hyperopt_optimization.ipynb

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    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t9vfGOg0uCfS",
        "outputId": "a5fd5d32-d9c6-488b-9800-9db5ebdef3dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting spektral\n",
            "  Downloading spektral-1.3.1-py3-none-any.whl (140 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.1/140.1 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Installing collected packages: spektral\n",
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          ]
        }
      ],
      "source": [
        "!pip install spektral\n",
        "!pip install hyperopt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "This example shows how to perform regression of molecular properties with the\n",
        "QM9 database, using a GNN based on edge-conditioned convolutions in batch mode.\n",
        "\"\"\"\n",
        "\n",
        "from hyperopt import fmin, tpe, hp, Trials\n",
        "\n",
        "import numpy as np\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "from spektral.data import BatchLoader\n",
        "from spektral.datasets import QM9\n",
        "from spektral.layers import ECCConv, GlobalSumPool, GraphMasking\n",
        "\n",
        "################################################################################\n",
        "# Config\n",
        "################################################################################\n",
        "learning_rate = 1e-3  # Learning rate\n",
        "epochs = 30  # Number of training epochs\n",
        "batch_size = 32  # Batch size\n",
        "neurons = [16, 32 ,48, 64]\n",
        "batches = [12, 18, 24, 30, 36]"
      ],
      "metadata": {
        "id": "F1dvXIkh-1aS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "################################################################################\n",
        "# Load data\n",
        "################################################################################\n",
        "dataset = QM9(amount=1000)  # Set amount=None to train on whole dataset\n",
        "\n",
        "\n",
        "# Parameters\n",
        "F = dataset.n_node_features  # Dimension of node features\n",
        "S = dataset.n_edge_features  # Dimension of edge features\n",
        "n_out = dataset.n_labels  # Dimension of the target\n",
        "\n",
        "# Train/test split\n",
        "idxs = np.random.permutation(len(dataset))\n",
        "split = int(0.9 * len(dataset))\n",
        "idx_tr, idx_te = np.split(idxs, [split])\n",
        "dataset_tr, dataset_te = dataset[idx_tr], dataset[idx_te]\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ceiBcVMk-5TM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d374e4b4-a866-48a6-8aab-047d4fadda99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/gdb9.tar.gz\n",
            "44852087/44852087 [==============================] - 2s 0us/step\n",
            "Loading QM9 dataset.\n",
            "Reading SDF\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████████████████████████████████| 1000/1000 [00:01<00:00, 635.48it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuning= {\"learning_rate\":hp.uniform(\"learning_rate\", 0.0001, 0.01),\n",
        "         \"batch_size\":hp.choice(\"batch_size\", batches),\n",
        "         \"neuron_layer1\":hp.choice(\"neuron_layer1\", neurons),\n",
        "         \"neuron_layer2\":hp.choice(\"neuron_layer2\", neurons)}"
      ],
      "metadata": {
        "id": "xIp_vwbCcMzm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "################################################################################\n",
        "# Build model\n",
        "################################################################################\n",
        "class Net(Model):\n",
        "    def __init__(self, neuron_layer1, neuron_layer2):\n",
        "        super().__init__()\n",
        "        self.masking = GraphMasking()\n",
        "        self.conv1 = ECCConv(neuron_layer1, activation=\"relu\")\n",
        "        self.conv2 = ECCConv(neuron_layer2, activation=\"relu\")\n",
        "        self.global_pool = GlobalSumPool()\n",
        "        self.dense = Dense(n_out)\n",
        "\n",
        "\n",
        "    def call(self, inputs):\n",
        "        x, a, e = inputs\n",
        "        x = self.masking(x)\n",
        "        x = self.conv1([x, a, e])\n",
        "        x = self.conv2([x, a, e])\n",
        "        output = self.global_pool(x)\n",
        "        output = self.dense(output)\n",
        "\n",
        "        return output"
      ],
      "metadata": {
        "id": "KvpCnLZU_BJr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def pruebas(tuning):\n",
        "  model = Net(tuning[\"neuron_layer1\"], tuning[\"neuron_layer2\"])\n",
        "  model.compile(optimizer=Adam(learning_rate=tuning[\"learning_rate\"]), loss=\"mse\")\n",
        "  loader_tr = BatchLoader(dataset_tr, batch_size=tuning[\"batch_size\"], mask=True)\n",
        "  model.fit(loader_tr.load(), steps_per_epoch=loader_tr.steps_per_epoch, epochs=epochs)\n",
        "  loader_te = BatchLoader(dataset_te, batch_size=tuning[\"batch_size\"], mask=True)\n",
        "  score = model.evaluate(loader_te.load(), steps=loader_te.steps_per_epoch)\n",
        "\n",
        "  return score\n"
      ],
      "metadata": {
        "id": "Hw-771MMaMeR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best = fmin(pruebas, tuning, algo=tpe.suggest, max_evals=20, trials=Trials())\n",
        "print(\"Best lr value:\", best)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s5OWEyWhbqJ3",
        "outputId": "0853f455-4187-4f25-b5c3-fbed1f6d8e47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "\n",
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            "48/50 [===========================>..] - ETA: 0s - loss: 22927248.0000\n",
            "50/50 [==============================] - 1s 11ms/step - loss: 22011808.0000\n",
            "\n",
            "Epoch 24/30\n",
            "\n",
            " 1/50 [..............................] - ETA: 0s - loss: 53105.9648\n",
            " 6/50 [==>...........................] - ETA: 0s - loss: 45888.0312\n",
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            "50/50 [==============================] - ETA: 0s - loss: 21939338.0000\n",
            "50/50 [==============================] - 1s 11ms/step - loss: 21939338.0000\n",
            "\n",
            "Epoch 25/30\n",
            "\n",
            " 1/50 [..............................] - ETA: 0s - loss: 117209.0547\n",
            " 6/50 [==>...........................] - ETA: 0s - loss: 92905.0469 \n",
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            "47/50 [===========================>..] - ETA: 0s - loss: 23270134.0000\n",
            "50/50 [==============================] - 1s 11ms/step - loss: 21877756.0000\n",
            "\n",
            "Epoch 26/30\n",
            "\n",
            " 1/50 [..............................] - ETA: 0s - loss: 173519.0469\n",
            " 7/50 [===>..........................] - ETA: 0s - loss: 154683.1562\n",
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            "50/50 [==============================] - 1s 10ms/step - loss: 21800780.0000\n",
            "\n",
            "Epoch 27/30\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch 9/30\n",
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            "\n",
            "Epoch 10/30\n",
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            "\n",
            "Epoch 11/30\n",
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            "\n",
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            "\n",
            "Epoch 13/30\n",
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            "\n",
            "Epoch 14/30\n",
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            "\n",
            "Epoch 15/30\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch 19/30\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch 24/30\n",
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            "\n",
            "Epoch 25/30\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch 28/30\n",
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            "\n",
            "Epoch 29/30\n",
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            "\n",
            "Epoch 30/30\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch 16/30\n",
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            "\n",
            "Epoch 17/30\n",
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            "\n",
            "Epoch 18/30\n",
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            "\n",
            "Epoch 19/30\n",
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            "\n",
            "Epoch 20/30\n",
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            "\n",
            "Epoch 21/30\n",
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            "\n",
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            "\n",
            "Epoch 23/30\n",
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            "\n",
            "Epoch 24/30\n",
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            "\n",
            "Epoch 25/30\n",
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            "\n",
            "Epoch 26/30\n",
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            "\n",
            "Epoch 27/30\n",
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            "\n",
            "Epoch 28/30\n",
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            "\n",
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            "\n",
            "Epoch 30/30\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch 11/30\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch 14/30\n",
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            "\n",
            "Epoch 15/30\n",
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            "\n",
            "Epoch 16/30\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch 22/30\n",
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            "\n",
            "Epoch 23/30\n",
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            "\n",
            "Epoch 24/30\n",
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            "\n",
            "Epoch 25/30\n",
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            "\n",
            "Epoch 26/30\n",
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            "\n",
            "Epoch 27/30\n",
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            "\n",
            "Epoch 28/30\n",
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            "\n",
            "Epoch 29/30\n",
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            "\n",
            "Epoch 30/30\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch 14/30\n",
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            "\n",
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            "\n",
            "Epoch 16/30\n",
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            "\n",
            "Epoch 17/30\n",
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            "\n",
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            "\n",
            "Epoch 19/30\n",
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            "\n",
            "Epoch 20/30\n",
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            "\n",
            "Epoch 21/30\n",
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            "\n",
            "Epoch 22/30\n",
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            "\n",
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            "\n",
            "Epoch 24/30\n",
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            "\n",
            "Epoch 25/30\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch 26/30\n",
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            "\n",
            "Epoch 27/30\n",
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            "\n",
            "Epoch 28/30\n",
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            "\n",
            "Epoch 29/30\n",
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            "\n",
            "Epoch 30/30\n",
            "\n",
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            "\n",
            "Epoch 1/30\n",
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            "\n",
            "Epoch 2/30\n",
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            "\n",
            "Epoch 3/30\n",
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            "\n",
            "Epoch 4/30\n",
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            "\n",
            "Epoch 5/30\n",
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            "\n",
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            "\n",
            "Epoch 7/30\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Epoch 11/30\n",
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            "\n",
            "Epoch 12/30\n",
            "\n",
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            "\n",
            "Epoch 13/30\n",
            "\n",
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            "\n",
            "Epoch 14/30\n",
            "\n",
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            "\n",
            "Epoch 15/30\n",
            "\n",
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            "\n",
            "Epoch 16/30\n",
            "\n",
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            "\n",
            "Epoch 17/30\n",
            "\n",
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            "\n",
            "Epoch 18/30\n",
            "\n",
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            "\n",
            "Epoch 19/30\n",
            "\n",
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            "\n",
            "Epoch 20/30\n",
            "\n",
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            "\n",
            "Epoch 21/30\n",
            "\n",
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            "\n",
            "Epoch 22/30\n",
            "\n",
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            "\n",
            "Epoch 23/30\n",
            "\n",
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            "\n",
            "Epoch 24/30\n",
            "\n",
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            "\n",
            "Epoch 25/30\n",
            "\n",
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            "72/75 [===========================>..] - ETA: 0s - loss: 18745194.0000\n",
            "75/75 [==============================] - 1s 11ms/step - loss: 18072302.0000\n",
            "\n",
            "Epoch 26/30\n",
            "\n",
            " 1/75 [..............................] - ETA: 1s - loss: 281039.1875\n",
            " 6/75 [=>............................] - ETA: 0s - loss: 190915168.0000\n",
            "10/75 [===>..........................] - ETA: 0s - loss: 114725640.0000\n",
            "15/75 [=====>........................] - ETA: 0s - loss: 76584440.0000 \n",
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            "29/75 [==========>...................] - ETA: 0s - loss: 40859920.0000\n",
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            "37/75 [=============>................] - ETA: 0s - loss: 32306076.0000\n",
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            "75/75 [==============================] - ETA: 0s - loss: 18069482.0000\n",
            "75/75 [==============================] - 1s 12ms/step - loss: 18069482.0000\n",
            "\n",
            "Epoch 27/30\n",
            "\n",
            " 1/75 [..............................] - ETA: 1s - loss: 78922.3359\n",
            " 6/75 [=>............................] - ETA: 0s - loss: 154166.5000\n",
            "11/75 [===>..........................] - ETA: 0s - loss: 106193584.0000\n",
            "16/75 [=====>........................] - ETA: 0s - loss: 73094232.0000 \n",
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            "69/75 [==========================>...] - ETA: 0s - loss: 18932186.0000\n",
            "74/75 [============================>.] - ETA: 0s - loss: 17687542.0000\n",
            "75/75 [==============================] - 1s 11ms/step - loss: 17699412.0000\n",
            "\n",
            "Epoch 28/30\n",
            "\n",
            " 1/75 [..............................] - ETA: 0s - loss: 106676.6016\n",
            " 6/75 [=>............................] - ETA: 0s - loss: 1300373.6250\n",
            "12/75 [===>..........................] - ETA: 0s - loss: 1059474.5000\n",
            "16/75 [=====>........................] - ETA: 0s - loss: 1873414.6250\n",
            "21/75 [=======>......................] - ETA: 0s - loss: 1568426.6250\n",
            "25/75 [=========>....................] - ETA: 0s - loss: 1462635.1250\n",
            "30/75 [===========>..................] - ETA: 0s - loss: 1611266.8750\n",
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            "39/75 [==============>...............] - ETA: 0s - loss: 1445680.8750\n",
            "44/75 [================>.............] - ETA: 0s - loss: 28989944.0000\n",
            "49/75 [==================>...........] - ETA: 0s - loss: 26239686.0000\n",
            "54/75 [====================>.........] - ETA: 0s - loss: 23864202.0000\n",
            "59/75 [======================>.......] - ETA: 0s - loss: 21894154.0000\n",
            "65/75 [=========================>....] - ETA: 0s - loss: 19978086.0000\n",
            "71/75 [===========================>..] - ETA: 0s - loss: 18324768.0000\n",
            "75/75 [==============================] - ETA: 0s - loss: 17403380.0000\n",
            "75/75 [==============================] - 1s 11ms/step - loss: 17403380.0000\n",
            "\n",
            "Epoch 29/30\n",
            "\n",
            " 1/75 [..............................] - ETA: 1s - loss: 2623672.7500\n",
            " 6/75 [=>............................] - ETA: 0s - loss: 3295414.2500\n",
            "11/75 [===>..........................] - ETA: 0s - loss: 2053705.7500\n",
            "16/75 [=====>........................] - ETA: 0s - loss: 2894449.7500\n",
            "21/75 [=======>......................] - ETA: 0s - loss: 2270419.0000\n",
            "26/75 [=========>....................] - ETA: 0s - loss: 1843104.6250\n",
            "32/75 [===========>..................] - ETA: 0s - loss: 1685319.5000\n",
            "38/75 [==============>...............] - ETA: 0s - loss: 1442406.5000\n",
            "44/75 [================>.............] - ETA: 0s - loss: 1380346.0000\n",
            "48/75 [==================>...........] - ETA: 0s - loss: 25251092.0000\n",
            "54/75 [====================>.........] - ETA: 0s - loss: 22797636.0000\n",
            "59/75 [======================>.......] - ETA: 0s - loss: 21262610.0000\n",
            "63/75 [========================>.....] - ETA: 0s - loss: 20025816.0000\n",
            "68/75 [==========================>...] - ETA: 0s - loss: 18802858.0000\n",
            "73/75 [============================>.] - ETA: 0s - loss: 17618660.0000\n",
            "75/75 [==============================] - 1s 11ms/step - loss: 17158122.0000\n",
            "\n",
            "Epoch 30/30\n",
            "\n",
            " 1/75 [..............................] - ETA: 1s - loss: 6829905.5000\n",
            " 5/75 [=>............................] - ETA: 0s - loss: 2176895.5000\n",
            "10/75 [===>..........................] - ETA: 0s - loss: 1432212.7500\n",
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            "29/75 [==========>...................] - ETA: 0s - loss: 1462994.0000\n",
            "34/75 [============>.................] - ETA: 0s - loss: 1328035.2500\n",
            "38/75 [==============>...............] - ETA: 0s - loss: 1326655.1250\n",
            "43/75 [================>.............] - ETA: 0s - loss: 1271325.2500\n",
            "48/75 [==================>...........] - ETA: 0s - loss: 1153280.8750\n",
            "54/75 [====================>.........] - ETA: 0s - loss: 1414187.1250\n",
            "59/75 [======================>.......] - ETA: 0s - loss: 1370504.7500\n",
            "65/75 [=========================>....] - ETA: 0s - loss: 1277095.7500\n",
            "71/75 [===========================>..] - ETA: 0s - loss: 17449750.0000\n",
            "74/75 [============================>.] - ETA: 0s - loss: 17060666.0000\n",
            "75/75 [==============================] - 1s 11ms/step - loss: 16875468.0000\n",
            "\n",
            "1/9 [==>...........................] - ETA: 2s - loss: 112389.4453\n",
            "7/9 [======================>.......] - ETA: 0s - loss: 2527627.0000\n",
            "9/9 [==============================] - 0s 10ms/step - loss: 2164656.5000\n",
            "\n",
            "100%|██████████| 20/20 [08:23<00:00, 25.16s/trial, best loss: 9901.251953125]\n",
            "Best lr value: {'batch_size': 2, 'learning_rate': 0.0004933536679832716, 'neuron_layer1': 0, 'neuron_layer2': 0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Net(neurons[best[\"neuron_layer1\"]], neurons[best[\"neuron_layer2\"]])\n",
        "optimizer = Adam(best[\"learning_rate\"])\n",
        "model.compile(optimizer=optimizer, loss=\"mse\")\n",
        "\n",
        "################################################################################\n",
        "# Fit model\n",
        "################################################################################\n",
        "loader_tr = BatchLoader(dataset_tr, batch_size=batch_size, mask=True)\n",
        "model.fit(loader_tr.load(), steps_per_epoch=loader_tr.steps_per_epoch, epochs=epochs)\n",
        "\n",
        "################################################################################\n",
        "# Evaluate model\n",
        "################################################################################\n",
        "print(\"Testing model\")\n",
        "loader_te = BatchLoader(dataset_te, batch_size=batch_size, mask=True)\n",
        "loss = model.evaluate(loader_te.load(), steps=loader_te.steps_per_epoch)\n",
        "print(\"Done. Test loss: {}\".format(loss))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b_xGr8EbuMxK",
        "outputId": "dfc01637-196c-47d2-e401-ddad58ca3948"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "29/29 [==============================] - 4s 97ms/step - loss: 22795394.0000\n",
            "Epoch 2/30\n",
            "29/29 [==============================] - 3s 103ms/step - loss: 22749512.0000\n",
            "Epoch 3/30\n",
            "29/29 [==============================] - 2s 82ms/step - loss: 22609228.0000\n",
            "Epoch 4/30\n",
            "29/29 [==============================] - 2s 85ms/step - loss: 22495344.0000\n",
            "Epoch 5/30\n",
            "29/29 [==============================] - 2s 80ms/step - loss: 22477152.0000\n",
            "Epoch 6/30\n",
            "29/29 [==============================] - 3s 102ms/step - loss: 22472176.0000\n",
            "Epoch 7/30\n",
            "29/29 [==============================] - 3s 97ms/step - loss: 22469606.0000\n",
            "Epoch 8/30\n",
            "29/29 [==============================] - 2s 84ms/step - loss: 22467330.0000\n",
            "Epoch 9/30\n",
            "29/29 [==============================] - 2s 79ms/step - loss: 22465440.0000\n",
            "Epoch 10/30\n",
            "29/29 [==============================] - 2s 83ms/step - loss: 22463416.0000\n",
            "Epoch 11/30\n",
            "29/29 [==============================] - 3s 113ms/step - loss: 22461654.0000\n",
            "Epoch 12/30\n",
            "29/29 [==============================] - 3s 89ms/step - loss: 22460128.0000\n",
            "Epoch 13/30\n",
            "29/29 [==============================] - 2s 83ms/step - loss: 22457854.0000\n",
            "Epoch 14/30\n",
            "29/29 [==============================] - 2s 85ms/step - loss: 22456224.0000\n",
            "Epoch 15/30\n",
            "29/29 [==============================] - 2s 82ms/step - loss: 22453880.0000\n",
            "Epoch 16/30\n",
            "29/29 [==============================] - 4s 128ms/step - loss: 22451644.0000\n",
            "Epoch 17/30\n",
            "29/29 [==============================] - 3s 109ms/step - loss: 22448952.0000\n",
            "Epoch 18/30\n",
            "29/29 [==============================] - 2s 84ms/step - loss: 22446328.0000\n",
            "Epoch 19/30\n",
            "29/29 [==============================] - 2s 84ms/step - loss: 22443910.0000\n",
            "Epoch 20/30\n",
            "29/29 [==============================] - 3s 91ms/step - loss: 22440342.0000\n",
            "Epoch 21/30\n",
            "29/29 [==============================] - 3s 108ms/step - loss: 22437572.0000\n",
            "Epoch 22/30\n",
            "29/29 [==============================] - 2s 85ms/step - loss: 22434036.0000\n",
            "Epoch 23/30\n",
            "29/29 [==============================] - 2s 86ms/step - loss: 22429236.0000\n",
            "Epoch 24/30\n",
            "29/29 [==============================] - 2s 84ms/step - loss: 22425216.0000\n",
            "Epoch 25/30\n",
            "29/29 [==============================] - 3s 99ms/step - loss: 22420430.0000\n",
            "Epoch 26/30\n",
            "29/29 [==============================] - 3s 98ms/step - loss: 22415596.0000\n",
            "Epoch 27/30\n",
            "29/29 [==============================] - 2s 84ms/step - loss: 22413196.0000\n",
            "Epoch 28/30\n",
            "29/29 [==============================] - 2s 86ms/step - loss: 22404140.0000\n",
            "Epoch 29/30\n",
            "29/29 [==============================] - 2s 84ms/step - loss: 22398130.0000\n",
            "Epoch 30/30\n",
            "29/29 [==============================] - 3s 114ms/step - loss: 22391936.0000\n",
            "Testing model\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 9434.4502\n",
            "Done. Test loss: 9434.4501953125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample = 0"
      ],
      "metadata": {
        "id": "EikdqG7F6og1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_prediction= model.predict(loader_te.load(), steps=loader_te.steps_per_epoch)\n",
        "test_prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dUjtR5uKuNbV",
        "outputId": "b3e24c49-9ef6-46cd-a45e-d8ccbe8734c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 24ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2.96197174e+02, -1.09542596e+00, -1.77664065e+00, ...,\n",
              "        -1.00814746e+03, -1.01777252e+03, -9.25648132e+02],\n",
              "       [ 4.00825714e+02,  2.03281116e+00, -7.94147921e+00, ...,\n",
              "        -1.58298438e+03, -1.63748218e+03, -1.46665576e+03],\n",
              "       [ 2.26709259e+02,  3.43180269e-01,  1.94384202e-01, ...,\n",
              "        -1.15994153e+03, -1.17544238e+03, -1.07453406e+03],\n",
              "       ...,\n",
              "       [ 6.01626099e+02, -5.26866722e+00, -6.89029360e+00, ...,\n",
              "        -1.56782214e+03, -1.58684082e+03, -1.42081165e+03],\n",
              "       [ 4.22688026e+01,  1.24362011e+01, -1.09629536e+00, ...,\n",
              "        -1.15505786e+03, -1.20730310e+03, -1.09595850e+03],\n",
              "       [ 1.37383881e+02, -1.88197541e+00,  3.32376385e+00, ...,\n",
              "        -1.41624304e+03, -1.43792200e+03, -1.31244775e+03]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_te[sample]['y']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvOLy8O-uZF3",
        "outputId": "7092c8d6-97e0-4ab0-9fa3-f7410809e0b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.19225200e+01,  3.07744000e+00,  2.77702000e+00,  9.50000000e-02,\n",
              "        5.29000000e+01, -2.72300000e-01,  9.86000000e-02,  3.70900000e-01,\n",
              "        5.14305300e+02,  1.37725000e-01, -1.96405320e+02, -1.96399392e+02,\n",
              "       -1.96398448e+02, -1.96434166e+02,  2.13640000e+01, -1.36089759e+03,\n",
              "       -1.37050915e+03, -1.37880858e+03, -1.26650832e+03])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_prediction[sample]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "823SQyILudjC",
        "outputId": "39fd6ba3-ca78-4c1f-ab11-da108d61896b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2.96197174e+02, -1.09542596e+00, -1.77664065e+00,  9.84849262e+00,\n",
              "        5.10482521e+01,  2.93655729e+00, -3.13356698e-01, -2.06878376e+00,\n",
              "        4.67406586e+02, -5.25722027e+00, -2.26810196e+02, -2.23029953e+02,\n",
              "       -2.28357086e+02, -2.16404984e+02,  1.89187431e+01, -1.01068134e+03,\n",
              "       -1.00814746e+03, -1.01777252e+03, -9.25648132e+02], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(dataset_te[sample]['y'],test_prediction[sample])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "8XhDzdpruhkN",
        "outputId": "62b04c9f-8d01-41ee-fc4d-03dd8ff0086c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7b7d60cc7d30>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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b5gwPAKD9CA9zaOS3YzXy27HBHgouE0RpAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABge0ELPIcOHdKUKVOUnJysqKgo9evXTzk5Oaqurg6o++ijjzRy5EhFRkYqKSlJS5YsabCvNWvWaODAgYqMjNSgQYO0YcOGtmoDAAC0A0ELPMXFxaqrq9Nzzz2nPXv2aOnSpVqxYoV+9atfWTV+v19jxoxR7969tX37dj3++ON65JFH9Pzzz1s1W7du1d13360pU6Zo586dGj9+vMaPH6+ioqJgtAUAAEKQwxhjgj2Ieo8//riWL1+uTz/9VJK0fPlyPfzww/J6vYqIiJAkzZ8/X6+//rqKi4slSRMmTNDJkye1bt06az/XX3+9hgwZohUrVjTpuH6/X263Wz6fTy6Xq4W7AgAAraE5n98hdQ2Pz+dT165drceFhYUaNWqUFXYkKSMjQ/v379exY8esmvT09ID9ZGRkqLCw8LzHqaqqkt/vD1gAoD2qrTMqPPhPvbHrcxUe/Kdq60Lm/2GBkNIh2AOod+DAAT3zzDN64oknrHVer1fJyckBdfHx8da2mJgYeb1ea925NV6v97zHWrx4sXJzc1tw9ADQ9vKLypW7dq/KfaetdYnuSOXcnqKxqYlBHBkQelr8DM/8+fPlcDguuNR/HVXv888/19ixY3XnnXdq6tSpLT2kBrKzs+Xz+azl8OHDrX5MAGhJ+UXlmr56R0DYkSSv77Smr96h/KLyII0MCE0tfoZnzpw5mjx58gVr+vbta/29rKxMo0eP1ogRIwIuRpakhIQEVVRUBKyrf5yQkHDBmvrtjXE6nXI6nRftBQBCUW2dUe7avWrsy6v6dblr9+rWlASFhznacmhAyGrxwBMbG6vY2Ngm1X7++ecaPXq0hg0bppUrVyosLPCEk8fj0cMPP6wzZ86oY8eOkqSCggINGDBAMTExVs2mTZs0c+ZM63kFBQXyeDwt0xAAhJhtJUcbnNn5unLfaW0rOSpPv25tNCogtAXtouXPP/9cN910k3r16qUnnnhCR44ckdfrDbj25ic/+YkiIiI0ZcoU7dmzR6+++qqefvppzZ4926p58MEHlZ+fryeffFLFxcV65JFH9OGHH2rGjBnBaAsAWp3Xf+Gw09w64HIQtIuWCwoKdODAAR04cEA9e/YM2FZ/p7zb7dZbb72lrKwsDRs2TN27d9fChQv1wAMPWLUjRozQyy+/rAULFuhXv/qVvvWtb+n1119Xampqm/YDAG3lSBODTFPrgMtBSM3DEyzMwwOgPXnwjzv0xu6LX5T8g8GJevruoW0wIiA42u08PACAizt1prZF64DLAYEHANqZ6/o07ULkptYBlwMCDwC0M5NG9JHjInebOxxn6wCcReABgHYmokOYHhiZfMGaB0YmK6IDb/FAvZD51RIAgKbL/l6KJOmF/y3Rub8+K8whTR2ZbG0HcBZ3aYm7tAC0X9U1dXqp8JD+cfQr9e4arYmePpzZwWWjOZ/fnOEBgHYsokOYpozse/FC4DLH/wYAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbI/AAAADbC4nAU1VVpSFDhsjhcGjXrl0B2z766CONHDlSkZGRSkpK0pIlSxo8f82aNRo4cKAiIyM1aNAgbdiwoY1GDgAA2oOQCDwPPfSQevTo0WC93+/XmDFj1Lt3b23fvl2PP/64HnnkET3//PNWzdatW3X33XdrypQp2rlzp8aPH6/x48erqKioLVsAAAAhzGGMMcEcwJtvvqnZs2frz3/+s66++mrt3LlTQ4YMkSQtX75cDz/8sLxeryIiIiRJ8+fP1+uvv67i4mJJ0oQJE3Ty5EmtW7fO2uf111+vIUOGaMWKFU0ag9/vl9vtls/nk8vlatkGAQBAq2jO53dQz/BUVFRo6tSpeumllxQdHd1ge2FhoUaNGmWFHUnKyMjQ/v37dezYMasmPT094HkZGRkqLCw873Grqqrk9/sDFgAAYF9BCzzGGE2ePFnTpk3Ttdde22iN1+tVfHx8wLr6x16v94I19dsbs3jxYrndbmtJSkr6Jq0AAIAQ1+KBZ/78+XI4HBdciouL9cwzz+jEiRPKzs5u6SFcVHZ2tnw+n7UcPny4zccAAADaToeW3uGcOXM0efLkC9b07dtXmzdvVmFhoZxOZ8C2a6+9Vvfcc49WrVqlhIQEVVRUBGyvf5yQkGD92VhN/fbGOJ3OBscFAAD21eKBJzY2VrGxsRet++1vf6v/+I//sB6XlZUpIyNDr776qtLS0iRJHo9HDz/8sM6cOaOOHTtKkgoKCjRgwADFxMRYNZs2bdLMmTOtfRUUFMjj8bRgVwAAoD1r8cDTVL169Qp43KlTJ0lSv3791LNnT0nST37yE+Xm5mrKlCmaN2+eioqK9PTTT2vp0qXW8x588EF997vf1ZNPPqnMzEy98sor+vDDDwNuXQcAAJe3kJiH53zcbrfeeustlZSUaNiwYZozZ44WLlyoBx54wKoZMWKEXn75ZT3//PMaPHiw/vSnP+n1119XampqEEcOAABCSdDn4QkFzMMDAED7027m4QEAAGgLBB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7QQ8869evV1pamqKiohQTE6Px48cHbC8tLVVmZqaio6MVFxenuXPnqqamJqBmy5YtGjp0qJxOp/r376+8vLy2awAAAIS8DsE8+J///GdNnTpVv/71r3XzzTerpqZGRUVF1vba2lplZmYqISFBW7duVXl5uX7605+qY8eO+vWvfy1JKikpUWZmpqZNm6Y//OEP2rRpk+6//34lJiYqIyMjWK0BAIAQ4jDGmGAcuKamRn369FFubq6mTJnSaM2bb76pcePGqaysTPHx8ZKkFStWaN68eTpy5IgiIiI0b948rV+/PiAo3XXXXTp+/Ljy8/ObNBa/3y+32y2fzyeXy/XNmwMAAK2uOZ/fQftKa8eOHfr8888VFhama665RomJibrtttsCgkthYaEGDRpkhR1JysjIkN/v1549e6ya9PT0gH1nZGSosLDwvMeuqqqS3+8PWAAAgH0FLfB8+umnkqRHHnlECxYs0Lp16xQTE6ObbrpJR48elSR5vd6AsCPJeuz1ei9Y4/f7derUqUaPvXjxYrndbmtJSkpq0d4AAEBoafHAM3/+fDkcjgsuxcXFqqurkyQ9/PDDuuOOOzRs2DCtXLlSDodDa9asaelhBcjOzpbP57OWw4cPt+rxAABAcLX4Rctz5szR5MmTL1jTt29flZeXS5JSUlKs9U6nU3379lVpaakkKSEhQdu2bQt4bkVFhbWt/s/6defWuFwuRUVFNXp8p9Mpp9PZ9KYAAEC71uKBJzY2VrGxsRetGzZsmJxOp/bv368bb7xRknTmzBkdOnRIvXv3liR5PB49+uijqqysVFxcnCSpoKBALpfLCkoej0cbNmwI2HdBQYE8Hk9LtgUAANqxoF3D43K5NG3aNOXk5Oitt97S/v37NX36dEnSnXfeKUkaM2aMUlJSNHHiRO3evVsbN27UggULlJWVZZ2hmTZtmj799FM99NBDKi4u1rJly/Taa69p1qxZwWoNAACEmKDOw/P444+rQ4cOmjhxok6dOqW0tDRt3rxZMTExkqTw8HCtW7dO06dPl8fj0RVXXKFJkyZp0aJF1j6Sk5O1fv16zZo1S08//bR69uypF198kTl4AACAJWjz8IQS5uEBAKD9aRfz8AAAALQVAg8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALA9Ag8AALC9oAaeTz75RD/4wQ/UvXt3uVwu3XjjjXrnnXcCakpLS5WZmano6GjFxcVp7ty5qqmpCajZsmWLhg4dKqfTqf79+ysvL68NuwAAAKEuqIFn3Lhxqqmp0ebNm7V9+3YNHjxY48aNk9frlSTV1tYqMzNT1dXV2rp1q1atWqW8vDwtXLjQ2kdJSYkyMzM1evRo7dq1SzNnztT999+vjRs3BqstAAAQYhzGGBOMA3/xxReKjY3Ve++9p5EjR0qSTpw4IZfLpYKCAqWnp+vNN9/UuHHjVFZWpvj4eEnSihUrNG/ePB05ckQRERGaN2+e1q9fr6KiImvfd911l44fP678/PwmjcXv98vtdsvn88nlcrV8swAAoMU15/M7aGd4unXrpgEDBui//uu/dPLkSdXU1Oi5555TXFychg0bJkkqLCzUoEGDrLAjSRkZGfL7/dqzZ49Vk56eHrDvjIwMFRYWnvfYVVVV8vv9AQsAALCvDsE6sMPh0Ntvv63x48erc+fOCgsLU1xcnPLz8xUTEyNJ8nq9AWFHkvW4/muv89X4/X6dOnVKUVFRDY69ePFi5ebmtkZbAAAgBLX4GZ758+fL4XBccCkuLpYxRllZWYqLi9P//u//atu2bRo/frxuv/12lZeXt/SwAmRnZ8vn81nL4cOHW/V4AAAguFr8DM+cOXM0efLkC9b07dtXmzdv1rp163Ts2DHre7dly5apoKBAq1at0vz585WQkKBt27YFPLeiokKSlJCQYP1Zv+7cGpfL1ejZHUlyOp1yOp2X0h4AAGiHWjzwxMbGKjY29qJ1X331lSQpLCzwJFNYWJjq6uokSR6PR48++qgqKysVFxcnSSooKJDL5VJKSopVs2HDhoB9FBQUyOPxfONeAACAPQTtomWPx6OYmBhNmjRJu3fv1ieffKK5c+dat5lL0pgxY5SSkqKJEydq9+7d2rhxoxYsWKCsrCzrDM20adP06aef6qGHHlJxcbGWLVum1157TbNmzQpWawAAIMQELfB0795d+fn5+vLLL3XzzTfr2muv1V//+le98cYbGjx4sCQpPDxc69atU3h4uDwej+6991799Kc/1aJFi6z9JCcna/369SooKNDgwYP15JNP6sUXX1RGRkawWgMAACEmaPPwhBLm4QEAoP1pF/PwAAAAtBUCDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsL0OwR4AWkZtndG2kqOqPHFacZ0jNTy5q8LDHMEeFgAAIYHAYwP5ReXKXbtX5b7T1rpEd6Rybk/R2NTEII4MAIDQwFda7Vx+Ubmmr94REHYkyes7remrdyi/qDxIIwMAIHQQeNqx2jqj3LV7ZRrZVr8ud+1e1dY1VgEAwOWDwNOObSs52uDMzrmMpHLfaW0rOdp2gwIAIAQReNqxyhPnDzuXUgcAgF0ReNqxuM6RLVoHAIBdEXjaseHJXZXojtT5bj536OzdWsOTu7blsAAACDkEnnYsPMyhnNtTJKlB6Kl/nHN7CvPxAAAuewSedm5saqKW3ztUCe7Ar60S3JFafu9Q5uEBAEBMPGgLY1MTdWtKAjMtAwBwHgQemwgPc8jTr1uwhwEAQEjiKy0AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7rRZ4Hn30UY0YMULR0dHq0qVLozWlpaXKzMxUdHS04uLiNHfuXNXU1ATUbNmyRUOHDpXT6VT//v2Vl5fXYD/PPvus+vTpo8jISKWlpWnbtm2t0BEAAGivWi3wVFdX684779T06dMb3V5bW6vMzExVV1dr69atWrVqlfLy8rRw4UKrpqSkRJmZmRo9erR27dqlmTNn6v7779fGjRutmldffVWzZ89WTk6OduzYocGDBysjI0OVlZWt1Vqz1dYZFR78p97Y9bkKD/5TtXUm2EMCAOCy4jDGtOqnb15enmbOnKnjx48HrH/zzTc1btw4lZWVKT4+XpK0YsUKzZs3T0eOHFFERITmzZun9evXq6ioyHreXXfdpePHjys/P1+SlJaWpuuuu07/+Z//KUmqq6tTUlKSfvGLX2j+/PlNGqPf75fb7ZbP55PL5WqBrv8lv6hcuWv3qtz3r99Y3iWqo+67IVkzbu7P5IAAAFyi5nx+B+0ansLCQg0aNMgKO5KUkZEhv9+vPXv2WDXp6ekBz8vIyFBhYaGks2eRtm/fHlATFham9PR0q6YxVVVV8vv9AUtr2PBRmaat3hEQdiTp+KkzWvr2Jxr2HwXKLypvlWMDAIB/CVrg8Xq9AWFHkvXY6/VesMbv9+vUqVP64osvVFtb22hN/T4as3jxYrndbmtJSkpqiZYCbPioXDP+uPOCNce/OqNpq3cQegAAaGXNCjzz58+Xw+G44FJcXNxaY20x2dnZ8vl81nL48OEW3X9+Ubl+/vIONfVSndy1e7muBwCAVtSs36U1Z84cTZ48+YI1ffv2bdK+EhISGtxNVVFRYW2r/7N+3bk1LpdLUVFRCg8PV3h4eKM19ftojNPplNPpbNI4m6u2zih37d5mPafcd1rbSo7yu7AAAGglzQo8sbGxio2NbZEDezwePfroo6qsrFRcXJwkqaCgQC6XSykpKVbNhg0bAp5XUFAgj8cjSYqIiNCwYcO0adMmjR8/XtLZi5Y3bdqkGTNmtMg4m2tbydEG1+w0ReWJ5j8HAAA0Tatdw1NaWqpdu3aptLRUtbW12rVrl3bt2qUvv/xSkjRmzBilpKRo4sSJ2r17tzZu3KgFCxYoKyvLOvsybdo0ffrpp3rooYdUXFysZcuW6bXXXtOsWbOs48yePVsvvPCCVq1apX379mn69Ok6efKk7rvvvtZq7YIuNbjEdY5s4ZEAAIB6zTrD0xwLFy7UqlWrrMfXXHONJOmdd97RTTfdpPDwcK1bt07Tp0+Xx+PRFVdcoUmTJmnRokXWc5KTk7V+/XrNmjVLTz/9tHr27KkXX3xRGRkZVs2ECRN05MgRLVy4UF6vV0OGDFF+fn6DC5nbSnODi0NSgjtSw5O7ts6AAABA68/D0x605Dw8tXVGN/5ms7y+07rYf9j6GXiW3ztUY1MTv9FxAQC43LSLeXjsKjzMoZzbz16DdLEpBRPckYQdAADaQKt9pXU5G5uaqOX3Dm0ww3KiO1J3XddLfbpHK67z2a+xmGkZAIDWR+BpJWNTE3VrSoK2lRxV5YnTBBwAAIKIwNOKwsMczK0DAEAI4BoeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABgewQeAABge8y0LKn+F8b7/f4gjwQAADRV/ed2/ef4hRB4JJ04cUKSlJSUFOSRAACA5jpx4oTcbvcFaxymKbHI5urq6lRWVqbOnTvL4bDPL/f0+/1KSkrS4cOH5XK5gj2cVnU59SpdXv1eTr1Kl1e/l1OvEv22BmOMTpw4oR49eigs7MJX6XCGR1JYWJh69uwZ7GG0GpfLdVn8cEmXV6/S5dXv5dSrdHn1ezn1KtFvS7vYmZ16XLQMAABsj8ADAABsj8BjY06nUzk5OXI6ncEeSqu7nHqVLq9+L6depcur38upV4l+g42LlgEAgO1xhgcAANgegQcAANgegQcAANgegQcAANgegacdevTRRzVixAhFR0erS5cuDbbv3r1bd999t5KSkhQVFaWrrrpKTz/9dEDNli1b5HA4Gixerzeg7tlnn1WfPn0UGRmptLQ0bdu2rTVba+BivUpSaWmpMjMzFR0drbi4OM2dO1c1NTUBNVu2bNHQoUPldDrVv39/5eXlNdhPsHv9uvO9Rg6HQ3/7298kSYcOHWp0+wcffBCwrzVr1mjgwIGKjIzUoEGDtGHDhmC0dFF9+vRp0Mtjjz0WUPPRRx9p5MiRioyMVFJSkpYsWdJgP6He76FDhzRlyhQlJycrKipK/fr1U05OjqqrqwNq7PTaNibUfuYuxeLFi3Xdddepc+fOiouL0/jx47V///6AmptuuqnB6zht2rSAmqa8jwXbI4880qCPgQMHWttPnz6trKwsdevWTZ06ddIdd9yhioqKgH0EtU+DdmfhwoXmqaeeMrNnzzZut7vB9t/97nfml7/8pdmyZYs5ePCgeemll0xUVJR55plnrJp33nnHSDL79+835eXl1lJbW2vVvPLKKyYiIsL8/ve/N3v27DFTp041Xbp0MRUVFW3RpjHm4r3W1NSY1NRUk56ebnbu3Gk2bNhgunfvbrKzs62aTz/91ERHR5vZs2ebvXv3mmeeecaEh4eb/Px8qyYUev26qqqqgNemvLzc3H///SY5OdnU1dUZY4wpKSkxkszbb78dUFddXW3t5/333zfh4eFmyZIlZu/evWbBggWmY8eO5uOPPw5Wa+fVu3dvs2jRooBevvzyS2u7z+cz8fHx5p577jFFRUXmj3/8o4mKijLPPfecVdMe+n3zzTfN5MmTzcaNG83BgwfNG2+8YeLi4sycOXOsGru9tl8Xij9zlyIjI8OsXLnSFBUVmV27dpnvfe97plevXgH/br/73e+aqVOnBryOPp/P2t6U97FQkJOTY66++uqAPo4cOWJtnzZtmklKSjKbNm0yH374obn++uvNiBEjrO3B7pPA046tXLmy0RDQmJ///Odm9OjR1uP6wHPs2LHzPmf48OEmKyvLelxbW2t69OhhFi9efKlDvmTn63XDhg0mLCzMeL1ea93y5cuNy+UyVVVVxhhjHnroIXP11VcHPG/ChAkmIyPDehxKvZ5PdXW1iY2NNYsWLbLW1X8o7ty587zP+/GPf2wyMzMD1qWlpZmf/exnrTXUS9a7d2+zdOnS825ftmyZiYmJsV5bY4yZN2+eGTBggPW4PfV7riVLlpjk5GTrsd1e269rDz9zl6KystJIMu+++6617rvf/a558MEHz/ucpryPhYKcnBwzePDgRrcdP37cdOzY0axZs8Zat2/fPiPJFBYWGmOC3ydfaV0mfD6funbt2mD9kCFDlJiYqFtvvVXvv/++tb66ulrbt29Xenq6tS4sLEzp6ekqLCxskzE3RWFhoQYNGqT4+HhrXUZGhvx+v/bs2WPVnNtHfU19H+2l17/85S/65z//qfvuu6/Btu9///uKi4vTjTfeqL/85S8B2y7Wf6h57LHH1K1bN11zzTV6/PHHA053FxYWatSoUYqIiLDWZWRkaP/+/Tp27JhV0576rXe+n1E7vbb12svP3KXw+XyS1OC1/MMf/qDu3bsrNTVV2dnZ+uqrr6xtTXkfCxV///vf1aNHD/Xt21f33HOPSktLJUnbt2/XmTNnAl7TgQMHqlevXtZrGuw++eWhl4GtW7fq1Vdf1fr16611iYmJWrFiha699lpVVVXpxRdf1E033aT/+7//09ChQ/XFF1+otrY24B+mJMXHx6u4uLitWzgvr9fb6Bjrt12oxu/369SpUzp27Fi76PV3v/udMjIyAn7RbadOnfTkk0/qhhtuUFhYmP785z9r/Pjxev311/X9739f0vn7//r1WqHgl7/8pYYOHaquXbtq69atys7OVnl5uZ566ilJZ3tJTk4OeM65r3dMTEy76rfegQMH9Mwzz+iJJ56w1tnttT1Xe3l/aa66ujrNnDlTN9xwg1JTU631P/nJT9S7d2/16NFDH330kebNm6f9+/frv//7vyU17X0sFKSlpSkvL08DBgxQeXm5cnNzNXLkSBUVFcnr9SoiIqLBtZbn/nsMdp8EnhAxf/58/eY3v7lgzb59+wIuEGuKoqIi/eAHP1BOTo7GjBljrR8wYIAGDBhgPR4xYoQOHjyopUuX6qWXXmre4JuptXptLy6l/88++0wbN27Ua6+9FlDXvXt3zZ4923p83XXXqaysTI8//rj1oRhszen33F6+853vKCIiQj/72c+0ePHikJme/kIu5bX9/PPPNXbsWN15552aOnWqtb49vLYIlJWVpaKiIv31r38NWP/AAw9Yfx80aJASExN1yy236ODBg+rXr19bD/OS3Xbbbdbfv/Od7ygtLU29e/fWa6+9pqioqCCOrGkIPCFizpw5mjx58gVr+vbt26x97t27V7fccoseeOABLViw4KL1w4cPt35Qu3fvrvDw8AZX2FdUVCghIaFZ4/i6luw1ISGhwZ0d9WOuH2dCQkKjfbhcLkVFRSk8PLzVem3MpfS/cuVKdevWrUkfdGlpaSooKLAen6//1uitMd/k9U5LS1NNTY0OHTqkAQMGnLcX6eKvd1v029xey8rKNHr0aI0YMULPP//8Rfcfaq/tpWrN95dgmTFjhtatW6f33nsv4CxsY9LS0iSdPbPXr1+/Jr2PhaIuXbro29/+tg4cOKBbb71V1dXVOn78eMBZnnNf06D32epXCaHVXOii5aKiIhMXF2fmzp3b5P2lp6ebH/7wh9bj4cOHmxkzZliPa2trzZVXXhmSFy2fe2fHc889Z1wulzl9+rQx5uxFy6mpqQHPu/vuuxtctBwqvX5dXV2dSU5ODriD50Luv/9+c80111iPf/zjH5tx48YF1Hg8nnZxYevq1atNWFiYOXr0qDHmXxctn3unUnZ2doOLlttDv5999pn51re+Ze666y5TU1PTpOfY6bUN5Z+55qirqzNZWVmmR48e5pNPPmnSc/76178aSWb37t3GmKa9j4WiEydOmJiYGPP0009bFy3/6U9/srYXFxc3etFysPok8LRD//jHP8zOnTtNbm6u6dSpk9m5c6fZuXOnOXHihDHGmI8//tjExsaae++9N+D2wcrKSmsfS5cuNa+//rr5+9//bj7++GPz4IMPmrCwMPP2229bNa+88opxOp0mLy/P7N271zzwwAOmS5cuAVfYB7vX+tscx4wZY3bt2mXy8/NNbGxso7elz5071+zbt888++yzjd6WHuxez+ftt982ksy+ffsabMvLyzMvv/yy2bdvn9m3b5959NFHTVhYmPn9739v1bz//vumQ4cO5oknnjD79u0zOTk5IXnr8tatW83SpUvNrl27zMGDB83q1atNbGys+elPf2rVHD9+3MTHx5uJEyeaoqIi88orr5jo6OgGt6WHer+fffaZ6d+/v7nlllvMZ599FvBzWs9Or21jQvlnrjmmT59u3G632bJlS8Dr+NVXXxljjDlw4IBZtGiR+fDDD01JSYl54403TN++fc2oUaOsfTTlfSwUzJkzx2zZssWUlJSY999/36Snp5vu3btbny3Tpk0zvXr1Mps3bzYffvih8Xg8xuPxWM8Pdp8EnnZo0qRJRlKD5Z133jHGnL11sLHtvXv3tvbxm9/8xvTr189ERkaarl27mptuusls3ry5wbGeeeYZ06tXLxMREWGGDx9uPvjggzbq8qyL9WqMMYcOHTK33XabiYqKMt27dzdz5swxZ86cCdjPO++8Y4YMGWIiIiJM3759zcqVKxscK9i9ns/dd98dMJfFufLy8sxVV11loqOjjcvlMsOHDw+4LbTea6+9Zr797W+biIgIc/XVV5v169e39rCbbfv27SYtLc243W4TGRlprrrqKvPrX/+6wf/57d6929x4443G6XSaK6+80jz22GMN9hXq/a5cubLRf9fnnnS302t7PqH6M9cc53sd699jSktLzahRo0zXrl2N0+k0/fv3N3Pnzg2Yh8eYpr2PBduECRNMYmKiiYiIMFdeeaWZMGGCOXDggLX91KlT5uc//7mJiYkx0dHR5oc//GFAiDcmuH06jDGm9b84AwAACB7m4QEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALb3/wB2QSMcdl21jgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k = 16 #@param {type:\"slider\", min:0, max:99, step:1}\n",
        "\n",
        "#plt.scatter(dataset_te[k]['y'],test_prediction[k])\n",
        "plt.plot(dataset_te[k]['y'])\n",
        "plt.plot(test_prediction[k])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "xbskidCcuouQ",
        "outputId": "480ce13c-6f4e-48ee-a3ab-59b322b3e808"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7b7d5f5256c0>]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pP8E3bGIw6cJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}