vietocr_gettingstart.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pbcquoc/vietocr/blob/master/vietocr_gettingstart.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "uPgu4i1yvhub"
      },
      "source": [
        "\n",
        "# Introduction\n",
        "<p align=\"center\">\n",
        "<img src=\"https://raw.githubusercontent.com/pbcquoc/vietocr/master/image/vietocr.jpg\" width=\"512\" height=\"512\">\n",
        "</p>\n",
        "This notebook describe how you can use VietOcr to train OCR model\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xEBHav_aljVN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "1b961df1-b4c7-4b02-ed86-86ba3b766603"
      },
      "source": [
        "! pip install --quiet vietocr==0.1.9"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[?25l\r\u001b[K     |████████                        | 10kB 31.6MB/s eta 0:00:01\r\u001b[K     |████████████████                | 20kB 6.4MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 30kB 7.5MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 40kB 3.9MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for gdown (PEP 517) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IS1cz5tKxio7",
        "colab_type": "text"
      },
      "source": [
        "# Inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsuT5Sa-xio8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "\n",
        "from vietocr.tool.predictor import Predictor\n",
        "from vietocr.tool.config import Cfg"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J7u5QgLWxipC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "config = Cfg.load_config_from_name('vgg_transformer')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5JIpythxipL",
        "colab_type": "text"
      },
      "source": [
        "Change weights to your weights or using default weights from our pretrained model. Path can be url or local file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjiubW0gxipL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# config['weights'] = './weights/transformerocr.pth'\n",
        "config['weights'] = 'https://drive.google.com/uc?id=13327Y1tz1ohsm5YZMyXVMPIOjoOA0OaA'\n",
        "config['device'] = 'cuda:0'\n",
        "config['predictor']['beamsearch']=False"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
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          "height": 174,
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      "source": [
        "detector = Predictor(config)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vgg19_bn-c79401a0.pth\" to /root/.cache/torch/checkpoints/vgg19_bn-c79401a0.pth\n"
          ],
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          "text": [
            "\n"
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          "name": "stdout"
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        {
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          "text": [
            "Cached Downloading: /root/.cache/gdown/https-COLON--SLASH--SLASH-drive.google.com-SLASH-uc-QUESTION-id-EQUAL-13327Y1tz1ohsm5YZMyXVMPIOjoOA0OaA\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=13327Y1tz1ohsm5YZMyXVMPIOjoOA0OaA\n",
            "To: /root/.cache/gdown/tmpi893y9wn/dl\n",
            "152MB [00:01, 89.3MB/s]\n"
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      "metadata": {
        "id": "BHtnhkpsxipZ",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "b58da845-0a6d-4877-f0b3-49e73a25cb7b"
      },
      "source": [
        "! gdown --id 1uMVd6EBjY4Q0G2IkU5iMOQ34X0bysm0b\n",
        "! unzip  -qq -o sample.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1uMVd6EBjY4Q0G2IkU5iMOQ34X0bysm0b\n",
            "To: /content/sample.zip\n",
            "\r  0% 0.00/306k [00:00<?, ?B/s]\r100% 306k/306k [00:00<00:00, 89.9MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGpmXki1xipe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "4491bd7d-c981-4861-a4e6-5c1a5746a59b"
      },
      "source": [
        "! ls sample | shuf |head -n 5"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2019_10_05_phung_vu_minh_phong_201910051031556_8.jpg\n",
            "457_statuses_74396.jpg\n",
            "463_Psi_60982.jpg\n",
            "24775.jpg\n",
            "030068003051.jpeg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B95BBXNExipj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "5b1a7b8d-61a8-4458-c96b-e12ad255a884"
      },
      "source": [
        "img = './sample/072199003062.jpeg'\n",
        "img = Image.open(img)\n",
        "plt.imshow(img)\n",
        "s = detector.predict(img)\n",
        "s"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'PHAN THỊ HỒNG THÂM'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "O9zjgHwN2vuC"
      },
      "source": [
        "# Download sample dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kMMAabbInNe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "733be0ac-49b9-4cc3-8ec1-2df9715849bb"
      },
      "source": [
        "! gdown https://drive.google.com/uc?id=1W2PZC94sjpA1lS7FN33VoIVleSnnWOaA "
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1W2PZC94sjpA1lS7FN33VoIVleSnnWOaA\n",
            "To: /content/data.zip\n",
            "1.49GB [00:15, 97.9MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZEBWugoTnvy0",
        "colab": {}
      },
      "source": [
        "! unzip -qq -o ./data.zip"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "F1lxSkEj20y0"
      },
      "source": [
        "# Train model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-MWgUSotv1sN"
      },
      "source": [
        "\n",
        "\n",
        "1.   Load your config\n",
        "2.   Train model using your dataset above\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "BuzRB0rxwC3m"
      },
      "source": [
        "Load the default config, we adopt VGG for image feature extraction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jMwREzEvm_jd",
        "colab": {}
      },
      "source": [
        "from vietocr.tool.config import Cfg\n",
        "from vietocr.model.trainer import Trainer"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7oKRCu2ewNE4"
      },
      "source": [
        "# Change the config \n",
        "\n",
        "* *data_root*: the folder save your all images\n",
        "* *train_annotation*: path to train annotation\n",
        "* *valid_annotation*: path to valid annotation\n",
        "* *print_every*: show train loss at every n steps\n",
        "* *valid_every*: show validation loss at every n steps\n",
        "* *iters*: number of iteration to train your model\n",
        "* *export*: export weights to folder that you can use for inference\n",
        "* *metrics*: number of sample in validation annotation you use for computing full_sequence_accuracy, for large dataset it will take too long, then you can reuduce this number\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "56VBD-Xy_ztj",
        "colab": {}
      },
      "source": [
        "config = Cfg.load_config_from_name('vgg_transformer')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ceKcT5eOnJ1G",
        "colab": {}
      },
      "source": [
        "dataset_params = {\n",
        "    'name':'hw',\n",
        "    'data_root':'./data/',\n",
        "    'train_annotation':'train_annotation.txt',\n",
        "    'valid_annotation':'test_annotation.txt'\n",
        "}\n",
        "\n",
        "params = {\n",
        "         'print_every':200,\n",
        "         'valid_every':15*200,\n",
        "          'iters':20000,\n",
        "          'checkpoint':'./checkpoint/transformerocr_checkpoint.pth',    \n",
        "          'export':'./weights/transformerocr.pth',\n",
        "          'metrics': 10000\n",
        "         }\n",
        "\n",
        "config['trainer'].update(params)\n",
        "config['dataset'].update(dataset_params)\n",
        "config['device'] = 'cuda:0'"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ufc86K0axiqN",
        "colab_type": "text"
      },
      "source": [
        "you can change any of these params in this full list below"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AwHpqqQEnHv1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "outputId": "5bff03b6-35b2-41aa-f770-3a6b08e15bbc"
      },
      "source": [
        "config"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'backbone': 'vgg19_bn',\n",
              " 'cnn': {'hidden': 256,\n",
              "  'ks': [[2, 2], [2, 2], [2, 1], [2, 1], [1, 1]],\n",
              "  'ss': [[2, 2], [2, 2], [2, 1], [2, 1], [1, 1]]},\n",
              " 'dataloader': {'num_workers': 3, 'pin_memory': True},\n",
              " 'dataset': {'data_root': './data/',\n",
              "  'image_height': 32,\n",
              "  'image_max_width': 512,\n",
              "  'image_min_width': 32,\n",
              "  'name': 'hw',\n",
              "  'train_annotation': 'train_annotation.txt',\n",
              "  'valid_annotation': 'test_annotation.txt'},\n",
              " 'device': 'cuda:0',\n",
              " 'optimizer': {'init_lr': 0.1, 'n_warmup_steps': 4000},\n",
              " 'predictor': {'beamsearch': True},\n",
              " 'pretrain': {'cached': '/tmp/tranformerorc.pth',\n",
              "  'id_or_url': '13327Y1tz1ohsm5YZMyXVMPIOjoOA0OaA',\n",
              "  'md5': '9cc606b70460261de374c56548aead5b'},\n",
              " 'quiet': False,\n",
              " 'trainer': {'batch_size': 32,\n",
              "  'checkpoint': './checkpoint/transformerocr_checkpoint.pth',\n",
              "  'export': './weights/transformerocr.pth',\n",
              "  'iters': 20000,\n",
              "  'log': './train.log',\n",
              "  'metrics': 10000,\n",
              "  'print_every': 200,\n",
              "  'valid_every': 3000},\n",
              " 'transformer': {'d_model': 256,\n",
              "  'dim_feedforward': 2048,\n",
              "  'max_seq_length': 1024,\n",
              "  'nhead': 8,\n",
              "  'num_decoder_layers': 6,\n",
              "  'num_encoder_layers': 6,\n",
              "  'pos_dropout': 0.1,\n",
              "  'trans_dropout': 0.1},\n",
              " 'vocab': 'aAàÀảẢãÃáÁạẠăĂằẰẳẲẵẴắẮặẶâÂầẦẩẨẫẪấẤậẬbBcCdDđĐeEèÈẻẺẽẼéÉẹẸêÊềỀểỂễỄếẾệỆfFgGhHiIìÌỉỈĩĨíÍịỊjJkKlLmMnNoOòÒỏỎõÕóÓọỌôÔồỒổỔỗỖốỐộỘơƠờỜởỞỡỠớỚợỢpPqQrRsStTuUùÙủỦũŨúÚụỤưƯừỪửỬữỮứỨựỰvVwWxXyYỳỲỷỶỹỸýÝỵỴzZ0123456789!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~ ',\n",
              " 'weights': 'https://drive.google.com/uc?id=12dTOZ9VP7ZVzwQgVvqBWz5JO5RXXW5NY'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "8dxTXpqa3Hd3"
      },
      "source": [
        "You should train model from our pretrained "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xtOyT8Cpo1gl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "7d77966b-34c4-432d-bab9-d6997a03d770"
      },
      "source": [
        "trainer = Trainer(config, pretrained=True)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cached Downloading: /tmp/tranformerorc.pth\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=13327Y1tz1ohsm5YZMyXVMPIOjoOA0OaA\n",
            "To: /root/.cache/gdown/tmpfn7zae3h/dl\n",
            "152MB [00:00, 205MB/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Computing MD5: /tmp/tranformerorc.pth\n",
            "MD5 matches: /tmp/tranformerorc.pth\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Create train_hw: 100%|███████████████████████████████████████| 88531/88531 [03:27<00:00, 426.18it/s]\n",
            "train_hw build cluster:  24%|██████▉                      | 21016/88530 [00:00<00:00, 210154.69it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Created dataset with 88530 samples\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "train_hw build cluster: 100%|█████████████████████████████| 88530/88530 [00:00<00:00, 202694.83it/s]\n",
            "Create valid_hw: 100%|███████████████████████████████████████| 29511/29511 [01:20<00:00, 365.41it/s]\n",
            "valid_hw build cluster: 100%|█████████████████████████████| 29510/29510 [00:00<00:00, 186004.23it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Created dataset with 29510 samples\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gyL8nmmTxiqV",
        "colab_type": "text"
      },
      "source": [
        "Visualize your dataset to check data augmentation is appropriate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OET9Vh8XxiqV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "da14d487-baf5-4bde-e8ee-64cb27f5e72c"
      },
      "source": [
        "trainer.visualize_dataset()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAR4AAAD3CAYAAADL9TsoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAMC0lEQVR4nO3dT4hkVxXH8d/p6p7+N/1v/tJhhsxGx7jJSkUICEZQV7oTFMlKUBnBgGCMIHGhxJ2CoiK4EjG6ECWguBZ0o5CVCgpDnDBRk0x3V/+v7r4uuhY9Wud05U7V6Xpd3w+ETN6Z9/p1vTe/eenz7r1WShEAZJo46xMAMH4IHgDpCB4A6QgeAOkIHgDpCB4A6QgepDKzu2b2oe6vzcx+ZWZ3zvq8kIvgQTUze8HMflK7fzl+iewZSZ8xs1uDOi+MPoIHZ6qUsibpU5KeOOtzQR6CZ0yY2ZfN7DUza5vZ38zs6e72CTN7zsz+YWZvmtnPzexSt3bLzIqZPWNmr5rZG2b21W7tI5Kel/QJM9s0s1cqzum9ZvYHSb+X9GMz+66ZXRjcd41RRfCMATO7LemOpPeUUhYkfVjS3W75C5I+LukDkh6T9EDS9/7nEE9Jui3paUlfM7MnSim/lfRNSS+VUi6WUp7sfq3nzOzlPk/tUNKzkq5Ien/3+J+v+ibRKJNnfQJIcShpWtK7zew/pZS7J2qflXSnlHJPOv65jaRXzezTJ37P10spO5Je6T7ZPCnpL72+UCnlxX5PqpTypxP/edfMfqjjAPx2v8dAM/HEMwZKKX+X9EVJL0j6t5n9zMwe65Yfl/RLM1szszUdB8qhpOsnDvH6iV9vS7o4iPMys3ea2ctm9rqZbej4CerKII6N0UbwjIlSyk9LKU/pOGiKpG91S/+U9NFSyvKJf2ZKKa/1c9hHPK3vS/qrpHeUUhZ1/DMje8RjogEInjFgZrfN7INmNi1pV9KOpKNu+QeSvmFmj3d/71Uz+1ifh/6XpFtmVnsfLUjakLRpZu+S9LnK46BhCJ7xMC3pRUlv6Ph/m65J+kq39h1Jv5b0OzNrS/qjpPf1edxfdP/9ppn9WZLM7Hkz+02f+39J0icltSX9SNJLfe6HhjMmAgOQjSceAOkIHgDpCB4A6QgeAOnCN5cLP3l+iJn/isnBwYFb63Q6PbfPzMxUfa3zelnG8Xs+zyy4oDzxAEhH8ABIR/AASEfwAEhH8ABIR/AASBeO1Tqv7fSobXt0dOTWNjc33Vr0Ue3t7fXcPjU15e6zsLDg1lqtlltrssPDQ7c2Oem/+XFOb9PGo50OYKQQPADSETwA0hE8ANIRPADSETwA0o3lulpR23Z9fd2tzc3NubVopLlnd3fXrW1vb7u1xcVFtzYqreXoPCYmev99d//+fXefS5cuubX5+Xm3Fr0eEb1WgeHiiQdAOoIHQDqCB0A6ggdAOoIHQLqx7GpFnatocGY0qLOmm3ThwgW3tr+//7aPN0pq5k++du2au8+9e/fc2s2bN91a9Bl7HS+6XcPHEw+AdAQPgHQED4B0BA+AdAQPgHQED4B0jW+nR63Pdrvdc/vs7Ky7z6Bb5pJ/jtGyx+eZ9zlOT0+7+6yurrq12la7d61HZaDtecYTD4B0BA+AdAQPgHQED4B0BA+AdAQPgHSNaKdHLfNOp+PWvHZ1NAI9s5UazascjapuOu96RvMjR/NdX7lyxa299dZbbs2buzp63SJCG75/PPEASEfwAEhH8ABIR/AASEfwAEhH8ABIZ1ELsIxIfzBqp29sbLg1b7RzNAo681uOzn1pacmtjchlaYTos9ra2uq5PVriOloueRgzGzSZBX9weeIBkI7gAZCO4AGQjuABkI7gAZCO4AGQrhGj06NRy1HNa5sPo7UZtfy9tu3kZCM+/kaLros3S0E0CX/0CkQ0o0A0un5iovff/+e5Bc8TD4B0BA+AdAQPgHQED4B0BA+AdCMzSLSmKyTFXS2va5HdLXjw4EHP7cvLy+4+0eeB4ar97KM5tPf29txazWDmVqvV/4mdkHnvM0gUwEgheACkI3gApCN4AKQjeACkI3gApGtEO73dbru1aLlZbxBm7bcVnePm5qZb81qf0bmf5wGC51V0f0TXc2dnp+f23d1dd59ofudosGrUoq8RfV+00wGMFIIHQDqCB0A6ggdAOoIHQDqCB0C6kZn0N2rLRSPQo1G6g25JR0vbdjodt3bx4sWe22mZny+119Objzl63SK637z2/Gk1rw0ftedrR8nzxAMgHcEDIB3BAyAdwQMgHcEDIB3BAyBdajs9Gr0btaqj/WpHBNccLxol77XMgdPU3KdRizuqRX/O9vf3e26PWve00wE0BsEDIB3BAyAdwQMgHcEDIB3BAyDdyIxOj9p8ExN1+ei1KaPjRaN3o9ZhNPk2o9AxaLX3VHQPR6PhB30ePPEASEfwAEhH8ABIR/AASEfwAEhH8ABI14h2ejRiPOLtF32tra0tt3b58mW3RsscTTAq9ylPPADSETwA0hE8ANIRPADSETwA0g2lq+X95DzqTm1vb7u1ycm60/S+3ubmprvP4uLi2z6eNDrdAqAJeOIBkI7gAZCO4AGQjuABkI7gAZCO4AGQbmQGiUZqW9Xe/MlRWzxa/pWWOTAYPPEASEfwAEhH8ABIR/AASEfwAEhH8ABI14h2eu2ocK+dvrKyUnU8AIPBEw+AdAQPgHQED4B0BA+AdAQPgHQED4B0jWint1ottxZNEj83N9dzO5O2j6bapaq5Zs3DEw+AdAQPgHQED4B0BA+AdAQPgHQED4B0FrUiy4D7lFG7dH9/361Fa51H66p766DTfh2+6FofHR313N7pdNx9omsWTdA/MeH/3cp9MFwW3AQ88QBIR/AASEfwAEhH8ABIR/AASDcyg0SjLsjW1pZbu3HjhlujazFc0TXb29tza97A3qg7FVlfX3drMzMzbm12drbndu6b4eOJB0A6ggdAOoIHQDqCB0A6ggdAOoIHQLqRaadHLfPp6Wm3VjMYsXZu33E06Ja5JC0tLfXcHg3ojHhza0vS2tqaW/MGGE9NTbn70GofDJ54AKQjeACkI3gApCN4AKQjeACkI3gApBtKO91rwUZz6kZt26hd6rXMTzsm+hN9vtErECsrK27Nuy61rerae2dnZ6fn9qgFf/XqVbdGq71/PPEASEfwAEhH8ABIR/AASEfwAEhH8ABIlzo6PVqKeGFhwa3t7u66tYODA7fmTR5O2/NhUTu63W67tfn5+apjZn7+0esA3rLZNTMenLYfHsYTD4B0BA+AdAQPgHQED4B0BA+AdAQPgHTV7fSodei1v1utln8izsTbp+3njTCW6tfiHjdRiziaUWBxcdGtDbplHt1vh4eHbi1aV90buU5bfPh44gGQjuABkI7gAZCO4AGQjuABkK66qxV1Lbzla5eXl6uONzs769a8gX6nHXMc1cyFHS0fXcu7LtESxlHnrWaZYsnvykXHw2DwxAMgHcEDIB3BAyAdwQMgHcEDIB3BAyBd2E6PBstFy9fOzMz03F7bLo3OIxpA6rX1o7mCx7EFHw2yjK5ZLe+YUVs/mq87uneiVzhwdnjiAZCO4AGQjuABkI7gAZCO4AGQjuABkC5sp0dtymgu29XV1Z7bo1Z17ZK3UWvcG2XszbWL/zeM+Ye9VzGi5aije2BpacmtTU1N9X9iXdErBLX3MB7GEw+AdAQPgHQED4B0BA+AdAQPgHQED4B01aPTr1+/PvCTqRGdo9c2j14FqJ2QvsmipZ7b7XbVMb1lrCPRtYxem4jOP3olxGub17bFa18JGUc88QBIR/AASEfwAEhH8ABIR/AASEfwAEhXvXZ6NMl6Zusw+lreut/RpOLRutnRKOgmjEz2PqtoVPje3p5biyb8j9rf3nrms7Oz7j6199ugr0vUno/OkVb7w3jiAZCO4AGQjuABkI7gAZCO4AGQjuABkM6iVl45p32+qLXprbcuxa1lr0Us+ROOR5OKR+dY2/72jlk7KjxqHw/aMG5F7/uO7oHahQc2NjbcmvcaQXRPNYEFNxZPPADSETwA0hE8ANIRPADSETwA0o1lVysSdXiiAYI1cwxHH2/0taJzjJbsremunXJ/uLUmDJr1RJ99NDA22i+6Lud1SW26WgBGCsEDIB3BAyAdwQMgHcEDIB3BAyAd7fQBaUL7mMv56Gpft6h9ZaHJaKcDGCkED4B0BA+AdAQPgHQED4B0BA+AdGE7HQCGgSceAOkIHgDpCB4A6QgeAOkIHgDpCB4A6f4Ls4dmuB4F2GYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAR4AAAD4CAYAAAA6o4n9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAM/0lEQVR4nO3dT2hlZxnH8eeZzGTyP7njZDJkwHZXVLBu1E1FsIIKhRYRXah1JbioIC60tqJVVKo7F0qX7WAF60IRoa07QUQQhO4ULAzaGVpIk0ySyZ97k3ldzB2Iw32eJO/c/HLPzfcDg53zzjn33D/z82R+932Pl1IMAJTOnPQJADh9CB4AcgQPADmCB4AcwQNAjuDBiXP3K+7+nLt/8KTPBRoED06Uu4+a2S/N7M9m9jN3XzjhU4KA8z0eAGpc8SDU/fHnV0f486+5+w97bH/c3d9297Pu/qK7/6i7/UF3L+6+cc+vL7r7q/t+33H39r7fv9DP5wm9syd9AhgqL5nZj939++X/L6W/bGYvl1J23b3XfnOllN17tr189z/c/UUze6uU8t1+nzBOBlc8Q8Ldv+3u19193d3/5e6Pdrefcfen3f1Nd3/X3V9x9wvdsbtXHF9x9/+4+5K7P9sd+7SZPWNmX+heZbxxiNP4vZm9x8w+tu+8Wmb2mJld7fNTRoMRPEPA3R8ys6fM7MOllGkz+5SZXesOf93MnjCzj5vZopmtmNkv7jnEI2b2kJk9ambfc/f3lVJeM7OfmNlvSilTpZSHu4/1tLv/sdd5lFK2zOwVM3ty3+bPm9k/SymHCS6cEgTPcNgzs/Nm9n53P1dKuVZKebM79jUze7aU8lYpZcfMnjOzz7n7/h+zf1BK2eqGwxtm9nD0QKWU50spjyXn8lL3+GPd3z/Z3ZZZcvfVfb8+dMCfR8PxbzxDoJTyb3f/ht0JlQ+4++tm9s1Syg0ze8DMfufut/ftsmdm+2vrt/f996aZTd3HufzF3ZfM7Al3/7uZfcTMPnvAbhd7/BsPhhhXPEOilPLrUsojdidoipn9tDv0XzP7TCllbt+vsVLK9cMctvJ0rtqdK50vmdnrpZR3Ko+DIUXwDAF3f8jdP+Hu581s28y2zOzuFc4LdqdpeqD7Z+fd/fFDHvodM3vQ3Y/6OblqZp80s6/awT9m4RQieIbDeTN73syW7M6PTZfM7DvdsZ+b2R/M7E/uvm5mfzOzjx7yuL/t/u+77v4PMzN3f8bdX812KqVcM7O/mtlk97EPsnrP93i+dcjzQ0PxzWUAclzx4MS5+0i3zXrvSZ8LNLjiASDHFQ8AOYIHgFz6BcIbN27wc9gxGhkZCcfa7XY4trGxEY7t7e0d+fFmZ2fDfZSWlpbCsVarFY6dPRt/jPmnhJOzuLjYc0awGVc8AE4AwQNAjuABIEfwAJAjeADIETwA5FiPp0+CtYTTsbW1tXCf7e3tcGxycjIcGxsbC8fOnOn9/zO3b9/uuf24ROcxMTER7tPpdMKxc+fOhWPU6YOJKx4AcgQPADmCB4AcwQNAjuABIEfwAJCjTu+TrJJeXl7uuX10dDTcZ35+vu/noa7Njyr7SsKgnzuOhiseAHIEDwA5ggeAHMEDQI7gASBHq3UEWeuSTWKMJnVOTU2F++zu7h7+xIZE9vpGE0vRTLybAOQIHgByBA8AOYIHgBzBA0CO4AEgR51+BNn6vdmEz6gmzir4rFpuguy1ip5bts70+Pj4fZ8TBgdXPADkCB4AcgQPADmCB4AcwQNAjuABIEed3idZfRyNNb0yz9Ssn5zNQM9uU8x6zM3DFQ8AOYIHgBzBA0CO4AEgR/AAkCN4AMhRp0Mu+nrBzMyM+Exwv7KvkWS44gEgR/AAkCN4AMgRPADkCB4AcgQPADnq9HvULFI+zOeB49fv9zM7Xr8/V7XnzhUPADmCB4AcwQNAjuABIEfwAJCj1brH2bPxS7K3t1d1zKhJyNYYzs4jW2O4Zu1nHF72nmWyz070fta+z9l+NY1XdrzsttMLCwvhGFc8AOQIHgByBA8AOYIHgBzBA0CO4AEgN9R1elR97u7uhvusrq6GY61WKxzLKseRkZGe27MqcmNjIxwbHx8Px7Jb/UZjtZMKT2M9v76+Ho51Op1wLHutRkdHe25vt9vhPjdv3gzHLl68GI5Fn8VM9vmIzv0gXPEAkCN4AMgRPADkCB4AcgQPADmCB4Bc4+v0rKbc3NzsuT2rAG/duhWOjY2NhWNTU1Ph2NraWs/t0fkd9FhZdZ/V8NF+2euRnUdW3fd7/d7a2dg1j7eyshLuk60akN2COZvVXvO1j+xzOjc3F45ls+RZcxnAUCN4AMgRPADkCB4AcgQPADmCB4Bc4+v0rM6LZhJns3dnZ2fDsay2zerNqOJeXFwM9zmOWeFRlVo7Sz57PbKqPZohnR0vq/xrZ0hHj5fV4tnzqr0ZQHQe2Wd7YmIiHMtmydd+ZaHfuOIBIEfwAJAjeADIETwA5AgeAHIEDwC5xtfp2eLV09PTPbdnC3ZnNWU2Wzg7j5r6uN+ziLP9Jicnw32ysewcs9cqGsvq3Kwirq3To9cje32z51X7vkSy1yM7j2wm/KAs0M8VDwA5ggeAHMEDQI7gASBH8ACQI3gAyKV1elb3ZpWdUs0i4LWLg2ePVTNrOTtebTVbc8zaWdXZZyCruM+fP1/1eJHa97NGvyvz4zrmoBuM9ABwqhA8AOQIHgByBA8AOYIHgFzaamW3cp2fnw/HBqVliBqe42iTMtltb2vOo3asZm3f47h1cNSGDcp6wE2QtYZNeB254gEgR/AAkCN4AMgRPADkCB4AcgQPALm0580mAW5tbYVj4+PjPbcra3azuuowO8faGj6qPrPHytbUzSZ1Zrcjjmr97Hg177NZvkZydB7Z521sbCwcyyadDkp9XKP2FtFNwBUPADmCB4AcwQNAjuABIEfwAJAjeADIpXX6zMxMOLa2thaOZTVrv2V1aVTPZue3ubl55OOZ1d32NnsNsyo1q5az5xbV2Nm5z87OhmO1s6CjsewrBNlKCQsLC+FYE0Sfq52dnXCfbCz7e6temSHCFQ8AOYIHgBzBA0CO4AEgR/AAkCN4AMildXrtgtLRDOmsBj6OmetRTZktvp5VutmM4JpZ7a1WK9wnq+5rZ1zXLH5fc7yDRJ+d7PN24cKFvp/HoIjOP/s7Vjs7fVBul8wVDwA5ggeAHMEDQI7gASBH8ACQI3gAyKV1elYRT05OhmPRAuFZnV4rqwfb7XbP7VlVPTIyEo5lNXy/7zGe1fqDUon2W/YaZvVx0+v0Gk1f4J4rHgByBA8AOYIHgBzBA0CO4AEgl7Za2b+OZxP61tfXe27PmpqsTaq9rXB0a97ssbI1l7MmoeZ2xFk7NazNVa0mNDWZmrWOs8/i9PT0fZ/TSeKKB4AcwQNAjuABIEfwAJAjeADIETwA5NI6PZPVvVHtnNWD2a1ys6o6G4vOMavTowreLJ9cmu0XTaituc2vGVV7E9Xc7jn7+kbt+t+DgiseAHIEDwA5ggeAHMEDQI7gASBH8ACQq67Ts8puYmKi5/aVlZWq49VUkQeNRbIKM6vTO51OOBbV9xsbG+E+2frUtWs/YzDt7Oz03J79ncg+i03Q7LMH0EgEDwA5ggeAHMEDQI7gASBH8ACQq67T04MGdW9WA9cubL26uhqORXV1NhM+m2VeW+tvb2/33J5Vok2vzPs9u752Rr7ytcoeK3s/l5eXe27PPovZCgtNuP01VzwA5AgeAHIEDwA5ggeAHMEDQI7gASB3LHV6NKs2WvTcLK/FW61WOBZV1WbxLPma+5yb1VeRW1tbPbdfvnw53Ee5EHzt8+p3xZ29L1m1XFtjK2VfnYied7biAbPTAeCICB4AcgQPADmCB4AcwQNA7lj+yT9qGbLbrmZrDF+/fj0cyybLXbp0qef29fX1cJ+pqalwLGtdRkdHw7ErV6703J41E8cxuTE6/3a7XXW8bL+shYrGss9Hbds4NzcXjiknkGafneh5Z41cE25TnOGKB4AcwQNAjuABIEfwAJAjeADIETwA5KQz6LIKMFtXOaudswo2ktXp2WNFk04PEj3vW7duhftkFXFtjR29VtlkxOxrDlkdnd0KOvrqQfba105IVdbO2Tlm70tU+dceb1DWVc5wxQNAjuABIEfwAJAjeADIETwA5AgeAHKDsSCt5bVntlZzzTq94+Pj4T7ZLOhaUb1Zux5wVutnlXQ0k792/d7aWzpH71ntOtNNFz23YX7OXPEAkCN4AMgRPADkCB4AcgQPADmCB4DcwNTpmdoZxtF+MzMzVcfLZgTXyGZwH4eonm36TGc0D1c8AOQIHgByBA8AOYIHgBzBA0CO4AEg14g6vd8G5b7TgzL7mMocalzxAJAjeADIETwA5AgeAHIEDwA5ggeAHMEDQI7gASBH8ACQI3gAyBE8AOQIHgByBA8AOYIHgBzBA0CO4AEgR/AAkCN4AMgRPADkTuWay6dVtLZyp9MJ99nZ2QnHpqenw7FBWdcag4krHgByBA8AOYIHgBzBA0CO4AEgR/AAkPNBuY0ugNODKx4AcgQPADmCB4AcwQNAjuABIEfwAJD7H49DEPBtz/ihAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6EkP2h64xiqZ",
        "colab_type": "text"
      },
      "source": [
        "Train now"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fpZEz_DPpV6y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2f407433-b50f-486b-ff31-fc299b83be7a"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "iter: 000200 - train loss: 1.530 - lr: 4.94e-06 - load time: 26.00 - gpu time: 38.40\n",
            "iter: 000400 - train loss: 1.092 - lr: 9.88e-06 - load time: 26.42 - gpu time: 36.96\n",
            "iter: 000600 - train loss: 0.948 - lr: 1.48e-05 - load time: 25.04 - gpu time: 38.00\n",
            "iter: 000800 - train loss: 0.875 - lr: 1.98e-05 - load time: 25.38 - gpu time: 37.39\n",
            "iter: 001000 - train loss: 0.838 - lr: 2.47e-05 - load time: 24.20 - gpu time: 38.70\n",
            "iter: 001200 - train loss: 0.807 - lr: 2.96e-05 - load time: 25.88 - gpu time: 37.31\n",
            "iter: 001400 - train loss: 0.733 - lr: 3.46e-05 - load time: 27.09 - gpu time: 37.49\n",
            "iter: 001600 - train loss: 0.734 - lr: 3.95e-05 - load time: 27.75 - gpu time: 37.34\n",
            "iter: 001800 - train loss: 0.761 - lr: 4.45e-05 - load time: 25.90 - gpu time: 37.13\n",
            "iter: 002000 - train loss: 0.726 - lr: 4.94e-05 - load time: 26.42 - gpu time: 37.96\n",
            "iter: 002200 - train loss: 0.741 - lr: 5.44e-05 - load time: 26.04 - gpu time: 37.58\n",
            "iter: 002400 - train loss: 0.743 - lr: 5.93e-05 - load time: 24.42 - gpu time: 38.61\n",
            "iter: 002600 - train loss: 0.729 - lr: 6.42e-05 - load time: 27.33 - gpu time: 37.52\n",
            "iter: 002800 - train loss: 0.741 - lr: 6.92e-05 - load time: 26.39 - gpu time: 37.95\n",
            "iter: 003000 - train loss: 0.694 - lr: 7.41e-05 - load time: 26.95 - gpu time: 38.01\n",
            "iter: 003000 - valid loss: 0.672 - acc full seq: 0.7210 - acc per char: 0.8650\n",
            "iter: 003200 - train loss: 0.702 - lr: 7.91e-05 - load time: 27.64 - gpu time: 37.00\n",
            "iter: 003400 - train loss: 0.755 - lr: 8.40e-05 - load time: 24.75 - gpu time: 38.00\n",
            "iter: 003600 - train loss: 0.734 - lr: 8.89e-05 - load time: 25.27 - gpu time: 37.25\n",
            "iter: 003800 - train loss: 0.675 - lr: 9.39e-05 - load time: 27.56 - gpu time: 37.51\n",
            "iter: 004000 - train loss: 0.733 - lr: 9.88e-05 - load time: 25.12 - gpu time: 37.32\n",
            "iter: 004200 - train loss: 0.730 - lr: 9.64e-05 - load time: 25.25 - gpu time: 37.82\n",
            "iter: 004400 - train loss: 0.749 - lr: 9.42e-05 - load time: 24.75 - gpu time: 37.82\n",
            "iter: 004600 - train loss: 0.683 - lr: 9.22e-05 - load time: 27.21 - gpu time: 36.74\n",
            "iter: 004800 - train loss: 0.691 - lr: 9.02e-05 - load time: 28.86 - gpu time: 36.46\n",
            "iter: 005000 - train loss: 0.670 - lr: 8.84e-05 - load time: 26.52 - gpu time: 37.77\n",
            "iter: 005200 - train loss: 0.667 - lr: 8.67e-05 - load time: 26.58 - gpu time: 37.68\n",
            "iter: 005400 - train loss: 0.668 - lr: 8.51e-05 - load time: 25.20 - gpu time: 37.98\n",
            "iter: 005600 - train loss: 0.671 - lr: 8.35e-05 - load time: 26.57 - gpu time: 37.22\n",
            "iter: 005800 - train loss: 0.647 - lr: 8.21e-05 - load time: 28.58 - gpu time: 37.07\n",
            "iter: 006000 - train loss: 0.645 - lr: 8.07e-05 - load time: 27.67 - gpu time: 37.20\n",
            "iter: 006000 - valid loss: 0.656 - acc full seq: 0.7805 - acc per char: 0.8712\n",
            "iter: 006200 - train loss: 0.654 - lr: 7.94e-05 - load time: 24.34 - gpu time: 41.64\n",
            "iter: 006400 - train loss: 0.669 - lr: 7.81e-05 - load time: 24.67 - gpu time: 41.11\n",
            "iter: 006600 - train loss: 0.668 - lr: 7.69e-05 - load time: 23.94 - gpu time: 41.40\n",
            "iter: 006800 - train loss: 0.666 - lr: 7.58e-05 - load time: 22.24 - gpu time: 40.84\n",
            "iter: 007000 - train loss: 0.661 - lr: 7.47e-05 - load time: 24.47 - gpu time: 40.03\n",
            "iter: 007200 - train loss: 0.655 - lr: 7.37e-05 - load time: 25.34 - gpu time: 40.94\n",
            "iter: 007400 - train loss: 0.629 - lr: 7.27e-05 - load time: 25.21 - gpu time: 41.00\n",
            "iter: 007600 - train loss: 0.663 - lr: 7.17e-05 - load time: 22.31 - gpu time: 41.57\n",
            "iter: 007800 - train loss: 0.656 - lr: 7.08e-05 - load time: 24.64 - gpu time: 40.67\n",
            "iter: 008000 - train loss: 0.640 - lr: 6.99e-05 - load time: 25.04 - gpu time: 40.74\n",
            "iter: 008200 - train loss: 0.652 - lr: 6.90e-05 - load time: 22.31 - gpu time: 40.96\n",
            "iter: 008400 - train loss: 0.615 - lr: 6.82e-05 - load time: 25.11 - gpu time: 40.65\n",
            "iter: 008600 - train loss: 0.618 - lr: 6.74e-05 - load time: 26.93 - gpu time: 40.33\n",
            "iter: 008800 - train loss: 0.615 - lr: 6.66e-05 - load time: 25.58 - gpu time: 40.61\n",
            "iter: 009000 - train loss: 0.639 - lr: 6.59e-05 - load time: 23.59 - gpu time: 40.43\n",
            "iter: 009000 - valid loss: 0.631 - acc full seq: 0.8189 - acc per char: 0.8907\n",
            "iter: 009200 - train loss: 0.619 - lr: 6.52e-05 - load time: 24.55 - gpu time: 41.20\n",
            "iter: 009400 - train loss: 0.625 - lr: 6.45e-05 - load time: 25.35 - gpu time: 43.69\n",
            "iter: 009600 - train loss: 0.628 - lr: 6.38e-05 - load time: 25.46 - gpu time: 44.54\n",
            "iter: 009800 - train loss: 0.623 - lr: 6.31e-05 - load time: 25.46 - gpu time: 42.29\n",
            "iter: 010000 - train loss: 0.613 - lr: 6.25e-05 - load time: 27.85 - gpu time: 40.80\n",
            "iter: 010200 - train loss: 0.632 - lr: 6.19e-05 - load time: 23.54 - gpu time: 41.52\n",
            "iter: 010400 - train loss: 0.639 - lr: 6.13e-05 - load time: 23.15 - gpu time: 40.59\n",
            "iter: 010600 - train loss: 0.643 - lr: 6.07e-05 - load time: 23.34 - gpu time: 40.46\n",
            "iter: 010800 - train loss: 0.645 - lr: 6.01e-05 - load time: 25.39 - gpu time: 40.11\n",
            "iter: 011000 - train loss: 0.613 - lr: 5.96e-05 - load time: 22.41 - gpu time: 39.90\n",
            "iter: 011200 - train loss: 0.616 - lr: 5.91e-05 - load time: 24.45 - gpu time: 41.18\n",
            "iter: 011400 - train loss: 0.610 - lr: 5.85e-05 - load time: 24.47 - gpu time: 40.63\n",
            "iter: 011600 - train loss: 0.626 - lr: 5.80e-05 - load time: 24.42 - gpu time: 41.00\n",
            "iter: 011800 - train loss: 0.617 - lr: 5.75e-05 - load time: 24.37 - gpu time: 39.75\n",
            "iter: 012000 - train loss: 0.580 - lr: 5.71e-05 - load time: 26.94 - gpu time: 39.08\n",
            "iter: 012000 - valid loss: 0.630 - acc full seq: 0.8335 - acc per char: 0.8996\n",
            "iter: 012200 - train loss: 0.624 - lr: 5.66e-05 - load time: 23.44 - gpu time: 40.22\n",
            "iter: 012400 - train loss: 0.630 - lr: 5.61e-05 - load time: 22.98 - gpu time: 39.90\n",
            "iter: 012600 - train loss: 0.594 - lr: 5.57e-05 - load time: 26.40 - gpu time: 39.04\n",
            "iter: 012800 - train loss: 0.599 - lr: 5.52e-05 - load time: 25.84 - gpu time: 39.24\n",
            "iter: 013000 - train loss: 0.623 - lr: 5.48e-05 - load time: 21.79 - gpu time: 41.91\n",
            "iter: 013200 - train loss: 0.608 - lr: 5.44e-05 - load time: 24.90 - gpu time: 41.31\n",
            "iter: 013400 - train loss: 0.602 - lr: 5.40e-05 - load time: 23.74 - gpu time: 40.79\n",
            "iter: 013600 - train loss: 0.601 - lr: 5.36e-05 - load time: 24.63 - gpu time: 41.11\n",
            "iter: 013800 - train loss: 0.621 - lr: 5.32e-05 - load time: 22.92 - gpu time: 41.00\n",
            "iter: 014000 - train loss: 0.608 - lr: 5.28e-05 - load time: 23.21 - gpu time: 40.43\n",
            "iter: 014200 - train loss: 0.589 - lr: 5.24e-05 - load time: 24.51 - gpu time: 40.29\n",
            "iter: 014400 - train loss: 0.599 - lr: 5.21e-05 - load time: 23.69 - gpu time: 41.36\n",
            "iter: 014600 - train loss: 0.589 - lr: 5.17e-05 - load time: 23.60 - gpu time: 40.17\n",
            "iter: 014800 - train loss: 0.603 - lr: 5.14e-05 - load time: 24.81 - gpu time: 39.43\n",
            "iter: 015000 - train loss: 0.590 - lr: 5.10e-05 - load time: 25.12 - gpu time: 39.83\n",
            "iter: 015000 - valid loss: 0.634 - acc full seq: 0.8375 - acc per char: 0.9000\n",
            "iter: 015200 - train loss: 0.614 - lr: 5.07e-05 - load time: 22.40 - gpu time: 40.78\n",
            "iter: 015400 - train loss: 0.601 - lr: 5.04e-05 - load time: 23.49 - gpu time: 41.19\n",
            "iter: 015600 - train loss: 0.588 - lr: 5.00e-05 - load time: 24.00 - gpu time: 40.53\n",
            "iter: 015800 - train loss: 0.584 - lr: 4.97e-05 - load time: 24.44 - gpu time: 41.42\n",
            "iter: 016000 - train loss: 0.615 - lr: 4.94e-05 - load time: 23.54 - gpu time: 40.73\n",
            "iter: 016200 - train loss: 0.601 - lr: 4.91e-05 - load time: 23.33 - gpu time: 41.18\n",
            "iter: 016400 - train loss: 0.614 - lr: 4.88e-05 - load time: 23.27 - gpu time: 40.51\n",
            "iter: 016600 - train loss: 0.581 - lr: 4.85e-05 - load time: 25.06 - gpu time: 40.19\n",
            "iter: 016800 - train loss: 0.587 - lr: 4.82e-05 - load time: 25.58 - gpu time: 39.76\n",
            "iter: 017000 - train loss: 0.604 - lr: 4.79e-05 - load time: 24.31 - gpu time: 40.64\n",
            "iter: 017200 - train loss: 0.588 - lr: 4.77e-05 - load time: 25.09 - gpu time: 39.67\n",
            "iter: 017400 - train loss: 0.586 - lr: 4.74e-05 - load time: 24.60 - gpu time: 40.34\n",
            "iter: 017600 - train loss: 0.579 - lr: 4.71e-05 - load time: 24.84 - gpu time: 40.92\n",
            "iter: 017800 - train loss: 0.589 - lr: 4.68e-05 - load time: 23.53 - gpu time: 40.99\n",
            "iter: 018000 - train loss: 0.584 - lr: 4.66e-05 - load time: 26.28 - gpu time: 39.75\n",
            "iter: 018000 - valid loss: 0.613 - acc full seq: 0.8438 - acc per char: 0.9046\n",
            "iter: 018200 - train loss: 0.601 - lr: 4.63e-05 - load time: 24.59 - gpu time: 42.76\n",
            "iter: 018400 - train loss: 0.604 - lr: 4.61e-05 - load time: 25.28 - gpu time: 44.09\n",
            "iter: 018600 - train loss: 0.585 - lr: 4.58e-05 - load time: 25.33 - gpu time: 42.96\n",
            "iter: 018800 - train loss: 0.598 - lr: 4.56e-05 - load time: 25.50 - gpu time: 43.26\n",
            "iter: 019000 - train loss: 0.576 - lr: 4.53e-05 - load time: 26.29 - gpu time: 42.55\n",
            "iter: 019200 - train loss: 0.595 - lr: 4.51e-05 - load time: 23.83 - gpu time: 42.75\n",
            "iter: 019400 - train loss: 0.585 - lr: 4.49e-05 - load time: 25.01 - gpu time: 42.99\n",
            "iter: 019600 - train loss: 0.575 - lr: 4.46e-05 - load time: 27.89 - gpu time: 41.32\n",
            "iter: 019800 - train loss: 0.588 - lr: 4.44e-05 - load time: 27.88 - gpu time: 42.17\n",
            "iter: 020000 - train loss: 0.598 - lr: 4.42e-05 - load time: 25.40 - gpu time: 42.98\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Iig6gpyb3jtz"
      },
      "source": [
        "Visualize prediction from our trained model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zeai5W02qXA9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "aa4c5d29-9e3c-4815-d9ae-4685906e65cb"
      },
      "source": [
        "trainer.visualize_prediction()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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drXVRGEtKyV0NSgAzMzMgIlRUVGhdFMaSEtfE4oiI4PF4YDKZUmaKFWNq4xCLo/n5eXg8Htjt9qQYH8dYIuIQiyOHwwGj0Yjs7Gyti8JY0uIQiyOn0wmPx4PKykoeuMpYnHCIxQkRwefzwe12o6SkROviMJa0OMTiaGRkBCUlJVwLYyyOOMTihIjQ29uLHTt2aF0UxpIah1icEBGGhoZQXl7ONTHG4ohDLE4kSYLRaOSR+ozFGYdYnMzPz0dXw2CMxQ+HWJzcvXsXNpuNm5KMxRmHWBwQEZ48eYLa2lqti8JY0uMQi4NgMAiHw8G7fTOmAg6xOPD7/XA6nSguLta6KIwlPQ6xOBgdHY2uo88Yiy8OMYXJ+2vW1tbybt+MqYBDTGE+nw+hUAgZGRm8HDVjKuCVXRXmdruRnp7OQysYUwlXFRT27NkzVFRUPLdtG2MsfjjEFERE6Ovrw2uvvRaddsQYiy8OMQU5nU5kZGTAbDZDkqSk346OsUTAIaagiYkJ2O12AOAQY0wlHGIKISIMDw+joqICkiQhHA5ziDGmAg4xhUiShOHhYVRWVkKSJK6JMaYSDjGFyEtRGwwGDjHGVMQhpgAiwrNnz1BZWfnc93mIBWPxxyGmACLC1NQUNm3a9FxwcYgxFn8cYgoIBAKYm5tDQUEBgEj/GAcYY+rgENsgeVejurq66FzJ+fl5XsGCMZVwiG0QEaG/vx+bN2+Ofm92dhbZ2dkaloqx1MEhtkE+nw9erxf5+fnR77ndbuTk5GhYKsZSB4fYBg0MDKC0tBQWiyX6PbfbzTUxxlTCIbYBkiTh4cOH2LVr13Md+bOzs1wTY0wlHGIb4HK5YDKZXurEDwaDMJvNGpWKsdTCIbYBY2Nj2LRp03Mj84lIwxIxlno4xNZJkiQ8ePAAu3fvfq4pSUS8ICJjKuIQW6fp6WlYrdZlm5IGg4EXRGRMJRxi60BEGBoaQnl5+Us1rmAwCKPRqMtNQogIP/74I6amphAKhbhpzHSBl1lYp76+Ppw8efKlEAuFQjAYDLoMMZfLhfPnz6OoqAihUAg7d+5EQ0MD1ypZQuMQW4fp6WlYLJZlx4LptSZGRLh37x6OHj2KLVu2wO12o7W1FUVFRSgsLNS6eIytSF9nWgIgIjx+/Bivvfbasp334XBYlzUxv9+P0dFRVFdXw2g0Ij8/HwUFBUnbpCQizMzMQJIkrYvCNkhfZ1oCkCQJY2NjKC8vX/Z++aTX09VJedfyurq6lNm13OPx4Je//CXa2to4yHSOQ2yNnE4nhBDIzc1d9n4i0l0tLBAI4IcffsDWrVu1Looq5Kbz22+/Dbfbjba2NoTDYa2LxdZJX2ebxuQ3/4tjw5byeDy6q82MjY0hIyNj2T6+ZGxOhkIhjIyMoK6uDsePH4fb7cbNmzc5yHSKQ2wN/H4/ZmZmUFxcvOLP6G0tMSLCrVu3cPDgwWWDWU/N4lh5vV4AQFZWFiwWC44fP47BwUGMjY1pXDK2HhxiazA2Nobs7GxkZGSs+DNCCF3VXjweD4QQKTVhvbu7G1u3bo0GtMViQXV1Ndxut8YlY+vBIRYjSZLQ0dGB5ubmVWsnVqsVfr9fxZJtjNvtRkZGxrI7M8k7NyUTeWu9Fy/M2Gw2+Hw+jUrFNoJDLEZOpxMGg2HFDn2Z1WrFwsKCSqXaOLn5u1wwWywWXQVyLOSgerHfkkNMvzjEYiBPx6moqHjl6HW9nfgDAwOorq5eNsTS0tJ09Vxi4fP5IISA1Wp97vs2my3aV8b0hUMsBqFQCD09PWhqanplR7eeOsKJCKOjoygtLdW6KKqRn++LfyeDwaCrvkz2/3GIvQIRYXBwEEVFRUhLS4v5d/TA4/HAZrMt2x9GREm34Yk826KhoeGl+wKBwHNLjDP94BCLwZMnT1BfX6+rWlYsfD7fildaJUmCJEnLBpxeeb1eBINBZGVlvXSf3++P+UOKJRYOsVfwer2YmppCdXX1mn5PD7WxUCi0Yh9fMBiEJEkv9R3pmbypy3LBzDUx/eIQWwUR4dGjR9i6dWvMU4mMRiOISBchFg6HV6xpBQKBpAoxSZJw//597NmzZ9kaNdfE9ItDbBWSJGFwcHBNtTC5g1gPIbZaTUyegqO3eaDLISI8e/YMeXl5K/bxcU1Mv/T/Do2jqamp6LI0sTIajZAkSTchtlJNbHp6GoWFhUnTD7hSh75sfn5+1ZkYLHElT6+twogId+7cwd69e9d0Iss1MT2MdF8pxOSreLt27dKgVMpbWFjA1NQUfvrTny77t5QkKeFCjIgQCATgdrujqwVbrVbYbDZYLJak+XBRAofYCubn5zE7O4uioqI1/Z6eamIrWVhYwOzsLDZt2qR1UTZMXiutsbFxxaazPMNCy/4/ObT8fj+Gh4fR3d2NYDAYHQJDRAgGgwgGgwiHw2hoaMCuXbuSorm/URxiyyAiPHz4EPX19Wvu7NVTx352djb6+/tf+v7U1BRyc3OTYgNgSZIwMjKClpaWFX/G7/cvO4pfDUQEl8uF9vZ2zM/PIxgMory8HG+++SYyMzNhsViitftwOIxQKIRAIIBvvvkGRLTihYpUwiG2DK/Xi4GBAZw+fXrNbxB50rQempP5+fmYmZmJ7pUpe/LkCerq6pLiU97j8cDj8ay6T4DL5UJ2drbqz9fv96OjowMDAwM4cOAAioqKkJGRsWw5hBAwGAwwm82w2Wx45513cP78eZjNZuzYsSOlg0z/79I4ePr0KcrKytb1yWw2mxEOh3URYmlpaQgEAs/VGgOBAIaHh1FbW6thyZRBRHjw4AF27Nix6pzX3t5ebNmyRbVyhcNh9PX14fPPP4fZbMaZM2dQV1eHrKysmIPUZrPh5MmTePz4Mfr6+nRR848XDrEXBAIBPHjw4JVL7qxE3iRED6uEyuuIyetoyVOsysrKkmKbNrlGXVdXt+LPEBEmJyfX3Pe5HkQEh8OBL7/8Er29vTh16hSam5vX3YxNT0/Hu+++i/b2dgQCAYVLqx/cnFyCiPDkyROUlZXBZrOt+zh2ux2Tk5O6WGiwrKwMw8PD0WEkIyMjKCsr07hUGydfXW5oaFh1uXCv1wur1RrX0JY77eWmY0tLS3TlkI02A7OystDQ0ID79++v+4NX77gm9oLOzk7s3LlzQ8eoqKjA0NCQQiWKL7vdjqmpqeiwkIGBAWzevFn3J8P09DRGRkawa9euVZ+LPAk+Xv1hRASn04lf//rXMBgMOHPmDGpqamAwGBR5jYUQaGxsxJMnTxAKhRQosf5wiC0iIvzwww/Izc1FQUHBht5gmzZtwuTkpIKlix+r1Qqv1xsdG1ZRUaH76TeSJOHmzZtobm5+5QR2n88Hq9UalxAjIvT29uLChQs4fPgwDh48CKvVqvgHRFZWFiorK/Ho0aOU7BvjEFskjydS4kqPPK5HD/1ihYWFcLvd8Pl86Onp0f22bfKHkRACtbW1r/xbSpKkWK1oqUAggO+++w6PHz/GBx98gPLy8rjVboUQ2LlzJ3p6elKyb4xDbNHExAQkSUJJScmGj2UymWA2m3WxTLXZbEZJSQlu374NIYSuB7gSEebm5nD79u0Vd296kdJb7BER3G43zp49CyEETp06hezs7Lg3z3NycpCZmYmJiYm4Pk4i4hBD5JJ3e3s7Dhw4oEizQg4xPSx3LITA9u3b8atf/QrNzc26Hhvm9/vx5ZdfoqWlBQUFBTH9jsPhWHUM2VoQEYaGhtDa2oqdO3fijTfegNlsVqV/UQiBLVu24OnTp3F/rESj33esgtxuNxYWFhS7zG4wGGA0GhEMBhU5nhq8Xm9CzR1cq0AggN/+9rfYvn07ampqYg6O6elp2O32DT22PEOjo6MD169fxwcffID6+nrVPxBKS0sxNjaWcv1iKT/EgojQ3t6Offv2KTbNRgiBjIwMeDweRY4XT3Jf4OHDh+Fyuda0YkeiCIfDuHbtGgoKCtbUpykvwb3cSq9rffwrV65AkiR8/PHHcem8j4XJZIIQAsFgMKWWFUr5mpjT6cTs7OyaV259lbKyMoyOjip6zHiYm5vD5OQkPv30U9y7d093n+LyeDC/349Dhw6tqfbj9/thNps3VGPyer04d+4c0tPTcezYMdhsNs2Gp8hTkubm5jR5fK2kdIjJ01K2bdum+FrypaWlCR9i8uDeqqoqFBUVIRQKYXp6WutixUyuRQ4PD+P48eNrDqOpqakNNSVdLhfOnj2L6upqtLS0aD5h3mg0wmKx6KIvVkkpG2JEhJmZGYyPjz+3pb1S5E10E3UOpdyU6u7uxt69e2E0GrF37160tbUlbJmXkodSdHd348SJE9Gm1Fp+3+FwrKv5TESYmJjAl19+iX379qGpqSkhLojI08hmZ2e1LoqqtH/lNdTZ2bmm9fPXQt4t3OFwKH5spbS1tWH37t1IS0uLjqsyGo0JP6GYiDAyMoJbt27h3XffRXp6+ro+hPx+/5r7r+Slri9evIhjx47FNBZNTeXl5RgZGdG6GKpK2RCbn5/H4OBgXGphQORTsaioCOPj4wkXCESE/v5++P1+NDY2Rp+/EAItLS3RPqZENTExgStXruDdd99Fbm7uuo8jL8EdK0mS0NXVhba2Npw6dWrZTXi1lpeXB5fLpXUxVJWSISZ3Bjc1NcXtKo4QAlVVVRgYGIjL8TfC7/ejvb0db7zxxnMTn4UQyM/PR0NDA27evJlwzUoiwtjYGC5duoTjx4/HPBZsJQ6HI+ZjhMNh3L17Fz09Pfjwww8T9ipueno694mlAp/Ph6GhIdTX18f1cUpKSuBwOBJqvJgkSWhvb0ddXd2KJ/COHTswOjqaMGOOiAjz8/O4evUqrl69iiNHjqC4uHhDtSB5es6rOuPlZaG/++47jI+P46OPPlJ0hL/S9LTbllJSLsSICLdu3UJTU1PclyM2GAyoqanB06dPE+ZNFcvqDhaLBe+88w4uXbqE3t5ezcpORPD7/bh9+zZaW1uRk5ODM2fOoKysbMPNuP7+/lcOiiUihEIhfPXVVwCAkydPrvkCghaMRqMu5u0qJeVCzOPxYGRkBPX19aq8GRNpqEUwGMSVK1fw1ltvrdqMFkKgoKAAZ86cwaNHj3D9+nWEw2HVwkxeFujRo0f4/PPPEQqFcPr0aezevVuRaTxEhOHh4VXXTSMiLCwsoLW1FcXFxTh8+DCMRmPCBxgQWfXV5/NpXQzVpFSIyePCtmzZsqFFD9eipKQEk5OTmvcvERHu3bsHu92OkpKSV56MQghkZmbi/fffRyAQwFdffRX3Ce1EBI/Hg87OTnz++ecYHx/HyZMn0dLSgoyMDMUCJBwOY3Z2Fnl5eSv+jNvtRmtrK7Zu3RodgqIHQoiU6xdLqWlH8/Pz+PHHH3HmzBnVPlEzMjJgMpngcrk23BG9XkSEp0+fYnBwEB9++OGahpSkpaXhyJEj+P7779Ha2oq9e/eiuLhYsZUZ5D6niYkJ9PT0YGJiAjU1NThy5Ajsdntc/k6BQABer3fFlXedTicuXLiAAwcOJNwQilikWk0spUKst7cXNTU1qi76J4TAjh078PDhQ7z11luqnxDylmBtbW14//331zWq3GAwYM+ePaisrMSDBw/w/fffw2g0Yt++fSgqKlrzVBs5uBYWFqIbXWRnZ6OxsRHHjh2Ly/peSw0NDaG8vPylMJeX0blw4QJaWlrWNJE8kZjN5pRaVyxlQszv96OrqwufffaZ6m9Mu92Ozs5OhEIh1aemBIPB6Mqiubm5637uQgjY7XYcO3YMwWAQLpcLHR0daG9vh9VqRUFBAQoLC1FQUIDc3NzngkiSJLjdbgwPD2N0dBSBQACSJCEQCKC2thYff/xxNAjV+Nv09vZi//79z31PXovs3LlzOHjwoG4DDEi9jv2UCDEiQl9fH6qqqjRZejk3NxcZGRno7u7Gzp07VTs5QqEQLl26hIaGBlRWViq2prvFYsGmTZtw8uRJLCwswO12w+12Y3p6Gj/88ANcLhckSYp2hIfDYWRnZ6OiogK7du2CxWKBxWJBenq66lf75E07lr4P5ClYZ8+eRUtLi+73GOAQS0JEhK6uLhw/flyTN6fRaMSbb76J1tZWlJSUqLJ6KhHh+++/h8FgiG/Ys+QAAASJSURBVNvcPiEEbDYbbDYbiouLn9u7UR6eACChhiXIF1jk1yMcDmNoaAjXr19PigBTqzabSJI+xOQpNjk5OZp1rAORkdSHDx/GN998g48++iiuNUJJktDX14f+/n589NFHiq/QEQshhOarOiwnGAxGa14//vgjOjs7kZ+fjzfffDOu6+Cz+En6EJMkCZ2dnWhubta0HEIIVFRU4NmzZ+jo6MChQ4cUP2HkptKNGzfgcDhw8uTJlFocLxaSJGFiYgI3btxAVVUVzpw5Ex1uwwGmT0kfYk6nE36/X5ENQDZKCIEDBw7gN7/5DQYHBxXrpwIiATY1NYWLFy+ivr5eV4Mz1WSz2fDzn/8cJpMp6V6fcDiMwcHBhJrmpoakHuwq7z948ODBhFjvCYhc/j5y5AiuXbum2ODRUCiEO3fu4OLFizh69Cj279+fUP1QiUQIAavVmrSvT2dnJ5xOZ8JMc1NDYpzZceJ2u+HxeBKiFraU3W5HU1MTzp49G919ez3kMWDnz5+Hy+XC6dOnUVpamjCBzdRlMBiQl5eHsbExTfpBtZK07/ZwOIw7d+5g+/btCdcvJG+Ttm/fPnz99de4d+8eAoFAzGEmd0zfvXsXX3zxBRoaGvD2228n9OoKLP6EENi2bRvu3LmD7OxsrYujmqQMMSJCT08P3G43GhsbtS7OsoQQ2Lx5Mz799FPMzMygtbUVDodjxSCTJ0XPzs7iypUrOHfuHPx+Pz777DO89tprSdk0YmuXk5MDr9ebkFeG4yXp6pzyBqb379/HJ598ktATd+WBo0ePHsXIyAi++uorVFZWLnupPxQKoa+vDy6XC01NTTh06FB0WWnGljIajXC5XHGbe5poki7EZmZm8O233+LEiROa7f+3VgaDARUVFfjkk0/Q09Oz7GqwQgg0NjairKwMFotFF8+LqY+IUFBQAIfDEfdFPxOFeEU/jK4ucXi9XnzxxRc4cOAAampqtC4OY6pzOBw4f/48zGYzfvaznyXTh92KTyRp+sQCgQC+/vprbNu2TfGNcBnTi4mJCdTW1iIYDKbMShZJEWKSJOHatWsoLCzE9u3bk+nTh7E1mZychN1uR3p6ekLvWKWkpAix0dFRTE9PJ9SgVsbURkTw+Xyw2WywWCwcYnoSCoVQUFCQUgP8GHuRPAzHaDTCYrFwc5Ixpi/hcBjBYDBaE+MQ06FUmi/G2HIkSYIQAmlpaSmzzn5ShBh35DOGaDPS5/MhLy8P09PTKfHBnhQhlpOTg8rKSq2LwZimhBAwmUwIBoPIz8+H2+3mENOL3Nxcnj/IUp686XF3dzfGx8fR29ubEmvtJ0WIMcYitmzZEh1ecfz48YSeO6yUpJp2xFiqe/F8TqLWyYpPhAdWMZZEkii0YsbNScaYrnGIMcZ0jUOMMaZrHGKMMV3jEGOM6RqHGGNM1zjEGGO6xiHGGNM1DjHGmK5xiDHGdI1DjDGmaxxijDFd4xBjjOkahxhjTNdetRRP6q3rwRjTFa6JMcZ0jUOMMaZrHGKMMV3jEGOM6RqHGGNM1zjEGGO69v8AX2z0Llk/5+kAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-bAXlHJv3ryW"
      },
      "source": [
        "Compute full seq accuracy for full valid dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eM806O42q_aT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9515c7d9-b1ef-42d3-dcce-52c340a82ff2"
      },
      "source": [
        "trainer.precision()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.8485497237569061, 0.9084443)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    }
  ]
}