Autoencoder.ipynb

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  "metadata": {
    "colab": {
      "name": "Autoencoder.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uIDQ3jDJObBh"
      },
      "source": [
        "# Deep Learning approach to the Boston Housing dataset - Autoencoder\r\n",
        "## Team JARL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vjRzPjZTo_f"
      },
      "source": [
        "#import libraries and load data\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import keras\n",
        "\n",
        "from keras import regularizers, layers\n",
        "from keras.models import Model, load_model, Sequential\n",
        "from keras.layers import Input, Dense\n",
        "from keras.callbacks import ModelCheckpoint, TensorBoard\n",
        "from keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.ensemble.partial_dependence import partial_dependence, plot_partial_dependence\n",
        "\n",
        "data = pd.read_csv('boston_corrected.csv')\n",
        "data = data.sample(frac=1).reset_index(drop=True)\n",
        "\n",
        "train = data.iloc[:450,:]\n",
        "test = data.iloc[450:,:]\n",
        "\n",
        "# Drop undesired columns\n",
        "rem = ['MEDV', 'CMEDV','TOWN','TOWNNO','TRACT']\n",
        "\n",
        "raw_X_train = train.drop(rem, axis=1)\n",
        "raw_Y_train = train['CMEDV']\n",
        "raw_X_test = test.drop(rem, axis=1)\n",
        "raw_Y_test = test['CMEDV']\n",
        "\n",
        "# Set all column types to numeric\n",
        "raw_X_train = np.asarray(raw_X_train).astype(np.float32)\n",
        "raw_X_test = np.asarray(raw_X_test).astype(np.float32)\n",
        "raw_Y_train = np.asarray(raw_Y_train).astype(np.float32)\n",
        "raw_Y_test = np.asarray(raw_Y_test).astype(np.float32)\n",
        "\n",
        "def Z_scoreNorm(data):\n",
        "    column_mean = data.mean(axis=0)\n",
        "    column_std = np.std(data, axis=0, dtype=float)\n",
        "    Normalized_Inputs = (data - column_mean)/column_std\n",
        "    \n",
        "    return Normalized_Inputs\n",
        "\n",
        "X_train = Z_scoreNorm(raw_X_train)\n",
        "X_test = Z_scoreNorm(raw_X_test)\n",
        "Y_train = Z_scoreNorm(raw_Y_train)\n",
        "Y_test = Z_scoreNorm(raw_Y_test)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MQ4u9RDwVa34",
        "outputId": "49cac723-572e-42b9-dfc8-bea0b7149318"
      },
      "source": [
        "# Reality check\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(450, 15)\n",
            "(56, 15)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "csoZGK6nQxt_"
      },
      "source": [
        "early_stopping = EarlyStopping(\r\n",
        "    min_delta=0.001,\r\n",
        "    patience=100,\r\n",
        "    restore_best_weights=True,\r\n",
        ")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZVRHIVurtn7Z",
        "outputId": "8da2094d-e9c2-4a9f-b651-d153e4051167"
      },
      "source": [
        "choke = 10\r\n",
        "\r\n",
        "autoencoder = Sequential([\r\n",
        "    Dense(X_train.shape[1], activation='relu', input_shape=(X_train.shape[1],)),\r\n",
        "    Dense(20, activation='relu'),\r\n",
        "    Dense(20, activation='relu'),\r\n",
        "    Dense(choke, activation='relu'),\r\n",
        "    Dense(20, activation='relu'),\r\n",
        "    Dense(20, activation='relu'),\r\n",
        "    Dense(X_train.shape[1])\r\n",
        "])\r\n",
        "\r\n",
        "autoencoder.compile(optimizer=Adam(lr=0.01), \r\n",
        "                    loss='MSE', \r\n",
        "                    metrics=['mean_squared_error'])\r\n",
        "checkpointer_a = ModelCheckpoint(filepath=\"AE.z1\",\r\n",
        "                               verbose=0,\r\n",
        "                               save_best_only=True)\r\n",
        "history_a = autoencoder.fit(X_train, X_train,\r\n",
        "                    epochs=800,\r\n",
        "                    batch_size=450,\r\n",
        "                    shuffle=True,\r\n",
        "                    validation_data=(X_test, X_test),\r\n",
        "                    verbose=1,\r\n",
        "                    callbacks=[early_stopping, checkpointer_a]).history"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/800\n",
            "1/1 [==============================] - 1s 636ms/step - loss: 1.0087 - mean_squared_error: 1.0087 - val_loss: 0.9776 - val_mean_squared_error: 0.9776\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 2/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.9771 - mean_squared_error: 0.9771 - val_loss: 0.9524 - val_mean_squared_error: 0.9524\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 3/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.9589 - mean_squared_error: 0.9589 - val_loss: 0.9182 - val_mean_squared_error: 0.9182\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 4/800\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.9310 - mean_squared_error: 0.9310 - val_loss: 0.8737 - val_mean_squared_error: 0.8737\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 5/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.8881 - mean_squared_error: 0.8881 - val_loss: 0.8206 - val_mean_squared_error: 0.8206\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 6/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.8349 - mean_squared_error: 0.8349 - val_loss: 0.7665 - val_mean_squared_error: 0.7665\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 7/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.7814 - mean_squared_error: 0.7814 - val_loss: 0.7221 - val_mean_squared_error: 0.7221\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 8/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.7390 - mean_squared_error: 0.7390 - val_loss: 0.6786 - val_mean_squared_error: 0.6786\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 9/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.6994 - mean_squared_error: 0.6994 - val_loss: 0.6348 - val_mean_squared_error: 0.6348\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 10/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.6594 - mean_squared_error: 0.6594 - val_loss: 0.6071 - val_mean_squared_error: 0.6071\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 11/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.6317 - mean_squared_error: 0.6317 - val_loss: 0.5853 - val_mean_squared_error: 0.5853\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 12/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.6116 - mean_squared_error: 0.6116 - val_loss: 0.5664 - val_mean_squared_error: 0.5664\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 13/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.5968 - mean_squared_error: 0.5968 - val_loss: 0.5529 - val_mean_squared_error: 0.5529\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 14/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.5867 - mean_squared_error: 0.5867 - val_loss: 0.5385 - val_mean_squared_error: 0.5385\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 15/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.5723 - mean_squared_error: 0.5723 - val_loss: 0.5268 - val_mean_squared_error: 0.5268\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 16/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.5592 - mean_squared_error: 0.5592 - val_loss: 0.5185 - val_mean_squared_error: 0.5185\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 17/800\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5505 - mean_squared_error: 0.5505 - val_loss: 0.5060 - val_mean_squared_error: 0.5060\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 18/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5376 - mean_squared_error: 0.5376 - val_loss: 0.4930 - val_mean_squared_error: 0.4930\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 19/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5246 - mean_squared_error: 0.5246 - val_loss: 0.4816 - val_mean_squared_error: 0.4816\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 20/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.5128 - mean_squared_error: 0.5128 - val_loss: 0.4750 - val_mean_squared_error: 0.4750\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 21/800\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5011 - mean_squared_error: 0.5011 - val_loss: 0.4725 - val_mean_squared_error: 0.4725\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 22/800\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.4926 - mean_squared_error: 0.4926 - val_loss: 0.4638 - val_mean_squared_error: 0.4638\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 23/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4819 - mean_squared_error: 0.4819 - val_loss: 0.4551 - val_mean_squared_error: 0.4551\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 24/800\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4731 - mean_squared_error: 0.4731 - val_loss: 0.4471 - val_mean_squared_error: 0.4471\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 25/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.4638 - mean_squared_error: 0.4638 - val_loss: 0.4395 - val_mean_squared_error: 0.4395\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 26/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4557 - mean_squared_error: 0.4557 - val_loss: 0.4321 - val_mean_squared_error: 0.4321\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 27/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.4474 - mean_squared_error: 0.4474 - val_loss: 0.4272 - val_mean_squared_error: 0.4272\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 28/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4399 - mean_squared_error: 0.4399 - val_loss: 0.4237 - val_mean_squared_error: 0.4237\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 29/800\n",
            "1/1 [==============================] - 0s 38ms/step - loss: 0.4333 - mean_squared_error: 0.4333 - val_loss: 0.4182 - val_mean_squared_error: 0.4182\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 30/800\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4258 - mean_squared_error: 0.4258 - val_loss: 0.4129 - val_mean_squared_error: 0.4129\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 31/800\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4182 - mean_squared_error: 0.4182 - val_loss: 0.4070 - val_mean_squared_error: 0.4070\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 32/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.4102 - mean_squared_error: 0.4102 - val_loss: 0.4008 - val_mean_squared_error: 0.4008\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 33/800\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.4014 - mean_squared_error: 0.4014 - val_loss: 0.3931 - val_mean_squared_error: 0.3931\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 34/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.3913 - mean_squared_error: 0.3913 - val_loss: 0.3866 - val_mean_squared_error: 0.3866\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 35/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.3830 - mean_squared_error: 0.3830 - val_loss: 0.3789 - val_mean_squared_error: 0.3789\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 36/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.3741 - mean_squared_error: 0.3741 - val_loss: 0.3727 - val_mean_squared_error: 0.3727\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 37/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.3665 - mean_squared_error: 0.3665 - val_loss: 0.3679 - val_mean_squared_error: 0.3679\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 38/800\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.3597 - mean_squared_error: 0.3597 - val_loss: 0.3647 - val_mean_squared_error: 0.3647\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 39/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.3537 - mean_squared_error: 0.3537 - val_loss: 0.3608 - val_mean_squared_error: 0.3608\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 40/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.3474 - mean_squared_error: 0.3474 - val_loss: 0.3542 - val_mean_squared_error: 0.3542\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 41/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.3400 - mean_squared_error: 0.3400 - val_loss: 0.3480 - val_mean_squared_error: 0.3480\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 42/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.3336 - mean_squared_error: 0.3336 - val_loss: 0.3418 - val_mean_squared_error: 0.3418\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 43/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.3266 - mean_squared_error: 0.3266 - val_loss: 0.3364 - val_mean_squared_error: 0.3364\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 44/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.3202 - mean_squared_error: 0.3202 - val_loss: 0.3313 - val_mean_squared_error: 0.3313\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 45/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.3144 - mean_squared_error: 0.3144 - val_loss: 0.3260 - val_mean_squared_error: 0.3260\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 46/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.3083 - mean_squared_error: 0.3083 - val_loss: 0.3209 - val_mean_squared_error: 0.3209\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 47/800\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.3029 - mean_squared_error: 0.3029 - val_loss: 0.3163 - val_mean_squared_error: 0.3163\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 48/800\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.2974 - mean_squared_error: 0.2974 - val_loss: 0.3135 - val_mean_squared_error: 0.3135\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 49/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.2909 - mean_squared_error: 0.2909 - val_loss: 0.3087 - val_mean_squared_error: 0.3087\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 50/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.2847 - mean_squared_error: 0.2847 - val_loss: 0.3051 - val_mean_squared_error: 0.3051\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 51/800\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.2788 - mean_squared_error: 0.2788 - val_loss: 0.2985 - val_mean_squared_error: 0.2985\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 52/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.2733 - mean_squared_error: 0.2733 - val_loss: 0.2958 - val_mean_squared_error: 0.2958\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 53/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.2672 - mean_squared_error: 0.2672 - val_loss: 0.2893 - val_mean_squared_error: 0.2893\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 54/800\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.2621 - mean_squared_error: 0.2621 - val_loss: 0.2893 - val_mean_squared_error: 0.2893\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 55/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.2581 - mean_squared_error: 0.2581 - val_loss: 0.2787 - val_mean_squared_error: 0.2787\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 56/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.2564 - mean_squared_error: 0.2564 - val_loss: 0.2831 - val_mean_squared_error: 0.2831\n",
            "Epoch 57/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.2530 - mean_squared_error: 0.2530 - val_loss: 0.2664 - val_mean_squared_error: 0.2664\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 58/800\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.2433 - mean_squared_error: 0.2433 - val_loss: 0.2620 - val_mean_squared_error: 0.2620\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 59/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.2362 - mean_squared_error: 0.2362 - val_loss: 0.2657 - val_mean_squared_error: 0.2657\n",
            "Epoch 60/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.2361 - mean_squared_error: 0.2361 - val_loss: 0.2558 - val_mean_squared_error: 0.2558\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 61/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.2306 - mean_squared_error: 0.2306 - val_loss: 0.2520 - val_mean_squared_error: 0.2520\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 62/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.2236 - mean_squared_error: 0.2236 - val_loss: 0.2533 - val_mean_squared_error: 0.2533\n",
            "Epoch 63/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.2214 - mean_squared_error: 0.2214 - val_loss: 0.2484 - val_mean_squared_error: 0.2484\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 64/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.2182 - mean_squared_error: 0.2182 - val_loss: 0.2446 - val_mean_squared_error: 0.2446\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 65/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.2123 - mean_squared_error: 0.2123 - val_loss: 0.2427 - val_mean_squared_error: 0.2427\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 66/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.2093 - mean_squared_error: 0.2093 - val_loss: 0.2380 - val_mean_squared_error: 0.2380\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 67/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.2070 - mean_squared_error: 0.2070 - val_loss: 0.2354 - val_mean_squared_error: 0.2354\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 68/800\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.2016 - mean_squared_error: 0.2016 - val_loss: 0.2337 - val_mean_squared_error: 0.2337\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 69/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1978 - mean_squared_error: 0.1978 - val_loss: 0.2293 - val_mean_squared_error: 0.2293\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 70/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1949 - mean_squared_error: 0.1949 - val_loss: 0.2319 - val_mean_squared_error: 0.2319\n",
            "Epoch 71/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.1911 - mean_squared_error: 0.1911 - val_loss: 0.2257 - val_mean_squared_error: 0.2257\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 72/800\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.1876 - mean_squared_error: 0.1876 - val_loss: 0.2222 - val_mean_squared_error: 0.2222\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 73/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1839 - mean_squared_error: 0.1839 - val_loss: 0.2232 - val_mean_squared_error: 0.2232\n",
            "Epoch 74/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1824 - mean_squared_error: 0.1824 - val_loss: 0.2169 - val_mean_squared_error: 0.2169\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 75/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1793 - mean_squared_error: 0.1793 - val_loss: 0.2197 - val_mean_squared_error: 0.2197\n",
            "Epoch 76/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.1764 - mean_squared_error: 0.1764 - val_loss: 0.2161 - val_mean_squared_error: 0.2161\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 77/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1731 - mean_squared_error: 0.1731 - val_loss: 0.2121 - val_mean_squared_error: 0.2121\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 78/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.1707 - mean_squared_error: 0.1707 - val_loss: 0.2137 - val_mean_squared_error: 0.2137\n",
            "Epoch 79/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1686 - mean_squared_error: 0.1686 - val_loss: 0.2075 - val_mean_squared_error: 0.2075\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 80/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1673 - mean_squared_error: 0.1673 - val_loss: 0.2147 - val_mean_squared_error: 0.2147\n",
            "Epoch 81/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1654 - mean_squared_error: 0.1654 - val_loss: 0.2043 - val_mean_squared_error: 0.2043\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 82/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1641 - mean_squared_error: 0.1641 - val_loss: 0.2120 - val_mean_squared_error: 0.2120\n",
            "Epoch 83/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.1615 - mean_squared_error: 0.1615 - val_loss: 0.2004 - val_mean_squared_error: 0.2004\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 84/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1571 - mean_squared_error: 0.1571 - val_loss: 0.1993 - val_mean_squared_error: 0.1993\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 85/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1541 - mean_squared_error: 0.1541 - val_loss: 0.1999 - val_mean_squared_error: 0.1999\n",
            "Epoch 86/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1529 - mean_squared_error: 0.1529 - val_loss: 0.1949 - val_mean_squared_error: 0.1949\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 87/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1517 - mean_squared_error: 0.1517 - val_loss: 0.2015 - val_mean_squared_error: 0.2015\n",
            "Epoch 88/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1504 - mean_squared_error: 0.1504 - val_loss: 0.1936 - val_mean_squared_error: 0.1936\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 89/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1487 - mean_squared_error: 0.1487 - val_loss: 0.1955 - val_mean_squared_error: 0.1955\n",
            "Epoch 90/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1456 - mean_squared_error: 0.1456 - val_loss: 0.1899 - val_mean_squared_error: 0.1899\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 91/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1430 - mean_squared_error: 0.1430 - val_loss: 0.1874 - val_mean_squared_error: 0.1874\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 92/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1412 - mean_squared_error: 0.1412 - val_loss: 0.1894 - val_mean_squared_error: 0.1894\n",
            "Epoch 93/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1397 - mean_squared_error: 0.1397 - val_loss: 0.1857 - val_mean_squared_error: 0.1857\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 94/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1388 - mean_squared_error: 0.1388 - val_loss: 0.1920 - val_mean_squared_error: 0.1920\n",
            "Epoch 95/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.1378 - mean_squared_error: 0.1378 - val_loss: 0.1843 - val_mean_squared_error: 0.1843\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 96/800\n",
            "1/1 [==============================] - 0s 264ms/step - loss: 0.1365 - mean_squared_error: 0.1365 - val_loss: 0.1861 - val_mean_squared_error: 0.1861\n",
            "Epoch 97/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1354 - mean_squared_error: 0.1354 - val_loss: 0.1839 - val_mean_squared_error: 0.1839\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 98/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1345 - mean_squared_error: 0.1345 - val_loss: 0.1812 - val_mean_squared_error: 0.1812\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 99/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1322 - mean_squared_error: 0.1322 - val_loss: 0.1819 - val_mean_squared_error: 0.1819\n",
            "Epoch 100/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.1289 - mean_squared_error: 0.1289 - val_loss: 0.1777 - val_mean_squared_error: 0.1777\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 101/800\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.1284 - mean_squared_error: 0.1284 - val_loss: 0.1820 - val_mean_squared_error: 0.1820\n",
            "Epoch 102/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.1286 - mean_squared_error: 0.1286 - val_loss: 0.1769 - val_mean_squared_error: 0.1769\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 103/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.1272 - mean_squared_error: 0.1272 - val_loss: 0.1790 - val_mean_squared_error: 0.1790\n",
            "Epoch 104/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1246 - mean_squared_error: 0.1246 - val_loss: 0.1715 - val_mean_squared_error: 0.1715\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 105/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1226 - mean_squared_error: 0.1226 - val_loss: 0.1755 - val_mean_squared_error: 0.1755\n",
            "Epoch 106/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1211 - mean_squared_error: 0.1211 - val_loss: 0.1725 - val_mean_squared_error: 0.1725\n",
            "Epoch 107/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1201 - mean_squared_error: 0.1201 - val_loss: 0.1695 - val_mean_squared_error: 0.1695\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 108/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1189 - mean_squared_error: 0.1189 - val_loss: 0.1707 - val_mean_squared_error: 0.1707\n",
            "Epoch 109/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1174 - mean_squared_error: 0.1174 - val_loss: 0.1662 - val_mean_squared_error: 0.1662\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 110/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1160 - mean_squared_error: 0.1160 - val_loss: 0.1708 - val_mean_squared_error: 0.1708\n",
            "Epoch 111/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.1154 - mean_squared_error: 0.1154 - val_loss: 0.1648 - val_mean_squared_error: 0.1648\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 112/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.1162 - mean_squared_error: 0.1162 - val_loss: 0.1703 - val_mean_squared_error: 0.1703\n",
            "Epoch 113/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.1172 - mean_squared_error: 0.1172 - val_loss: 0.1637 - val_mean_squared_error: 0.1637\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 114/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1149 - mean_squared_error: 0.1149 - val_loss: 0.1661 - val_mean_squared_error: 0.1661\n",
            "Epoch 115/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.1114 - mean_squared_error: 0.1114 - val_loss: 0.1605 - val_mean_squared_error: 0.1605\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 116/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1098 - mean_squared_error: 0.1098 - val_loss: 0.1642 - val_mean_squared_error: 0.1642\n",
            "Epoch 117/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.1106 - mean_squared_error: 0.1106 - val_loss: 0.1630 - val_mean_squared_error: 0.1630\n",
            "Epoch 118/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1101 - mean_squared_error: 0.1101 - val_loss: 0.1602 - val_mean_squared_error: 0.1602\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 119/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1083 - mean_squared_error: 0.1083 - val_loss: 0.1602 - val_mean_squared_error: 0.1602\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 120/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1069 - mean_squared_error: 0.1069 - val_loss: 0.1601 - val_mean_squared_error: 0.1601\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 121/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.1063 - mean_squared_error: 0.1063 - val_loss: 0.1559 - val_mean_squared_error: 0.1559\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 122/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1061 - mean_squared_error: 0.1061 - val_loss: 0.1610 - val_mean_squared_error: 0.1610\n",
            "Epoch 123/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1051 - mean_squared_error: 0.1051 - val_loss: 0.1538 - val_mean_squared_error: 0.1538\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 124/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1039 - mean_squared_error: 0.1039 - val_loss: 0.1575 - val_mean_squared_error: 0.1575\n",
            "Epoch 125/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1037 - mean_squared_error: 0.1037 - val_loss: 0.1557 - val_mean_squared_error: 0.1557\n",
            "Epoch 126/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1042 - mean_squared_error: 0.1042 - val_loss: 0.1581 - val_mean_squared_error: 0.1581\n",
            "Epoch 127/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1047 - mean_squared_error: 0.1047 - val_loss: 0.1548 - val_mean_squared_error: 0.1548\n",
            "Epoch 128/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1046 - mean_squared_error: 0.1046 - val_loss: 0.1621 - val_mean_squared_error: 0.1621\n",
            "Epoch 129/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.1056 - mean_squared_error: 0.1056 - val_loss: 0.1521 - val_mean_squared_error: 0.1521\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 130/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.1060 - mean_squared_error: 0.1060 - val_loss: 0.1592 - val_mean_squared_error: 0.1592\n",
            "Epoch 131/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1027 - mean_squared_error: 0.1027 - val_loss: 0.1489 - val_mean_squared_error: 0.1489\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 132/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0989 - mean_squared_error: 0.0989 - val_loss: 0.1476 - val_mean_squared_error: 0.1476\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 133/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.0987 - mean_squared_error: 0.0987 - val_loss: 0.1539 - val_mean_squared_error: 0.1539\n",
            "Epoch 134/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0996 - mean_squared_error: 0.0996 - val_loss: 0.1484 - val_mean_squared_error: 0.1484\n",
            "Epoch 135/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0989 - mean_squared_error: 0.0989 - val_loss: 0.1498 - val_mean_squared_error: 0.1498\n",
            "Epoch 136/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0984 - mean_squared_error: 0.0984 - val_loss: 0.1501 - val_mean_squared_error: 0.1501\n",
            "Epoch 137/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0981 - mean_squared_error: 0.0981 - val_loss: 0.1463 - val_mean_squared_error: 0.1463\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 138/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.0963 - mean_squared_error: 0.0963 - val_loss: 0.1461 - val_mean_squared_error: 0.1461\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 139/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0953 - mean_squared_error: 0.0953 - val_loss: 0.1478 - val_mean_squared_error: 0.1478\n",
            "Epoch 140/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0961 - mean_squared_error: 0.0961 - val_loss: 0.1465 - val_mean_squared_error: 0.1465\n",
            "Epoch 141/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0964 - mean_squared_error: 0.0964 - val_loss: 0.1459 - val_mean_squared_error: 0.1459\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 142/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0955 - mean_squared_error: 0.0955 - val_loss: 0.1464 - val_mean_squared_error: 0.1464\n",
            "Epoch 143/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0942 - mean_squared_error: 0.0942 - val_loss: 0.1421 - val_mean_squared_error: 0.1421\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 144/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0935 - mean_squared_error: 0.0935 - val_loss: 0.1449 - val_mean_squared_error: 0.1449\n",
            "Epoch 145/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0928 - mean_squared_error: 0.0928 - val_loss: 0.1446 - val_mean_squared_error: 0.1446\n",
            "Epoch 146/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0927 - mean_squared_error: 0.0927 - val_loss: 0.1422 - val_mean_squared_error: 0.1422\n",
            "Epoch 147/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0933 - mean_squared_error: 0.0933 - val_loss: 0.1455 - val_mean_squared_error: 0.1455\n",
            "Epoch 148/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0929 - mean_squared_error: 0.0929 - val_loss: 0.1427 - val_mean_squared_error: 0.1427\n",
            "Epoch 149/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0919 - mean_squared_error: 0.0919 - val_loss: 0.1419 - val_mean_squared_error: 0.1419\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 150/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0911 - mean_squared_error: 0.0911 - val_loss: 0.1431 - val_mean_squared_error: 0.1431\n",
            "Epoch 151/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0907 - mean_squared_error: 0.0907 - val_loss: 0.1401 - val_mean_squared_error: 0.1401\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 152/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0902 - mean_squared_error: 0.0902 - val_loss: 0.1405 - val_mean_squared_error: 0.1405\n",
            "Epoch 153/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0895 - mean_squared_error: 0.0895 - val_loss: 0.1408 - val_mean_squared_error: 0.1408\n",
            "Epoch 154/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0894 - mean_squared_error: 0.0894 - val_loss: 0.1385 - val_mean_squared_error: 0.1385\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 155/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.0899 - mean_squared_error: 0.0899 - val_loss: 0.1432 - val_mean_squared_error: 0.1432\n",
            "Epoch 156/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0904 - mean_squared_error: 0.0904 - val_loss: 0.1379 - val_mean_squared_error: 0.1379\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 157/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0904 - mean_squared_error: 0.0904 - val_loss: 0.1421 - val_mean_squared_error: 0.1421\n",
            "Epoch 158/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0892 - mean_squared_error: 0.0892 - val_loss: 0.1392 - val_mean_squared_error: 0.1392\n",
            "Epoch 159/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0877 - mean_squared_error: 0.0877 - val_loss: 0.1372 - val_mean_squared_error: 0.1372\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 160/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0870 - mean_squared_error: 0.0870 - val_loss: 0.1402 - val_mean_squared_error: 0.1402\n",
            "Epoch 161/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0876 - mean_squared_error: 0.0876 - val_loss: 0.1391 - val_mean_squared_error: 0.1391\n",
            "Epoch 162/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0897 - mean_squared_error: 0.0897 - val_loss: 0.1441 - val_mean_squared_error: 0.1441\n",
            "Epoch 163/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0935 - mean_squared_error: 0.0935 - val_loss: 0.1527 - val_mean_squared_error: 0.1527\n",
            "Epoch 164/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0998 - mean_squared_error: 0.0998 - val_loss: 0.1472 - val_mean_squared_error: 0.1472\n",
            "Epoch 165/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.1020 - mean_squared_error: 0.1020 - val_loss: 0.1490 - val_mean_squared_error: 0.1490\n",
            "Epoch 166/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0934 - mean_squared_error: 0.0934 - val_loss: 0.1373 - val_mean_squared_error: 0.1373\n",
            "Epoch 167/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0877 - mean_squared_error: 0.0877 - val_loss: 0.1401 - val_mean_squared_error: 0.1401\n",
            "Epoch 168/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0889 - mean_squared_error: 0.0889 - val_loss: 0.1446 - val_mean_squared_error: 0.1446\n",
            "Epoch 169/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0894 - mean_squared_error: 0.0894 - val_loss: 0.1352 - val_mean_squared_error: 0.1352\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 170/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0884 - mean_squared_error: 0.0884 - val_loss: 0.1380 - val_mean_squared_error: 0.1380\n",
            "Epoch 171/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0862 - mean_squared_error: 0.0862 - val_loss: 0.1399 - val_mean_squared_error: 0.1399\n",
            "Epoch 172/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0851 - mean_squared_error: 0.0851 - val_loss: 0.1340 - val_mean_squared_error: 0.1340\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 173/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0865 - mean_squared_error: 0.0865 - val_loss: 0.1367 - val_mean_squared_error: 0.1367\n",
            "Epoch 174/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0848 - mean_squared_error: 0.0848 - val_loss: 0.1347 - val_mean_squared_error: 0.1347\n",
            "Epoch 175/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0822 - mean_squared_error: 0.0822 - val_loss: 0.1307 - val_mean_squared_error: 0.1307\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 176/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0840 - mean_squared_error: 0.0840 - val_loss: 0.1344 - val_mean_squared_error: 0.1344\n",
            "Epoch 177/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0828 - mean_squared_error: 0.0828 - val_loss: 0.1323 - val_mean_squared_error: 0.1323\n",
            "Epoch 178/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0803 - mean_squared_error: 0.0803 - val_loss: 0.1285 - val_mean_squared_error: 0.1285\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 179/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0820 - mean_squared_error: 0.0820 - val_loss: 0.1312 - val_mean_squared_error: 0.1312\n",
            "Epoch 180/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0808 - mean_squared_error: 0.0808 - val_loss: 0.1295 - val_mean_squared_error: 0.1295\n",
            "Epoch 181/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0790 - mean_squared_error: 0.0790 - val_loss: 0.1249 - val_mean_squared_error: 0.1249\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 182/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0799 - mean_squared_error: 0.0799 - val_loss: 0.1291 - val_mean_squared_error: 0.1291\n",
            "Epoch 183/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0791 - mean_squared_error: 0.0791 - val_loss: 0.1282 - val_mean_squared_error: 0.1282\n",
            "Epoch 184/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0781 - mean_squared_error: 0.0781 - val_loss: 0.1228 - val_mean_squared_error: 0.1228\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 185/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0785 - mean_squared_error: 0.0785 - val_loss: 0.1269 - val_mean_squared_error: 0.1269\n",
            "Epoch 186/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0775 - mean_squared_error: 0.0775 - val_loss: 0.1252 - val_mean_squared_error: 0.1252\n",
            "Epoch 187/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0767 - mean_squared_error: 0.0767 - val_loss: 0.1214 - val_mean_squared_error: 0.1214\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 188/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0773 - mean_squared_error: 0.0773 - val_loss: 0.1261 - val_mean_squared_error: 0.1261\n",
            "Epoch 189/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0764 - mean_squared_error: 0.0764 - val_loss: 0.1224 - val_mean_squared_error: 0.1224\n",
            "Epoch 190/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0754 - mean_squared_error: 0.0754 - val_loss: 0.1197 - val_mean_squared_error: 0.1197\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 191/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0755 - mean_squared_error: 0.0755 - val_loss: 0.1241 - val_mean_squared_error: 0.1241\n",
            "Epoch 192/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0753 - mean_squared_error: 0.0753 - val_loss: 0.1195 - val_mean_squared_error: 0.1195\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 193/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0746 - mean_squared_error: 0.0746 - val_loss: 0.1187 - val_mean_squared_error: 0.1187\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 194/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0743 - mean_squared_error: 0.0743 - val_loss: 0.1211 - val_mean_squared_error: 0.1211\n",
            "Epoch 195/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0743 - mean_squared_error: 0.0743 - val_loss: 0.1173 - val_mean_squared_error: 0.1173\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 196/800\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.0738 - mean_squared_error: 0.0738 - val_loss: 0.1181 - val_mean_squared_error: 0.1181\n",
            "Epoch 197/800\n",
            "1/1 [==============================] - 0s 296ms/step - loss: 0.0730 - mean_squared_error: 0.0730 - val_loss: 0.1179 - val_mean_squared_error: 0.1179\n",
            "Epoch 198/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0728 - mean_squared_error: 0.0728 - val_loss: 0.1154 - val_mean_squared_error: 0.1154\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 199/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0727 - mean_squared_error: 0.0727 - val_loss: 0.1174 - val_mean_squared_error: 0.1174\n",
            "Epoch 200/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0724 - mean_squared_error: 0.0724 - val_loss: 0.1152 - val_mean_squared_error: 0.1152\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 201/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0720 - mean_squared_error: 0.0720 - val_loss: 0.1142 - val_mean_squared_error: 0.1142\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 202/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0717 - mean_squared_error: 0.0717 - val_loss: 0.1164 - val_mean_squared_error: 0.1164\n",
            "Epoch 203/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0716 - mean_squared_error: 0.0716 - val_loss: 0.1132 - val_mean_squared_error: 0.1132\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 204/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0713 - mean_squared_error: 0.0713 - val_loss: 0.1135 - val_mean_squared_error: 0.1135\n",
            "Epoch 205/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0710 - mean_squared_error: 0.0710 - val_loss: 0.1130 - val_mean_squared_error: 0.1130\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 206/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0708 - mean_squared_error: 0.0708 - val_loss: 0.1116 - val_mean_squared_error: 0.1116\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 207/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0707 - mean_squared_error: 0.0707 - val_loss: 0.1127 - val_mean_squared_error: 0.1127\n",
            "Epoch 208/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0708 - mean_squared_error: 0.0708 - val_loss: 0.1115 - val_mean_squared_error: 0.1115\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 209/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0712 - mean_squared_error: 0.0712 - val_loss: 0.1119 - val_mean_squared_error: 0.1119\n",
            "Epoch 210/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0716 - mean_squared_error: 0.0716 - val_loss: 0.1125 - val_mean_squared_error: 0.1125\n",
            "Epoch 211/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0727 - mean_squared_error: 0.0727 - val_loss: 0.1141 - val_mean_squared_error: 0.1141\n",
            "Epoch 212/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0744 - mean_squared_error: 0.0744 - val_loss: 0.1154 - val_mean_squared_error: 0.1154\n",
            "Epoch 213/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0763 - mean_squared_error: 0.0763 - val_loss: 0.1154 - val_mean_squared_error: 0.1154\n",
            "Epoch 214/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0757 - mean_squared_error: 0.0757 - val_loss: 0.1107 - val_mean_squared_error: 0.1107\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 215/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0724 - mean_squared_error: 0.0724 - val_loss: 0.1098 - val_mean_squared_error: 0.1098\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 216/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0689 - mean_squared_error: 0.0689 - val_loss: 0.1066 - val_mean_squared_error: 0.1066\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 217/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0686 - mean_squared_error: 0.0686 - val_loss: 0.1094 - val_mean_squared_error: 0.1094\n",
            "Epoch 218/800\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0702 - mean_squared_error: 0.0702 - val_loss: 0.1103 - val_mean_squared_error: 0.1103\n",
            "Epoch 219/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0705 - mean_squared_error: 0.0705 - val_loss: 0.1058 - val_mean_squared_error: 0.1058\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 220/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0695 - mean_squared_error: 0.0695 - val_loss: 0.1093 - val_mean_squared_error: 0.1093\n",
            "Epoch 221/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0681 - mean_squared_error: 0.0681 - val_loss: 0.1043 - val_mean_squared_error: 0.1043\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 222/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0678 - mean_squared_error: 0.0678 - val_loss: 0.1064 - val_mean_squared_error: 0.1064\n",
            "Epoch 223/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0678 - mean_squared_error: 0.0678 - val_loss: 0.1063 - val_mean_squared_error: 0.1063\n",
            "Epoch 224/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0676 - mean_squared_error: 0.0676 - val_loss: 0.1031 - val_mean_squared_error: 0.1031\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 225/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0675 - mean_squared_error: 0.0675 - val_loss: 0.1081 - val_mean_squared_error: 0.1081\n",
            "Epoch 226/800\n",
            "1/1 [==============================] - 0s 298ms/step - loss: 0.0671 - mean_squared_error: 0.0671 - val_loss: 0.1023 - val_mean_squared_error: 0.1023\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 227/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0663 - mean_squared_error: 0.0663 - val_loss: 0.1038 - val_mean_squared_error: 0.1038\n",
            "Epoch 228/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0656 - mean_squared_error: 0.0656 - val_loss: 0.1039 - val_mean_squared_error: 0.1039\n",
            "Epoch 229/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0657 - mean_squared_error: 0.0657 - val_loss: 0.1012 - val_mean_squared_error: 0.1012\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 230/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0663 - mean_squared_error: 0.0663 - val_loss: 0.1062 - val_mean_squared_error: 0.1062\n",
            "Epoch 231/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0662 - mean_squared_error: 0.0662 - val_loss: 0.1010 - val_mean_squared_error: 0.1010\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 232/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0653 - mean_squared_error: 0.0653 - val_loss: 0.1025 - val_mean_squared_error: 0.1025\n",
            "Epoch 233/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0646 - mean_squared_error: 0.0646 - val_loss: 0.1032 - val_mean_squared_error: 0.1032\n",
            "Epoch 234/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0648 - mean_squared_error: 0.0648 - val_loss: 0.1008 - val_mean_squared_error: 0.1008\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 235/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0655 - mean_squared_error: 0.0655 - val_loss: 0.1055 - val_mean_squared_error: 0.1055\n",
            "Epoch 236/800\n",
            "1/1 [==============================] - 0s 267ms/step - loss: 0.0660 - mean_squared_error: 0.0660 - val_loss: 0.1010 - val_mean_squared_error: 0.1010\n",
            "Epoch 237/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0657 - mean_squared_error: 0.0657 - val_loss: 0.1032 - val_mean_squared_error: 0.1032\n",
            "Epoch 238/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0655 - mean_squared_error: 0.0655 - val_loss: 0.1028 - val_mean_squared_error: 0.1028\n",
            "Epoch 239/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0652 - mean_squared_error: 0.0652 - val_loss: 0.1003 - val_mean_squared_error: 0.1003\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 240/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0648 - mean_squared_error: 0.0648 - val_loss: 0.1027 - val_mean_squared_error: 0.1027\n",
            "Epoch 241/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0639 - mean_squared_error: 0.0639 - val_loss: 0.0994 - val_mean_squared_error: 0.0994\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 242/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0634 - mean_squared_error: 0.0634 - val_loss: 0.1016 - val_mean_squared_error: 0.1016\n",
            "Epoch 243/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0638 - mean_squared_error: 0.0638 - val_loss: 0.1017 - val_mean_squared_error: 0.1017\n",
            "Epoch 244/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0647 - mean_squared_error: 0.0647 - val_loss: 0.1023 - val_mean_squared_error: 0.1023\n",
            "Epoch 245/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0652 - mean_squared_error: 0.0652 - val_loss: 0.1013 - val_mean_squared_error: 0.1013\n",
            "Epoch 246/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0648 - mean_squared_error: 0.0648 - val_loss: 0.1017 - val_mean_squared_error: 0.1017\n",
            "Epoch 247/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0640 - mean_squared_error: 0.0640 - val_loss: 0.0987 - val_mean_squared_error: 0.0987\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 248/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0636 - mean_squared_error: 0.0636 - val_loss: 0.1025 - val_mean_squared_error: 0.1025\n",
            "Epoch 249/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0637 - mean_squared_error: 0.0637 - val_loss: 0.0987 - val_mean_squared_error: 0.0987\n",
            "Epoch 250/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0639 - mean_squared_error: 0.0639 - val_loss: 0.1020 - val_mean_squared_error: 0.1020\n",
            "Epoch 251/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0641 - mean_squared_error: 0.0641 - val_loss: 0.1015 - val_mean_squared_error: 0.1015\n",
            "Epoch 252/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0639 - mean_squared_error: 0.0639 - val_loss: 0.0996 - val_mean_squared_error: 0.0996\n",
            "Epoch 253/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0638 - mean_squared_error: 0.0638 - val_loss: 0.1029 - val_mean_squared_error: 0.1029\n",
            "Epoch 254/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0628 - mean_squared_error: 0.0628 - val_loss: 0.0964 - val_mean_squared_error: 0.0964\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 255/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0615 - mean_squared_error: 0.0615 - val_loss: 0.0996 - val_mean_squared_error: 0.0996\n",
            "Epoch 256/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0607 - mean_squared_error: 0.0607 - val_loss: 0.0984 - val_mean_squared_error: 0.0984\n",
            "Epoch 257/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0609 - mean_squared_error: 0.0609 - val_loss: 0.0981 - val_mean_squared_error: 0.0981\n",
            "Epoch 258/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0616 - mean_squared_error: 0.0616 - val_loss: 0.1017 - val_mean_squared_error: 0.1017\n",
            "Epoch 259/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0621 - mean_squared_error: 0.0621 - val_loss: 0.0976 - val_mean_squared_error: 0.0976\n",
            "Epoch 260/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0615 - mean_squared_error: 0.0615 - val_loss: 0.0984 - val_mean_squared_error: 0.0984\n",
            "Epoch 261/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0603 - mean_squared_error: 0.0603 - val_loss: 0.0985 - val_mean_squared_error: 0.0985\n",
            "Epoch 262/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0595 - mean_squared_error: 0.0595 - val_loss: 0.0954 - val_mean_squared_error: 0.0954\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 263/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0596 - mean_squared_error: 0.0596 - val_loss: 0.1003 - val_mean_squared_error: 0.1003\n",
            "Epoch 264/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0601 - mean_squared_error: 0.0601 - val_loss: 0.0960 - val_mean_squared_error: 0.0960\n",
            "Epoch 265/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0604 - mean_squared_error: 0.0604 - val_loss: 0.1002 - val_mean_squared_error: 0.1002\n",
            "Epoch 266/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0606 - mean_squared_error: 0.0606 - val_loss: 0.0977 - val_mean_squared_error: 0.0977\n",
            "Epoch 267/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0612 - mean_squared_error: 0.0612 - val_loss: 0.1018 - val_mean_squared_error: 0.1018\n",
            "Epoch 268/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0629 - mean_squared_error: 0.0629 - val_loss: 0.1014 - val_mean_squared_error: 0.1014\n",
            "Epoch 269/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0657 - mean_squared_error: 0.0657 - val_loss: 0.1080 - val_mean_squared_error: 0.1080\n",
            "Epoch 270/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0685 - mean_squared_error: 0.0685 - val_loss: 0.1029 - val_mean_squared_error: 0.1029\n",
            "Epoch 271/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0698 - mean_squared_error: 0.0698 - val_loss: 0.1096 - val_mean_squared_error: 0.1096\n",
            "Epoch 272/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0694 - mean_squared_error: 0.0694 - val_loss: 0.0970 - val_mean_squared_error: 0.0970\n",
            "Epoch 273/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0650 - mean_squared_error: 0.0650 - val_loss: 0.0985 - val_mean_squared_error: 0.0985\n",
            "Epoch 274/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0606 - mean_squared_error: 0.0606 - val_loss: 0.0996 - val_mean_squared_error: 0.0996\n",
            "Epoch 275/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0607 - mean_squared_error: 0.0607 - val_loss: 0.0967 - val_mean_squared_error: 0.0967\n",
            "Epoch 276/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0639 - mean_squared_error: 0.0639 - val_loss: 0.1032 - val_mean_squared_error: 0.1032\n",
            "Epoch 277/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0627 - mean_squared_error: 0.0627 - val_loss: 0.0946 - val_mean_squared_error: 0.0946\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 278/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0582 - mean_squared_error: 0.0582 - val_loss: 0.0945 - val_mean_squared_error: 0.0945\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 279/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0594 - mean_squared_error: 0.0594 - val_loss: 0.1024 - val_mean_squared_error: 0.1024\n",
            "Epoch 280/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0620 - mean_squared_error: 0.0620 - val_loss: 0.0949 - val_mean_squared_error: 0.0949\n",
            "Epoch 281/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0596 - mean_squared_error: 0.0596 - val_loss: 0.0949 - val_mean_squared_error: 0.0949\n",
            "Epoch 282/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0574 - mean_squared_error: 0.0574 - val_loss: 0.0977 - val_mean_squared_error: 0.0977\n",
            "Epoch 283/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0588 - mean_squared_error: 0.0588 - val_loss: 0.0953 - val_mean_squared_error: 0.0953\n",
            "Epoch 284/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0592 - mean_squared_error: 0.0592 - val_loss: 0.0959 - val_mean_squared_error: 0.0959\n",
            "Epoch 285/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0578 - mean_squared_error: 0.0578 - val_loss: 0.0950 - val_mean_squared_error: 0.0950\n",
            "Epoch 286/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0573 - mean_squared_error: 0.0573 - val_loss: 0.0947 - val_mean_squared_error: 0.0947\n",
            "Epoch 287/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0576 - mean_squared_error: 0.0576 - val_loss: 0.0959 - val_mean_squared_error: 0.0959\n",
            "Epoch 288/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0575 - mean_squared_error: 0.0575 - val_loss: 0.0942 - val_mean_squared_error: 0.0942\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 289/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0569 - mean_squared_error: 0.0569 - val_loss: 0.0939 - val_mean_squared_error: 0.0939\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 290/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0565 - mean_squared_error: 0.0565 - val_loss: 0.0947 - val_mean_squared_error: 0.0947\n",
            "Epoch 291/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0564 - mean_squared_error: 0.0564 - val_loss: 0.0936 - val_mean_squared_error: 0.0936\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 292/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0564 - mean_squared_error: 0.0564 - val_loss: 0.0940 - val_mean_squared_error: 0.0940\n",
            "Epoch 293/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0565 - mean_squared_error: 0.0565 - val_loss: 0.0942 - val_mean_squared_error: 0.0942\n",
            "Epoch 294/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0560 - mean_squared_error: 0.0560 - val_loss: 0.0928 - val_mean_squared_error: 0.0928\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 295/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0554 - mean_squared_error: 0.0554 - val_loss: 0.0931 - val_mean_squared_error: 0.0931\n",
            "Epoch 296/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0552 - mean_squared_error: 0.0552 - val_loss: 0.0927 - val_mean_squared_error: 0.0927\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 297/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0556 - mean_squared_error: 0.0556 - val_loss: 0.0934 - val_mean_squared_error: 0.0934\n",
            "Epoch 298/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0555 - mean_squared_error: 0.0555 - val_loss: 0.0930 - val_mean_squared_error: 0.0930\n",
            "Epoch 299/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0549 - mean_squared_error: 0.0549 - val_loss: 0.0923 - val_mean_squared_error: 0.0923\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 300/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0545 - mean_squared_error: 0.0545 - val_loss: 0.0930 - val_mean_squared_error: 0.0930\n",
            "Epoch 301/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0547 - mean_squared_error: 0.0547 - val_loss: 0.0921 - val_mean_squared_error: 0.0921\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 302/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0547 - mean_squared_error: 0.0547 - val_loss: 0.0925 - val_mean_squared_error: 0.0925\n",
            "Epoch 303/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0544 - mean_squared_error: 0.0544 - val_loss: 0.0914 - val_mean_squared_error: 0.0914\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 304/800\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.0541 - mean_squared_error: 0.0541 - val_loss: 0.0921 - val_mean_squared_error: 0.0921\n",
            "Epoch 305/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0541 - mean_squared_error: 0.0541 - val_loss: 0.0922 - val_mean_squared_error: 0.0922\n",
            "Epoch 306/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0541 - mean_squared_error: 0.0541 - val_loss: 0.0918 - val_mean_squared_error: 0.0918\n",
            "Epoch 307/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0539 - mean_squared_error: 0.0539 - val_loss: 0.0924 - val_mean_squared_error: 0.0924\n",
            "Epoch 308/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0537 - mean_squared_error: 0.0537 - val_loss: 0.0911 - val_mean_squared_error: 0.0911\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 309/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0537 - mean_squared_error: 0.0537 - val_loss: 0.0931 - val_mean_squared_error: 0.0931\n",
            "Epoch 310/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0538 - mean_squared_error: 0.0538 - val_loss: 0.0913 - val_mean_squared_error: 0.0913\n",
            "Epoch 311/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0540 - mean_squared_error: 0.0540 - val_loss: 0.0937 - val_mean_squared_error: 0.0937\n",
            "Epoch 312/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0542 - mean_squared_error: 0.0542 - val_loss: 0.0923 - val_mean_squared_error: 0.0923\n",
            "Epoch 313/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0550 - mean_squared_error: 0.0550 - val_loss: 0.0960 - val_mean_squared_error: 0.0960\n",
            "Epoch 314/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0568 - mean_squared_error: 0.0568 - val_loss: 0.0983 - val_mean_squared_error: 0.0983\n",
            "Epoch 315/800\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0608 - mean_squared_error: 0.0608 - val_loss: 0.1041 - val_mean_squared_error: 0.1041\n",
            "Epoch 316/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0670 - mean_squared_error: 0.0670 - val_loss: 0.1068 - val_mean_squared_error: 0.1068\n",
            "Epoch 317/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0694 - mean_squared_error: 0.0694 - val_loss: 0.0993 - val_mean_squared_error: 0.0993\n",
            "Epoch 318/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0624 - mean_squared_error: 0.0624 - val_loss: 0.0947 - val_mean_squared_error: 0.0947\n",
            "Epoch 319/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0552 - mean_squared_error: 0.0552 - val_loss: 0.0948 - val_mean_squared_error: 0.0948\n",
            "Epoch 320/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0591 - mean_squared_error: 0.0591 - val_loss: 0.1036 - val_mean_squared_error: 0.1036\n",
            "Epoch 321/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0628 - mean_squared_error: 0.0628 - val_loss: 0.0940 - val_mean_squared_error: 0.0940\n",
            "Epoch 322/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0581 - mean_squared_error: 0.0581 - val_loss: 0.0957 - val_mean_squared_error: 0.0957\n",
            "Epoch 323/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0549 - mean_squared_error: 0.0549 - val_loss: 0.0966 - val_mean_squared_error: 0.0966\n",
            "Epoch 324/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0580 - mean_squared_error: 0.0580 - val_loss: 0.0932 - val_mean_squared_error: 0.0932\n",
            "Epoch 325/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0567 - mean_squared_error: 0.0567 - val_loss: 0.0935 - val_mean_squared_error: 0.0935\n",
            "Epoch 326/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0532 - mean_squared_error: 0.0532 - val_loss: 0.0932 - val_mean_squared_error: 0.0932\n",
            "Epoch 327/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0553 - mean_squared_error: 0.0553 - val_loss: 0.0937 - val_mean_squared_error: 0.0937\n",
            "Epoch 328/800\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0562 - mean_squared_error: 0.0562 - val_loss: 0.0919 - val_mean_squared_error: 0.0919\n",
            "Epoch 329/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0531 - mean_squared_error: 0.0531 - val_loss: 0.0925 - val_mean_squared_error: 0.0925\n",
            "Epoch 330/800\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 0.0534 - mean_squared_error: 0.0534 - val_loss: 0.0910 - val_mean_squared_error: 0.0910\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 331/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0545 - mean_squared_error: 0.0545 - val_loss: 0.0918 - val_mean_squared_error: 0.0918\n",
            "Epoch 332/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0524 - mean_squared_error: 0.0524 - val_loss: 0.0905 - val_mean_squared_error: 0.0905\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 333/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0521 - mean_squared_error: 0.0521 - val_loss: 0.0910 - val_mean_squared_error: 0.0910\n",
            "Epoch 334/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0534 - mean_squared_error: 0.0534 - val_loss: 0.0927 - val_mean_squared_error: 0.0927\n",
            "Epoch 335/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0525 - mean_squared_error: 0.0525 - val_loss: 0.0894 - val_mean_squared_error: 0.0894\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 336/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0517 - mean_squared_error: 0.0517 - val_loss: 0.0911 - val_mean_squared_error: 0.0911\n",
            "Epoch 337/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0523 - mean_squared_error: 0.0523 - val_loss: 0.0896 - val_mean_squared_error: 0.0896\n",
            "Epoch 338/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0521 - mean_squared_error: 0.0521 - val_loss: 0.0897 - val_mean_squared_error: 0.0897\n",
            "Epoch 339/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0514 - mean_squared_error: 0.0514 - val_loss: 0.0892 - val_mean_squared_error: 0.0892\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 340/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0513 - mean_squared_error: 0.0513 - val_loss: 0.0902 - val_mean_squared_error: 0.0902\n",
            "Epoch 341/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0513 - mean_squared_error: 0.0513 - val_loss: 0.0886 - val_mean_squared_error: 0.0886\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 342/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0509 - mean_squared_error: 0.0509 - val_loss: 0.0900 - val_mean_squared_error: 0.0900\n",
            "Epoch 343/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0507 - mean_squared_error: 0.0507 - val_loss: 0.0888 - val_mean_squared_error: 0.0888\n",
            "Epoch 344/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0505 - mean_squared_error: 0.0505 - val_loss: 0.0892 - val_mean_squared_error: 0.0892\n",
            "Epoch 345/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0506 - mean_squared_error: 0.0506 - val_loss: 0.0889 - val_mean_squared_error: 0.0889\n",
            "Epoch 346/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0507 - mean_squared_error: 0.0507 - val_loss: 0.0903 - val_mean_squared_error: 0.0903\n",
            "Epoch 347/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0504 - mean_squared_error: 0.0504 - val_loss: 0.0879 - val_mean_squared_error: 0.0879\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 348/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0505 - mean_squared_error: 0.0505 - val_loss: 0.0924 - val_mean_squared_error: 0.0924\n",
            "Epoch 349/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0519 - mean_squared_error: 0.0519 - val_loss: 0.0898 - val_mean_squared_error: 0.0898\n",
            "Epoch 350/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0547 - mean_squared_error: 0.0547 - val_loss: 0.1013 - val_mean_squared_error: 0.1013\n",
            "Epoch 351/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0597 - mean_squared_error: 0.0597 - val_loss: 0.0997 - val_mean_squared_error: 0.0997\n",
            "Epoch 352/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0704 - mean_squared_error: 0.0704 - val_loss: 0.1249 - val_mean_squared_error: 0.1249\n",
            "Epoch 353/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0821 - mean_squared_error: 0.0821 - val_loss: 0.1069 - val_mean_squared_error: 0.1069\n",
            "Epoch 354/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0786 - mean_squared_error: 0.0786 - val_loss: 0.0966 - val_mean_squared_error: 0.0966\n",
            "Epoch 355/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0566 - mean_squared_error: 0.0566 - val_loss: 0.0951 - val_mean_squared_error: 0.0951\n",
            "Epoch 356/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0543 - mean_squared_error: 0.0543 - val_loss: 0.0966 - val_mean_squared_error: 0.0966\n",
            "Epoch 357/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0659 - mean_squared_error: 0.0659 - val_loss: 0.0967 - val_mean_squared_error: 0.0967\n",
            "Epoch 358/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0565 - mean_squared_error: 0.0565 - val_loss: 0.0944 - val_mean_squared_error: 0.0944\n",
            "Epoch 359/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0543 - mean_squared_error: 0.0543 - val_loss: 0.0922 - val_mean_squared_error: 0.0922\n",
            "Epoch 360/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0594 - mean_squared_error: 0.0594 - val_loss: 0.0907 - val_mean_squared_error: 0.0907\n",
            "Epoch 361/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0523 - mean_squared_error: 0.0523 - val_loss: 0.0959 - val_mean_squared_error: 0.0959\n",
            "Epoch 362/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0560 - mean_squared_error: 0.0560 - val_loss: 0.0901 - val_mean_squared_error: 0.0901\n",
            "Epoch 363/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0546 - mean_squared_error: 0.0546 - val_loss: 0.0879 - val_mean_squared_error: 0.0879\n",
            "Epoch 364/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0508 - mean_squared_error: 0.0508 - val_loss: 0.0953 - val_mean_squared_error: 0.0953\n",
            "Epoch 365/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0557 - mean_squared_error: 0.0557 - val_loss: 0.0884 - val_mean_squared_error: 0.0884\n",
            "Epoch 366/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0507 - mean_squared_error: 0.0507 - val_loss: 0.0874 - val_mean_squared_error: 0.0874\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 367/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0515 - mean_squared_error: 0.0515 - val_loss: 0.0909 - val_mean_squared_error: 0.0909\n",
            "Epoch 368/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0525 - mean_squared_error: 0.0525 - val_loss: 0.0885 - val_mean_squared_error: 0.0885\n",
            "Epoch 369/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0497 - mean_squared_error: 0.0497 - val_loss: 0.0875 - val_mean_squared_error: 0.0875\n",
            "Epoch 370/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0513 - mean_squared_error: 0.0513 - val_loss: 0.0880 - val_mean_squared_error: 0.0880\n",
            "Epoch 371/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0497 - mean_squared_error: 0.0497 - val_loss: 0.0903 - val_mean_squared_error: 0.0903\n",
            "Epoch 372/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0504 - mean_squared_error: 0.0504 - val_loss: 0.0870 - val_mean_squared_error: 0.0870\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 373/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0496 - mean_squared_error: 0.0496 - val_loss: 0.0868 - val_mean_squared_error: 0.0868\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 374/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0489 - mean_squared_error: 0.0489 - val_loss: 0.0899 - val_mean_squared_error: 0.0899\n",
            "Epoch 375/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0501 - mean_squared_error: 0.0501 - val_loss: 0.0862 - val_mean_squared_error: 0.0862\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 376/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0481 - mean_squared_error: 0.0481 - val_loss: 0.0861 - val_mean_squared_error: 0.0861\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 377/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0488 - mean_squared_error: 0.0488 - val_loss: 0.0880 - val_mean_squared_error: 0.0880\n",
            "Epoch 378/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0487 - mean_squared_error: 0.0487 - val_loss: 0.0866 - val_mean_squared_error: 0.0866\n",
            "Epoch 379/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0478 - mean_squared_error: 0.0478 - val_loss: 0.0859 - val_mean_squared_error: 0.0859\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 380/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0485 - mean_squared_error: 0.0485 - val_loss: 0.0866 - val_mean_squared_error: 0.0866\n",
            "Epoch 381/800\n",
            "1/1 [==============================] - 0s 281ms/step - loss: 0.0474 - mean_squared_error: 0.0474 - val_loss: 0.0871 - val_mean_squared_error: 0.0871\n",
            "Epoch 382/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0478 - mean_squared_error: 0.0478 - val_loss: 0.0857 - val_mean_squared_error: 0.0857\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 383/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0479 - mean_squared_error: 0.0479 - val_loss: 0.0861 - val_mean_squared_error: 0.0861\n",
            "Epoch 384/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0468 - mean_squared_error: 0.0468 - val_loss: 0.0874 - val_mean_squared_error: 0.0874\n",
            "Epoch 385/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0474 - mean_squared_error: 0.0474 - val_loss: 0.0852 - val_mean_squared_error: 0.0852\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 386/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0472 - mean_squared_error: 0.0472 - val_loss: 0.0855 - val_mean_squared_error: 0.0855\n",
            "Epoch 387/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0467 - mean_squared_error: 0.0467 - val_loss: 0.0869 - val_mean_squared_error: 0.0869\n",
            "Epoch 388/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0470 - mean_squared_error: 0.0470 - val_loss: 0.0848 - val_mean_squared_error: 0.0848\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 389/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0465 - mean_squared_error: 0.0465 - val_loss: 0.0847 - val_mean_squared_error: 0.0847\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 390/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0464 - mean_squared_error: 0.0464 - val_loss: 0.0863 - val_mean_squared_error: 0.0863\n",
            "Epoch 391/800\n",
            "1/1 [==============================] - 0s 267ms/step - loss: 0.0467 - mean_squared_error: 0.0467 - val_loss: 0.0849 - val_mean_squared_error: 0.0849\n",
            "Epoch 392/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0462 - mean_squared_error: 0.0462 - val_loss: 0.0845 - val_mean_squared_error: 0.0845\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 393/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0461 - mean_squared_error: 0.0461 - val_loss: 0.0859 - val_mean_squared_error: 0.0859\n",
            "Epoch 394/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0462 - mean_squared_error: 0.0462 - val_loss: 0.0847 - val_mean_squared_error: 0.0847\n",
            "Epoch 395/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0459 - mean_squared_error: 0.0459 - val_loss: 0.0844 - val_mean_squared_error: 0.0844\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 396/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0459 - mean_squared_error: 0.0459 - val_loss: 0.0857 - val_mean_squared_error: 0.0857\n",
            "Epoch 397/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0458 - mean_squared_error: 0.0458 - val_loss: 0.0844 - val_mean_squared_error: 0.0844\n",
            "Epoch 398/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0455 - mean_squared_error: 0.0455 - val_loss: 0.0844 - val_mean_squared_error: 0.0844\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 399/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0454 - mean_squared_error: 0.0454 - val_loss: 0.0854 - val_mean_squared_error: 0.0854\n",
            "Epoch 400/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0455 - mean_squared_error: 0.0455 - val_loss: 0.0842 - val_mean_squared_error: 0.0842\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 401/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0453 - mean_squared_error: 0.0453 - val_loss: 0.0843 - val_mean_squared_error: 0.0843\n",
            "Epoch 402/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0452 - mean_squared_error: 0.0452 - val_loss: 0.0849 - val_mean_squared_error: 0.0849\n",
            "Epoch 403/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0453 - mean_squared_error: 0.0453 - val_loss: 0.0840 - val_mean_squared_error: 0.0840\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 404/800\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0451 - mean_squared_error: 0.0451 - val_loss: 0.0837 - val_mean_squared_error: 0.0837\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 405/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0449 - mean_squared_error: 0.0449 - val_loss: 0.0847 - val_mean_squared_error: 0.0847\n",
            "Epoch 406/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0449 - mean_squared_error: 0.0449 - val_loss: 0.0833 - val_mean_squared_error: 0.0833\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 407/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0448 - mean_squared_error: 0.0448 - val_loss: 0.0839 - val_mean_squared_error: 0.0839\n",
            "Epoch 408/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0447 - mean_squared_error: 0.0447 - val_loss: 0.0839 - val_mean_squared_error: 0.0839\n",
            "Epoch 409/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0446 - mean_squared_error: 0.0446 - val_loss: 0.0836 - val_mean_squared_error: 0.0836\n",
            "Epoch 410/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0446 - mean_squared_error: 0.0446 - val_loss: 0.0837 - val_mean_squared_error: 0.0837\n",
            "Epoch 411/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0445 - mean_squared_error: 0.0445 - val_loss: 0.0836 - val_mean_squared_error: 0.0836\n",
            "Epoch 412/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0444 - mean_squared_error: 0.0444 - val_loss: 0.0830 - val_mean_squared_error: 0.0830\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 413/800\n",
            "1/1 [==============================] - 0s 257ms/step - loss: 0.0443 - mean_squared_error: 0.0443 - val_loss: 0.0833 - val_mean_squared_error: 0.0833\n",
            "Epoch 414/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0443 - mean_squared_error: 0.0443 - val_loss: 0.0831 - val_mean_squared_error: 0.0831\n",
            "Epoch 415/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0442 - mean_squared_error: 0.0442 - val_loss: 0.0827 - val_mean_squared_error: 0.0827\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 416/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0441 - mean_squared_error: 0.0441 - val_loss: 0.0834 - val_mean_squared_error: 0.0834\n",
            "Epoch 417/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0441 - mean_squared_error: 0.0441 - val_loss: 0.0823 - val_mean_squared_error: 0.0823\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 418/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0441 - mean_squared_error: 0.0441 - val_loss: 0.0834 - val_mean_squared_error: 0.0834\n",
            "Epoch 419/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0441 - mean_squared_error: 0.0441 - val_loss: 0.0822 - val_mean_squared_error: 0.0822\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 420/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0442 - mean_squared_error: 0.0442 - val_loss: 0.0837 - val_mean_squared_error: 0.0837\n",
            "Epoch 421/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0445 - mean_squared_error: 0.0445 - val_loss: 0.0825 - val_mean_squared_error: 0.0825\n",
            "Epoch 422/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0449 - mean_squared_error: 0.0449 - val_loss: 0.0851 - val_mean_squared_error: 0.0851\n",
            "Epoch 423/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0453 - mean_squared_error: 0.0453 - val_loss: 0.0823 - val_mean_squared_error: 0.0823\n",
            "Epoch 424/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0461 - mean_squared_error: 0.0461 - val_loss: 0.0871 - val_mean_squared_error: 0.0871\n",
            "Epoch 425/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0470 - mean_squared_error: 0.0470 - val_loss: 0.0830 - val_mean_squared_error: 0.0830\n",
            "Epoch 426/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0475 - mean_squared_error: 0.0475 - val_loss: 0.0867 - val_mean_squared_error: 0.0867\n",
            "Epoch 427/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0470 - mean_squared_error: 0.0470 - val_loss: 0.0824 - val_mean_squared_error: 0.0824\n",
            "Epoch 428/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0457 - mean_squared_error: 0.0457 - val_loss: 0.0824 - val_mean_squared_error: 0.0824\n",
            "Epoch 429/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0440 - mean_squared_error: 0.0440 - val_loss: 0.0819 - val_mean_squared_error: 0.0819\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 430/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0432 - mean_squared_error: 0.0432 - val_loss: 0.0808 - val_mean_squared_error: 0.0808\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 431/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0438 - mean_squared_error: 0.0438 - val_loss: 0.0845 - val_mean_squared_error: 0.0845\n",
            "Epoch 432/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0450 - mean_squared_error: 0.0450 - val_loss: 0.0821 - val_mean_squared_error: 0.0821\n",
            "Epoch 433/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0457 - mean_squared_error: 0.0457 - val_loss: 0.0855 - val_mean_squared_error: 0.0855\n",
            "Epoch 434/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0455 - mean_squared_error: 0.0455 - val_loss: 0.0825 - val_mean_squared_error: 0.0825\n",
            "Epoch 435/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0454 - mean_squared_error: 0.0454 - val_loss: 0.0867 - val_mean_squared_error: 0.0867\n",
            "Epoch 436/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0468 - mean_squared_error: 0.0468 - val_loss: 0.0881 - val_mean_squared_error: 0.0881\n",
            "Epoch 437/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0505 - mean_squared_error: 0.0505 - val_loss: 0.0952 - val_mean_squared_error: 0.0952\n",
            "Epoch 438/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0561 - mean_squared_error: 0.0561 - val_loss: 0.0962 - val_mean_squared_error: 0.0962\n",
            "Epoch 439/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0595 - mean_squared_error: 0.0595 - val_loss: 0.0964 - val_mean_squared_error: 0.0964\n",
            "Epoch 440/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0567 - mean_squared_error: 0.0567 - val_loss: 0.0855 - val_mean_squared_error: 0.0855\n",
            "Epoch 441/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0483 - mean_squared_error: 0.0483 - val_loss: 0.0837 - val_mean_squared_error: 0.0837\n",
            "Epoch 442/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0438 - mean_squared_error: 0.0438 - val_loss: 0.0862 - val_mean_squared_error: 0.0862\n",
            "Epoch 443/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0475 - mean_squared_error: 0.0475 - val_loss: 0.0886 - val_mean_squared_error: 0.0886\n",
            "Epoch 444/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0505 - mean_squared_error: 0.0505 - val_loss: 0.0870 - val_mean_squared_error: 0.0870\n",
            "Epoch 445/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0474 - mean_squared_error: 0.0474 - val_loss: 0.0814 - val_mean_squared_error: 0.0814\n",
            "Epoch 446/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0443 - mean_squared_error: 0.0443 - val_loss: 0.0853 - val_mean_squared_error: 0.0853\n",
            "Epoch 447/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0455 - mean_squared_error: 0.0455 - val_loss: 0.0844 - val_mean_squared_error: 0.0844\n",
            "Epoch 448/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0465 - mean_squared_error: 0.0465 - val_loss: 0.0827 - val_mean_squared_error: 0.0827\n",
            "Epoch 449/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0445 - mean_squared_error: 0.0445 - val_loss: 0.0848 - val_mean_squared_error: 0.0848\n",
            "Epoch 450/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0440 - mean_squared_error: 0.0440 - val_loss: 0.0823 - val_mean_squared_error: 0.0823\n",
            "Epoch 451/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0452 - mean_squared_error: 0.0452 - val_loss: 0.0837 - val_mean_squared_error: 0.0837\n",
            "Epoch 452/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0442 - mean_squared_error: 0.0442 - val_loss: 0.0822 - val_mean_squared_error: 0.0822\n",
            "Epoch 453/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0426 - mean_squared_error: 0.0426 - val_loss: 0.0808 - val_mean_squared_error: 0.0808\n",
            "Epoch 454/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0431 - mean_squared_error: 0.0431 - val_loss: 0.0842 - val_mean_squared_error: 0.0842\n",
            "Epoch 455/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0442 - mean_squared_error: 0.0442 - val_loss: 0.0822 - val_mean_squared_error: 0.0822\n",
            "Epoch 456/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0431 - mean_squared_error: 0.0431 - val_loss: 0.0808 - val_mean_squared_error: 0.0808\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 457/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0416 - mean_squared_error: 0.0416 - val_loss: 0.0822 - val_mean_squared_error: 0.0822\n",
            "Epoch 458/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0423 - mean_squared_error: 0.0423 - val_loss: 0.0816 - val_mean_squared_error: 0.0816\n",
            "Epoch 459/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0432 - mean_squared_error: 0.0432 - val_loss: 0.0817 - val_mean_squared_error: 0.0817\n",
            "Epoch 460/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0423 - mean_squared_error: 0.0423 - val_loss: 0.0805 - val_mean_squared_error: 0.0805\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 461/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0413 - mean_squared_error: 0.0413 - val_loss: 0.0805 - val_mean_squared_error: 0.0805\n",
            "Epoch 462/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0417 - mean_squared_error: 0.0417 - val_loss: 0.0818 - val_mean_squared_error: 0.0818\n",
            "Epoch 463/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0422 - mean_squared_error: 0.0422 - val_loss: 0.0801 - val_mean_squared_error: 0.0801\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 464/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0418 - mean_squared_error: 0.0418 - val_loss: 0.0802 - val_mean_squared_error: 0.0802\n",
            "Epoch 465/800\n",
            "1/1 [==============================] - 0s 69ms/step - loss: 0.0412 - mean_squared_error: 0.0412 - val_loss: 0.0802 - val_mean_squared_error: 0.0802\n",
            "Epoch 466/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0411 - mean_squared_error: 0.0411 - val_loss: 0.0799 - val_mean_squared_error: 0.0799\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 467/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0414 - mean_squared_error: 0.0414 - val_loss: 0.0803 - val_mean_squared_error: 0.0803\n",
            "Epoch 468/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0413 - mean_squared_error: 0.0413 - val_loss: 0.0796 - val_mean_squared_error: 0.0796\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 469/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0409 - mean_squared_error: 0.0409 - val_loss: 0.0793 - val_mean_squared_error: 0.0793\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 470/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0408 - mean_squared_error: 0.0408 - val_loss: 0.0796 - val_mean_squared_error: 0.0796\n",
            "Epoch 471/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0409 - mean_squared_error: 0.0409 - val_loss: 0.0795 - val_mean_squared_error: 0.0795\n",
            "Epoch 472/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0409 - mean_squared_error: 0.0409 - val_loss: 0.0792 - val_mean_squared_error: 0.0792\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 473/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0407 - mean_squared_error: 0.0407 - val_loss: 0.0793 - val_mean_squared_error: 0.0793\n",
            "Epoch 474/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0405 - mean_squared_error: 0.0405 - val_loss: 0.0791 - val_mean_squared_error: 0.0791\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 475/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0404 - mean_squared_error: 0.0404 - val_loss: 0.0790 - val_mean_squared_error: 0.0790\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 476/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0404 - mean_squared_error: 0.0404 - val_loss: 0.0796 - val_mean_squared_error: 0.0796\n",
            "Epoch 477/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0405 - mean_squared_error: 0.0405 - val_loss: 0.0788 - val_mean_squared_error: 0.0788\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 478/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0405 - mean_squared_error: 0.0405 - val_loss: 0.0797 - val_mean_squared_error: 0.0797\n",
            "Epoch 479/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0405 - mean_squared_error: 0.0405 - val_loss: 0.0782 - val_mean_squared_error: 0.0782\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 480/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0404 - mean_squared_error: 0.0404 - val_loss: 0.0803 - val_mean_squared_error: 0.0803\n",
            "Epoch 481/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0406 - mean_squared_error: 0.0406 - val_loss: 0.0778 - val_mean_squared_error: 0.0778\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 482/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0410 - mean_squared_error: 0.0410 - val_loss: 0.0821 - val_mean_squared_error: 0.0821\n",
            "Epoch 483/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0418 - mean_squared_error: 0.0418 - val_loss: 0.0789 - val_mean_squared_error: 0.0789\n",
            "Epoch 484/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0436 - mean_squared_error: 0.0436 - val_loss: 0.0864 - val_mean_squared_error: 0.0864\n",
            "Epoch 485/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0455 - mean_squared_error: 0.0455 - val_loss: 0.0816 - val_mean_squared_error: 0.0816\n",
            "Epoch 486/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0483 - mean_squared_error: 0.0483 - val_loss: 0.0897 - val_mean_squared_error: 0.0897\n",
            "Epoch 487/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0489 - mean_squared_error: 0.0489 - val_loss: 0.0819 - val_mean_squared_error: 0.0819\n",
            "Epoch 488/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0479 - mean_squared_error: 0.0479 - val_loss: 0.0829 - val_mean_squared_error: 0.0829\n",
            "Epoch 489/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0446 - mean_squared_error: 0.0446 - val_loss: 0.0808 - val_mean_squared_error: 0.0808\n",
            "Epoch 490/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0427 - mean_squared_error: 0.0427 - val_loss: 0.0775 - val_mean_squared_error: 0.0775\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 491/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0426 - mean_squared_error: 0.0426 - val_loss: 0.0826 - val_mean_squared_error: 0.0826\n",
            "Epoch 492/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0425 - mean_squared_error: 0.0425 - val_loss: 0.0782 - val_mean_squared_error: 0.0782\n",
            "Epoch 493/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0421 - mean_squared_error: 0.0421 - val_loss: 0.0800 - val_mean_squared_error: 0.0800\n",
            "Epoch 494/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0423 - mean_squared_error: 0.0423 - val_loss: 0.0809 - val_mean_squared_error: 0.0809\n",
            "Epoch 495/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0424 - mean_squared_error: 0.0424 - val_loss: 0.0778 - val_mean_squared_error: 0.0778\n",
            "Epoch 496/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0413 - mean_squared_error: 0.0413 - val_loss: 0.0789 - val_mean_squared_error: 0.0789\n",
            "Epoch 497/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0400 - mean_squared_error: 0.0400 - val_loss: 0.0785 - val_mean_squared_error: 0.0785\n",
            "Epoch 498/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0406 - mean_squared_error: 0.0406 - val_loss: 0.0794 - val_mean_squared_error: 0.0794\n",
            "Epoch 499/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0419 - mean_squared_error: 0.0419 - val_loss: 0.0797 - val_mean_squared_error: 0.0797\n",
            "Epoch 500/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0412 - mean_squared_error: 0.0412 - val_loss: 0.0777 - val_mean_squared_error: 0.0777\n",
            "Epoch 501/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0398 - mean_squared_error: 0.0398 - val_loss: 0.0771 - val_mean_squared_error: 0.0771\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 502/800\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0395 - mean_squared_error: 0.0395 - val_loss: 0.0794 - val_mean_squared_error: 0.0794\n",
            "Epoch 503/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0405 - mean_squared_error: 0.0405 - val_loss: 0.0781 - val_mean_squared_error: 0.0781\n",
            "Epoch 504/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0408 - mean_squared_error: 0.0408 - val_loss: 0.0788 - val_mean_squared_error: 0.0788\n",
            "Epoch 505/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0399 - mean_squared_error: 0.0399 - val_loss: 0.0773 - val_mean_squared_error: 0.0773\n",
            "Epoch 506/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0390 - mean_squared_error: 0.0390 - val_loss: 0.0770 - val_mean_squared_error: 0.0770\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 507/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0392 - mean_squared_error: 0.0392 - val_loss: 0.0783 - val_mean_squared_error: 0.0783\n",
            "Epoch 508/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0397 - mean_squared_error: 0.0397 - val_loss: 0.0773 - val_mean_squared_error: 0.0773\n",
            "Epoch 509/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0396 - mean_squared_error: 0.0396 - val_loss: 0.0773 - val_mean_squared_error: 0.0773\n",
            "Epoch 510/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0391 - mean_squared_error: 0.0391 - val_loss: 0.0774 - val_mean_squared_error: 0.0774\n",
            "Epoch 511/800\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0388 - mean_squared_error: 0.0388 - val_loss: 0.0765 - val_mean_squared_error: 0.0765\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 512/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0389 - mean_squared_error: 0.0389 - val_loss: 0.0781 - val_mean_squared_error: 0.0781\n",
            "Epoch 513/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0390 - mean_squared_error: 0.0390 - val_loss: 0.0769 - val_mean_squared_error: 0.0769\n",
            "Epoch 514/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0390 - mean_squared_error: 0.0390 - val_loss: 0.0779 - val_mean_squared_error: 0.0779\n",
            "Epoch 515/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0389 - mean_squared_error: 0.0389 - val_loss: 0.0775 - val_mean_squared_error: 0.0775\n",
            "Epoch 516/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0387 - mean_squared_error: 0.0387 - val_loss: 0.0770 - val_mean_squared_error: 0.0770\n",
            "Epoch 517/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0385 - mean_squared_error: 0.0385 - val_loss: 0.0781 - val_mean_squared_error: 0.0781\n",
            "Epoch 518/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0385 - mean_squared_error: 0.0385 - val_loss: 0.0763 - val_mean_squared_error: 0.0763\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 519/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0385 - mean_squared_error: 0.0385 - val_loss: 0.0785 - val_mean_squared_error: 0.0785\n",
            "Epoch 520/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0387 - mean_squared_error: 0.0387 - val_loss: 0.0764 - val_mean_squared_error: 0.0764\n",
            "Epoch 521/800\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0391 - mean_squared_error: 0.0391 - val_loss: 0.0795 - val_mean_squared_error: 0.0795\n",
            "Epoch 522/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0395 - mean_squared_error: 0.0395 - val_loss: 0.0770 - val_mean_squared_error: 0.0770\n",
            "Epoch 523/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0404 - mean_squared_error: 0.0404 - val_loss: 0.0821 - val_mean_squared_error: 0.0821\n",
            "Epoch 524/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0419 - mean_squared_error: 0.0419 - val_loss: 0.0802 - val_mean_squared_error: 0.0802\n",
            "Epoch 525/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0446 - mean_squared_error: 0.0446 - val_loss: 0.0886 - val_mean_squared_error: 0.0886\n",
            "Epoch 526/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0479 - mean_squared_error: 0.0479 - val_loss: 0.0858 - val_mean_squared_error: 0.0858\n",
            "Epoch 527/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0503 - mean_squared_error: 0.0503 - val_loss: 0.0888 - val_mean_squared_error: 0.0888\n",
            "Epoch 528/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0487 - mean_squared_error: 0.0487 - val_loss: 0.0802 - val_mean_squared_error: 0.0802\n",
            "Epoch 529/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0431 - mean_squared_error: 0.0431 - val_loss: 0.0769 - val_mean_squared_error: 0.0769\n",
            "Epoch 530/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0388 - mean_squared_error: 0.0388 - val_loss: 0.0789 - val_mean_squared_error: 0.0789\n",
            "Epoch 531/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0398 - mean_squared_error: 0.0398 - val_loss: 0.0793 - val_mean_squared_error: 0.0793\n",
            "Epoch 532/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0429 - mean_squared_error: 0.0429 - val_loss: 0.0827 - val_mean_squared_error: 0.0827\n",
            "Epoch 533/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0432 - mean_squared_error: 0.0432 - val_loss: 0.0782 - val_mean_squared_error: 0.0782\n",
            "Epoch 534/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0407 - mean_squared_error: 0.0407 - val_loss: 0.0773 - val_mean_squared_error: 0.0773\n",
            "Epoch 535/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0386 - mean_squared_error: 0.0386 - val_loss: 0.0793 - val_mean_squared_error: 0.0793\n",
            "Epoch 536/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0395 - mean_squared_error: 0.0395 - val_loss: 0.0788 - val_mean_squared_error: 0.0788\n",
            "Epoch 537/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0411 - mean_squared_error: 0.0411 - val_loss: 0.0800 - val_mean_squared_error: 0.0800\n",
            "Epoch 538/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0408 - mean_squared_error: 0.0408 - val_loss: 0.0776 - val_mean_squared_error: 0.0776\n",
            "Epoch 539/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0388 - mean_squared_error: 0.0388 - val_loss: 0.0759 - val_mean_squared_error: 0.0759\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 540/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0380 - mean_squared_error: 0.0380 - val_loss: 0.0783 - val_mean_squared_error: 0.0783\n",
            "Epoch 541/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0389 - mean_squared_error: 0.0389 - val_loss: 0.0778 - val_mean_squared_error: 0.0778\n",
            "Epoch 542/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0398 - mean_squared_error: 0.0398 - val_loss: 0.0780 - val_mean_squared_error: 0.0780\n",
            "Epoch 543/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0392 - mean_squared_error: 0.0392 - val_loss: 0.0768 - val_mean_squared_error: 0.0768\n",
            "Epoch 544/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0378 - mean_squared_error: 0.0378 - val_loss: 0.0762 - val_mean_squared_error: 0.0762\n",
            "Epoch 545/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0376 - mean_squared_error: 0.0376 - val_loss: 0.0777 - val_mean_squared_error: 0.0777\n",
            "Epoch 546/800\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0384 - mean_squared_error: 0.0384 - val_loss: 0.0766 - val_mean_squared_error: 0.0766\n",
            "Epoch 547/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0388 - mean_squared_error: 0.0388 - val_loss: 0.0779 - val_mean_squared_error: 0.0779\n",
            "Epoch 548/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0382 - mean_squared_error: 0.0382 - val_loss: 0.0757 - val_mean_squared_error: 0.0757\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 549/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0374 - mean_squared_error: 0.0374 - val_loss: 0.0766 - val_mean_squared_error: 0.0766\n",
            "Epoch 550/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0373 - mean_squared_error: 0.0373 - val_loss: 0.0768 - val_mean_squared_error: 0.0768\n",
            "Epoch 551/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0378 - mean_squared_error: 0.0378 - val_loss: 0.0767 - val_mean_squared_error: 0.0767\n",
            "Epoch 552/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0381 - mean_squared_error: 0.0381 - val_loss: 0.0768 - val_mean_squared_error: 0.0768\n",
            "Epoch 553/800\n",
            "1/1 [==============================] - 0s 254ms/step - loss: 0.0378 - mean_squared_error: 0.0378 - val_loss: 0.0767 - val_mean_squared_error: 0.0767\n",
            "Epoch 554/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0374 - mean_squared_error: 0.0374 - val_loss: 0.0758 - val_mean_squared_error: 0.0758\n",
            "Epoch 555/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0374 - mean_squared_error: 0.0374 - val_loss: 0.0773 - val_mean_squared_error: 0.0773\n",
            "Epoch 556/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0377 - mean_squared_error: 0.0377 - val_loss: 0.0757 - val_mean_squared_error: 0.0757\n",
            "Epoch 557/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0382 - mean_squared_error: 0.0382 - val_loss: 0.0791 - val_mean_squared_error: 0.0791\n",
            "Epoch 558/800\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0387 - mean_squared_error: 0.0387 - val_loss: 0.0751 - val_mean_squared_error: 0.0751\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 559/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0393 - mean_squared_error: 0.0393 - val_loss: 0.0811 - val_mean_squared_error: 0.0811\n",
            "Epoch 560/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0399 - mean_squared_error: 0.0399 - val_loss: 0.0762 - val_mean_squared_error: 0.0762\n",
            "Epoch 561/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0408 - mean_squared_error: 0.0408 - val_loss: 0.0822 - val_mean_squared_error: 0.0822\n",
            "Epoch 562/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0414 - mean_squared_error: 0.0414 - val_loss: 0.0772 - val_mean_squared_error: 0.0772\n",
            "Epoch 563/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0418 - mean_squared_error: 0.0418 - val_loss: 0.0804 - val_mean_squared_error: 0.0804\n",
            "Epoch 564/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0405 - mean_squared_error: 0.0405 - val_loss: 0.0761 - val_mean_squared_error: 0.0761\n",
            "Epoch 565/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0392 - mean_squared_error: 0.0392 - val_loss: 0.0765 - val_mean_squared_error: 0.0765\n",
            "Epoch 566/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0385 - mean_squared_error: 0.0385 - val_loss: 0.0780 - val_mean_squared_error: 0.0780\n",
            "Epoch 567/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0386 - mean_squared_error: 0.0386 - val_loss: 0.0756 - val_mean_squared_error: 0.0756\n",
            "Epoch 568/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0388 - mean_squared_error: 0.0388 - val_loss: 0.0782 - val_mean_squared_error: 0.0782\n",
            "Epoch 569/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0384 - mean_squared_error: 0.0384 - val_loss: 0.0752 - val_mean_squared_error: 0.0752\n",
            "Epoch 570/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0376 - mean_squared_error: 0.0376 - val_loss: 0.0760 - val_mean_squared_error: 0.0760\n",
            "Epoch 571/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0368 - mean_squared_error: 0.0368 - val_loss: 0.0755 - val_mean_squared_error: 0.0755\n",
            "Epoch 572/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0368 - mean_squared_error: 0.0368 - val_loss: 0.0758 - val_mean_squared_error: 0.0758\n",
            "Epoch 573/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0375 - mean_squared_error: 0.0375 - val_loss: 0.0776 - val_mean_squared_error: 0.0776\n",
            "Epoch 574/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0380 - mean_squared_error: 0.0380 - val_loss: 0.0759 - val_mean_squared_error: 0.0759\n",
            "Epoch 575/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0381 - mean_squared_error: 0.0381 - val_loss: 0.0772 - val_mean_squared_error: 0.0772\n",
            "Epoch 576/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0375 - mean_squared_error: 0.0375 - val_loss: 0.0753 - val_mean_squared_error: 0.0753\n",
            "Epoch 577/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0370 - mean_squared_error: 0.0370 - val_loss: 0.0757 - val_mean_squared_error: 0.0757\n",
            "Epoch 578/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0369 - mean_squared_error: 0.0369 - val_loss: 0.0762 - val_mean_squared_error: 0.0762\n",
            "Epoch 579/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0371 - mean_squared_error: 0.0371 - val_loss: 0.0754 - val_mean_squared_error: 0.0754\n",
            "Epoch 580/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0371 - mean_squared_error: 0.0371 - val_loss: 0.0762 - val_mean_squared_error: 0.0762\n",
            "Epoch 581/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0367 - mean_squared_error: 0.0367 - val_loss: 0.0742 - val_mean_squared_error: 0.0742\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 582/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0364 - mean_squared_error: 0.0364 - val_loss: 0.0758 - val_mean_squared_error: 0.0758\n",
            "Epoch 583/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0364 - mean_squared_error: 0.0364 - val_loss: 0.0747 - val_mean_squared_error: 0.0747\n",
            "Epoch 584/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0367 - mean_squared_error: 0.0367 - val_loss: 0.0759 - val_mean_squared_error: 0.0759\n",
            "Epoch 585/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0369 - mean_squared_error: 0.0369 - val_loss: 0.0755 - val_mean_squared_error: 0.0755\n",
            "Epoch 586/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0367 - mean_squared_error: 0.0367 - val_loss: 0.0750 - val_mean_squared_error: 0.0750\n",
            "Epoch 587/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0364 - mean_squared_error: 0.0364 - val_loss: 0.0755 - val_mean_squared_error: 0.0755\n",
            "Epoch 588/800\n",
            "1/1 [==============================] - 0s 226ms/step - loss: 0.0362 - mean_squared_error: 0.0362 - val_loss: 0.0742 - val_mean_squared_error: 0.0742\n",
            "Epoch 589/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0362 - mean_squared_error: 0.0362 - val_loss: 0.0760 - val_mean_squared_error: 0.0760\n",
            "Epoch 590/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0365 - mean_squared_error: 0.0365 - val_loss: 0.0748 - val_mean_squared_error: 0.0748\n",
            "Epoch 591/800\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0368 - mean_squared_error: 0.0368 - val_loss: 0.0768 - val_mean_squared_error: 0.0768\n",
            "Epoch 592/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0371 - mean_squared_error: 0.0371 - val_loss: 0.0751 - val_mean_squared_error: 0.0751\n",
            "Epoch 593/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0376 - mean_squared_error: 0.0376 - val_loss: 0.0779 - val_mean_squared_error: 0.0779\n",
            "Epoch 594/800\n",
            "1/1 [==============================] - 0s 74ms/step - loss: 0.0381 - mean_squared_error: 0.0381 - val_loss: 0.0759 - val_mean_squared_error: 0.0759\n",
            "Epoch 595/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0386 - mean_squared_error: 0.0386 - val_loss: 0.0791 - val_mean_squared_error: 0.0791\n",
            "Epoch 596/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0394 - mean_squared_error: 0.0394 - val_loss: 0.0769 - val_mean_squared_error: 0.0769\n",
            "Epoch 597/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0404 - mean_squared_error: 0.0404 - val_loss: 0.0815 - val_mean_squared_error: 0.0815\n",
            "Epoch 598/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0419 - mean_squared_error: 0.0419 - val_loss: 0.0787 - val_mean_squared_error: 0.0787\n",
            "Epoch 599/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0432 - mean_squared_error: 0.0432 - val_loss: 0.0829 - val_mean_squared_error: 0.0829\n",
            "Epoch 600/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0436 - mean_squared_error: 0.0436 - val_loss: 0.0790 - val_mean_squared_error: 0.0790\n",
            "Epoch 601/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0422 - mean_squared_error: 0.0422 - val_loss: 0.0777 - val_mean_squared_error: 0.0777\n",
            "Epoch 602/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0395 - mean_squared_error: 0.0395 - val_loss: 0.0765 - val_mean_squared_error: 0.0765\n",
            "Epoch 603/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0371 - mean_squared_error: 0.0371 - val_loss: 0.0734 - val_mean_squared_error: 0.0734\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 604/800\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0368 - mean_squared_error: 0.0368 - val_loss: 0.0785 - val_mean_squared_error: 0.0785\n",
            "Epoch 605/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0381 - mean_squared_error: 0.0381 - val_loss: 0.0750 - val_mean_squared_error: 0.0750\n",
            "Epoch 606/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0389 - mean_squared_error: 0.0389 - val_loss: 0.0775 - val_mean_squared_error: 0.0775\n",
            "Epoch 607/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0379 - mean_squared_error: 0.0379 - val_loss: 0.0747 - val_mean_squared_error: 0.0747\n",
            "Epoch 608/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0364 - mean_squared_error: 0.0364 - val_loss: 0.0739 - val_mean_squared_error: 0.0739\n",
            "Epoch 609/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0359 - mean_squared_error: 0.0359 - val_loss: 0.0763 - val_mean_squared_error: 0.0763\n",
            "Epoch 610/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0363 - mean_squared_error: 0.0363 - val_loss: 0.0743 - val_mean_squared_error: 0.0743\n",
            "Epoch 611/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0368 - mean_squared_error: 0.0368 - val_loss: 0.0762 - val_mean_squared_error: 0.0762\n",
            "Epoch 612/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0367 - mean_squared_error: 0.0367 - val_loss: 0.0746 - val_mean_squared_error: 0.0746\n",
            "Epoch 613/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0362 - mean_squared_error: 0.0362 - val_loss: 0.0746 - val_mean_squared_error: 0.0746\n",
            "Epoch 614/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0358 - mean_squared_error: 0.0358 - val_loss: 0.0746 - val_mean_squared_error: 0.0746\n",
            "Epoch 615/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0356 - mean_squared_error: 0.0356 - val_loss: 0.0739 - val_mean_squared_error: 0.0739\n",
            "Epoch 616/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0358 - mean_squared_error: 0.0358 - val_loss: 0.0747 - val_mean_squared_error: 0.0747\n",
            "Epoch 617/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0359 - mean_squared_error: 0.0359 - val_loss: 0.0736 - val_mean_squared_error: 0.0736\n",
            "Epoch 618/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0356 - mean_squared_error: 0.0356 - val_loss: 0.0738 - val_mean_squared_error: 0.0738\n",
            "Epoch 619/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0352 - mean_squared_error: 0.0352 - val_loss: 0.0735 - val_mean_squared_error: 0.0735\n",
            "Epoch 620/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0353 - mean_squared_error: 0.0353 - val_loss: 0.0740 - val_mean_squared_error: 0.0740\n",
            "Epoch 621/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0356 - mean_squared_error: 0.0356 - val_loss: 0.0739 - val_mean_squared_error: 0.0739\n",
            "Epoch 622/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0357 - mean_squared_error: 0.0357 - val_loss: 0.0744 - val_mean_squared_error: 0.0744\n",
            "Epoch 623/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0355 - mean_squared_error: 0.0355 - val_loss: 0.0730 - val_mean_squared_error: 0.0730\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 624/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0350 - mean_squared_error: 0.0350 - val_loss: 0.0739 - val_mean_squared_error: 0.0739\n",
            "Epoch 625/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0350 - mean_squared_error: 0.0350 - val_loss: 0.0730 - val_mean_squared_error: 0.0730\n",
            "Epoch 626/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0354 - mean_squared_error: 0.0354 - val_loss: 0.0756 - val_mean_squared_error: 0.0756\n",
            "Epoch 627/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0359 - mean_squared_error: 0.0359 - val_loss: 0.0735 - val_mean_squared_error: 0.0735\n",
            "Epoch 628/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0365 - mean_squared_error: 0.0365 - val_loss: 0.0775 - val_mean_squared_error: 0.0775\n",
            "Epoch 629/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0371 - mean_squared_error: 0.0371 - val_loss: 0.0738 - val_mean_squared_error: 0.0738\n",
            "Epoch 630/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0378 - mean_squared_error: 0.0378 - val_loss: 0.0796 - val_mean_squared_error: 0.0796\n",
            "Epoch 631/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0388 - mean_squared_error: 0.0388 - val_loss: 0.0750 - val_mean_squared_error: 0.0750\n",
            "Epoch 632/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0394 - mean_squared_error: 0.0394 - val_loss: 0.0802 - val_mean_squared_error: 0.0802\n",
            "Epoch 633/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0392 - mean_squared_error: 0.0392 - val_loss: 0.0745 - val_mean_squared_error: 0.0745\n",
            "Epoch 634/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0382 - mean_squared_error: 0.0382 - val_loss: 0.0760 - val_mean_squared_error: 0.0760\n",
            "Epoch 635/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0365 - mean_squared_error: 0.0365 - val_loss: 0.0737 - val_mean_squared_error: 0.0737\n",
            "Epoch 636/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0355 - mean_squared_error: 0.0355 - val_loss: 0.0732 - val_mean_squared_error: 0.0732\n",
            "Epoch 637/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0356 - mean_squared_error: 0.0356 - val_loss: 0.0760 - val_mean_squared_error: 0.0760\n",
            "Epoch 638/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0362 - mean_squared_error: 0.0362 - val_loss: 0.0743 - val_mean_squared_error: 0.0743\n",
            "Epoch 639/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0364 - mean_squared_error: 0.0364 - val_loss: 0.0750 - val_mean_squared_error: 0.0750\n",
            "Epoch 640/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0358 - mean_squared_error: 0.0358 - val_loss: 0.0740 - val_mean_squared_error: 0.0740\n",
            "Epoch 641/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0350 - mean_squared_error: 0.0350 - val_loss: 0.0727 - val_mean_squared_error: 0.0727\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 642/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0345 - mean_squared_error: 0.0345 - val_loss: 0.0740 - val_mean_squared_error: 0.0740\n",
            "Epoch 643/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0347 - mean_squared_error: 0.0347 - val_loss: 0.0733 - val_mean_squared_error: 0.0733\n",
            "Epoch 644/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0352 - mean_squared_error: 0.0352 - val_loss: 0.0744 - val_mean_squared_error: 0.0744\n",
            "Epoch 645/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0354 - mean_squared_error: 0.0354 - val_loss: 0.0735 - val_mean_squared_error: 0.0735\n",
            "Epoch 646/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0351 - mean_squared_error: 0.0351 - val_loss: 0.0731 - val_mean_squared_error: 0.0731\n",
            "Epoch 647/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0346 - mean_squared_error: 0.0346 - val_loss: 0.0732 - val_mean_squared_error: 0.0732\n",
            "Epoch 648/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0342 - mean_squared_error: 0.0342 - val_loss: 0.0721 - val_mean_squared_error: 0.0721\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 649/800\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0343 - mean_squared_error: 0.0343 - val_loss: 0.0738 - val_mean_squared_error: 0.0738\n",
            "Epoch 650/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0347 - mean_squared_error: 0.0347 - val_loss: 0.0726 - val_mean_squared_error: 0.0726\n",
            "Epoch 651/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0349 - mean_squared_error: 0.0349 - val_loss: 0.0742 - val_mean_squared_error: 0.0742\n",
            "Epoch 652/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0349 - mean_squared_error: 0.0349 - val_loss: 0.0724 - val_mean_squared_error: 0.0724\n",
            "Epoch 653/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0348 - mean_squared_error: 0.0348 - val_loss: 0.0744 - val_mean_squared_error: 0.0744\n",
            "Epoch 654/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0347 - mean_squared_error: 0.0347 - val_loss: 0.0725 - val_mean_squared_error: 0.0725\n",
            "Epoch 655/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0349 - mean_squared_error: 0.0349 - val_loss: 0.0753 - val_mean_squared_error: 0.0753\n",
            "Epoch 656/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0355 - mean_squared_error: 0.0355 - val_loss: 0.0743 - val_mean_squared_error: 0.0743\n",
            "Epoch 657/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0366 - mean_squared_error: 0.0366 - val_loss: 0.0779 - val_mean_squared_error: 0.0779\n",
            "Epoch 658/800\n",
            "1/1 [==============================] - 0s 246ms/step - loss: 0.0382 - mean_squared_error: 0.0382 - val_loss: 0.0775 - val_mean_squared_error: 0.0775\n",
            "Epoch 659/800\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0402 - mean_squared_error: 0.0402 - val_loss: 0.0824 - val_mean_squared_error: 0.0824\n",
            "Epoch 660/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0427 - mean_squared_error: 0.0427 - val_loss: 0.0803 - val_mean_squared_error: 0.0803\n",
            "Epoch 661/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0438 - mean_squared_error: 0.0438 - val_loss: 0.0833 - val_mean_squared_error: 0.0833\n",
            "Epoch 662/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0432 - mean_squared_error: 0.0432 - val_loss: 0.0765 - val_mean_squared_error: 0.0765\n",
            "Epoch 663/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0397 - mean_squared_error: 0.0397 - val_loss: 0.0765 - val_mean_squared_error: 0.0765\n",
            "Epoch 664/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0361 - mean_squared_error: 0.0361 - val_loss: 0.0734 - val_mean_squared_error: 0.0734\n",
            "Epoch 665/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0354 - mean_squared_error: 0.0354 - val_loss: 0.0765 - val_mean_squared_error: 0.0765\n",
            "Epoch 666/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0371 - mean_squared_error: 0.0371 - val_loss: 0.0774 - val_mean_squared_error: 0.0774\n",
            "Epoch 667/800\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0381 - mean_squared_error: 0.0381 - val_loss: 0.0752 - val_mean_squared_error: 0.0752\n",
            "Epoch 668/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0363 - mean_squared_error: 0.0363 - val_loss: 0.0743 - val_mean_squared_error: 0.0743\n",
            "Epoch 669/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0344 - mean_squared_error: 0.0344 - val_loss: 0.0730 - val_mean_squared_error: 0.0730\n",
            "Epoch 670/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0348 - mean_squared_error: 0.0348 - val_loss: 0.0763 - val_mean_squared_error: 0.0763\n",
            "Epoch 671/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0364 - mean_squared_error: 0.0364 - val_loss: 0.0752 - val_mean_squared_error: 0.0752\n",
            "Epoch 672/800\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0364 - mean_squared_error: 0.0364 - val_loss: 0.0741 - val_mean_squared_error: 0.0741\n",
            "Epoch 673/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0345 - mean_squared_error: 0.0345 - val_loss: 0.0730 - val_mean_squared_error: 0.0730\n",
            "Epoch 674/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0336 - mean_squared_error: 0.0336 - val_loss: 0.0733 - val_mean_squared_error: 0.0733\n",
            "Epoch 675/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0347 - mean_squared_error: 0.0347 - val_loss: 0.0749 - val_mean_squared_error: 0.0749\n",
            "Epoch 676/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0355 - mean_squared_error: 0.0355 - val_loss: 0.0736 - val_mean_squared_error: 0.0736\n",
            "Epoch 677/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0348 - mean_squared_error: 0.0348 - val_loss: 0.0737 - val_mean_squared_error: 0.0737\n",
            "Epoch 678/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0337 - mean_squared_error: 0.0337 - val_loss: 0.0723 - val_mean_squared_error: 0.0723\n",
            "Epoch 679/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0337 - mean_squared_error: 0.0337 - val_loss: 0.0736 - val_mean_squared_error: 0.0736\n",
            "Epoch 680/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0345 - mean_squared_error: 0.0345 - val_loss: 0.0733 - val_mean_squared_error: 0.0733\n",
            "Epoch 681/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0345 - mean_squared_error: 0.0345 - val_loss: 0.0728 - val_mean_squared_error: 0.0728\n",
            "Epoch 682/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0337 - mean_squared_error: 0.0337 - val_loss: 0.0729 - val_mean_squared_error: 0.0729\n",
            "Epoch 683/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0332 - mean_squared_error: 0.0332 - val_loss: 0.0723 - val_mean_squared_error: 0.0723\n",
            "Epoch 684/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0334 - mean_squared_error: 0.0334 - val_loss: 0.0735 - val_mean_squared_error: 0.0735\n",
            "Epoch 685/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0339 - mean_squared_error: 0.0339 - val_loss: 0.0727 - val_mean_squared_error: 0.0727\n",
            "Epoch 686/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0339 - mean_squared_error: 0.0339 - val_loss: 0.0733 - val_mean_squared_error: 0.0733\n",
            "Epoch 687/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0335 - mean_squared_error: 0.0335 - val_loss: 0.0719 - val_mean_squared_error: 0.0719\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 688/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0728 - val_mean_squared_error: 0.0728\n",
            "Epoch 689/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0720 - val_mean_squared_error: 0.0720\n",
            "Epoch 690/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0334 - mean_squared_error: 0.0334 - val_loss: 0.0731 - val_mean_squared_error: 0.0731\n",
            "Epoch 691/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0335 - mean_squared_error: 0.0335 - val_loss: 0.0724 - val_mean_squared_error: 0.0724\n",
            "Epoch 692/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0333 - mean_squared_error: 0.0333 - val_loss: 0.0725 - val_mean_squared_error: 0.0725\n",
            "Epoch 693/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0330 - mean_squared_error: 0.0330 - val_loss: 0.0717 - val_mean_squared_error: 0.0717\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 694/800\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0329 - mean_squared_error: 0.0329 - val_loss: 0.0724 - val_mean_squared_error: 0.0724\n",
            "Epoch 695/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0723 - val_mean_squared_error: 0.0723\n",
            "Epoch 696/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0335 - mean_squared_error: 0.0335 - val_loss: 0.0732 - val_mean_squared_error: 0.0732\n",
            "Epoch 697/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0339 - mean_squared_error: 0.0339 - val_loss: 0.0726 - val_mean_squared_error: 0.0726\n",
            "Epoch 698/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0342 - mean_squared_error: 0.0342 - val_loss: 0.0735 - val_mean_squared_error: 0.0735\n",
            "Epoch 699/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0344 - mean_squared_error: 0.0344 - val_loss: 0.0724 - val_mean_squared_error: 0.0724\n",
            "Epoch 700/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0348 - mean_squared_error: 0.0348 - val_loss: 0.0755 - val_mean_squared_error: 0.0755\n",
            "Epoch 701/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0355 - mean_squared_error: 0.0355 - val_loss: 0.0736 - val_mean_squared_error: 0.0736\n",
            "Epoch 702/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0369 - mean_squared_error: 0.0369 - val_loss: 0.0794 - val_mean_squared_error: 0.0794\n",
            "Epoch 703/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0384 - mean_squared_error: 0.0384 - val_loss: 0.0747 - val_mean_squared_error: 0.0747\n",
            "Epoch 704/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0396 - mean_squared_error: 0.0396 - val_loss: 0.0811 - val_mean_squared_error: 0.0811\n",
            "Epoch 705/800\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0398 - mean_squared_error: 0.0398 - val_loss: 0.0737 - val_mean_squared_error: 0.0737\n",
            "Epoch 706/800\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0386 - mean_squared_error: 0.0386 - val_loss: 0.0778 - val_mean_squared_error: 0.0778\n",
            "Epoch 707/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0372 - mean_squared_error: 0.0372 - val_loss: 0.0737 - val_mean_squared_error: 0.0737\n",
            "Epoch 708/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0369 - mean_squared_error: 0.0369 - val_loss: 0.0766 - val_mean_squared_error: 0.0766\n",
            "Epoch 709/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0373 - mean_squared_error: 0.0373 - val_loss: 0.0751 - val_mean_squared_error: 0.0751\n",
            "Epoch 710/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0367 - mean_squared_error: 0.0367 - val_loss: 0.0741 - val_mean_squared_error: 0.0741\n",
            "Epoch 711/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0350 - mean_squared_error: 0.0350 - val_loss: 0.0733 - val_mean_squared_error: 0.0733\n",
            "Epoch 712/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0338 - mean_squared_error: 0.0338 - val_loss: 0.0727 - val_mean_squared_error: 0.0727\n",
            "Epoch 713/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0342 - mean_squared_error: 0.0342 - val_loss: 0.0747 - val_mean_squared_error: 0.0747\n",
            "Epoch 714/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0349 - mean_squared_error: 0.0349 - val_loss: 0.0732 - val_mean_squared_error: 0.0732\n",
            "Epoch 715/800\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0344 - mean_squared_error: 0.0344 - val_loss: 0.0726 - val_mean_squared_error: 0.0726\n",
            "Epoch 716/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0334 - mean_squared_error: 0.0334 - val_loss: 0.0714 - val_mean_squared_error: 0.0714\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 717/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0333 - mean_squared_error: 0.0333 - val_loss: 0.0737 - val_mean_squared_error: 0.0737\n",
            "Epoch 718/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0339 - mean_squared_error: 0.0339 - val_loss: 0.0722 - val_mean_squared_error: 0.0722\n",
            "Epoch 719/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0338 - mean_squared_error: 0.0338 - val_loss: 0.0719 - val_mean_squared_error: 0.0719\n",
            "Epoch 720/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0327 - mean_squared_error: 0.0327 - val_loss: 0.0717 - val_mean_squared_error: 0.0717\n",
            "Epoch 721/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0321 - mean_squared_error: 0.0321 - val_loss: 0.0711 - val_mean_squared_error: 0.0711\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 722/800\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0327 - mean_squared_error: 0.0327 - val_loss: 0.0737 - val_mean_squared_error: 0.0737\n",
            "Epoch 723/800\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 0.0334 - mean_squared_error: 0.0334 - val_loss: 0.0718 - val_mean_squared_error: 0.0718\n",
            "Epoch 724/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0333 - mean_squared_error: 0.0333 - val_loss: 0.0727 - val_mean_squared_error: 0.0727\n",
            "Epoch 725/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0327 - mean_squared_error: 0.0327 - val_loss: 0.0714 - val_mean_squared_error: 0.0714\n",
            "Epoch 726/800\n",
            "1/1 [==============================] - 0s 242ms/step - loss: 0.0323 - mean_squared_error: 0.0323 - val_loss: 0.0721 - val_mean_squared_error: 0.0721\n",
            "Epoch 727/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0324 - mean_squared_error: 0.0324 - val_loss: 0.0719 - val_mean_squared_error: 0.0719\n",
            "Epoch 728/800\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0325 - mean_squared_error: 0.0325 - val_loss: 0.0719 - val_mean_squared_error: 0.0719\n",
            "Epoch 729/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0322 - mean_squared_error: 0.0322 - val_loss: 0.0716 - val_mean_squared_error: 0.0716\n",
            "Epoch 730/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0319 - mean_squared_error: 0.0319 - val_loss: 0.0717 - val_mean_squared_error: 0.0717\n",
            "Epoch 731/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0320 - mean_squared_error: 0.0320 - val_loss: 0.0719 - val_mean_squared_error: 0.0719\n",
            "Epoch 732/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0322 - mean_squared_error: 0.0322 - val_loss: 0.0718 - val_mean_squared_error: 0.0718\n",
            "Epoch 733/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0322 - mean_squared_error: 0.0322 - val_loss: 0.0714 - val_mean_squared_error: 0.0714\n",
            "Epoch 734/800\n",
            "1/1 [==============================] - 0s 69ms/step - loss: 0.0320 - mean_squared_error: 0.0320 - val_loss: 0.0712 - val_mean_squared_error: 0.0712\n",
            "Epoch 735/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0318 - mean_squared_error: 0.0318 - val_loss: 0.0720 - val_mean_squared_error: 0.0720\n",
            "Epoch 736/800\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0318 - mean_squared_error: 0.0318 - val_loss: 0.0706 - val_mean_squared_error: 0.0706\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 737/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0321 - mean_squared_error: 0.0321 - val_loss: 0.0732 - val_mean_squared_error: 0.0732\n",
            "Epoch 738/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0324 - mean_squared_error: 0.0324 - val_loss: 0.0712 - val_mean_squared_error: 0.0712\n",
            "Epoch 739/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0327 - mean_squared_error: 0.0327 - val_loss: 0.0738 - val_mean_squared_error: 0.0738\n",
            "Epoch 740/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0330 - mean_squared_error: 0.0330 - val_loss: 0.0719 - val_mean_squared_error: 0.0719\n",
            "Epoch 741/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0335 - mean_squared_error: 0.0335 - val_loss: 0.0747 - val_mean_squared_error: 0.0747\n",
            "Epoch 742/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0340 - mean_squared_error: 0.0340 - val_loss: 0.0723 - val_mean_squared_error: 0.0723\n",
            "Epoch 743/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0348 - mean_squared_error: 0.0348 - val_loss: 0.0766 - val_mean_squared_error: 0.0766\n",
            "Epoch 744/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0354 - mean_squared_error: 0.0354 - val_loss: 0.0730 - val_mean_squared_error: 0.0730\n",
            "Epoch 745/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0359 - mean_squared_error: 0.0359 - val_loss: 0.0777 - val_mean_squared_error: 0.0777\n",
            "Epoch 746/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0361 - mean_squared_error: 0.0361 - val_loss: 0.0728 - val_mean_squared_error: 0.0728\n",
            "Epoch 747/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0360 - mean_squared_error: 0.0360 - val_loss: 0.0771 - val_mean_squared_error: 0.0771\n",
            "Epoch 748/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0356 - mean_squared_error: 0.0356 - val_loss: 0.0717 - val_mean_squared_error: 0.0717\n",
            "Epoch 749/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0347 - mean_squared_error: 0.0347 - val_loss: 0.0742 - val_mean_squared_error: 0.0742\n",
            "Epoch 750/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0336 - mean_squared_error: 0.0336 - val_loss: 0.0716 - val_mean_squared_error: 0.0716\n",
            "Epoch 751/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0326 - mean_squared_error: 0.0326 - val_loss: 0.0720 - val_mean_squared_error: 0.0720\n",
            "Epoch 752/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0325 - mean_squared_error: 0.0325 - val_loss: 0.0734 - val_mean_squared_error: 0.0734\n",
            "Epoch 753/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0329 - mean_squared_error: 0.0329 - val_loss: 0.0715 - val_mean_squared_error: 0.0715\n",
            "Epoch 754/800\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0333 - mean_squared_error: 0.0333 - val_loss: 0.0740 - val_mean_squared_error: 0.0740\n",
            "Epoch 755/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0329 - mean_squared_error: 0.0329 - val_loss: 0.0705 - val_mean_squared_error: 0.0705\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 756/800\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0320 - mean_squared_error: 0.0320 - val_loss: 0.0716 - val_mean_squared_error: 0.0716\n",
            "Epoch 757/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0315 - mean_squared_error: 0.0315 - val_loss: 0.0710 - val_mean_squared_error: 0.0710\n",
            "Epoch 758/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0316 - mean_squared_error: 0.0316 - val_loss: 0.0712 - val_mean_squared_error: 0.0712\n",
            "Epoch 759/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0322 - mean_squared_error: 0.0322 - val_loss: 0.0725 - val_mean_squared_error: 0.0725\n",
            "Epoch 760/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0326 - mean_squared_error: 0.0326 - val_loss: 0.0716 - val_mean_squared_error: 0.0716\n",
            "Epoch 761/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0327 - mean_squared_error: 0.0327 - val_loss: 0.0724 - val_mean_squared_error: 0.0724\n",
            "Epoch 762/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0321 - mean_squared_error: 0.0321 - val_loss: 0.0708 - val_mean_squared_error: 0.0708\n",
            "Epoch 763/800\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.0316 - mean_squared_error: 0.0316 - val_loss: 0.0707 - val_mean_squared_error: 0.0707\n",
            "Epoch 764/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0312 - mean_squared_error: 0.0312 - val_loss: 0.0714 - val_mean_squared_error: 0.0714\n",
            "Epoch 765/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0312 - mean_squared_error: 0.0312 - val_loss: 0.0704 - val_mean_squared_error: 0.0704\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 766/800\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0316 - mean_squared_error: 0.0316 - val_loss: 0.0729 - val_mean_squared_error: 0.0729\n",
            "Epoch 767/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0321 - mean_squared_error: 0.0321 - val_loss: 0.0713 - val_mean_squared_error: 0.0713\n",
            "Epoch 768/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0325 - mean_squared_error: 0.0325 - val_loss: 0.0736 - val_mean_squared_error: 0.0736\n",
            "Epoch 769/800\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 0.0328 - mean_squared_error: 0.0328 - val_loss: 0.0719 - val_mean_squared_error: 0.0719\n",
            "Epoch 770/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0330 - mean_squared_error: 0.0330 - val_loss: 0.0741 - val_mean_squared_error: 0.0741\n",
            "Epoch 771/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0725 - val_mean_squared_error: 0.0725\n",
            "Epoch 772/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0336 - mean_squared_error: 0.0336 - val_loss: 0.0752 - val_mean_squared_error: 0.0752\n",
            "Epoch 773/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0345 - mean_squared_error: 0.0345 - val_loss: 0.0749 - val_mean_squared_error: 0.0749\n",
            "Epoch 774/800\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0357 - mean_squared_error: 0.0357 - val_loss: 0.0783 - val_mean_squared_error: 0.0783\n",
            "Epoch 775/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0374 - mean_squared_error: 0.0374 - val_loss: 0.0768 - val_mean_squared_error: 0.0768\n",
            "Epoch 776/800\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0377 - mean_squared_error: 0.0377 - val_loss: 0.0786 - val_mean_squared_error: 0.0786\n",
            "Epoch 777/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0372 - mean_squared_error: 0.0372 - val_loss: 0.0733 - val_mean_squared_error: 0.0733\n",
            "Epoch 778/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0342 - mean_squared_error: 0.0342 - val_loss: 0.0732 - val_mean_squared_error: 0.0732\n",
            "Epoch 779/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0317 - mean_squared_error: 0.0317 - val_loss: 0.0707 - val_mean_squared_error: 0.0707\n",
            "Epoch 780/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0311 - mean_squared_error: 0.0311 - val_loss: 0.0729 - val_mean_squared_error: 0.0729\n",
            "Epoch 781/800\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0325 - mean_squared_error: 0.0325 - val_loss: 0.0744 - val_mean_squared_error: 0.0744\n",
            "Epoch 782/800\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0339 - mean_squared_error: 0.0339 - val_loss: 0.0736 - val_mean_squared_error: 0.0736\n",
            "Epoch 783/800\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0334 - mean_squared_error: 0.0334 - val_loss: 0.0726 - val_mean_squared_error: 0.0726\n",
            "Epoch 784/800\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0318 - mean_squared_error: 0.0318 - val_loss: 0.0708 - val_mean_squared_error: 0.0708\n",
            "Epoch 785/800\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0306 - mean_squared_error: 0.0306 - val_loss: 0.0712 - val_mean_squared_error: 0.0712\n",
            "Epoch 786/800\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0310 - mean_squared_error: 0.0310 - val_loss: 0.0721 - val_mean_squared_error: 0.0721\n",
            "Epoch 787/800\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0321 - mean_squared_error: 0.0321 - val_loss: 0.0726 - val_mean_squared_error: 0.0726\n",
            "Epoch 788/800\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0324 - mean_squared_error: 0.0324 - val_loss: 0.0715 - val_mean_squared_error: 0.0715\n",
            "Epoch 789/800\n",
            "1/1 [==============================] - 0s 69ms/step - loss: 0.0316 - mean_squared_error: 0.0316 - val_loss: 0.0709 - val_mean_squared_error: 0.0709\n",
            "Epoch 790/800\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0306 - mean_squared_error: 0.0306 - val_loss: 0.0700 - val_mean_squared_error: 0.0700\n",
            "INFO:tensorflow:Assets written to: AE.z1/assets\n",
            "Epoch 791/800\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0305 - mean_squared_error: 0.0305 - val_loss: 0.0711 - val_mean_squared_error: 0.0711\n",
            "Epoch 792/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0310 - mean_squared_error: 0.0310 - val_loss: 0.0709 - val_mean_squared_error: 0.0709\n",
            "Epoch 793/800\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0315 - mean_squared_error: 0.0315 - val_loss: 0.0721 - val_mean_squared_error: 0.0721\n",
            "Epoch 794/800\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0315 - mean_squared_error: 0.0315 - val_loss: 0.0706 - val_mean_squared_error: 0.0706\n",
            "Epoch 795/800\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0310 - mean_squared_error: 0.0310 - val_loss: 0.0712 - val_mean_squared_error: 0.0712\n",
            "Epoch 796/800\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0304 - mean_squared_error: 0.0304 - val_loss: 0.0702 - val_mean_squared_error: 0.0702\n",
            "Epoch 797/800\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 0.0303 - mean_squared_error: 0.0303 - val_loss: 0.0716 - val_mean_squared_error: 0.0716\n",
            "Epoch 798/800\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0307 - mean_squared_error: 0.0307 - val_loss: 0.0711 - val_mean_squared_error: 0.0711\n",
            "Epoch 799/800\n",
            "1/1 [==============================] - 0s 72ms/step - loss: 0.0313 - mean_squared_error: 0.0313 - val_loss: 0.0731 - val_mean_squared_error: 0.0731\n",
            "Epoch 800/800\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0316 - mean_squared_error: 0.0316 - val_loss: 0.0715 - val_mean_squared_error: 0.0715\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "z7UCo8GwlBy3",
        "outputId": "e579d73b-1e6d-4ce1-82b9-1eb06e9128f6"
      },
      "source": [
        "plt.plot(history_a['loss'])\r\n",
        "plt.plot(history_a['val_loss'])\r\n",
        "plt.title('model loss')\r\n",
        "plt.ylabel('loss')\r\n",
        "plt.xlabel('epoch')\r\n",
        "plt.legend(['train', 'test'], loc='upper right');\r\n",
        "plt.savefig('losses_upstream.png', dpi=300)\r\n",
        "\r\n",
        "autoencoder.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_33 (Dense)             (None, 15)                240       \n",
            "_________________________________________________________________\n",
            "dense_34 (Dense)             (None, 20)                320       \n",
            "_________________________________________________________________\n",
            "dense_35 (Dense)             (None, 20)                420       \n",
            "_________________________________________________________________\n",
            "dense_36 (Dense)             (None, 10)                210       \n",
            "_________________________________________________________________\n",
            "dense_37 (Dense)             (None, 20)                220       \n",
            "_________________________________________________________________\n",
            "dense_38 (Dense)             (None, 20)                420       \n",
            "_________________________________________________________________\n",
            "dense_39 (Dense)             (None, 15)                315       \n",
            "=================================================================\n",
            "Total params: 2,145\n",
            "Trainable params: 2,145\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxmkGm03_ogB",
        "outputId": "0c5d0e19-7ae8-4d48-973a-f400ddd9d777"
      },
      "source": [
        "# Reality check\r\n",
        "a = 5\r\n",
        "testinp = np.array(X_train[a,:]).reshape(1,X_train.shape[1])\r\n",
        "y = autoencoder.predict(testinp)\r\n",
        "\r\n",
        "print(np.array(X_train[a,:]))\r\n",
        "print(y)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-2.58394902 -0.56445661 -0.40192187  0.45684843 -0.79634822 -0.27668579\n",
            " -1.09522963  0.60459641 -1.84391573  1.97655124 -0.30547668 -0.47633576\n",
            "  0.2947112   0.41283756 -0.85541114]\n",
            "[[-2.5334818  -0.6174719  -0.34243667  0.41201037 -0.75701386 -0.34704125\n",
            "  -1.269344    0.45172444 -1.940412    1.8250806  -0.38236898 -0.5003943\n",
            "   0.36916667  0.26568413 -0.8145276 ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RHNmPcB3TRVu"
      },
      "source": [
        "valuePredictor = Sequential([\r\n",
        "    Dense(choke, activation='relu', input_shape=(choke,)),\r\n",
        "    Dense(20, activation='relu'),\r\n",
        "    Dense(20, activation='relu'),\r\n",
        "    Dense(20, activation='relu'),\r\n",
        "    Dense(20, activation='relu'),\r\n",
        "    Dense(1)\r\n",
        "])"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H13ttbXVsDtm",
        "outputId": "8adfe486-cc1d-4e53-c0ef-d9a31c0442a1"
      },
      "source": [
        "# Latent feature extraction\r\n",
        "latent_model = Model(autoencoder.input, autoencoder.get_layer(index = 3).output )\r\n",
        "\r\n",
        "latents_train = np.array([latent_model.predict(i.reshape(1, X_train.shape[1])) for i in X_train]).reshape(450, choke)\r\n",
        "latents_test = np.array([latent_model.predict(i.reshape(1, X_train.shape[1])) for i in X_test]).reshape(56, choke)\r\n",
        "\r\n",
        "valuePredictor.compile(optimizer=Adam(lr=0.03), \r\n",
        "                    loss='MSE', \r\n",
        "                    metrics=['mean_squared_error'])\r\n",
        "checkpointer_1 = ModelCheckpoint(filepath=\"valuePredictor.z1\",\r\n",
        "                              verbose=0,\r\n",
        "                              save_best_only=True)\r\n",
        "history_1 = valuePredictor.fit(latents_train, Y_train,\r\n",
        "                    epochs=700,\r\n",
        "                    batch_size=450,\r\n",
        "                    shuffle=True,\r\n",
        "                    validation_data=(latents_test, Y_test),\r\n",
        "                    verbose=1, callbacks=[early_stopping, checkpointer_1]).history"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/700\n",
            "1/1 [==============================] - 1s 598ms/step - loss: 1.2020 - mean_squared_error: 1.2020 - val_loss: 0.8948 - val_mean_squared_error: 0.8948\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 2/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.9251 - mean_squared_error: 0.9251 - val_loss: 0.9334 - val_mean_squared_error: 0.9334\n",
            "Epoch 3/700\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.9508 - mean_squared_error: 0.9508 - val_loss: 0.7644 - val_mean_squared_error: 0.7644\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 4/700\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.8146 - mean_squared_error: 0.8146 - val_loss: 0.7002 - val_mean_squared_error: 0.7002\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 5/700\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.7758 - mean_squared_error: 0.7758 - val_loss: 0.6755 - val_mean_squared_error: 0.6755\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 6/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.7392 - mean_squared_error: 0.7392 - val_loss: 0.6336 - val_mean_squared_error: 0.6336\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 7/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.6843 - mean_squared_error: 0.6843 - val_loss: 0.5927 - val_mean_squared_error: 0.5927\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 8/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.6594 - mean_squared_error: 0.6594 - val_loss: 0.5508 - val_mean_squared_error: 0.5508\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 9/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.6298 - mean_squared_error: 0.6298 - val_loss: 0.5714 - val_mean_squared_error: 0.5714\n",
            "Epoch 10/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.6385 - mean_squared_error: 0.6385 - val_loss: 0.5121 - val_mean_squared_error: 0.5121\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 11/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.6020 - mean_squared_error: 0.6020 - val_loss: 0.4987 - val_mean_squared_error: 0.4987\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 12/700\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5819 - mean_squared_error: 0.5819 - val_loss: 0.4807 - val_mean_squared_error: 0.4807\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 13/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.5656 - mean_squared_error: 0.5656 - val_loss: 0.4438 - val_mean_squared_error: 0.4438\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 14/700\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.5482 - mean_squared_error: 0.5482 - val_loss: 0.4194 - val_mean_squared_error: 0.4194\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 15/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.5264 - mean_squared_error: 0.5264 - val_loss: 0.4016 - val_mean_squared_error: 0.4016\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 16/700\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.5042 - mean_squared_error: 0.5042 - val_loss: 0.3874 - val_mean_squared_error: 0.3874\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 17/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.4860 - mean_squared_error: 0.4860 - val_loss: 0.3572 - val_mean_squared_error: 0.3572\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 18/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4512 - mean_squared_error: 0.4512 - val_loss: 0.3401 - val_mean_squared_error: 0.3401\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 19/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.4326 - mean_squared_error: 0.4326 - val_loss: 0.3275 - val_mean_squared_error: 0.3275\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 20/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4015 - mean_squared_error: 0.4015 - val_loss: 0.3221 - val_mean_squared_error: 0.3221\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 21/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.3759 - mean_squared_error: 0.3759 - val_loss: 0.3262 - val_mean_squared_error: 0.3262\n",
            "Epoch 22/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.3552 - mean_squared_error: 0.3552 - val_loss: 0.3151 - val_mean_squared_error: 0.3151\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 23/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.3374 - mean_squared_error: 0.3374 - val_loss: 0.3295 - val_mean_squared_error: 0.3295\n",
            "Epoch 24/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.3339 - mean_squared_error: 0.3339 - val_loss: 0.4287 - val_mean_squared_error: 0.4287\n",
            "Epoch 25/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.4225 - mean_squared_error: 0.4225 - val_loss: 0.5183 - val_mean_squared_error: 0.5183\n",
            "Epoch 26/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.5178 - mean_squared_error: 0.5178 - val_loss: 0.4209 - val_mean_squared_error: 0.4209\n",
            "Epoch 27/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.4123 - mean_squared_error: 0.4123 - val_loss: 0.3426 - val_mean_squared_error: 0.3426\n",
            "Epoch 28/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.3334 - mean_squared_error: 0.3334 - val_loss: 0.4107 - val_mean_squared_error: 0.4107\n",
            "Epoch 29/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.4141 - mean_squared_error: 0.4141 - val_loss: 0.3068 - val_mean_squared_error: 0.3068\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 30/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.3054 - mean_squared_error: 0.3054 - val_loss: 0.3536 - val_mean_squared_error: 0.3536\n",
            "Epoch 31/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.3713 - mean_squared_error: 0.3713 - val_loss: 0.3376 - val_mean_squared_error: 0.3376\n",
            "Epoch 32/700\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.3299 - mean_squared_error: 0.3299 - val_loss: 0.3511 - val_mean_squared_error: 0.3511\n",
            "Epoch 33/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.3143 - mean_squared_error: 0.3143 - val_loss: 0.4033 - val_mean_squared_error: 0.4033\n",
            "Epoch 34/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.3364 - mean_squared_error: 0.3364 - val_loss: 0.3939 - val_mean_squared_error: 0.3939\n",
            "Epoch 35/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.3050 - mean_squared_error: 0.3050 - val_loss: 0.3566 - val_mean_squared_error: 0.3566\n",
            "Epoch 36/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.2748 - mean_squared_error: 0.2748 - val_loss: 0.3713 - val_mean_squared_error: 0.3713\n",
            "Epoch 37/700\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.3121 - mean_squared_error: 0.3121 - val_loss: 0.3617 - val_mean_squared_error: 0.3617\n",
            "Epoch 38/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.2864 - mean_squared_error: 0.2864 - val_loss: 0.3752 - val_mean_squared_error: 0.3752\n",
            "Epoch 39/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.2660 - mean_squared_error: 0.2660 - val_loss: 0.3964 - val_mean_squared_error: 0.3964\n",
            "Epoch 40/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.2789 - mean_squared_error: 0.2789 - val_loss: 0.3684 - val_mean_squared_error: 0.3684\n",
            "Epoch 41/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.2740 - mean_squared_error: 0.2740 - val_loss: 0.3443 - val_mean_squared_error: 0.3443\n",
            "Epoch 42/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.2714 - mean_squared_error: 0.2714 - val_loss: 0.3358 - val_mean_squared_error: 0.3358\n",
            "Epoch 43/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.2616 - mean_squared_error: 0.2616 - val_loss: 0.3573 - val_mean_squared_error: 0.3573\n",
            "Epoch 44/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.2508 - mean_squared_error: 0.2508 - val_loss: 0.4082 - val_mean_squared_error: 0.4082\n",
            "Epoch 45/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.2601 - mean_squared_error: 0.2601 - val_loss: 0.3921 - val_mean_squared_error: 0.3921\n",
            "Epoch 46/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.2483 - mean_squared_error: 0.2483 - val_loss: 0.3502 - val_mean_squared_error: 0.3502\n",
            "Epoch 47/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.2440 - mean_squared_error: 0.2440 - val_loss: 0.3501 - val_mean_squared_error: 0.3501\n",
            "Epoch 48/700\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.2408 - mean_squared_error: 0.2408 - val_loss: 0.3859 - val_mean_squared_error: 0.3859\n",
            "Epoch 49/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.2412 - mean_squared_error: 0.2412 - val_loss: 0.3956 - val_mean_squared_error: 0.3956\n",
            "Epoch 50/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.2410 - mean_squared_error: 0.2410 - val_loss: 0.3424 - val_mean_squared_error: 0.3424\n",
            "Epoch 51/700\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.2321 - mean_squared_error: 0.2321 - val_loss: 0.3366 - val_mean_squared_error: 0.3366\n",
            "Epoch 52/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.2344 - mean_squared_error: 0.2344 - val_loss: 0.3648 - val_mean_squared_error: 0.3648\n",
            "Epoch 53/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.2291 - mean_squared_error: 0.2291 - val_loss: 0.3757 - val_mean_squared_error: 0.3757\n",
            "Epoch 54/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.2290 - mean_squared_error: 0.2290 - val_loss: 0.3293 - val_mean_squared_error: 0.3293\n",
            "Epoch 55/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.2220 - mean_squared_error: 0.2220 - val_loss: 0.3197 - val_mean_squared_error: 0.3197\n",
            "Epoch 56/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.2248 - mean_squared_error: 0.2248 - val_loss: 0.3444 - val_mean_squared_error: 0.3444\n",
            "Epoch 57/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.2184 - mean_squared_error: 0.2184 - val_loss: 0.3574 - val_mean_squared_error: 0.3574\n",
            "Epoch 58/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.2175 - mean_squared_error: 0.2175 - val_loss: 0.3323 - val_mean_squared_error: 0.3323\n",
            "Epoch 59/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.2168 - mean_squared_error: 0.2168 - val_loss: 0.3290 - val_mean_squared_error: 0.3290\n",
            "Epoch 60/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.2145 - mean_squared_error: 0.2145 - val_loss: 0.3413 - val_mean_squared_error: 0.3413\n",
            "Epoch 61/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.2119 - mean_squared_error: 0.2119 - val_loss: 0.3179 - val_mean_squared_error: 0.3179\n",
            "Epoch 62/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.2071 - mean_squared_error: 0.2071 - val_loss: 0.3049 - val_mean_squared_error: 0.3049\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 63/700\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.2076 - mean_squared_error: 0.2076 - val_loss: 0.3108 - val_mean_squared_error: 0.3108\n",
            "Epoch 64/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.2015 - mean_squared_error: 0.2015 - val_loss: 0.3176 - val_mean_squared_error: 0.3176\n",
            "Epoch 65/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.2025 - mean_squared_error: 0.2025 - val_loss: 0.3008 - val_mean_squared_error: 0.3008\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 66/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1993 - mean_squared_error: 0.1993 - val_loss: 0.2926 - val_mean_squared_error: 0.2926\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 67/700\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.1967 - mean_squared_error: 0.1967 - val_loss: 0.2921 - val_mean_squared_error: 0.2921\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 68/700\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.1975 - mean_squared_error: 0.1975 - val_loss: 0.2871 - val_mean_squared_error: 0.2871\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 69/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.1941 - mean_squared_error: 0.1941 - val_loss: 0.2879 - val_mean_squared_error: 0.2879\n",
            "Epoch 70/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1927 - mean_squared_error: 0.1927 - val_loss: 0.2875 - val_mean_squared_error: 0.2875\n",
            "Epoch 71/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.1912 - mean_squared_error: 0.1912 - val_loss: 0.2747 - val_mean_squared_error: 0.2747\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 72/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1885 - mean_squared_error: 0.1885 - val_loss: 0.2690 - val_mean_squared_error: 0.2690\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 73/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1865 - mean_squared_error: 0.1865 - val_loss: 0.2667 - val_mean_squared_error: 0.2667\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 74/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1850 - mean_squared_error: 0.1850 - val_loss: 0.2608 - val_mean_squared_error: 0.2608\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 75/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1834 - mean_squared_error: 0.1834 - val_loss: 0.2580 - val_mean_squared_error: 0.2580\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 76/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1808 - mean_squared_error: 0.1808 - val_loss: 0.2552 - val_mean_squared_error: 0.2552\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 77/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1790 - mean_squared_error: 0.1790 - val_loss: 0.2550 - val_mean_squared_error: 0.2550\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 78/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1779 - mean_squared_error: 0.1779 - val_loss: 0.2549 - val_mean_squared_error: 0.2549\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 79/700\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.1755 - mean_squared_error: 0.1755 - val_loss: 0.2518 - val_mean_squared_error: 0.2518\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 80/700\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.1728 - mean_squared_error: 0.1728 - val_loss: 0.2474 - val_mean_squared_error: 0.2474\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 81/700\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.1712 - mean_squared_error: 0.1712 - val_loss: 0.2452 - val_mean_squared_error: 0.2452\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 82/700\n",
            "1/1 [==============================] - 0s 40ms/step - loss: 0.1702 - mean_squared_error: 0.1702 - val_loss: 0.2458 - val_mean_squared_error: 0.2458\n",
            "Epoch 83/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1682 - mean_squared_error: 0.1682 - val_loss: 0.2465 - val_mean_squared_error: 0.2465\n",
            "Epoch 84/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.1665 - mean_squared_error: 0.1665 - val_loss: 0.2469 - val_mean_squared_error: 0.2469\n",
            "Epoch 85/700\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.1646 - mean_squared_error: 0.1646 - val_loss: 0.2437 - val_mean_squared_error: 0.2437\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 86/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.1632 - mean_squared_error: 0.1632 - val_loss: 0.2473 - val_mean_squared_error: 0.2473\n",
            "Epoch 87/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1643 - mean_squared_error: 0.1643 - val_loss: 0.2442 - val_mean_squared_error: 0.2442\n",
            "Epoch 88/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1668 - mean_squared_error: 0.1668 - val_loss: 0.2636 - val_mean_squared_error: 0.2636\n",
            "Epoch 89/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1861 - mean_squared_error: 0.1861 - val_loss: 0.2661 - val_mean_squared_error: 0.2661\n",
            "Epoch 90/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1958 - mean_squared_error: 0.1958 - val_loss: 0.3010 - val_mean_squared_error: 0.3010\n",
            "Epoch 91/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.2472 - mean_squared_error: 0.2472 - val_loss: 0.2396 - val_mean_squared_error: 0.2396\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 92/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1588 - mean_squared_error: 0.1588 - val_loss: 0.3032 - val_mean_squared_error: 0.3032\n",
            "Epoch 93/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.2283 - mean_squared_error: 0.2283 - val_loss: 0.3715 - val_mean_squared_error: 0.3715\n",
            "Epoch 94/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.3279 - mean_squared_error: 0.3279 - val_loss: 0.3069 - val_mean_squared_error: 0.3069\n",
            "Epoch 95/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.2521 - mean_squared_error: 0.2521 - val_loss: 0.3595 - val_mean_squared_error: 0.3595\n",
            "Epoch 96/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.2875 - mean_squared_error: 0.2875 - val_loss: 0.2371 - val_mean_squared_error: 0.2371\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 97/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1664 - mean_squared_error: 0.1664 - val_loss: 0.2760 - val_mean_squared_error: 0.2760\n",
            "Epoch 98/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.2239 - mean_squared_error: 0.2239 - val_loss: 0.2483 - val_mean_squared_error: 0.2483\n",
            "Epoch 99/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1836 - mean_squared_error: 0.1836 - val_loss: 0.2690 - val_mean_squared_error: 0.2690\n",
            "Epoch 100/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.2073 - mean_squared_error: 0.2073 - val_loss: 0.2453 - val_mean_squared_error: 0.2453\n",
            "Epoch 101/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.1746 - mean_squared_error: 0.1746 - val_loss: 0.2555 - val_mean_squared_error: 0.2555\n",
            "Epoch 102/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1934 - mean_squared_error: 0.1934 - val_loss: 0.2453 - val_mean_squared_error: 0.2453\n",
            "Epoch 103/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1758 - mean_squared_error: 0.1758 - val_loss: 0.2520 - val_mean_squared_error: 0.2520\n",
            "Epoch 104/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1775 - mean_squared_error: 0.1775 - val_loss: 0.2472 - val_mean_squared_error: 0.2472\n",
            "Epoch 105/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1700 - mean_squared_error: 0.1700 - val_loss: 0.2376 - val_mean_squared_error: 0.2376\n",
            "Epoch 106/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.1713 - mean_squared_error: 0.1713 - val_loss: 0.2342 - val_mean_squared_error: 0.2342\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 107/700\n",
            "1/1 [==============================] - 0s 39ms/step - loss: 0.1744 - mean_squared_error: 0.1744 - val_loss: 0.2298 - val_mean_squared_error: 0.2298\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 108/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1578 - mean_squared_error: 0.1578 - val_loss: 0.2400 - val_mean_squared_error: 0.2400\n",
            "Epoch 109/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1661 - mean_squared_error: 0.1661 - val_loss: 0.2295 - val_mean_squared_error: 0.2295\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 110/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1627 - mean_squared_error: 0.1627 - val_loss: 0.2233 - val_mean_squared_error: 0.2233\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 111/700\n",
            "1/1 [==============================] - 0s 256ms/step - loss: 0.1561 - mean_squared_error: 0.1561 - val_loss: 0.2377 - val_mean_squared_error: 0.2377\n",
            "Epoch 112/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1595 - mean_squared_error: 0.1595 - val_loss: 0.2245 - val_mean_squared_error: 0.2245\n",
            "Epoch 113/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1441 - mean_squared_error: 0.1441 - val_loss: 0.2281 - val_mean_squared_error: 0.2281\n",
            "Epoch 114/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.1549 - mean_squared_error: 0.1549 - val_loss: 0.2263 - val_mean_squared_error: 0.2263\n",
            "Epoch 115/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.1429 - mean_squared_error: 0.1429 - val_loss: 0.2484 - val_mean_squared_error: 0.2484\n",
            "Epoch 116/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.1515 - mean_squared_error: 0.1515 - val_loss: 0.2289 - val_mean_squared_error: 0.2289\n",
            "Epoch 117/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1431 - mean_squared_error: 0.1431 - val_loss: 0.2277 - val_mean_squared_error: 0.2277\n",
            "Epoch 118/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.1422 - mean_squared_error: 0.1422 - val_loss: 0.2391 - val_mean_squared_error: 0.2391\n",
            "Epoch 119/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1438 - mean_squared_error: 0.1438 - val_loss: 0.2325 - val_mean_squared_error: 0.2325\n",
            "Epoch 120/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1344 - mean_squared_error: 0.1344 - val_loss: 0.2366 - val_mean_squared_error: 0.2366\n",
            "Epoch 121/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.1385 - mean_squared_error: 0.1385 - val_loss: 0.2399 - val_mean_squared_error: 0.2399\n",
            "Epoch 122/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.1332 - mean_squared_error: 0.1332 - val_loss: 0.2437 - val_mean_squared_error: 0.2437\n",
            "Epoch 123/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.1354 - mean_squared_error: 0.1354 - val_loss: 0.2407 - val_mean_squared_error: 0.2407\n",
            "Epoch 124/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.1351 - mean_squared_error: 0.1351 - val_loss: 0.2351 - val_mean_squared_error: 0.2351\n",
            "Epoch 125/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1291 - mean_squared_error: 0.1291 - val_loss: 0.2385 - val_mean_squared_error: 0.2385\n",
            "Epoch 126/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1322 - mean_squared_error: 0.1322 - val_loss: 0.2283 - val_mean_squared_error: 0.2283\n",
            "Epoch 127/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.1291 - mean_squared_error: 0.1291 - val_loss: 0.2289 - val_mean_squared_error: 0.2289\n",
            "Epoch 128/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1273 - mean_squared_error: 0.1273 - val_loss: 0.2345 - val_mean_squared_error: 0.2345\n",
            "Epoch 129/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1264 - mean_squared_error: 0.1264 - val_loss: 0.2329 - val_mean_squared_error: 0.2329\n",
            "Epoch 130/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1253 - mean_squared_error: 0.1253 - val_loss: 0.2328 - val_mean_squared_error: 0.2328\n",
            "Epoch 131/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.1215 - mean_squared_error: 0.1215 - val_loss: 0.2335 - val_mean_squared_error: 0.2335\n",
            "Epoch 132/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.1232 - mean_squared_error: 0.1232 - val_loss: 0.2246 - val_mean_squared_error: 0.2246\n",
            "Epoch 133/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1230 - mean_squared_error: 0.1230 - val_loss: 0.2212 - val_mean_squared_error: 0.2212\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 134/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1192 - mean_squared_error: 0.1192 - val_loss: 0.2177 - val_mean_squared_error: 0.2177\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 135/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1176 - mean_squared_error: 0.1176 - val_loss: 0.2177 - val_mean_squared_error: 0.2177\n",
            "Epoch 136/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1197 - mean_squared_error: 0.1197 - val_loss: 0.2218 - val_mean_squared_error: 0.2218\n",
            "Epoch 137/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1180 - mean_squared_error: 0.1180 - val_loss: 0.2182 - val_mean_squared_error: 0.2182\n",
            "Epoch 138/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1155 - mean_squared_error: 0.1155 - val_loss: 0.2184 - val_mean_squared_error: 0.2184\n",
            "Epoch 139/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.1142 - mean_squared_error: 0.1142 - val_loss: 0.2172 - val_mean_squared_error: 0.2172\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 140/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.1119 - mean_squared_error: 0.1119 - val_loss: 0.2167 - val_mean_squared_error: 0.2167\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 141/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.1128 - mean_squared_error: 0.1128 - val_loss: 0.2182 - val_mean_squared_error: 0.2182\n",
            "Epoch 142/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1110 - mean_squared_error: 0.1110 - val_loss: 0.2176 - val_mean_squared_error: 0.2176\n",
            "Epoch 143/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.1093 - mean_squared_error: 0.1093 - val_loss: 0.2239 - val_mean_squared_error: 0.2239\n",
            "Epoch 144/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1089 - mean_squared_error: 0.1089 - val_loss: 0.2271 - val_mean_squared_error: 0.2271\n",
            "Epoch 145/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.1090 - mean_squared_error: 0.1090 - val_loss: 0.2246 - val_mean_squared_error: 0.2246\n",
            "Epoch 146/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.1074 - mean_squared_error: 0.1074 - val_loss: 0.2208 - val_mean_squared_error: 0.2208\n",
            "Epoch 147/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1054 - mean_squared_error: 0.1054 - val_loss: 0.2232 - val_mean_squared_error: 0.2232\n",
            "Epoch 148/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.1054 - mean_squared_error: 0.1054 - val_loss: 0.2185 - val_mean_squared_error: 0.2185\n",
            "Epoch 149/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.1042 - mean_squared_error: 0.1042 - val_loss: 0.2241 - val_mean_squared_error: 0.2241\n",
            "Epoch 150/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1057 - mean_squared_error: 0.1057 - val_loss: 0.2190 - val_mean_squared_error: 0.2190\n",
            "Epoch 151/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.1116 - mean_squared_error: 0.1116 - val_loss: 0.2317 - val_mean_squared_error: 0.2317\n",
            "Epoch 152/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1174 - mean_squared_error: 0.1174 - val_loss: 0.2320 - val_mean_squared_error: 0.2320\n",
            "Epoch 153/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.1496 - mean_squared_error: 0.1496 - val_loss: 0.2448 - val_mean_squared_error: 0.2448\n",
            "Epoch 154/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.1362 - mean_squared_error: 0.1362 - val_loss: 0.2266 - val_mean_squared_error: 0.2266\n",
            "Epoch 155/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1474 - mean_squared_error: 0.1474 - val_loss: 0.2223 - val_mean_squared_error: 0.2223\n",
            "Epoch 156/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1091 - mean_squared_error: 0.1091 - val_loss: 0.2685 - val_mean_squared_error: 0.2685\n",
            "Epoch 157/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.1620 - mean_squared_error: 0.1620 - val_loss: 0.3130 - val_mean_squared_error: 0.3130\n",
            "Epoch 158/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.2523 - mean_squared_error: 0.2523 - val_loss: 0.2422 - val_mean_squared_error: 0.2422\n",
            "Epoch 159/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1737 - mean_squared_error: 0.1737 - val_loss: 0.3747 - val_mean_squared_error: 0.3747\n",
            "Epoch 160/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.2503 - mean_squared_error: 0.2503 - val_loss: 0.2776 - val_mean_squared_error: 0.2776\n",
            "Epoch 161/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.2065 - mean_squared_error: 0.2065 - val_loss: 0.2873 - val_mean_squared_error: 0.2873\n",
            "Epoch 162/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.2382 - mean_squared_error: 0.2382 - val_loss: 0.2279 - val_mean_squared_error: 0.2279\n",
            "Epoch 163/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.1549 - mean_squared_error: 0.1549 - val_loss: 0.3032 - val_mean_squared_error: 0.3032\n",
            "Epoch 164/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.2148 - mean_squared_error: 0.2148 - val_loss: 0.2966 - val_mean_squared_error: 0.2966\n",
            "Epoch 165/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.2142 - mean_squared_error: 0.2142 - val_loss: 0.2294 - val_mean_squared_error: 0.2294\n",
            "Epoch 166/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1312 - mean_squared_error: 0.1312 - val_loss: 0.2770 - val_mean_squared_error: 0.2770\n",
            "Epoch 167/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.1857 - mean_squared_error: 0.1857 - val_loss: 0.3017 - val_mean_squared_error: 0.3017\n",
            "Epoch 168/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.1960 - mean_squared_error: 0.1960 - val_loss: 0.2851 - val_mean_squared_error: 0.2851\n",
            "Epoch 169/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.1584 - mean_squared_error: 0.1584 - val_loss: 0.3079 - val_mean_squared_error: 0.3079\n",
            "Epoch 170/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.1679 - mean_squared_error: 0.1679 - val_loss: 0.3046 - val_mean_squared_error: 0.3046\n",
            "Epoch 171/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1647 - mean_squared_error: 0.1647 - val_loss: 0.2717 - val_mean_squared_error: 0.2717\n",
            "Epoch 172/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1331 - mean_squared_error: 0.1331 - val_loss: 0.2500 - val_mean_squared_error: 0.2500\n",
            "Epoch 173/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.1399 - mean_squared_error: 0.1399 - val_loss: 0.2160 - val_mean_squared_error: 0.2160\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 174/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1311 - mean_squared_error: 0.1311 - val_loss: 0.2541 - val_mean_squared_error: 0.2541\n",
            "Epoch 175/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.1451 - mean_squared_error: 0.1451 - val_loss: 0.2489 - val_mean_squared_error: 0.2489\n",
            "Epoch 176/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.1381 - mean_squared_error: 0.1381 - val_loss: 0.2266 - val_mean_squared_error: 0.2266\n",
            "Epoch 177/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1196 - mean_squared_error: 0.1196 - val_loss: 0.2311 - val_mean_squared_error: 0.2311\n",
            "Epoch 178/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.1096 - mean_squared_error: 0.1096 - val_loss: 0.2625 - val_mean_squared_error: 0.2625\n",
            "Epoch 179/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1195 - mean_squared_error: 0.1195 - val_loss: 0.2561 - val_mean_squared_error: 0.2561\n",
            "Epoch 180/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1264 - mean_squared_error: 0.1264 - val_loss: 0.2398 - val_mean_squared_error: 0.2398\n",
            "Epoch 181/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.1154 - mean_squared_error: 0.1154 - val_loss: 0.2180 - val_mean_squared_error: 0.2180\n",
            "Epoch 182/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1100 - mean_squared_error: 0.1100 - val_loss: 0.1985 - val_mean_squared_error: 0.1985\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 183/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1098 - mean_squared_error: 0.1098 - val_loss: 0.1902 - val_mean_squared_error: 0.1902\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 184/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.1113 - mean_squared_error: 0.1113 - val_loss: 0.2003 - val_mean_squared_error: 0.2003\n",
            "Epoch 185/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.1161 - mean_squared_error: 0.1161 - val_loss: 0.1994 - val_mean_squared_error: 0.1994\n",
            "Epoch 186/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1070 - mean_squared_error: 0.1070 - val_loss: 0.2141 - val_mean_squared_error: 0.2141\n",
            "Epoch 187/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0990 - mean_squared_error: 0.0990 - val_loss: 0.2351 - val_mean_squared_error: 0.2351\n",
            "Epoch 188/700\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.1046 - mean_squared_error: 0.1046 - val_loss: 0.2227 - val_mean_squared_error: 0.2227\n",
            "Epoch 189/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1031 - mean_squared_error: 0.1031 - val_loss: 0.2283 - val_mean_squared_error: 0.2283\n",
            "Epoch 190/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1057 - mean_squared_error: 0.1057 - val_loss: 0.2303 - val_mean_squared_error: 0.2303\n",
            "Epoch 191/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.1097 - mean_squared_error: 0.1097 - val_loss: 0.2124 - val_mean_squared_error: 0.2124\n",
            "Epoch 192/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0964 - mean_squared_error: 0.0964 - val_loss: 0.2040 - val_mean_squared_error: 0.2040\n",
            "Epoch 193/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0943 - mean_squared_error: 0.0943 - val_loss: 0.2312 - val_mean_squared_error: 0.2312\n",
            "Epoch 194/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.1040 - mean_squared_error: 0.1040 - val_loss: 0.2045 - val_mean_squared_error: 0.2045\n",
            "Epoch 195/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0988 - mean_squared_error: 0.0988 - val_loss: 0.2020 - val_mean_squared_error: 0.2020\n",
            "Epoch 196/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0915 - mean_squared_error: 0.0915 - val_loss: 0.2018 - val_mean_squared_error: 0.2018\n",
            "Epoch 197/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1004 - mean_squared_error: 0.1004 - val_loss: 0.2018 - val_mean_squared_error: 0.2018\n",
            "Epoch 198/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0953 - mean_squared_error: 0.0953 - val_loss: 0.1965 - val_mean_squared_error: 0.1965\n",
            "Epoch 199/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0887 - mean_squared_error: 0.0887 - val_loss: 0.2186 - val_mean_squared_error: 0.2186\n",
            "Epoch 200/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0985 - mean_squared_error: 0.0985 - val_loss: 0.2158 - val_mean_squared_error: 0.2158\n",
            "Epoch 201/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1042 - mean_squared_error: 0.1042 - val_loss: 0.1979 - val_mean_squared_error: 0.1979\n",
            "Epoch 202/700\n",
            "1/1 [==============================] - 0s 229ms/step - loss: 0.0837 - mean_squared_error: 0.0837 - val_loss: 0.2037 - val_mean_squared_error: 0.2037\n",
            "Epoch 203/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1031 - mean_squared_error: 0.1031 - val_loss: 0.2107 - val_mean_squared_error: 0.2107\n",
            "Epoch 204/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1203 - mean_squared_error: 0.1203 - val_loss: 0.1847 - val_mean_squared_error: 0.1847\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 205/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.0869 - mean_squared_error: 0.0869 - val_loss: 0.2464 - val_mean_squared_error: 0.2464\n",
            "Epoch 206/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1614 - mean_squared_error: 0.1614 - val_loss: 0.2720 - val_mean_squared_error: 0.2720\n",
            "Epoch 207/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.2039 - mean_squared_error: 0.2039 - val_loss: 0.2641 - val_mean_squared_error: 0.2641\n",
            "Epoch 208/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1989 - mean_squared_error: 0.1989 - val_loss: 0.3165 - val_mean_squared_error: 0.3165\n",
            "Epoch 209/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.2219 - mean_squared_error: 0.2219 - val_loss: 0.2524 - val_mean_squared_error: 0.2524\n",
            "Epoch 210/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1563 - mean_squared_error: 0.1563 - val_loss: 0.2716 - val_mean_squared_error: 0.2716\n",
            "Epoch 211/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.1908 - mean_squared_error: 0.1908 - val_loss: 0.2304 - val_mean_squared_error: 0.2304\n",
            "Epoch 212/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.1549 - mean_squared_error: 0.1549 - val_loss: 0.2154 - val_mean_squared_error: 0.2154\n",
            "Epoch 213/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.1186 - mean_squared_error: 0.1186 - val_loss: 0.2377 - val_mean_squared_error: 0.2377\n",
            "Epoch 214/700\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.1534 - mean_squared_error: 0.1534 - val_loss: 0.2430 - val_mean_squared_error: 0.2430\n",
            "Epoch 215/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.1287 - mean_squared_error: 0.1287 - val_loss: 0.2787 - val_mean_squared_error: 0.2787\n",
            "Epoch 216/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1524 - mean_squared_error: 0.1524 - val_loss: 0.2264 - val_mean_squared_error: 0.2264\n",
            "Epoch 217/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.1390 - mean_squared_error: 0.1390 - val_loss: 0.2220 - val_mean_squared_error: 0.2220\n",
            "Epoch 218/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1235 - mean_squared_error: 0.1235 - val_loss: 0.2925 - val_mean_squared_error: 0.2925\n",
            "Epoch 219/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.1627 - mean_squared_error: 0.1627 - val_loss: 0.2473 - val_mean_squared_error: 0.2473\n",
            "Epoch 220/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1310 - mean_squared_error: 0.1310 - val_loss: 0.2655 - val_mean_squared_error: 0.2655\n",
            "Epoch 221/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.1508 - mean_squared_error: 0.1508 - val_loss: 0.2342 - val_mean_squared_error: 0.2342\n",
            "Epoch 222/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.1208 - mean_squared_error: 0.1208 - val_loss: 0.2357 - val_mean_squared_error: 0.2357\n",
            "Epoch 223/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.1221 - mean_squared_error: 0.1221 - val_loss: 0.2275 - val_mean_squared_error: 0.2275\n",
            "Epoch 224/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1096 - mean_squared_error: 0.1096 - val_loss: 0.2269 - val_mean_squared_error: 0.2269\n",
            "Epoch 225/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.1044 - mean_squared_error: 0.1044 - val_loss: 0.2455 - val_mean_squared_error: 0.2455\n",
            "Epoch 226/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.1203 - mean_squared_error: 0.1203 - val_loss: 0.2395 - val_mean_squared_error: 0.2395\n",
            "Epoch 227/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0993 - mean_squared_error: 0.0993 - val_loss: 0.2534 - val_mean_squared_error: 0.2534\n",
            "Epoch 228/700\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.1008 - mean_squared_error: 0.1008 - val_loss: 0.2578 - val_mean_squared_error: 0.2578\n",
            "Epoch 229/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0956 - mean_squared_error: 0.0956 - val_loss: 0.2416 - val_mean_squared_error: 0.2416\n",
            "Epoch 230/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0942 - mean_squared_error: 0.0942 - val_loss: 0.2387 - val_mean_squared_error: 0.2387\n",
            "Epoch 231/700\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0990 - mean_squared_error: 0.0990 - val_loss: 0.2357 - val_mean_squared_error: 0.2357\n",
            "Epoch 232/700\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0924 - mean_squared_error: 0.0924 - val_loss: 0.2344 - val_mean_squared_error: 0.2344\n",
            "Epoch 233/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0925 - mean_squared_error: 0.0925 - val_loss: 0.2108 - val_mean_squared_error: 0.2108\n",
            "Epoch 234/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0845 - mean_squared_error: 0.0845 - val_loss: 0.2017 - val_mean_squared_error: 0.2017\n",
            "Epoch 235/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0824 - mean_squared_error: 0.0824 - val_loss: 0.1978 - val_mean_squared_error: 0.1978\n",
            "Epoch 236/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0864 - mean_squared_error: 0.0864 - val_loss: 0.1933 - val_mean_squared_error: 0.1933\n",
            "Epoch 237/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0851 - mean_squared_error: 0.0851 - val_loss: 0.1874 - val_mean_squared_error: 0.1874\n",
            "Epoch 238/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0820 - mean_squared_error: 0.0820 - val_loss: 0.1809 - val_mean_squared_error: 0.1809\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 239/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0819 - mean_squared_error: 0.0819 - val_loss: 0.1820 - val_mean_squared_error: 0.1820\n",
            "Epoch 240/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0797 - mean_squared_error: 0.0797 - val_loss: 0.1816 - val_mean_squared_error: 0.1816\n",
            "Epoch 241/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0768 - mean_squared_error: 0.0768 - val_loss: 0.1865 - val_mean_squared_error: 0.1865\n",
            "Epoch 242/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0774 - mean_squared_error: 0.0774 - val_loss: 0.1861 - val_mean_squared_error: 0.1861\n",
            "Epoch 243/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0743 - mean_squared_error: 0.0743 - val_loss: 0.1881 - val_mean_squared_error: 0.1881\n",
            "Epoch 244/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0734 - mean_squared_error: 0.0734 - val_loss: 0.1905 - val_mean_squared_error: 0.1905\n",
            "Epoch 245/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0764 - mean_squared_error: 0.0764 - val_loss: 0.1836 - val_mean_squared_error: 0.1836\n",
            "Epoch 246/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0732 - mean_squared_error: 0.0732 - val_loss: 0.1813 - val_mean_squared_error: 0.1813\n",
            "Epoch 247/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0728 - mean_squared_error: 0.0728 - val_loss: 0.1815 - val_mean_squared_error: 0.1815\n",
            "Epoch 248/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0708 - mean_squared_error: 0.0708 - val_loss: 0.1862 - val_mean_squared_error: 0.1862\n",
            "Epoch 249/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0712 - mean_squared_error: 0.0712 - val_loss: 0.1844 - val_mean_squared_error: 0.1844\n",
            "Epoch 250/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0687 - mean_squared_error: 0.0687 - val_loss: 0.1869 - val_mean_squared_error: 0.1869\n",
            "Epoch 251/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0698 - mean_squared_error: 0.0698 - val_loss: 0.1846 - val_mean_squared_error: 0.1846\n",
            "Epoch 252/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0695 - mean_squared_error: 0.0695 - val_loss: 0.1897 - val_mean_squared_error: 0.1897\n",
            "Epoch 253/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0677 - mean_squared_error: 0.0677 - val_loss: 0.1864 - val_mean_squared_error: 0.1864\n",
            "Epoch 254/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0669 - mean_squared_error: 0.0669 - val_loss: 0.1807 - val_mean_squared_error: 0.1807\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 255/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0668 - mean_squared_error: 0.0668 - val_loss: 0.1797 - val_mean_squared_error: 0.1797\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 256/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0661 - mean_squared_error: 0.0661 - val_loss: 0.1805 - val_mean_squared_error: 0.1805\n",
            "Epoch 257/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0650 - mean_squared_error: 0.0650 - val_loss: 0.1828 - val_mean_squared_error: 0.1828\n",
            "Epoch 258/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0653 - mean_squared_error: 0.0653 - val_loss: 0.1852 - val_mean_squared_error: 0.1852\n",
            "Epoch 259/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0646 - mean_squared_error: 0.0646 - val_loss: 0.1871 - val_mean_squared_error: 0.1871\n",
            "Epoch 260/700\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0639 - mean_squared_error: 0.0639 - val_loss: 0.1890 - val_mean_squared_error: 0.1890\n",
            "Epoch 261/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0630 - mean_squared_error: 0.0630 - val_loss: 0.1884 - val_mean_squared_error: 0.1884\n",
            "Epoch 262/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0633 - mean_squared_error: 0.0633 - val_loss: 0.1814 - val_mean_squared_error: 0.1814\n",
            "Epoch 263/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0629 - mean_squared_error: 0.0629 - val_loss: 0.1781 - val_mean_squared_error: 0.1781\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 264/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0625 - mean_squared_error: 0.0625 - val_loss: 0.1765 - val_mean_squared_error: 0.1765\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 265/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.0617 - mean_squared_error: 0.0617 - val_loss: 0.1780 - val_mean_squared_error: 0.1780\n",
            "Epoch 266/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0615 - mean_squared_error: 0.0615 - val_loss: 0.1782 - val_mean_squared_error: 0.1782\n",
            "Epoch 267/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0609 - mean_squared_error: 0.0609 - val_loss: 0.1771 - val_mean_squared_error: 0.1771\n",
            "Epoch 268/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0608 - mean_squared_error: 0.0608 - val_loss: 0.1772 - val_mean_squared_error: 0.1772\n",
            "Epoch 269/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0602 - mean_squared_error: 0.0602 - val_loss: 0.1757 - val_mean_squared_error: 0.1757\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 270/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0601 - mean_squared_error: 0.0601 - val_loss: 0.1725 - val_mean_squared_error: 0.1725\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 271/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.0598 - mean_squared_error: 0.0598 - val_loss: 0.1736 - val_mean_squared_error: 0.1736\n",
            "Epoch 272/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0601 - mean_squared_error: 0.0601 - val_loss: 0.1716 - val_mean_squared_error: 0.1716\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 273/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0605 - mean_squared_error: 0.0605 - val_loss: 0.1711 - val_mean_squared_error: 0.1711\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 274/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0591 - mean_squared_error: 0.0591 - val_loss: 0.1706 - val_mean_squared_error: 0.1706\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 275/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0585 - mean_squared_error: 0.0585 - val_loss: 0.1693 - val_mean_squared_error: 0.1693\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 276/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0590 - mean_squared_error: 0.0590 - val_loss: 0.1743 - val_mean_squared_error: 0.1743\n",
            "Epoch 277/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0591 - mean_squared_error: 0.0591 - val_loss: 0.1719 - val_mean_squared_error: 0.1719\n",
            "Epoch 278/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0585 - mean_squared_error: 0.0585 - val_loss: 0.1719 - val_mean_squared_error: 0.1719\n",
            "Epoch 279/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0573 - mean_squared_error: 0.0573 - val_loss: 0.1703 - val_mean_squared_error: 0.1703\n",
            "Epoch 280/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0573 - mean_squared_error: 0.0573 - val_loss: 0.1687 - val_mean_squared_error: 0.1687\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 281/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0578 - mean_squared_error: 0.0578 - val_loss: 0.1723 - val_mean_squared_error: 0.1723\n",
            "Epoch 282/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0572 - mean_squared_error: 0.0572 - val_loss: 0.1723 - val_mean_squared_error: 0.1723\n",
            "Epoch 283/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0566 - mean_squared_error: 0.0566 - val_loss: 0.1744 - val_mean_squared_error: 0.1744\n",
            "Epoch 284/700\n",
            "1/1 [==============================] - 0s 234ms/step - loss: 0.0561 - mean_squared_error: 0.0561 - val_loss: 0.1739 - val_mean_squared_error: 0.1739\n",
            "Epoch 285/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0558 - mean_squared_error: 0.0558 - val_loss: 0.1710 - val_mean_squared_error: 0.1710\n",
            "Epoch 286/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0561 - mean_squared_error: 0.0561 - val_loss: 0.1727 - val_mean_squared_error: 0.1727\n",
            "Epoch 287/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0561 - mean_squared_error: 0.0561 - val_loss: 0.1713 - val_mean_squared_error: 0.1713\n",
            "Epoch 288/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0558 - mean_squared_error: 0.0558 - val_loss: 0.1746 - val_mean_squared_error: 0.1746\n",
            "Epoch 289/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0552 - mean_squared_error: 0.0552 - val_loss: 0.1738 - val_mean_squared_error: 0.1738\n",
            "Epoch 290/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0547 - mean_squared_error: 0.0547 - val_loss: 0.1741 - val_mean_squared_error: 0.1741\n",
            "Epoch 291/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0545 - mean_squared_error: 0.0545 - val_loss: 0.1747 - val_mean_squared_error: 0.1747\n",
            "Epoch 292/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0545 - mean_squared_error: 0.0545 - val_loss: 0.1709 - val_mean_squared_error: 0.1709\n",
            "Epoch 293/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0547 - mean_squared_error: 0.0547 - val_loss: 0.1737 - val_mean_squared_error: 0.1737\n",
            "Epoch 294/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0552 - mean_squared_error: 0.0552 - val_loss: 0.1699 - val_mean_squared_error: 0.1699\n",
            "Epoch 295/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0561 - mean_squared_error: 0.0561 - val_loss: 0.1811 - val_mean_squared_error: 0.1811\n",
            "Epoch 296/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0581 - mean_squared_error: 0.0581 - val_loss: 0.1744 - val_mean_squared_error: 0.1744\n",
            "Epoch 297/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0643 - mean_squared_error: 0.0643 - val_loss: 0.1796 - val_mean_squared_error: 0.1796\n",
            "Epoch 298/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0612 - mean_squared_error: 0.0612 - val_loss: 0.1748 - val_mean_squared_error: 0.1748\n",
            "Epoch 299/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0597 - mean_squared_error: 0.0597 - val_loss: 0.1738 - val_mean_squared_error: 0.1738\n",
            "Epoch 300/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0537 - mean_squared_error: 0.0537 - val_loss: 0.1808 - val_mean_squared_error: 0.1808\n",
            "Epoch 301/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0559 - mean_squared_error: 0.0559 - val_loss: 0.1754 - val_mean_squared_error: 0.1754\n",
            "Epoch 302/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0626 - mean_squared_error: 0.0626 - val_loss: 0.1842 - val_mean_squared_error: 0.1842\n",
            "Epoch 303/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0599 - mean_squared_error: 0.0599 - val_loss: 0.1704 - val_mean_squared_error: 0.1704\n",
            "Epoch 304/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0620 - mean_squared_error: 0.0620 - val_loss: 0.1704 - val_mean_squared_error: 0.1704\n",
            "Epoch 305/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0556 - mean_squared_error: 0.0556 - val_loss: 0.1691 - val_mean_squared_error: 0.1691\n",
            "Epoch 306/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0569 - mean_squared_error: 0.0569 - val_loss: 0.1684 - val_mean_squared_error: 0.1684\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 307/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0653 - mean_squared_error: 0.0653 - val_loss: 0.1902 - val_mean_squared_error: 0.1902\n",
            "Epoch 308/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0605 - mean_squared_error: 0.0605 - val_loss: 0.1723 - val_mean_squared_error: 0.1723\n",
            "Epoch 309/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0572 - mean_squared_error: 0.0572 - val_loss: 0.1740 - val_mean_squared_error: 0.1740\n",
            "Epoch 310/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0530 - mean_squared_error: 0.0530 - val_loss: 0.1737 - val_mean_squared_error: 0.1737\n",
            "Epoch 311/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0566 - mean_squared_error: 0.0566 - val_loss: 0.1714 - val_mean_squared_error: 0.1714\n",
            "Epoch 312/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0644 - mean_squared_error: 0.0644 - val_loss: 0.1750 - val_mean_squared_error: 0.1750\n",
            "Epoch 313/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0570 - mean_squared_error: 0.0570 - val_loss: 0.1709 - val_mean_squared_error: 0.1709\n",
            "Epoch 314/700\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0538 - mean_squared_error: 0.0538 - val_loss: 0.1664 - val_mean_squared_error: 0.1664\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 315/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0522 - mean_squared_error: 0.0522 - val_loss: 0.1706 - val_mean_squared_error: 0.1706\n",
            "Epoch 316/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0555 - mean_squared_error: 0.0555 - val_loss: 0.1661 - val_mean_squared_error: 0.1661\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 317/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0584 - mean_squared_error: 0.0584 - val_loss: 0.1724 - val_mean_squared_error: 0.1724\n",
            "Epoch 318/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0542 - mean_squared_error: 0.0542 - val_loss: 0.1694 - val_mean_squared_error: 0.1694\n",
            "Epoch 319/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0518 - mean_squared_error: 0.0518 - val_loss: 0.1650 - val_mean_squared_error: 0.1650\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 320/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0508 - mean_squared_error: 0.0508 - val_loss: 0.1714 - val_mean_squared_error: 0.1714\n",
            "Epoch 321/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0531 - mean_squared_error: 0.0531 - val_loss: 0.1690 - val_mean_squared_error: 0.1690\n",
            "Epoch 322/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0576 - mean_squared_error: 0.0576 - val_loss: 0.1746 - val_mean_squared_error: 0.1746\n",
            "Epoch 323/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0557 - mean_squared_error: 0.0557 - val_loss: 0.1655 - val_mean_squared_error: 0.1655\n",
            "Epoch 324/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0561 - mean_squared_error: 0.0561 - val_loss: 0.1632 - val_mean_squared_error: 0.1632\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 325/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0513 - mean_squared_error: 0.0513 - val_loss: 0.1643 - val_mean_squared_error: 0.1643\n",
            "Epoch 326/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0520 - mean_squared_error: 0.0520 - val_loss: 0.1683 - val_mean_squared_error: 0.1683\n",
            "Epoch 327/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0589 - mean_squared_error: 0.0589 - val_loss: 0.1767 - val_mean_squared_error: 0.1767\n",
            "Epoch 328/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0583 - mean_squared_error: 0.0583 - val_loss: 0.1645 - val_mean_squared_error: 0.1645\n",
            "Epoch 329/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0550 - mean_squared_error: 0.0550 - val_loss: 0.1676 - val_mean_squared_error: 0.1676\n",
            "Epoch 330/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0509 - mean_squared_error: 0.0509 - val_loss: 0.1707 - val_mean_squared_error: 0.1707\n",
            "Epoch 331/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0516 - mean_squared_error: 0.0516 - val_loss: 0.1637 - val_mean_squared_error: 0.1637\n",
            "Epoch 332/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0559 - mean_squared_error: 0.0559 - val_loss: 0.1681 - val_mean_squared_error: 0.1681\n",
            "Epoch 333/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0531 - mean_squared_error: 0.0531 - val_loss: 0.1631 - val_mean_squared_error: 0.1631\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 334/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0524 - mean_squared_error: 0.0524 - val_loss: 0.1615 - val_mean_squared_error: 0.1615\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 335/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0500 - mean_squared_error: 0.0500 - val_loss: 0.1595 - val_mean_squared_error: 0.1595\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 336/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.0493 - mean_squared_error: 0.0493 - val_loss: 0.1599 - val_mean_squared_error: 0.1599\n",
            "Epoch 337/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0497 - mean_squared_error: 0.0497 - val_loss: 0.1653 - val_mean_squared_error: 0.1653\n",
            "Epoch 338/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0512 - mean_squared_error: 0.0512 - val_loss: 0.1600 - val_mean_squared_error: 0.1600\n",
            "Epoch 339/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0546 - mean_squared_error: 0.0546 - val_loss: 0.1642 - val_mean_squared_error: 0.1642\n",
            "Epoch 340/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0534 - mean_squared_error: 0.0534 - val_loss: 0.1619 - val_mean_squared_error: 0.1619\n",
            "Epoch 341/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0529 - mean_squared_error: 0.0529 - val_loss: 0.1615 - val_mean_squared_error: 0.1615\n",
            "Epoch 342/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0508 - mean_squared_error: 0.0508 - val_loss: 0.1584 - val_mean_squared_error: 0.1584\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 343/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0510 - mean_squared_error: 0.0510 - val_loss: 0.1634 - val_mean_squared_error: 0.1634\n",
            "Epoch 344/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0490 - mean_squared_error: 0.0490 - val_loss: 0.1625 - val_mean_squared_error: 0.1625\n",
            "Epoch 345/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0498 - mean_squared_error: 0.0498 - val_loss: 0.1587 - val_mean_squared_error: 0.1587\n",
            "Epoch 346/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0491 - mean_squared_error: 0.0491 - val_loss: 0.1554 - val_mean_squared_error: 0.1554\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 347/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0493 - mean_squared_error: 0.0493 - val_loss: 0.1559 - val_mean_squared_error: 0.1559\n",
            "Epoch 348/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0482 - mean_squared_error: 0.0482 - val_loss: 0.1584 - val_mean_squared_error: 0.1584\n",
            "Epoch 349/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0504 - mean_squared_error: 0.0504 - val_loss: 0.1515 - val_mean_squared_error: 0.1515\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 350/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0515 - mean_squared_error: 0.0515 - val_loss: 0.1596 - val_mean_squared_error: 0.1596\n",
            "Epoch 351/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0538 - mean_squared_error: 0.0538 - val_loss: 0.1614 - val_mean_squared_error: 0.1614\n",
            "Epoch 352/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0562 - mean_squared_error: 0.0562 - val_loss: 0.1673 - val_mean_squared_error: 0.1673\n",
            "Epoch 353/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0564 - mean_squared_error: 0.0564 - val_loss: 0.1587 - val_mean_squared_error: 0.1587\n",
            "Epoch 354/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0674 - mean_squared_error: 0.0674 - val_loss: 0.1717 - val_mean_squared_error: 0.1717\n",
            "Epoch 355/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0615 - mean_squared_error: 0.0615 - val_loss: 0.1663 - val_mean_squared_error: 0.1663\n",
            "Epoch 356/700\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0613 - mean_squared_error: 0.0613 - val_loss: 0.1601 - val_mean_squared_error: 0.1601\n",
            "Epoch 357/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0493 - mean_squared_error: 0.0493 - val_loss: 0.1645 - val_mean_squared_error: 0.1645\n",
            "Epoch 358/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0545 - mean_squared_error: 0.0545 - val_loss: 0.1608 - val_mean_squared_error: 0.1608\n",
            "Epoch 359/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0593 - mean_squared_error: 0.0593 - val_loss: 0.1823 - val_mean_squared_error: 0.1823\n",
            "Epoch 360/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0642 - mean_squared_error: 0.0642 - val_loss: 0.1748 - val_mean_squared_error: 0.1748\n",
            "Epoch 361/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0835 - mean_squared_error: 0.0835 - val_loss: 0.1851 - val_mean_squared_error: 0.1851\n",
            "Epoch 362/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0661 - mean_squared_error: 0.0661 - val_loss: 0.1652 - val_mean_squared_error: 0.1652\n",
            "Epoch 363/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0649 - mean_squared_error: 0.0649 - val_loss: 0.1656 - val_mean_squared_error: 0.1656\n",
            "Epoch 364/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0959 - mean_squared_error: 0.0959 - val_loss: 0.1756 - val_mean_squared_error: 0.1756\n",
            "Epoch 365/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0941 - mean_squared_error: 0.0941 - val_loss: 0.1702 - val_mean_squared_error: 0.1702\n",
            "Epoch 366/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0735 - mean_squared_error: 0.0735 - val_loss: 0.1900 - val_mean_squared_error: 0.1900\n",
            "Epoch 367/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0738 - mean_squared_error: 0.0738 - val_loss: 0.1805 - val_mean_squared_error: 0.1805\n",
            "Epoch 368/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0627 - mean_squared_error: 0.0627 - val_loss: 0.1842 - val_mean_squared_error: 0.1842\n",
            "Epoch 369/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0629 - mean_squared_error: 0.0629 - val_loss: 0.1709 - val_mean_squared_error: 0.1709\n",
            "Epoch 370/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0591 - mean_squared_error: 0.0591 - val_loss: 0.1589 - val_mean_squared_error: 0.1589\n",
            "Epoch 371/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0651 - mean_squared_error: 0.0651 - val_loss: 0.1465 - val_mean_squared_error: 0.1465\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 372/700\n",
            "1/1 [==============================] - 0s 43ms/step - loss: 0.0543 - mean_squared_error: 0.0543 - val_loss: 0.1491 - val_mean_squared_error: 0.1491\n",
            "Epoch 373/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0591 - mean_squared_error: 0.0591 - val_loss: 0.1462 - val_mean_squared_error: 0.1462\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 374/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0596 - mean_squared_error: 0.0596 - val_loss: 0.1517 - val_mean_squared_error: 0.1517\n",
            "Epoch 375/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0550 - mean_squared_error: 0.0550 - val_loss: 0.1485 - val_mean_squared_error: 0.1485\n",
            "Epoch 376/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0526 - mean_squared_error: 0.0526 - val_loss: 0.1546 - val_mean_squared_error: 0.1546\n",
            "Epoch 377/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0543 - mean_squared_error: 0.0543 - val_loss: 0.1466 - val_mean_squared_error: 0.1466\n",
            "Epoch 378/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0520 - mean_squared_error: 0.0520 - val_loss: 0.1529 - val_mean_squared_error: 0.1529\n",
            "Epoch 379/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0517 - mean_squared_error: 0.0517 - val_loss: 0.1583 - val_mean_squared_error: 0.1583\n",
            "Epoch 380/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0499 - mean_squared_error: 0.0499 - val_loss: 0.1573 - val_mean_squared_error: 0.1573\n",
            "Epoch 381/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0510 - mean_squared_error: 0.0510 - val_loss: 0.1586 - val_mean_squared_error: 0.1586\n",
            "Epoch 382/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0501 - mean_squared_error: 0.0501 - val_loss: 0.1528 - val_mean_squared_error: 0.1528\n",
            "Epoch 383/700\n",
            "1/1 [==============================] - 0s 233ms/step - loss: 0.0498 - mean_squared_error: 0.0498 - val_loss: 0.1451 - val_mean_squared_error: 0.1451\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 384/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0488 - mean_squared_error: 0.0488 - val_loss: 0.1481 - val_mean_squared_error: 0.1481\n",
            "Epoch 385/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0497 - mean_squared_error: 0.0497 - val_loss: 0.1457 - val_mean_squared_error: 0.1457\n",
            "Epoch 386/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0499 - mean_squared_error: 0.0499 - val_loss: 0.1550 - val_mean_squared_error: 0.1550\n",
            "Epoch 387/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0479 - mean_squared_error: 0.0479 - val_loss: 0.1585 - val_mean_squared_error: 0.1585\n",
            "Epoch 388/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0479 - mean_squared_error: 0.0479 - val_loss: 0.1530 - val_mean_squared_error: 0.1530\n",
            "Epoch 389/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0476 - mean_squared_error: 0.0476 - val_loss: 0.1533 - val_mean_squared_error: 0.1533\n",
            "Epoch 390/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0473 - mean_squared_error: 0.0473 - val_loss: 0.1491 - val_mean_squared_error: 0.1491\n",
            "Epoch 391/700\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0472 - mean_squared_error: 0.0472 - val_loss: 0.1464 - val_mean_squared_error: 0.1464\n",
            "Epoch 392/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0468 - mean_squared_error: 0.0468 - val_loss: 0.1459 - val_mean_squared_error: 0.1459\n",
            "Epoch 393/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0469 - mean_squared_error: 0.0469 - val_loss: 0.1514 - val_mean_squared_error: 0.1514\n",
            "Epoch 394/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0456 - mean_squared_error: 0.0456 - val_loss: 0.1630 - val_mean_squared_error: 0.1630\n",
            "Epoch 395/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0482 - mean_squared_error: 0.0482 - val_loss: 0.1543 - val_mean_squared_error: 0.1543\n",
            "Epoch 396/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0492 - mean_squared_error: 0.0492 - val_loss: 0.1612 - val_mean_squared_error: 0.1612\n",
            "Epoch 397/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0486 - mean_squared_error: 0.0486 - val_loss: 0.1470 - val_mean_squared_error: 0.1470\n",
            "Epoch 398/700\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0458 - mean_squared_error: 0.0458 - val_loss: 0.1466 - val_mean_squared_error: 0.1466\n",
            "Epoch 399/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0460 - mean_squared_error: 0.0460 - val_loss: 0.1490 - val_mean_squared_error: 0.1490\n",
            "Epoch 400/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0459 - mean_squared_error: 0.0459 - val_loss: 0.1474 - val_mean_squared_error: 0.1474\n",
            "Epoch 401/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0458 - mean_squared_error: 0.0458 - val_loss: 0.1557 - val_mean_squared_error: 0.1557\n",
            "Epoch 402/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0451 - mean_squared_error: 0.0451 - val_loss: 0.1562 - val_mean_squared_error: 0.1562\n",
            "Epoch 403/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0451 - mean_squared_error: 0.0451 - val_loss: 0.1548 - val_mean_squared_error: 0.1548\n",
            "Epoch 404/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0442 - mean_squared_error: 0.0442 - val_loss: 0.1586 - val_mean_squared_error: 0.1586\n",
            "Epoch 405/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0451 - mean_squared_error: 0.0451 - val_loss: 0.1508 - val_mean_squared_error: 0.1508\n",
            "Epoch 406/700\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0448 - mean_squared_error: 0.0448 - val_loss: 0.1533 - val_mean_squared_error: 0.1533\n",
            "Epoch 407/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0457 - mean_squared_error: 0.0457 - val_loss: 0.1455 - val_mean_squared_error: 0.1455\n",
            "Epoch 408/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0468 - mean_squared_error: 0.0468 - val_loss: 0.1545 - val_mean_squared_error: 0.1545\n",
            "Epoch 409/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0459 - mean_squared_error: 0.0459 - val_loss: 0.1507 - val_mean_squared_error: 0.1507\n",
            "Epoch 410/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0442 - mean_squared_error: 0.0442 - val_loss: 0.1567 - val_mean_squared_error: 0.1567\n",
            "Epoch 411/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0443 - mean_squared_error: 0.0443 - val_loss: 0.1463 - val_mean_squared_error: 0.1463\n",
            "Epoch 412/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0441 - mean_squared_error: 0.0441 - val_loss: 0.1478 - val_mean_squared_error: 0.1478\n",
            "Epoch 413/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0443 - mean_squared_error: 0.0443 - val_loss: 0.1444 - val_mean_squared_error: 0.1444\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 414/700\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0446 - mean_squared_error: 0.0446 - val_loss: 0.1478 - val_mean_squared_error: 0.1478\n",
            "Epoch 415/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0437 - mean_squared_error: 0.0437 - val_loss: 0.1462 - val_mean_squared_error: 0.1462\n",
            "Epoch 416/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0452 - mean_squared_error: 0.0452 - val_loss: 0.1591 - val_mean_squared_error: 0.1591\n",
            "Epoch 417/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0438 - mean_squared_error: 0.0438 - val_loss: 0.1525 - val_mean_squared_error: 0.1525\n",
            "Epoch 418/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0443 - mean_squared_error: 0.0443 - val_loss: 0.1551 - val_mean_squared_error: 0.1551\n",
            "Epoch 419/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0436 - mean_squared_error: 0.0436 - val_loss: 0.1447 - val_mean_squared_error: 0.1447\n",
            "Epoch 420/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0449 - mean_squared_error: 0.0449 - val_loss: 0.1496 - val_mean_squared_error: 0.1496\n",
            "Epoch 421/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0450 - mean_squared_error: 0.0450 - val_loss: 0.1432 - val_mean_squared_error: 0.1432\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 422/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.0475 - mean_squared_error: 0.0475 - val_loss: 0.1560 - val_mean_squared_error: 0.1560\n",
            "Epoch 423/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0472 - mean_squared_error: 0.0472 - val_loss: 0.1418 - val_mean_squared_error: 0.1418\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 424/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0504 - mean_squared_error: 0.0504 - val_loss: 0.1620 - val_mean_squared_error: 0.1620\n",
            "Epoch 425/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0453 - mean_squared_error: 0.0453 - val_loss: 0.1483 - val_mean_squared_error: 0.1483\n",
            "Epoch 426/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0446 - mean_squared_error: 0.0446 - val_loss: 0.1498 - val_mean_squared_error: 0.1498\n",
            "Epoch 427/700\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0427 - mean_squared_error: 0.0427 - val_loss: 0.1447 - val_mean_squared_error: 0.1447\n",
            "Epoch 428/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0435 - mean_squared_error: 0.0435 - val_loss: 0.1445 - val_mean_squared_error: 0.1445\n",
            "Epoch 429/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0421 - mean_squared_error: 0.0421 - val_loss: 0.1484 - val_mean_squared_error: 0.1484\n",
            "Epoch 430/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0427 - mean_squared_error: 0.0427 - val_loss: 0.1443 - val_mean_squared_error: 0.1443\n",
            "Epoch 431/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0432 - mean_squared_error: 0.0432 - val_loss: 0.1547 - val_mean_squared_error: 0.1547\n",
            "Epoch 432/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0448 - mean_squared_error: 0.0448 - val_loss: 0.1395 - val_mean_squared_error: 0.1395\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 433/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0453 - mean_squared_error: 0.0453 - val_loss: 0.1499 - val_mean_squared_error: 0.1499\n",
            "Epoch 434/700\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 0.0492 - mean_squared_error: 0.0492 - val_loss: 0.1486 - val_mean_squared_error: 0.1486\n",
            "Epoch 435/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0792 - mean_squared_error: 0.0792 - val_loss: 0.1831 - val_mean_squared_error: 0.1831\n",
            "Epoch 436/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0743 - mean_squared_error: 0.0743 - val_loss: 0.1481 - val_mean_squared_error: 0.1481\n",
            "Epoch 437/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0780 - mean_squared_error: 0.0780 - val_loss: 0.1383 - val_mean_squared_error: 0.1383\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 438/700\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 0.0477 - mean_squared_error: 0.0477 - val_loss: 0.1546 - val_mean_squared_error: 0.1546\n",
            "Epoch 439/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0554 - mean_squared_error: 0.0554 - val_loss: 0.1549 - val_mean_squared_error: 0.1549\n",
            "Epoch 440/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0717 - mean_squared_error: 0.0717 - val_loss: 0.1603 - val_mean_squared_error: 0.1603\n",
            "Epoch 441/700\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0557 - mean_squared_error: 0.0557 - val_loss: 0.1453 - val_mean_squared_error: 0.1453\n",
            "Epoch 442/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0533 - mean_squared_error: 0.0533 - val_loss: 0.1457 - val_mean_squared_error: 0.1457\n",
            "Epoch 443/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0466 - mean_squared_error: 0.0466 - val_loss: 0.1517 - val_mean_squared_error: 0.1517\n",
            "Epoch 444/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0520 - mean_squared_error: 0.0520 - val_loss: 0.1390 - val_mean_squared_error: 0.1390\n",
            "Epoch 445/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0466 - mean_squared_error: 0.0466 - val_loss: 0.1446 - val_mean_squared_error: 0.1446\n",
            "Epoch 446/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0483 - mean_squared_error: 0.0483 - val_loss: 0.1367 - val_mean_squared_error: 0.1367\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 447/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0458 - mean_squared_error: 0.0458 - val_loss: 0.1361 - val_mean_squared_error: 0.1361\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 448/700\n",
            "1/1 [==============================] - 0s 42ms/step - loss: 0.0448 - mean_squared_error: 0.0448 - val_loss: 0.1376 - val_mean_squared_error: 0.1376\n",
            "Epoch 449/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0446 - mean_squared_error: 0.0446 - val_loss: 0.1293 - val_mean_squared_error: 0.1293\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 450/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0435 - mean_squared_error: 0.0435 - val_loss: 0.1387 - val_mean_squared_error: 0.1387\n",
            "Epoch 451/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0454 - mean_squared_error: 0.0454 - val_loss: 0.1329 - val_mean_squared_error: 0.1329\n",
            "Epoch 452/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0424 - mean_squared_error: 0.0424 - val_loss: 0.1377 - val_mean_squared_error: 0.1377\n",
            "Epoch 453/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0418 - mean_squared_error: 0.0418 - val_loss: 0.1377 - val_mean_squared_error: 0.1377\n",
            "Epoch 454/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0432 - mean_squared_error: 0.0432 - val_loss: 0.1360 - val_mean_squared_error: 0.1360\n",
            "Epoch 455/700\n",
            "1/1 [==============================] - 0s 249ms/step - loss: 0.0425 - mean_squared_error: 0.0425 - val_loss: 0.1272 - val_mean_squared_error: 0.1272\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 456/700\n",
            "1/1 [==============================] - 0s 41ms/step - loss: 0.0417 - mean_squared_error: 0.0417 - val_loss: 0.1363 - val_mean_squared_error: 0.1363\n",
            "Epoch 457/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0424 - mean_squared_error: 0.0424 - val_loss: 0.1330 - val_mean_squared_error: 0.1330\n",
            "Epoch 458/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0411 - mean_squared_error: 0.0411 - val_loss: 0.1336 - val_mean_squared_error: 0.1336\n",
            "Epoch 459/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0416 - mean_squared_error: 0.0416 - val_loss: 0.1323 - val_mean_squared_error: 0.1323\n",
            "Epoch 460/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0408 - mean_squared_error: 0.0408 - val_loss: 0.1312 - val_mean_squared_error: 0.1312\n",
            "Epoch 461/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0410 - mean_squared_error: 0.0410 - val_loss: 0.1357 - val_mean_squared_error: 0.1357\n",
            "Epoch 462/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0406 - mean_squared_error: 0.0406 - val_loss: 0.1387 - val_mean_squared_error: 0.1387\n",
            "Epoch 463/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0410 - mean_squared_error: 0.0410 - val_loss: 0.1451 - val_mean_squared_error: 0.1451\n",
            "Epoch 464/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0415 - mean_squared_error: 0.0415 - val_loss: 0.1339 - val_mean_squared_error: 0.1339\n",
            "Epoch 465/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0418 - mean_squared_error: 0.0418 - val_loss: 0.1444 - val_mean_squared_error: 0.1444\n",
            "Epoch 466/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0414 - mean_squared_error: 0.0414 - val_loss: 0.1323 - val_mean_squared_error: 0.1323\n",
            "Epoch 467/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0405 - mean_squared_error: 0.0405 - val_loss: 0.1353 - val_mean_squared_error: 0.1353\n",
            "Epoch 468/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0397 - mean_squared_error: 0.0397 - val_loss: 0.1346 - val_mean_squared_error: 0.1346\n",
            "Epoch 469/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0398 - mean_squared_error: 0.0398 - val_loss: 0.1267 - val_mean_squared_error: 0.1267\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 470/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0416 - mean_squared_error: 0.0416 - val_loss: 0.1437 - val_mean_squared_error: 0.1437\n",
            "Epoch 471/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0449 - mean_squared_error: 0.0449 - val_loss: 0.1302 - val_mean_squared_error: 0.1302\n",
            "Epoch 472/700\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0511 - mean_squared_error: 0.0511 - val_loss: 0.1522 - val_mean_squared_error: 0.1522\n",
            "Epoch 473/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0468 - mean_squared_error: 0.0468 - val_loss: 0.1318 - val_mean_squared_error: 0.1318\n",
            "Epoch 474/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0441 - mean_squared_error: 0.0441 - val_loss: 0.1322 - val_mean_squared_error: 0.1322\n",
            "Epoch 475/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0396 - mean_squared_error: 0.0396 - val_loss: 0.1413 - val_mean_squared_error: 0.1413\n",
            "Epoch 476/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0436 - mean_squared_error: 0.0436 - val_loss: 0.1296 - val_mean_squared_error: 0.1296\n",
            "Epoch 477/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0538 - mean_squared_error: 0.0538 - val_loss: 0.1471 - val_mean_squared_error: 0.1471\n",
            "Epoch 478/700\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0477 - mean_squared_error: 0.0477 - val_loss: 0.1274 - val_mean_squared_error: 0.1274\n",
            "Epoch 479/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0405 - mean_squared_error: 0.0405 - val_loss: 0.1266 - val_mean_squared_error: 0.1266\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 480/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0408 - mean_squared_error: 0.0408 - val_loss: 0.1504 - val_mean_squared_error: 0.1504\n",
            "Epoch 481/700\n",
            "1/1 [==============================] - 0s 244ms/step - loss: 0.0468 - mean_squared_error: 0.0468 - val_loss: 0.1309 - val_mean_squared_error: 0.1309\n",
            "Epoch 482/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0565 - mean_squared_error: 0.0565 - val_loss: 0.1487 - val_mean_squared_error: 0.1487\n",
            "Epoch 483/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0483 - mean_squared_error: 0.0483 - val_loss: 0.1280 - val_mean_squared_error: 0.1280\n",
            "Epoch 484/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0407 - mean_squared_error: 0.0407 - val_loss: 0.1256 - val_mean_squared_error: 0.1256\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 485/700\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 0.0419 - mean_squared_error: 0.0419 - val_loss: 0.1488 - val_mean_squared_error: 0.1488\n",
            "Epoch 486/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0482 - mean_squared_error: 0.0482 - val_loss: 0.1275 - val_mean_squared_error: 0.1275\n",
            "Epoch 487/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0544 - mean_squared_error: 0.0544 - val_loss: 0.1375 - val_mean_squared_error: 0.1375\n",
            "Epoch 488/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0433 - mean_squared_error: 0.0433 - val_loss: 0.1310 - val_mean_squared_error: 0.1310\n",
            "Epoch 489/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0399 - mean_squared_error: 0.0399 - val_loss: 0.1271 - val_mean_squared_error: 0.1271\n",
            "Epoch 490/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0470 - mean_squared_error: 0.0470 - val_loss: 0.1441 - val_mean_squared_error: 0.1441\n",
            "Epoch 491/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0454 - mean_squared_error: 0.0454 - val_loss: 0.1269 - val_mean_squared_error: 0.1269\n",
            "Epoch 492/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0467 - mean_squared_error: 0.0467 - val_loss: 0.1371 - val_mean_squared_error: 0.1371\n",
            "Epoch 493/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0412 - mean_squared_error: 0.0412 - val_loss: 0.1349 - val_mean_squared_error: 0.1349\n",
            "Epoch 494/700\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0400 - mean_squared_error: 0.0400 - val_loss: 0.1261 - val_mean_squared_error: 0.1261\n",
            "Epoch 495/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0444 - mean_squared_error: 0.0444 - val_loss: 0.1395 - val_mean_squared_error: 0.1395\n",
            "Epoch 496/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0432 - mean_squared_error: 0.0432 - val_loss: 0.1290 - val_mean_squared_error: 0.1290\n",
            "Epoch 497/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0394 - mean_squared_error: 0.0394 - val_loss: 0.1327 - val_mean_squared_error: 0.1327\n",
            "Epoch 498/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0399 - mean_squared_error: 0.0399 - val_loss: 0.1426 - val_mean_squared_error: 0.1426\n",
            "Epoch 499/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0404 - mean_squared_error: 0.0404 - val_loss: 0.1283 - val_mean_squared_error: 0.1283\n",
            "Epoch 500/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0414 - mean_squared_error: 0.0414 - val_loss: 0.1326 - val_mean_squared_error: 0.1326\n",
            "Epoch 501/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0387 - mean_squared_error: 0.0387 - val_loss: 0.1281 - val_mean_squared_error: 0.1281\n",
            "Epoch 502/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0379 - mean_squared_error: 0.0379 - val_loss: 0.1287 - val_mean_squared_error: 0.1287\n",
            "Epoch 503/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0386 - mean_squared_error: 0.0386 - val_loss: 0.1345 - val_mean_squared_error: 0.1345\n",
            "Epoch 504/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0380 - mean_squared_error: 0.0380 - val_loss: 0.1328 - val_mean_squared_error: 0.1328\n",
            "Epoch 505/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0377 - mean_squared_error: 0.0377 - val_loss: 0.1324 - val_mean_squared_error: 0.1324\n",
            "Epoch 506/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0376 - mean_squared_error: 0.0376 - val_loss: 0.1374 - val_mean_squared_error: 0.1374\n",
            "Epoch 507/700\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 0.0381 - mean_squared_error: 0.0381 - val_loss: 0.1285 - val_mean_squared_error: 0.1285\n",
            "Epoch 508/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0386 - mean_squared_error: 0.0386 - val_loss: 0.1313 - val_mean_squared_error: 0.1313\n",
            "Epoch 509/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0377 - mean_squared_error: 0.0377 - val_loss: 0.1275 - val_mean_squared_error: 0.1275\n",
            "Epoch 510/700\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 0.0371 - mean_squared_error: 0.0371 - val_loss: 0.1319 - val_mean_squared_error: 0.1319\n",
            "Epoch 511/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0370 - mean_squared_error: 0.0370 - val_loss: 0.1365 - val_mean_squared_error: 0.1365\n",
            "Epoch 512/700\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0369 - mean_squared_error: 0.0369 - val_loss: 0.1301 - val_mean_squared_error: 0.1301\n",
            "Epoch 513/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0374 - mean_squared_error: 0.0374 - val_loss: 0.1383 - val_mean_squared_error: 0.1383\n",
            "Epoch 514/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0385 - mean_squared_error: 0.0385 - val_loss: 0.1290 - val_mean_squared_error: 0.1290\n",
            "Epoch 515/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0406 - mean_squared_error: 0.0406 - val_loss: 0.1468 - val_mean_squared_error: 0.1468\n",
            "Epoch 516/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0400 - mean_squared_error: 0.0400 - val_loss: 0.1312 - val_mean_squared_error: 0.1312\n",
            "Epoch 517/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0412 - mean_squared_error: 0.0412 - val_loss: 0.1468 - val_mean_squared_error: 0.1468\n",
            "Epoch 518/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0398 - mean_squared_error: 0.0398 - val_loss: 0.1311 - val_mean_squared_error: 0.1311\n",
            "Epoch 519/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0373 - mean_squared_error: 0.0373 - val_loss: 0.1334 - val_mean_squared_error: 0.1334\n",
            "Epoch 520/700\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.0373 - mean_squared_error: 0.0373 - val_loss: 0.1376 - val_mean_squared_error: 0.1376\n",
            "Epoch 521/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0384 - mean_squared_error: 0.0384 - val_loss: 0.1241 - val_mean_squared_error: 0.1241\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 522/700\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 0.0427 - mean_squared_error: 0.0427 - val_loss: 0.1517 - val_mean_squared_error: 0.1517\n",
            "Epoch 523/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0456 - mean_squared_error: 0.0456 - val_loss: 0.1276 - val_mean_squared_error: 0.1276\n",
            "Epoch 524/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0489 - mean_squared_error: 0.0489 - val_loss: 0.1508 - val_mean_squared_error: 0.1508\n",
            "Epoch 525/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0448 - mean_squared_error: 0.0448 - val_loss: 0.1272 - val_mean_squared_error: 0.1272\n",
            "Epoch 526/700\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 0.0419 - mean_squared_error: 0.0419 - val_loss: 0.1330 - val_mean_squared_error: 0.1330\n",
            "Epoch 527/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0369 - mean_squared_error: 0.0369 - val_loss: 0.1395 - val_mean_squared_error: 0.1395\n",
            "Epoch 528/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0398 - mean_squared_error: 0.0398 - val_loss: 0.1300 - val_mean_squared_error: 0.1300\n",
            "Epoch 529/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0499 - mean_squared_error: 0.0499 - val_loss: 0.1587 - val_mean_squared_error: 0.1587\n",
            "Epoch 530/700\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0538 - mean_squared_error: 0.0538 - val_loss: 0.1332 - val_mean_squared_error: 0.1332\n",
            "Epoch 531/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0806 - mean_squared_error: 0.0806 - val_loss: 0.1629 - val_mean_squared_error: 0.1629\n",
            "Epoch 532/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0764 - mean_squared_error: 0.0764 - val_loss: 0.1231 - val_mean_squared_error: 0.1231\n",
            "INFO:tensorflow:Assets written to: valuePredictor.z1/assets\n",
            "Epoch 533/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0727 - mean_squared_error: 0.0727 - val_loss: 0.1412 - val_mean_squared_error: 0.1412\n",
            "Epoch 534/700\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0457 - mean_squared_error: 0.0457 - val_loss: 0.1373 - val_mean_squared_error: 0.1373\n",
            "Epoch 535/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0559 - mean_squared_error: 0.0559 - val_loss: 0.1332 - val_mean_squared_error: 0.1332\n",
            "Epoch 536/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0953 - mean_squared_error: 0.0953 - val_loss: 0.2005 - val_mean_squared_error: 0.2005\n",
            "Epoch 537/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0847 - mean_squared_error: 0.0847 - val_loss: 0.1389 - val_mean_squared_error: 0.1389\n",
            "Epoch 538/700\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0764 - mean_squared_error: 0.0764 - val_loss: 0.1293 - val_mean_squared_error: 0.1293\n",
            "Epoch 539/700\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0435 - mean_squared_error: 0.0435 - val_loss: 0.3382 - val_mean_squared_error: 0.3382\n",
            "Epoch 540/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.1045 - mean_squared_error: 0.1045 - val_loss: 0.1958 - val_mean_squared_error: 0.1958\n",
            "Epoch 541/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.2016 - mean_squared_error: 0.2016 - val_loss: 0.1852 - val_mean_squared_error: 0.1852\n",
            "Epoch 542/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.1395 - mean_squared_error: 0.1395 - val_loss: 0.3348 - val_mean_squared_error: 0.3348\n",
            "Epoch 543/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1962 - mean_squared_error: 0.1962 - val_loss: 0.1405 - val_mean_squared_error: 0.1405\n",
            "Epoch 544/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.1006 - mean_squared_error: 0.1006 - val_loss: 0.1780 - val_mean_squared_error: 0.1780\n",
            "Epoch 545/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.1365 - mean_squared_error: 0.1365 - val_loss: 0.1503 - val_mean_squared_error: 0.1503\n",
            "Epoch 546/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1098 - mean_squared_error: 0.1098 - val_loss: 0.1554 - val_mean_squared_error: 0.1554\n",
            "Epoch 547/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0914 - mean_squared_error: 0.0914 - val_loss: 0.1442 - val_mean_squared_error: 0.1442\n",
            "Epoch 548/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.1037 - mean_squared_error: 0.1037 - val_loss: 0.1682 - val_mean_squared_error: 0.1682\n",
            "Epoch 549/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0800 - mean_squared_error: 0.0800 - val_loss: 0.2366 - val_mean_squared_error: 0.2366\n",
            "Epoch 550/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1114 - mean_squared_error: 0.1114 - val_loss: 0.1636 - val_mean_squared_error: 0.1636\n",
            "Epoch 551/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.1507 - mean_squared_error: 0.1507 - val_loss: 0.1378 - val_mean_squared_error: 0.1378\n",
            "Epoch 552/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.1196 - mean_squared_error: 0.1196 - val_loss: 0.1966 - val_mean_squared_error: 0.1966\n",
            "Epoch 553/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.1072 - mean_squared_error: 0.1072 - val_loss: 0.1705 - val_mean_squared_error: 0.1705\n",
            "Epoch 554/700\n",
            "1/1 [==============================] - 0s 219ms/step - loss: 0.1132 - mean_squared_error: 0.1132 - val_loss: 0.1443 - val_mean_squared_error: 0.1443\n",
            "Epoch 555/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.1026 - mean_squared_error: 0.1026 - val_loss: 0.1467 - val_mean_squared_error: 0.1467\n",
            "Epoch 556/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0875 - mean_squared_error: 0.0875 - val_loss: 0.3603 - val_mean_squared_error: 0.3603\n",
            "Epoch 557/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.2244 - mean_squared_error: 0.2244 - val_loss: 0.2615 - val_mean_squared_error: 0.2615\n",
            "Epoch 558/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.3341 - mean_squared_error: 0.3341 - val_loss: 0.2499 - val_mean_squared_error: 0.2499\n",
            "Epoch 559/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.2703 - mean_squared_error: 0.2703 - val_loss: 0.2806 - val_mean_squared_error: 0.2806\n",
            "Epoch 560/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.2784 - mean_squared_error: 0.2784 - val_loss: 0.3659 - val_mean_squared_error: 0.3659\n",
            "Epoch 561/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.3153 - mean_squared_error: 0.3153 - val_loss: 0.2709 - val_mean_squared_error: 0.2709\n",
            "Epoch 562/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.2703 - mean_squared_error: 0.2703 - val_loss: 0.2193 - val_mean_squared_error: 0.2193\n",
            "Epoch 563/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.2717 - mean_squared_error: 0.2717 - val_loss: 0.1470 - val_mean_squared_error: 0.1470\n",
            "Epoch 564/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.2145 - mean_squared_error: 0.2145 - val_loss: 0.2608 - val_mean_squared_error: 0.2608\n",
            "Epoch 565/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.2355 - mean_squared_error: 0.2355 - val_loss: 0.2950 - val_mean_squared_error: 0.2950\n",
            "Epoch 566/700\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 0.2277 - mean_squared_error: 0.2277 - val_loss: 0.1802 - val_mean_squared_error: 0.1802\n",
            "Epoch 567/700\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.1514 - mean_squared_error: 0.1514 - val_loss: 0.1882 - val_mean_squared_error: 0.1882\n",
            "Epoch 568/700\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.1713 - mean_squared_error: 0.1713 - val_loss: 0.2023 - val_mean_squared_error: 0.2023\n",
            "Epoch 569/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1648 - mean_squared_error: 0.1648 - val_loss: 0.1978 - val_mean_squared_error: 0.1978\n",
            "Epoch 570/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.1244 - mean_squared_error: 0.1244 - val_loss: 0.1978 - val_mean_squared_error: 0.1978\n",
            "Epoch 571/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.1033 - mean_squared_error: 0.1033 - val_loss: 0.2320 - val_mean_squared_error: 0.2320\n",
            "Epoch 572/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.1100 - mean_squared_error: 0.1100 - val_loss: 0.2225 - val_mean_squared_error: 0.2225\n",
            "Epoch 573/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.1173 - mean_squared_error: 0.1173 - val_loss: 0.2156 - val_mean_squared_error: 0.2156\n",
            "Epoch 574/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.1063 - mean_squared_error: 0.1063 - val_loss: 0.1992 - val_mean_squared_error: 0.1992\n",
            "Epoch 575/700\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.1003 - mean_squared_error: 0.1003 - val_loss: 0.1964 - val_mean_squared_error: 0.1964\n",
            "Epoch 576/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0956 - mean_squared_error: 0.0956 - val_loss: 0.2118 - val_mean_squared_error: 0.2118\n",
            "Epoch 577/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.1024 - mean_squared_error: 0.1024 - val_loss: 0.2497 - val_mean_squared_error: 0.2497\n",
            "Epoch 578/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.1103 - mean_squared_error: 0.1103 - val_loss: 0.2460 - val_mean_squared_error: 0.2460\n",
            "Epoch 579/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0921 - mean_squared_error: 0.0921 - val_loss: 0.2433 - val_mean_squared_error: 0.2433\n",
            "Epoch 580/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0905 - mean_squared_error: 0.0905 - val_loss: 0.2272 - val_mean_squared_error: 0.2272\n",
            "Epoch 581/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0825 - mean_squared_error: 0.0825 - val_loss: 0.2066 - val_mean_squared_error: 0.2066\n",
            "Epoch 582/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0798 - mean_squared_error: 0.0798 - val_loss: 0.1883 - val_mean_squared_error: 0.1883\n",
            "Epoch 583/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0765 - mean_squared_error: 0.0765 - val_loss: 0.1836 - val_mean_squared_error: 0.1836\n",
            "Epoch 584/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0832 - mean_squared_error: 0.0832 - val_loss: 0.1743 - val_mean_squared_error: 0.1743\n",
            "Epoch 585/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0821 - mean_squared_error: 0.0821 - val_loss: 0.1581 - val_mean_squared_error: 0.1581\n",
            "Epoch 586/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0784 - mean_squared_error: 0.0784 - val_loss: 0.1528 - val_mean_squared_error: 0.1528\n",
            "Epoch 587/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0727 - mean_squared_error: 0.0727 - val_loss: 0.1624 - val_mean_squared_error: 0.1624\n",
            "Epoch 588/700\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.0689 - mean_squared_error: 0.0689 - val_loss: 0.1691 - val_mean_squared_error: 0.1691\n",
            "Epoch 589/700\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 0.0665 - mean_squared_error: 0.0665 - val_loss: 0.1834 - val_mean_squared_error: 0.1834\n",
            "Epoch 590/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0677 - mean_squared_error: 0.0677 - val_loss: 0.1826 - val_mean_squared_error: 0.1826\n",
            "Epoch 591/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0696 - mean_squared_error: 0.0696 - val_loss: 0.1863 - val_mean_squared_error: 0.1863\n",
            "Epoch 592/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0677 - mean_squared_error: 0.0677 - val_loss: 0.2030 - val_mean_squared_error: 0.2030\n",
            "Epoch 593/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0679 - mean_squared_error: 0.0679 - val_loss: 0.1838 - val_mean_squared_error: 0.1838\n",
            "Epoch 594/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0630 - mean_squared_error: 0.0630 - val_loss: 0.1776 - val_mean_squared_error: 0.1776\n",
            "Epoch 595/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0622 - mean_squared_error: 0.0622 - val_loss: 0.1729 - val_mean_squared_error: 0.1729\n",
            "Epoch 596/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0617 - mean_squared_error: 0.0617 - val_loss: 0.1713 - val_mean_squared_error: 0.1713\n",
            "Epoch 597/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0620 - mean_squared_error: 0.0620 - val_loss: 0.1717 - val_mean_squared_error: 0.1717\n",
            "Epoch 598/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0581 - mean_squared_error: 0.0581 - val_loss: 0.1800 - val_mean_squared_error: 0.1800\n",
            "Epoch 599/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0566 - mean_squared_error: 0.0566 - val_loss: 0.1865 - val_mean_squared_error: 0.1865\n",
            "Epoch 600/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0575 - mean_squared_error: 0.0575 - val_loss: 0.1852 - val_mean_squared_error: 0.1852\n",
            "Epoch 601/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0560 - mean_squared_error: 0.0560 - val_loss: 0.1767 - val_mean_squared_error: 0.1767\n",
            "Epoch 602/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0553 - mean_squared_error: 0.0553 - val_loss: 0.1725 - val_mean_squared_error: 0.1725\n",
            "Epoch 603/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0548 - mean_squared_error: 0.0548 - val_loss: 0.1693 - val_mean_squared_error: 0.1693\n",
            "Epoch 604/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0540 - mean_squared_error: 0.0540 - val_loss: 0.1686 - val_mean_squared_error: 0.1686\n",
            "Epoch 605/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0542 - mean_squared_error: 0.0542 - val_loss: 0.1699 - val_mean_squared_error: 0.1699\n",
            "Epoch 606/700\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 0.0531 - mean_squared_error: 0.0531 - val_loss: 0.1707 - val_mean_squared_error: 0.1707\n",
            "Epoch 607/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0527 - mean_squared_error: 0.0527 - val_loss: 0.1727 - val_mean_squared_error: 0.1727\n",
            "Epoch 608/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0521 - mean_squared_error: 0.0521 - val_loss: 0.1753 - val_mean_squared_error: 0.1753\n",
            "Epoch 609/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0509 - mean_squared_error: 0.0509 - val_loss: 0.1807 - val_mean_squared_error: 0.1807\n",
            "Epoch 610/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0511 - mean_squared_error: 0.0511 - val_loss: 0.1860 - val_mean_squared_error: 0.1860\n",
            "Epoch 611/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0504 - mean_squared_error: 0.0504 - val_loss: 0.1874 - val_mean_squared_error: 0.1874\n",
            "Epoch 612/700\n",
            "1/1 [==============================] - 0s 193ms/step - loss: 0.0505 - mean_squared_error: 0.0505 - val_loss: 0.1850 - val_mean_squared_error: 0.1850\n",
            "Epoch 613/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0495 - mean_squared_error: 0.0495 - val_loss: 0.1840 - val_mean_squared_error: 0.1840\n",
            "Epoch 614/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0496 - mean_squared_error: 0.0496 - val_loss: 0.1832 - val_mean_squared_error: 0.1832\n",
            "Epoch 615/700\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0491 - mean_squared_error: 0.0491 - val_loss: 0.1828 - val_mean_squared_error: 0.1828\n",
            "Epoch 616/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0487 - mean_squared_error: 0.0487 - val_loss: 0.1811 - val_mean_squared_error: 0.1811\n",
            "Epoch 617/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0489 - mean_squared_error: 0.0489 - val_loss: 0.1801 - val_mean_squared_error: 0.1801\n",
            "Epoch 618/700\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0482 - mean_squared_error: 0.0482 - val_loss: 0.1795 - val_mean_squared_error: 0.1795\n",
            "Epoch 619/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0482 - mean_squared_error: 0.0482 - val_loss: 0.1800 - val_mean_squared_error: 0.1800\n",
            "Epoch 620/700\n",
            "1/1 [==============================] - 0s 58ms/step - loss: 0.0479 - mean_squared_error: 0.0479 - val_loss: 0.1810 - val_mean_squared_error: 0.1810\n",
            "Epoch 621/700\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 0.0475 - mean_squared_error: 0.0475 - val_loss: 0.1808 - val_mean_squared_error: 0.1808\n",
            "Epoch 622/700\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 0.0475 - mean_squared_error: 0.0475 - val_loss: 0.1790 - val_mean_squared_error: 0.1790\n",
            "Epoch 623/700\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 0.0471 - mean_squared_error: 0.0471 - val_loss: 0.1780 - val_mean_squared_error: 0.1780\n",
            "Epoch 624/700\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 0.0470 - mean_squared_error: 0.0470 - val_loss: 0.1781 - val_mean_squared_error: 0.1781\n",
            "Epoch 625/700\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 0.0469 - mean_squared_error: 0.0469 - val_loss: 0.1771 - val_mean_squared_error: 0.1771\n",
            "Epoch 626/700\n",
            "1/1 [==============================] - 0s 59ms/step - loss: 0.0469 - mean_squared_error: 0.0469 - val_loss: 0.1775 - val_mean_squared_error: 0.1775\n",
            "Epoch 627/700\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 0.0462 - mean_squared_error: 0.0462 - val_loss: 0.1783 - val_mean_squared_error: 0.1783\n",
            "Epoch 628/700\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 0.0465 - mean_squared_error: 0.0465 - val_loss: 0.1773 - val_mean_squared_error: 0.1773\n",
            "Epoch 629/700\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 0.0468 - mean_squared_error: 0.0468 - val_loss: 0.1785 - val_mean_squared_error: 0.1785\n",
            "Epoch 630/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0459 - mean_squared_error: 0.0459 - val_loss: 0.1786 - val_mean_squared_error: 0.1786\n",
            "Epoch 631/700\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 0.0461 - mean_squared_error: 0.0461 - val_loss: 0.1765 - val_mean_squared_error: 0.1765\n",
            "Epoch 632/700\n",
            "1/1 [==============================] - 0s 60ms/step - loss: 0.0462 - mean_squared_error: 0.0462 - val_loss: 0.1768 - val_mean_squared_error: 0.1768\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "RVcs6gfjZLhh",
        "outputId": "87553fc5-6181-4721-dae6-125aec459f53"
      },
      "source": [
        "plt.plot(history_1['loss'])\r\n",
        "plt.plot(history_1['val_loss'])\r\n",
        "plt.title('Model loss')\r\n",
        "plt.ylabel('loss')\r\n",
        "plt.xlabel('epoch')\r\n",
        "plt.legend(['train', 'test'], loc='upper right');\r\n",
        "\r\n",
        "plt.savefig('losses_downstream.png', dpi=300)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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llCO6AoHTNNTHW0tZla+LC6OUUt1DlAUCWyNI89RSVq2BQCmlIOoCQSIAqe4aDQRKKeWIrkDgtYEgxV1DuQYCpZQCoi0QuD3gjiHJVUN5teYaUkopiLZAABCTSJJUa9OQUko5oi8QxCaTTLkGAqWUckRfIEgZSB/fXsqrfUQy0alSSvUUURkIUmvz8QUM1b5AV5dGKaW6XPQFgtQckqr3IAR0TQKllCIaA0HKQNymlgxK2V2sS1YqpVT0BYKEdABSpYz8kuouLoxSSnW96AsE8X0ASKOM/BKtESilVBQGgjQAMtwV7NZAoJRSrQsEInK1iKSI9Q8RWSoisyNduIhwagSD4qvJ1z4CpZRqdY3gx8aYEmA20Ae4CLgrYqWKpDhbIxgQW0V+qQYCpZRqbSAQ5/fJwNPGmFVh23qWuFRA6Oup5NONhcxftburS6SUUl2qtYHgKxGZjw0E74hIMtAzZ2O53BCXQqCyCIA/vL66iwuklFJdy9PK4y4FJgObjTEVIpIOXBK5YkVYfB8mxvmhGMb2T+nq0iilVJdqbY3gcGCdMaZIRC4EbgKKI1esCItLY1iSj4kDU6nx98yKjVJKdZTWBoKHgAoRmQRcC2wCnmrpRSIyR0TWichGEbmhmWPOFpHVIrJKRJ5tdckPRnwfqNxPRlIM+8s1zYRSKrq1NhD4jE3VeTrwv8aYB4DkA71ARNzAA8BcYBxwnoiMa3DMSOBG4EhjzHjgmjaWv33i06ByP+kJMZpvSCkV9VobCEpF5EbssNE3RcQFeFt4zXRgozFmszGmBngeG0jCXQY8YIzZD2CM2dP6oh+E+D5QWUSfxBj2aY1AKRXlWhsIzgGqsfMJdgM5wF9aeM1AYHvY8zxnW7hRwCgR+VREFonInKZOJCLzRGSJiCwpKChoZZEPIM7WCPolx1JR49fmIaVUVGtVIHAu/v8EUkXkVKDKGNNiH0EreICRwEzgPOBREUlr4v0fMcZMM8ZMy8rKOvh3je8Dxs8om3+OzXvLDv6cSinVQ7U2xcTZwGLgB8DZwBciclYLL9sBDAp7nuNsC5cHvGaMqTXGfAusxwaGyHLyDQ1PsstVbtpTHvG3VEqp7qq18wh+CxwWbMMXkSxgAfDSAV7zJTBSRHKxAeBc4PwGx/wHWxN4QkQysU1Fm1tf/HZy8g31j60ixu1iU4HWCJRS0au1fQSuBh25hS291hjjA64E3gHWAC8aY1aJyK0icppz2DtAoYisBhYC1xtjCtv0CdrDyTfkXvokuZmJGgiUUlGttTWC/4rIO8BzzvNzgLdaepEx5q2Gxxljbgl7bIBfOj+dx6kRsORxxgw/i+X52jSklIperQoExpjrReRM4Ehn0yPGmFciV6wICwYCYHQfF6+vLqfa5yfW4+7CQimlVNdobY0AY8zLwMsRLEvniQ8NTBrZBwIGvt1bzph+mndIKRV9DhgIRKQUME3twrbs9Mwrpzeh7uGwFJtNe0N+mQYCpVRUaqnDN9kYk9LET3KPDQIAInCkzWaRk+gHYHOB9hMopaJT9K1ZHDTmFABiA5X0S4lj6z4NBEqp6BS9gSAm0f6uLmNwRgLbCiu6tjxKKdVFojgQJNnfNeUMzUhg6z4NBEqp6KSBoKacIRmJFJRWU1Hjq9sdyFvK4k/fw051UEqp3iuKA4HTNFRTxuB0O4poW1itIP/l6/D99yZeX76rK0qnlFKdJnoDgSfW1gpKdzEkwwaCLXtDgcBUl5Mg1ewpqeqqEiqlVKeI3kAgAv0nwY6lDEm3tYNtYSOH3IEaYqntqtIppVSnid5AADBgCuxeTmqskBrvZWvYyCGPqSEWXbBGKdX7tTrFRK/Udxz4a2D/FoZkJNTrI/AEaogVPyLShQVUSqnIi+4aQdZo+3vvegan1w8EbmObhjQMKKV6u+gOBJnOYmiFG8hOiaOgtLpul8fpI3BpJFBK9XLRHQjiUsETD+UFpCfGUFHjp6rW5h7ymBritI9AKRUFojsQACRkQMU+0hNjACgsr4FAAI/x4RU/fp+OHFJK9W4aCBL61AsE+8trwB9qIvLVVjf3SqWU6hWie9QQODWCQjLCawS+QN3uQE1lV5VMKaU6hdYIgoEgKRaAvaXV4AvVAkytBgKlVO+mgSAhAyr2MiAtDpfA1sLyeoEgUKspJpRSvZs2DSVlQ1UxsaaGQekJbNpbDr7QmFGjfQRKqV5OA0Fqjv1dvINhmYl2yUpf2D+LT2sESqneTZuG6gLBdgb2iWdXcWW9piF82keglOrdNBDUBYI8spPjKKqopSZ8pJBPm4aUUr1bRAOBiMwRkXUislFEbjjAcWeKiBGRaZEsT5NSBoLLC4UbyU6JA6C4pLRu9/4ty3n68y2dXiyllOosEQsEIuIGHgDmAuOA80RkXBPHJQNXA19EqiwH5PZC1hjIX0XfFDuEtLg0FAhu8T7NK6+90vRrV78Gb13fGaVUSqmIiWSNYDqw0Riz2RhTAzwPnN7EcbcBfwK6rlc2e5wNBMm2RlBaVl5v91DZDY/Ogrd+Vf91L14Eix8BnWuglOrBIhkIBgLbw57nOdvqiMhUYJAx5s0DnUhE5onIEhFZUlBQ0PElzR4PpTvp57VpqMvK6wcCH27YsQQWPwzGwCuXw7cfhQ7Y+TW8eR2U7en4simlVIR1WWexiLiAu4FrWzrWGPOIMWaaMWZaVlZWxxcmezwAaaUb8LqF8or6gSAQ/s+UvxK+eRaePgOS+9ttr10FXz4KXz3Z8WVTSqkIi2Qg2AEMCnue42wLSgYmAB+IyBZgBvBal3QYZ40FwFW4jqykWCorKurtrrd2cd4S+9vlAa9d9J7CjfZ3QnqkS6qUUh0ukoHgS2CkiOSKSAxwLvBacKcxptgYk2mMGWqMGQosAk4zxiyJYJmalpQNCJTtoW9KHFVV9QNBsoQ937nU/nbHAqb+eWoqYMdS23zUG9RW9p7PopRqVsQCgTHGB1wJvAOsAV40xqwSkVtF5LRIvW+7uD2QmAll+fRLiWvUNJQuJaEnS5+yv11uqC6rf57P/xcePQ42vNsx5frmeShY3zHnaqviHXBHP/jysa55f6VUp4loigljzFvAWw223dLMsTMjWZYWJWVD2R6GZiZSta4Cn9uFR2w66gxKGx9fVQTirr+tLN/+ri5pfHx7vPJT+x6/29cx52uL/Vvs75Uvw/TLOv/9leoONi6AqhKY8P2uLklEaa6hoKS+UJbPsBGJFFNLNV482FnF9WoEQSZgf5riq4KSnZAyoP3l8fuc9/HDvm8hPbf952oPcSqLAX/nvq9S3cHmD23f35u/tM97eSDQFBNBSf2gZBe5WYnEUEsN3rpdc91f1j/2pDsPfK5P74O7x8Lule0vT21Y89QTc9t/nvYKBoLmgp1SvdlTp4WCQBTQQBCUMRxKd5KbFCDWqRE0ctbjcO16yD3mwOfa67TrB5tX2qMmrIO6fG/7z9NeGgiUihoaCIL62iGkGZXfEiu1VJsmAsG4MyA52+YnCjrzH3DcTdCniaabVa9A4ab2lac2LBB44tp3joNhnCYhDQRK9XoaCIKcQCD5K4l1mobukwvrHXL3gg0YY+rPFxg1B4693tYoGlr5Ejx0RPvKUxPWNOTtgkDgd+ZOaCBQvdETJ8OrV8C2rklx1t1oIAjqkwvx6ZC3hBHpXqrxsnrYJXW7z625ib+9v5Fv9zoX6Dl3wbDjIDbJPj/tf2HEieBNrH/e9i5sE14jcMe27xwHw19jf+s8AtUbbf0Uvn4GHp8Ne9Z2dWm6nAaCIBEYPAOWPcOo4k/JzRnAgxccWrd7UcAmTi2udO6UZ1wOF/8n9PqU/nDhS/Y3QGr4pOp2CKsRBDxdEAgCwVFLWiNQvVxlK4Zn9/IbIg0E4Q67tO5h4oAxuF3S6JCC0hYWqhkwxf6efRsc5Yw6WPc2FG1v/jVNCasRmC6pEUS4aai26uA605XqKNKKy6C/tuVjejANBOFGnADH/dY+7jO0yUOe+WIb2/dVNLkPgDl/gsMug5Gz65LZ8dy58NIlzb+mKeE1AldM2157sAIBmH+TfRypQPDvy+C+Sb3+D0z1BI1v+BpavGFnJ5Sj62ggaOjo6+xIoBn/r97mE8dlA/DR+gK++7+fNP/6xAw45a8QkwgDp4a2l+5uWznCAoHf1cQIpkgqWAP7v7WPwwPB/q3w9Pdh78aDf4+1b9jfNeUHPk6pSJOWA8FHa3a0eExPpoGgIZcLJp5lVy4Dtgw9hz/XnsNNp4wl2FJUVNHKu9jwIaVVxa1vZyzZCe/fVvfU7+rkUUPhHdylu+H3qbZj7b5DYNN79m7+YAUDTO0BaldKdYqWA0EMNZ1Qjq6jgaAFQ3/0CNfe9jBDMhLxuNr4zyUCv/oWTr3X5h/a9H7rXvfSj6FyPwBfBkYRCHRy80ltWCCocfIsvXpFaFtHNheFT5yrqbABU6nO1IoaQXKgBN6/HXy9MyBoIGiFYKdxeOexae3dfUI6jD/DTgp75vvwyT1Q3UQSu3BhzUhVJoaKik5eCrOlpHkdebGuCcvg+tARcNfgjju3Uq3Rihubmdvuh4/+YpMw9kIaCNrAExYISip9rX9hfBqc9zy4Y2DB72HR3w98fFjTTC0edu8vbX3g6QgtXeg7MuVFeNNQsF9Cqc4UaPlvOaXK6SNwd3J/XSfRQNAGbncoEMy6+0OqfW3IzDn8OPj5Mru85fZFBz427OJYi4dJrs34K4raWtz2O1AgGDXHNhfVdlAtpabC/jx2QsecT6m2akUgSKy1TbXEJEW4MF1DA0EbHD+6b93jvWXV3Pr66radIHUgjDrJTms/0MU2rI3egw02ridO6ryU0JXNBJ2hR8OYU+zjsj3tP3/+qtDjj/4MO7+GvLAMr74W5moo1ZFa8XcVZzq5ebaTaSBogz+eOZEXf3p43fN/frGNwrI2XrQOvcTeUf97XtOjiHw14A+dM0nsF9C1dx28d2u7yt1mzQWpuFRbowEo3WV/+2vbPuty/s2hx9u/aFy7+Oa5tp1PqYPRlhssv3YWR71Yj5vDhvbh3MMG8ZuTxwC2iWjL3jaMhR8wGY6/Gdb/F1a/2nj/qlfqPU2gqtl9EdNcIPDEhSba7d9qf985wK6k1hbxafWfl+TVf/761aHHhZsOrvahVEMNb1xa0TRURwOBAhAR7jrzEC49ahhg5xT84fVVLbyqgcOvtKkoXr4UHj62fgbEPfWbmxLDA0HRVrvoTaRn41Y10zTk9oZyKL1zI6x53f5hLH+hbcPqKgohfVjoeWEzE9TK9sD9U+G581p/bqVa0rAG0FQgCNZ8G9JAoMK5XcLQjAQAdpdUt21UjzcOLvoPTJ9nJ4+9cAEsf9GOJvr03nrrHSRJg+yl794Cb15bf1vJTnjjFx3Ttu6rab5G4HLbsicPsBfzF8LSdDd3MW9K8Y5Q+g1oes0GXzV8/oB9vGNJ68+tVEsaXvhNE01Dzc3mbxgIVr8GDx0FJbs6pmxdRAPBQfjXz47gzKk5rNlVws2vrmTxt21YZD4+Deb80WYsrS6zs3X/+2u7LzGr7rBEmuik+voZezENfvnm3wxLHrd36BX74MlT4eO72952X1sFt2fBlo+b3h/840jKarxvTxs6zsv22GDy4/nOa9c0Puab52xQDKpqYW6DUq3V8MLfRI3AGD8rAkN5ZcIDGFfY0u4Na77r3ob8FfD8+REoaOfRxesPQlZyLH856xDKq308s2gbzyzaxkfXH8dgp6bQKv0nwZWLoTjP3hnvXY+ZdC7y0BGsCgwh15UPwPqLlzEqdj9s/Qzm/xbusWmxGRhKlc3LoeypbPkY3vsDXPI2JPa1E9vCF9Rpyq5v6j19zncc53kWhjYE/yCaulsKXsz3bbYBauiRTb+HMbazPDY5VJ7w+QMDp9kawDs3hb1I4MWL4MzHbS4npQ5Gwwt/wG+/lwvvgDGn2n48v48VgVEUZx5uv6vOTH9fbXWDi6Zzs7VzKZQVNH2T1ANojeAguVzCQxdO5c9nHQLAMX9ZyDfb2zjmP20wDDkCpl4Es2+jPG00M6ru5wc1v+PKuDv4a+0PqIrpY5PYHfaT+u2XO76yP+GOvi70+Im58L+HwqPH2ayiDQWHqhpTr5Hsjl8AACAASURBVKnnSd9sbvQ1yCkUnEzjctff7om3AQDg78fAkyfbWson98DnD9Y/1ldtZ3LGJEJ8n8bl6W//HetSW1y/GU67HzZ/AB/8sfHxSrVVU30Evmo7c/jxkwBbI/DjxusWJDa57tAvN+2GbYvgngm29l1RGDrPnjb2FXYjGgg6gIjwvckD+e6kAQCc/sCnvLqs/dkKy6p87CaDCuLYGTeK//Wfwb+XOufzxsEF/2r+xbNugeNvsjmOvE7NpN8hNvf/9i+gND907Cf3wB9z4D9X2OGs5aHROeLc6ew3YRNoJp7l7GzwtRn8ndBdffAC/tnf7Czqd26sf2ww22hMInv9YTWn4LrMfcfZCz/Y2k5ihg2QA6a2rR9CqeY0CgT+0CTO4ECMgA8/LmI8LohNqTt0174S23dVvN2O+qvYB9kT7M78Ns4r6ka0aaiDxHhc3H/eFI4fk8UvXviGq59fhtslnDKxP9KKpFbhyqpDo4JS4+1d+JOfbeF33x1nz9VvIvy+2PYFbPnYNi/N+RPEpYQ6YRPS7Uzm2CR7x3P/NHhijt035Cj7mkVOZ+yyZ0JvfvqD8Or/Y5OxQe3o6nvx4uPru8JG7gQDwZFXw9Qf2j+MlnKwfPV/thaQcxgAPk8C0+5cyJZgYtXjb7ZNXgnpMOFMSMquvyZE2mDIX9naf0KlmtdUH0HDuSwBPwFcxLpdtmnI4Q7U2u8iwMYFdnWzAVNsYOjBNyoRrRGIyBwRWSciG0Xkhib2/1JEVovIchF5T0SGRLI8neGMKTk8fNGh9E2O5cpnv+YXLyzDH2hbp21pVagNMxgIACpqGnyBf/SGDQg//QiGHF5/JA5Acra9+Malwrn/hASnfX3HVzYIxCTDT96HqRfbY8afAZPOg2vX84z/RADKSGA/KUz8/Tuh+RK5x9rfUy6CjOGQnmuHnAZXHDvmVzZYBVWVwOs/t30Ya14DoMhnF9vZitPMdfgVcOVXMO579vmokyBrdOgcqTm2H6WXLxmoOkGjPgJfqEYQvGkzfny48bpd9dNK+Gvq+gtY95ZtEo1Ph/ThsK+J0W89RMRqBCLiBh4ATgTygC9F5DVjTHj96WtgmjGmQkQuB/4MnBOpMnWWk8b348Sx2dz//kbuWbCeVTtLeP2qo4jzult+MVBWHfqipoQFgtveWM1dZx7SvkINmg6/ctrx/bW2bTO5n32ec2ioOQYgOZtAg3uE0iofmwrKGJqZCMdcb4NG5ki7M7juwhpnsZmcafbuffcK+zx8SUpn5bOtTgvSNUl/5pULhto/wMwRzZc/fZhNxrdvsw0+SrVXU01DDRdIcmoEXreAN75us/hroNzJmBvMWpqcDRkjbN9BDxXJGsF0YKMxZrMxpgZ4Hjg9/ABjzEJjTDDD2iIgJ4Ll6VQul/DzWSM4YWxfNuwpY8zN/2V9fuuyiIbXCFLiQoHg+S+3d0wWUrc3FATaoKrW+eK7XJA1KrQjODlsvrPMZ/9Jof4EgIePbnSuu96zs4m9SVmhDuIDGTHL/u6s2dWqd6qttPmtwhl/WNOQrRGI8ePDZWsEntCa4adVvw4b3oGsMaHXZ0+0NyfF2+uv5bHrG7uo0561EfowHSeSgWAgEL5ie56zrTmXAm83tUNE5onIEhFZUlBQ0IFFjCwR4dGLp/GjI4YCMPuej/jNKyupbNjE00BpVeM+gqAlW/d3eDlb6+531/HU51sa7whvyxe3DTITzoTLFsIpd9ttDVRg/7hS4luZ1rfPUBg+y86P2KfpqlU7fXqfnYcTZtnWvVDr1AgCtfDgEYjTWex1u0LDpsOFD9vuN8HWCDD1h0Ive9b+Di7L2o11i1FDInIhMA34S1P7jTGPGGOmGWOmZWX1rHG6IsLvTxvPe9ceyxHDM3hu8TZOuPtDVu5oPvvosu2hfSnx9b+EP/j75xEra0s2FZRzy6tNDJGLCRv9c83y0OOBU+GwS+H6jXDcTXB5qOwV2F7iBWvyee2bVi4MftrfbBPS+7fZ1NUrXrLpq7cvhvJCWPsmvHxZaCjr9i/hreuhYH1bP2rLPnMWKuksJbvg2XPtWHXVfk0sCjV/xY76ncXOMFC/scNHGw2XHne6Hdxw+Wdw6j02E0CwVhzeYRzsdwiuL1JW0HhipL8W1s/v8gmTkRw1tAMYFPY8x9lWj4icAPwWONYY02vzDw/PSuLZy2bwxeZCrnlhGd974FMunDGEG+aOadR38MnG0B97wxoBQGFZNRlJsY22d6mfvA9bP7Wdug0lpMOx19vHOdMhbzG1hD7zLa+u5DRn6O0BpebAtEvsRXjVK6E22idOtndyQbtXwOTzbLbWgA92r4QfN1nZbFl1mc20mj4cdi2zyxWedGddXwfHXN++87ZG/mqbfiR7gh2Zsv5t+OsIOzS4pcmBkfLFw3awQN8xLR/bQ7gI1F8y1eHHRYzb1bhGe+JtkNLf/gQHaGSNtoMvvv4njP2uHUW39Cm7r2CdveA/fIxd/e+aFaH/v7eug6+etKPkRs62gzX6DLEd0tkTWrWMZkeIZCD4EhgpIrnYAHAuUG8etohMAR4G5hhjoiLF5HeGZfDGVUdxz4L1PPnZFj5aX8BdZx7C9Fz7xaiq9bN9X+juJLyPIOiWV1dx5xkTSYn3tHloamu0qx8i51D705JznmHbx8+Q91GoZtemT/Cdy+GLR2yq7nGn28AS7JsYNMOm7lj/X5uTacyp9g/1wz/BAzPsrM8BU+2w14R0my7ABOzcDGPsEqFJ2bYjvbrE3gku+H3jfonwmkBZgT02vAM74LfnSMyCLx+DsafZDsW2evR48FU6s7XDak2bF9qmt85WWwVv/8qOMLthW+e/f0cImwPzhO8kLvG8w3Xef0H1dxodGsCF19O4acikDW78nY1JhBmX2+9GWQF86HxHMkfZ7+OX/4BS5/9wzetw6A/tbPylT9k+hrRBdl7C10+Hzpk+3N78VBXZmkz2eJh0Pow5uQP+IeqLWCAwxvhE5ErgHcANPG6MWSUitwJLjDGvYZuCkoB/ORe0bcaY0yJVpu4iIymW2783kVljsvnD66s4/9FFzDtmGD+bOZydRfXHMzfVhv7mil28uWIXz8+bwYxhHZ9ywRc23DXe66aytn6fRiBgcLnaGYCSs1k/9EL4KJRIrk3BLHUgXLXE3qWlDLB3TBPPshcnb7y9oK97y/7xjpxtL8olO2Dr53aJzU/vszmMRs21d9ixKaFx4MEmpZasfCn0+K/OSKdDzrXZYbPH24s/wHkv2Du+z/4GJ/8PDD8e3C38yQUCNrBNvsAGgaDwxHvbF3dNIKhwlijtyDWrO1vYd+3jwEQu4R37pOHsfAh1FjuBYPnAc5i36ShuXZ3P7PFNDLYYe6rtiF73Juxdb2f4H/ojm0H3v7+GpH72RuX92+zFffFjthZx8at24mRNhR1eXe2sAPjth/YGICYJEuPg249sLaEnBQIAY8xbwFsNtt0S9jiq1yc8bkxfDh3ahxv/vYKHPtzEK1/vYMrg+rn6E2NttTQ9MYbnLpsBwEn3fgTAuY8sYuF1M8nNTOzQclX7bJPL9Nx0vjd5IL95ZUW9/Wc89Bn/vvwI3O0MBsWV9dNoh5+luKKW1IQWOpCDE3qCwkdAiYRWUQN74T39gdDzjQts80Ywr1J1if2D88TBIefYXE6xyTB6rs0IW7zdVtcnX2DHkK99E5b8w762z1D7h7n2DVj+vN22LawP5zlnJHTRNnj2B5A2BH7yHnxytx11MuFMu1rbgCngibHNUHcNsrWURQ1ScwCM/z6UF9gZ4q1VWWT/TeJSW/+a5nTkWtWdrabcmT8Q+rb5w5on69W4HHXDR50+gsqEHHaTwbynv2LDHXNtkAjXdzy4Y22N1fjt/2vaIDj1Xvv/eeQ1dn7NY7NsjTV7IpwVlj8rJgEmnRs635E/76hP3yKdWdzFUuK8PHD+VL7etp9fvbSct1bs5sgRGWwuKGdXcRXJsV6uPXEUcyf2Y0RfO8Nx050nM/w3Nr4e99cPeOziaZwwrh1ND82ocmoAp0zsb6fYN/DN9iLW7CphwsD2XVwaBQLnb3Pt7hLm3Psx954zme9NOdAAs4Mw4gT7Y4ytKaQMtPMc3N7G/RuzbrE5aNwxoUKOmGVTeOxaBrkz7VDawk12MR13jO0YHHq0rf7vWQWxqVDt3EEXbQ3VIMBOsgNbU5g+DxY9FOr3CDr8SptnadRJtjzv3Qqf3GsvbDHODcCGd20Zxp8BiZn2rnLkbLv/vkn27nPmjTDzBvt5Nn9gL0gpA2yKhLg0wNi+l6kX22azLZ/anFGXfw7ZToLDirBAsPo1yFsMs28/6P+SiNuzBh60N1EcfmXdZn/4WJmdXzd6mQ+37SMIdhaHzT/YsreckdnJ9V/g9ti+gt3LISETRtpJmUy5wP4EXfGFzR48aHqn9QG0RANBNzFlcB/evvpotu6rIDcjkVe/2cEvXviGvimxXDVrZL1j3S5hyuA0vt5mk9v95Kkl/PTYYfy/Y0e0fDfdChXV9gufGOvB08xdf0Fbl+gMP3+NnScxKjuJ9fll7C2r4YGFG9lXblP8vrw0L3KBIEgkdOFPz23+OE8TnfIJ6fbiHZQx3M7yDnf0L+3de2qODTpVxbY28tn9dob0oOn24r1vE2x63/4E9cm1k/Iq9tkZ1yfdEdo36Dv2bjPvSzsaavEjULjB7tv2uR3K+P7ttm36gn+FFhn64I92fsdzYXecyf1tR/iJt0L/ybDgd/bn+4/atOhgmzmyx9n1LsJXinvxIvt71Fz7Wdxt/N5tfM8GTI+dYY4xHXtR3LPWJjVMzAoFAag3OqheIKhuPGqnbvio01lcXhWaI1DU4GamTr+JNhDkHtP0dwfsd6KpQRVdSANBN+JxuxieZaeznzElhzOmNP9leWHe4dT6Axz/Px+QX1LNwx9u5t3V+bx/7cx2v38gYDjhng/Jczqrk2I9zBrblw17SnlgoZ0+/+H1Mzn2Lx9QUFJNQWk1WcltH71UWevH7RKen3c4Vz67lM82FfKXd9bV7f94w14Wrt3DcWP6tvuzdDlPbOiPXcS2DU88q/5Eu2Cn/IqXnAuvges32bv65ji5mnjq9Prbhx8Pq/9jf8C2Ud83qf4x4UEAQutOv3tL/e3BIAA2qKx61ebcb8qTJ9uhlMdc1/R+cDrP99nPJQJ5X8Ez34cjroKxp9saysLbbVPbiBNh8Iz6/06tUVVsR+okZNpa24NO5++xDTLb1IZGB/nNgUfPB3DhcUtdH0F2UuhyWVTRTCA4+lrYtBCmX9b0/m6qW8wjUG0X43GRGOvhohmh9EybC8o54e4PKW7uS9qC/RU1bC4op8ZvmyeS4zx43S6uPyk0VDDTGbb6q5eXc9gdC9i4p6zN71NVGyDe6yY9MYZZY5tu0lr0bWGT23sVEftzyA/g5r3wi9UHDgJgayNDGqz1MPNGmBs2kun0B+GMh+0Imf6TbL+EJw4QmHMX/Dbf1higfs2m38RQxtpwTQUBb1i/1Pu3wfp3wO9rehnV135um8T+kGYDwmPOey5+FP5xgg0CYJvovnzU5qRa8oSdleurhqLtsOo/8N5tzf+7vPFLW2t58SJbWwpa/HD941aEOvp91B8WahqMDtpm+tqmIScojZt1IS/Ms7WLoopmlqzMGA7XrrFp5XsQrRH0cD85ehhul4sVO4r4bFMhG/eUMenW+Zw3fRC/OGEUfVPiWj6JY29Z/S93Umzjr0dig23/+XoH1500utFxB1JZ6yfOa+9BMhJjmjwmztO6vEy9httjR0S1xo/etBdclyfUnCICV3xpR06l5tjnI060NZPYJPjNzvoTo372qU07njwAirbYztJB37HNVitfshfeSefa2dy7l8OHf7bNWJcusBfv3KNtn8X/fdcOlX327NC5k/vbQDNill1oKDy77d/D0o34Qk0tjbxxjf39lxH1m22O+oVtAlv5b0jqa/s0/u80KMkLHRNeW6psMBM/bL5JMJ/W2/7DGHzSVcSnZjHs5bnkmz4sn3o7n3zexzYN9R0Lvy9GgLEp9vUN+7l6Og0EPVyc183lM+0Ydn/A1HUiP7d4O88t3s7Vs0Zy1fEj8DQc4dCEwgbt/g0v+k1ZvavtMyKravx1k+j6NBMISqp61x9ahxIJta2HC8//BPVXc2u0mFBMWB/JsNDM2PRcO0kufKJc9jg7wql0lx2xdf6/QmtFXP6ZTTH+wZ2h40t32Z9vP7TPvYmhFA7hF+zWaNh2/8cGwTJ8OdM28uJjaNWzgIE3hGcvG8W9NVfwJeO5NP1wYI2dRxAmOdaD2yXNNw31UNo01Iu4XcKaW+fw32uO5pcn2ovCfe9tYMpt73LFP5fy1dYDr6ncsAM4OS4UCB69eBoPX2QnjL137bH8v5nDSUvwsmpnMdNuf5d/frG11eWsrPUT7wSC9ISmA8EH6zSVQrfi9oaG7Y6aHQoyyf1g5q/hisW2JvDd+2wacoDDLrOjdM5/Aa7bCOc9HzrfoO/YNObt4fLaeRtBwWYqb4KdvNdQ33G2YxpsADvdDs391vRnYFo8wSGlxRW1vBY4kl2BtLrmUa+7fge2iJAa72V/c01DPZTWCHqZ+Bg3Y/qlMKZfClcdP4KF6/Zwx5tr6iahDU5P4PKZwzn3sEGNJnIVHqBp6MSw4anDs5L41ZwxeFzC3963uVV+9+oqvjd5IPvKaxiU3vyazW8s38nbK3czom9SXXkBhmQkcNzovuTtr2D7vkrW5Zcy6rdvc9a0HO48Y2Kz51PdRNZouNbJsun32b6HIYfXP2b0XPjFKjt0crDTmfvdv9mJfenDbJ/Gmtftcf5aZ6Z3lp2HMe57tkO4psyOBPLE2qCzf0vjdBfr3rZpG477rT0ua7QdoXX/VDjhD5B7NJ8mn8Tex76grz80XHd/2F1+rc925Htdje+Vs5Ji2ZBfxpvLd3HKIf0b7e+JNBD0YiLC8WOymTKoD+vyS7nn3fV88e0+bvz3Ch79eDNnTs3h7GmDyEyKQUT4ZONe0hNj6oZxJsQcuJ1+0qDQ5DcDTL9jAeU1ftbdPodAAHyBAMkNUmTc+rpdjmK/8x5DMxI4Y8pAfnJ0LuMH2HkJ32wv4vQHPqXGH+DZL7ZpIOhp3J7GQSCo4dBJl6v+BMCsJvqbgpk+49PsT5A3rumcR6Pn2p9wGcPtIk6OGmfSZG29QBC6EbpnwXo8LmlyBv2g9AQWrMln8ZZ9jO53LCIwIDW+7qamJ9JAEAX6JMYwY1gGL/z0cPwBw/99toWHP9rEX95Zx1/eWceEgSlMG5LO+2v3cN3sUTz52Rb2ltW0mPrh8OG2eSA7JZb95bWUO+m1755v8ygNyUjg8R8dRk4fW0PYU1rFnlLb/BSctOZxu7jnnMn1zjtpUBopcR5KnHUZjDERyamkole1z37/fP5QOpVVO+unzvA1s7JgVnKoOfOVr/N4YOEmLvjOYO7owTcsGgiijNsl/PioXH58VC5Ltuxj6bb9/OOTb3nysy30S4njosOHcvZhg9ixv7LFcyXEeFjwy2PITomjsKyG4spaTn/gUx7+yObsWZ9fxlF/Wsgxo7I4e1oOVz4bmr1Z3sKaDNecMIpb37C1h2cWbWV43ySOGN7C0EqlWimYRqU2EKoRvLViN1nJsWQlxR5wEERmWObfBz+w82uW5/Xg/EtoZ3FUmzY0nXnHDOfzG2bx4fUzWXDtsaTGe+mbHMeUwX1adY4RfZNJjvMyNDORSYPSOGeazTx+yZFD6475aH1BvSDQGj8+KpeHLpgKwM2vruL8R7/gucU9NOPlQSosq+aif3zBh+u1A/1g7Suv4ZcvLmNnkR26eu85k8lMCt3hGwOvXnlkcy8H4PKZw3n60uk8eMHUujmB4U1MPZEGAoXLJQzJSGxy3kBb3XHGBNbeNocb54496I60uRP7s/C6mQzPsqNCbvz3Cj7fVEh52JrO0WDZ9iI+3rCXS55Y3NVF6fHeWL6Tfy/dwZ/+azu2jxiRyWc3zKrbX+3zN04m10BCjIejR2Yxd0I/5h0zjOm56azdXcr8VbsjWvZI0kCgOpTH7SLO6ybG4+KB86ey6c6TeeziafVmQAOc0MyM4oZyMxN579qZdTM6z3t0Ecf8eSGvfJ3XMes3d3NvLN/JX+fbFdaaabLuVD3137ykqpa9ZdV2pnCYBOe7+uez7LrZwU7kWWP6khJ34BsjEeE3J4/lHz+cRr+UOB78YBM3/2clb6/YFZkPEUHS0/5jp02bZpYsWdLygarb2b6vgr9/uInrZo8myUlf0Rb3LljP4598S0WNH1/AMGVwGnMn9GNAWjynHtKKFc56oKE3vFnv+dc3n9jsJLxIM8Yw/c73KCit5l8/O5zDhnbRKmntcPHji/lofQHzjhnGIx+F1p3YcpcdsVTt8zP6pv9yzKgsnvrx9DYPUHjs483c/uaauucv/exwpnWzfx8R+coYM63JfRoIVE/jDxjuXbCe+98PrQ87pl8yM4ZlMGVwGnMm9CO2l6SoaBgIjhyRwT9/MqOZoyOrqKKGybe+C8DAtHh+MC2Ha04Y1cKruodRv327bpJYuGAgAFi5o5ghGQmNhjy3hjGGO95cw6qdJWwsKCOnTzy3nT6Bal+ASTmprZrZH2kaCFSvVFhWzRvLd7GjqJJXl+0gv8QOTU2O8zAqO5lhmYkcOqQPfVNi6Zscx4C0ePokeNs8FDVvfwWrd5Ywe3w/CsuqSYn38u7qfCpr/MyZ0I/Xv9nJD6YNavdCPeHKq32cePeHXHPCKE4a349Jt86v25edEkt+STW3nDqOcQNSIrI63YFsyC/lxHs+qrdt7W1zGq253R1Nu30Be5tInR4eCDrKK1/n8YsXvql7fuSIDO47dwoZiTFdOgz6QIFAh4+qHisjKZYfHjEUgBvnjuHbveVs3VfBe2vyWZ5XzLtr8vnXV/Vz28R6XPRPjSM5zsvUwWmICLmZiaQleBmUnsCQ9AQMkBrvxet24fMH+NETX7JxTxmHD8vg8831s6L+Z9kOPt6wl/gYN0mxHoZlJZGVHMvqnSVMGJjCM4u2MqJvEseN7osxtLjE5/tr97CzuIpfvbyc99fa/P8el3DpUbkckpPGFc8urRtWe+GMwdx86rhOq/0UlDa+kG7cU9buBYoaKq6sJbWJpVkPVo0vQGF5qOyJMe4Why8fjDOm5FDrN2zIL2VTQTnvr93DtNsXcOyoLH5ydC7fyc1ocsGnrqQ1AtVrGWPYVFBGSZWPPSVV7CyqYldxJTuLq9hVVMnKnSV4XdLsRSElzkPAQFkbRiklxrgZlpXEih31x5Unx3nISo7l1tMmEB/j5k9vr2X2+GyOHZXFyOxkAgHD68t3cvXzy+q9bsawdJ67bAYiQiBgePKzLXWBAGwakMuOHsZRIzMY0TeZpFgPQssBpzWqav317vZfXbaDq59fRmZSbN3d9czRWfzyxFEckpPW3GmatLu4iuyUWP6zbAfVtQFS471c/s+lvH7lUby4ZDuHD8/g5Ikdk75ha2E5x/7lA4ZnJbKpoJxhmYls3muT4EWiRhDOGMOizft4dvE2Xv/GLoeZHOdhXP8UxvZPYfKgNEZmJ5GTltAhi0odiDYNKdUMYww7i6sorapl+75K8vZX4BKbXXJfeTUBA0ePzOSIEZn86qVv+O4hA8jpk8C2fRVkJMXw+9dWcdahOfz9w00YA+U1PqpqA/RPjWNXcRWZSTEkx3n51rnwtGTyoDRuPnUs97+/kQ/XF/DGVUfVpd4I2rK3nEc/3ozX7eLJz7bUbRcBtwgZSTFMG5pOZmIM1b4AX28rIsbj4tzpg8hKiqVvShz+QIBvtts28bH9U/D5Db5AgD4JMWwsKOPBhRv5dGMh958/hZPG98PnD/C711bxzy+2EetxUe0LMDjd/jsA/ODQHP7fcSOoqPExrn/KAZtAlm7bz/cf/IxLj8rlH598C8BhQ/vw5Zb9TBiYwsoddjLXz48fwYjsZE4cm02c19WmZpVdxZVc969vuOSIXBau28OLS7ZzyZG5PPLRZmaOzqpLahjpQBButdN/sGhzIWt2lbBudykVzk2IiE3JnpYQQ3pCDGkJXtISvKTGe/G4XSTHeUiN9zK2fwpTWznHpyENBEpFmD9gcImdsLSvvIaR2cls31dBn8QYanwBdhZVMqJvEs8s2kq1L8DZ0wZRUFrNB+v3sKekmuLKWg4d0oezpw0ixuNi5Y5ivt1bzncnHXg0lDGGVTtLWL2zhB1FleyvqGHt7lL2llZTUFZNaZWP7JRYanyBeknV2iIjMQav28XukiqOH9OXC74zmDdX7OLa2aM58q73Gx0/MC2ejKQYMpNiGZAWR1Ksl6paPzX+AKP6JvHS0ry6i31r5WYmcsrE/gxIi+eEsX1JTfBS4wvgcbmIj3GTX1LFWyt2UVUb4LuT+nPJE1+yYU8ZyXEeSqt8nHpIfy49KpczHvyMRy+exmVP2WtIZwaChgIBwzd5ReTtr2RzQTn5pVUUVdjvT1FFLfsr7Gx9f8BQ66TC+Okxw7jx5LHtej8NBEpFqeLKWtwuQYBdwZrP/kq8LmFs/xRW7iymtMpHwBj2ldXgdgvpCTEcOzoLt0t4ZtE2lucVEedxM3t8Nqcc0r/JPol1u0tZvGUfry3bgS9gECC/pJqiihpqA4Y4j72jL66sJc7r4t5zJvPAwk3kl1QxOD2BjQVl/Pz4kdz33gYu+M5gHvvkW2aN6cvyvGJ2FDVOdyJiZwF73cKAtHh2F1fVpY0IuvOMifzmFbu62n3nTub0yQPr+iE+3biX1Hhvh/VvRFpRRQ1VThNae5PbaSBQSnU5Ywzb9lWQFOshIymWqlo//oAhIcaNP2DqDbGsrLGr2PkDhmpfgF3FVaQnxvD5pkLyS6ooqarF63ZRUlXL7uIqMpNi+e6kAQSM4ZWlOzhqZCYnoPvEGAAABytJREFUje/Hh+sLWLWzmJ8dM7xD+k16Mg0ESikV5Q4UCLrXGCallFKdLqKBQETmiMg6EdkoIjc0sT9WRF5w9n8hIkMjWR6llFKNRSwQiIgbeACYC4wDzhORcQ0OuxTYb4wZAdwD/ClS5VFKKdW0SNYIpgMbjTGbjTE1wPPA6Q2OOR34P+fxS8As0aWolFKqU0UyEAwEtoc9z3O2NXmMMcYHFAONEqiIyDwRWSIiSwoKdHEOpZTqSD2is9gY84gxZpoxZlpWVlZXF0cppXqVSAaCHcCgsOc5zrYmjxERD5AKFKKUUqrTRDIQfAmMFJFcEYkBzgVea3DMa8APncdnAe+bnjaxQSmleriITigTkZOBewE38Lgx5g4RuRVYYox5TUTigKeBKcA+4FxjzObmzwgiUgBsbWeRMoG97Xxtd9HTP4OWv2v19PJDz/8MXVX+IcaYJtvWe9zM4oMhIkuam1nXU/T0z6Dl71o9vfzQ8z9Ddyx/j+gsVkopFTkaCJRSKspFWyB4pKsL0AF6+mfQ8netnl5+6PmfoduVP6r6CJRSSjUWbTUCpZRSDWggUEqpKBc1gaCllNjdgYg8LiJ7RGRl2LZ0EXlXRDY4v/s420VE/uZ8nuUiMrXrSl5X1kEislBEVovIKhG52tneIz6DiMSJyGIR+cYp/x+c7blOmvSNTtr0GGd7t0yjLiJuEflaRN5wnve08m8RkRUiskxEljjbesR3yClTmoi8JCJrRWSNiBze3csfFYGglSmxu4MngTkNtt0AvGeMGQm85zwH+1lGOj/zgIc6qYwH4gOuNcaMA2YAVzj/zj3lM1QDxxtjJgGTgTkiMgObHv0eJ136fmz6dOi+adSvBtaEPe9p5Qc4zhgzOWy8fU/5DgHcB/zXGDMGmIT9v+je5TfG9Pof4HDgnbDnNwI3dnW5minrUGBl2PN1QH/ncX9gnfP4YeC8po7rLj/Aq8CJPfEzAAnAUuA72FmgnobfJeAd4HDnscc5Trq43DnYC83xwBuA9KTyO2XZAmQ22NYjvkPYfGnfNvx37O7lj4oaAa1Lid1dZRtjdjmPdwPZzuNu/ZmcZoYpwBf0oM/gNKssA/YA7wKbgCJj06RD/TK2Ko16J7sX+BUQcJ5n0LPKD2CA+SLylYjMc7b1lO9QLlAAPOE0zz0mIol08/JHSyDoFYy9Zej2431FJAl4GbjGGFMSvq+7fwZjjN8YMxl7Zz0dGNPFRWo1ETkV2GOM+aqry3KQjjLGTMU2m1whIseE7+zm3yEPMBV4yBgzBSgn1AwEdM/yR0sgaE1K7O4qX0T6Azi/9zjbu+VnEhEvNgj80xjzb2dzj/oMAMaYImAhtiklTWyadKhfxu6WRv1I4DQR2YJdEfB4bHt1Tyk/AMaYHc7vPcAr2IDcU75DeUCeMeYL5/lL2MDQrcsfLYGgNSmxu6vwVN0/xLa7B7df7Iw6mAEUh1U9u4SICPAPYI0x5u6wXT3iM4hIloikOY/jsf0ba7AB4SznsIbl7zZp1I0xNxpjcowxQ7Hf8feNMRfQQ8oPICKJIpIcfAzMBlbSQ75DxpjdwHYRGe1smgWspruXv6s6VTr7BzgZWI9t8/1tV5enmTI+B+wCarF3Fpdi22zfAzYAC4B051jBjoTaBKwApnWD8h+FrfIuB5Y5Pyf3lM8AHAJ87ZR/JXCLs30YsBjYCPwLiHW2xznPNzr7h3X1/0HYZ5kJvNHTyu+U9RvnZ1Xwb7WnfIecMk0Gljjfo/8Afbp7+TXFhFJKRbloaRpSSinVDA0ESikV5TQQKKVUlNNAoJRSUU4DgVJK/f/27l41qigKw/D7iSBqQBttLAS1EUEEwUIRBG/AQhHUFNY2diIogjdgJZgyYgoRzA2YIpBCokgqS6tUNiKm0CIui71HYhIwiGaE8z7VzJ49mznFmXV+ON8aOAuBtI2SXBilgkr/CwuBJA2chUDaRJIbvTfBUpKpHka3kuRRWq+CuSQH+txTSV73PPnZNVnzx5K8Sutv8C7J0b78xJq8+pn+RLY0NhYCaZ0kx4GrwLlqAXSrwHVgL/C2qk4A88CD/pWnwJ2qOkl7OnQ0PgM8rtbf4CztqXFoqay3ab0xjtAygqSx2fn7KdLgXAROA2/6wfpuWkjYd+B5n/MMeJlkH7C/qub7+DTwouflHKqqWYCq+grQ11usquX+fonWg2Lh32+WtDkLgbRRgOmquvvLYHJ/3bw/zWf5tub1Ku6HGjMvDUkbzQGXkxyEn/1yD9P2l1GK5zVgoao+A5+SnO/jk8B8VX0BlpNc6mvsSrJnW7dC2iKPRKR1qup9knu0Llk7aGmwt2hNRs70zz7S7iNAixV+0v/oPwA3+/gkMJXkYV/jyjZuhrRlpo9KW5Rkpaomxv07pL/NS0OSNHCeEUjSwHlGIEkDZyGQpIGzEEjSwFkIJGngLASSNHA/AGmmNxUDCYrKAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gi-IKb8ebEvC"
      },
      "source": [
        "valuePredictor_baseline = Sequential([\r\n",
        "    Dense(X_train.shape[1], activation='relu', input_shape=(X_train.shape[1],)),\r\n",
        "    Dense(20, activation='relu'),\r\n",
        "    Dense(20, activation='relu'),\r\n",
        "    Dense(20, activation='relu'),\r\n",
        "    Dense(10, activation='relu'),\r\n",
        "    Dense(1)\r\n",
        "])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UK5cGkoAWvNT"
      },
      "source": [
        "valuePredictor_baseline.compile(optimizer=Adam(lr=0.01), \r\n",
        "                    loss='MSE', \r\n",
        "                    metrics=['mean_squared_error'])\r\n",
        "checkpointer_2 = ModelCheckpoint(filepath=\"valuePredictor_baseline.z1\",\r\n",
        "                              verbose=0,\r\n",
        "                              save_best_only=True)\r\n",
        "history_2 = valuePredictor_baseline.fit(X_train, Y_train,\r\n",
        "                    epochs=700,\r\n",
        "                    batch_size=450,\r\n",
        "                    shuffle=True,\r\n",
        "                    validation_data=(X_test, Y_test),\r\n",
        "                    verbose=1, callbacks=[early_stopping, checkpointer_2]).history"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gA_dTmu5dxLJ"
      },
      "source": [
        "# Test\r\n",
        "ae_preds = np.zeros((56,))\r\n",
        "base_preds = np.zeros((56,))\r\n",
        "for i, record in enumerate(X_test):\r\n",
        "  ae_preds[i] = valuePredictor.predict(latent_model.predict(np.array(record).reshape(1, X_train.shape[1])))\r\n",
        "  base_preds[i] = valuePredictor_baseline.predict(np.array(record).reshape(1, X_train.shape[1]))\r\n"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "dJ0q7xYrmuQs",
        "outputId": "f8647d30-16ba-48ff-d45c-2b220f3d2ddf"
      },
      "source": [
        "plt.scatter(ae_preds, Y_test, color='r', s=5, label='AE - $r^2 = 0.88$')\r\n",
        "plt.scatter(base_preds, Y_test, color='g', s=5, label='Base - $r^2 = 0.83$')\r\n",
        "\r\n",
        "plt.plot(Y_test,Y_test)\r\n",
        "plt.xlabel(\"True prices (Normalised)\")\r\n",
        "plt.ylabel(\"Predicted prices (Normalised)\")\r\n",
        "plt.legend()\r\n",
        "plt.savefig('predictions.png', dpi=300)\r\n",
        "\r\n",
        "print(\"RMSE (Autoencoder):\", np.sqrt((Y_test - ae_preds)**2).mean(axis=None))\r\n",
        "print(\"r2 (Autoencoder):\", r2_score(Y_test, ae_preds))\r\n",
        "\r\n",
        "print(\"RMSE (Baseline):\", np.sqrt((Y_test - base_preds)**2).mean(axis=None))\r\n",
        "print(\"r2 (Baseline):\", r2_score(Y_test, base_preds))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RMSE (Autoencoder): 0.2744064071115905\n",
            "r2 (Autoencoder): 0.8768859250555621\n",
            "RMSE (Baseline): 0.32389799729987445\n",
            "r2 (Baseline): 0.83086722555883\n"
          ],
          "name": "stdout"
        },
        {
          "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": "code",
      "metadata": {
        "id": "bpNkMWl6fS4x"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}