167\n",
            "2022-03-11 08:17:27,190 | INFO : \t train_acc: 99.12; train_loss: 0.208; test_loss: 28.754; lr: [0.001]\n",
            "2022-03-11 08:17:27,190 | INFO : \t test_acc: 92.71 (best: 92.99 from #128)\n",
            "2022-03-11 08:18:17,938 | INFO : Epoch #168\n",
            "2022-03-11 08:18:17,938 | INFO : \t train_acc: 99.224; train_loss: 0.019; test_loss: 30.237; lr: [0.001]\n",
            "2022-03-11 08:18:17,939 | INFO : \t test_acc: 92.4 (best: 92.99 from #128)\n",
            "2022-03-11 08:19:08,668 | INFO : Epoch #169\n",
            "2022-03-11 08:19:08,668 | INFO : \t train_acc: 99.122; train_loss: 0.032; test_loss: 29.813; lr: [0.001]\n",
            "2022-03-11 08:19:08,668 | INFO : \t test_acc: 92.57 (best: 92.99 from #128)\n",
            "2022-03-11 08:19:59,432 | INFO : Epoch #170\n",
            "2022-03-11 08:19:59,433 | INFO : \t train_acc: 99.198; train_loss: 0.035; test_loss: 30.363; lr: [0.001]\n",
            "2022-03-11 08:19:59,433 | INFO : \t test_acc: 92.45 (best: 92.99 from #128)\n",
            "2022-03-11 08:20:50,124 | INFO : Epoch #171\n",
            "2022-03-11 08:20:50,124 | INFO : \t train_acc: 99.142; train_loss: 0.019; test_loss: 30.116; lr: [0.001]\n",
            "2022-03-11 08:20:50,124 | INFO : \t test_acc: 92.48 (best: 92.99 from #128)\n",
            "2022-03-11 08:21:40,802 | INFO : Epoch #172\n",
            "2022-03-11 08:21:40,803 | INFO : \t train_acc: 99.244; train_loss: 0.084; test_loss: 31.456; lr: [0.001]\n",
            "2022-03-11 08:21:40,803 | INFO : \t test_acc: 92.44 (best: 92.99 from #128)\n",
            "2022-03-11 08:22:31,498 | INFO : Epoch #173\n",
            "2022-03-11 08:22:31,498 | INFO : \t train_acc: 99.294; train_loss: 0.128; test_loss: 31.119; lr: [0.001]\n",
            "2022-03-11 08:22:31,498 | INFO : \t test_acc: 92.39 (best: 92.99 from #128)\n",
            "2022-03-11 08:23:22,197 | INFO : Epoch #174\n",
            "2022-03-11 08:23:22,198 | INFO : \t train_acc: 99.226; train_loss: 0.007; test_loss: 31.185; lr: [0.001]\n",
            "2022-03-11 08:23:22,198 | INFO : \t test_acc: 92.54 (best: 92.99 from #128)\n",
            "2022-03-11 08:24:12,871 | INFO : Epoch #175\n",
            "2022-03-11 08:24:12,871 | INFO : \t train_acc: 99.23; train_loss: 0.019; test_loss: 32.065; lr: [0.001]\n",
            "2022-03-11 08:24:12,872 | INFO : \t test_acc: 92.3 (best: 92.99 from #128)\n",
            "2022-03-11 08:25:03,550 | INFO : Epoch #176\n",
            "2022-03-11 08:25:03,551 | INFO : \t train_acc: 99.156; train_loss: 0.549; test_loss: 30.354; lr: [0.001]\n",
            "2022-03-11 08:25:03,551 | INFO : \t test_acc: 92.74 (best: 92.99 from #128)\n",
            "2022-03-11 08:25:54,270 | INFO : Epoch #177\n",
            "2022-03-11 08:25:54,270 | INFO : \t train_acc: 99.17; train_loss: 0.002; test_loss: 31.065; lr: [0.001]\n",
            "2022-03-11 08:25:54,271 | INFO : \t test_acc: 92.3 (best: 92.99 from #128)\n",
            "2022-03-11 08:26:44,982 | INFO : Epoch #178\n",
            "2022-03-11 08:26:44,983 | INFO : \t train_acc: 99.152; train_loss: 0.001; test_loss: 30.978; lr: [0.001]\n",
            "2022-03-11 08:26:44,983 | INFO : \t test_acc: 92.54 (best: 92.99 from #128)\n",
            "2022-03-11 08:27:35,729 | INFO : Epoch #179\n",
            "2022-03-11 08:27:35,729 | INFO : \t train_acc: 99.122; train_loss: 0.006; test_loss: 30.674; lr: [0.001]\n",
            "2022-03-11 08:27:35,730 | INFO : \t test_acc: 92.47 (best: 92.99 from #128)\n",
            "2022-03-11 08:28:26,472 | INFO : Epoch #180\n",
            "2022-03-11 08:28:26,472 | INFO : \t train_acc: 99.166; train_loss: 0.040; test_loss: 30.431; lr: [0.001]\n",
            "2022-03-11 08:28:26,472 | INFO : \t test_acc: 92.66 (best: 92.99 from #128)\n",
            "2022-03-11 08:29:17,180 | INFO : Epoch #181\n",
            "2022-03-11 08:29:17,180 | INFO : \t train_acc: 99.156; train_loss: 0.056; test_loss: 31.107; lr: [0.001]\n",
            "2022-03-11 08:29:17,180 | INFO : \t test_acc: 92.53 (best: 92.99 from #128)\n",
            "2022-03-11 08:30:08,014 | INFO : Epoch #182\n",
            "2022-03-11 08:30:08,015 | INFO : \t train_acc: 99.238; train_loss: 0.006; test_loss: 30.222; lr: [0.001]\n",
            "2022-03-11 08:30:08,015 | INFO : \t test_acc: 92.61 (best: 92.99 from #128)\n",
            "2022-03-11 08:30:58,861 | INFO : Epoch #183\n",
            "2022-03-11 08:30:58,862 | INFO : \t train_acc: 99.238; train_loss: 0.066; test_loss: 30.708; lr: [0.001]\n",
            "2022-03-11 08:30:58,862 | INFO : \t test_acc: 92.6 (best: 92.99 from #128)\n",
            "2022-03-11 08:31:49,616 | INFO : Epoch #184\n",
            "2022-03-11 08:31:49,617 | INFO : \t train_acc: 99.146; train_loss: 0.097; test_loss: 30.760; lr: [0.001]\n",
            "2022-03-11 08:31:49,617 | INFO : \t test_acc: 92.34 (best: 92.99 from #128)\n",
            "2022-03-11 08:32:40,368 | INFO : Epoch #185\n",
            "2022-03-11 08:32:40,368 | INFO : \t train_acc: 99.172; train_loss: 0.066; test_loss: 32.222; lr: [0.001]\n",
            "2022-03-11 08:32:40,368 | INFO : \t test_acc: 92.15 (best: 92.99 from #128)\n",
            "2022-03-11 08:33:31,139 | INFO : Epoch #186\n",
            "2022-03-11 08:33:31,139 | INFO : \t train_acc: 99.21; train_loss: 0.017; test_loss: 31.140; lr: [0.001]\n",
            "2022-03-11 08:33:31,139 | INFO : \t test_acc: 92.37 (best: 92.99 from #128)\n",
            "2022-03-11 08:34:21,908 | INFO : Epoch #187\n",
            "2022-03-11 08:34:21,909 | INFO : \t train_acc: 99.072; train_loss: 0.015; test_loss: 30.811; lr: [0.001]\n",
            "2022-03-11 08:34:21,909 | INFO : \t test_acc: 92.36 (best: 92.99 from #128)\n",
            "2022-03-11 08:35:12,670 | INFO : Epoch #188\n",
            "2022-03-11 08:35:12,670 | INFO : \t train_acc: 99.21; train_loss: 0.030; test_loss: 31.584; lr: [0.001]\n",
            "2022-03-11 08:35:12,670 | INFO : \t test_acc: 92.11 (best: 92.99 from #128)\n",
            "2022-03-11 08:36:03,310 | INFO : Epoch #189\n",
            "2022-03-11 08:36:03,311 | INFO : \t train_acc: 99.096; train_loss: 0.115; test_loss: 30.421; lr: [0.001]\n",
            "2022-03-11 08:36:03,311 | INFO : \t test_acc: 92.69 (best: 92.99 from #128)\n",
            "2022-03-11 08:36:54,079 | INFO : Epoch #190\n",
            "2022-03-11 08:36:54,079 | INFO : \t train_acc: 99.088; train_loss: 0.064; test_loss: 30.311; lr: [0.001]\n",
            "2022-03-11 08:36:54,079 | INFO : \t test_acc: 92.71 (best: 92.99 from #128)\n",
            "2022-03-11 08:37:44,826 | INFO : Epoch #191\n",
            "2022-03-11 08:37:44,827 | INFO : \t train_acc: 99.168; train_loss: 0.001; test_loss: 32.358; lr: [0.001]\n",
            "2022-03-11 08:37:44,827 | INFO : \t test_acc: 92.34 (best: 92.99 from #128)\n",
            "2022-03-11 08:38:35,570 | INFO : Epoch #192\n",
            "2022-03-11 08:38:35,571 | INFO : \t train_acc: 99.3; train_loss: 0.152; test_loss: 31.808; lr: [0.001]\n",
            "2022-03-11 08:38:35,571 | INFO : \t test_acc: 92.2 (best: 92.99 from #128)\n",
            "2022-03-11 08:39:26,298 | INFO : Epoch #193\n",
            "2022-03-11 08:39:26,298 | INFO : \t train_acc: 99.128; train_loss: 0.059; test_loss: 32.012; lr: [0.001]\n",
            "2022-03-11 08:39:26,299 | INFO : \t test_acc: 92.28 (best: 92.99 from #128)\n",
            "2022-03-11 08:40:17,043 | INFO : Epoch #194\n",
            "2022-03-11 08:40:17,044 | INFO : \t train_acc: 99.252; train_loss: 0.150; test_loss: 31.758; lr: [0.001]\n",
            "2022-03-11 08:40:17,044 | INFO : \t test_acc: 92.12 (best: 92.99 from #128)\n",
            "2022-03-11 08:41:07,765 | INFO : Epoch #195\n",
            "2022-03-11 08:41:07,766 | INFO : \t train_acc: 99.282; train_loss: 0.139; test_loss: 31.170; lr: [0.001]\n",
            "2022-03-11 08:41:07,766 | INFO : \t test_acc: 92.32 (best: 92.99 from #128)\n",
            "2022-03-11 08:41:58,515 | INFO : Epoch #196\n",
            "2022-03-11 08:41:58,515 | INFO : \t train_acc: 99.166; train_loss: 0.104; test_loss: 30.558; lr: [0.001]\n",
            "2022-03-11 08:41:58,516 | INFO : \t test_acc: 92.33 (best: 92.99 from #128)\n",
            "2022-03-11 08:42:49,303 | INFO : Epoch #197\n",
            "2022-03-11 08:42:49,304 | INFO : \t train_acc: 99.064; train_loss: 0.177; test_loss: 30.503; lr: [0.001]\n",
            "2022-03-11 08:42:49,304 | INFO : \t test_acc: 92.35 (best: 92.99 from #128)\n",
            "2022-03-11 08:43:40,033 | INFO : Epoch #198\n",
            "2022-03-11 08:43:40,034 | INFO : \t train_acc: 99.168; train_loss: 0.028; test_loss: 31.486; lr: [0.001]\n",
            "2022-03-11 08:43:40,034 | INFO : \t test_acc: 92.46 (best: 92.99 from #128)\n",
            "2022-03-11 08:44:30,754 | INFO : Epoch #199\n",
            "2022-03-11 08:44:30,754 | INFO : \t train_acc: 99.094; train_loss: 0.008; test_loss: 31.252; lr: [0.001]\n",
            "2022-03-11 08:44:30,754 | INFO : \t test_acc: 92.44 (best: 92.99 from #128)\n",
            "2022-03-11 08:45:21,519 | INFO : Epoch #200\n",
            "2022-03-11 08:45:21,519 | INFO : \t train_acc: 99.422; train_loss: 0.008; test_loss: 29.625; lr: [0.0001]\n",
            "2022-03-11 08:45:21,519 | INFO : \t test_acc: 92.71 (best: 92.99 from #128)\n",
            "2022-03-11 08:46:12,389 | INFO : Epoch #201\n",
            "2022-03-11 08:46:12,389 | INFO : \t train_acc: 99.544; train_loss: 0.024; test_loss: 29.616; lr: [0.0001]\n",
            "2022-03-11 08:46:12,389 | INFO : \t test_acc: 92.79 (best: 92.99 from #128)\n",
            "2022-03-11 08:47:03,170 | INFO : Epoch #202\n",
            "2022-03-11 08:47:03,170 | INFO : \t train_acc: 99.632; train_loss: 0.044; test_loss: 29.982; lr: [0.0001]\n",
            "2022-03-11 08:47:03,171 | INFO : \t test_acc: 92.74 (best: 92.99 from #128)\n",
            "2022-03-11 08:47:53,901 | INFO : Epoch #203\n",
            "2022-03-11 08:47:53,901 | INFO : \t train_acc: 99.634; train_loss: 0.023; test_loss: 29.649; lr: [0.0001]\n",
            "2022-03-11 08:47:53,901 | INFO : \t test_acc: 92.87 (best: 92.99 from #128)\n",
            "2022-03-11 08:48:44,697 | INFO : Epoch #204\n",
            "2022-03-11 08:48:44,698 | INFO : \t train_acc: 99.6; train_loss: 0.006; test_loss: 29.543; lr: [0.0001]\n",
            "2022-03-11 08:48:44,698 | INFO : \t test_acc: 92.78 (best: 92.99 from #128)\n",
            "2022-03-11 08:49:35,477 | INFO : Epoch #205\n",
            "2022-03-11 08:49:35,478 | INFO : \t train_acc: 99.604; train_loss: 0.001; test_loss: 30.040; lr: [0.0001]\n",
            "2022-03-11 08:49:35,478 | INFO : \t test_acc: 92.68 (best: 92.99 from #128)\n",
            "2022-03-11 08:50:26,259 | INFO : Epoch #206\n",
            "2022-03-11 08:50:26,260 | INFO : \t train_acc: 99.656; train_loss: 0.083; test_loss: 29.887; lr: [0.0001]\n",
            "2022-03-11 08:50:26,260 | INFO : \t test_acc: 92.73 (best: 92.99 from #128)\n",
            "2022-03-11 08:51:17,041 | INFO : Epoch #207\n",
            "2022-03-11 08:51:17,042 | INFO : \t train_acc: 99.654; train_loss: 0.019; test_loss: 29.824; lr: [0.0001]\n",
            "2022-03-11 08:51:17,042 | INFO : \t test_acc: 92.76 (best: 92.99 from #128)\n",
            "2022-03-11 08:52:07,804 | INFO : Epoch #208\n",
            "2022-03-11 08:52:07,804 | INFO : \t train_acc: 99.672; train_loss: 0.058; test_loss: 29.717; lr: [0.0001]\n",
            "2022-03-11 08:52:07,804 | INFO : \t test_acc: 92.76 (best: 92.99 from #128)\n",
            "2022-03-11 08:52:58,503 | INFO : Epoch #209\n",
            "2022-03-11 08:52:58,503 | INFO : \t train_acc: 99.642; train_loss: 0.088; test_loss: 29.884; lr: [0.0001]\n",
            "2022-03-11 08:52:58,503 | INFO : \t test_acc: 92.62 (best: 92.99 from #128)\n",
            "2022-03-11 08:53:49,296 | INFO : Epoch #210\n",
            "2022-03-11 08:53:49,296 | INFO : \t train_acc: 99.658; train_loss: 0.044; test_loss: 29.883; lr: [0.0001]\n",
            "2022-03-11 08:53:49,297 | INFO : \t test_acc: 92.74 (best: 92.99 from #128)\n",
            "2022-03-11 08:54:39,980 | INFO : Epoch #211\n",
            "2022-03-11 08:54:39,981 | INFO : \t train_acc: 99.646; train_loss: 0.064; test_loss: 30.413; lr: [0.0001]\n",
            "2022-03-11 08:54:39,981 | INFO : \t test_acc: 92.63 (best: 92.99 from #128)\n",
            "2022-03-11 08:55:30,677 | INFO : Epoch #212\n",
            "2022-03-11 08:55:30,678 | INFO : \t train_acc: 99.702; train_loss: 0.019; test_loss: 30.205; lr: [0.0001]\n",
            "2022-03-11 08:55:30,678 | INFO : \t test_acc: 92.61 (best: 92.99 from #128)\n",
            "2022-03-11 08:56:21,398 | INFO : Epoch #213\n",
            "2022-03-11 08:56:21,398 | INFO : \t train_acc: 99.674; train_loss: 0.009; test_loss: 30.186; lr: [0.0001]\n",
            "2022-03-11 08:56:21,398 | INFO : \t test_acc: 92.83 (best: 92.99 from #128)\n",
            "2022-03-11 08:57:12,138 | INFO : Epoch #214\n",
            "2022-03-11 08:57:12,139 | INFO : \t train_acc: 99.698; train_loss: 0.016; test_loss: 29.900; lr: [0.0001]\n",
            "2022-03-11 08:57:12,139 | INFO : \t test_acc: 92.85 (best: 92.99 from #128)\n",
            "2022-03-11 08:58:02,863 | INFO : Epoch #215\n",
            "2022-03-11 08:58:02,863 | INFO : \t train_acc: 99.638; train_loss: 0.011; test_loss: 29.927; lr: [0.0001]\n",
            "2022-03-11 08:58:02,864 | INFO : \t test_acc: 92.73 (best: 92.99 from #128)\n",
            "2022-03-11 08:58:53,637 | INFO : Epoch #216\n",
            "2022-03-11 08:58:53,638 | INFO : \t train_acc: 99.662; train_loss: 0.033; test_loss: 30.393; lr: [0.0001]\n",
            "2022-03-11 08:58:53,638 | INFO : \t test_acc: 92.77 (best: 92.99 from #128)\n",
            "2022-03-11 08:59:44,405 | INFO : Epoch #217\n",
            "2022-03-11 08:59:44,406 | INFO : \t train_acc: 99.734; train_loss: 0.091; test_loss: 30.056; lr: [0.0001]\n",
            "2022-03-11 08:59:44,406 | INFO : \t test_acc: 92.76 (best: 92.99 from #128)\n",
            "2022-03-11 09:00:35,127 | INFO : Epoch #218\n",
            "2022-03-11 09:00:35,127 | INFO : \t train_acc: 99.684; train_loss: 0.022; test_loss: 30.329; lr: [0.0001]\n",
            "2022-03-11 09:00:35,127 | INFO : \t test_acc: 92.83 (best: 92.99 from #128)\n",
            "2022-03-11 09:01:25,908 | INFO : Epoch #219\n",
            "2022-03-11 09:01:25,908 | INFO : \t train_acc: 99.722; train_loss: 0.061; test_loss: 30.215; lr: [0.0001]\n",
            "2022-03-11 09:01:25,908 | INFO : \t test_acc: 92.81 (best: 92.99 from #128)\n",
            "2022-03-11 09:02:16,787 | INFO : Epoch #220\n",
            "2022-03-11 09:02:16,788 | INFO : \t train_acc: 99.69; train_loss: 0.252; test_loss: 30.573; lr: [0.0001]\n",
            "2022-03-11 09:02:16,788 | INFO : \t test_acc: 92.6 (best: 92.99 from #128)\n",
            "2022-03-11 09:03:07,507 | INFO : Epoch #221\n",
            "2022-03-11 09:03:07,508 | INFO : \t train_acc: 99.748; train_loss: 0.038; test_loss: 30.117; lr: [0.0001]\n",
            "2022-03-11 09:03:07,508 | INFO : \t test_acc: 92.89 (best: 92.99 from #128)\n",
            "2022-03-11 09:03:58,236 | INFO : Epoch #222\n",
            "2022-03-11 09:03:58,236 | INFO : \t train_acc: 99.706; train_loss: 0.001; test_loss: 30.092; lr: [0.0001]\n",
            "2022-03-11 09:03:58,236 | INFO : \t test_acc: 92.84 (best: 92.99 from #128)\n",
            "2022-03-11 09:04:48,974 | INFO : Epoch #223\n",
            "2022-03-11 09:04:48,974 | INFO : \t train_acc: 99.718; train_loss: 0.244; test_loss: 30.388; lr: [0.0001]\n",
            "2022-03-11 09:04:48,974 | INFO : \t test_acc: 92.83 (best: 92.99 from #128)\n",
            "2022-03-11 09:05:39,602 | INFO : Epoch #224\n",
            "2022-03-11 09:05:39,603 | INFO : \t train_acc: 99.746; train_loss: 0.048; test_loss: 30.327; lr: [0.0001]\n",
            "2022-03-11 09:05:39,603 | INFO : \t test_acc: 92.89 (best: 92.99 from #128)\n",
            "2022-03-11 09:06:30,336 | INFO : Epoch #225\n",
            "2022-03-11 09:06:30,336 | INFO : \t train_acc: 99.748; train_loss: 0.009; test_loss: 30.627; lr: [0.0001]\n",
            "2022-03-11 09:06:30,337 | INFO : \t test_acc: 92.81 (best: 92.99 from #128)\n",
            "2022-03-11 09:07:21,021 | INFO : Epoch #226\n",
            "2022-03-11 09:07:21,021 | INFO : \t train_acc: 99.724; train_loss: 0.038; test_loss: 30.487; lr: [0.0001]\n",
            "2022-03-11 09:07:21,022 | INFO : \t test_acc: 92.62 (best: 92.99 from #128)\n",
            "2022-03-11 09:08:11,745 | INFO : Epoch #227\n",
            "2022-03-11 09:08:11,745 | INFO : \t train_acc: 99.706; train_loss: 0.006; test_loss: 30.298; lr: [0.0001]\n",
            "2022-03-11 09:08:11,745 | INFO : \t test_acc: 92.73 (best: 92.99 from #128)\n",
            "2022-03-11 09:09:02,465 | INFO : Epoch #228\n",
            "2022-03-11 09:09:02,466 | INFO : \t train_acc: 99.72; train_loss: 0.114; test_loss: 30.322; lr: [0.0001]\n",
            "2022-03-11 09:09:02,466 | INFO : \t test_acc: 92.83 (best: 92.99 from #128)\n",
            "2022-03-11 09:09:53,082 | INFO : Epoch #229\n",
            "2022-03-11 09:09:53,083 | INFO : \t train_acc: 99.714; train_loss: 0.009; test_loss: 30.367; lr: [0.0001]\n",
            "2022-03-11 09:09:53,083 | INFO : \t test_acc: 92.82 (best: 92.99 from #128)\n",
            "2022-03-11 09:10:43,806 | INFO : Epoch #230\n",
            "2022-03-11 09:10:43,806 | INFO : \t train_acc: 99.722; train_loss: 0.130; test_loss: 30.132; lr: [0.0001]\n",
            "2022-03-11 09:10:43,807 | INFO : \t test_acc: 92.71 (best: 92.99 from #128)\n",
            "2022-03-11 09:11:34,530 | INFO : Epoch #231\n",
            "2022-03-11 09:11:34,531 | INFO : \t train_acc: 99.748; train_loss: 0.093; test_loss: 30.261; lr: [0.0001]\n",
            "2022-03-11 09:11:34,531 | INFO : \t test_acc: 92.84 (best: 92.99 from #128)\n",
            "2022-03-11 09:12:25,168 | INFO : Epoch #232\n",
            "2022-03-11 09:12:25,169 | INFO : \t train_acc: 99.734; train_loss: 0.568; test_loss: 30.085; lr: [0.0001]\n",
            "2022-03-11 09:12:25,169 | INFO : \t test_acc: 92.92 (best: 92.99 from #128)\n",
            "2022-03-11 09:13:15,857 | INFO : Epoch #233\n",
            "2022-03-11 09:13:15,858 | INFO : \t train_acc: 99.732; train_loss: 0.015; test_loss: 30.348; lr: [0.0001]\n",
            "2022-03-11 09:13:15,858 | INFO : \t test_acc: 92.75 (best: 92.99 from #128)\n",
            "2022-03-11 09:14:06,568 | INFO : Epoch #234\n",
            "2022-03-11 09:14:06,569 | INFO : \t train_acc: 99.708; train_loss: 0.230; test_loss: 30.444; lr: [0.0001]\n",
            "2022-03-11 09:14:06,569 | INFO : \t test_acc: 92.72 (best: 92.99 from #128)\n",
            "2022-03-11 09:14:57,217 | INFO : Epoch #235\n",
            "2022-03-11 09:14:57,217 | INFO : \t train_acc: 99.726; train_loss: 0.037; test_loss: 30.096; lr: [0.0001]\n",
            "2022-03-11 09:14:57,217 | INFO : \t test_acc: 92.83 (best: 92.99 from #128)\n",
            "2022-03-11 09:15:47,852 | INFO : Epoch #236\n",
            "2022-03-11 09:15:47,853 | INFO : \t train_acc: 99.712; train_loss: 0.221; test_loss: 30.383; lr: [0.0001]\n",
            "2022-03-11 09:15:47,853 | INFO : \t test_acc: 92.75 (best: 92.99 from #128)\n",
            "2022-03-11 09:16:38,459 | INFO : Epoch #237\n",
            "2022-03-11 09:16:38,460 | INFO : \t train_acc: 99.732; train_loss: 0.007; test_loss: 29.879; lr: [0.0001]\n",
            "2022-03-11 09:16:38,460 | INFO : \t test_acc: 92.84 (best: 92.99 from #128)\n",
            "2022-03-11 09:17:29,147 | INFO : Epoch #238\n",
            "2022-03-11 09:17:29,148 | INFO : \t train_acc: 99.77; train_loss: 0.026; test_loss: 30.272; lr: [0.0001]\n",
            "2022-03-11 09:17:29,148 | INFO : \t test_acc: 92.82 (best: 92.99 from #128)\n",
            "2022-03-11 09:18:19,884 | INFO : Epoch #239\n",
            "2022-03-11 09:18:19,884 | INFO : \t train_acc: 99.728; train_loss: 0.031; test_loss: 30.058; lr: [0.0001]\n",
            "2022-03-11 09:18:19,884 | INFO : \t test_acc: 92.87 (best: 92.99 from #128)\n",
            "2022-03-11 09:19:10,626 | INFO : Epoch #240\n",
            "2022-03-11 09:19:10,626 | INFO : \t train_acc: 99.722; train_loss: 0.011; test_loss: 30.451; lr: [0.0001]\n",
            "2022-03-11 09:19:10,627 | INFO : \t test_acc: 92.77 (best: 92.99 from #128)\n",
            "2022-03-11 09:20:01,225 | INFO : Epoch #241\n",
            "2022-03-11 09:20:01,226 | INFO : \t train_acc: 99.734; train_loss: 0.008; test_loss: 30.313; lr: [0.0001]\n",
            "2022-03-11 09:20:01,226 | INFO : \t test_acc: 92.9 (best: 92.99 from #128)\n",
            "2022-03-11 09:20:51,943 | INFO : Epoch #242\n",
            "2022-03-11 09:20:51,944 | INFO : \t train_acc: 99.796; train_loss: 0.055; test_loss: 30.216; lr: [0.0001]\n",
            "2022-03-11 09:20:51,944 | INFO : \t test_acc: 92.79 (best: 92.99 from #128)\n",
            "2022-03-11 09:21:42,589 | INFO : Epoch #243\n",
            "2022-03-11 09:21:42,590 | INFO : \t train_acc: 99.758; train_loss: 0.019; test_loss: 30.012; lr: [0.0001]\n",
            "2022-03-11 09:21:42,590 | INFO : \t test_acc: 92.71 (best: 92.99 from #128)\n",
            "2022-03-11 09:22:33,427 | INFO : Epoch #244\n",
            "2022-03-11 09:22:33,428 | INFO : \t train_acc: 99.736; train_loss: 0.089; test_loss: 30.035; lr: [0.0001]\n",
            "2022-03-11 09:22:33,428 | INFO : \t test_acc: 92.97 (best: 92.99 from #128)\n",
            "2022-03-11 09:23:24,128 | INFO : Epoch #245\n",
            "2022-03-11 09:23:24,129 | INFO : \t train_acc: 99.746; train_loss: 0.091; test_loss: 30.332; lr: [0.0001]\n",
            "2022-03-11 09:23:24,129 | INFO : \t test_acc: 92.82 (best: 92.99 from #128)\n",
            "2022-03-11 09:24:14,715 | INFO : Epoch #246\n",
            "2022-03-11 09:24:14,715 | INFO : \t train_acc: 99.78; train_loss: 0.019; test_loss: 30.050; lr: [0.0001]\n",
            "2022-03-11 09:24:14,716 | INFO : \t test_acc: 92.84 (best: 92.99 from #128)\n",
            "2022-03-11 09:25:05,325 | INFO : Epoch #247\n",
            "2022-03-11 09:25:05,326 | INFO : \t train_acc: 99.75; train_loss: 0.015; test_loss: 30.131; lr: [0.0001]\n",
            "2022-03-11 09:25:05,326 | INFO : \t test_acc: 92.95 (best: 92.99 from #128)\n",
            "2022-03-11 09:25:55,939 | INFO : Epoch #248\n",
            "2022-03-11 09:25:55,940 | INFO : \t train_acc: 99.74; train_loss: 0.123; test_loss: 30.433; lr: [0.0001]\n",
            "2022-03-11 09:25:55,940 | INFO : \t test_acc: 92.86 (best: 92.99 from #128)\n",
            "2022-03-11 09:26:46,695 | INFO : Epoch #249\n",
            "2022-03-11 09:26:46,695 | INFO : \t train_acc: 99.78; train_loss: 0.039; test_loss: 30.261; lr: [0.0001]\n",
            "2022-03-11 09:26:46,696 | INFO : \t test_acc: 92.78 (best: 92.99 from #128)\n",
            "2022-03-11 09:27:37,505 | INFO : Epoch #250\n",
            "2022-03-11 09:27:37,506 | INFO : \t train_acc: 99.794; train_loss: 0.125; test_loss: 29.975; lr: [0.0001]\n",
            "2022-03-11 09:27:37,506 | INFO : \t test_acc: 92.83 (best: 92.99 from #128)\n",
            "2022-03-11 09:28:28,348 | INFO : Epoch #251\n",
            "2022-03-11 09:28:28,349 | INFO : \t train_acc: 99.766; train_loss: 0.014; test_loss: 30.415; lr: [0.0001]\n",
            "2022-03-11 09:28:28,349 | INFO : \t test_acc: 92.8 (best: 92.99 from #128)\n",
            "2022-03-11 09:29:19,155 | INFO : Epoch #252\n",
            "2022-03-11 09:29:19,155 | INFO : \t train_acc: 99.768; train_loss: 0.006; test_loss: 30.068; lr: [0.0001]\n",
            "2022-03-11 09:29:19,155 | INFO : \t test_acc: 92.77 (best: 92.99 from #128)\n",
            "2022-03-11 09:30:09,961 | INFO : Epoch #253\n",
            "2022-03-11 09:30:09,961 | INFO : \t train_acc: 99.792; train_loss: 0.004; test_loss: 30.398; lr: [0.0001]\n",
            "2022-03-11 09:30:09,961 | INFO : \t test_acc: 92.78 (best: 92.99 from #128)\n",
            "2022-03-11 09:31:00,789 | INFO : Epoch #254\n",
            "2022-03-11 09:31:00,790 | INFO : \t train_acc: 99.768; train_loss: 0.007; test_loss: 30.327; lr: [0.0001]\n",
            "2022-03-11 09:31:00,790 | INFO : \t test_acc: 92.92 (best: 92.99 from #128)\n",
            "2022-03-11 09:31:51,814 | INFO : Epoch #255\n",
            "2022-03-11 09:31:51,814 | INFO : \t train_acc: 99.78; train_loss: 0.099; test_loss: 30.095; lr: [0.0001]\n",
            "2022-03-11 09:31:51,815 | INFO : \t test_acc: 92.93 (best: 92.99 from #128)\n",
            "2022-03-11 09:32:42,642 | INFO : Epoch #256\n",
            "2022-03-11 09:32:42,643 | INFO : \t train_acc: 99.772; train_loss: 0.004; test_loss: 30.015; lr: [0.0001]\n",
            "2022-03-11 09:32:42,643 | INFO : \t test_acc: 92.9 (best: 92.99 from #128)\n",
            "2022-03-11 09:33:33,494 | INFO : Epoch #257\n",
            "2022-03-11 09:33:33,494 | INFO : \t train_acc: 99.788; train_loss: 0.094; test_loss: 30.341; lr: [0.0001]\n",
            "2022-03-11 09:33:33,494 | INFO : \t test_acc: 92.91 (best: 92.99 from #128)\n",
            "2022-03-11 09:34:24,369 | INFO : Epoch #258\n",
            "2022-03-11 09:34:24,369 | INFO : \t train_acc: 99.724; train_loss: 0.005; test_loss: 30.256; lr: [0.0001]\n",
            "2022-03-11 09:34:24,369 | INFO : \t test_acc: 92.89 (best: 92.99 from #128)\n",
            "2022-03-11 09:35:15,179 | INFO : Epoch #259\n",
            "2022-03-11 09:35:15,179 | INFO : \t train_acc: 99.792; train_loss: 0.004; test_loss: 30.064; lr: [0.0001]\n",
            "2022-03-11 09:35:15,179 | INFO : \t test_acc: 92.85 (best: 92.99 from #128)\n",
            "2022-03-11 09:36:06,001 | INFO : Epoch #260\n",
            "2022-03-11 09:36:06,002 | INFO : \t train_acc: 99.784; train_loss: 0.020; test_loss: 30.490; lr: [0.0001]\n",
            "2022-03-11 09:36:06,002 | INFO : \t test_acc: 92.9 (best: 92.99 from #128)\n",
            "2022-03-11 09:36:56,863 | INFO : Epoch #261\n",
            "2022-03-11 09:36:56,863 | INFO : \t train_acc: 99.778; train_loss: 0.025; test_loss: 30.441; lr: [0.0001]\n",
            "2022-03-11 09:36:56,863 | INFO : \t test_acc: 92.8 (best: 92.99 from #128)\n",
            "2022-03-11 09:37:47,777 | INFO : Epoch #262\n",
            "2022-03-11 09:37:47,777 | INFO : \t train_acc: 99.796; train_loss: 0.015; test_loss: 30.189; lr: [0.0001]\n",
            "2022-03-11 09:37:47,777 | INFO : \t test_acc: 92.95 (best: 92.99 from #128)\n",
            "2022-03-11 09:38:38,672 | INFO : Epoch #263\n",
            "2022-03-11 09:38:38,673 | INFO : \t train_acc: 99.81; train_loss: 0.014; test_loss: 30.569; lr: [0.0001]\n",
            "2022-03-11 09:38:38,673 | INFO : \t test_acc: 92.75 (best: 92.99 from #128)\n",
            "2022-03-11 09:39:29,561 | INFO : Epoch #264\n",
            "2022-03-11 09:39:29,562 | INFO : \t train_acc: 99.744; train_loss: 0.112; test_loss: 30.917; lr: [0.0001]\n",
            "2022-03-11 09:39:29,562 | INFO : \t test_acc: 92.71 (best: 92.99 from #128)\n",
            "2022-03-11 09:40:20,444 | INFO : Epoch #265\n",
            "2022-03-11 09:40:20,445 | INFO : \t train_acc: 99.77; train_loss: 0.216; test_loss: 30.687; lr: [0.0001]\n",
            "2022-03-11 09:40:20,445 | INFO : \t test_acc: 92.83 (best: 92.99 from #128)\n",
            "2022-03-11 09:41:11,258 | INFO : Epoch #266\n",
            "2022-03-11 09:41:11,258 | INFO : \t train_acc: 99.746; train_loss: 0.019; test_loss: 30.644; lr: [0.0001]\n",
            "2022-03-11 09:41:11,258 | INFO : \t test_acc: 92.83 (best: 92.99 from #128)\n",
            "2022-03-11 09:42:02,174 | INFO : Epoch #267\n",
            "2022-03-11 09:42:02,174 | INFO : \t train_acc: 99.748; train_loss: 0.001; test_loss: 30.809; lr: [0.0001]\n",
            "2022-03-11 09:42:02,175 | INFO : \t test_acc: 92.73 (best: 92.99 from #128)\n",
            "2022-03-11 09:42:53,054 | INFO : Epoch #268\n",
            "2022-03-11 09:42:53,054 | INFO : \t train_acc: 99.814; train_loss: 0.003; test_loss: 30.854; lr: [0.0001]\n",
            "2022-03-11 09:42:53,054 | INFO : \t test_acc: 92.81 (best: 92.99 from #128)\n",
            "2022-03-11 09:43:43,950 | INFO : Epoch #269\n",
            "2022-03-11 09:43:43,951 | INFO : \t train_acc: 99.762; train_loss: 0.010; test_loss: 30.545; lr: [0.0001]\n",
            "2022-03-11 09:43:43,951 | INFO : \t test_acc: 92.83 (best: 92.99 from #128)\n",
            "2022-03-11 09:44:34,862 | INFO : Epoch #270\n",
            "2022-03-11 09:44:34,863 | INFO : \t train_acc: 99.804; train_loss: 0.037; test_loss: 30.407; lr: [0.0001]\n",
            "2022-03-11 09:44:34,863 | INFO : \t test_acc: 92.78 (best: 92.99 from #128)\n",
            "2022-03-11 09:45:25,738 | INFO : Epoch #271\n",
            "2022-03-11 09:45:25,739 | INFO : \t train_acc: 99.772; train_loss: 0.538; test_loss: 31.166; lr: [0.0001]\n",
            "2022-03-11 09:45:25,739 | INFO : \t test_acc: 92.66 (best: 92.99 from #128)\n",
            "2022-03-11 09:46:16,609 | INFO : Epoch #272\n",
            "2022-03-11 09:46:16,609 | INFO : \t train_acc: 99.808; train_loss: 0.036; test_loss: 30.262; lr: [0.0001]\n",
            "2022-03-11 09:46:16,610 | INFO : \t test_acc: 92.8 (best: 92.99 from #128)\n",
            "2022-03-11 09:47:07,494 | INFO : Epoch #273\n",
            "2022-03-11 09:47:07,495 | INFO : \t train_acc: 99.796; train_loss: 0.015; test_loss: 30.364; lr: [0.0001]\n",
            "2022-03-11 09:47:07,495 | INFO : \t test_acc: 92.74 (best: 92.99 from #128)\n",
            "2022-03-11 09:47:58,360 | INFO : Epoch #274\n",
            "2022-03-11 09:47:58,361 | INFO : \t train_acc: 99.802; train_loss: 0.010; test_loss: 30.494; lr: [0.0001]\n",
            "2022-03-11 09:47:58,361 | INFO : \t test_acc: 92.7 (best: 92.99 from #128)\n",
            "2022-03-11 09:48:49,192 | INFO : Epoch #275\n",
            "2022-03-11 09:48:49,192 | INFO : \t train_acc: 99.802; train_loss: 0.287; test_loss: 30.481; lr: [0.0001]\n",
            "2022-03-11 09:48:49,193 | INFO : \t test_acc: 92.85 (best: 92.99 from #128)\n",
            "2022-03-11 09:49:40,016 | INFO : Epoch #276\n",
            "2022-03-11 09:49:40,017 | INFO : \t train_acc: 99.814; train_loss: 0.035; test_loss: 30.333; lr: [0.0001]\n",
            "2022-03-11 09:49:40,017 | INFO : \t test_acc: 92.74 (best: 92.99 from #128)\n",
            "2022-03-11 09:50:30,914 | INFO : Epoch #277\n",
            "2022-03-11 09:50:30,914 | INFO : \t train_acc: 99.766; train_loss: 0.038; test_loss: 30.466; lr: [0.0001]\n",
            "2022-03-11 09:50:30,914 | INFO : \t test_acc: 92.91 (best: 92.99 from #128)\n",
            "2022-03-11 09:51:21,826 | INFO : Epoch #278\n",
            "2022-03-11 09:51:21,826 | INFO : \t train_acc: 99.752; train_loss: 0.077; test_loss: 30.827; lr: [0.0001]\n",
            "2022-03-11 09:51:21,826 | INFO : \t test_acc: 92.91 (best: 92.99 from #128)\n",
            "2022-03-11 09:52:12,784 | INFO : Epoch #279\n",
            "2022-03-11 09:52:12,784 | INFO : \t train_acc: 99.788; train_loss: 0.011; test_loss: 30.166; lr: [0.0001]\n",
            "2022-03-11 09:52:12,784 | INFO : \t test_acc: 92.87 (best: 92.99 from #128)\n",
            "2022-03-11 09:53:03,733 | INFO : Epoch #280\n",
            "2022-03-11 09:53:03,733 | INFO : \t train_acc: 99.764; train_loss: 0.048; test_loss: 30.818; lr: [0.0001]\n",
            "2022-03-11 09:53:03,733 | INFO : \t test_acc: 92.77 (best: 92.99 from #128)\n",
            "2022-03-11 09:53:54,634 | INFO : Epoch #281\n",
            "2022-03-11 09:53:54,634 | INFO : \t train_acc: 99.798; train_loss: 0.090; test_loss: 30.586; lr: [0.0001]\n",
            "2022-03-11 09:53:54,634 | INFO : \t test_acc: 92.84 (best: 92.99 from #128)\n",
            "2022-03-11 09:54:45,544 | INFO : Epoch #282\n",
            "2022-03-11 09:54:45,545 | INFO : \t train_acc: 99.81; train_loss: 0.004; test_loss: 30.445; lr: [0.0001]\n",
            "2022-03-11 09:54:45,545 | INFO : \t test_acc: 92.88 (best: 92.99 from #128)\n",
            "2022-03-11 09:55:36,420 | INFO : Epoch #283\n",
            "2022-03-11 09:55:36,421 | INFO : \t train_acc: 99.772; train_loss: 0.035; test_loss: 30.134; lr: [0.0001]\n",
            "2022-03-11 09:55:36,421 | INFO : \t test_acc: 92.85 (best: 92.99 from #128)\n",
            "2022-03-11 09:56:27,310 | INFO : Epoch #284\n",
            "2022-03-11 09:56:27,311 | INFO : \t train_acc: 99.762; train_loss: 0.002; test_loss: 30.611; lr: [0.0001]\n",
            "2022-03-11 09:56:27,311 | INFO : \t test_acc: 92.78 (best: 92.99 from #128)\n",
            "2022-03-11 09:57:18,214 | INFO : Epoch #285\n",
            "2022-03-11 09:57:18,215 | INFO : \t train_acc: 99.796; train_loss: 0.024; test_loss: 30.576; lr: [0.0001]\n",
            "2022-03-11 09:57:18,215 | INFO : \t test_acc: 92.83 (best: 92.99 from #128)\n",
            "2022-03-11 09:58:09,117 | INFO : Epoch #286\n",
            "2022-03-11 09:58:09,118 | INFO : \t train_acc: 99.792; train_loss: 0.032; test_loss: 30.471; lr: [0.0001]\n",
            "2022-03-11 09:58:09,118 | INFO : \t test_acc: 92.88 (best: 92.99 from #128)\n",
            "2022-03-11 09:59:00,062 | INFO : Epoch #287\n",
            "2022-03-11 09:59:00,062 | INFO : \t train_acc: 99.806; train_loss: 0.004; test_loss: 30.519; lr: [0.0001]\n",
            "2022-03-11 09:59:00,062 | INFO : \t test_acc: 92.88 (best: 92.99 from #128)\n",
            "2022-03-11 09:59:50,921 | INFO : Epoch #288\n",
            "2022-03-11 09:59:50,921 | INFO : \t train_acc: 99.828; train_loss: 0.154; test_loss: 30.419; lr: [0.0001]\n",
            "2022-03-11 09:59:50,921 | INFO : \t test_acc: 92.73 (best: 92.99 from #128)\n",
            "2022-03-11 10:00:41,872 | INFO : Epoch #289\n",
            "2022-03-11 10:00:41,873 | INFO : \t train_acc: 99.802; train_loss: 0.003; test_loss: 30.443; lr: [0.0001]\n",
            "2022-03-11 10:00:41,873 | INFO : \t test_acc: 92.84 (best: 92.99 from #128)\n",
            "2022-03-11 10:01:32,809 | INFO : Epoch #290\n",
            "2022-03-11 10:01:32,809 | INFO : \t train_acc: 99.772; train_loss: 0.011; test_loss: 30.997; lr: [0.0001]\n",
            "2022-03-11 10:01:32,810 | INFO : \t test_acc: 92.79 (best: 92.99 from #128)\n",
            "2022-03-11 10:02:23,711 | INFO : Epoch #291\n",
            "2022-03-11 10:02:23,712 | INFO : \t train_acc: 99.8; train_loss: 0.039; test_loss: 30.401; lr: [0.0001]\n",
            "2022-03-11 10:02:23,712 | INFO : \t test_acc: 92.79 (best: 92.99 from #128)\n",
            "2022-03-11 10:03:14,643 | INFO : Epoch #292\n",
            "2022-03-11 10:03:14,643 | INFO : \t train_acc: 99.8; train_loss: 0.001; test_loss: 30.308; lr: [0.0001]\n",
            "2022-03-11 10:03:14,643 | INFO : \t test_acc: 92.8 (best: 92.99 from #128)\n",
            "2022-03-11 10:04:05,534 | INFO : Epoch #293\n",
            "2022-03-11 10:04:05,534 | INFO : \t train_acc: 99.826; train_loss: 0.088; test_loss: 30.345; lr: [0.0001]\n",
            "2022-03-11 10:04:05,534 | INFO : \t test_acc: 92.83 (best: 92.99 from #128)\n",
            "2022-03-11 10:04:56,480 | INFO : Epoch #294\n",
            "2022-03-11 10:04:56,480 | INFO : \t train_acc: 99.786; train_loss: 0.009; test_loss: 30.183; lr: [0.0001]\n",
            "2022-03-11 10:04:56,480 | INFO : \t test_acc: 92.85 (best: 92.99 from #128)\n",
            "2022-03-11 10:05:47,395 | INFO : Epoch #295\n",
            "2022-03-11 10:05:47,395 | INFO : \t train_acc: 99.832; train_loss: 0.017; test_loss: 30.349; lr: [0.0001]\n",
            "2022-03-11 10:05:47,395 | INFO : \t test_acc: 92.92 (best: 92.99 from #128)\n",
            "2022-03-11 10:06:38,328 | INFO : Epoch #296\n",
            "2022-03-11 10:06:38,328 | INFO : \t train_acc: 99.802; train_loss: 0.065; test_loss: 30.333; lr: [0.0001]\n",
            "2022-03-11 10:06:38,328 | INFO : \t test_acc: 93.04 (best: 93.04 from #296)\n",
            "2022-03-11 10:07:29,237 | INFO : Epoch #297\n",
            "2022-03-11 10:07:29,237 | INFO : \t train_acc: 99.826; train_loss: 0.004; test_loss: 30.415; lr: [0.0001]\n",
            "2022-03-11 10:07:29,238 | INFO : \t test_acc: 92.87 (best: 93.04 from #296)\n",
            "2022-03-11 10:08:20,141 | INFO : Epoch #298\n",
            "2022-03-11 10:08:20,142 | INFO : \t train_acc: 99.81; train_loss: 0.001; test_loss: 31.036; lr: [0.0001]\n",
            "2022-03-11 10:08:20,142 | INFO : \t test_acc: 93.02 (best: 93.04 from #296)\n",
            "2022-03-11 10:09:11,072 | INFO : Epoch #299\n",
            "2022-03-11 10:09:11,073 | INFO : \t train_acc: 99.83; train_loss: 0.011; test_loss: 30.650; lr: [0.0001]\n",
            "2022-03-11 10:09:11,073 | INFO : \t test_acc: 92.91 (best: 93.04 from #296)\n",
            "2022-03-11 10:09:11,149 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet32_cifar10_baseline finetuned 18.1 --> 93.04.\n",
            "2022-03-11 10:09:11,149 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet32_cifar10_baseline pruned, grouped, and finetuned: 92.82 --> 18.1 --> 93.04.\n",
            "\n",
            "2022-03-11 10:09:11,153 | INFO : setting\n",
            "2022-03-11 10:09:11,155 | INFO : Saving setting file to /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet32_cifar10_demo_run/setting.json...\n",
            "2022-03-11 10:09:11,159 | INFO : Experiment resnet32_cifar10_demo (task: finetune) ends at 2022-03-11T10:09:11.150119; result: 92.82 --> 93.04.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python main.py \\\n",
        "--exp_desc resnet56_cifar10_demo  \\\n",
        "--setting_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/settings/resnet56_cifar10_reported.json \\\n",
        "--model_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet56_cifar10_baseline \\\n",
        "--snapshot_folder_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/snapshots/resnet56/ \\\n",
        "--output_folder_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet56_cifar10_demo_run \\\n",
        "--baseline True \\\n",
        "--task finetune"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8rSHiiFdDZw",
        "outputId": "5ad5a24f-3e3d-4906-9555-5d814e0114a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-03-11 10:16:08,058 | INFO : Experiment resnet56_cifar10_demo starts at 2022-03-11T10:16:08.054065.\n",
            "2022-03-11 10:16:08,058 | INFO : Parsed setting file from /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/settings/resnet56_cifar10_reported.json...\n",
            "2022-03-11 10:16:08,074 | INFO : ==> Preparing dataset: cifar10\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "2022-03-11 10:16:10,366 | INFO : ==> Building baseline model: /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet56_cifar10_baseline (dataset: cifar10)\n",
            "2022-03-11 10:16:15,250 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet56_cifar10_baseline loaded (<class 'models.resnet.CifarResNet'>)\n",
            "2022-03-11 10:16:16,612 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet56_cifar10_baseline <class 'models.resnet.CifarResNet'> has a baseline of 93.78.\n",
            "2022-03-11 10:16:16,635 | INFO : pruning_strategy: greedy (tickets magnitute increase); n_clusters: 8; eval_pruned_kernel_relationship: True; cost_balancer: 2; eval_kept_kernel_ratio: 0.2\n",
            "2022-03-11 10:18:41,050 | INFO : Layer 2-0-0; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 27, 'K-PCA': 9});\tSequence: {'K-PCA': 6, 'DBSCAN': 13});\n",
            "2022-03-11 10:19:59,927 | INFO : Layer 2-0-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 21, 'DBSCAN': 15});\tSequence: {'DBSCAN': 2, 'K-PCA': 6});\n",
            "2022-03-11 10:21:17,476 | INFO : Layer 2-1-0; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 20, 'K-PCA': 10, 'Spectral': 6});\tSequence: {'Spectral': 2, 'K-PCA': 2, 'DBSCAN': 8});\n",
            "2022-03-11 10:22:39,910 | INFO : Layer 2-1-2; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 20, 'K-PCA': 15, 'DBSCAN': 1});\tSequence: {'Spectral': 7, 'K-PCA': 4});\n",
            "2022-03-11 10:23:57,901 | INFO : Layer 2-2-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 18, 'Spectral': 16, 'DBSCAN': 2});\tSequence: {'K-PCA': 9, 'Spectral': 9});\n",
            "2022-03-11 10:25:16,690 | INFO : Layer 2-2-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 24, 'DBSCAN': 7, 'Spectral': 5});\tSequence: {'K-PCA': 14, 'DBSCAN': 3, 'Spectral': 3});\n",
            "2022-03-11 10:26:38,516 | INFO : Layer 2-3-0; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 20, 'K-PCA': 16});\tSequence: {'DBSCAN': 12, 'K-PCA': 10});\n",
            "2022-03-11 10:27:59,193 | INFO : Layer 2-3-2; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 19, 'K-PCA': 14, 'Spectral': 3});\tSequence: {'K-PCA': 4, 'DBSCAN': 8});\n",
            "2022-03-11 10:29:17,508 | INFO : Layer 2-4-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 27, 'DBSCAN': 6, 'Spectral': 3});\tSequence: {'DBSCAN': 1, 'K-PCA': 14, 'Spectral': 1});\n",
            "2022-03-11 10:30:38,417 | INFO : Layer 2-4-2; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 15, 'Spectral': 11, 'K-PCA': 10});\tSequence: {'Spectral': 6, 'K-PCA': 4, 'DBSCAN': 5});\n",
            "2022-03-11 10:31:57,398 | INFO : Layer 2-5-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 25, 'DBSCAN': 6, 'Spectral': 5});\tSequence: {'K-PCA': 6, 'Spectral': 1, 'DBSCAN': 3});\n",
            "2022-03-11 10:33:12,242 | INFO : Layer 2-5-2; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 23, 'K-PCA': 13});\tSequence: {'DBSCAN': 7, 'K-PCA': 4});\n",
            "2022-03-11 10:34:31,998 | INFO : Layer 2-6-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 33, 'Spectral': 3});\tSequence: {'K-PCA': 15, 'Spectral': 1});\n",
            "2022-03-11 10:35:52,818 | INFO : Layer 2-6-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 24, 'DBSCAN': 9, 'Spectral': 3});\tSequence: {'K-PCA': 16, 'DBSCAN': 2});\n",
            "2022-03-11 10:37:09,030 | INFO : Layer 2-7-0; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 25, 'K-PCA': 7, 'DBSCAN': 4});\tSequence: {'Spectral': 5, 'K-PCA': 1});\n",
            "2022-03-11 10:38:26,846 | INFO : Layer 2-7-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 26, 'Spectral': 8, 'DBSCAN': 2});\tSequence: {'K-PCA': 9, 'Spectral': 2, 'DBSCAN': 1});\n",
            "2022-03-11 10:39:46,633 | INFO : Layer 2-8-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 26, 'DBSCAN': 6, 'Spectral': 4});\tSequence: {'K-PCA': 15, 'Spectral': 3, 'DBSCAN': 2});\n",
            "2022-03-11 10:41:08,777 | INFO : Layer 2-8-2; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 23, 'DBSCAN': 11, 'K-PCA': 2});\tSequence: {'DBSCAN': 3, 'Spectral': 5, 'K-PCA': 1});\n",
            "2022-03-11 10:43:05,301 | INFO : Layer 3-0-0; Shape (32, 16, 3, 3) -> (32, 144); Method: K-PCA; Occurrence: ({'K-PCA': 17, 'Spectral': 12, 'DBSCAN': 7});\tSequence: {'DBSCAN': 1, 'Spectral': 3, 'K-PCA': 15});\n",
            "2022-03-11 10:45:05,800 | INFO : Layer 3-0-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 35, 'DBSCAN': 1});\tSequence: {'K-PCA': 34});\n",
            "2022-03-11 10:47:06,104 | INFO : Layer 3-1-0; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 25, 'K-PCA': 6, 'DBSCAN': 5});\tSequence: {'Spectral': 14, 'DBSCAN': 2, 'K-PCA': 2});\n",
            "2022-03-11 10:49:27,518 | INFO : Layer 3-1-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 22, 'DBSCAN': 7, 'K-PCA': 7});\tSequence: {'DBSCAN': 2, 'Spectral': 9, 'K-PCA': 2});\n",
            "2022-03-11 10:51:22,908 | INFO : Layer 3-2-0; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 22, 'K-PCA': 13, 'DBSCAN': 1});\tSequence: {'Spectral': 6, 'K-PCA': 4});\n",
            "2022-03-11 10:53:33,160 | INFO : Layer 3-2-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 22, 'Spectral': 7, 'DBSCAN': 7});\tSequence: {'Spectral': 1, 'K-PCA': 13, 'DBSCAN': 3});\n",
            "2022-03-11 10:55:34,107 | INFO : Layer 3-3-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 31, 'DBSCAN': 5});\tSequence: {'K-PCA': 19, 'DBSCAN': 1});\n",
            "2022-03-11 10:58:37,839 | INFO : Layer 3-3-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 28, 'K-PCA': 4, 'DBSCAN': 4});\tSequence: {'Spectral': 6, 'K-PCA': 1});\n",
            "2022-03-11 11:00:54,266 | INFO : Layer 3-4-0; Shape (32, 32, 3, 3) -> (32, 288); Method: DBSCAN; Occurrence: ({'DBSCAN': 21, 'Spectral': 14, 'K-PCA': 1});\tSequence: {'DBSCAN': 4, 'Spectral': 3});\n",
            "2022-03-11 11:02:55,901 | INFO : Layer 3-4-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 29, 'DBSCAN': 7});\tSequence: {'K-PCA': 10, 'DBSCAN': 2});\n",
            "2022-03-11 11:04:54,128 | INFO : Layer 3-5-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 20, 'Spectral': 16});\tSequence: {'K-PCA': 6, 'Spectral': 6});\n",
            "2022-03-11 11:06:49,345 | INFO : Layer 3-5-2; Shape (32, 32, 3, 3) -> (32, 288); Method: DBSCAN; Occurrence: ({'DBSCAN': 26, 'K-PCA': 7, 'Spectral': 3});\tSequence: {'DBSCAN': 6, 'K-PCA': 2, 'Spectral': 1});\n",
            "2022-03-11 11:09:23,981 | INFO : Layer 3-6-0; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 25, 'K-PCA': 9, 'DBSCAN': 2});\tSequence: {'K-PCA': 5, 'Spectral': 16, 'DBSCAN': 1});\n",
            "2022-03-11 11:11:47,745 | INFO : Layer 3-6-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 21, 'DBSCAN': 8, 'Spectral': 7});\tSequence: {'K-PCA': 11, 'DBSCAN': 2, 'Spectral': 3});\n",
            "2022-03-11 11:14:46,299 | INFO : Layer 3-7-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 20, 'DBSCAN': 13, 'Spectral': 3});\tSequence: {'K-PCA': 6, 'DBSCAN': 3, 'Spectral': 1});\n",
            "2022-03-11 11:16:45,482 | INFO : Layer 3-7-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 18, 'K-PCA': 15, 'DBSCAN': 3});\tSequence: {'DBSCAN': 2, 'K-PCA': 6, 'Spectral': 5});\n",
            "2022-03-11 11:18:42,140 | INFO : Layer 3-8-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 21, 'DBSCAN': 8, 'Spectral': 7});\tSequence: {'K-PCA': 7, 'DBSCAN': 2});\n",
            "2022-03-11 11:21:00,582 | INFO : Layer 3-8-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 34, 'Spectral': 1, 'DBSCAN': 1});\tSequence: {'K-PCA': 21});\n",
            "2022-03-11 11:26:39,742 | INFO : Layer 4-0-0; Shape (64, 32, 3, 3) -> (64, 288); Method: DBSCAN; Occurrence: ({'DBSCAN': 22, 'K-PCA': 14});\tSequence: {'DBSCAN': 11, 'K-PCA': 6});\n",
            "2022-03-11 11:30:25,728 | INFO : Layer 4-0-2; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 13, 'DBSCAN': 12, 'K-PCA': 11});\tSequence: {'DBSCAN': 4, 'K-PCA': 5, 'Spectral': 6});\n",
            "2022-03-11 11:34:12,624 | INFO : Layer 4-1-0; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 34, 'DBSCAN': 2});\tSequence: {'DBSCAN': 1, 'K-PCA': 33});\n",
            "2022-03-11 11:38:14,429 | INFO : Layer 4-1-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 26, 'DBSCAN': 9, 'Spectral': 1});\tSequence: {'K-PCA': 3, 'DBSCAN': 1});\n",
            "2022-03-11 11:42:41,626 | INFO : Layer 4-2-0; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 18, 'DBSCAN': 15, 'K-PCA': 3});\tSequence: {'DBSCAN': 5, 'Spectral': 5});\n",
            "2022-03-11 11:46:20,891 | INFO : Layer 4-2-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 22, 'DBSCAN': 12, 'Spectral': 2});\tSequence: {'DBSCAN': 4, 'K-PCA': 8});\n",
            "2022-03-11 11:51:40,684 | INFO : Layer 4-3-0; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 21, 'K-PCA': 14, 'Spectral': 1});\tSequence: {'K-PCA': 2, 'DBSCAN': 5});\n",
            "2022-03-11 11:55:21,183 | INFO : Layer 4-3-2; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 33, 'K-PCA': 3});\tSequence: {'K-PCA': 1, 'DBSCAN': 31});\n",
            "2022-03-11 12:00:47,571 | INFO : Layer 4-4-0; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 16, 'K-PCA': 13, 'DBSCAN': 7});\tSequence: {'DBSCAN': 2, 'K-PCA': 3, 'Spectral': 4});\n",
            "2022-03-11 12:05:17,657 | INFO : Layer 4-4-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 19, 'Spectral': 15, 'DBSCAN': 2});\tSequence: {'Spectral': 3, 'K-PCA': 9});\n",
            "2022-03-11 12:12:15,333 | INFO : Layer 4-5-0; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 16, 'K-PCA': 12, 'Spectral': 8});\tSequence: {'DBSCAN': 6, 'K-PCA': 2, 'Spectral': 3});\n",
            "2022-03-11 12:16:08,940 | INFO : Layer 4-5-2; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 30, 'DBSCAN': 5, 'K-PCA': 1});\tSequence: {'Spectral': 11});\n",
            "2022-03-11 12:19:53,856 | INFO : Layer 4-6-0; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 14, 'DBSCAN': 14, 'K-PCA': 8});\tSequence: {'Spectral': 10, 'DBSCAN': 6, 'K-PCA': 7});\n",
            "2022-03-11 12:25:55,935 | INFO : Layer 4-6-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 32, 'DBSCAN': 4});\tSequence: {'DBSCAN': 1, 'K-PCA': 29});\n",
            "2022-03-11 12:30:42,211 | INFO : Layer 4-7-0; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 19, 'Spectral': 17});\tSequence: {'K-PCA': 5, 'Spectral': 5});\n",
            "2022-03-11 12:34:40,038 | INFO : Layer 4-7-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 19, 'Spectral': 14, 'DBSCAN': 3});\tSequence: {'Spectral': 12, 'K-PCA': 9});\n",
            "2022-03-11 12:44:46,378 | INFO : Layer 4-8-0; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 26, 'K-PCA': 6, 'DBSCAN': 4});\tSequence: {'DBSCAN': 3, 'Spectral': 21, 'K-PCA': 2});\n",
            "2022-03-11 12:49:21,291 | INFO : Layer 4-8-2; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 23, 'K-PCA': 13});\tSequence: {'DBSCAN': 13, 'K-PCA': 9});\n",
            "2022-03-11 12:49:23,092 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet56_cifar10_baseline <class 'prune_cifar.ModifiedResNet'> now pruned.\n",
            "GroupResNet pruning rate: 0.4375; kernel_gcd: 16\n",
            "2022-03-11 12:49:23,174 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet56_cifar10_baseline <class 'group_cifar.GroupResNet'> now grouped.\n",
            "2022-03-11 12:49:23,176 | INFO : ==> Starting task: finetune for 300 epochs; multi_prune_flag: False)\n",
            "2022-03-11 12:49:28,141 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet56_cifar10_baseline (type: <class 'group_cifar.GroupResNet'>) pruned and grouped (grouped baseline: 19.57); now to finetune with 300 epochs.\n",
            "\n",
            "2022-03-11 12:49:33,064 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet56_cifar10_baseline has a baseline of 19.57.\n",
            "2022-03-11 12:51:02,819 | INFO : Epoch #0\n",
            "2022-03-11 12:51:02,819 | INFO : \t train_acc: 82.134; train_loss: 0.340; test_loss: 72.430; lr: [0.01]\n",
            "2022-03-11 12:51:02,819 | INFO : \t test_acc: 78.02 (best: 78.02 from #0)\n",
            "2022-03-11 12:52:33,037 | INFO : Epoch #1\n",
            "2022-03-11 12:52:33,037 | INFO : \t train_acc: 87.084; train_loss: 0.481; test_loss: 51.347; lr: [0.01]\n",
            "2022-03-11 12:52:33,037 | INFO : \t test_acc: 83.55 (best: 83.55 from #1)\n",
            "2022-03-11 12:54:03,241 | INFO : Epoch #2\n",
            "2022-03-11 12:54:03,242 | INFO : \t train_acc: 88.494; train_loss: 0.716; test_loss: 45.128; lr: [0.01]\n",
            "2022-03-11 12:54:03,242 | INFO : \t test_acc: 85.12 (best: 85.12 from #2)\n",
            "2022-03-11 12:55:33,479 | INFO : Epoch #3\n",
            "2022-03-11 12:55:33,479 | INFO : \t train_acc: 89.73; train_loss: 0.607; test_loss: 47.071; lr: [0.01]\n",
            "2022-03-11 12:55:33,479 | INFO : \t test_acc: 84.85 (best: 85.12 from #2)\n",
            "2022-03-11 12:57:03,651 | INFO : Epoch #4\n",
            "2022-03-11 12:57:03,651 | INFO : \t train_acc: 90.144; train_loss: 0.387; test_loss: 36.685; lr: [0.01]\n",
            "2022-03-11 12:57:03,651 | INFO : \t test_acc: 88.01 (best: 88.01 from #4)\n",
            "2022-03-11 12:58:33,759 | INFO : Epoch #5\n",
            "2022-03-11 12:58:33,759 | INFO : \t train_acc: 90.682; train_loss: 0.547; test_loss: 40.860; lr: [0.01]\n",
            "2022-03-11 12:58:33,760 | INFO : \t test_acc: 87.1 (best: 88.01 from #4)\n",
            "2022-03-11 13:00:03,844 | INFO : Epoch #6\n",
            "2022-03-11 13:00:03,844 | INFO : \t train_acc: 91.03; train_loss: 0.288; test_loss: 42.801; lr: [0.01]\n",
            "2022-03-11 13:00:03,845 | INFO : \t test_acc: 86.82 (best: 88.01 from #4)\n",
            "2022-03-11 13:01:33,916 | INFO : Epoch #7\n",
            "2022-03-11 13:01:33,917 | INFO : \t train_acc: 91.328; train_loss: 0.102; test_loss: 37.617; lr: [0.01]\n",
            "2022-03-11 13:01:33,917 | INFO : \t test_acc: 87.93 (best: 88.01 from #4)\n",
            "2022-03-11 13:03:04,054 | INFO : Epoch #8\n",
            "2022-03-11 13:03:04,055 | INFO : \t train_acc: 91.874; train_loss: 0.161; test_loss: 44.512; lr: [0.01]\n",
            "2022-03-11 13:03:04,055 | INFO : \t test_acc: 85.94 (best: 88.01 from #4)\n",
            "2022-03-11 13:04:34,159 | INFO : Epoch #9\n",
            "2022-03-11 13:04:34,159 | INFO : \t train_acc: 91.906; train_loss: 0.276; test_loss: 39.053; lr: [0.01]\n",
            "2022-03-11 13:04:34,160 | INFO : \t test_acc: 87.33 (best: 88.01 from #4)\n",
            "2022-03-11 13:06:04,417 | INFO : Epoch #10\n",
            "2022-03-11 13:06:04,418 | INFO : \t train_acc: 92.078; train_loss: 0.405; test_loss: 38.887; lr: [0.01]\n",
            "2022-03-11 13:06:04,418 | INFO : \t test_acc: 87.4 (best: 88.01 from #4)\n",
            "2022-03-11 13:07:34,527 | INFO : Epoch #11\n",
            "2022-03-11 13:07:34,528 | INFO : \t train_acc: 92.118; train_loss: 0.345; test_loss: 35.660; lr: [0.01]\n",
            "2022-03-11 13:07:34,528 | INFO : \t test_acc: 88.6 (best: 88.6 from #11)\n",
            "2022-03-11 13:09:04,691 | INFO : Epoch #12\n",
            "2022-03-11 13:09:04,691 | INFO : \t train_acc: 92.546; train_loss: 0.314; test_loss: 35.845; lr: [0.01]\n",
            "2022-03-11 13:09:04,691 | INFO : \t test_acc: 88.85 (best: 88.85 from #12)\n",
            "2022-03-11 13:10:34,890 | INFO : Epoch #13\n",
            "2022-03-11 13:10:34,891 | INFO : \t train_acc: 92.424; train_loss: 0.489; test_loss: 33.792; lr: [0.01]\n",
            "2022-03-11 13:10:34,891 | INFO : \t test_acc: 89.12 (best: 89.12 from #13)\n",
            "2022-03-11 13:12:05,078 | INFO : Epoch #14\n",
            "2022-03-11 13:12:05,079 | INFO : \t train_acc: 92.554; train_loss: 0.062; test_loss: 36.318; lr: [0.01]\n",
            "2022-03-11 13:12:05,079 | INFO : \t test_acc: 88.44 (best: 89.12 from #13)\n",
            "2022-03-11 13:13:35,197 | INFO : Epoch #15\n",
            "2022-03-11 13:13:35,197 | INFO : \t train_acc: 92.766; train_loss: 0.352; test_loss: 32.203; lr: [0.01]\n",
            "2022-03-11 13:13:35,197 | INFO : \t test_acc: 89.57 (best: 89.57 from #15)\n",
            "2022-03-11 13:15:05,380 | INFO : Epoch #16\n",
            "2022-03-11 13:15:05,381 | INFO : \t train_acc: 92.936; train_loss: 0.198; test_loss: 41.464; lr: [0.01]\n",
            "2022-03-11 13:15:05,381 | INFO : \t test_acc: 87.61 (best: 89.57 from #15)\n",
            "2022-03-11 13:16:35,512 | INFO : Epoch #17\n",
            "2022-03-11 13:16:35,512 | INFO : \t train_acc: 92.692; train_loss: 0.105; test_loss: 39.030; lr: [0.01]\n",
            "2022-03-11 13:16:35,512 | INFO : \t test_acc: 87.78 (best: 89.57 from #15)\n",
            "2022-03-11 13:18:05,655 | INFO : Epoch #18\n",
            "2022-03-11 13:18:05,656 | INFO : \t train_acc: 93.028; train_loss: 0.099; test_loss: 37.737; lr: [0.01]\n",
            "2022-03-11 13:18:05,656 | INFO : \t test_acc: 88.37 (best: 89.57 from #15)\n",
            "2022-03-11 13:19:35,750 | INFO : Epoch #19\n",
            "2022-03-11 13:19:35,751 | INFO : \t train_acc: 92.98; train_loss: 0.351; test_loss: 36.864; lr: [0.01]\n",
            "2022-03-11 13:19:35,751 | INFO : \t test_acc: 88.44 (best: 89.57 from #15)\n",
            "2022-03-11 13:21:05,992 | INFO : Epoch #20\n",
            "2022-03-11 13:21:05,992 | INFO : \t train_acc: 93.11; train_loss: 0.220; test_loss: 40.199; lr: [0.01]\n",
            "2022-03-11 13:21:05,992 | INFO : \t test_acc: 87.51 (best: 89.57 from #15)\n",
            "2022-03-11 13:22:36,149 | INFO : Epoch #21\n",
            "2022-03-11 13:22:36,150 | INFO : \t train_acc: 93.102; train_loss: 0.242; test_loss: 33.278; lr: [0.01]\n",
            "2022-03-11 13:22:36,150 | INFO : \t test_acc: 88.9 (best: 89.57 from #15)\n",
            "2022-03-11 13:24:06,248 | INFO : Epoch #22\n",
            "2022-03-11 13:24:06,248 | INFO : \t train_acc: 93.18; train_loss: 0.258; test_loss: 37.855; lr: [0.01]\n",
            "2022-03-11 13:24:06,248 | INFO : \t test_acc: 88.4 (best: 89.57 from #15)\n",
            "2022-03-11 13:25:36,364 | INFO : Epoch #23\n",
            "2022-03-11 13:25:36,364 | INFO : \t train_acc: 93.308; train_loss: 0.627; test_loss: 34.229; lr: [0.01]\n",
            "2022-03-11 13:25:36,364 | INFO : \t test_acc: 89.07 (best: 89.57 from #15)\n",
            "2022-03-11 13:27:06,523 | INFO : Epoch #24\n",
            "2022-03-11 13:27:06,523 | INFO : \t train_acc: 93.028; train_loss: 0.338; test_loss: 35.802; lr: [0.01]\n",
            "2022-03-11 13:27:06,524 | INFO : \t test_acc: 88.7 (best: 89.57 from #15)\n",
            "2022-03-11 13:28:36,656 | INFO : Epoch #25\n",
            "2022-03-11 13:28:36,657 | INFO : \t train_acc: 93.324; train_loss: 0.136; test_loss: 40.554; lr: [0.01]\n",
            "2022-03-11 13:28:36,657 | INFO : \t test_acc: 87.47 (best: 89.57 from #15)\n",
            "2022-03-11 13:30:06,737 | INFO : Epoch #26\n",
            "2022-03-11 13:30:06,738 | INFO : \t train_acc: 93.378; train_loss: 0.046; test_loss: 36.534; lr: [0.01]\n",
            "2022-03-11 13:30:06,738 | INFO : \t test_acc: 88.09 (best: 89.57 from #15)\n",
            "2022-03-11 13:31:36,824 | INFO : Epoch #27\n",
            "2022-03-11 13:31:36,825 | INFO : \t train_acc: 93.512; train_loss: 0.218; test_loss: 39.292; lr: [0.01]\n",
            "2022-03-11 13:31:36,825 | INFO : \t test_acc: 88.31 (best: 89.57 from #15)\n",
            "2022-03-11 13:33:06,898 | INFO : Epoch #28\n",
            "2022-03-11 13:33:06,898 | INFO : \t train_acc: 93.826; train_loss: 0.717; test_loss: 41.290; lr: [0.01]\n",
            "2022-03-11 13:33:06,898 | INFO : \t test_acc: 87.39 (best: 89.57 from #15)\n",
            "2022-03-11 13:34:37,035 | INFO : Epoch #29\n",
            "2022-03-11 13:34:37,036 | INFO : \t train_acc: 93.324; train_loss: 0.127; test_loss: 35.555; lr: [0.01]\n",
            "2022-03-11 13:34:37,036 | INFO : \t test_acc: 88.98 (best: 89.57 from #15)\n",
            "2022-03-11 13:36:07,180 | INFO : Epoch #30\n",
            "2022-03-11 13:36:07,180 | INFO : \t train_acc: 93.426; train_loss: 0.054; test_loss: 43.555; lr: [0.01]\n",
            "2022-03-11 13:36:07,181 | INFO : \t test_acc: 86.82 (best: 89.57 from #15)\n",
            "2022-03-11 13:37:37,237 | INFO : Epoch #31\n",
            "2022-03-11 13:37:37,238 | INFO : \t train_acc: 93.532; train_loss: 0.265; test_loss: 39.689; lr: [0.01]\n",
            "2022-03-11 13:37:37,238 | INFO : \t test_acc: 87.45 (best: 89.57 from #15)\n",
            "2022-03-11 13:39:07,310 | INFO : Epoch #32\n",
            "2022-03-11 13:39:07,311 | INFO : \t train_acc: 93.546; train_loss: 0.262; test_loss: 35.720; lr: [0.01]\n",
            "2022-03-11 13:39:07,311 | INFO : \t test_acc: 89.01 (best: 89.57 from #15)\n",
            "2022-03-11 13:40:37,361 | INFO : Epoch #33\n",
            "2022-03-11 13:40:37,361 | INFO : \t train_acc: 93.53; train_loss: 0.064; test_loss: 31.504; lr: [0.01]\n",
            "2022-03-11 13:40:37,361 | INFO : \t test_acc: 90.1 (best: 90.1 from #33)\n",
            "2022-03-11 13:42:07,526 | INFO : Epoch #34\n",
            "2022-03-11 13:42:07,527 | INFO : \t train_acc: 93.742; train_loss: 0.193; test_loss: 35.212; lr: [0.01]\n",
            "2022-03-11 13:42:07,527 | INFO : \t test_acc: 89.01 (best: 90.1 from #33)\n",
            "2022-03-11 13:43:37,624 | INFO : Epoch #35\n",
            "2022-03-11 13:43:37,624 | INFO : \t train_acc: 93.798; train_loss: 0.214; test_loss: 37.224; lr: [0.01]\n",
            "2022-03-11 13:43:37,624 | INFO : \t test_acc: 88.35 (best: 90.1 from #33)\n",
            "2022-03-11 13:45:07,738 | INFO : Epoch #36\n",
            "2022-03-11 13:45:07,739 | INFO : \t train_acc: 93.84; train_loss: 0.472; test_loss: 37.355; lr: [0.01]\n",
            "2022-03-11 13:45:07,739 | INFO : \t test_acc: 88.31 (best: 90.1 from #33)\n",
            "2022-03-11 13:46:37,807 | INFO : Epoch #37\n",
            "2022-03-11 13:46:37,808 | INFO : \t train_acc: 93.678; train_loss: 0.027; test_loss: 37.030; lr: [0.01]\n",
            "2022-03-11 13:46:37,808 | INFO : \t test_acc: 88.36 (best: 90.1 from #33)\n",
            "2022-03-11 13:48:07,845 | INFO : Epoch #38\n",
            "2022-03-11 13:48:07,845 | INFO : \t train_acc: 93.784; train_loss: 0.101; test_loss: 41.255; lr: [0.01]\n",
            "2022-03-11 13:48:07,845 | INFO : \t test_acc: 87.44 (best: 90.1 from #33)\n",
            "2022-03-11 13:49:37,936 | INFO : Epoch #39\n",
            "2022-03-11 13:49:37,937 | INFO : \t train_acc: 93.99; train_loss: 0.111; test_loss: 38.473; lr: [0.01]\n",
            "2022-03-11 13:49:37,937 | INFO : \t test_acc: 88.28 (best: 90.1 from #33)\n",
            "2022-03-11 13:51:08,068 | INFO : Epoch #40\n",
            "2022-03-11 13:51:08,069 | INFO : \t train_acc: 93.888; train_loss: 0.327; test_loss: 39.005; lr: [0.01]\n",
            "2022-03-11 13:51:08,069 | INFO : \t test_acc: 88.23 (best: 90.1 from #33)\n",
            "2022-03-11 13:52:38,166 | INFO : Epoch #41\n",
            "2022-03-11 13:52:38,167 | INFO : \t train_acc: 93.632; train_loss: 0.417; test_loss: 38.496; lr: [0.01]\n",
            "2022-03-11 13:52:38,167 | INFO : \t test_acc: 88.77 (best: 90.1 from #33)\n",
            "2022-03-11 13:54:08,269 | INFO : Epoch #42\n",
            "2022-03-11 13:54:08,270 | INFO : \t train_acc: 93.788; train_loss: 0.250; test_loss: 35.697; lr: [0.01]\n",
            "2022-03-11 13:54:08,270 | INFO : \t test_acc: 89.2 (best: 90.1 from #33)\n",
            "2022-03-11 13:55:38,369 | INFO : Epoch #43\n",
            "2022-03-11 13:55:38,369 | INFO : \t train_acc: 93.656; train_loss: 0.107; test_loss: 41.543; lr: [0.01]\n",
            "2022-03-11 13:55:38,369 | INFO : \t test_acc: 87.45 (best: 90.1 from #33)\n",
            "2022-03-11 13:57:08,467 | INFO : Epoch #44\n",
            "2022-03-11 13:57:08,468 | INFO : \t train_acc: 93.9; train_loss: 0.089; test_loss: 37.677; lr: [0.01]\n",
            "2022-03-11 13:57:08,468 | INFO : \t test_acc: 88.39 (best: 90.1 from #33)\n",
            "2022-03-11 13:58:38,563 | INFO : Epoch #45\n",
            "2022-03-11 13:58:38,563 | INFO : \t train_acc: 93.788; train_loss: 0.096; test_loss: 38.406; lr: [0.01]\n",
            "2022-03-11 13:58:38,563 | INFO : \t test_acc: 88.27 (best: 90.1 from #33)\n",
            "2022-03-11 14:00:08,607 | INFO : Epoch #46\n",
            "2022-03-11 14:00:08,607 | INFO : \t train_acc: 93.878; train_loss: 0.215; test_loss: 40.050; lr: [0.01]\n",
            "2022-03-11 14:00:08,607 | INFO : \t test_acc: 88.28 (best: 90.1 from #33)\n",
            "2022-03-11 14:01:38,668 | INFO : Epoch #47\n",
            "2022-03-11 14:01:38,669 | INFO : \t train_acc: 93.92; train_loss: 0.412; test_loss: 35.370; lr: [0.01]\n",
            "2022-03-11 14:01:38,669 | INFO : \t test_acc: 89.27 (best: 90.1 from #33)\n",
            "2022-03-11 14:03:08,761 | INFO : Epoch #48\n",
            "2022-03-11 14:03:08,762 | INFO : \t train_acc: 93.776; train_loss: 0.914; test_loss: 38.184; lr: [0.01]\n",
            "2022-03-11 14:03:08,762 | INFO : \t test_acc: 87.84 (best: 90.1 from #33)\n",
            "2022-03-11 14:04:38,832 | INFO : Epoch #49\n",
            "2022-03-11 14:04:38,832 | INFO : \t train_acc: 93.756; train_loss: 0.094; test_loss: 34.255; lr: [0.01]\n",
            "2022-03-11 14:04:38,832 | INFO : \t test_acc: 89.11 (best: 90.1 from #33)\n",
            "2022-03-11 14:06:09,058 | INFO : Epoch #50\n",
            "2022-03-11 14:06:09,059 | INFO : \t train_acc: 93.996; train_loss: 0.234; test_loss: 41.223; lr: [0.01]\n",
            "2022-03-11 14:06:09,059 | INFO : \t test_acc: 87.51 (best: 90.1 from #33)\n",
            "2022-03-11 14:07:39,224 | INFO : Epoch #51\n",
            "2022-03-11 14:07:39,225 | INFO : \t train_acc: 93.964; train_loss: 0.540; test_loss: 34.460; lr: [0.01]\n",
            "2022-03-11 14:07:39,225 | INFO : \t test_acc: 89.49 (best: 90.1 from #33)\n",
            "2022-03-11 14:09:09,314 | INFO : Epoch #52\n",
            "2022-03-11 14:09:09,314 | INFO : \t train_acc: 94.128; train_loss: 0.394; test_loss: 41.344; lr: [0.01]\n",
            "2022-03-11 14:09:09,314 | INFO : \t test_acc: 87.31 (best: 90.1 from #33)\n",
            "2022-03-11 14:10:39,418 | INFO : Epoch #53\n",
            "2022-03-11 14:10:39,419 | INFO : \t train_acc: 94.048; train_loss: 0.077; test_loss: 33.336; lr: [0.01]\n",
            "2022-03-11 14:10:39,419 | INFO : \t test_acc: 89.71 (best: 90.1 from #33)\n",
            "2022-03-11 14:12:09,522 | INFO : Epoch #54\n",
            "2022-03-11 14:12:09,522 | INFO : \t train_acc: 93.896; train_loss: 0.064; test_loss: 37.267; lr: [0.01]\n",
            "2022-03-11 14:12:09,523 | INFO : \t test_acc: 88.7 (best: 90.1 from #33)\n",
            "2022-03-11 14:13:39,613 | INFO : Epoch #55\n",
            "2022-03-11 14:13:39,614 | INFO : \t train_acc: 93.828; train_loss: 0.040; test_loss: 32.715; lr: [0.01]\n",
            "2022-03-11 14:13:39,614 | INFO : \t test_acc: 89.7 (best: 90.1 from #33)\n",
            "2022-03-11 14:15:09,722 | INFO : Epoch #56\n",
            "2022-03-11 14:15:09,722 | INFO : \t train_acc: 93.948; train_loss: 0.153; test_loss: 34.566; lr: [0.01]\n",
            "2022-03-11 14:15:09,722 | INFO : \t test_acc: 89.41 (best: 90.1 from #33)\n",
            "2022-03-11 14:16:39,814 | INFO : Epoch #57\n",
            "2022-03-11 14:16:39,814 | INFO : \t train_acc: 94.066; train_loss: 0.374; test_loss: 41.196; lr: [0.01]\n",
            "2022-03-11 14:16:39,814 | INFO : \t test_acc: 87.45 (best: 90.1 from #33)\n",
            "2022-03-11 14:18:09,942 | INFO : Epoch #58\n",
            "2022-03-11 14:18:09,942 | INFO : \t train_acc: 94.15; train_loss: 0.244; test_loss: 38.462; lr: [0.01]\n",
            "2022-03-11 14:18:09,943 | INFO : \t test_acc: 88.38 (best: 90.1 from #33)\n",
            "2022-03-11 14:19:40,039 | INFO : Epoch #59\n",
            "2022-03-11 14:19:40,040 | INFO : \t train_acc: 94.052; train_loss: 0.202; test_loss: 38.636; lr: [0.01]\n",
            "2022-03-11 14:19:40,040 | INFO : \t test_acc: 87.94 (best: 90.1 from #33)\n",
            "2022-03-11 14:21:10,167 | INFO : Epoch #60\n",
            "2022-03-11 14:21:10,168 | INFO : \t train_acc: 94.112; train_loss: 0.448; test_loss: 34.609; lr: [0.01]\n",
            "2022-03-11 14:21:10,168 | INFO : \t test_acc: 89.29 (best: 90.1 from #33)\n",
            "2022-03-11 14:22:40,283 | INFO : Epoch #61\n",
            "2022-03-11 14:22:40,283 | INFO : \t train_acc: 94.058; train_loss: 0.385; test_loss: 37.389; lr: [0.01]\n",
            "2022-03-11 14:22:40,284 | INFO : \t test_acc: 88.36 (best: 90.1 from #33)\n",
            "2022-03-11 14:24:10,403 | INFO : Epoch #62\n",
            "2022-03-11 14:24:10,403 | INFO : \t train_acc: 93.948; train_loss: 0.306; test_loss: 40.483; lr: [0.01]\n",
            "2022-03-11 14:24:10,403 | INFO : \t test_acc: 87.64 (best: 90.1 from #33)\n",
            "2022-03-11 14:25:40,138 | INFO : Epoch #63\n",
            "2022-03-11 14:25:40,138 | INFO : \t train_acc: 93.994; train_loss: 0.530; test_loss: 41.780; lr: [0.01]\n",
            "2022-03-11 14:25:40,139 | INFO : \t test_acc: 87.79 (best: 90.1 from #33)\n",
            "2022-03-11 14:27:09,718 | INFO : Epoch #64\n",
            "2022-03-11 14:27:09,719 | INFO : \t train_acc: 93.886; train_loss: 0.303; test_loss: 34.415; lr: [0.01]\n",
            "2022-03-11 14:27:09,719 | INFO : \t test_acc: 89.11 (best: 90.1 from #33)\n",
            "2022-03-11 14:28:39,432 | INFO : Epoch #65\n",
            "2022-03-11 14:28:39,433 | INFO : \t train_acc: 94.094; train_loss: 0.355; test_loss: 41.759; lr: [0.01]\n",
            "2022-03-11 14:28:39,433 | INFO : \t test_acc: 87.45 (best: 90.1 from #33)\n",
            "2022-03-11 14:30:09,199 | INFO : Epoch #66\n",
            "2022-03-11 14:30:09,200 | INFO : \t train_acc: 94.31; train_loss: 0.254; test_loss: 35.085; lr: [0.01]\n",
            "2022-03-11 14:30:09,200 | INFO : \t test_acc: 89.17 (best: 90.1 from #33)\n",
            "2022-03-11 14:31:38,933 | INFO : Epoch #67\n",
            "2022-03-11 14:31:38,933 | INFO : \t train_acc: 94.078; train_loss: 0.315; test_loss: 44.543; lr: [0.01]\n",
            "2022-03-11 14:31:38,934 | INFO : \t test_acc: 87.03 (best: 90.1 from #33)\n",
            "2022-03-11 14:33:08,712 | INFO : Epoch #68\n",
            "2022-03-11 14:33:08,713 | INFO : \t train_acc: 93.978; train_loss: 0.066; test_loss: 35.713; lr: [0.01]\n",
            "2022-03-11 14:33:08,713 | INFO : \t test_acc: 88.88 (best: 90.1 from #33)\n",
            "2022-03-11 14:34:38,486 | INFO : Epoch #69\n",
            "2022-03-11 14:34:38,486 | INFO : \t train_acc: 94.052; train_loss: 0.106; test_loss: 40.255; lr: [0.01]\n",
            "2022-03-11 14:34:38,486 | INFO : \t test_acc: 86.99 (best: 90.1 from #33)\n",
            "2022-03-11 14:36:08,249 | INFO : Epoch #70\n",
            "2022-03-11 14:36:08,249 | INFO : \t train_acc: 94.16; train_loss: 0.607; test_loss: 38.244; lr: [0.01]\n",
            "2022-03-11 14:36:08,250 | INFO : \t test_acc: 88.12 (best: 90.1 from #33)\n",
            "2022-03-11 14:37:38,014 | INFO : Epoch #71\n",
            "2022-03-11 14:37:38,014 | INFO : \t train_acc: 94.028; train_loss: 0.158; test_loss: 34.236; lr: [0.01]\n",
            "2022-03-11 14:37:38,014 | INFO : \t test_acc: 89.53 (best: 90.1 from #33)\n",
            "2022-03-11 14:39:07,792 | INFO : Epoch #72\n",
            "2022-03-11 14:39:07,793 | INFO : \t train_acc: 94.29; train_loss: 0.188; test_loss: 37.288; lr: [0.01]\n",
            "2022-03-11 14:39:07,793 | INFO : \t test_acc: 88.74 (best: 90.1 from #33)\n",
            "2022-03-11 14:40:37,546 | INFO : Epoch #73\n",
            "2022-03-11 14:40:37,547 | INFO : \t train_acc: 94.11; train_loss: 0.134; test_loss: 36.035; lr: [0.01]\n",
            "2022-03-11 14:40:37,547 | INFO : \t test_acc: 88.77 (best: 90.1 from #33)\n",
            "2022-03-11 14:42:07,308 | INFO : Epoch #74\n",
            "2022-03-11 14:42:07,309 | INFO : \t train_acc: 93.878; train_loss: 0.153; test_loss: 34.900; lr: [0.01]\n",
            "2022-03-11 14:42:07,309 | INFO : \t test_acc: 89.25 (best: 90.1 from #33)\n",
            "2022-03-11 14:43:37,022 | INFO : Epoch #75\n",
            "2022-03-11 14:43:37,022 | INFO : \t train_acc: 94.032; train_loss: 0.246; test_loss: 34.876; lr: [0.01]\n",
            "2022-03-11 14:43:37,022 | INFO : \t test_acc: 89.39 (best: 90.1 from #33)\n",
            "2022-03-11 14:45:06,818 | INFO : Epoch #76\n",
            "2022-03-11 14:45:06,818 | INFO : \t train_acc: 94.22; train_loss: 0.044; test_loss: 41.380; lr: [0.01]\n",
            "2022-03-11 14:45:06,818 | INFO : \t test_acc: 88.26 (best: 90.1 from #33)\n",
            "2022-03-11 14:46:36,532 | INFO : Epoch #77\n",
            "2022-03-11 14:46:36,533 | INFO : \t train_acc: 94.336; train_loss: 0.386; test_loss: 36.233; lr: [0.01]\n",
            "2022-03-11 14:46:36,533 | INFO : \t test_acc: 88.84 (best: 90.1 from #33)\n",
            "2022-03-11 14:48:06,302 | INFO : Epoch #78\n",
            "2022-03-11 14:48:06,303 | INFO : \t train_acc: 94.024; train_loss: 0.035; test_loss: 36.093; lr: [0.01]\n",
            "2022-03-11 14:48:06,303 | INFO : \t test_acc: 89.57 (best: 90.1 from #33)\n",
            "2022-03-11 14:49:36,066 | INFO : Epoch #79\n",
            "2022-03-11 14:49:36,066 | INFO : \t train_acc: 94.21; train_loss: 0.129; test_loss: 34.097; lr: [0.01]\n",
            "2022-03-11 14:49:36,066 | INFO : \t test_acc: 89.74 (best: 90.1 from #33)\n",
            "2022-03-11 14:51:05,850 | INFO : Epoch #80\n",
            "2022-03-11 14:51:05,851 | INFO : \t train_acc: 94.28; train_loss: 0.342; test_loss: 36.480; lr: [0.01]\n",
            "2022-03-11 14:51:05,851 | INFO : \t test_acc: 89.0 (best: 90.1 from #33)\n",
            "2022-03-11 14:52:35,592 | INFO : Epoch #81\n",
            "2022-03-11 14:52:35,592 | INFO : \t train_acc: 94.29; train_loss: 0.534; test_loss: 37.449; lr: [0.01]\n",
            "2022-03-11 14:52:35,592 | INFO : \t test_acc: 88.52 (best: 90.1 from #33)\n",
            "2022-03-11 14:54:05,393 | INFO : Epoch #82\n",
            "2022-03-11 14:54:05,393 | INFO : \t train_acc: 94.142; train_loss: 0.161; test_loss: 37.829; lr: [0.01]\n",
            "2022-03-11 14:54:05,394 | INFO : \t test_acc: 88.24 (best: 90.1 from #33)\n",
            "2022-03-11 14:55:35,146 | INFO : Epoch #83\n",
            "2022-03-11 14:55:35,147 | INFO : \t train_acc: 94.22; train_loss: 0.256; test_loss: 36.578; lr: [0.01]\n",
            "2022-03-11 14:55:35,147 | INFO : \t test_acc: 89.28 (best: 90.1 from #33)\n",
            "2022-03-11 14:57:04,861 | INFO : Epoch #84\n",
            "2022-03-11 14:57:04,862 | INFO : \t train_acc: 94.264; train_loss: 0.456; test_loss: 42.121; lr: [0.01]\n",
            "2022-03-11 14:57:04,862 | INFO : \t test_acc: 87.67 (best: 90.1 from #33)\n",
            "2022-03-11 14:58:34,635 | INFO : Epoch #85\n",
            "2022-03-11 14:58:34,636 | INFO : \t train_acc: 94.248; train_loss: 0.047; test_loss: 35.321; lr: [0.01]\n",
            "2022-03-11 14:58:34,636 | INFO : \t test_acc: 89.18 (best: 90.1 from #33)\n",
            "2022-03-11 15:00:04,470 | INFO : Epoch #86\n",
            "2022-03-11 15:00:04,471 | INFO : \t train_acc: 94.344; train_loss: 0.043; test_loss: 41.455; lr: [0.01]\n",
            "2022-03-11 15:00:04,471 | INFO : \t test_acc: 87.64 (best: 90.1 from #33)\n",
            "2022-03-11 15:01:34,246 | INFO : Epoch #87\n",
            "2022-03-11 15:01:34,246 | INFO : \t train_acc: 94.178; train_loss: 0.199; test_loss: 36.553; lr: [0.01]\n",
            "2022-03-11 15:01:34,246 | INFO : \t test_acc: 88.86 (best: 90.1 from #33)\n",
            "2022-03-11 15:03:04,040 | INFO : Epoch #88\n",
            "2022-03-11 15:03:04,041 | INFO : \t train_acc: 94.344; train_loss: 0.466; test_loss: 33.811; lr: [0.01]\n",
            "2022-03-11 15:03:04,041 | INFO : \t test_acc: 89.69 (best: 90.1 from #33)\n",
            "2022-03-11 15:04:33,813 | INFO : Epoch #89\n",
            "2022-03-11 15:04:33,813 | INFO : \t train_acc: 94.112; train_loss: 0.075; test_loss: 32.153; lr: [0.01]\n",
            "2022-03-11 15:04:33,814 | INFO : \t test_acc: 90.06 (best: 90.1 from #33)\n",
            "2022-03-11 15:06:03,641 | INFO : Epoch #90\n",
            "2022-03-11 15:06:03,642 | INFO : \t train_acc: 94.306; train_loss: 0.066; test_loss: 37.089; lr: [0.01]\n",
            "2022-03-11 15:06:03,642 | INFO : \t test_acc: 89.03 (best: 90.1 from #33)\n",
            "2022-03-11 15:07:33,414 | INFO : Epoch #91\n",
            "2022-03-11 15:07:33,414 | INFO : \t train_acc: 94.332; train_loss: 0.253; test_loss: 32.972; lr: [0.01]\n",
            "2022-03-11 15:07:33,415 | INFO : \t test_acc: 89.69 (best: 90.1 from #33)\n",
            "2022-03-11 15:09:03,186 | INFO : Epoch #92\n",
            "2022-03-11 15:09:03,186 | INFO : \t train_acc: 94.43; train_loss: 0.509; test_loss: 35.666; lr: [0.01]\n",
            "2022-03-11 15:09:03,186 | INFO : \t test_acc: 89.09 (best: 90.1 from #33)\n",
            "2022-03-11 15:10:32,970 | INFO : Epoch #93\n",
            "2022-03-11 15:10:32,970 | INFO : \t train_acc: 94.116; train_loss: 0.039; test_loss: 32.208; lr: [0.01]\n",
            "2022-03-11 15:10:32,970 | INFO : \t test_acc: 89.94 (best: 90.1 from #33)\n",
            "2022-03-11 15:12:02,697 | INFO : Epoch #94\n",
            "2022-03-11 15:12:02,698 | INFO : \t train_acc: 94.324; train_loss: 0.206; test_loss: 35.214; lr: [0.01]\n",
            "2022-03-11 15:12:02,698 | INFO : \t test_acc: 88.98 (best: 90.1 from #33)\n",
            "2022-03-11 15:13:32,585 | INFO : Epoch #95\n",
            "2022-03-11 15:13:32,586 | INFO : \t train_acc: 94.304; train_loss: 0.145; test_loss: 30.168; lr: [0.01]\n",
            "2022-03-11 15:13:32,586 | INFO : \t test_acc: 90.03 (best: 90.1 from #33)\n",
            "2022-03-11 15:15:02,429 | INFO : Epoch #96\n",
            "2022-03-11 15:15:02,429 | INFO : \t train_acc: 94.256; train_loss: 0.197; test_loss: 36.136; lr: [0.01]\n",
            "2022-03-11 15:15:02,429 | INFO : \t test_acc: 88.49 (best: 90.1 from #33)\n",
            "2022-03-11 15:16:32,308 | INFO : Epoch #97\n",
            "2022-03-11 15:16:32,309 | INFO : \t train_acc: 94.482; train_loss: 0.250; test_loss: 34.184; lr: [0.01]\n",
            "2022-03-11 15:16:32,309 | INFO : \t test_acc: 89.5 (best: 90.1 from #33)\n",
            "2022-03-11 15:18:02,154 | INFO : Epoch #98\n",
            "2022-03-11 15:18:02,155 | INFO : \t train_acc: 94.342; train_loss: 0.149; test_loss: 35.156; lr: [0.01]\n",
            "2022-03-11 15:18:02,155 | INFO : \t test_acc: 89.08 (best: 90.1 from #33)\n",
            "2022-03-11 15:19:32,136 | INFO : Epoch #99\n",
            "2022-03-11 15:19:32,137 | INFO : \t train_acc: 94.366; train_loss: 0.256; test_loss: 37.019; lr: [0.01]\n",
            "2022-03-11 15:19:32,137 | INFO : \t test_acc: 88.62 (best: 90.1 from #33)\n",
            "2022-03-11 15:21:02,167 | INFO : Epoch #100\n",
            "2022-03-11 15:21:02,168 | INFO : \t train_acc: 97.118; train_loss: 0.137; test_loss: 23.381; lr: [0.001]\n",
            "2022-03-11 15:21:02,168 | INFO : \t test_acc: 92.7 (best: 92.7 from #100)\n",
            "2022-03-11 15:22:32,168 | INFO : Epoch #101\n",
            "2022-03-11 15:22:32,168 | INFO : \t train_acc: 98.08; train_loss: 0.011; test_loss: 22.777; lr: [0.001]\n",
            "2022-03-11 15:22:32,169 | INFO : \t test_acc: 92.95 (best: 92.95 from #101)\n",
            "2022-03-11 15:24:02,193 | INFO : Epoch #102\n",
            "2022-03-11 15:24:02,194 | INFO : \t train_acc: 98.314; train_loss: 0.206; test_loss: 22.956; lr: [0.001]\n",
            "2022-03-11 15:24:02,194 | INFO : \t test_acc: 93.25 (best: 93.25 from #102)\n",
            "2022-03-11 15:25:32,189 | INFO : Epoch #103\n",
            "2022-03-11 15:25:32,190 | INFO : \t train_acc: 98.56; train_loss: 0.126; test_loss: 23.428; lr: [0.001]\n",
            "2022-03-11 15:25:32,190 | INFO : \t test_acc: 93.15 (best: 93.25 from #102)\n",
            "2022-03-11 15:27:02,171 | INFO : Epoch #104\n",
            "2022-03-11 15:27:02,172 | INFO : \t train_acc: 98.724; train_loss: 0.045; test_loss: 23.452; lr: [0.001]\n",
            "2022-03-11 15:27:02,172 | INFO : \t test_acc: 93.16 (best: 93.25 from #102)\n",
            "2022-03-11 15:28:32,178 | INFO : Epoch #105\n",
            "2022-03-11 15:28:32,178 | INFO : \t train_acc: 98.746; train_loss: 0.036; test_loss: 23.675; lr: [0.001]\n",
            "2022-03-11 15:28:32,178 | INFO : \t test_acc: 93.31 (best: 93.31 from #105)\n",
            "2022-03-11 15:30:02,185 | INFO : Epoch #106\n",
            "2022-03-11 15:30:02,185 | INFO : \t train_acc: 99.006; train_loss: 0.055; test_loss: 23.525; lr: [0.001]\n",
            "2022-03-11 15:30:02,186 | INFO : \t test_acc: 93.45 (best: 93.45 from #106)\n",
            "2022-03-11 15:31:32,127 | INFO : Epoch #107\n",
            "2022-03-11 15:31:32,128 | INFO : \t train_acc: 98.956; train_loss: 0.011; test_loss: 23.813; lr: [0.001]\n",
            "2022-03-11 15:31:32,128 | INFO : \t test_acc: 93.26 (best: 93.45 from #106)\n",
            "2022-03-11 15:33:02,113 | INFO : Epoch #108\n",
            "2022-03-11 15:33:02,113 | INFO : \t train_acc: 99.04; train_loss: 0.043; test_loss: 24.172; lr: [0.001]\n",
            "2022-03-11 15:33:02,113 | INFO : \t test_acc: 93.38 (best: 93.45 from #106)\n",
            "2022-03-11 15:34:32,064 | INFO : Epoch #109\n",
            "2022-03-11 15:34:32,064 | INFO : \t train_acc: 99.098; train_loss: 0.047; test_loss: 24.529; lr: [0.001]\n",
            "2022-03-11 15:34:32,065 | INFO : \t test_acc: 93.26 (best: 93.45 from #106)\n",
            "2022-03-11 15:36:02,125 | INFO : Epoch #110\n",
            "2022-03-11 15:36:02,125 | INFO : \t train_acc: 99.106; train_loss: 0.035; test_loss: 24.119; lr: [0.001]\n",
            "2022-03-11 15:36:02,126 | INFO : \t test_acc: 93.38 (best: 93.45 from #106)\n",
            "2022-03-11 15:37:32,112 | INFO : Epoch #111\n",
            "2022-03-11 15:37:32,112 | INFO : \t train_acc: 99.18; train_loss: 0.031; test_loss: 24.704; lr: [0.001]\n",
            "2022-03-11 15:37:32,112 | INFO : \t test_acc: 93.21 (best: 93.45 from #106)\n",
            "2022-03-11 15:39:02,066 | INFO : Epoch #112\n",
            "2022-03-11 15:39:02,067 | INFO : \t train_acc: 99.218; train_loss: 0.024; test_loss: 24.504; lr: [0.001]\n",
            "2022-03-11 15:39:02,067 | INFO : \t test_acc: 93.39 (best: 93.45 from #106)\n",
            "2022-03-11 15:40:32,046 | INFO : Epoch #113\n",
            "2022-03-11 15:40:32,047 | INFO : \t train_acc: 99.244; train_loss: 0.167; test_loss: 24.694; lr: [0.001]\n",
            "2022-03-11 15:40:32,047 | INFO : \t test_acc: 93.33 (best: 93.45 from #106)\n",
            "2022-03-11 15:42:02,039 | INFO : Epoch #114\n",
            "2022-03-11 15:42:02,040 | INFO : \t train_acc: 99.37; train_loss: 0.138; test_loss: 24.300; lr: [0.001]\n",
            "2022-03-11 15:42:02,040 | INFO : \t test_acc: 93.41 (best: 93.45 from #106)\n",
            "2022-03-11 15:43:32,019 | INFO : Epoch #115\n",
            "2022-03-11 15:43:32,020 | INFO : \t train_acc: 99.348; train_loss: 0.008; test_loss: 25.089; lr: [0.001]\n",
            "2022-03-11 15:43:32,020 | INFO : \t test_acc: 93.38 (best: 93.45 from #106)\n",
            "2022-03-11 15:45:02,018 | INFO : Epoch #116\n",
            "2022-03-11 15:45:02,019 | INFO : \t train_acc: 99.348; train_loss: 0.037; test_loss: 24.966; lr: [0.001]\n",
            "2022-03-11 15:45:02,019 | INFO : \t test_acc: 93.32 (best: 93.45 from #106)\n",
            "2022-03-11 15:46:31,992 | INFO : Epoch #117\n",
            "2022-03-11 15:46:31,992 | INFO : \t train_acc: 99.422; train_loss: 0.035; test_loss: 25.537; lr: [0.001]\n",
            "2022-03-11 15:46:31,992 | INFO : \t test_acc: 93.4 (best: 93.45 from #106)\n",
            "2022-03-11 15:48:01,969 | INFO : Epoch #118\n",
            "2022-03-11 15:48:01,969 | INFO : \t train_acc: 99.428; train_loss: 0.079; test_loss: 25.530; lr: [0.001]\n",
            "2022-03-11 15:48:01,969 | INFO : \t test_acc: 93.39 (best: 93.45 from #106)\n",
            "2022-03-11 15:49:31,932 | INFO : Epoch #119\n",
            "2022-03-11 15:49:31,932 | INFO : \t train_acc: 99.47; train_loss: 0.008; test_loss: 26.252; lr: [0.001]\n",
            "2022-03-11 15:49:31,932 | INFO : \t test_acc: 93.21 (best: 93.45 from #106)\n",
            "2022-03-11 15:51:02,001 | INFO : Epoch #120\n",
            "2022-03-11 15:51:02,001 | INFO : \t train_acc: 99.504; train_loss: 0.021; test_loss: 25.502; lr: [0.001]\n",
            "2022-03-11 15:51:02,001 | INFO : \t test_acc: 93.42 (best: 93.45 from #106)\n",
            "2022-03-11 15:52:31,962 | INFO : Epoch #121\n",
            "2022-03-11 15:52:31,963 | INFO : \t train_acc: 99.446; train_loss: 0.039; test_loss: 25.588; lr: [0.001]\n",
            "2022-03-11 15:52:31,963 | INFO : \t test_acc: 93.47 (best: 93.47 from #121)\n",
            "2022-03-11 15:54:01,985 | INFO : Epoch #122\n",
            "2022-03-11 15:54:01,985 | INFO : \t train_acc: 99.474; train_loss: 0.188; test_loss: 25.564; lr: [0.001]\n",
            "2022-03-11 15:54:01,986 | INFO : \t test_acc: 93.4 (best: 93.47 from #121)\n",
            "2022-03-11 15:55:31,953 | INFO : Epoch #123\n",
            "2022-03-11 15:55:31,953 | INFO : \t train_acc: 99.55; train_loss: 0.001; test_loss: 25.989; lr: [0.001]\n",
            "2022-03-11 15:55:31,954 | INFO : \t test_acc: 93.3 (best: 93.47 from #121)\n",
            "2022-03-11 15:57:01,941 | INFO : Epoch #124\n",
            "2022-03-11 15:57:01,942 | INFO : \t train_acc: 99.476; train_loss: 0.005; test_loss: 26.107; lr: [0.001]\n",
            "2022-03-11 15:57:01,942 | INFO : \t test_acc: 93.47 (best: 93.47 from #124)\n",
            "2022-03-11 15:58:31,938 | INFO : Epoch #125\n",
            "2022-03-11 15:58:31,939 | INFO : \t train_acc: 99.562; train_loss: 0.335; test_loss: 25.797; lr: [0.001]\n",
            "2022-03-11 15:58:31,939 | INFO : \t test_acc: 93.36 (best: 93.47 from #124)\n",
            "2022-03-11 16:00:01,892 | INFO : Epoch #126\n",
            "2022-03-11 16:00:01,893 | INFO : \t train_acc: 99.57; train_loss: 0.131; test_loss: 26.391; lr: [0.001]\n",
            "2022-03-11 16:00:01,893 | INFO : \t test_acc: 93.25 (best: 93.47 from #124)\n",
            "2022-03-11 16:01:31,871 | INFO : Epoch #127\n",
            "2022-03-11 16:01:31,871 | INFO : \t train_acc: 99.602; train_loss: 0.016; test_loss: 27.119; lr: [0.001]\n",
            "2022-03-11 16:01:31,871 | INFO : \t test_acc: 93.19 (best: 93.47 from #124)\n",
            "2022-03-11 16:03:01,826 | INFO : Epoch #128\n",
            "2022-03-11 16:03:01,826 | INFO : \t train_acc: 99.564; train_loss: 0.042; test_loss: 26.907; lr: [0.001]\n",
            "2022-03-11 16:03:01,826 | INFO : \t test_acc: 93.29 (best: 93.47 from #124)\n",
            "2022-03-11 16:04:31,801 | INFO : Epoch #129\n",
            "2022-03-11 16:04:31,802 | INFO : \t train_acc: 99.616; train_loss: 0.005; test_loss: 26.894; lr: [0.001]\n",
            "2022-03-11 16:04:31,802 | INFO : \t test_acc: 93.31 (best: 93.47 from #124)\n",
            "2022-03-11 16:06:01,817 | INFO : Epoch #130\n",
            "2022-03-11 16:06:01,818 | INFO : \t train_acc: 99.622; train_loss: 0.041; test_loss: 26.522; lr: [0.001]\n",
            "2022-03-11 16:06:01,818 | INFO : \t test_acc: 93.36 (best: 93.47 from #124)\n",
            "2022-03-11 16:07:31,798 | INFO : Epoch #131\n",
            "2022-03-11 16:07:31,799 | INFO : \t train_acc: 99.608; train_loss: 0.187; test_loss: 27.310; lr: [0.001]\n",
            "2022-03-11 16:07:31,799 | INFO : \t test_acc: 93.11 (best: 93.47 from #124)\n",
            "2022-03-11 16:09:01,805 | INFO : Epoch #132\n",
            "2022-03-11 16:09:01,805 | INFO : \t train_acc: 99.668; train_loss: 0.028; test_loss: 27.110; lr: [0.001]\n",
            "2022-03-11 16:09:01,806 | INFO : \t test_acc: 93.42 (best: 93.47 from #124)\n",
            "2022-03-11 16:10:31,733 | INFO : Epoch #133\n",
            "2022-03-11 16:10:31,733 | INFO : \t train_acc: 99.612; train_loss: 0.216; test_loss: 27.532; lr: [0.001]\n",
            "2022-03-11 16:10:31,733 | INFO : \t test_acc: 93.19 (best: 93.47 from #124)\n",
            "2022-03-11 16:12:01,679 | INFO : Epoch #134\n",
            "2022-03-11 16:12:01,679 | INFO : \t train_acc: 99.604; train_loss: 0.011; test_loss: 27.507; lr: [0.001]\n",
            "2022-03-11 16:12:01,679 | INFO : \t test_acc: 93.43 (best: 93.47 from #124)\n",
            "2022-03-11 16:13:31,603 | INFO : Epoch #135\n",
            "2022-03-11 16:13:31,603 | INFO : \t train_acc: 99.678; train_loss: 0.002; test_loss: 27.602; lr: [0.001]\n",
            "2022-03-11 16:13:31,603 | INFO : \t test_acc: 93.32 (best: 93.47 from #124)\n",
            "2022-03-11 16:15:01,534 | INFO : Epoch #136\n",
            "2022-03-11 16:15:01,534 | INFO : \t train_acc: 99.678; train_loss: 0.093; test_loss: 27.732; lr: [0.001]\n",
            "2022-03-11 16:15:01,534 | INFO : \t test_acc: 93.48 (best: 93.48 from #136)\n",
            "2022-03-11 16:16:31,579 | INFO : Epoch #137\n",
            "2022-03-11 16:16:31,579 | INFO : \t train_acc: 99.65; train_loss: 0.007; test_loss: 27.160; lr: [0.001]\n",
            "2022-03-11 16:16:31,580 | INFO : \t test_acc: 93.57 (best: 93.57 from #137)\n",
            "2022-03-11 16:18:01,551 | INFO : Epoch #138\n",
            "2022-03-11 16:18:01,552 | INFO : \t train_acc: 99.658; train_loss: 0.043; test_loss: 28.128; lr: [0.001]\n",
            "2022-03-11 16:18:01,552 | INFO : \t test_acc: 93.23 (best: 93.57 from #137)\n",
            "2022-03-11 16:19:31,493 | INFO : Epoch #139\n",
            "2022-03-11 16:19:31,493 | INFO : \t train_acc: 99.646; train_loss: 0.109; test_loss: 27.496; lr: [0.001]\n",
            "2022-03-11 16:19:31,494 | INFO : \t test_acc: 93.52 (best: 93.57 from #137)\n",
            "2022-03-11 16:21:01,503 | INFO : Epoch #140\n",
            "2022-03-11 16:21:01,504 | INFO : \t train_acc: 99.628; train_loss: 0.086; test_loss: 27.613; lr: [0.001]\n",
            "2022-03-11 16:21:01,504 | INFO : \t test_acc: 93.31 (best: 93.57 from #137)\n",
            "2022-03-11 16:22:31,436 | INFO : Epoch #141\n",
            "2022-03-11 16:22:31,437 | INFO : \t train_acc: 99.612; train_loss: 0.094; test_loss: 27.995; lr: [0.001]\n",
            "2022-03-11 16:22:31,437 | INFO : \t test_acc: 93.34 (best: 93.57 from #137)\n",
            "2022-03-11 16:24:01,357 | INFO : Epoch #142\n",
            "2022-03-11 16:24:01,357 | INFO : \t train_acc: 99.656; train_loss: 0.013; test_loss: 27.195; lr: [0.001]\n",
            "2022-03-11 16:24:01,357 | INFO : \t test_acc: 93.4 (best: 93.57 from #137)\n",
            "2022-03-11 16:25:31,291 | INFO : Epoch #143\n",
            "2022-03-11 16:25:31,291 | INFO : \t train_acc: 99.624; train_loss: 0.012; test_loss: 27.706; lr: [0.001]\n",
            "2022-03-11 16:25:31,291 | INFO : \t test_acc: 93.64 (best: 93.64 from #143)\n",
            "2022-03-11 16:27:01,307 | INFO : Epoch #144\n",
            "2022-03-11 16:27:01,307 | INFO : \t train_acc: 99.698; train_loss: 0.002; test_loss: 28.026; lr: [0.001]\n",
            "2022-03-11 16:27:01,307 | INFO : \t test_acc: 93.46 (best: 93.64 from #143)\n",
            "2022-03-11 16:28:31,313 | INFO : Epoch #145\n",
            "2022-03-11 16:28:31,313 | INFO : \t train_acc: 99.764; train_loss: 0.004; test_loss: 28.139; lr: [0.001]\n",
            "2022-03-11 16:28:31,313 | INFO : \t test_acc: 93.38 (best: 93.64 from #143)\n",
            "2022-03-11 16:30:01,292 | INFO : Epoch #146\n",
            "2022-03-11 16:30:01,293 | INFO : \t train_acc: 99.694; train_loss: 0.020; test_loss: 29.187; lr: [0.001]\n",
            "2022-03-11 16:30:01,293 | INFO : \t test_acc: 93.31 (best: 93.64 from #143)\n",
            "2022-03-11 16:31:31,198 | INFO : Epoch #147\n",
            "2022-03-11 16:31:31,199 | INFO : \t train_acc: 99.72; train_loss: 0.005; test_loss: 28.666; lr: [0.001]\n",
            "2022-03-11 16:31:31,199 | INFO : \t test_acc: 93.21 (best: 93.64 from #143)\n",
            "2022-03-11 16:33:01,174 | INFO : Epoch #148\n",
            "2022-03-11 16:33:01,175 | INFO : \t train_acc: 99.716; train_loss: 0.171; test_loss: 27.879; lr: [0.001]\n",
            "2022-03-11 16:33:01,175 | INFO : \t test_acc: 93.27 (best: 93.64 from #143)\n",
            "2022-03-11 16:34:31,168 | INFO : Epoch #149\n",
            "2022-03-11 16:34:31,168 | INFO : \t train_acc: 99.664; train_loss: 0.004; test_loss: 28.190; lr: [0.001]\n",
            "2022-03-11 16:34:31,168 | INFO : \t test_acc: 93.32 (best: 93.64 from #143)\n",
            "2022-03-11 16:36:01,177 | INFO : Epoch #150\n",
            "2022-03-11 16:36:01,177 | INFO : \t train_acc: 99.746; train_loss: 0.059; test_loss: 28.733; lr: [0.001]\n",
            "2022-03-11 16:36:01,177 | INFO : \t test_acc: 93.26 (best: 93.64 from #143)\n",
            "2022-03-11 16:37:31,137 | INFO : Epoch #151\n",
            "2022-03-11 16:37:31,138 | INFO : \t train_acc: 99.74; train_loss: 0.234; test_loss: 28.706; lr: [0.001]\n",
            "2022-03-11 16:37:31,138 | INFO : \t test_acc: 93.48 (best: 93.64 from #143)\n",
            "2022-03-11 16:39:01,053 | INFO : Epoch #152\n",
            "2022-03-11 16:39:01,053 | INFO : \t train_acc: 99.704; train_loss: 0.001; test_loss: 28.437; lr: [0.001]\n",
            "2022-03-11 16:39:01,053 | INFO : \t test_acc: 93.24 (best: 93.64 from #143)\n",
            "2022-03-11 16:40:30,933 | INFO : Epoch #153\n",
            "2022-03-11 16:40:30,934 | INFO : \t train_acc: 99.736; train_loss: 0.204; test_loss: 28.571; lr: [0.001]\n",
            "2022-03-11 16:40:30,934 | INFO : \t test_acc: 93.3 (best: 93.64 from #143)\n",
            "2022-03-11 16:42:00,863 | INFO : Epoch #154\n",
            "2022-03-11 16:42:00,863 | INFO : \t train_acc: 99.722; train_loss: 0.153; test_loss: 28.304; lr: [0.001]\n",
            "2022-03-11 16:42:00,863 | INFO : \t test_acc: 93.52 (best: 93.64 from #143)\n",
            "2022-03-11 16:43:30,786 | INFO : Epoch #155\n",
            "2022-03-11 16:43:30,787 | INFO : \t train_acc: 99.698; train_loss: 0.019; test_loss: 28.173; lr: [0.001]\n",
            "2022-03-11 16:43:30,787 | INFO : \t test_acc: 93.47 (best: 93.64 from #143)\n",
            "2022-03-11 16:45:00,685 | INFO : Epoch #156\n",
            "2022-03-11 16:45:00,685 | INFO : \t train_acc: 99.752; train_loss: 0.003; test_loss: 28.394; lr: [0.001]\n",
            "2022-03-11 16:45:00,685 | INFO : \t test_acc: 93.56 (best: 93.64 from #143)\n",
            "2022-03-11 16:46:30,615 | INFO : Epoch #157\n",
            "2022-03-11 16:46:30,616 | INFO : \t train_acc: 99.722; train_loss: 0.010; test_loss: 28.247; lr: [0.001]\n",
            "2022-03-11 16:46:30,616 | INFO : \t test_acc: 93.38 (best: 93.64 from #143)\n",
            "2022-03-11 16:48:00,536 | INFO : Epoch #158\n",
            "2022-03-11 16:48:00,537 | INFO : \t train_acc: 99.788; train_loss: 0.187; test_loss: 27.993; lr: [0.001]\n",
            "2022-03-11 16:48:00,537 | INFO : \t test_acc: 93.43 (best: 93.64 from #143)\n",
            "2022-03-11 16:49:30,493 | INFO : Epoch #159\n",
            "2022-03-11 16:49:30,494 | INFO : \t train_acc: 99.734; train_loss: 0.000; test_loss: 28.942; lr: [0.001]\n",
            "2022-03-11 16:49:30,494 | INFO : \t test_acc: 93.34 (best: 93.64 from #143)\n",
            "2022-03-11 16:51:00,436 | INFO : Epoch #160\n",
            "2022-03-11 16:51:00,436 | INFO : \t train_acc: 99.752; train_loss: 0.036; test_loss: 28.762; lr: [0.001]\n",
            "2022-03-11 16:51:00,436 | INFO : \t test_acc: 93.42 (best: 93.64 from #143)\n",
            "2022-03-11 16:52:30,326 | INFO : Epoch #161\n",
            "2022-03-11 16:52:30,327 | INFO : \t train_acc: 99.718; train_loss: 0.007; test_loss: 28.693; lr: [0.001]\n",
            "2022-03-11 16:52:30,327 | INFO : \t test_acc: 93.64 (best: 93.64 from #161)\n",
            "2022-03-11 16:54:00,338 | INFO : Epoch #162\n",
            "2022-03-11 16:54:00,338 | INFO : \t train_acc: 99.728; train_loss: 0.137; test_loss: 29.123; lr: [0.001]\n",
            "2022-03-11 16:54:00,339 | INFO : \t test_acc: 93.44 (best: 93.64 from #161)\n",
            "2022-03-11 16:55:30,326 | INFO : Epoch #163\n",
            "2022-03-11 16:55:30,327 | INFO : \t train_acc: 99.682; train_loss: 0.041; test_loss: 29.363; lr: [0.001]\n",
            "2022-03-11 16:55:30,327 | INFO : \t test_acc: 93.21 (best: 93.64 from #161)\n",
            "2022-03-11 16:57:00,275 | INFO : Epoch #164\n",
            "2022-03-11 16:57:00,276 | INFO : \t train_acc: 99.774; train_loss: 0.188; test_loss: 28.242; lr: [0.001]\n",
            "2022-03-11 16:57:00,276 | INFO : \t test_acc: 93.44 (best: 93.64 from #161)\n",
            "2022-03-11 16:58:30,226 | INFO : Epoch #165\n",
            "2022-03-11 16:58:30,227 | INFO : \t train_acc: 99.69; train_loss: 0.133; test_loss: 28.524; lr: [0.001]\n",
            "2022-03-11 16:58:30,227 | INFO : \t test_acc: 93.39 (best: 93.64 from #161)\n",
            "2022-03-11 17:00:00,145 | INFO : Epoch #166\n",
            "2022-03-11 17:00:00,145 | INFO : \t train_acc: 99.688; train_loss: 0.004; test_loss: 28.540; lr: [0.001]\n",
            "2022-03-11 17:00:00,145 | INFO : \t test_acc: 93.3 (best: 93.64 from #161)\n",
            "2022-03-11 17:01:30,135 | INFO : Epoch #167\n",
            "2022-03-11 17:01:30,136 | INFO : \t train_acc: 99.736; train_loss: 0.007; test_loss: 28.996; lr: [0.001]\n",
            "2022-03-11 17:01:30,136 | INFO : \t test_acc: 93.31 (best: 93.64 from #161)\n",
            "2022-03-11 17:03:00,119 | INFO : Epoch #168\n",
            "2022-03-11 17:03:00,120 | INFO : \t train_acc: 99.77; train_loss: 0.003; test_loss: 28.659; lr: [0.001]\n",
            "2022-03-11 17:03:00,120 | INFO : \t test_acc: 93.48 (best: 93.64 from #161)\n",
            "2022-03-11 17:04:30,070 | INFO : Epoch #169\n",
            "2022-03-11 17:04:30,071 | INFO : \t train_acc: 99.732; train_loss: 0.145; test_loss: 29.053; lr: [0.001]\n",
            "2022-03-11 17:04:30,071 | INFO : \t test_acc: 93.3 (best: 93.64 from #161)\n",
            "2022-03-11 17:06:00,062 | INFO : Epoch #170\n",
            "2022-03-11 17:06:00,062 | INFO : \t train_acc: 99.762; train_loss: 0.030; test_loss: 28.940; lr: [0.001]\n",
            "2022-03-11 17:06:00,063 | INFO : \t test_acc: 93.44 (best: 93.64 from #161)\n",
            "2022-03-11 17:07:30,039 | INFO : Epoch #171\n",
            "2022-03-11 17:07:30,039 | INFO : \t train_acc: 99.786; train_loss: 0.002; test_loss: 29.004; lr: [0.001]\n",
            "2022-03-11 17:07:30,039 | INFO : \t test_acc: 93.38 (best: 93.64 from #161)\n",
            "2022-03-11 17:09:00,001 | INFO : Epoch #172\n",
            "2022-03-11 17:09:00,002 | INFO : \t train_acc: 99.782; train_loss: 0.049; test_loss: 29.319; lr: [0.001]\n",
            "2022-03-11 17:09:00,002 | INFO : \t test_acc: 93.29 (best: 93.64 from #161)\n",
            "2022-03-11 17:10:29,977 | INFO : Epoch #173\n",
            "2022-03-11 17:10:29,977 | INFO : \t train_acc: 99.77; train_loss: 0.002; test_loss: 28.829; lr: [0.001]\n",
            "2022-03-11 17:10:29,978 | INFO : \t test_acc: 93.51 (best: 93.64 from #161)\n",
            "2022-03-11 17:11:59,906 | INFO : Epoch #174\n",
            "2022-03-11 17:11:59,907 | INFO : \t train_acc: 99.782; train_loss: 0.009; test_loss: 29.183; lr: [0.001]\n",
            "2022-03-11 17:11:59,907 | INFO : \t test_acc: 93.41 (best: 93.64 from #161)\n",
            "2022-03-11 17:13:29,937 | INFO : Epoch #175\n",
            "2022-03-11 17:13:29,938 | INFO : \t train_acc: 99.714; train_loss: 0.004; test_loss: 28.932; lr: [0.001]\n",
            "2022-03-11 17:13:29,938 | INFO : \t test_acc: 93.26 (best: 93.64 from #161)\n",
            "2022-03-11 17:15:00,077 | INFO : Epoch #176\n",
            "2022-03-11 17:15:00,077 | INFO : \t train_acc: 99.758; train_loss: 0.061; test_loss: 28.932; lr: [0.001]\n",
            "2022-03-11 17:15:00,078 | INFO : \t test_acc: 93.38 (best: 93.64 from #161)\n",
            "2022-03-11 17:16:30,162 | INFO : Epoch #177\n",
            "2022-03-11 17:16:30,162 | INFO : \t train_acc: 99.784; train_loss: 0.016; test_loss: 28.394; lr: [0.001]\n",
            "2022-03-11 17:16:30,162 | INFO : \t test_acc: 93.6 (best: 93.64 from #161)\n",
            "2022-03-11 17:18:00,264 | INFO : Epoch #178\n",
            "2022-03-11 17:18:00,264 | INFO : \t train_acc: 99.766; train_loss: 0.021; test_loss: 28.957; lr: [0.001]\n",
            "2022-03-11 17:18:00,265 | INFO : \t test_acc: 93.29 (best: 93.64 from #161)\n",
            "2022-03-11 17:19:30,364 | INFO : Epoch #179\n",
            "2022-03-11 17:19:30,365 | INFO : \t train_acc: 99.744; train_loss: 0.188; test_loss: 28.661; lr: [0.001]\n",
            "2022-03-11 17:19:30,365 | INFO : \t test_acc: 93.47 (best: 93.64 from #161)\n",
            "2022-03-11 17:21:00,500 | INFO : Epoch #180\n",
            "2022-03-11 17:21:00,500 | INFO : \t train_acc: 99.742; train_loss: 0.006; test_loss: 29.086; lr: [0.001]\n",
            "2022-03-11 17:21:00,501 | INFO : \t test_acc: 93.26 (best: 93.64 from #161)\n",
            "2022-03-11 17:22:30,606 | INFO : Epoch #181\n",
            "2022-03-11 17:22:30,607 | INFO : \t train_acc: 99.8; train_loss: 0.013; test_loss: 28.562; lr: [0.001]\n",
            "2022-03-11 17:22:30,607 | INFO : \t test_acc: 93.3 (best: 93.64 from #161)\n",
            "2022-03-11 17:24:00,742 | INFO : Epoch #182\n",
            "2022-03-11 17:24:00,742 | INFO : \t train_acc: 99.742; train_loss: 0.001; test_loss: 29.696; lr: [0.001]\n",
            "2022-03-11 17:24:00,742 | INFO : \t test_acc: 93.26 (best: 93.64 from #161)\n",
            "2022-03-11 17:25:30,852 | INFO : Epoch #183\n",
            "2022-03-11 17:25:30,852 | INFO : \t train_acc: 99.754; train_loss: 0.005; test_loss: 28.770; lr: [0.001]\n",
            "2022-03-11 17:25:30,853 | INFO : \t test_acc: 93.47 (best: 93.64 from #161)\n",
            "2022-03-11 17:27:00,949 | INFO : Epoch #184\n",
            "2022-03-11 17:27:00,949 | INFO : \t train_acc: 99.756; train_loss: 0.070; test_loss: 30.315; lr: [0.001]\n",
            "2022-03-11 17:27:00,949 | INFO : \t test_acc: 93.27 (best: 93.64 from #161)\n",
            "2022-03-11 17:28:31,116 | INFO : Epoch #185\n",
            "2022-03-11 17:28:31,117 | INFO : \t train_acc: 99.752; train_loss: 0.067; test_loss: 29.171; lr: [0.001]\n",
            "2022-03-11 17:28:31,117 | INFO : \t test_acc: 93.6 (best: 93.64 from #161)\n",
            "2022-03-11 17:30:01,253 | INFO : Epoch #186\n",
            "2022-03-11 17:30:01,254 | INFO : \t train_acc: 99.742; train_loss: 0.027; test_loss: 30.917; lr: [0.001]\n",
            "2022-03-11 17:30:01,254 | INFO : \t test_acc: 93.14 (best: 93.64 from #161)\n",
            "2022-03-11 17:31:31,390 | INFO : Epoch #187\n",
            "2022-03-11 17:31:31,390 | INFO : \t train_acc: 99.766; train_loss: 0.056; test_loss: 29.229; lr: [0.001]\n",
            "2022-03-11 17:31:31,390 | INFO : \t test_acc: 93.4 (best: 93.64 from #161)\n",
            "2022-03-11 17:33:01,517 | INFO : Epoch #188\n",
            "2022-03-11 17:33:01,518 | INFO : \t train_acc: 99.764; train_loss: 0.001; test_loss: 29.328; lr: [0.001]\n",
            "2022-03-11 17:33:01,518 | INFO : \t test_acc: 93.4 (best: 93.64 from #161)\n",
            "2022-03-11 17:34:31,617 | INFO : Epoch #189\n",
            "2022-03-11 17:34:31,618 | INFO : \t train_acc: 99.74; train_loss: 0.009; test_loss: 29.282; lr: [0.001]\n",
            "2022-03-11 17:34:31,618 | INFO : \t test_acc: 93.46 (best: 93.64 from #161)\n",
            "2022-03-11 17:36:01,797 | INFO : Epoch #190\n",
            "2022-03-11 17:36:01,797 | INFO : \t train_acc: 99.758; train_loss: 0.237; test_loss: 28.925; lr: [0.001]\n",
            "2022-03-11 17:36:01,798 | INFO : \t test_acc: 93.43 (best: 93.64 from #161)\n",
            "2022-03-11 17:37:31,901 | INFO : Epoch #191\n",
            "2022-03-11 17:37:31,902 | INFO : \t train_acc: 99.744; train_loss: 0.030; test_loss: 29.020; lr: [0.001]\n",
            "2022-03-11 17:37:31,902 | INFO : \t test_acc: 93.26 (best: 93.64 from #161)\n",
            "2022-03-11 17:39:02,098 | INFO : Epoch #192\n",
            "2022-03-11 17:39:02,099 | INFO : \t train_acc: 99.752; train_loss: 0.009; test_loss: 29.464; lr: [0.001]\n",
            "2022-03-11 17:39:02,099 | INFO : \t test_acc: 93.48 (best: 93.64 from #161)\n",
            "2022-03-11 17:40:32,267 | INFO : Epoch #193\n",
            "2022-03-11 17:40:32,267 | INFO : \t train_acc: 99.744; train_loss: 0.002; test_loss: 29.637; lr: [0.001]\n",
            "2022-03-11 17:40:32,267 | INFO : \t test_acc: 93.43 (best: 93.64 from #161)\n",
            "2022-03-11 17:42:02,399 | INFO : Epoch #194\n",
            "2022-03-11 17:42:02,400 | INFO : \t train_acc: 99.734; train_loss: 0.092; test_loss: 28.600; lr: [0.001]\n",
            "2022-03-11 17:42:02,400 | INFO : \t test_acc: 93.56 (best: 93.64 from #161)\n",
            "2022-03-11 17:43:32,541 | INFO : Epoch #195\n",
            "2022-03-11 17:43:32,541 | INFO : \t train_acc: 99.71; train_loss: 0.007; test_loss: 30.555; lr: [0.001]\n",
            "2022-03-11 17:43:32,542 | INFO : \t test_acc: 93.03 (best: 93.64 from #161)\n",
            "2022-03-11 17:45:02,686 | INFO : Epoch #196\n",
            "2022-03-11 17:45:02,686 | INFO : \t train_acc: 99.726; train_loss: 0.016; test_loss: 30.299; lr: [0.001]\n",
            "2022-03-11 17:45:02,686 | INFO : \t test_acc: 93.08 (best: 93.64 from #161)\n",
            "2022-03-11 17:46:32,804 | INFO : Epoch #197\n",
            "2022-03-11 17:46:32,804 | INFO : \t train_acc: 99.798; train_loss: 0.003; test_loss: 29.505; lr: [0.001]\n",
            "2022-03-11 17:46:32,804 | INFO : \t test_acc: 93.41 (best: 93.64 from #161)\n",
            "2022-03-11 17:48:02,888 | INFO : Epoch #198\n",
            "2022-03-11 17:48:02,889 | INFO : \t train_acc: 99.752; train_loss: 0.001; test_loss: 29.702; lr: [0.001]\n",
            "2022-03-11 17:48:02,889 | INFO : \t test_acc: 93.23 (best: 93.64 from #161)\n",
            "2022-03-11 17:49:33,030 | INFO : Epoch #199\n",
            "2022-03-11 17:49:33,031 | INFO : \t train_acc: 99.752; train_loss: 0.024; test_loss: 29.171; lr: [0.001]\n",
            "2022-03-11 17:49:33,031 | INFO : \t test_acc: 93.57 (best: 93.64 from #161)\n",
            "2022-03-11 17:51:03,303 | INFO : Epoch #200\n",
            "2022-03-11 17:51:03,304 | INFO : \t train_acc: 99.824; train_loss: 0.002; test_loss: 28.668; lr: [0.0001]\n",
            "2022-03-11 17:51:03,304 | INFO : \t test_acc: 93.63 (best: 93.64 from #161)\n",
            "2022-03-11 17:52:33,433 | INFO : Epoch #201\n",
            "2022-03-11 17:52:33,433 | INFO : \t train_acc: 99.87; train_loss: 0.056; test_loss: 28.642; lr: [0.0001]\n",
            "2022-03-11 17:52:33,434 | INFO : \t test_acc: 93.59 (best: 93.64 from #161)\n",
            "2022-03-11 17:54:03,554 | INFO : Epoch #202\n",
            "2022-03-11 17:54:03,555 | INFO : \t train_acc: 99.864; train_loss: 0.137; test_loss: 28.946; lr: [0.0001]\n",
            "2022-03-11 17:54:03,555 | INFO : \t test_acc: 93.55 (best: 93.64 from #161)\n",
            "2022-03-11 17:55:33,686 | INFO : Epoch #203\n",
            "2022-03-11 17:55:33,687 | INFO : \t train_acc: 99.84; train_loss: 0.003; test_loss: 28.489; lr: [0.0001]\n",
            "2022-03-11 17:55:33,687 | INFO : \t test_acc: 93.64 (best: 93.64 from #203)\n",
            "2022-03-11 17:57:03,849 | INFO : Epoch #204\n",
            "2022-03-11 17:57:03,849 | INFO : \t train_acc: 99.888; train_loss: 0.026; test_loss: 28.621; lr: [0.0001]\n",
            "2022-03-11 17:57:03,849 | INFO : \t test_acc: 93.62 (best: 93.64 from #203)\n",
            "2022-03-11 17:58:33,975 | INFO : Epoch #205\n",
            "2022-03-11 17:58:33,976 | INFO : \t train_acc: 99.888; train_loss: 0.002; test_loss: 28.516; lr: [0.0001]\n",
            "2022-03-11 17:58:33,976 | INFO : \t test_acc: 93.66 (best: 93.66 from #205)\n",
            "2022-03-11 18:00:04,139 | INFO : Epoch #206\n",
            "2022-03-11 18:00:04,139 | INFO : \t train_acc: 99.904; train_loss: 0.200; test_loss: 28.228; lr: [0.0001]\n",
            "2022-03-11 18:00:04,139 | INFO : \t test_acc: 93.6 (best: 93.66 from #205)\n",
            "2022-03-11 18:01:34,238 | INFO : Epoch #207\n",
            "2022-03-11 18:01:34,238 | INFO : \t train_acc: 99.862; train_loss: 0.004; test_loss: 28.187; lr: [0.0001]\n",
            "2022-03-11 18:01:34,238 | INFO : \t test_acc: 93.64 (best: 93.66 from #205)\n",
            "2022-03-11 18:03:04,326 | INFO : Epoch #208\n",
            "2022-03-11 18:03:04,327 | INFO : \t train_acc: 99.89; train_loss: 0.015; test_loss: 28.384; lr: [0.0001]\n",
            "2022-03-11 18:03:04,327 | INFO : \t test_acc: 93.73 (best: 93.73 from #208)\n",
            "2022-03-11 18:04:34,452 | INFO : Epoch #209\n",
            "2022-03-11 18:04:34,452 | INFO : \t train_acc: 99.922; train_loss: 0.126; test_loss: 28.250; lr: [0.0001]\n",
            "2022-03-11 18:04:34,452 | INFO : \t test_acc: 93.7 (best: 93.73 from #208)\n",
            "2022-03-11 18:06:04,656 | INFO : Epoch #210\n",
            "2022-03-11 18:06:04,656 | INFO : \t train_acc: 99.902; train_loss: 0.328; test_loss: 28.302; lr: [0.0001]\n",
            "2022-03-11 18:06:04,657 | INFO : \t test_acc: 93.74 (best: 93.74 from #210)\n",
            "2022-03-11 18:07:34,790 | INFO : Epoch #211\n",
            "2022-03-11 18:07:34,790 | INFO : \t train_acc: 99.894; train_loss: 0.003; test_loss: 28.722; lr: [0.0001]\n",
            "2022-03-11 18:07:34,790 | INFO : \t test_acc: 93.59 (best: 93.74 from #210)\n",
            "2022-03-11 18:09:04,796 | INFO : Epoch #212\n",
            "2022-03-11 18:09:04,796 | INFO : \t train_acc: 99.904; train_loss: 0.007; test_loss: 28.682; lr: [0.0001]\n",
            "2022-03-11 18:09:04,796 | INFO : \t test_acc: 93.49 (best: 93.74 from #210)\n",
            "2022-03-11 18:10:34,787 | INFO : Epoch #213\n",
            "2022-03-11 18:10:34,788 | INFO : \t train_acc: 99.912; train_loss: 0.043; test_loss: 28.503; lr: [0.0001]\n",
            "2022-03-11 18:10:34,788 | INFO : \t test_acc: 93.67 (best: 93.74 from #210)\n",
            "2022-03-11 18:12:04,725 | INFO : Epoch #214\n",
            "2022-03-11 18:12:04,725 | INFO : \t train_acc: 99.918; train_loss: 0.009; test_loss: 28.766; lr: [0.0001]\n",
            "2022-03-11 18:12:04,725 | INFO : \t test_acc: 93.69 (best: 93.74 from #210)\n",
            "2022-03-11 18:13:34,637 | INFO : Epoch #215\n",
            "2022-03-11 18:13:34,638 | INFO : \t train_acc: 99.906; train_loss: 0.001; test_loss: 28.500; lr: [0.0001]\n",
            "2022-03-11 18:13:34,638 | INFO : \t test_acc: 93.69 (best: 93.74 from #210)\n",
            "2022-03-11 18:15:04,679 | INFO : Epoch #216\n",
            "2022-03-11 18:15:04,679 | INFO : \t train_acc: 99.918; train_loss: 0.004; test_loss: 28.144; lr: [0.0001]\n",
            "2022-03-11 18:15:04,680 | INFO : \t test_acc: 93.73 (best: 93.74 from #210)\n",
            "2022-03-11 18:16:34,641 | INFO : Epoch #217\n",
            "2022-03-11 18:16:34,642 | INFO : \t train_acc: 99.932; train_loss: 0.024; test_loss: 28.208; lr: [0.0001]\n",
            "2022-03-11 18:16:34,642 | INFO : \t test_acc: 93.75 (best: 93.75 from #217)\n",
            "2022-03-11 18:18:04,644 | INFO : Epoch #218\n",
            "2022-03-11 18:18:04,644 | INFO : \t train_acc: 99.902; train_loss: 0.008; test_loss: 28.246; lr: [0.0001]\n",
            "2022-03-11 18:18:04,644 | INFO : \t test_acc: 93.61 (best: 93.75 from #217)\n",
            "2022-03-11 18:19:34,569 | INFO : Epoch #219\n",
            "2022-03-11 18:19:34,570 | INFO : \t train_acc: 99.906; train_loss: 0.005; test_loss: 28.003; lr: [0.0001]\n",
            "2022-03-11 18:19:34,570 | INFO : \t test_acc: 93.76 (best: 93.76 from #219)\n",
            "2022-03-11 18:21:04,693 | INFO : Epoch #220\n",
            "2022-03-11 18:21:04,694 | INFO : \t train_acc: 99.93; train_loss: 0.003; test_loss: 28.458; lr: [0.0001]\n",
            "2022-03-11 18:21:04,694 | INFO : \t test_acc: 93.54 (best: 93.76 from #219)\n",
            "2022-03-11 18:22:34,602 | INFO : Epoch #221\n",
            "2022-03-11 18:22:34,602 | INFO : \t train_acc: 99.936; train_loss: 0.003; test_loss: 27.993; lr: [0.0001]\n",
            "2022-03-11 18:22:34,602 | INFO : \t test_acc: 93.64 (best: 93.76 from #219)\n",
            "2022-03-11 18:24:04,567 | INFO : Epoch #222\n",
            "2022-03-11 18:24:04,567 | INFO : \t train_acc: 99.928; train_loss: 0.007; test_loss: 28.099; lr: [0.0001]\n",
            "2022-03-11 18:24:04,568 | INFO : \t test_acc: 93.77 (best: 93.77 from #222)\n",
            "2022-03-11 18:25:34,668 | INFO : Epoch #223\n",
            "2022-03-11 18:25:34,668 | INFO : \t train_acc: 99.922; train_loss: 0.003; test_loss: 28.179; lr: [0.0001]\n",
            "2022-03-11 18:25:34,669 | INFO : \t test_acc: 93.71 (best: 93.77 from #222)\n",
            "2022-03-11 18:27:04,699 | INFO : Epoch #224\n",
            "2022-03-11 18:27:04,699 | INFO : \t train_acc: 99.932; train_loss: 0.001; test_loss: 28.402; lr: [0.0001]\n",
            "2022-03-11 18:27:04,700 | INFO : \t test_acc: 93.59 (best: 93.77 from #222)\n",
            "2022-03-11 18:28:34,588 | INFO : Epoch #225\n",
            "2022-03-11 18:28:34,588 | INFO : \t train_acc: 99.912; train_loss: 0.004; test_loss: 28.660; lr: [0.0001]\n",
            "2022-03-11 18:28:34,588 | INFO : \t test_acc: 93.69 (best: 93.77 from #222)\n",
            "2022-03-11 18:30:04,535 | INFO : Epoch #226\n",
            "2022-03-11 18:30:04,536 | INFO : \t train_acc: 99.93; train_loss: 0.002; test_loss: 28.349; lr: [0.0001]\n",
            "2022-03-11 18:30:04,536 | INFO : \t test_acc: 93.66 (best: 93.77 from #222)\n",
            "2022-03-11 18:31:34,460 | INFO : Epoch #227\n",
            "2022-03-11 18:31:34,460 | INFO : \t train_acc: 99.918; train_loss: 0.017; test_loss: 28.380; lr: [0.0001]\n",
            "2022-03-11 18:31:34,461 | INFO : \t test_acc: 93.72 (best: 93.77 from #222)\n",
            "2022-03-11 18:33:04,372 | INFO : Epoch #228\n",
            "2022-03-11 18:33:04,372 | INFO : \t train_acc: 99.916; train_loss: 0.130; test_loss: 28.574; lr: [0.0001]\n",
            "2022-03-11 18:33:04,373 | INFO : \t test_acc: 93.67 (best: 93.77 from #222)\n",
            "2022-03-11 18:34:34,423 | INFO : Epoch #229\n",
            "2022-03-11 18:34:34,423 | INFO : \t train_acc: 99.938; train_loss: 0.048; test_loss: 28.438; lr: [0.0001]\n",
            "2022-03-11 18:34:34,423 | INFO : \t test_acc: 93.58 (best: 93.77 from #222)\n",
            "2022-03-11 18:36:04,482 | INFO : Epoch #230\n",
            "2022-03-11 18:36:04,483 | INFO : \t train_acc: 99.938; train_loss: 0.165; test_loss: 28.318; lr: [0.0001]\n",
            "2022-03-11 18:36:04,483 | INFO : \t test_acc: 93.66 (best: 93.77 from #222)\n",
            "2022-03-11 18:37:34,408 | INFO : Epoch #231\n",
            "2022-03-11 18:37:34,409 | INFO : \t train_acc: 99.932; train_loss: 0.010; test_loss: 28.752; lr: [0.0001]\n",
            "2022-03-11 18:37:34,409 | INFO : \t test_acc: 93.74 (best: 93.77 from #222)\n",
            "2022-03-11 18:39:04,346 | INFO : Epoch #232\n",
            "2022-03-11 18:39:04,346 | INFO : \t train_acc: 99.938; train_loss: 0.002; test_loss: 28.527; lr: [0.0001]\n",
            "2022-03-11 18:39:04,346 | INFO : \t test_acc: 93.64 (best: 93.77 from #222)\n",
            "2022-03-11 18:40:34,244 | INFO : Epoch #233\n",
            "2022-03-11 18:40:34,245 | INFO : \t train_acc: 99.918; train_loss: 0.180; test_loss: 28.386; lr: [0.0001]\n",
            "2022-03-11 18:40:34,245 | INFO : \t test_acc: 93.67 (best: 93.77 from #222)\n",
            "2022-03-11 18:42:04,198 | INFO : Epoch #234\n",
            "2022-03-11 18:42:04,198 | INFO : \t train_acc: 99.934; train_loss: 0.007; test_loss: 28.364; lr: [0.0001]\n",
            "2022-03-11 18:42:04,198 | INFO : \t test_acc: 93.58 (best: 93.77 from #222)\n",
            "2022-03-11 18:43:34,191 | INFO : Epoch #235\n",
            "2022-03-11 18:43:34,192 | INFO : \t train_acc: 99.912; train_loss: 0.001; test_loss: 28.261; lr: [0.0001]\n",
            "2022-03-11 18:43:34,192 | INFO : \t test_acc: 93.81 (best: 93.81 from #235)\n",
            "2022-03-11 18:45:04,182 | INFO : Epoch #236\n",
            "2022-03-11 18:45:04,182 | INFO : \t train_acc: 99.932; train_loss: 0.057; test_loss: 28.557; lr: [0.0001]\n",
            "2022-03-11 18:45:04,182 | INFO : \t test_acc: 93.65 (best: 93.81 from #235)\n",
            "2022-03-11 18:46:34,114 | INFO : Epoch #237\n",
            "2022-03-11 18:46:34,115 | INFO : \t train_acc: 99.932; train_loss: 0.004; test_loss: 28.278; lr: [0.0001]\n",
            "2022-03-11 18:46:34,115 | INFO : \t test_acc: 93.74 (best: 93.81 from #235)\n",
            "2022-03-11 18:48:04,021 | INFO : Epoch #238\n",
            "2022-03-11 18:48:04,022 | INFO : \t train_acc: 99.96; train_loss: 0.003; test_loss: 28.412; lr: [0.0001]\n",
            "2022-03-11 18:48:04,022 | INFO : \t test_acc: 93.65 (best: 93.81 from #235)\n",
            "2022-03-11 18:49:33,955 | INFO : Epoch #239\n",
            "2022-03-11 18:49:33,956 | INFO : \t train_acc: 99.928; train_loss: 0.008; test_loss: 27.876; lr: [0.0001]\n",
            "2022-03-11 18:49:33,956 | INFO : \t test_acc: 93.73 (best: 93.81 from #235)\n",
            "2022-03-11 18:51:04,000 | INFO : Epoch #240\n",
            "2022-03-11 18:51:04,000 | INFO : \t train_acc: 99.932; train_loss: 0.005; test_loss: 28.284; lr: [0.0001]\n",
            "2022-03-11 18:51:04,001 | INFO : \t test_acc: 93.78 (best: 93.81 from #235)\n",
            "2022-03-11 18:52:33,967 | INFO : Epoch #241\n",
            "2022-03-11 18:52:33,967 | INFO : \t train_acc: 99.928; train_loss: 0.003; test_loss: 28.252; lr: [0.0001]\n",
            "2022-03-11 18:52:33,968 | INFO : \t test_acc: 93.7 (best: 93.81 from #235)\n",
            "2022-03-11 18:54:03,946 | INFO : Epoch #242\n",
            "2022-03-11 18:54:03,946 | INFO : \t train_acc: 99.922; train_loss: 0.020; test_loss: 28.168; lr: [0.0001]\n",
            "2022-03-11 18:54:03,947 | INFO : \t test_acc: 93.73 (best: 93.81 from #235)\n",
            "2022-03-11 18:55:33,919 | INFO : Epoch #243\n",
            "2022-03-11 18:55:33,920 | INFO : \t train_acc: 99.95; train_loss: 0.002; test_loss: 28.167; lr: [0.0001]\n",
            "2022-03-11 18:55:33,920 | INFO : \t test_acc: 93.69 (best: 93.81 from #235)\n",
            "2022-03-11 18:57:03,907 | INFO : Epoch #244\n",
            "2022-03-11 18:57:03,908 | INFO : \t train_acc: 99.92; train_loss: 0.188; test_loss: 28.005; lr: [0.0001]\n",
            "2022-03-11 18:57:03,908 | INFO : \t test_acc: 93.79 (best: 93.81 from #235)\n",
            "2022-03-11 18:58:34,166 | INFO : Epoch #245\n",
            "2022-03-11 18:58:34,166 | INFO : \t train_acc: 99.942; train_loss: 0.014; test_loss: 28.356; lr: [0.0001]\n",
            "2022-03-11 18:58:34,167 | INFO : \t test_acc: 93.64 (best: 93.81 from #235)\n",
            "2022-03-11 19:00:04,404 | INFO : Epoch #246\n",
            "2022-03-11 19:00:04,405 | INFO : \t train_acc: 99.942; train_loss: 0.003; test_loss: 28.085; lr: [0.0001]\n",
            "2022-03-11 19:00:04,405 | INFO : \t test_acc: 93.77 (best: 93.81 from #235)\n",
            "2022-03-11 19:01:34,651 | INFO : Epoch #247\n",
            "2022-03-11 19:01:34,652 | INFO : \t train_acc: 99.908; train_loss: 0.001; test_loss: 28.256; lr: [0.0001]\n",
            "2022-03-11 19:01:34,652 | INFO : \t test_acc: 93.79 (best: 93.81 from #235)\n",
            "2022-03-11 19:03:04,960 | INFO : Epoch #248\n",
            "2022-03-11 19:03:04,961 | INFO : \t train_acc: 99.908; train_loss: 0.055; test_loss: 28.531; lr: [0.0001]\n",
            "2022-03-11 19:03:04,961 | INFO : \t test_acc: 93.81 (best: 93.81 from #248)\n",
            "2022-03-11 19:04:35,270 | INFO : Epoch #249\n",
            "2022-03-11 19:04:35,271 | INFO : \t train_acc: 99.948; train_loss: 0.001; test_loss: 28.344; lr: [0.0001]\n",
            "2022-03-11 19:04:35,271 | INFO : \t test_acc: 93.72 (best: 93.81 from #248)\n",
            "2022-03-11 19:06:05,616 | INFO : Epoch #250\n",
            "2022-03-11 19:06:05,617 | INFO : \t train_acc: 99.944; train_loss: 0.021; test_loss: 28.542; lr: [0.0001]\n",
            "2022-03-11 19:06:05,617 | INFO : \t test_acc: 93.74 (best: 93.81 from #248)\n",
            "2022-03-11 19:07:35,874 | INFO : Epoch #251\n",
            "2022-03-11 19:07:35,874 | INFO : \t train_acc: 99.94; train_loss: 0.002; test_loss: 28.382; lr: [0.0001]\n",
            "2022-03-11 19:07:35,874 | INFO : \t test_acc: 93.85 (best: 93.85 from #251)\n",
            "2022-03-11 19:09:06,027 | INFO : Epoch #252\n",
            "2022-03-11 19:09:06,028 | INFO : \t train_acc: 99.952; train_loss: 0.011; test_loss: 27.955; lr: [0.0001]\n",
            "2022-03-11 19:09:06,028 | INFO : \t test_acc: 93.77 (best: 93.85 from #251)\n",
            "2022-03-11 19:10:36,029 | INFO : Epoch #253\n",
            "2022-03-11 19:10:36,030 | INFO : \t train_acc: 99.94; train_loss: 0.003; test_loss: 27.994; lr: [0.0001]\n",
            "2022-03-11 19:10:36,030 | INFO : \t test_acc: 93.78 (best: 93.85 from #251)\n",
            "2022-03-11 19:12:06,100 | INFO : Epoch #254\n",
            "2022-03-11 19:12:06,101 | INFO : \t train_acc: 99.95; train_loss: 0.015; test_loss: 28.292; lr: [0.0001]\n",
            "2022-03-11 19:12:06,101 | INFO : \t test_acc: 93.86 (best: 93.86 from #254)\n",
            "2022-03-11 19:13:36,124 | INFO : Epoch #255\n",
            "2022-03-11 19:13:36,124 | INFO : \t train_acc: 99.956; train_loss: 0.078; test_loss: 28.035; lr: [0.0001]\n",
            "2022-03-11 19:13:36,124 | INFO : \t test_acc: 93.86 (best: 93.86 from #255)\n",
            "2022-03-11 19:15:06,089 | INFO : Epoch #256\n",
            "2022-03-11 19:15:06,089 | INFO : \t train_acc: 99.944; train_loss: 0.001; test_loss: 28.330; lr: [0.0001]\n",
            "2022-03-11 19:15:06,089 | INFO : \t test_acc: 93.81 (best: 93.86 from #255)\n",
            "2022-03-11 19:16:36,056 | INFO : Epoch #257\n",
            "2022-03-11 19:16:36,056 | INFO : \t train_acc: 99.932; train_loss: 0.001; test_loss: 28.726; lr: [0.0001]\n",
            "2022-03-11 19:16:36,056 | INFO : \t test_acc: 93.76 (best: 93.86 from #255)\n",
            "2022-03-11 19:18:05,981 | INFO : Epoch #258\n",
            "2022-03-11 19:18:05,981 | INFO : \t train_acc: 99.932; train_loss: 0.083; test_loss: 28.210; lr: [0.0001]\n",
            "2022-03-11 19:18:05,982 | INFO : \t test_acc: 93.86 (best: 93.86 from #258)\n",
            "2022-03-11 19:19:35,944 | INFO : Epoch #259\n",
            "2022-03-11 19:19:35,945 | INFO : \t train_acc: 99.944; train_loss: 0.007; test_loss: 28.429; lr: [0.0001]\n",
            "2022-03-11 19:19:35,945 | INFO : \t test_acc: 93.81 (best: 93.86 from #258)\n",
            "2022-03-11 19:21:05,735 | INFO : Epoch #260\n",
            "2022-03-11 19:21:05,735 | INFO : \t train_acc: 99.934; train_loss: 0.005; test_loss: 28.474; lr: [0.0001]\n",
            "2022-03-11 19:21:05,736 | INFO : \t test_acc: 93.63 (best: 93.86 from #258)\n",
            "2022-03-11 19:22:35,533 | INFO : Epoch #261\n",
            "2022-03-11 19:22:35,534 | INFO : \t train_acc: 99.936; train_loss: 0.010; test_loss: 28.402; lr: [0.0001]\n",
            "2022-03-11 19:22:35,534 | INFO : \t test_acc: 93.92 (best: 93.92 from #261)\n",
            "2022-03-11 19:24:05,265 | INFO : Epoch #262\n",
            "2022-03-11 19:24:05,266 | INFO : \t train_acc: 99.95; train_loss: 0.033; test_loss: 28.051; lr: [0.0001]\n",
            "2022-03-11 19:24:05,266 | INFO : \t test_acc: 94.0 (best: 94.0 from #262)\n",
            "2022-03-11 19:25:34,962 | INFO : Epoch #263\n",
            "2022-03-11 19:25:34,963 | INFO : \t train_acc: 99.946; train_loss: 0.002; test_loss: 28.575; lr: [0.0001]\n",
            "2022-03-11 19:25:34,963 | INFO : \t test_acc: 93.72 (best: 94.0 from #262)\n",
            "2022-03-11 19:27:04,726 | INFO : Epoch #264\n",
            "2022-03-11 19:27:04,727 | INFO : \t train_acc: 99.938; train_loss: 0.005; test_loss: 28.621; lr: [0.0001]\n",
            "2022-03-11 19:27:04,727 | INFO : \t test_acc: 93.78 (best: 94.0 from #262)\n",
            "2022-03-11 19:28:34,431 | INFO : Epoch #265\n",
            "2022-03-11 19:28:34,432 | INFO : \t train_acc: 99.936; train_loss: 0.045; test_loss: 28.405; lr: [0.0001]\n",
            "2022-03-11 19:28:34,432 | INFO : \t test_acc: 93.78 (best: 94.0 from #262)\n",
            "2022-03-11 19:30:04,051 | INFO : Epoch #266\n",
            "2022-03-11 19:30:04,051 | INFO : \t train_acc: 99.94; train_loss: 0.172; test_loss: 28.593; lr: [0.0001]\n",
            "2022-03-11 19:30:04,051 | INFO : \t test_acc: 93.81 (best: 94.0 from #262)\n",
            "2022-03-11 19:31:33,657 | INFO : Epoch #267\n",
            "2022-03-11 19:31:33,658 | INFO : \t train_acc: 99.948; train_loss: 0.004; test_loss: 28.103; lr: [0.0001]\n",
            "2022-03-11 19:31:33,658 | INFO : \t test_acc: 93.84 (best: 94.0 from #262)\n",
            "2022-03-11 19:33:03,251 | INFO : Epoch #268\n",
            "2022-03-11 19:33:03,252 | INFO : \t train_acc: 99.94; train_loss: 0.008; test_loss: 28.103; lr: [0.0001]\n",
            "2022-03-11 19:33:03,252 | INFO : \t test_acc: 93.83 (best: 94.0 from #262)\n",
            "2022-03-11 19:34:32,865 | INFO : Epoch #269\n",
            "2022-03-11 19:34:32,865 | INFO : \t train_acc: 99.932; train_loss: 0.072; test_loss: 28.289; lr: [0.0001]\n",
            "2022-03-11 19:34:32,866 | INFO : \t test_acc: 93.81 (best: 94.0 from #262)\n",
            "2022-03-11 19:36:02,629 | INFO : Epoch #270\n",
            "2022-03-11 19:36:02,629 | INFO : \t train_acc: 99.938; train_loss: 0.013; test_loss: 28.398; lr: [0.0001]\n",
            "2022-03-11 19:36:02,630 | INFO : \t test_acc: 93.73 (best: 94.0 from #262)\n",
            "2022-03-11 19:37:32,459 | INFO : Epoch #271\n",
            "2022-03-11 19:37:32,460 | INFO : \t train_acc: 99.942; train_loss: 0.004; test_loss: 28.222; lr: [0.0001]\n",
            "2022-03-11 19:37:32,460 | INFO : \t test_acc: 93.77 (best: 94.0 from #262)\n",
            "2022-03-11 19:39:02,245 | INFO : Epoch #272\n",
            "2022-03-11 19:39:02,245 | INFO : \t train_acc: 99.954; train_loss: 0.006; test_loss: 28.577; lr: [0.0001]\n",
            "2022-03-11 19:39:02,245 | INFO : \t test_acc: 93.71 (best: 94.0 from #262)\n",
            "2022-03-11 19:40:31,889 | INFO : Epoch #273\n",
            "2022-03-11 19:40:31,890 | INFO : \t train_acc: 99.952; train_loss: 0.042; test_loss: 28.351; lr: [0.0001]\n",
            "2022-03-11 19:40:31,890 | INFO : \t test_acc: 93.8 (best: 94.0 from #262)\n",
            "2022-03-11 19:42:01,536 | INFO : Epoch #274\n",
            "2022-03-11 19:42:01,536 | INFO : \t train_acc: 99.962; train_loss: 0.004; test_loss: 28.752; lr: [0.0001]\n",
            "2022-03-11 19:42:01,536 | INFO : \t test_acc: 93.73 (best: 94.0 from #262)\n",
            "2022-03-11 19:43:31,165 | INFO : Epoch #275\n",
            "2022-03-11 19:43:31,165 | INFO : \t train_acc: 99.93; train_loss: 0.019; test_loss: 28.853; lr: [0.0001]\n",
            "2022-03-11 19:43:31,165 | INFO : \t test_acc: 93.71 (best: 94.0 from #262)\n",
            "2022-03-11 19:45:01,083 | INFO : Epoch #276\n",
            "2022-03-11 19:45:01,083 | INFO : \t train_acc: 99.946; train_loss: 0.027; test_loss: 28.222; lr: [0.0001]\n",
            "2022-03-11 19:45:01,083 | INFO : \t test_acc: 93.77 (best: 94.0 from #262)\n",
            "2022-03-11 19:46:30,758 | INFO : Epoch #277\n",
            "2022-03-11 19:46:30,759 | INFO : \t train_acc: 99.936; train_loss: 0.259; test_loss: 28.414; lr: [0.0001]\n",
            "2022-03-11 19:46:30,759 | INFO : \t test_acc: 93.7 (best: 94.0 from #262)\n",
            "2022-03-11 19:48:00,451 | INFO : Epoch #278\n",
            "2022-03-11 19:48:00,452 | INFO : \t train_acc: 99.944; train_loss: 0.009; test_loss: 28.708; lr: [0.0001]\n",
            "2022-03-11 19:48:00,452 | INFO : \t test_acc: 93.87 (best: 94.0 from #262)\n",
            "2022-03-11 19:49:30,271 | INFO : Epoch #279\n",
            "2022-03-11 19:49:30,271 | INFO : \t train_acc: 99.94; train_loss: 0.056; test_loss: 28.598; lr: [0.0001]\n",
            "2022-03-11 19:49:30,271 | INFO : \t test_acc: 93.89 (best: 94.0 from #262)\n",
            "2022-03-11 19:51:00,077 | INFO : Epoch #280\n",
            "2022-03-11 19:51:00,077 | INFO : \t train_acc: 99.96; train_loss: 0.005; test_loss: 28.601; lr: [0.0001]\n",
            "2022-03-11 19:51:00,078 | INFO : \t test_acc: 93.71 (best: 94.0 from #262)\n",
            "2022-03-11 19:52:29,897 | INFO : Epoch #281\n",
            "2022-03-11 19:52:29,897 | INFO : \t train_acc: 99.95; train_loss: 0.098; test_loss: 28.271; lr: [0.0001]\n",
            "2022-03-11 19:52:29,897 | INFO : \t test_acc: 93.74 (best: 94.0 from #262)\n",
            "2022-03-11 19:53:59,796 | INFO : Epoch #282\n",
            "2022-03-11 19:53:59,797 | INFO : \t train_acc: 99.954; train_loss: 0.106; test_loss: 28.748; lr: [0.0001]\n",
            "2022-03-11 19:53:59,797 | INFO : \t test_acc: 93.86 (best: 94.0 from #262)\n",
            "2022-03-11 19:55:29,558 | INFO : Epoch #283\n",
            "2022-03-11 19:55:29,559 | INFO : \t train_acc: 99.958; train_loss: 0.006; test_loss: 28.319; lr: [0.0001]\n",
            "2022-03-11 19:55:29,559 | INFO : \t test_acc: 93.82 (best: 94.0 from #262)\n",
            "2022-03-11 19:56:59,319 | INFO : Epoch #284\n",
            "2022-03-11 19:56:59,319 | INFO : \t train_acc: 99.956; train_loss: 0.034; test_loss: 28.314; lr: [0.0001]\n",
            "2022-03-11 19:56:59,320 | INFO : \t test_acc: 93.86 (best: 94.0 from #262)\n",
            "2022-03-11 19:58:29,081 | INFO : Epoch #285\n",
            "2022-03-11 19:58:29,081 | INFO : \t train_acc: 99.952; train_loss: 0.000; test_loss: 28.574; lr: [0.0001]\n",
            "2022-03-11 19:58:29,081 | INFO : \t test_acc: 93.82 (best: 94.0 from #262)\n",
            "2022-03-11 19:59:58,819 | INFO : Epoch #286\n",
            "2022-03-11 19:59:58,820 | INFO : \t train_acc: 99.95; train_loss: 0.003; test_loss: 28.532; lr: [0.0001]\n",
            "2022-03-11 19:59:58,820 | INFO : \t test_acc: 93.88 (best: 94.0 from #262)\n",
            "2022-03-11 20:01:28,567 | INFO : Epoch #287\n",
            "2022-03-11 20:01:28,568 | INFO : \t train_acc: 99.936; train_loss: 0.000; test_loss: 28.611; lr: [0.0001]\n",
            "2022-03-11 20:01:28,568 | INFO : \t test_acc: 93.83 (best: 94.0 from #262)\n",
            "2022-03-11 20:02:58,324 | INFO : Epoch #288\n",
            "2022-03-11 20:02:58,325 | INFO : \t train_acc: 99.954; train_loss: 0.042; test_loss: 28.390; lr: [0.0001]\n",
            "2022-03-11 20:02:58,325 | INFO : \t test_acc: 93.88 (best: 94.0 from #262)\n",
            "2022-03-11 20:04:28,038 | INFO : Epoch #289\n",
            "2022-03-11 20:04:28,039 | INFO : \t train_acc: 99.95; train_loss: 0.001; test_loss: 28.562; lr: [0.0001]\n",
            "2022-03-11 20:04:28,039 | INFO : \t test_acc: 93.8 (best: 94.0 from #262)\n",
            "2022-03-11 20:05:57,841 | INFO : Epoch #290\n",
            "2022-03-11 20:05:57,841 | INFO : \t train_acc: 99.954; train_loss: 0.001; test_loss: 28.820; lr: [0.0001]\n",
            "2022-03-11 20:05:57,841 | INFO : \t test_acc: 93.76 (best: 94.0 from #262)\n",
            "2022-03-11 20:07:27,530 | INFO : Epoch #291\n",
            "2022-03-11 20:07:27,530 | INFO : \t train_acc: 99.95; train_loss: 0.002; test_loss: 28.454; lr: [0.0001]\n",
            "2022-03-11 20:07:27,530 | INFO : \t test_acc: 93.81 (best: 94.0 from #262)\n",
            "2022-03-11 20:08:57,146 | INFO : Epoch #292\n",
            "2022-03-11 20:08:57,147 | INFO : \t train_acc: 99.952; train_loss: 0.003; test_loss: 28.321; lr: [0.0001]\n",
            "2022-03-11 20:08:57,147 | INFO : \t test_acc: 93.8 (best: 94.0 from #262)\n",
            "2022-03-11 20:10:26,844 | INFO : Epoch #293\n",
            "2022-03-11 20:10:26,845 | INFO : \t train_acc: 99.958; train_loss: 0.024; test_loss: 28.646; lr: [0.0001]\n",
            "2022-03-11 20:10:26,845 | INFO : \t test_acc: 93.71 (best: 94.0 from #262)\n",
            "2022-03-11 20:11:56,600 | INFO : Epoch #294\n",
            "2022-03-11 20:11:56,600 | INFO : \t train_acc: 99.946; train_loss: 0.440; test_loss: 28.479; lr: [0.0001]\n",
            "2022-03-11 20:11:56,600 | INFO : \t test_acc: 93.84 (best: 94.0 from #262)\n",
            "2022-03-11 20:13:26,218 | INFO : Epoch #295\n",
            "2022-03-11 20:13:26,219 | INFO : \t train_acc: 99.952; train_loss: 0.004; test_loss: 28.448; lr: [0.0001]\n",
            "2022-03-11 20:13:26,219 | INFO : \t test_acc: 93.85 (best: 94.0 from #262)\n",
            "2022-03-11 20:14:55,852 | INFO : Epoch #296\n",
            "2022-03-11 20:14:55,852 | INFO : \t train_acc: 99.942; train_loss: 0.002; test_loss: 28.200; lr: [0.0001]\n",
            "2022-03-11 20:14:55,852 | INFO : \t test_acc: 93.86 (best: 94.0 from #262)\n",
            "2022-03-11 20:16:25,529 | INFO : Epoch #297\n",
            "2022-03-11 20:16:25,530 | INFO : \t train_acc: 99.964; train_loss: 0.002; test_loss: 28.713; lr: [0.0001]\n",
            "2022-03-11 20:16:25,530 | INFO : \t test_acc: 93.82 (best: 94.0 from #262)\n",
            "2022-03-11 20:17:55,320 | INFO : Epoch #298\n",
            "2022-03-11 20:17:55,321 | INFO : \t train_acc: 99.964; train_loss: 0.015; test_loss: 28.272; lr: [0.0001]\n",
            "2022-03-11 20:17:55,321 | INFO : \t test_acc: 93.77 (best: 94.0 from #262)\n",
            "2022-03-11 20:19:25,154 | INFO : Epoch #299\n",
            "2022-03-11 20:19:25,155 | INFO : \t train_acc: 99.962; train_loss: 0.006; test_loss: 28.391; lr: [0.0001]\n",
            "2022-03-11 20:19:25,155 | INFO : \t test_acc: 93.76 (best: 94.0 from #262)\n",
            "2022-03-11 20:19:25,284 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet56_cifar10_baseline finetuned 19.57 --> 94.0.\n",
            "2022-03-11 20:19:25,285 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet56_cifar10_baseline pruned, grouped, and finetuned: 93.78 --> 19.57 --> 94.0.\n",
            "\n",
            "2022-03-11 20:19:25,289 | INFO : setting\n",
            "2022-03-11 20:19:25,291 | INFO : Saving setting file to /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet56_cifar10_demo_run/setting.json...\n",
            "2022-03-11 20:19:25,295 | INFO : Experiment resnet56_cifar10_demo (task: finetune) ends at 2022-03-11T20:19:25.285282; result: 93.78 --> 94.0.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python main.py \\\n",
        "--exp_desc resnet110_cifar10_demo  \\\n",
        "--setting_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/settings/resnet110_cifar10_reported.json \\\n",
        "--model_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet110_cifar10_baseline \\\n",
        "--snapshot_folder_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/snapshots/resnet110/ \\\n",
        "--output_folder_dir /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/output/resnet110_cifar10_demo \\\n",
        "--baseline True \\\n",
        "--task finetune"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ND5ltSdYYLtO",
        "outputId": "dd23acd6-2b25-44db-d80c-7dca9b1e9058"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-03-14 03:14:04,895 | INFO : Experiment resnet110_cifar10_demo starts at 2022-03-14T03:14:04.136283.\n",
            "2022-03-14 03:14:04,895 | INFO : Parsed setting file from /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/settings/resnet110_cifar10_reported.json...\n",
            "2022-03-14 03:14:04,924 | INFO : ==> Preparing dataset: cifar10\n",
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n",
            "2022-03-14 03:14:07,204 | INFO : ==> Building baseline model: /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet110_cifar10_baseline (dataset: cifar10)\n",
            "2022-03-14 03:14:11,873 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet110_cifar10_baseline loaded (<class 'models.resnet.CifarResNet'>)\n",
            "2022-03-14 03:14:14,962 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet110_cifar10_baseline <class 'models.resnet.CifarResNet'> has a baseline of 94.26.\n",
            "2022-03-14 03:14:15,012 | INFO : pruning_strategy: greedy (tickets magnitute increase); n_clusters: 8; eval_pruned_kernel_relationship: True; cost_balancer: auto; eval_kept_kernel_ratio: 0.2\n",
            "2022-03-14 03:15:44,716 | INFO : Layer 2-0-0; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 33, 'K-PCA': 3});\tSequence: {'Spectral': 30, 'K-PCA': 2});\n",
            "2022-03-14 03:17:12,986 | INFO : Layer 2-0-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 25, 'DBSCAN': 9, 'Spectral': 2});\tSequence: {'K-PCA': 6, 'DBSCAN': 1, 'Spectral': 1});\n",
            "2022-03-14 03:18:44,116 | INFO : Layer 2-1-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 25, 'Spectral': 10, 'DBSCAN': 1});\tSequence: {'K-PCA': 7, 'Spectral': 2});\n",
            "2022-03-14 03:20:16,730 | INFO : Layer 2-1-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 28, 'Spectral': 8});\tSequence: {'Spectral': 6, 'K-PCA': 26});\n",
            "2022-03-14 03:21:44,150 | INFO : Layer 2-2-0; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 36});\tSequence: {'Spectral': 35});\n",
            "2022-03-14 03:23:14,989 | INFO : Layer 2-2-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 23, 'DBSCAN': 13});\tSequence: {'K-PCA': 10, 'DBSCAN': 4});\n",
            "2022-03-14 03:24:41,295 | INFO : Layer 2-3-0; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 36});\tSequence: {'Spectral': 35});\n",
            "2022-03-14 03:26:11,992 | INFO : Layer 2-3-2; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 23, 'DBSCAN': 13});\tSequence: {'Spectral': 8, 'DBSCAN': 8});\n",
            "2022-03-14 03:27:40,316 | INFO : Layer 2-4-0; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 19, 'K-PCA': 17});\tSequence: {'Spectral': 9, 'K-PCA': 7});\n",
            "2022-03-14 03:29:09,832 | INFO : Layer 2-4-2; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 30, 'Spectral': 3, 'K-PCA': 3});\tSequence: {'Spectral': 1, 'DBSCAN': 16});\n",
            "2022-03-14 03:30:37,879 | INFO : Layer 2-5-0; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 19, 'K-PCA': 16, 'Spectral': 1});\tSequence: {'DBSCAN': 7, 'K-PCA': 7});\n",
            "2022-03-14 03:32:05,156 | INFO : Layer 2-5-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 16, 'Spectral': 14, 'DBSCAN': 6});\tSequence: {'K-PCA': 4, 'DBSCAN': 2, 'Spectral': 8});\n",
            "2022-03-14 03:33:33,266 | INFO : Layer 2-6-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 20, 'Spectral': 14, 'DBSCAN': 2});\tSequence: {'Spectral': 3, 'K-PCA': 5});\n",
            "2022-03-14 03:35:06,835 | INFO : Layer 2-6-2; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 33, 'DBSCAN': 2, 'K-PCA': 1});\tSequence: {'Spectral': 22});\n",
            "2022-03-14 03:36:38,120 | INFO : Layer 2-7-0; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 20, 'K-PCA': 16});\tSequence: {'Spectral': 13, 'K-PCA': 10});\n",
            "2022-03-14 03:38:07,608 | INFO : Layer 2-7-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 27, 'Spectral': 7, 'DBSCAN': 2});\tSequence: {'K-PCA': 10, 'Spectral': 1});\n",
            "2022-03-14 03:39:41,392 | INFO : Layer 2-8-0; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 28, 'K-PCA': 7, 'DBSCAN': 1});\tSequence: {'Spectral': 10, 'K-PCA': 1});\n",
            "2022-03-14 03:41:15,478 | INFO : Layer 2-8-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 24, 'DBSCAN': 12});\tSequence: {'K-PCA': 8, 'DBSCAN': 3});\n",
            "2022-03-14 03:42:51,915 | INFO : Layer 2-9-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 24, 'Spectral': 9, 'DBSCAN': 3});\tSequence: {'K-PCA': 10, 'DBSCAN': 1, 'Spectral': 1});\n",
            "2022-03-14 03:44:23,696 | INFO : Layer 2-9-2; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 24, 'K-PCA': 6, 'Spectral': 6});\tSequence: {'DBSCAN': 8, 'Spectral': 2, 'K-PCA': 2});\n",
            "2022-03-14 03:45:55,456 | INFO : Layer 2-10-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 35, 'DBSCAN': 1});\tSequence: {'K-PCA': 34});\n",
            "2022-03-14 03:47:26,025 | INFO : Layer 2-10-2; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 16, 'K-PCA': 12, 'DBSCAN': 8});\tSequence: {'K-PCA': 4, 'DBSCAN': 1, 'Spectral': 6});\n",
            "2022-03-14 03:48:54,717 | INFO : Layer 2-11-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 20, 'DBSCAN': 12, 'Spectral': 4});\tSequence: {'DBSCAN': 2, 'K-PCA': 12});\n",
            "2022-03-14 03:50:16,585 | INFO : Layer 2-11-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 25, 'DBSCAN': 6, 'Spectral': 5});\tSequence: {'Spectral': 1, 'K-PCA': 11});\n",
            "2022-03-14 03:51:40,261 | INFO : Layer 2-12-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 20, 'DBSCAN': 16});\tSequence: {'DBSCAN': 5, 'K-PCA': 11});\n",
            "2022-03-14 03:53:09,057 | INFO : Layer 2-12-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 15, 'DBSCAN': 13, 'Spectral': 8});\tSequence: {'K-PCA': 9, 'DBSCAN': 5, 'Spectral': 3});\n",
            "2022-03-14 03:55:05,942 | INFO : Layer 2-13-0; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 26, 'DBSCAN': 10});\tSequence: {'DBSCAN': 3, 'Spectral': 13});\n",
            "2022-03-14 03:56:32,823 | INFO : Layer 2-13-2; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 18, 'K-PCA': 11, 'Spectral': 7});\tSequence: {'Spectral': 3, 'DBSCAN': 5, 'K-PCA': 4});\n",
            "2022-03-14 03:58:15,713 | INFO : Layer 2-14-0; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 18, 'K-PCA': 10, 'Spectral': 8});\tSequence: {'Spectral': 3, 'DBSCAN': 4, 'K-PCA': 3});\n",
            "2022-03-14 03:59:47,438 | INFO : Layer 2-14-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 23, 'Spectral': 9, 'DBSCAN': 4});\tSequence: {'Spectral': 2, 'DBSCAN': 1, 'K-PCA': 16});\n",
            "2022-03-14 04:01:17,643 | INFO : Layer 2-15-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 27, 'DBSCAN': 5, 'Spectral': 4});\tSequence: {'DBSCAN': 1, 'K-PCA': 19, 'Spectral': 2});\n",
            "2022-03-14 04:02:47,515 | INFO : Layer 2-15-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'DBSCAN': 18, 'K-PCA': 18});\tSequence: {'DBSCAN': 7, 'K-PCA': 11});\n",
            "2022-03-14 04:04:07,883 | INFO : Layer 2-16-0; Shape (16, 16, 3, 3) -> (16, 144); Method: DBSCAN; Occurrence: ({'DBSCAN': 22, 'K-PCA': 11, 'Spectral': 3});\tSequence: {'DBSCAN': 19, 'K-PCA': 7});\n",
            "2022-03-14 04:05:37,494 | INFO : Layer 2-16-2; Shape (16, 16, 3, 3) -> (16, 144); Method: Spectral; Occurrence: ({'Spectral': 19, 'K-PCA': 11, 'DBSCAN': 6});\tSequence: {'DBSCAN': 2, 'Spectral': 9, 'K-PCA': 4});\n",
            "2022-03-14 04:07:08,833 | INFO : Layer 2-17-0; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 16, 'Spectral': 11, 'DBSCAN': 9});\tSequence: {'Spectral': 4, 'DBSCAN': 4, 'K-PCA': 4});\n",
            "2022-03-14 04:08:39,719 | INFO : Layer 2-17-2; Shape (16, 16, 3, 3) -> (16, 144); Method: K-PCA; Occurrence: ({'K-PCA': 27, 'Spectral': 7, 'DBSCAN': 2});\tSequence: {'K-PCA': 15, 'Spectral': 5});\n",
            "2022-03-14 04:10:51,064 | INFO : Layer 3-0-0; Shape (32, 16, 3, 3) -> (32, 144); Method: Spectral; Occurrence: ({'Spectral': 15, 'DBSCAN': 15, 'K-PCA': 6});\tSequence: {'K-PCA': 2, 'Spectral': 6, 'DBSCAN': 4});\n",
            "2022-03-14 04:13:52,332 | INFO : Layer 3-0-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 20, 'K-PCA': 10, 'DBSCAN': 6});\tSequence: {'Spectral': 8, 'DBSCAN': 2, 'K-PCA': 3});\n",
            "2022-03-14 04:16:47,866 | INFO : Layer 3-1-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 33, 'Spectral': 3});\tSequence: {'K-PCA': 24, 'Spectral': 1});\n",
            "2022-03-14 04:19:02,055 | INFO : Layer 3-1-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 16, 'K-PCA': 13, 'DBSCAN': 7});\tSequence: {'DBSCAN': 4, 'Spectral': 3, 'K-PCA': 5});\n",
            "2022-03-14 04:21:40,641 | INFO : Layer 3-2-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 25, 'Spectral': 9, 'DBSCAN': 2});\tSequence: {'Spectral': 6, 'K-PCA': 6});\n",
            "2022-03-14 04:23:55,818 | INFO : Layer 3-2-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 26, 'K-PCA': 8, 'DBSCAN': 2});\tSequence: {'K-PCA': 1, 'Spectral': 13, 'DBSCAN': 1});\n",
            "2022-03-14 04:26:48,019 | INFO : Layer 3-3-0; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 29, 'DBSCAN': 6, 'K-PCA': 1});\tSequence: {'Spectral': 11, 'DBSCAN': 1});\n",
            "2022-03-14 04:29:00,971 | INFO : Layer 3-3-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 28, 'K-PCA': 7, 'DBSCAN': 1});\tSequence: {'K-PCA': 2, 'Spectral': 13});\n",
            "2022-03-14 04:31:10,116 | INFO : Layer 3-4-0; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 26, 'DBSCAN': 8, 'K-PCA': 2});\tSequence: {'DBSCAN': 6, 'Spectral': 20, 'K-PCA': 1});\n",
            "2022-03-14 04:33:24,393 | INFO : Layer 3-4-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 28, 'Spectral': 8});\tSequence: {'K-PCA': 11, 'Spectral': 2});\n",
            "2022-03-14 04:35:37,685 | INFO : Layer 3-5-0; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 32, 'K-PCA': 2, 'DBSCAN': 2});\tSequence: {'Spectral': 20, 'K-PCA': 1, 'DBSCAN': 1});\n",
            "2022-03-14 04:38:16,373 | INFO : Layer 3-5-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 16, 'Spectral': 12, 'DBSCAN': 8});\tSequence: {'Spectral': 2, 'K-PCA': 4, 'DBSCAN': 2});\n",
            "2022-03-14 04:42:17,047 | INFO : Layer 3-6-0; Shape (32, 32, 3, 3) -> (32, 288); Method: DBSCAN; Occurrence: ({'DBSCAN': 22, 'Spectral': 14});\tSequence: {'DBSCAN': 10, 'Spectral': 6});\n",
            "2022-03-14 04:44:31,229 | INFO : Layer 3-6-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 20, 'Spectral': 12, 'DBSCAN': 4});\tSequence: {'Spectral': 2, 'K-PCA': 4, 'DBSCAN': 1});\n",
            "2022-03-14 04:47:28,240 | INFO : Layer 3-7-0; Shape (32, 32, 3, 3) -> (32, 288); Method: DBSCAN; Occurrence: ({'DBSCAN': 28, 'K-PCA': 7, 'Spectral': 1});\tSequence: {'DBSCAN': 7, 'K-PCA': 1});\n",
            "2022-03-14 04:50:03,766 | INFO : Layer 3-7-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 18, 'K-PCA': 16, 'DBSCAN': 2});\tSequence: {'DBSCAN': 1, 'K-PCA': 5, 'Spectral': 7});\n",
            "2022-03-14 04:52:17,369 | INFO : Layer 3-8-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 29, 'DBSCAN': 4, 'Spectral': 3});\tSequence: {'K-PCA': 8, 'DBSCAN': 1, 'Spectral': 1});\n",
            "2022-03-14 04:54:47,648 | INFO : Layer 3-8-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 19, 'K-PCA': 9, 'DBSCAN': 8});\tSequence: {'DBSCAN': 2, 'Spectral': 12, 'K-PCA': 3});\n",
            "2022-03-14 04:57:01,046 | INFO : Layer 3-9-0; Shape (32, 32, 3, 3) -> (32, 288); Method: DBSCAN; Occurrence: ({'DBSCAN': 21, 'Spectral': 12, 'K-PCA': 3});\tSequence: {'DBSCAN': 9, 'Spectral': 4});\n",
            "2022-03-14 04:59:11,225 | INFO : Layer 3-9-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 17, 'DBSCAN': 15, 'Spectral': 4});\tSequence: {'DBSCAN': 7, 'Spectral': 1, 'K-PCA': 8});\n",
            "2022-03-14 05:01:48,655 | INFO : Layer 3-10-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 22, 'DBSCAN': 10, 'Spectral': 4});\tSequence: {'DBSCAN': 4, 'Spectral': 1, 'K-PCA': 9});\n",
            "2022-03-14 05:04:28,230 | INFO : Layer 3-10-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 15, 'Spectral': 13, 'DBSCAN': 8});\tSequence: {'K-PCA': 4, 'Spectral': 5, 'DBSCAN': 5});\n",
            "2022-03-14 05:06:39,153 | INFO : Layer 3-11-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 22, 'Spectral': 13, 'DBSCAN': 1});\tSequence: {'K-PCA': 10, 'Spectral': 6});\n",
            "2022-03-14 05:09:09,861 | INFO : Layer 3-11-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 24, 'Spectral': 7, 'DBSCAN': 5});\tSequence: {'K-PCA': 15, 'DBSCAN': 2, 'Spectral': 5});\n",
            "2022-03-14 05:12:22,890 | INFO : Layer 3-12-0; Shape (32, 32, 3, 3) -> (32, 288); Method: DBSCAN; Occurrence: ({'DBSCAN': 17, 'K-PCA': 16, 'Spectral': 3});\tSequence: {'DBSCAN': 5, 'Spectral': 1, 'K-PCA': 4});\n",
            "2022-03-14 05:14:54,413 | INFO : Layer 3-12-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 19, 'K-PCA': 14, 'DBSCAN': 3});\tSequence: {'Spectral': 5, 'K-PCA': 2, 'DBSCAN': 1});\n",
            "2022-03-14 05:17:32,063 | INFO : Layer 3-13-0; Shape (32, 32, 3, 3) -> (32, 288); Method: DBSCAN; Occurrence: ({'DBSCAN': 24, 'Spectral': 7, 'K-PCA': 5});\tSequence: {'DBSCAN': 13, 'Spectral': 5, 'K-PCA': 1});\n",
            "2022-03-14 05:19:46,776 | INFO : Layer 3-13-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 13, 'DBSCAN': 12, 'K-PCA': 11});\tSequence: {'DBSCAN': 3, 'K-PCA': 1, 'Spectral': 2});\n",
            "2022-03-14 05:21:56,661 | INFO : Layer 3-14-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 32, 'DBSCAN': 3, 'Spectral': 1});\tSequence: {'K-PCA': 16});\n",
            "2022-03-14 05:24:10,690 | INFO : Layer 3-14-2; Shape (32, 32, 3, 3) -> (32, 288); Method: Spectral; Occurrence: ({'Spectral': 22, 'DBSCAN': 12, 'K-PCA': 2});\tSequence: {'Spectral': 14, 'DBSCAN': 7, 'K-PCA': 1});\n",
            "2022-03-14 05:26:24,771 | INFO : Layer 3-15-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 21, 'Spectral': 8, 'DBSCAN': 7});\tSequence: {'K-PCA': 10, 'DBSCAN': 1, 'Spectral': 2});\n",
            "2022-03-14 05:28:35,866 | INFO : Layer 3-15-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 20, 'Spectral': 10, 'DBSCAN': 6});\tSequence: {'K-PCA': 8, 'Spectral': 4, 'DBSCAN': 2});\n",
            "2022-03-14 05:30:52,477 | INFO : Layer 3-16-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 35, 'Spectral': 1});\tSequence: {'K-PCA': 33});\n",
            "2022-03-14 05:33:29,439 | INFO : Layer 3-16-2; Shape (32, 32, 3, 3) -> (32, 288); Method: DBSCAN; Occurrence: ({'DBSCAN': 25, 'Spectral': 6, 'K-PCA': 5});\tSequence: {'Spectral': 1, 'DBSCAN': 10, 'K-PCA': 1});\n",
            "2022-03-14 05:36:18,287 | INFO : Layer 3-17-0; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 26, 'Spectral': 10});\tSequence: {'K-PCA': 10, 'Spectral': 1});\n",
            "2022-03-14 05:39:41,812 | INFO : Layer 3-17-2; Shape (32, 32, 3, 3) -> (32, 288); Method: K-PCA; Occurrence: ({'K-PCA': 22, 'Spectral': 14});\tSequence: {'Spectral': 3, 'K-PCA': 19});\n",
            "2022-03-14 05:43:56,656 | INFO : Layer 4-0-0; Shape (64, 32, 3, 3) -> (64, 288); Method: Spectral; Occurrence: ({'Spectral': 18, 'K-PCA': 17, 'DBSCAN': 1});\tSequence: {'K-PCA': 4, 'Spectral': 8});\n",
            "2022-03-14 05:48:56,268 | INFO : Layer 4-0-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 26, 'DBSCAN': 8, 'Spectral': 2});\tSequence: {'K-PCA': 19, 'DBSCAN': 3});\n",
            "2022-03-14 05:53:07,166 | INFO : Layer 4-1-0; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 18, 'Spectral': 15, 'DBSCAN': 3});\tSequence: {'Spectral': 4, 'K-PCA': 11, 'DBSCAN': 1});\n",
            "2022-03-14 05:58:22,960 | INFO : Layer 4-1-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 32, 'DBSCAN': 4});\tSequence: {'DBSCAN': 1, 'K-PCA': 29});\n",
            "2022-03-14 06:02:32,945 | INFO : Layer 4-2-0; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 30, 'Spectral': 3, 'DBSCAN': 3});\tSequence: {'Spectral': 2, 'K-PCA': 20});\n",
            "2022-03-14 06:06:41,412 | INFO : Layer 4-2-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 22, 'DBSCAN': 14});\tSequence: {'DBSCAN': 13, 'K-PCA': 21});\n",
            "2022-03-14 06:11:41,746 | INFO : Layer 4-3-0; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 22, 'K-PCA': 9, 'DBSCAN': 5});\tSequence: {'DBSCAN': 1, 'K-PCA': 2, 'Spectral': 9});\n",
            "2022-03-14 06:18:48,678 | INFO : Layer 4-3-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 28, 'DBSCAN': 6, 'Spectral': 2});\tSequence: {'K-PCA': 15, 'DBSCAN': 3});\n",
            "2022-03-14 06:23:01,557 | INFO : Layer 4-4-0; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 28, 'K-PCA': 8});\tSequence: {'Spectral': 8, 'K-PCA': 3});\n",
            "2022-03-14 06:27:12,952 | INFO : Layer 4-4-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 31, 'Spectral': 3, 'DBSCAN': 2});\tSequence: {'K-PCA': 17, 'Spectral': 2});\n",
            "2022-03-14 06:31:20,310 | INFO : Layer 4-5-0; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 26, 'Spectral': 6, 'K-PCA': 4});\tSequence: {'DBSCAN': 16, 'Spectral': 2, 'K-PCA': 3});\n",
            "2022-03-14 06:35:23,055 | INFO : Layer 4-5-2; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 20, 'K-PCA': 16});\tSequence: {'Spectral': 15, 'K-PCA': 5});\n",
            "2022-03-14 06:40:23,769 | INFO : Layer 4-6-0; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 22, 'DBSCAN': 11, 'Spectral': 3});\tSequence: {'DBSCAN': 2, 'Spectral': 1, 'K-PCA': 5});\n",
            "2022-03-14 06:44:45,640 | INFO : Layer 4-6-2; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 25, 'K-PCA': 7, 'Spectral': 4});\tSequence: {'DBSCAN': 12, 'Spectral': 1, 'K-PCA': 3});\n",
            "2022-03-14 06:48:59,961 | INFO : Layer 4-7-0; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 21, 'K-PCA': 8, 'Spectral': 7});\tSequence: {'DBSCAN': 6, 'Spectral': 1, 'K-PCA': 4});\n",
            "2022-03-14 06:53:06,739 | INFO : Layer 4-7-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 28, 'Spectral': 8});\tSequence: {'K-PCA': 8, 'Spectral': 2});\n",
            "2022-03-14 06:58:04,133 | INFO : Layer 4-8-0; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 34, 'Spectral': 2});\tSequence: {'DBSCAN': 30, 'Spectral': 1});\n",
            "2022-03-14 07:02:14,598 | INFO : Layer 4-8-2; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 18, 'DBSCAN': 13, 'K-PCA': 5});\tSequence: {'Spectral': 6, 'DBSCAN': 5, 'K-PCA': 1});\n",
            "2022-03-14 07:07:16,473 | INFO : Layer 4-9-0; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 28, 'DBSCAN': 8});\tSequence: {'K-PCA': 26, 'DBSCAN': 5});\n",
            "2022-03-14 07:11:21,882 | INFO : Layer 4-9-2; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 19, 'Spectral': 13, 'K-PCA': 4});\tSequence: {'DBSCAN': 4, 'K-PCA': 2, 'Spectral': 7});\n",
            "2022-03-14 07:15:32,779 | INFO : Layer 4-10-0; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 23, 'DBSCAN': 12, 'Spectral': 1});\tSequence: {'K-PCA': 9, 'DBSCAN': 4});\n",
            "2022-03-14 07:20:49,857 | INFO : Layer 4-10-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 27, 'DBSCAN': 6, 'Spectral': 3});\tSequence: {'K-PCA': 18, 'DBSCAN': 3, 'Spectral': 1});\n",
            "2022-03-14 07:25:46,958 | INFO : Layer 4-11-0; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 19, 'DBSCAN': 17});\tSequence: {'DBSCAN': 4, 'K-PCA': 8});\n",
            "2022-03-14 07:29:58,665 | INFO : Layer 4-11-2; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 16, 'DBSCAN': 13, 'K-PCA': 7});\tSequence: {'DBSCAN': 7, 'K-PCA': 2, 'Spectral': 6});\n",
            "2022-03-14 07:34:14,209 | INFO : Layer 4-12-0; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 19, 'K-PCA': 9, 'DBSCAN': 8});\tSequence: {'DBSCAN': 2, 'Spectral': 7, 'K-PCA': 2});\n",
            "2022-03-14 07:40:07,418 | INFO : Layer 4-12-2; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 19, 'DBSCAN': 14, 'K-PCA': 3});\tSequence: {'DBSCAN': 3, 'Spectral': 7, 'K-PCA': 1});\n",
            "2022-03-14 07:45:12,525 | INFO : Layer 4-13-0; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 33, 'Spectral': 3});\tSequence: {'K-PCA': 14});\n",
            "2022-03-14 07:50:08,053 | INFO : Layer 4-13-2; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 16, 'K-PCA': 12, 'Spectral': 8});\tSequence: {'Spectral': 7, 'DBSCAN': 6, 'K-PCA': 5});\n",
            "2022-03-14 07:54:13,063 | INFO : Layer 4-14-0; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 33, 'Spectral': 3});\tSequence: {'K-PCA': 17});\n",
            "2022-03-14 07:58:27,622 | INFO : Layer 4-14-2; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 26, 'K-PCA': 10});\tSequence: {'Spectral': 17, 'K-PCA': 3});\n",
            "2022-03-14 08:06:30,989 | INFO : Layer 4-15-0; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 19, 'Spectral': 17});\tSequence: {'Spectral': 7, 'DBSCAN': 7});\n",
            "2022-03-14 08:10:44,162 | INFO : Layer 4-15-2; Shape (64, 64, 3, 3) -> (64, 576); Method: K-PCA; Occurrence: ({'K-PCA': 15, 'Spectral': 13, 'DBSCAN': 8});\tSequence: {'K-PCA': 8, 'Spectral': 11, 'DBSCAN': 4});\n",
            "2022-03-14 08:21:48,580 | INFO : Layer 4-16-0; Shape (64, 64, 3, 3) -> (64, 576); Method: DBSCAN; Occurrence: ({'DBSCAN': 28, 'K-PCA': 5, 'Spectral': 3});\tSequence: {'DBSCAN': 9, 'Spectral': 1, 'K-PCA': 1});\n",
            "2022-03-14 08:25:52,531 | INFO : Layer 4-16-2; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 23, 'K-PCA': 11, 'DBSCAN': 2});\tSequence: {'Spectral': 11, 'K-PCA': 10});\n",
            "2022-03-14 08:33:56,600 | INFO : Layer 4-17-0; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 27, 'DBSCAN': 9});\tSequence: {'DBSCAN': 7, 'Spectral': 18});\n",
            "2022-03-14 08:38:10,000 | INFO : Layer 4-17-2; Shape (64, 64, 3, 3) -> (64, 576); Method: Spectral; Occurrence: ({'Spectral': 36});\tSequence: {'Spectral': 35});\n",
            "2022-03-14 08:38:13,632 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet110_cifar10_baseline <class 'prune_cifar.ModifiedResNet'> now pruned.\n",
            "GroupResNet pruning rate: 0.4375; kernel_gcd: 16\n",
            "2022-03-14 08:38:14,929 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet110_cifar10_baseline <class 'group_cifar.GroupResNet'> now grouped.\n",
            "2022-03-14 08:38:14,934 | INFO : ==> Starting task: finetune for 300 epochs; multi_prune_flag: False)\n",
            "2022-03-14 08:39:08,133 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet110_cifar10_baseline (type: <class 'group_cifar.GroupResNet'>) pruned and grouped (grouped baseline: 16.87); now to finetune with 300 epochs.\n",
            "\n",
            "2022-03-14 08:40:01,192 | INFO : Model /content/drive/MyDrive/lottery_regulated_grouped_kernel_pruning/baseline/resnet110_cifar10_baseline has a baseline of 16.87.\n",
            "2022-03-14 08:44:16,735 | INFO : Epoch #0\n",
            "2022-03-14 08:44:16,735 | INFO : \t train_acc: 83.064; train_loss: 0.479; test_loss: 55.361; lr: [0.01]\n",
            "2022-03-14 08:44:16,735 | INFO : \t test_acc: 81.92 (best: 81.92 from #0)\n",
            "2022-03-14 08:48:37,024 | INFO : Epoch #1\n",
            "2022-03-14 08:48:37,025 | INFO : \t train_acc: 87.95; train_loss: 0.531; test_loss: 58.765; lr: [0.01]\n",
            "2022-03-14 08:48:37,025 | INFO : \t test_acc: 81.31 (best: 81.92 from #0)\n",
            "2022-03-14 08:52:52,753 | INFO : Epoch #2\n",
            "2022-03-14 08:52:52,753 | INFO : \t train_acc: 89.638; train_loss: 0.091; test_loss: 46.618; lr: [0.01]\n",
            "2022-03-14 08:52:52,753 | INFO : \t test_acc: 85.06 (best: 85.06 from #2)\n",
            "2022-03-14 08:57:08,265 | INFO : Epoch #3\n",
            "2022-03-14 08:57:08,266 | INFO : \t train_acc: 90.488; train_loss: 0.441; test_loss: 41.157; lr: [0.01]\n",
            "2022-03-14 08:57:08,266 | INFO : \t test_acc: 86.49 (best: 86.49 from #3)\n",
            "2022-03-14 09:01:24,076 | INFO : Epoch #4\n",
            "2022-03-14 09:01:24,077 | INFO : \t train_acc: 91.224; train_loss: 0.106; test_loss: 43.789; lr: [0.01]\n",
            "2022-03-14 09:01:24,077 | INFO : \t test_acc: 86.23 (best: 86.49 from #3)\n",
            "2022-03-14 09:05:39,419 | INFO : Epoch #5\n",
            "2022-03-14 09:05:39,420 | INFO : \t train_acc: 91.73; train_loss: 0.388; test_loss: 39.928; lr: [0.01]\n",
            "2022-03-14 09:05:39,420 | INFO : \t test_acc: 87.63 (best: 87.63 from #5)\n",
            "2022-03-14 09:09:55,162 | INFO : Epoch #6\n",
            "2022-03-14 09:09:55,162 | INFO : \t train_acc: 92.096; train_loss: 0.487; test_loss: 35.803; lr: [0.01]\n",
            "2022-03-14 09:09:55,162 | INFO : \t test_acc: 88.03 (best: 88.03 from #6)\n",
            "2022-03-14 09:14:10,639 | INFO : Epoch #7\n",
            "2022-03-14 09:14:10,639 | INFO : \t train_acc: 92.452; train_loss: 0.096; test_loss: 37.042; lr: [0.01]\n",
            "2022-03-14 09:14:10,640 | INFO : \t test_acc: 88.3 (best: 88.3 from #7)\n",
            "2022-03-14 09:18:26,253 | INFO : Epoch #8\n",
            "2022-03-14 09:18:26,254 | INFO : \t train_acc: 92.802; train_loss: 0.022; test_loss: 34.850; lr: [0.01]\n",
            "2022-03-14 09:18:26,254 | INFO : \t test_acc: 88.89 (best: 88.89 from #8)\n",
            "2022-03-14 09:22:41,738 | INFO : Epoch #9\n",
            "2022-03-14 09:22:41,738 | INFO : \t train_acc: 92.858; train_loss: 0.067; test_loss: 45.386; lr: [0.01]\n",
            "2022-03-14 09:22:41,738 | INFO : \t test_acc: 86.14 (best: 88.89 from #8)\n",
            "2022-03-14 09:26:58,969 | INFO : Epoch #10\n",
            "2022-03-14 09:26:58,969 | INFO : \t train_acc: 92.958; train_loss: 0.401; test_loss: 33.196; lr: [0.01]\n",
            "2022-03-14 09:26:58,969 | INFO : \t test_acc: 89.21 (best: 89.21 from #10)\n",
            "2022-03-14 09:31:14,329 | INFO : Epoch #11\n",
            "2022-03-14 09:31:14,330 | INFO : \t train_acc: 93.276; train_loss: 0.053; test_loss: 35.817; lr: [0.01]\n",
            "2022-03-14 09:31:14,330 | INFO : \t test_acc: 89.08 (best: 89.21 from #10)\n",
            "2022-03-14 09:35:29,906 | INFO : Epoch #12\n",
            "2022-03-14 09:35:29,907 | INFO : \t train_acc: 93.6; train_loss: 0.433; test_loss: 33.734; lr: [0.01]\n",
            "2022-03-14 09:35:29,907 | INFO : \t test_acc: 89.21 (best: 89.21 from #12)\n",
            "2022-03-14 09:39:45,306 | INFO : Epoch #13\n",
            "2022-03-14 09:39:45,306 | INFO : \t train_acc: 93.47; train_loss: 0.176; test_loss: 29.670; lr: [0.01]\n",
            "2022-03-14 09:39:45,306 | INFO : \t test_acc: 90.34 (best: 90.34 from #13)\n",
            "2022-03-14 09:44:00,869 | INFO : Epoch #14\n",
            "2022-03-14 09:44:00,870 | INFO : \t train_acc: 93.736; train_loss: 0.501; test_loss: 42.194; lr: [0.01]\n",
            "2022-03-14 09:44:00,870 | INFO : \t test_acc: 87.35 (best: 90.34 from #13)\n",
            "2022-03-14 09:48:16,353 | INFO : Epoch #15\n",
            "2022-03-14 09:48:16,354 | INFO : \t train_acc: 93.714; train_loss: 0.051; test_loss: 40.190; lr: [0.01]\n",
            "2022-03-14 09:48:16,354 | INFO : \t test_acc: 87.67 (best: 90.34 from #13)\n",
            "2022-03-14 09:52:32,064 | INFO : Epoch #16\n",
            "2022-03-14 09:52:32,065 | INFO : \t train_acc: 93.782; train_loss: 0.586; test_loss: 32.860; lr: [0.01]\n",
            "2022-03-14 09:52:32,065 | INFO : \t test_acc: 89.51 (best: 90.34 from #13)\n",
            "2022-03-14 09:56:47,422 | INFO : Epoch #17\n",
            "2022-03-14 09:56:47,422 | INFO : \t train_acc: 94.182; train_loss: 0.014; test_loss: 37.383; lr: [0.01]\n",
            "2022-03-14 09:56:47,423 | INFO : \t test_acc: 88.89 (best: 90.34 from #13)\n",
            "2022-03-14 10:01:03,047 | INFO : Epoch #18\n",
            "2022-03-14 10:01:03,047 | INFO : \t train_acc: 93.962; train_loss: 0.044; test_loss: 41.119; lr: [0.01]\n",
            "2022-03-14 10:01:03,048 | INFO : \t test_acc: 88.14 (best: 90.34 from #13)\n",
            "2022-03-14 10:05:18,428 | INFO : Epoch #19\n",
            "2022-03-14 10:05:18,429 | INFO : \t train_acc: 94.16; train_loss: 0.068; test_loss: 37.847; lr: [0.01]\n",
            "2022-03-14 10:05:18,429 | INFO : \t test_acc: 88.65 (best: 90.34 from #13)\n",
            "2022-03-14 10:09:35,791 | INFO : Epoch #20\n",
            "2022-03-14 10:09:35,792 | INFO : \t train_acc: 94.092; train_loss: 0.559; test_loss: 43.413; lr: [0.01]\n",
            "2022-03-14 10:09:35,792 | INFO : \t test_acc: 86.96 (best: 90.34 from #13)\n",
            "2022-03-14 10:13:50,994 | INFO : Epoch #21\n",
            "2022-03-14 10:13:50,995 | INFO : \t train_acc: 94.124; train_loss: 0.660; test_loss: 37.291; lr: [0.01]\n",
            "2022-03-14 10:13:50,995 | INFO : \t test_acc: 88.85 (best: 90.34 from #13)\n",
            "2022-03-14 10:18:06,404 | INFO : Epoch #22\n",
            "2022-03-14 10:18:06,405 | INFO : \t train_acc: 94.372; train_loss: 0.496; test_loss: 37.567; lr: [0.01]\n",
            "2022-03-14 10:18:06,405 | INFO : \t test_acc: 88.4 (best: 90.34 from #13)\n",
            "2022-03-14 10:22:21,764 | INFO : Epoch #23\n",
            "2022-03-14 10:22:21,765 | INFO : \t train_acc: 94.146; train_loss: 0.506; test_loss: 32.780; lr: [0.01]\n",
            "2022-03-14 10:22:21,765 | INFO : \t test_acc: 89.88 (best: 90.34 from #13)\n",
            "2022-03-14 10:26:37,386 | INFO : Epoch #24\n",
            "2022-03-14 10:26:37,386 | INFO : \t train_acc: 94.2; train_loss: 0.376; test_loss: 34.737; lr: [0.01]\n",
            "2022-03-14 10:26:37,387 | INFO : \t test_acc: 89.99 (best: 90.34 from #13)\n",
            "2022-03-14 10:30:52,659 | INFO : Epoch #25\n",
            "2022-03-14 10:30:52,660 | INFO : \t train_acc: 94.398; train_loss: 0.177; test_loss: 33.541; lr: [0.01]\n",
            "2022-03-14 10:30:52,660 | INFO : \t test_acc: 89.59 (best: 90.34 from #13)\n",
            "2022-03-14 10:35:08,645 | INFO : Epoch #26\n",
            "2022-03-14 10:35:08,645 | INFO : \t train_acc: 94.494; train_loss: 0.117; test_loss: 36.642; lr: [0.01]\n",
            "2022-03-14 10:35:08,645 | INFO : \t test_acc: 89.08 (best: 90.34 from #13)\n",
            "2022-03-14 10:39:24,102 | INFO : Epoch #27\n",
            "2022-03-14 10:39:24,103 | INFO : \t train_acc: 94.682; train_loss: 0.324; test_loss: 33.264; lr: [0.01]\n",
            "2022-03-14 10:39:24,103 | INFO : \t test_acc: 89.65 (best: 90.34 from #13)\n",
            "2022-03-14 10:43:39,775 | INFO : Epoch #28\n",
            "2022-03-14 10:43:39,775 | INFO : \t train_acc: 94.446; train_loss: 0.257; test_loss: 34.482; lr: [0.01]\n",
            "2022-03-14 10:43:39,776 | INFO : \t test_acc: 89.71 (best: 90.34 from #13)\n",
            "2022-03-14 10:47:55,000 | INFO : Epoch #29\n",
            "2022-03-14 10:47:55,000 | INFO : \t train_acc: 94.51; train_loss: 0.042; test_loss: 36.548; lr: [0.01]\n",
            "2022-03-14 10:47:55,001 | INFO : \t test_acc: 89.51 (best: 90.34 from #13)\n",
            "2022-03-14 10:52:12,405 | INFO : Epoch #30\n",
            "2022-03-14 10:52:12,406 | INFO : \t train_acc: 94.53; train_loss: 0.101; test_loss: 34.556; lr: [0.01]\n",
            "2022-03-14 10:52:12,406 | INFO : \t test_acc: 90.05 (best: 90.34 from #13)\n",
            "2022-03-14 10:56:27,953 | INFO : Epoch #31\n",
            "2022-03-14 10:56:27,953 | INFO : \t train_acc: 94.596; train_loss: 0.213; test_loss: 37.346; lr: [0.01]\n",
            "2022-03-14 10:56:27,953 | INFO : \t test_acc: 88.65 (best: 90.34 from #13)\n",
            "2022-03-14 11:00:44,008 | INFO : Epoch #32\n",
            "2022-03-14 11:00:44,008 | INFO : \t train_acc: 94.532; train_loss: 0.502; test_loss: 32.934; lr: [0.01]\n",
            "2022-03-14 11:00:44,008 | INFO : \t test_acc: 89.42 (best: 90.34 from #13)\n",
            "2022-03-14 11:04:59,692 | INFO : Epoch #33\n",
            "2022-03-14 11:04:59,693 | INFO : \t train_acc: 94.656; train_loss: 0.193; test_loss: 41.255; lr: [0.01]\n",
            "2022-03-14 11:04:59,693 | INFO : \t test_acc: 87.87 (best: 90.34 from #13)\n",
            "2022-03-14 11:09:14,764 | INFO : Epoch #34\n",
            "2022-03-14 11:09:14,764 | INFO : \t train_acc: 94.764; train_loss: 0.528; test_loss: 39.893; lr: [0.01]\n",
            "2022-03-14 11:09:14,764 | INFO : \t test_acc: 88.72 (best: 90.34 from #13)\n",
            "2022-03-14 11:13:29,635 | INFO : Epoch #35\n",
            "2022-03-14 11:13:29,636 | INFO : \t train_acc: 94.524; train_loss: 0.056; test_loss: 36.189; lr: [0.01]\n",
            "2022-03-14 11:13:29,636 | INFO : \t test_acc: 89.16 (best: 90.34 from #13)\n",
            "2022-03-14 11:17:44,655 | INFO : Epoch #36\n",
            "2022-03-14 11:17:44,656 | INFO : \t train_acc: 94.684; train_loss: 0.316; test_loss: 42.178; lr: [0.01]\n",
            "2022-03-14 11:17:44,656 | INFO : \t test_acc: 87.75 (best: 90.34 from #13)\n",
            "2022-03-14 11:21:59,613 | INFO : Epoch #37\n",
            "2022-03-14 11:21:59,614 | INFO : \t train_acc: 94.744; train_loss: 0.129; test_loss: 37.642; lr: [0.01]\n",
            "2022-03-14 11:21:59,614 | INFO : \t test_acc: 89.36 (best: 90.34 from #13)\n",
            "2022-03-14 11:26:14,658 | INFO : Epoch #38\n",
            "2022-03-14 11:26:14,658 | INFO : \t train_acc: 94.798; train_loss: 0.344; test_loss: 39.808; lr: [0.01]\n",
            "2022-03-14 11:26:14,659 | INFO : \t test_acc: 88.2 (best: 90.34 from #13)\n",
            "2022-03-14 11:30:29,648 | INFO : Epoch #39\n",
            "2022-03-14 11:30:29,648 | INFO : \t train_acc: 94.844; train_loss: 0.081; test_loss: 35.532; lr: [0.01]\n",
            "2022-03-14 11:30:29,648 | INFO : \t test_acc: 89.52 (best: 90.34 from #13)\n",
            "2022-03-14 11:34:46,115 | INFO : Epoch #40\n",
            "2022-03-14 11:34:46,115 | INFO : \t train_acc: 94.864; train_loss: 0.090; test_loss: 47.565; lr: [0.01]\n",
            "2022-03-14 11:34:46,115 | INFO : \t test_acc: 86.49 (best: 90.34 from #13)\n",
            "2022-03-14 11:39:00,591 | INFO : Epoch #41\n",
            "2022-03-14 11:39:00,591 | INFO : \t train_acc: 94.754; train_loss: 0.043; test_loss: 38.738; lr: [0.01]\n",
            "2022-03-14 11:39:00,592 | INFO : \t test_acc: 88.82 (best: 90.34 from #13)\n",
            "2022-03-14 11:43:15,533 | INFO : Epoch #42\n",
            "2022-03-14 11:43:15,533 | INFO : \t train_acc: 94.878; train_loss: 0.086; test_loss: 36.559; lr: [0.01]\n",
            "2022-03-14 11:43:15,533 | INFO : \t test_acc: 88.43 (best: 90.34 from #13)\n",
            "2022-03-14 11:47:30,599 | INFO : Epoch #43\n",
            "2022-03-14 11:47:30,599 | INFO : \t train_acc: 95.076; train_loss: 0.268; test_loss: 36.004; lr: [0.01]\n",
            "2022-03-14 11:47:30,599 | INFO : \t test_acc: 89.68 (best: 90.34 from #13)\n",
            "2022-03-14 11:51:45,706 | INFO : Epoch #44\n",
            "2022-03-14 11:51:45,707 | INFO : \t train_acc: 94.83; train_loss: 0.138; test_loss: 38.815; lr: [0.01]\n",
            "2022-03-14 11:51:45,707 | INFO : \t test_acc: 88.55 (best: 90.34 from #13)\n",
            "2022-03-14 11:56:00,718 | INFO : Epoch #45\n",
            "2022-03-14 11:56:00,719 | INFO : \t train_acc: 94.858; train_loss: 0.060; test_loss: 41.976; lr: [0.01]\n",
            "2022-03-14 11:56:00,719 | INFO : \t test_acc: 87.58 (best: 90.34 from #13)\n",
            "2022-03-14 12:00:15,670 | INFO : Epoch #46\n",
            "2022-03-14 12:00:15,671 | INFO : \t train_acc: 94.92; train_loss: 0.135; test_loss: 35.653; lr: [0.01]\n",
            "2022-03-14 12:00:15,671 | INFO : \t test_acc: 89.57 (best: 90.34 from #13)\n",
            "2022-03-14 12:04:30,491 | INFO : Epoch #47\n",
            "2022-03-14 12:04:30,492 | INFO : \t train_acc: 94.894; train_loss: 0.607; test_loss: 35.340; lr: [0.01]\n",
            "2022-03-14 12:04:30,492 | INFO : \t test_acc: 89.47 (best: 90.34 from #13)\n",
            "2022-03-14 12:08:45,625 | INFO : Epoch #48\n",
            "2022-03-14 12:08:45,626 | INFO : \t train_acc: 94.788; train_loss: 0.043; test_loss: 40.745; lr: [0.01]\n",
            "2022-03-14 12:08:45,626 | INFO : \t test_acc: 88.0 (best: 90.34 from #13)\n",
            "2022-03-14 12:13:00,262 | INFO : Epoch #49\n",
            "2022-03-14 12:13:00,262 | INFO : \t train_acc: 94.908; train_loss: 0.274; test_loss: 31.932; lr: [0.01]\n",
            "2022-03-14 12:13:00,262 | INFO : \t test_acc: 90.21 (best: 90.34 from #13)\n",
            "2022-03-14 12:17:16,955 | INFO : Epoch #50\n",
            "2022-03-14 12:17:16,955 | INFO : \t train_acc: 94.976; train_loss: 0.009; test_loss: 33.607; lr: [0.01]\n",
            "2022-03-14 12:17:16,955 | INFO : \t test_acc: 90.13 (best: 90.34 from #13)\n",
            "2022-03-14 12:21:31,817 | INFO : Epoch #51\n",
            "2022-03-14 12:21:31,817 | INFO : \t train_acc: 94.832; train_loss: 0.034; test_loss: 34.503; lr: [0.01]\n",
            "2022-03-14 12:21:31,818 | INFO : \t test_acc: 89.42 (best: 90.34 from #13)\n",
            "2022-03-14 12:25:46,884 | INFO : Epoch #52\n",
            "2022-03-14 12:25:46,885 | INFO : \t train_acc: 95.176; train_loss: 0.418; test_loss: 32.029; lr: [0.01]\n",
            "2022-03-14 12:25:46,885 | INFO : \t test_acc: 90.34 (best: 90.34 from #52)\n",
            "2022-03-14 12:30:01,856 | INFO : Epoch #53\n",
            "2022-03-14 12:30:01,856 | INFO : \t train_acc: 94.912; train_loss: 0.056; test_loss: 33.203; lr: [0.01]\n",
            "2022-03-14 12:30:01,856 | INFO : \t test_acc: 90.39 (best: 90.39 from #53)\n",
            "2022-03-14 12:34:17,315 | INFO : Epoch #54\n",
            "2022-03-14 12:34:17,316 | INFO : \t train_acc: 94.876; train_loss: 0.320; test_loss: 36.584; lr: [0.01]\n",
            "2022-03-14 12:34:17,316 | INFO : \t test_acc: 89.44 (best: 90.39 from #53)\n",
            "2022-03-14 12:38:32,582 | INFO : Epoch #55\n",
            "2022-03-14 12:38:32,583 | INFO : \t train_acc: 95.062; train_loss: 0.067; test_loss: 36.036; lr: [0.01]\n",
            "2022-03-14 12:38:32,583 | INFO : \t test_acc: 89.22 (best: 90.39 from #53)\n",
            "2022-03-14 12:42:48,143 | INFO : Epoch #56\n",
            "2022-03-14 12:42:48,144 | INFO : \t train_acc: 95.064; train_loss: 0.292; test_loss: 37.797; lr: [0.01]\n",
            "2022-03-14 12:42:48,144 | INFO : \t test_acc: 88.72 (best: 90.39 from #53)\n",
            "2022-03-14 12:47:04,101 | INFO : Epoch #57\n",
            "2022-03-14 12:47:04,101 | INFO : \t train_acc: 94.978; train_loss: 0.266; test_loss: 38.014; lr: [0.01]\n",
            "2022-03-14 12:47:04,101 | INFO : \t test_acc: 88.84 (best: 90.39 from #53)\n",
            "2022-03-14 12:51:20,326 | INFO : Epoch #58\n",
            "2022-03-14 12:51:20,326 | INFO : \t train_acc: 95.066; train_loss: 0.132; test_loss: 39.564; lr: [0.01]\n",
            "2022-03-14 12:51:20,327 | INFO : \t test_acc: 88.57 (best: 90.39 from #53)\n",
            "2022-03-14 12:55:36,484 | INFO : Epoch #59\n",
            "2022-03-14 12:55:36,485 | INFO : \t train_acc: 95.13; train_loss: 0.722; test_loss: 35.492; lr: [0.01]\n",
            "2022-03-14 12:55:36,485 | INFO : \t test_acc: 89.31 (best: 90.39 from #53)\n",
            "2022-03-14 12:59:54,406 | INFO : Epoch #60\n",
            "2022-03-14 12:59:54,406 | INFO : \t train_acc: 94.956; train_loss: 0.281; test_loss: 32.352; lr: [0.01]\n",
            "2022-03-14 12:59:54,407 | INFO : \t test_acc: 90.65 (best: 90.65 from #60)\n",
            "2022-03-14 13:04:10,482 | INFO : Epoch #61\n",
            "2022-03-14 13:04:10,482 | INFO : \t train_acc: 95.25; train_loss: 0.048; test_loss: 32.846; lr: [0.01]\n",
            "2022-03-14 13:04:10,482 | INFO : \t test_acc: 90.0 (best: 90.65 from #60)\n",
            "2022-03-14 13:08:26,751 | INFO : Epoch #62\n",
            "2022-03-14 13:08:26,752 | INFO : \t train_acc: 95.08; train_loss: 0.159; test_loss: 43.397; lr: [0.01]\n",
            "2022-03-14 13:08:26,752 | INFO : \t test_acc: 87.37 (best: 90.65 from #60)\n",
            "2022-03-14 13:12:42,693 | INFO : Epoch #63\n",
            "2022-03-14 13:12:42,693 | INFO : \t train_acc: 95.2; train_loss: 0.094; test_loss: 34.447; lr: [0.01]\n",
            "2022-03-14 13:12:42,694 | INFO : \t test_acc: 89.82 (best: 90.65 from #60)\n",
            "2022-03-14 13:16:58,920 | INFO : Epoch #64\n",
            "2022-03-14 13:16:58,920 | INFO : \t train_acc: 95.248; train_loss: 0.445; test_loss: 37.195; lr: [0.01]\n",
            "2022-03-14 13:16:58,920 | INFO : \t test_acc: 88.88 (best: 90.65 from #60)\n",
            "2022-03-14 13:21:14,773 | INFO : Epoch #65\n",
            "2022-03-14 13:21:14,774 | INFO : \t train_acc: 95.088; train_loss: 0.705; test_loss: 34.907; lr: [0.01]\n",
            "2022-03-14 13:21:14,774 | INFO : \t test_acc: 89.48 (best: 90.65 from #60)\n",
            "2022-03-14 13:25:30,024 | INFO : Epoch #66\n",
            "2022-03-14 13:25:30,024 | INFO : \t train_acc: 95.102; train_loss: 0.236; test_loss: 36.665; lr: [0.01]\n",
            "2022-03-14 13:25:30,025 | INFO : \t test_acc: 89.34 (best: 90.65 from #60)\n",
            "2022-03-14 13:29:44,967 | INFO : Epoch #67\n",
            "2022-03-14 13:29:44,968 | INFO : \t train_acc: 95.014; train_loss: 0.185; test_loss: 40.342; lr: [0.01]\n",
            "2022-03-14 13:29:44,968 | INFO : \t test_acc: 88.22 (best: 90.65 from #60)\n",
            "2022-03-14 13:34:00,176 | INFO : Epoch #68\n",
            "2022-03-14 13:34:00,177 | INFO : \t train_acc: 95.126; train_loss: 0.166; test_loss: 40.467; lr: [0.01]\n",
            "2022-03-14 13:34:00,177 | INFO : \t test_acc: 88.38 (best: 90.65 from #60)\n",
            "2022-03-14 13:38:15,175 | INFO : Epoch #69\n",
            "2022-03-14 13:38:15,175 | INFO : \t train_acc: 95.21; train_loss: 0.269; test_loss: 42.614; lr: [0.01]\n",
            "2022-03-14 13:38:15,175 | INFO : \t test_acc: 87.94 (best: 90.65 from #60)\n",
            "2022-03-14 13:42:31,724 | INFO : Epoch #70\n",
            "2022-03-14 13:42:31,725 | INFO : \t train_acc: 94.98; train_loss: 0.023; test_loss: 32.234; lr: [0.01]\n",
            "2022-03-14 13:42:31,725 | INFO : \t test_acc: 90.46 (best: 90.65 from #60)\n",
            "2022-03-14 13:46:46,659 | INFO : Epoch #71\n",
            "2022-03-14 13:46:46,660 | INFO : \t train_acc: 95.424; train_loss: 0.083; test_loss: 32.978; lr: [0.01]\n",
            "2022-03-14 13:46:46,660 | INFO : \t test_acc: 90.32 (best: 90.65 from #60)\n",
            "2022-03-14 13:51:01,942 | INFO : Epoch #72\n",
            "2022-03-14 13:51:01,943 | INFO : \t train_acc: 95.222; train_loss: 0.295; test_loss: 32.962; lr: [0.01]\n",
            "2022-03-14 13:51:01,943 | INFO : \t test_acc: 90.32 (best: 90.65 from #60)\n",
            "2022-03-14 13:55:16,688 | INFO : Epoch #73\n",
            "2022-03-14 13:55:16,688 | INFO : \t train_acc: 95.128; train_loss: 0.045; test_loss: 33.575; lr: [0.01]\n",
            "2022-03-14 13:55:16,688 | INFO : \t test_acc: 90.09 (best: 90.65 from #60)\n",
            "2022-03-14 13:59:31,854 | INFO : Epoch #74\n",
            "2022-03-14 13:59:31,854 | INFO : \t train_acc: 95.182; train_loss: 0.626; test_loss: 35.665; lr: [0.01]\n",
            "2022-03-14 13:59:31,854 | INFO : \t test_acc: 89.43 (best: 90.65 from #60)\n",
            "2022-03-14 14:03:46,791 | INFO : Epoch #75\n",
            "2022-03-14 14:03:46,792 | INFO : \t train_acc: 95.258; train_loss: 0.122; test_loss: 37.650; lr: [0.01]\n",
            "2022-03-14 14:03:46,792 | INFO : \t test_acc: 89.17 (best: 90.65 from #60)\n",
            "2022-03-14 14:08:01,714 | INFO : Epoch #76\n",
            "2022-03-14 14:08:01,715 | INFO : \t train_acc: 95.252; train_loss: 0.441; test_loss: 32.936; lr: [0.01]\n",
            "2022-03-14 14:08:01,715 | INFO : \t test_acc: 90.08 (best: 90.65 from #60)\n",
            "2022-03-14 14:12:16,670 | INFO : Epoch #77\n",
            "2022-03-14 14:12:16,670 | INFO : \t train_acc: 95.166; train_loss: 0.104; test_loss: 32.252; lr: [0.01]\n",
            "2022-03-14 14:12:16,670 | INFO : \t test_acc: 90.51 (best: 90.65 from #60)\n",
            "2022-03-14 14:16:31,946 | INFO : Epoch #78\n",
            "2022-03-14 14:16:31,946 | INFO : \t train_acc: 95.382; train_loss: 0.255; test_loss: 30.286; lr: [0.01]\n",
            "2022-03-14 14:16:31,947 | INFO : \t test_acc: 90.72 (best: 90.72 from #78)\n",
            "2022-03-14 14:20:47,013 | INFO : Epoch #79\n",
            "2022-03-14 14:20:47,014 | INFO : \t train_acc: 95.248; train_loss: 0.483; test_loss: 37.674; lr: [0.01]\n",
            "2022-03-14 14:20:47,014 | INFO : \t test_acc: 89.29 (best: 90.72 from #78)\n",
            "2022-03-14 14:25:04,039 | INFO : Epoch #80\n",
            "2022-03-14 14:25:04,039 | INFO : \t train_acc: 95.062; train_loss: 0.555; test_loss: 38.500; lr: [0.01]\n",
            "2022-03-14 14:25:04,039 | INFO : \t test_acc: 88.5 (best: 90.72 from #78)\n",
            "2022-03-14 14:29:19,395 | INFO : Epoch #81\n",
            "2022-03-14 14:29:19,396 | INFO : \t train_acc: 95.158; train_loss: 0.045; test_loss: 34.637; lr: [0.01]\n",
            "2022-03-14 14:29:19,396 | INFO : \t test_acc: 89.91 (best: 90.72 from #78)\n",
            "2022-03-14 14:33:35,056 | INFO : Epoch #82\n",
            "2022-03-14 14:33:35,056 | INFO : \t train_acc: 95.364; train_loss: 0.038; test_loss: 36.631; lr: [0.01]\n",
            "2022-03-14 14:33:35,057 | INFO : \t test_acc: 89.33 (best: 90.72 from #78)\n",
            "2022-03-14 14:37:50,778 | INFO : Epoch #83\n",
            "2022-03-14 14:37:50,779 | INFO : \t train_acc: 95.278; train_loss: 0.265; test_loss: 42.881; lr: [0.01]\n",
            "2022-03-14 14:37:50,779 | INFO : \t test_acc: 88.1 (best: 90.72 from #78)\n",
            "2022-03-14 14:42:06,367 | INFO : Epoch #84\n",
            "2022-03-14 14:42:06,367 | INFO : \t train_acc: 95.04; train_loss: 0.368; test_loss: 40.253; lr: [0.01]\n",
            "2022-03-14 14:42:06,368 | INFO : \t test_acc: 88.56 (best: 90.72 from #78)\n",
            "2022-03-14 14:46:21,947 | INFO : Epoch #85\n",
            "2022-03-14 14:46:21,948 | INFO : \t train_acc: 95.422; train_loss: 0.028; test_loss: 35.078; lr: [0.01]\n",
            "2022-03-14 14:46:21,948 | INFO : \t test_acc: 89.63 (best: 90.72 from #78)\n",
            "2022-03-14 14:50:37,487 | INFO : Epoch #86\n",
            "2022-03-14 14:50:37,487 | INFO : \t train_acc: 95.372; train_loss: 0.187; test_loss: 35.249; lr: [0.01]\n",
            "2022-03-14 14:50:37,488 | INFO : \t test_acc: 89.63 (best: 90.72 from #78)\n",
            "2022-03-14 14:54:52,794 | INFO : Epoch #87\n",
            "2022-03-14 14:54:52,795 | INFO : \t train_acc: 95.346; train_loss: 0.149; test_loss: 31.222; lr: [0.01]\n",
            "2022-03-14 14:54:52,795 | INFO : \t test_acc: 90.41 (best: 90.72 from #78)\n",
            "2022-03-14 14:59:08,525 | INFO : Epoch #88\n",
            "2022-03-14 14:59:08,525 | INFO : \t train_acc: 95.522; train_loss: 0.052; test_loss: 39.518; lr: [0.01]\n",
            "2022-03-14 14:59:08,526 | INFO : \t test_acc: 88.61 (best: 90.72 from #78)\n",
            "2022-03-14 15:03:24,195 | INFO : Epoch #89\n",
            "2022-03-14 15:03:24,196 | INFO : \t train_acc: 95.218; train_loss: 0.139; test_loss: 36.176; lr: [0.01]\n",
            "2022-03-14 15:03:24,196 | INFO : \t test_acc: 89.37 (best: 90.72 from #78)\n",
            "2022-03-14 15:07:41,900 | INFO : Epoch #90\n",
            "2022-03-14 15:07:41,901 | INFO : \t train_acc: 95.208; train_loss: 0.063; test_loss: 37.888; lr: [0.01]\n",
            "2022-03-14 15:07:41,901 | INFO : \t test_acc: 89.33 (best: 90.72 from #78)\n",
            "2022-03-14 15:11:57,511 | INFO : Epoch #91\n",
            "2022-03-14 15:11:57,512 | INFO : \t train_acc: 95.374; train_loss: 0.170; test_loss: 35.878; lr: [0.01]\n",
            "2022-03-14 15:11:57,512 | INFO : \t test_acc: 89.67 (best: 90.72 from #78)\n",
            "2022-03-14 15:16:13,274 | INFO : Epoch #92\n",
            "2022-03-14 15:16:13,274 | INFO : \t train_acc: 95.484; train_loss: 0.117; test_loss: 43.319; lr: [0.01]\n",
            "2022-03-14 15:16:13,274 | INFO : \t test_acc: 87.61 (best: 90.72 from #78)\n",
            "2022-03-14 15:20:28,668 | INFO : Epoch #93\n",
            "2022-03-14 15:20:28,669 | INFO : \t train_acc: 95.086; train_loss: 0.665; test_loss: 32.856; lr: [0.01]\n",
            "2022-03-14 15:20:28,669 | INFO : \t test_acc: 90.25 (best: 90.72 from #78)\n",
            "2022-03-14 15:24:44,970 | INFO : Epoch #94\n",
            "2022-03-14 15:24:44,970 | INFO : \t train_acc: 95.152; train_loss: 0.139; test_loss: 35.015; lr: [0.01]\n",
            "2022-03-14 15:24:44,971 | INFO : \t test_acc: 89.65 (best: 90.72 from #78)\n",
            "2022-03-14 15:29:00,922 | INFO : Epoch #95\n",
            "2022-03-14 15:29:00,922 | INFO : \t train_acc: 95.188; train_loss: 0.494; test_loss: 34.522; lr: [0.01]\n",
            "2022-03-14 15:29:00,922 | INFO : \t test_acc: 90.07 (best: 90.72 from #78)\n",
            "2022-03-14 15:33:17,144 | INFO : Epoch #96\n",
            "2022-03-14 15:33:17,144 | INFO : \t train_acc: 95.242; train_loss: 0.171; test_loss: 35.800; lr: [0.01]\n",
            "2022-03-14 15:33:17,144 | INFO : \t test_acc: 89.95 (best: 90.72 from #78)\n",
            "2022-03-14 15:37:33,272 | INFO : Epoch #97\n",
            "2022-03-14 15:37:33,272 | INFO : \t train_acc: 95.704; train_loss: 0.010; test_loss: 33.166; lr: [0.01]\n",
            "2022-03-14 15:37:33,273 | INFO : \t test_acc: 90.31 (best: 90.72 from #78)\n",
            "2022-03-14 15:41:49,815 | INFO : Epoch #98\n",
            "2022-03-14 15:41:49,816 | INFO : \t train_acc: 95.466; train_loss: 0.333; test_loss: 37.031; lr: [0.01]\n",
            "2022-03-14 15:41:49,816 | INFO : \t test_acc: 89.36 (best: 90.72 from #78)\n",
            "2022-03-14 15:46:06,018 | INFO : Epoch #99\n",
            "2022-03-14 15:46:06,018 | INFO : \t train_acc: 95.296; train_loss: 0.031; test_loss: 35.593; lr: [0.01]\n",
            "2022-03-14 15:46:06,019 | INFO : \t test_acc: 89.51 (best: 90.72 from #78)\n",
            "2022-03-14 15:50:24,304 | INFO : Epoch #100\n",
            "2022-03-14 15:50:24,304 | INFO : \t train_acc: 97.962; train_loss: 0.041; test_loss: 21.052; lr: [0.001]\n",
            "2022-03-14 15:50:24,305 | INFO : \t test_acc: 93.65 (best: 93.65 from #100)\n",
            "2022-03-14 15:54:40,594 | INFO : Epoch #101\n",
            "2022-03-14 15:54:40,594 | INFO : \t train_acc: 98.9; train_loss: 0.007; test_loss: 21.010; lr: [0.001]\n",
            "2022-03-14 15:54:40,595 | INFO : \t test_acc: 93.76 (best: 93.76 from #101)\n",
            "2022-03-14 15:58:56,954 | INFO : Epoch #102\n",
            "2022-03-14 15:58:56,955 | INFO : \t train_acc: 99.012; train_loss: 0.008; test_loss: 20.779; lr: [0.001]\n",
            "2022-03-14 15:58:56,955 | INFO : \t test_acc: 93.88 (best: 93.88 from #102)\n",
            "2022-03-14 16:03:13,368 | INFO : Epoch #103\n",
            "2022-03-14 16:03:13,369 | INFO : \t train_acc: 99.222; train_loss: 0.043; test_loss: 21.410; lr: [0.001]\n",
            "2022-03-14 16:03:13,369 | INFO : \t test_acc: 93.99 (best: 93.99 from #103)\n",
            "2022-03-14 16:07:29,216 | INFO : Epoch #104\n",
            "2022-03-14 16:07:29,217 | INFO : \t train_acc: 99.39; train_loss: 0.003; test_loss: 21.276; lr: [0.001]\n",
            "2022-03-14 16:07:29,217 | INFO : \t test_acc: 94.11 (best: 94.11 from #104)\n",
            "2022-03-14 16:11:44,895 | INFO : Epoch #105\n",
            "2022-03-14 16:11:44,895 | INFO : \t train_acc: 99.368; train_loss: 0.034; test_loss: 21.627; lr: [0.001]\n",
            "2022-03-14 16:11:44,895 | INFO : \t test_acc: 94.03 (best: 94.11 from #104)\n",
            "2022-03-14 16:16:00,924 | INFO : Epoch #106\n",
            "2022-03-14 16:16:00,924 | INFO : \t train_acc: 99.478; train_loss: 0.212; test_loss: 21.848; lr: [0.001]\n",
            "2022-03-14 16:16:00,924 | INFO : \t test_acc: 94.07 (best: 94.11 from #104)\n",
            "2022-03-14 16:20:16,648 | INFO : Epoch #107\n",
            "2022-03-14 16:20:16,648 | INFO : \t train_acc: 99.57; train_loss: 0.092; test_loss: 21.845; lr: [0.001]\n",
            "2022-03-14 16:20:16,648 | INFO : \t test_acc: 94.15 (best: 94.15 from #107)\n",
            "2022-03-14 16:24:32,348 | INFO : Epoch #108\n",
            "2022-03-14 16:24:32,348 | INFO : \t train_acc: 99.556; train_loss: 0.001; test_loss: 21.662; lr: [0.001]\n",
            "2022-03-14 16:24:32,349 | INFO : \t test_acc: 94.08 (best: 94.15 from #107)\n",
            "2022-03-14 16:28:47,896 | INFO : Epoch #109\n",
            "2022-03-14 16:28:47,896 | INFO : \t train_acc: 99.628; train_loss: 0.020; test_loss: 22.260; lr: [0.001]\n",
            "2022-03-14 16:28:47,896 | INFO : \t test_acc: 94.01 (best: 94.15 from #107)\n",
            "2022-03-14 16:33:05,705 | INFO : Epoch #110\n",
            "2022-03-14 16:33:05,706 | INFO : \t train_acc: 99.628; train_loss: 0.073; test_loss: 22.207; lr: [0.001]\n",
            "2022-03-14 16:33:05,706 | INFO : \t test_acc: 94.08 (best: 94.15 from #107)\n",
            "2022-03-14 16:37:21,462 | INFO : Epoch #111\n",
            "2022-03-14 16:37:21,462 | INFO : \t train_acc: 99.648; train_loss: 0.054; test_loss: 22.878; lr: [0.001]\n",
            "2022-03-14 16:37:21,462 | INFO : \t test_acc: 93.93 (best: 94.15 from #107)\n",
            "2022-03-14 16:41:37,239 | INFO : Epoch #112\n",
            "2022-03-14 16:41:37,240 | INFO : \t train_acc: 99.688; train_loss: 0.010; test_loss: 22.486; lr: [0.001]\n",
            "2022-03-14 16:41:37,240 | INFO : \t test_acc: 94.15 (best: 94.15 from #112)\n",
            "2022-03-14 16:45:52,919 | INFO : Epoch #113\n",
            "2022-03-14 16:45:52,919 | INFO : \t train_acc: 99.698; train_loss: 0.001; test_loss: 22.459; lr: [0.001]\n",
            "2022-03-14 16:45:52,920 | INFO : \t test_acc: 94.3 (best: 94.3 from #113)\n",
            "2022-03-14 16:50:08,710 | INFO : Epoch #114\n",
            "2022-03-14 16:50:08,711 | INFO : \t train_acc: 99.76; train_loss: 0.000; test_loss: 22.389; lr: [0.001]\n",
            "2022-03-14 16:50:08,711 | INFO : \t test_acc: 94.38 (best: 94.38 from #114)\n",
            "2022-03-14 16:54:24,490 | INFO : Epoch #115\n",
            "2022-03-14 16:54:24,491 | INFO : \t train_acc: 99.772; train_loss: 0.085; test_loss: 22.761; lr: [0.001]\n",
            "2022-03-14 16:54:24,491 | INFO : \t test_acc: 94.25 (best: 94.38 from #114)\n",
            "2022-03-14 16:58:40,616 | INFO : Epoch #116\n",
            "2022-03-14 16:58:40,616 | INFO : \t train_acc: 99.73; train_loss: 0.003; test_loss: 23.011; lr: [0.001]\n",
            "2022-03-14 16:58:40,617 | INFO : \t test_acc: 94.25 (best: 94.38 from #114)\n",
            "2022-03-14 17:02:56,178 | INFO : Epoch #117\n",
            "2022-03-14 17:02:56,178 | INFO : \t train_acc: 99.784; train_loss: 0.001; test_loss: 23.102; lr: [0.001]\n",
            "2022-03-14 17:02:56,179 | INFO : \t test_acc: 94.17 (best: 94.38 from #114)\n",
            "2022-03-14 17:07:12,174 | INFO : Epoch #118\n",
            "2022-03-14 17:07:12,175 | INFO : \t train_acc: 99.764; train_loss: 0.003; test_loss: 23.045; lr: [0.001]\n",
            "2022-03-14 17:07:12,175 | INFO : \t test_acc: 94.17 (best: 94.38 from #114)\n",
            "2022-03-14 17:11:27,689 | INFO : Epoch #119\n",
            "2022-03-14 17:11:27,689 | INFO : \t train_acc: 99.766; train_loss: 0.003; test_loss: 22.896; lr: [0.001]\n",
            "2022-03-14 17:11:27,689 | INFO : \t test_acc: 94.32 (best: 94.38 from #114)\n",
            "2022-03-14 17:15:45,412 | INFO : Epoch #120\n",
            "2022-03-14 17:15:45,413 | INFO : \t train_acc: 99.78; train_loss: 0.005; test_loss: 23.526; lr: [0.001]\n",
            "2022-03-14 17:15:45,413 | INFO : \t test_acc: 94.12 (best: 94.38 from #114)\n",
            "2022-03-14 17:20:00,956 | INFO : Epoch #121\n",
            "2022-03-14 17:20:00,956 | INFO : \t train_acc: 99.792; train_loss: 0.023; test_loss: 24.092; lr: [0.001]\n",
            "2022-03-14 17:20:00,957 | INFO : \t test_acc: 94.25 (best: 94.38 from #114)\n",
            "2022-03-14 17:24:16,786 | INFO : Epoch #122\n",
            "2022-03-14 17:24:16,787 | INFO : \t train_acc: 99.836; train_loss: 0.001; test_loss: 24.453; lr: [0.001]\n",
            "2022-03-14 17:24:16,787 | INFO : \t test_acc: 94.19 (best: 94.38 from #114)\n",
            "2022-03-14 17:28:32,449 | INFO : Epoch #123\n",
            "2022-03-14 17:28:32,450 | INFO : \t train_acc: 99.812; train_loss: 0.104; test_loss: 23.488; lr: [0.001]\n",
            "2022-03-14 17:28:32,450 | INFO : \t test_acc: 94.11 (best: 94.38 from #114)\n",
            "2022-03-14 17:32:48,208 | INFO : Epoch #124\n",
            "2022-03-14 17:32:48,209 | INFO : \t train_acc: 99.798; train_loss: 0.019; test_loss: 23.752; lr: [0.001]\n",
            "2022-03-14 17:32:48,209 | INFO : \t test_acc: 94.2 (best: 94.38 from #114)\n",
            "2022-03-14 17:37:03,740 | INFO : Epoch #125\n",
            "2022-03-14 17:37:03,740 | INFO : \t train_acc: 99.812; train_loss: 0.207; test_loss: 23.438; lr: [0.001]\n",
            "2022-03-14 17:37:03,741 | INFO : \t test_acc: 94.43 (best: 94.43 from #125)\n",
            "2022-03-14 17:41:19,709 | INFO : Epoch #126\n",
            "2022-03-14 17:41:19,709 | INFO : \t train_acc: 99.844; train_loss: 0.152; test_loss: 23.889; lr: [0.001]\n",
            "2022-03-14 17:41:19,709 | INFO : \t test_acc: 94.18 (best: 94.43 from #125)\n",
            "2022-03-14 17:45:35,334 | INFO : Epoch #127\n",
            "2022-03-14 17:45:35,335 | INFO : \t train_acc: 99.828; train_loss: 0.002; test_loss: 24.395; lr: [0.001]\n",
            "2022-03-14 17:45:35,335 | INFO : \t test_acc: 94.12 (best: 94.43 from #125)\n",
            "2022-03-14 17:49:51,204 | INFO : Epoch #128\n",
            "2022-03-14 17:49:51,204 | INFO : \t train_acc: 99.846; train_loss: 0.006; test_loss: 24.006; lr: [0.001]\n",
            "2022-03-14 17:49:51,204 | INFO : \t test_acc: 94.37 (best: 94.43 from #125)\n",
            "2022-03-14 17:54:06,716 | INFO : Epoch #129\n",
            "2022-03-14 17:54:06,717 | INFO : \t train_acc: 99.834; train_loss: 0.015; test_loss: 24.053; lr: [0.001]\n",
            "2022-03-14 17:54:06,717 | INFO : \t test_acc: 94.36 (best: 94.43 from #125)\n",
            "2022-03-14 17:58:24,376 | INFO : Epoch #130\n",
            "2022-03-14 17:58:24,376 | INFO : \t train_acc: 99.838; train_loss: 0.076; test_loss: 25.169; lr: [0.001]\n",
            "2022-03-14 17:58:24,377 | INFO : \t test_acc: 94.15 (best: 94.43 from #125)\n",
            "2022-03-14 18:02:40,035 | INFO : Epoch #131\n",
            "2022-03-14 18:02:40,035 | INFO : \t train_acc: 99.84; train_loss: 0.013; test_loss: 23.633; lr: [0.001]\n",
            "2022-03-14 18:02:40,035 | INFO : \t test_acc: 94.41 (best: 94.43 from #125)\n",
            "2022-03-14 18:06:55,892 | INFO : Epoch #132\n",
            "2022-03-14 18:06:55,893 | INFO : \t train_acc: 99.884; train_loss: 0.001; test_loss: 23.424; lr: [0.001]\n",
            "2022-03-14 18:06:55,893 | INFO : \t test_acc: 94.52 (best: 94.52 from #132)\n",
            "2022-03-14 18:11:11,626 | INFO : Epoch #133\n",
            "2022-03-14 18:11:11,627 | INFO : \t train_acc: 99.866; train_loss: 0.046; test_loss: 23.793; lr: [0.001]\n",
            "2022-03-14 18:11:11,627 | INFO : \t test_acc: 94.32 (best: 94.52 from #132)\n",
            "2022-03-14 18:15:27,513 | INFO : Epoch #134\n",
            "2022-03-14 18:15:27,514 | INFO : \t train_acc: 99.872; train_loss: 0.001; test_loss: 24.263; lr: [0.001]\n",
            "2022-03-14 18:15:27,514 | INFO : \t test_acc: 94.3 (best: 94.52 from #132)\n",
            "2022-03-14 18:19:43,024 | INFO : Epoch #135\n",
            "2022-03-14 18:19:43,024 | INFO : \t train_acc: 99.896; train_loss: 0.159; test_loss: 23.490; lr: [0.001]\n",
            "2022-03-14 18:19:43,024 | INFO : \t test_acc: 94.46 (best: 94.52 from #132)\n",
            "2022-03-14 18:23:58,647 | INFO : Epoch #136\n",
            "2022-03-14 18:23:58,648 | INFO : \t train_acc: 99.88; train_loss: 0.003; test_loss: 24.026; lr: [0.001]\n",
            "2022-03-14 18:23:58,648 | INFO : \t test_acc: 94.37 (best: 94.52 from #132)\n",
            "2022-03-14 18:28:14,029 | INFO : Epoch #137\n",
            "2022-03-14 18:28:14,030 | INFO : \t train_acc: 99.9; train_loss: 0.004; test_loss: 23.975; lr: [0.001]\n",
            "2022-03-14 18:28:14,030 | INFO : \t test_acc: 94.48 (best: 94.52 from #132)\n",
            "2022-03-14 18:32:29,810 | INFO : Epoch #138\n",
            "2022-03-14 18:32:29,810 | INFO : \t train_acc: 99.856; train_loss: 0.021; test_loss: 24.244; lr: [0.001]\n",
            "2022-03-14 18:32:29,810 | INFO : \t test_acc: 94.48 (best: 94.52 from #132)\n",
            "2022-03-14 18:36:45,243 | INFO : Epoch #139\n",
            "2022-03-14 18:36:45,243 | INFO : \t train_acc: 99.858; train_loss: 0.003; test_loss: 24.217; lr: [0.001]\n",
            "2022-03-14 18:36:45,243 | INFO : \t test_acc: 94.47 (best: 94.52 from #132)\n",
            "2022-03-14 18:41:02,545 | INFO : Epoch #140\n",
            "2022-03-14 18:41:02,546 | INFO : \t train_acc: 99.922; train_loss: 0.004; test_loss: 24.093; lr: [0.001]\n",
            "2022-03-14 18:41:02,546 | INFO : \t test_acc: 94.38 (best: 94.52 from #132)\n",
            "2022-03-14 18:45:18,138 | INFO : Epoch #141\n",
            "2022-03-14 18:45:18,138 | INFO : \t train_acc: 99.868; train_loss: 0.002; test_loss: 24.253; lr: [0.001]\n",
            "2022-03-14 18:45:18,139 | INFO : \t test_acc: 94.42 (best: 94.52 from #132)\n",
            "2022-03-14 18:49:33,846 | INFO : Epoch #142\n",
            "2022-03-14 18:49:33,847 | INFO : \t train_acc: 99.894; train_loss: 0.032; test_loss: 23.529; lr: [0.001]\n",
            "2022-03-14 18:49:33,847 | INFO : \t test_acc: 94.44 (best: 94.52 from #132)\n",
            "2022-03-14 18:53:49,644 | INFO : Epoch #143\n",
            "2022-03-14 18:53:49,644 | INFO : \t train_acc: 99.892; train_loss: 0.001; test_loss: 23.913; lr: [0.001]\n",
            "2022-03-14 18:53:49,644 | INFO : \t test_acc: 94.3 (best: 94.52 from #132)\n",
            "2022-03-14 18:58:05,544 | INFO : Epoch #144\n",
            "2022-03-14 18:58:05,545 | INFO : \t train_acc: 99.87; train_loss: 0.021; test_loss: 24.086; lr: [0.001]\n",
            "2022-03-14 18:58:05,545 | INFO : \t test_acc: 94.42 (best: 94.52 from #132)\n",
            "2022-03-14 19:02:20,967 | INFO : Epoch #145\n",
            "2022-03-14 19:02:20,967 | INFO : \t train_acc: 99.896; train_loss: 0.003; test_loss: 24.472; lr: [0.001]\n",
            "2022-03-14 19:02:20,967 | INFO : \t test_acc: 94.34 (best: 94.52 from #132)\n",
            "2022-03-14 19:06:36,830 | INFO : Epoch #146\n",
            "2022-03-14 19:06:36,830 | INFO : \t train_acc: 99.874; train_loss: 0.002; test_loss: 24.008; lr: [0.001]\n",
            "2022-03-14 19:06:36,830 | INFO : \t test_acc: 94.47 (best: 94.52 from #132)\n",
            "2022-03-14 19:10:52,278 | INFO : Epoch #147\n",
            "2022-03-14 19:10:52,278 | INFO : \t train_acc: 99.896; train_loss: 0.001; test_loss: 23.894; lr: [0.001]\n",
            "2022-03-14 19:10:52,278 | INFO : \t test_acc: 94.42 (best: 94.52 from #132)\n",
            "2022-03-14 19:15:07,836 | INFO : Epoch #148\n",
            "2022-03-14 19:15:07,837 | INFO : \t train_acc: 99.898; train_loss: 0.001; test_loss: 23.694; lr: [0.001]\n",
            "2022-03-14 19:15:07,837 | INFO : \t test_acc: 94.65 (best: 94.65 from #148)\n",
            "2022-03-14 19:19:23,503 | INFO : Epoch #149\n",
            "2022-03-14 19:19:23,504 | INFO : \t train_acc: 99.896; train_loss: 0.086; test_loss: 24.849; lr: [0.001]\n",
            "2022-03-14 19:19:23,504 | INFO : \t test_acc: 94.46 (best: 94.65 from #148)\n",
            "2022-03-14 19:23:40,979 | INFO : Epoch #150\n",
            "2022-03-14 19:23:40,980 | INFO : \t train_acc: 99.904; train_loss: 0.125; test_loss: 24.572; lr: [0.001]\n",
            "2022-03-14 19:23:40,980 | INFO : \t test_acc: 94.59 (best: 94.65 from #148)\n",
            "2022-03-14 19:27:56,578 | INFO : Epoch #151\n",
            "2022-03-14 19:27:56,579 | INFO : \t train_acc: 99.902; train_loss: 0.004; test_loss: 24.000; lr: [0.001]\n",
            "2022-03-14 19:27:56,579 | INFO : \t test_acc: 94.58 (best: 94.65 from #148)\n",
            "2022-03-14 19:32:12,087 | INFO : Epoch #152\n",
            "2022-03-14 19:32:12,087 | INFO : \t train_acc: 99.898; train_loss: 0.017; test_loss: 25.185; lr: [0.001]\n",
            "2022-03-14 19:32:12,088 | INFO : \t test_acc: 94.39 (best: 94.65 from #148)\n",
            "2022-03-14 19:36:27,710 | INFO : Epoch #153\n",
            "2022-03-14 19:36:27,710 | INFO : \t train_acc: 99.89; train_loss: 0.001; test_loss: 24.444; lr: [0.001]\n",
            "2022-03-14 19:36:27,710 | INFO : \t test_acc: 94.49 (best: 94.65 from #148)\n",
            "2022-03-14 19:40:43,309 | INFO : Epoch #154\n",
            "2022-03-14 19:40:43,310 | INFO : \t train_acc: 99.916; train_loss: 0.002; test_loss: 24.325; lr: [0.001]\n",
            "2022-03-14 19:40:43,310 | INFO : \t test_acc: 94.53 (best: 94.65 from #148)\n",
            "2022-03-14 19:44:58,718 | INFO : Epoch #155\n",
            "2022-03-14 19:44:58,719 | INFO : \t train_acc: 99.906; train_loss: 0.015; test_loss: 24.252; lr: [0.001]\n",
            "2022-03-14 19:44:58,719 | INFO : \t test_acc: 94.47 (best: 94.65 from #148)\n",
            "2022-03-14 19:49:14,254 | INFO : Epoch #156\n",
            "2022-03-14 19:49:14,254 | INFO : \t train_acc: 99.916; train_loss: 0.029; test_loss: 23.375; lr: [0.001]\n",
            "2022-03-14 19:49:14,254 | INFO : \t test_acc: 94.54 (best: 94.65 from #148)\n",
            "2022-03-14 19:53:29,950 | INFO : Epoch #157\n",
            "2022-03-14 19:53:29,950 | INFO : \t train_acc: 99.894; train_loss: 0.002; test_loss: 23.456; lr: [0.001]\n",
            "2022-03-14 19:53:29,950 | INFO : \t test_acc: 94.54 (best: 94.65 from #148)\n",
            "2022-03-14 19:57:45,688 | INFO : Epoch #158\n",
            "2022-03-14 19:57:45,688 | INFO : \t train_acc: 99.908; train_loss: 0.004; test_loss: 24.251; lr: [0.001]\n",
            "2022-03-14 19:57:45,688 | INFO : \t test_acc: 94.55 (best: 94.65 from #148)\n",
            "2022-03-14 20:02:01,268 | INFO : Epoch #159\n",
            "2022-03-14 20:02:01,269 | INFO : \t train_acc: 99.896; train_loss: 0.418; test_loss: 24.004; lr: [0.001]\n",
            "2022-03-14 20:02:01,269 | INFO : \t test_acc: 94.63 (best: 94.65 from #148)\n",
            "2022-03-14 20:06:18,621 | INFO : Epoch #160\n",
            "2022-03-14 20:06:18,622 | INFO : \t train_acc: 99.882; train_loss: 0.005; test_loss: 25.116; lr: [0.001]\n",
            "2022-03-14 20:06:18,622 | INFO : \t test_acc: 94.36 (best: 94.65 from #148)\n",
            "2022-03-14 20:10:34,543 | INFO : Epoch #161\n",
            "2022-03-14 20:10:34,544 | INFO : \t train_acc: 99.902; train_loss: 0.092; test_loss: 24.611; lr: [0.001]\n",
            "2022-03-14 20:10:34,544 | INFO : \t test_acc: 94.51 (best: 94.65 from #148)\n",
            "2022-03-14 20:14:50,436 | INFO : Epoch #162\n",
            "2022-03-14 20:14:50,436 | INFO : \t train_acc: 99.914; train_loss: 0.002; test_loss: 24.539; lr: [0.001]\n",
            "2022-03-14 20:14:50,437 | INFO : \t test_acc: 94.72 (best: 94.72 from #162)\n",
            "2022-03-14 20:19:06,261 | INFO : Epoch #163\n",
            "2022-03-14 20:19:06,261 | INFO : \t train_acc: 99.902; train_loss: 0.020; test_loss: 24.537; lr: [0.001]\n",
            "2022-03-14 20:19:06,261 | INFO : \t test_acc: 94.47 (best: 94.72 from #162)\n",
            "2022-03-14 20:23:22,418 | INFO : Epoch #164\n",
            "2022-03-14 20:23:22,419 | INFO : \t train_acc: 99.928; train_loss: 0.059; test_loss: 24.798; lr: [0.001]\n",
            "2022-03-14 20:23:22,419 | INFO : \t test_acc: 94.52 (best: 94.72 from #162)\n",
            "2022-03-14 20:27:38,343 | INFO : Epoch #165\n",
            "2022-03-14 20:27:38,343 | INFO : \t train_acc: 99.882; train_loss: 0.010; test_loss: 24.559; lr: [0.001]\n",
            "2022-03-14 20:27:38,343 | INFO : \t test_acc: 94.41 (best: 94.72 from #162)\n",
            "2022-03-14 20:31:54,250 | INFO : Epoch #166\n",
            "2022-03-14 20:31:54,251 | INFO : \t train_acc: 99.882; train_loss: 0.001; test_loss: 24.904; lr: [0.001]\n",
            "2022-03-14 20:31:54,251 | INFO : \t test_acc: 94.47 (best: 94.72 from #162)\n",
            "2022-03-14 20:36:10,136 | INFO : Epoch #167\n",
            "2022-03-14 20:36:10,136 | INFO : \t train_acc: 99.912; train_loss: 0.009; test_loss: 24.942; lr: [0.001]\n",
            "2022-03-14 20:36:10,136 | INFO : \t test_acc: 94.24 (best: 94.72 from #162)\n",
            "2022-03-14 20:40:25,941 | INFO : Epoch #168\n",
            "2022-03-14 20:40:25,942 | INFO : \t train_acc: 99.906; train_loss: 0.021; test_loss: 25.595; lr: [0.001]\n",
            "2022-03-14 20:40:25,942 | INFO : \t test_acc: 94.33 (best: 94.72 from #162)\n",
            "2022-03-14 20:44:41,581 | INFO : Epoch #169\n",
            "2022-03-14 20:44:41,582 | INFO : \t train_acc: 99.87; train_loss: 0.001; test_loss: 24.432; lr: [0.001]\n",
            "2022-03-14 20:44:41,582 | INFO : \t test_acc: 94.37 (best: 94.72 from #162)\n",
            "2022-03-14 20:48:59,475 | INFO : Epoch #170\n",
            "2022-03-14 20:48:59,476 | INFO : \t train_acc: 99.918; train_loss: 0.000; test_loss: 25.749; lr: [0.001]\n",
            "2022-03-14 20:48:59,476 | INFO : \t test_acc: 94.34 (best: 94.72 from #162)\n",
            "2022-03-14 20:53:15,218 | INFO : Epoch #171\n",
            "2022-03-14 20:53:15,218 | INFO : \t train_acc: 99.902; train_loss: 0.007; test_loss: 24.997; lr: [0.001]\n",
            "2022-03-14 20:53:15,219 | INFO : \t test_acc: 94.4 (best: 94.72 from #162)\n",
            "2022-03-14 20:57:31,058 | INFO : Epoch #172\n",
            "2022-03-14 20:57:31,059 | INFO : \t train_acc: 99.936; train_loss: 0.008; test_loss: 25.130; lr: [0.001]\n",
            "2022-03-14 20:57:31,059 | INFO : \t test_acc: 94.35 (best: 94.72 from #162)\n",
            "2022-03-14 21:01:47,023 | INFO : Epoch #173\n",
            "2022-03-14 21:01:47,023 | INFO : \t train_acc: 99.944; train_loss: 0.001; test_loss: 25.719; lr: [0.001]\n",
            "2022-03-14 21:01:47,023 | INFO : \t test_acc: 94.41 (best: 94.72 from #162)\n",
            "2022-03-14 21:06:02,770 | INFO : Epoch #174\n",
            "2022-03-14 21:06:02,770 | INFO : \t train_acc: 99.908; train_loss: 0.058; test_loss: 25.779; lr: [0.001]\n",
            "2022-03-14 21:06:02,770 | INFO : \t test_acc: 94.26 (best: 94.72 from #162)\n",
            "2022-03-14 21:10:18,432 | INFO : Epoch #175\n",
            "2022-03-14 21:10:18,433 | INFO : \t train_acc: 99.93; train_loss: 0.004; test_loss: 25.829; lr: [0.001]\n",
            "2022-03-14 21:10:18,433 | INFO : \t test_acc: 94.19 (best: 94.72 from #162)\n",
            "2022-03-14 21:14:34,258 | INFO : Epoch #176\n",
            "2022-03-14 21:14:34,258 | INFO : \t train_acc: 99.89; train_loss: 0.004; test_loss: 25.732; lr: [0.001]\n",
            "2022-03-14 21:14:34,259 | INFO : \t test_acc: 94.24 (best: 94.72 from #162)\n",
            "2022-03-14 21:18:49,959 | INFO : Epoch #177\n",
            "2022-03-14 21:18:49,959 | INFO : \t train_acc: 99.924; train_loss: 0.056; test_loss: 26.058; lr: [0.001]\n",
            "2022-03-14 21:18:49,960 | INFO : \t test_acc: 94.39 (best: 94.72 from #162)\n",
            "2022-03-14 21:23:05,872 | INFO : Epoch #178\n",
            "2022-03-14 21:23:05,873 | INFO : \t train_acc: 99.936; train_loss: 0.002; test_loss: 25.534; lr: [0.001]\n",
            "2022-03-14 21:23:05,873 | INFO : \t test_acc: 94.47 (best: 94.72 from #162)\n",
            "2022-03-14 21:27:21,565 | INFO : Epoch #179\n",
            "2022-03-14 21:27:21,565 | INFO : \t train_acc: 99.912; train_loss: 0.021; test_loss: 26.178; lr: [0.001]\n",
            "2022-03-14 21:27:21,565 | INFO : \t test_acc: 94.31 (best: 94.72 from #162)\n",
            "2022-03-14 21:31:39,115 | INFO : Epoch #180\n",
            "2022-03-14 21:31:39,116 | INFO : \t train_acc: 99.928; train_loss: 0.043; test_loss: 26.596; lr: [0.001]\n",
            "2022-03-14 21:31:39,116 | INFO : \t test_acc: 94.14 (best: 94.72 from #162)\n",
            "2022-03-14 21:35:54,536 | INFO : Epoch #181\n",
            "2022-03-14 21:35:54,537 | INFO : \t train_acc: 99.892; train_loss: 0.001; test_loss: 26.313; lr: [0.001]\n",
            "2022-03-14 21:35:54,537 | INFO : \t test_acc: 94.13 (best: 94.72 from #162)\n",
            "2022-03-14 21:40:10,223 | INFO : Epoch #182\n",
            "2022-03-14 21:40:10,224 | INFO : \t train_acc: 99.938; train_loss: 0.003; test_loss: 26.708; lr: [0.001]\n",
            "2022-03-14 21:40:10,224 | INFO : \t test_acc: 94.2 (best: 94.72 from #162)\n",
            "2022-03-14 21:44:25,995 | INFO : Epoch #183\n",
            "2022-03-14 21:44:25,995 | INFO : \t train_acc: 99.878; train_loss: 0.004; test_loss: 25.542; lr: [0.001]\n",
            "2022-03-14 21:44:25,995 | INFO : \t test_acc: 94.25 (best: 94.72 from #162)\n",
            "2022-03-14 21:48:41,956 | INFO : Epoch #184\n",
            "2022-03-14 21:48:41,956 | INFO : \t train_acc: 99.928; train_loss: 0.039; test_loss: 25.906; lr: [0.001]\n",
            "2022-03-14 21:48:41,956 | INFO : \t test_acc: 94.3 (best: 94.72 from #162)\n",
            "2022-03-14 21:52:57,703 | INFO : Epoch #185\n",
            "2022-03-14 21:52:57,703 | INFO : \t train_acc: 99.894; train_loss: 0.022; test_loss: 26.599; lr: [0.001]\n",
            "2022-03-14 21:52:57,703 | INFO : \t test_acc: 94.09 (best: 94.72 from #162)\n",
            "2022-03-14 21:57:13,298 | INFO : Epoch #186\n",
            "2022-03-14 21:57:13,298 | INFO : \t train_acc: 99.92; train_loss: 0.002; test_loss: 26.252; lr: [0.001]\n",
            "2022-03-14 21:57:13,298 | INFO : \t test_acc: 94.19 (best: 94.72 from #162)\n",
            "2022-03-14 22:01:28,850 | INFO : Epoch #187\n",
            "2022-03-14 22:01:28,850 | INFO : \t train_acc: 99.884; train_loss: 0.004; test_loss: 25.644; lr: [0.001]\n",
            "2022-03-14 22:01:28,850 | INFO : \t test_acc: 94.19 (best: 94.72 from #162)\n",
            "2022-03-14 22:05:44,616 | INFO : Epoch #188\n",
            "2022-03-14 22:05:44,616 | INFO : \t train_acc: 99.912; train_loss: 0.002; test_loss: 26.387; lr: [0.001]\n",
            "2022-03-14 22:05:44,616 | INFO : \t test_acc: 94.33 (best: 94.72 from #162)\n",
            "2022-03-14 22:10:00,255 | INFO : Epoch #189\n",
            "2022-03-14 22:10:00,256 | INFO : \t train_acc: 99.888; train_loss: 0.033; test_loss: 25.322; lr: [0.001]\n",
            "2022-03-14 22:10:00,256 | INFO : \t test_acc: 94.4 (best: 94.72 from #162)\n",
            "2022-03-14 22:14:17,972 | INFO : Epoch #190\n",
            "2022-03-14 22:14:17,973 | INFO : \t train_acc: 99.884; train_loss: 0.030; test_loss: 26.468; lr: [0.001]\n",
            "2022-03-14 22:14:17,973 | INFO : \t test_acc: 94.01 (best: 94.72 from #162)\n",
            "2022-03-14 22:18:33,816 | INFO : Epoch #191\n",
            "2022-03-14 22:18:33,816 | INFO : \t train_acc: 99.856; train_loss: 0.002; test_loss: 26.177; lr: [0.001]\n",
            "2022-03-14 22:18:33,817 | INFO : \t test_acc: 94.35 (best: 94.72 from #162)\n",
            "2022-03-14 22:22:49,836 | INFO : Epoch #192\n",
            "2022-03-14 22:22:49,836 | INFO : \t train_acc: 99.918; train_loss: 0.001; test_loss: 26.187; lr: [0.001]\n",
            "2022-03-14 22:22:49,836 | INFO : \t test_acc: 94.24 (best: 94.72 from #162)\n",
            "2022-03-14 22:27:05,700 | INFO : Epoch #193\n",
            "2022-03-14 22:27:05,701 | INFO : \t train_acc: 99.904; train_loss: 0.007; test_loss: 27.237; lr: [0.001]\n",
            "2022-03-14 22:27:05,701 | INFO : \t test_acc: 94.19 (best: 94.72 from #162)\n",
            "2022-03-14 22:31:21,673 | INFO : Epoch #194\n",
            "2022-03-14 22:31:21,674 | INFO : \t train_acc: 99.922; train_loss: 0.008; test_loss: 25.694; lr: [0.001]\n",
            "2022-03-14 22:31:21,674 | INFO : \t test_acc: 94.52 (best: 94.72 from #162)\n",
            "2022-03-14 22:35:37,393 | INFO : Epoch #195\n",
            "2022-03-14 22:35:37,393 | INFO : \t train_acc: 99.914; train_loss: 0.001; test_loss: 25.760; lr: [0.001]\n",
            "2022-03-14 22:35:37,393 | INFO : \t test_acc: 94.56 (best: 94.72 from #162)\n",
            "2022-03-14 22:39:53,056 | INFO : Epoch #196\n",
            "2022-03-14 22:39:53,057 | INFO : \t train_acc: 99.924; train_loss: 0.046; test_loss: 25.689; lr: [0.001]\n",
            "2022-03-14 22:39:53,057 | INFO : \t test_acc: 94.39 (best: 94.72 from #162)\n",
            "2022-03-14 22:44:08,770 | INFO : Epoch #197\n",
            "2022-03-14 22:44:08,771 | INFO : \t train_acc: 99.902; train_loss: 0.021; test_loss: 26.740; lr: [0.001]\n",
            "2022-03-14 22:44:08,771 | INFO : \t test_acc: 94.03 (best: 94.72 from #162)\n",
            "2022-03-14 22:48:24,427 | INFO : Epoch #198\n",
            "2022-03-14 22:48:24,427 | INFO : \t train_acc: 99.882; train_loss: 0.007; test_loss: 26.524; lr: [0.001]\n",
            "2022-03-14 22:48:24,428 | INFO : \t test_acc: 94.38 (best: 94.72 from #162)\n",
            "2022-03-14 22:52:40,163 | INFO : Epoch #199\n",
            "2022-03-14 22:52:40,163 | INFO : \t train_acc: 99.926; train_loss: 0.158; test_loss: 26.562; lr: [0.001]\n",
            "2022-03-14 22:52:40,164 | INFO : \t test_acc: 94.21 (best: 94.72 from #162)\n",
            "2022-03-14 22:56:57,665 | INFO : Epoch #200\n",
            "2022-03-14 22:56:57,666 | INFO : \t train_acc: 99.914; train_loss: 0.002; test_loss: 25.207; lr: [0.0001]\n",
            "2022-03-14 22:56:57,666 | INFO : \t test_acc: 94.41 (best: 94.72 from #162)\n",
            "2022-03-14 23:01:13,623 | INFO : Epoch #201\n",
            "2022-03-14 23:01:13,624 | INFO : \t train_acc: 99.922; train_loss: 0.002; test_loss: 25.644; lr: [0.0001]\n",
            "2022-03-14 23:01:13,624 | INFO : \t test_acc: 94.5 (best: 94.72 from #162)\n",
            "2022-03-14 23:05:29,459 | INFO : Epoch #202\n",
            "2022-03-14 23:05:29,460 | INFO : \t train_acc: 99.944; train_loss: 0.001; test_loss: 25.314; lr: [0.0001]\n",
            "2022-03-14 23:05:29,460 | INFO : \t test_acc: 94.57 (best: 94.72 from #162)\n",
            "2022-03-14 23:09:45,433 | INFO : Epoch #203\n",
            "2022-03-14 23:09:45,433 | INFO : \t train_acc: 99.958; train_loss: 0.002; test_loss: 25.304; lr: [0.0001]\n",
            "2022-03-14 23:09:45,433 | INFO : \t test_acc: 94.51 (best: 94.72 from #162)\n",
            "2022-03-14 23:14:01,337 | INFO : Epoch #204\n",
            "2022-03-14 23:14:01,338 | INFO : \t train_acc: 99.96; train_loss: 0.003; test_loss: 25.065; lr: [0.0001]\n",
            "2022-03-14 23:14:01,338 | INFO : \t test_acc: 94.62 (best: 94.72 from #162)\n",
            "2022-03-14 23:18:17,321 | INFO : Epoch #205\n",
            "2022-03-14 23:18:17,321 | INFO : \t train_acc: 99.948; train_loss: 0.004; test_loss: 25.389; lr: [0.0001]\n",
            "2022-03-14 23:18:17,322 | INFO : \t test_acc: 94.53 (best: 94.72 from #162)\n",
            "2022-03-14 23:22:33,139 | INFO : Epoch #206\n",
            "2022-03-14 23:22:33,140 | INFO : \t train_acc: 99.94; train_loss: 0.042; test_loss: 25.656; lr: [0.0001]\n",
            "2022-03-14 23:22:33,140 | INFO : \t test_acc: 94.47 (best: 94.72 from #162)\n",
            "2022-03-14 23:26:48,707 | INFO : Epoch #207\n",
            "2022-03-14 23:26:48,708 | INFO : \t train_acc: 99.978; train_loss: 0.022; test_loss: 25.135; lr: [0.0001]\n",
            "2022-03-14 23:26:48,708 | INFO : \t test_acc: 94.62 (best: 94.72 from #162)\n",
            "2022-03-14 23:31:05,048 | INFO : Epoch #208\n",
            "2022-03-14 23:31:05,048 | INFO : \t train_acc: 99.972; train_loss: 0.012; test_loss: 25.576; lr: [0.0001]\n",
            "2022-03-14 23:31:05,048 | INFO : \t test_acc: 94.44 (best: 94.72 from #162)\n",
            "2022-03-14 23:35:20,611 | INFO : Epoch #209\n",
            "2022-03-14 23:35:20,612 | INFO : \t train_acc: 99.944; train_loss: 0.002; test_loss: 25.174; lr: [0.0001]\n",
            "2022-03-14 23:35:20,612 | INFO : \t test_acc: 94.54 (best: 94.72 from #162)\n",
            "2022-03-14 23:39:38,369 | INFO : Epoch #210\n",
            "2022-03-14 23:39:38,370 | INFO : \t train_acc: 99.974; train_loss: 0.047; test_loss: 25.433; lr: [0.0001]\n",
            "2022-03-14 23:39:38,370 | INFO : \t test_acc: 94.52 (best: 94.72 from #162)\n",
            "2022-03-14 23:43:54,252 | INFO : Epoch #211\n",
            "2022-03-14 23:43:54,253 | INFO : \t train_acc: 99.966; train_loss: 0.137; test_loss: 25.113; lr: [0.0001]\n",
            "2022-03-14 23:43:54,253 | INFO : \t test_acc: 94.49 (best: 94.72 from #162)\n",
            "2022-03-14 23:48:10,006 | INFO : Epoch #212\n",
            "2022-03-14 23:48:10,006 | INFO : \t train_acc: 99.97; train_loss: 0.003; test_loss: 25.153; lr: [0.0001]\n",
            "2022-03-14 23:48:10,006 | INFO : \t test_acc: 94.46 (best: 94.72 from #162)\n",
            "2022-03-14 23:52:25,753 | INFO : Epoch #213\n",
            "2022-03-14 23:52:25,754 | INFO : \t train_acc: 99.962; train_loss: 0.003; test_loss: 25.376; lr: [0.0001]\n",
            "2022-03-14 23:52:25,754 | INFO : \t test_acc: 94.53 (best: 94.72 from #162)\n",
            "2022-03-14 23:56:41,474 | INFO : Epoch #214\n",
            "2022-03-14 23:56:41,475 | INFO : \t train_acc: 99.976; train_loss: 0.002; test_loss: 25.082; lr: [0.0001]\n",
            "2022-03-14 23:56:41,475 | INFO : \t test_acc: 94.51 (best: 94.72 from #162)\n",
            "2022-03-15 00:00:57,215 | INFO : Epoch #215\n",
            "2022-03-15 00:00:57,216 | INFO : \t train_acc: 99.942; train_loss: 0.002; test_loss: 25.108; lr: [0.0001]\n",
            "2022-03-15 00:00:57,216 | INFO : \t test_acc: 94.54 (best: 94.72 from #162)\n",
            "2022-03-15 00:05:13,009 | INFO : Epoch #216\n",
            "2022-03-15 00:05:13,010 | INFO : \t train_acc: 99.978; train_loss: 0.004; test_loss: 25.230; lr: [0.0001]\n",
            "2022-03-15 00:05:13,010 | INFO : \t test_acc: 94.51 (best: 94.72 from #162)\n",
            "2022-03-15 00:09:28,709 | INFO : Epoch #217\n",
            "2022-03-15 00:09:28,709 | INFO : \t train_acc: 99.976; train_loss: 0.003; test_loss: 25.109; lr: [0.0001]\n",
            "2022-03-15 00:09:28,709 | INFO : \t test_acc: 94.5 (best: 94.72 from #162)\n",
            "2022-03-15 00:13:44,616 | INFO : Epoch #218\n",
            "2022-03-15 00:13:44,617 | INFO : \t train_acc: 99.978; train_loss: 0.001; test_loss: 25.290; lr: [0.0001]\n",
            "2022-03-15 00:13:44,617 | INFO : \t test_acc: 94.59 (best: 94.72 from #162)\n",
            "2022-03-15 00:18:00,295 | INFO : Epoch #219\n",
            "2022-03-15 00:18:00,295 | INFO : \t train_acc: 99.982; train_loss: 0.128; test_loss: 25.375; lr: [0.0001]\n",
            "2022-03-15 00:18:00,296 | INFO : \t test_acc: 94.52 (best: 94.72 from #162)\n",
            "2022-03-15 00:22:18,459 | INFO : Epoch #220\n",
            "2022-03-15 00:22:18,459 | INFO : \t train_acc: 99.96; train_loss: 0.005; test_loss: 25.085; lr: [0.0001]\n",
            "2022-03-15 00:22:18,460 | INFO : \t test_acc: 94.59 (best: 94.72 from #162)\n",
            "2022-03-15 00:26:34,052 | INFO : Epoch #221\n",
            "2022-03-15 00:26:34,053 | INFO : \t train_acc: 99.972; train_loss: 0.010; test_loss: 25.153; lr: [0.0001]\n",
            "2022-03-15 00:26:34,053 | INFO : \t test_acc: 94.53 (best: 94.72 from #162)\n",
            "2022-03-15 00:30:49,983 | INFO : Epoch #222\n",
            "2022-03-15 00:30:49,983 | INFO : \t train_acc: 99.984; train_loss: 0.008; test_loss: 24.871; lr: [0.0001]\n",
            "2022-03-15 00:30:49,984 | INFO : \t test_acc: 94.48 (best: 94.72 from #162)\n",
            "2022-03-15 00:35:05,788 | INFO : Epoch #223\n",
            "2022-03-15 00:35:05,789 | INFO : \t train_acc: 99.984; train_loss: 0.002; test_loss: 25.082; lr: [0.0001]\n",
            "2022-03-15 00:35:05,789 | INFO : \t test_acc: 94.65 (best: 94.72 from #162)\n",
            "2022-03-15 00:39:22,080 | INFO : Epoch #224\n",
            "2022-03-15 00:39:22,081 | INFO : \t train_acc: 99.978; train_loss: 0.003; test_loss: 24.713; lr: [0.0001]\n",
            "2022-03-15 00:39:22,081 | INFO : \t test_acc: 94.52 (best: 94.72 from #162)\n",
            "2022-03-15 00:43:37,843 | INFO : Epoch #225\n",
            "2022-03-15 00:43:37,844 | INFO : \t train_acc: 99.974; train_loss: 0.016; test_loss: 24.943; lr: [0.0001]\n",
            "2022-03-15 00:43:37,844 | INFO : \t test_acc: 94.59 (best: 94.72 from #162)\n",
            "2022-03-15 00:47:53,704 | INFO : Epoch #226\n",
            "2022-03-15 00:47:53,705 | INFO : \t train_acc: 99.98; train_loss: 0.014; test_loss: 24.881; lr: [0.0001]\n",
            "2022-03-15 00:47:53,705 | INFO : \t test_acc: 94.74 (best: 94.74 from #226)\n",
            "2022-03-15 00:52:09,503 | INFO : Epoch #227\n",
            "2022-03-15 00:52:09,504 | INFO : \t train_acc: 99.98; train_loss: 0.001; test_loss: 24.963; lr: [0.0001]\n",
            "2022-03-15 00:52:09,504 | INFO : \t test_acc: 94.62 (best: 94.74 from #226)\n",
            "2022-03-15 00:56:25,411 | INFO : Epoch #228\n",
            "2022-03-15 00:56:25,411 | INFO : \t train_acc: 99.978; train_loss: 0.006; test_loss: 25.214; lr: [0.0001]\n",
            "2022-03-15 00:56:25,412 | INFO : \t test_acc: 94.58 (best: 94.74 from #226)\n",
            "2022-03-15 01:00:41,024 | INFO : Epoch #229\n",
            "2022-03-15 01:00:41,025 | INFO : \t train_acc: 99.972; train_loss: 0.221; test_loss: 25.331; lr: [0.0001]\n",
            "2022-03-15 01:00:41,025 | INFO : \t test_acc: 94.49 (best: 94.74 from #226)\n",
            "2022-03-15 01:04:58,832 | INFO : Epoch #230\n",
            "2022-03-15 01:04:58,832 | INFO : \t train_acc: 99.976; train_loss: 0.002; test_loss: 25.112; lr: [0.0001]\n",
            "2022-03-15 01:04:58,833 | INFO : \t test_acc: 94.62 (best: 94.74 from #226)\n",
            "2022-03-15 01:09:14,518 | INFO : Epoch #231\n",
            "2022-03-15 01:09:14,519 | INFO : \t train_acc: 99.982; train_loss: 0.060; test_loss: 25.337; lr: [0.0001]\n",
            "2022-03-15 01:09:14,519 | INFO : \t test_acc: 94.44 (best: 94.74 from #226)\n",
            "2022-03-15 01:13:30,501 | INFO : Epoch #232\n",
            "2022-03-15 01:13:30,501 | INFO : \t train_acc: 99.956; train_loss: 0.011; test_loss: 24.720; lr: [0.0001]\n",
            "2022-03-15 01:13:30,502 | INFO : \t test_acc: 94.65 (best: 94.74 from #226)\n",
            "2022-03-15 01:17:46,172 | INFO : Epoch #233\n",
            "2022-03-15 01:17:46,173 | INFO : \t train_acc: 99.976; train_loss: 0.001; test_loss: 25.314; lr: [0.0001]\n",
            "2022-03-15 01:17:46,173 | INFO : \t test_acc: 94.62 (best: 94.74 from #226)\n",
            "2022-03-15 01:22:02,240 | INFO : Epoch #234\n",
            "2022-03-15 01:22:02,241 | INFO : \t train_acc: 99.974; train_loss: 0.003; test_loss: 24.883; lr: [0.0001]\n",
            "2022-03-15 01:22:02,241 | INFO : \t test_acc: 94.57 (best: 94.74 from #226)\n",
            "2022-03-15 01:26:18,064 | INFO : Epoch #235\n",
            "2022-03-15 01:26:18,065 | INFO : \t train_acc: 99.968; train_loss: 0.001; test_loss: 25.106; lr: [0.0001]\n",
            "2022-03-15 01:26:18,065 | INFO : \t test_acc: 94.58 (best: 94.74 from #226)\n",
            "2022-03-15 01:30:33,910 | INFO : Epoch #236\n",
            "2022-03-15 01:30:33,910 | INFO : \t train_acc: 99.97; train_loss: 0.004; test_loss: 25.165; lr: [0.0001]\n",
            "2022-03-15 01:30:33,910 | INFO : \t test_acc: 94.56 (best: 94.74 from #226)\n",
            "2022-03-15 01:34:49,507 | INFO : Epoch #237\n",
            "2022-03-15 01:34:49,507 | INFO : \t train_acc: 99.98; train_loss: 0.015; test_loss: 24.845; lr: [0.0001]\n",
            "2022-03-15 01:34:49,507 | INFO : \t test_acc: 94.61 (best: 94.74 from #226)\n",
            "2022-03-15 01:39:05,422 | INFO : Epoch #238\n",
            "2022-03-15 01:39:05,423 | INFO : \t train_acc: 99.974; train_loss: 0.020; test_loss: 24.968; lr: [0.0001]\n",
            "2022-03-15 01:39:05,423 | INFO : \t test_acc: 94.62 (best: 94.74 from #226)\n",
            "2022-03-15 01:43:21,340 | INFO : Epoch #239\n",
            "2022-03-15 01:43:21,340 | INFO : \t train_acc: 99.966; train_loss: 0.047; test_loss: 25.140; lr: [0.0001]\n",
            "2022-03-15 01:43:21,341 | INFO : \t test_acc: 94.53 (best: 94.74 from #226)\n",
            "2022-03-15 01:47:39,084 | INFO : Epoch #240\n",
            "2022-03-15 01:47:39,085 | INFO : \t train_acc: 99.972; train_loss: 0.010; test_loss: 25.210; lr: [0.0001]\n",
            "2022-03-15 01:47:39,085 | INFO : \t test_acc: 94.44 (best: 94.74 from #226)\n",
            "2022-03-15 01:51:54,990 | INFO : Epoch #241\n",
            "2022-03-15 01:51:54,990 | INFO : \t train_acc: 99.98; train_loss: 0.001; test_loss: 25.244; lr: [0.0001]\n",
            "2022-03-15 01:51:54,990 | INFO : \t test_acc: 94.63 (best: 94.74 from #226)\n",
            "2022-03-15 01:56:11,307 | INFO : Epoch #242\n",
            "2022-03-15 01:56:11,308 | INFO : \t train_acc: 99.972; train_loss: 0.071; test_loss: 25.354; lr: [0.0001]\n",
            "2022-03-15 01:56:11,308 | INFO : \t test_acc: 94.49 (best: 94.74 from #226)\n",
            "2022-03-15 02:00:27,026 | INFO : Epoch #243\n",
            "2022-03-15 02:00:27,026 | INFO : \t train_acc: 99.984; train_loss: 0.002; test_loss: 25.265; lr: [0.0001]\n",
            "2022-03-15 02:00:27,026 | INFO : \t test_acc: 94.65 (best: 94.74 from #226)\n",
            "2022-03-15 02:04:42,822 | INFO : Epoch #244\n",
            "2022-03-15 02:04:42,822 | INFO : \t train_acc: 99.984; train_loss: 0.001; test_loss: 24.851; lr: [0.0001]\n",
            "2022-03-15 02:04:42,822 | INFO : \t test_acc: 94.61 (best: 94.74 from #226)\n",
            "2022-03-15 02:08:58,403 | INFO : Epoch #245\n",
            "2022-03-15 02:08:58,404 | INFO : \t train_acc: 99.968; train_loss: 0.002; test_loss: 25.146; lr: [0.0001]\n",
            "2022-03-15 02:08:58,404 | INFO : \t test_acc: 94.48 (best: 94.74 from #226)\n",
            "2022-03-15 02:13:14,200 | INFO : Epoch #246\n",
            "2022-03-15 02:13:14,200 | INFO : \t train_acc: 99.976; train_loss: 0.017; test_loss: 25.514; lr: [0.0001]\n",
            "2022-03-15 02:13:14,201 | INFO : \t test_acc: 94.59 (best: 94.74 from #226)\n",
            "2022-03-15 02:17:30,061 | INFO : Epoch #247\n",
            "2022-03-15 02:17:30,061 | INFO : \t train_acc: 99.98; train_loss: 0.002; test_loss: 24.799; lr: [0.0001]\n",
            "2022-03-15 02:17:30,061 | INFO : \t test_acc: 94.51 (best: 94.74 from #226)\n",
            "2022-03-15 02:21:45,926 | INFO : Epoch #248\n",
            "2022-03-15 02:21:45,926 | INFO : \t train_acc: 99.978; train_loss: 0.097; test_loss: 25.122; lr: [0.0001]\n",
            "2022-03-15 02:21:45,927 | INFO : \t test_acc: 94.6 (best: 94.74 from #226)\n",
            "2022-03-15 02:26:01,775 | INFO : Epoch #249\n",
            "2022-03-15 02:26:01,776 | INFO : \t train_acc: 99.98; train_loss: 0.002; test_loss: 24.747; lr: [0.0001]\n",
            "2022-03-15 02:26:01,776 | INFO : \t test_acc: 94.53 (best: 94.74 from #226)\n",
            "2022-03-15 02:30:19,608 | INFO : Epoch #250\n",
            "2022-03-15 02:30:19,608 | INFO : \t train_acc: 99.972; train_loss: 0.002; test_loss: 25.138; lr: [0.0001]\n",
            "2022-03-15 02:30:19,608 | INFO : \t test_acc: 94.6 (best: 94.74 from #226)\n",
            "2022-03-15 02:34:35,041 | INFO : Epoch #251\n",
            "2022-03-15 02:34:35,041 | INFO : \t train_acc: 99.984; train_loss: 0.005; test_loss: 25.042; lr: [0.0001]\n",
            "2022-03-15 02:34:35,041 | INFO : \t test_acc: 94.58 (best: 94.74 from #226)\n",
            "2022-03-15 02:38:50,880 | INFO : Epoch #252\n",
            "2022-03-15 02:38:50,880 | INFO : \t train_acc: 99.97; train_loss: 0.005; test_loss: 24.818; lr: [0.0001]\n",
            "2022-03-15 02:38:50,880 | INFO : \t test_acc: 94.6 (best: 94.74 from #226)\n",
            "2022-03-15 02:43:06,540 | INFO : Epoch #253\n",
            "2022-03-15 02:43:06,540 | INFO : \t train_acc: 99.988; train_loss: 0.003; test_loss: 25.060; lr: [0.0001]\n",
            "2022-03-15 02:43:06,540 | INFO : \t test_acc: 94.51 (best: 94.74 from #226)\n",
            "2022-03-15 02:47:22,374 | INFO : Epoch #254\n",
            "2022-03-15 02:47:22,375 | INFO : \t train_acc: 99.978; train_loss: 0.001; test_loss: 25.009; lr: [0.0001]\n",
            "2022-03-15 02:47:22,375 | INFO : \t test_acc: 94.5 (best: 94.74 from #226)\n",
            "2022-03-15 02:51:37,865 | INFO : Epoch #255\n",
            "2022-03-15 02:51:37,865 | INFO : \t train_acc: 99.974; train_loss: 0.002; test_loss: 24.792; lr: [0.0001]\n",
            "2022-03-15 02:51:37,865 | INFO : \t test_acc: 94.54 (best: 94.74 from #226)\n",
            "2022-03-15 02:55:53,735 | INFO : Epoch #256\n",
            "2022-03-15 02:55:53,735 | INFO : \t train_acc: 99.982; train_loss: 0.001; test_loss: 24.717; lr: [0.0001]\n",
            "2022-03-15 02:55:53,736 | INFO : \t test_acc: 94.55 (best: 94.74 from #226)\n",
            "2022-03-15 03:00:09,415 | INFO : Epoch #257\n",
            "2022-03-15 03:00:09,416 | INFO : \t train_acc: 99.984; train_loss: 0.004; test_loss: 24.598; lr: [0.0001]\n",
            "2022-03-15 03:00:09,416 | INFO : \t test_acc: 94.58 (best: 94.74 from #226)\n",
            "2022-03-15 03:04:25,415 | INFO : Epoch #258\n",
            "2022-03-15 03:04:25,416 | INFO : \t train_acc: 99.992; train_loss: 0.002; test_loss: 24.839; lr: [0.0001]\n",
            "2022-03-15 03:04:25,416 | INFO : \t test_acc: 94.51 (best: 94.74 from #226)\n"
          ]
        }
      ]
    }
  ]
}