Simple Neural Network.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\n\ntrain_df = pd.DataFrame([\n    [0, 0, 0],\n    [0, 1, 1],\n    [1, 0, 1],\n    [1, 1, 2],\n    [2, 0, 2],\n    [2, 1, 3],\n    [3, 0, 3],\n    [3, 1, 4],\n    [4, 0, 4],\n    [4, 1, 5]\n])\n\nx_train = train_df.iloc[:, 0:2]\ny_train = train_df.iloc[:, 2]",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "test_df = pd.DataFrame([\n    [0, 3, 3],\n    [1, 3, 4],\n    [2, 3, 5],\n    [3, 2, 5],\n    [0, 4, 4]\n])\n\nx_test = test_df.iloc[:, 0:2]\ny_test = test_df.iloc[:, 2]",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from keras.models import Sequential\nfrom keras.layers import Dense\n\nmodel = Sequential()\nmodel.add(Dense(3, input_dim=2, activation='relu'))\nmodel.add(Dense(1)) \nmodel.compile(loss='mae', optimizer='adam', metrics=['mae'])\nmodel.summary()",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Using TensorFlow backend.\n",
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": "Model: \"sequential_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense_1 (Dense)              (None, 3)                 9         \n_________________________________________________________________\ndense_2 (Dense)              (None, 1)                 4         \n=================================================================\nTotal params: 13\nTrainable params: 13\nNon-trainable params: 0\n_________________________________________________________________\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(model.get_weights())",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": "[array([[-0.14091301, -0.15582293,  0.8588767 ],\n       [-0.566372  , -0.102009  ,  0.4461006 ]], dtype=float32), array([0., 0., 0.], dtype=float32), array([[ 0.51683176],\n       [-0.95711666],\n       [ 0.6962602 ]], dtype=float32), array([0.], dtype=float32)]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "hist = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=500, batch_size=1)",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Train on 10 samples, validate on 5 samples\nEpoch 1/500\n10/10 [==============================] - 0s 17ms/step - loss: 1.1305 - mae: 1.1305 - val_loss: 2.4981 - val_mae: 2.4981\nEpoch 2/500\n10/10 [==============================] - 0s 3ms/step - loss: 1.0859 - mae: 1.0859 - val_loss: 2.4425 - val_mae: 2.4425\nEpoch 3/500\n10/10 [==============================] - 0s 3ms/step - loss: 1.0475 - mae: 1.0475 - val_loss: 2.3819 - val_mae: 2.3819\nEpoch 4/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.9993 - mae: 0.9993 - val_loss: 2.3256 - val_mae: 2.3256\nEpoch 5/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.9581 - mae: 0.9581 - val_loss: 2.2667 - val_mae: 2.2667\nEpoch 6/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.9163 - mae: 0.9163 - val_loss: 2.2023 - val_mae: 2.2023\nEpoch 7/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.8691 - mae: 0.8691 - val_loss: 2.1426 - val_mae: 2.1426\nEpoch 8/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.8210 - mae: 0.8210 - val_loss: 2.0835 - val_mae: 2.0835\nEpoch 9/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.7781 - mae: 0.7781 - val_loss: 2.0170 - val_mae: 2.0170\nEpoch 10/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.7304 - mae: 0.7304 - val_loss: 1.9525 - val_mae: 1.9525\nEpoch 11/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.6836 - mae: 0.6836 - val_loss: 1.8876 - val_mae: 1.8876\nEpoch 12/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.6340 - mae: 0.6340 - val_loss: 1.8219 - val_mae: 1.8219\nEpoch 13/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.5876 - mae: 0.5876 - val_loss: 1.7522 - val_mae: 1.7522\nEpoch 14/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.5329 - mae: 0.5329 - val_loss: 1.6875 - val_mae: 1.6875\nEpoch 15/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.4917 - mae: 0.4917 - val_loss: 1.6199 - val_mae: 1.6199\nEpoch 16/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.4495 - mae: 0.4495 - val_loss: 1.5556 - val_mae: 1.5556\nEpoch 17/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.4093 - mae: 0.4093 - val_loss: 1.4911 - val_mae: 1.4911\nEpoch 18/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.3690 - mae: 0.3690 - val_loss: 1.4246 - val_mae: 1.4246\nEpoch 19/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.3347 - mae: 0.3347 - val_loss: 1.3566 - val_mae: 1.3566\nEpoch 20/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.2967 - mae: 0.2967 - val_loss: 1.3102 - val_mae: 1.3102\nEpoch 21/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.2747 - mae: 0.2747 - val_loss: 1.2502 - val_mae: 1.2502\nEpoch 22/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.2587 - mae: 0.2587 - val_loss: 1.1897 - val_mae: 1.1897\nEpoch 23/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.2370 - mae: 0.2370 - val_loss: 1.1455 - val_mae: 1.1455\nEpoch 24/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.2235 - mae: 0.2235 - val_loss: 1.1180 - val_mae: 1.1180\nEpoch 25/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.2211 - mae: 0.2211 - val_loss: 1.0780 - val_mae: 1.0780\nEpoch 26/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.2162 - mae: 0.2162 - val_loss: 1.0525 - val_mae: 1.0525\nEpoch 27/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.2158 - mae: 0.2158 - val_loss: 1.0146 - val_mae: 1.0146\nEpoch 28/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.2093 - mae: 0.2093 - val_loss: 0.9998 - val_mae: 0.9998\nEpoch 29/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.2058 - mae: 0.2058 - val_loss: 0.9751 - val_mae: 0.9751\nEpoch 30/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.2016 - mae: 0.2016 - val_loss: 0.9442 - val_mae: 0.9442\nEpoch 31/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1973 - mae: 0.1973 - val_loss: 0.9239 - val_mae: 0.9239\nEpoch 32/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1936 - mae: 0.1936 - val_loss: 0.9047 - val_mae: 0.9047\nEpoch 33/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1906 - mae: 0.1906 - val_loss: 0.8708 - val_mae: 0.8708\nEpoch 34/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1860 - mae: 0.1860 - val_loss: 0.8436 - val_mae: 0.8436\nEpoch 35/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1816 - mae: 0.1816 - val_loss: 0.8184 - val_mae: 0.8184\nEpoch 36/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1777 - mae: 0.1777 - val_loss: 0.7981 - val_mae: 0.7981\nEpoch 37/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1748 - mae: 0.1748 - val_loss: 0.7693 - val_mae: 0.7693\nEpoch 38/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1699 - mae: 0.1699 - val_loss: 0.7442 - val_mae: 0.7442\nEpoch 39/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1664 - mae: 0.1664 - val_loss: 0.7174 - val_mae: 0.7174\nEpoch 40/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1622 - mae: 0.1622 - val_loss: 0.6969 - val_mae: 0.6969\nEpoch 41/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1593 - mae: 0.1593 - val_loss: 0.6640 - val_mae: 0.6640\nEpoch 42/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1572 - mae: 0.1572 - val_loss: 0.6427 - val_mae: 0.6427\nEpoch 43/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1566 - mae: 0.1566 - val_loss: 0.6438 - val_mae: 0.6438\nEpoch 44/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1531 - mae: 0.1531 - val_loss: 0.6238 - val_mae: 0.6238\nEpoch 45/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1510 - mae: 0.1510 - val_loss: 0.6187 - val_mae: 0.6187\nEpoch 46/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1479 - mae: 0.1479 - val_loss: 0.5980 - val_mae: 0.5980\nEpoch 47/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1455 - mae: 0.1455 - val_loss: 0.5871 - val_mae: 0.5871\nEpoch 48/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1450 - mae: 0.1450 - val_loss: 0.5678 - val_mae: 0.5678\nEpoch 49/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1422 - mae: 0.1422 - val_loss: 0.5540 - val_mae: 0.5540\nEpoch 50/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1393 - mae: 0.1393 - val_loss: 0.5475 - val_mae: 0.5475\nEpoch 51/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1380 - mae: 0.1380 - val_loss: 0.5469 - val_mae: 0.5469\nEpoch 52/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1356 - mae: 0.1356 - val_loss: 0.5268 - val_mae: 0.5268\nEpoch 53/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1329 - mae: 0.1329 - val_loss: 0.5217 - val_mae: 0.5217\nEpoch 54/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1302 - mae: 0.1302 - val_loss: 0.5102 - val_mae: 0.5102\nEpoch 55/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.1288 - mae: 0.1288 - val_loss: 0.4954 - val_mae: 0.4954\nEpoch 56/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.1265 - mae: 0.1265 - val_loss: 0.4928 - val_mae: 0.4928\nEpoch 57/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1237 - mae: 0.1237 - val_loss: 0.4749 - val_mae: 0.4749\nEpoch 58/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1212 - mae: 0.1212 - val_loss: 0.4660 - val_mae: 0.4660\nEpoch 59/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1196 - mae: 0.1196 - val_loss: 0.4605 - val_mae: 0.4605\nEpoch 60/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.1181 - mae: 0.1181 - val_loss: 0.4423 - val_mae: 0.4423\nEpoch 61/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1141 - mae: 0.1141 - val_loss: 0.4453 - val_mae: 0.4453\nEpoch 62/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1127 - mae: 0.1127 - val_loss: 0.4479 - val_mae: 0.4479\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "Epoch 63/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1126 - mae: 0.1126 - val_loss: 0.4313 - val_mae: 0.4313\nEpoch 64/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1093 - mae: 0.1093 - val_loss: 0.4289 - val_mae: 0.4289\nEpoch 65/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.1068 - mae: 0.1068 - val_loss: 0.4155 - val_mae: 0.4155\nEpoch 66/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.1058 - mae: 0.1058 - val_loss: 0.4124 - val_mae: 0.4124\nEpoch 67/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.1037 - mae: 0.1037 - val_loss: 0.4098 - val_mae: 0.4098\nEpoch 68/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.1001 - mae: 0.1001 - val_loss: 0.3870 - val_mae: 0.3870\nEpoch 69/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0997 - mae: 0.0997 - val_loss: 0.3640 - val_mae: 0.3640\nEpoch 70/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0959 - mae: 0.0959 - val_loss: 0.3615 - val_mae: 0.3615\nEpoch 71/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0933 - mae: 0.0933 - val_loss: 0.3524 - val_mae: 0.3524\nEpoch 72/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0923 - mae: 0.0923 - val_loss: 0.3313 - val_mae: 0.3313\nEpoch 73/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0900 - mae: 0.0900 - val_loss: 0.3337 - val_mae: 0.3337\nEpoch 74/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0876 - mae: 0.0876 - val_loss: 0.3469 - val_mae: 0.3469\nEpoch 75/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0849 - mae: 0.0849 - val_loss: 0.3335 - val_mae: 0.3335\nEpoch 76/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0828 - mae: 0.0828 - val_loss: 0.3200 - val_mae: 0.3200\nEpoch 77/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0814 - mae: 0.0814 - val_loss: 0.3055 - val_mae: 0.3055\nEpoch 78/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0784 - mae: 0.0784 - val_loss: 0.3040 - val_mae: 0.3040\nEpoch 79/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0768 - mae: 0.0768 - val_loss: 0.2871 - val_mae: 0.2871\nEpoch 80/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0756 - mae: 0.0756 - val_loss: 0.2877 - val_mae: 0.2877\nEpoch 81/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0722 - mae: 0.0722 - val_loss: 0.2691 - val_mae: 0.2691\nEpoch 82/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0718 - mae: 0.0718 - val_loss: 0.2539 - val_mae: 0.2539\nEpoch 83/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0665 - mae: 0.0665 - val_loss: 0.2538 - val_mae: 0.2538\nEpoch 84/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0667 - mae: 0.0667 - val_loss: 0.2575 - val_mae: 0.2575\nEpoch 85/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0640 - mae: 0.0640 - val_loss: 0.2338 - val_mae: 0.2338\nEpoch 86/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0606 - mae: 0.0606 - val_loss: 0.2289 - val_mae: 0.2289\nEpoch 87/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0581 - mae: 0.0581 - val_loss: 0.2162 - val_mae: 0.2162\nEpoch 88/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0569 - mae: 0.0569 - val_loss: 0.1955 - val_mae: 0.1955\nEpoch 89/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0552 - mae: 0.0552 - val_loss: 0.2013 - val_mae: 0.2013\nEpoch 90/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0524 - mae: 0.0524 - val_loss: 0.1984 - val_mae: 0.1984\nEpoch 91/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0514 - mae: 0.0514 - val_loss: 0.1747 - val_mae: 0.1747\nEpoch 92/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0511 - mae: 0.0511 - val_loss: 0.1826 - val_mae: 0.1826\nEpoch 93/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0465 - mae: 0.0465 - val_loss: 0.1639 - val_mae: 0.1639\nEpoch 94/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0436 - mae: 0.0436 - val_loss: 0.1676 - val_mae: 0.1676\nEpoch 95/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0403 - mae: 0.0403 - val_loss: 0.1568 - val_mae: 0.1568\nEpoch 96/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0404 - mae: 0.0404 - val_loss: 0.1328 - val_mae: 0.1328\nEpoch 97/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0360 - mae: 0.0360 - val_loss: 0.1336 - val_mae: 0.1336\nEpoch 98/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0343 - mae: 0.0343 - val_loss: 0.1245 - val_mae: 0.1245\nEpoch 99/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0329 - mae: 0.0329 - val_loss: 0.1242 - val_mae: 0.1242\nEpoch 100/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0301 - mae: 0.0301 - val_loss: 0.1080 - val_mae: 0.1080\nEpoch 101/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0283 - mae: 0.0283 - val_loss: 0.0855 - val_mae: 0.0855\nEpoch 102/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0254 - mae: 0.0254 - val_loss: 0.0887 - val_mae: 0.0887\nEpoch 103/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0243 - mae: 0.0243 - val_loss: 0.0940 - val_mae: 0.0940\nEpoch 104/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0210 - mae: 0.0210 - val_loss: 0.0732 - val_mae: 0.0732\nEpoch 105/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0179 - mae: 0.0179 - val_loss: 0.0620 - val_mae: 0.0620\nEpoch 106/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0168 - mae: 0.0168 - val_loss: 0.0489 - val_mae: 0.0489\nEpoch 107/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0144 - mae: 0.0144 - val_loss: 0.0540 - val_mae: 0.0540\nEpoch 108/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0124 - mae: 0.0124 - val_loss: 0.0356 - val_mae: 0.0356\nEpoch 109/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0103 - mae: 0.0103 - val_loss: 0.0239 - val_mae: 0.0239\nEpoch 110/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0082 - mae: 0.0082 - val_loss: 0.0142 - val_mae: 0.0142\nEpoch 111/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0060 - mae: 0.0060 - val_loss: 0.0113 - val_mae: 0.0113\nEpoch 112/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0034 - mae: 0.0034 - val_loss: 0.0082 - val_mae: 0.0082\nEpoch 113/500\n10/10 [==============================] - 0s 6ms/step - loss: 0.0028 - mae: 0.0028 - val_loss: 0.0028 - val_mae: 0.0028\nEpoch 114/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0026 - mae: 0.0026 - val_loss: 0.0101 - val_mae: 0.0101\nEpoch 115/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0030 - mae: 0.0030 - val_loss: 8.6265e-04 - val_mae: 8.6265e-04\nEpoch 116/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 9.5334e-04 - val_mae: 9.5334e-04\nEpoch 117/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 5.2385e-04 - val_mae: 5.2385e-04\nEpoch 118/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0022 - val_mae: 0.0022\nEpoch 119/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 4.7035e-04 - val_mae: 4.7035e-04\nEpoch 120/500\n10/10 [==============================] - 0s 3ms/step - loss: 6.6907e-04 - mae: 6.6907e-04 - val_loss: 0.0016 - val_mae: 0.0016\nEpoch 121/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0022 - val_mae: 0.0022\nEpoch 122/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 4.7555e-04 - val_mae: 4.7555e-04\nEpoch 123/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 0.0034 - val_mae: 0.0034\nEpoch 124/500\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "10/10 [==============================] - 0s 2ms/step - loss: 0.0028 - mae: 0.0028 - val_loss: 0.0021 - val_mae: 0.0021\nEpoch 125/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0030 - mae: 0.0030 - val_loss: 0.0047 - val_mae: 0.0047\nEpoch 126/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0048 - mae: 0.0048 - val_loss: 0.0021 - val_mae: 0.0021\nEpoch 127/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0025 - mae: 0.0025 - val_loss: 0.0036 - val_mae: 0.0036\nEpoch 128/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 0.0046 - val_mae: 0.0046\nEpoch 129/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0038 - mae: 0.0038 - val_loss: 0.0115 - val_mae: 0.0115\nEpoch 130/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0039 - mae: 0.0039 - val_loss: 0.0030 - val_mae: 0.0030\nEpoch 131/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0035 - mae: 0.0035 - val_loss: 0.0061 - val_mae: 0.0061\nEpoch 132/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 0.0014 - val_mae: 0.0014\nEpoch 133/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0029 - mae: 0.0029 - val_loss: 0.0049 - val_mae: 0.0049\nEpoch 134/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 0.0032 - val_mae: 0.0032\nEpoch 135/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 8.6832e-04 - val_mae: 8.6832e-04\nEpoch 136/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 0.0026 - val_mae: 0.0026\nEpoch 137/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0029 - val_mae: 0.0029\nEpoch 138/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 0.0021 - val_mae: 0.0021\nEpoch 139/500\n10/10 [==============================] - 0s 3ms/step - loss: 6.0104e-04 - mae: 6.0104e-04 - val_loss: 5.7402e-04 - val_mae: 5.7402e-04\nEpoch 140/500\n10/10 [==============================] - 0s 3ms/step - loss: 7.0715e-04 - mae: 7.0715e-04 - val_loss: 0.0021 - val_mae: 0.0021\nEpoch 141/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 0.0030 - val_mae: 0.0030\nEpoch 142/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0025 - val_mae: 0.0025\nEpoch 143/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0036 - val_mae: 0.0036\nEpoch 144/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0028 - mae: 0.0028 - val_loss: 3.8538e-04 - val_mae: 3.8538e-04\nEpoch 145/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 0.0068 - val_mae: 0.0068\nEpoch 146/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0034 - mae: 0.0034 - val_loss: 0.0044 - val_mae: 0.0044\nEpoch 147/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 7.1321e-04 - val_mae: 7.1321e-04\nEpoch 148/500\n10/10 [==============================] - 0s 2ms/step - loss: 8.2906e-04 - mae: 8.2906e-04 - val_loss: 4.1928e-04 - val_mae: 4.1928e-04\nEpoch 149/500\n10/10 [==============================] - 0s 2ms/step - loss: 7.9842e-04 - mae: 7.9842e-04 - val_loss: 3.7317e-04 - val_mae: 3.7317e-04\nEpoch 150/500\n10/10 [==============================] - 0s 2ms/step - loss: 3.6263e-04 - mae: 3.6263e-04 - val_loss: 0.0025 - val_mae: 0.0025\nEpoch 151/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0092 - val_mae: 0.0092\nEpoch 152/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0045 - val_mae: 0.0045\nEpoch 153/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 2.6917e-04 - val_mae: 2.6917e-04\nEpoch 154/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0037 - mae: 0.0037 - val_loss: 0.0031 - val_mae: 0.0031\nEpoch 155/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0046 - mae: 0.0046 - val_loss: 0.0107 - val_mae: 0.0107\nEpoch 156/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0035 - mae: 0.0035 - val_loss: 0.0030 - val_mae: 0.0030\nEpoch 157/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0021 - mae: 0.0021 - val_loss: 0.0022 - val_mae: 0.0022\nEpoch 158/500\n10/10 [==============================] - 0s 2ms/step - loss: 9.8934e-04 - mae: 9.8934e-04 - val_loss: 0.0037 - val_mae: 0.0037\nEpoch 159/500\n10/10 [==============================] - 0s 2ms/step - loss: 6.5028e-04 - mae: 6.5028e-04 - val_loss: 5.9595e-04 - val_mae: 5.9595e-04\nEpoch 160/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 7.5026e-04 - val_mae: 7.5026e-04\nEpoch 161/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 0.0043 - val_mae: 0.0043\nEpoch 162/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0025 - mae: 0.0025 - val_loss: 0.0045 - val_mae: 0.0045\nEpoch 163/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0040 - mae: 0.0040 - val_loss: 0.0018 - val_mae: 0.0018\nEpoch 164/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0011 - val_mae: 0.0011\nEpoch 165/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0025 - val_mae: 0.0025\nEpoch 166/500\n10/10 [==============================] - ETA: 0s - loss: 0.0013 - mae: 0.001 - 0s 2ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0035 - val_mae: 0.0035\nEpoch 167/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0022 - mae: 0.0022 - val_loss: 0.0094 - val_mae: 0.0094\nEpoch 168/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0024 - mae: 0.0024 - val_loss: 0.0067 - val_mae: 0.0067\nEpoch 169/500\n10/10 [==============================] - 0s 3ms/step - loss: 9.4309e-04 - mae: 9.4309e-04 - val_loss: 0.0022 - val_mae: 0.0022\nEpoch 170/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 8.4805e-04 - val_mae: 8.4805e-04\nEpoch 171/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0045 - val_mae: 0.0045\nEpoch 172/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0020 - val_mae: 0.0020\nEpoch 173/500\n10/10 [==============================] - 0s 6ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0012 - val_mae: 0.0012\nEpoch 174/500\n10/10 [==============================] - 0s 6ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0024 - val_mae: 0.0024\nEpoch 175/500\n10/10 [==============================] - 0s 6ms/step - loss: 0.0025 - mae: 0.0025 - val_loss: 0.0017 - val_mae: 0.0017\nEpoch 176/500\n10/10 [==============================] - 0s 4ms/step - loss: 9.8532e-04 - mae: 9.8532e-04 - val_loss: 0.0033 - val_mae: 0.0033\nEpoch 177/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 7.6761e-04 - val_mae: 7.6761e-04\nEpoch 178/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0010 - mae: 0.0010 - val_loss: 3.0189e-04 - val_mae: 3.0189e-04\nEpoch 179/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 8.6212e-04 - val_mae: 8.6212e-04\nEpoch 180/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 0.0033 - val_mae: 0.0033\nEpoch 181/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0029 - val_mae: 0.0029\nEpoch 182/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0090 - val_mae: 0.0090\nEpoch 183/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 0.0064 - val_mae: 0.0064\nEpoch 184/500\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "10/10 [==============================] - 0s 5ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0041 - val_mae: 0.0041\nEpoch 185/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0057 - val_mae: 0.0057\nEpoch 186/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0071 - val_mae: 0.0071\nEpoch 187/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0011 - val_mae: 0.0011\nEpoch 188/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 0.0030 - val_mae: 0.0030\nEpoch 189/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 6.7391e-04 - val_mae: 6.7391e-04\nEpoch 190/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0010 - mae: 0.0010 - val_loss: 0.0030 - val_mae: 0.0030\nEpoch 191/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 9.0313e-04 - val_mae: 9.0313e-04\nEpoch 192/500\n10/10 [==============================] - 0s 7ms/step - loss: 0.0025 - mae: 0.0025 - val_loss: 0.0045 - val_mae: 0.0045\nEpoch 193/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0022 - mae: 0.0022 - val_loss: 0.0078 - val_mae: 0.0078\nEpoch 194/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0066 - mae: 0.0066 - val_loss: 0.0026 - val_mae: 0.0026\nEpoch 195/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0033 - mae: 0.0033 - val_loss: 0.0087 - val_mae: 0.0087\nEpoch 196/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0028 - mae: 0.0028 - val_loss: 0.0045 - val_mae: 0.0045\nEpoch 197/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0021 - mae: 0.0021 - val_loss: 0.0059 - val_mae: 0.0059\nEpoch 198/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 6.2323e-04 - val_mae: 6.2323e-04\nEpoch 199/500\n10/10 [==============================] - 0s 3ms/step - loss: 9.9678e-04 - mae: 9.9678e-04 - val_loss: 9.9330e-04 - val_mae: 9.9330e-04\nEpoch 200/500\n10/10 [==============================] - 0s 4ms/step - loss: 9.3419e-04 - mae: 9.3419e-04 - val_loss: 0.0023 - val_mae: 0.0023\nEpoch 201/500\n10/10 [==============================] - 0s 4ms/step - loss: 4.1348e-04 - mae: 4.1348e-04 - val_loss: 8.1711e-04 - val_mae: 8.1711e-04\nEpoch 202/500\n10/10 [==============================] - 0s 5ms/step - loss: 8.2362e-04 - mae: 8.2362e-04 - val_loss: 0.0017 - val_mae: 0.0017\nEpoch 203/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0058 - val_mae: 0.0058\nEpoch 204/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0064 - val_mae: 0.0064\nEpoch 205/500\n10/10 [==============================] - 0s 3ms/step - loss: 6.2267e-04 - mae: 6.2267e-04 - val_loss: 0.0022 - val_mae: 0.0022\nEpoch 206/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 0.0075 - val_mae: 0.0075\nEpoch 207/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0047 - val_mae: 0.0047\nEpoch 208/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 0.0026 - val_mae: 0.0026\nEpoch 209/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0033 - mae: 0.0033 - val_loss: 0.0011 - val_mae: 0.0011\nEpoch 210/500\n10/10 [==============================] - 0s 6ms/step - loss: 0.0053 - mae: 0.0053 - val_loss: 2.7056e-04 - val_mae: 2.7056e-04\nEpoch 211/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0044 - mae: 0.0044 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 212/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 0.0042 - val_mae: 0.0042\nEpoch 213/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0067 - val_mae: 0.0067\nEpoch 214/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 4.9820e-04 - val_mae: 4.9820e-04\nEpoch 215/500\n10/10 [==============================] - 0s 3ms/step - loss: 7.2438e-04 - mae: 7.2438e-04 - val_loss: 0.0050 - val_mae: 0.0050\nEpoch 216/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0024 - mae: 0.0024 - val_loss: 0.0036 - val_mae: 0.0036\nEpoch 217/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0024 - mae: 0.0024 - val_loss: 0.0057 - val_mae: 0.0057\nEpoch 218/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0071 - val_mae: 0.0071\nEpoch 219/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0026 - mae: 0.0026 - val_loss: 0.0033 - val_mae: 0.0033\nEpoch 220/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0035 - mae: 0.0035 - val_loss: 0.0041 - val_mae: 0.0041\nEpoch 221/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0068 - mae: 0.0068 - val_loss: 0.0044 - val_mae: 0.0044\nEpoch 222/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0060 - mae: 0.0060 - val_loss: 0.0123 - val_mae: 0.0123\nEpoch 223/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0028 - mae: 0.0028 - val_loss: 0.0048 - val_mae: 0.0048\nEpoch 224/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0033 - val_mae: 0.0033\nEpoch 225/500\n10/10 [==============================] - 0s 4ms/step - loss: 9.8194e-04 - mae: 9.8194e-04 - val_loss: 0.0059 - val_mae: 0.0059\nEpoch 226/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 0.0062 - val_mae: 0.0062\nEpoch 227/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 2.0151e-04 - val_mae: 2.0151e-04\nEpoch 228/500\n10/10 [==============================] - 0s 3ms/step - loss: 8.7851e-04 - mae: 8.7851e-04 - val_loss: 8.9612e-04 - val_mae: 8.9612e-04\nEpoch 229/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0010 - mae: 0.0010 - val_loss: 0.0017 - val_mae: 0.0017\nEpoch 230/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0015 - val_mae: 0.0015\nEpoch 231/500\n10/10 [==============================] - 0s 3ms/step - loss: 6.9143e-04 - mae: 6.9143e-04 - val_loss: 0.0030 - val_mae: 0.0030\nEpoch 232/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 7.5326e-04 - val_mae: 7.5326e-04\nEpoch 233/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 0.0017 - val_mae: 0.0017\nEpoch 234/500\n10/10 [==============================] - 0s 3ms/step - loss: 4.3683e-04 - mae: 4.3683e-04 - val_loss: 0.0075 - val_mae: 0.0075\nEpoch 235/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0023 - mae: 0.0023 - val_loss: 0.0039 - val_mae: 0.0039\nEpoch 236/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 0.0034 - val_mae: 0.0034\nEpoch 237/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 0.0050 - val_mae: 0.0050\nEpoch 238/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0024 - mae: 0.0024 - val_loss: 0.0076 - val_mae: 0.0076\nEpoch 239/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0078 - val_mae: 0.0078\nEpoch 240/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0021 - mae: 0.0021 - val_loss: 8.9250e-04 - val_mae: 8.9250e-04\nEpoch 241/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 7.5498e-04 - val_mae: 7.5498e-04\nEpoch 242/500\n10/10 [==============================] - 0s 3ms/step - loss: 5.7258e-04 - mae: 5.7258e-04 - val_loss: 5.4722e-04 - val_mae: 5.4722e-04\nEpoch 243/500\n10/10 [==============================] - 0s 7ms/step - loss: 9.2282e-04 - mae: 9.2282e-04 - val_loss: 7.6575e-04 - val_mae: 7.6575e-04\nEpoch 244/500\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "10/10 [==============================] - 0s 4ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0039 - val_mae: 0.0039\nEpoch 245/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0021 - mae: 0.0021 - val_loss: 0.0099 - val_mae: 0.0099\nEpoch 246/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0038 - mae: 0.0038 - val_loss: 0.0047 - val_mae: 0.0047\nEpoch 247/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0023 - mae: 0.0023 - val_loss: 0.0052 - val_mae: 0.0052\nEpoch 248/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0031 - mae: 0.0031 - val_loss: 0.0040 - val_mae: 0.0040\nEpoch 249/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0032 - mae: 0.0032 - val_loss: 0.0065 - val_mae: 0.0065\nEpoch 250/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 0.0012 - val_mae: 0.0012\nEpoch 251/500\n10/10 [==============================] - 0s 5ms/step - loss: 8.5493e-04 - mae: 8.5493e-04 - val_loss: 7.4897e-04 - val_mae: 7.4897e-04\nEpoch 252/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0024 - mae: 0.0024 - val_loss: 0.0014 - val_mae: 0.0014\nEpoch 253/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0037 - val_mae: 0.0037\nEpoch 254/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0010 - mae: 0.0010 - val_loss: 3.4828e-04 - val_mae: 3.4828e-04\nEpoch 255/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0036 - val_mae: 0.0036\nEpoch 256/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0021 - mae: 0.0021 - val_loss: 0.0018 - val_mae: 0.0018\nEpoch 257/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 0.0024 - val_mae: 0.0024\nEpoch 258/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0035 - mae: 0.0035 - val_loss: 0.0149 - val_mae: 0.0149\nEpoch 259/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0039 - mae: 0.0039 - val_loss: 0.0015 - val_mae: 0.0015\nEpoch 260/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0035 - mae: 0.0035 - val_loss: 0.0145 - val_mae: 0.0145\nEpoch 261/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0029 - mae: 0.0029 - val_loss: 0.0043 - val_mae: 0.0043\nEpoch 262/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0051 - mae: 0.0051 - val_loss: 0.0149 - val_mae: 0.0149\nEpoch 263/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0047 - mae: 0.0047 - val_loss: 0.0017 - val_mae: 0.0017\nEpoch 264/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0011 - val_mae: 0.0011\nEpoch 265/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0027 - val_mae: 0.0027\nEpoch 266/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0010 - mae: 0.0010 - val_loss: 0.0052 - val_mae: 0.0052\nEpoch 267/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 0.0024 - val_mae: 0.0024\nEpoch 268/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0010 - mae: 0.0010 - val_loss: 0.0035 - val_mae: 0.0035\nEpoch 269/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0026 - mae: 0.0026 - val_loss: 0.0115 - val_mae: 0.0115\nEpoch 270/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0081 - val_mae: 0.0081\nEpoch 271/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0017 - val_mae: 0.0017\nEpoch 272/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0093 - val_mae: 0.0093\nEpoch 273/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 0.0057 - val_mae: 0.0057\nEpoch 274/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0025 - mae: 0.0025 - val_loss: 0.0055 - val_mae: 0.0055\nEpoch 275/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0046 - val_mae: 0.0046\nEpoch 276/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0012 - val_mae: 0.0012\nEpoch 277/500\n10/10 [==============================] - 0s 3ms/step - loss: 8.2055e-04 - mae: 8.2055e-04 - val_loss: 0.0052 - val_mae: 0.0052\nEpoch 278/500\n10/10 [==============================] - 0s 7ms/step - loss: 0.0010 - mae: 0.0010 - val_loss: 0.0051 - val_mae: 0.0051\nEpoch 279/500\n10/10 [==============================] - 0s 6ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 0.0072 - val_mae: 0.0072\nEpoch 280/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0029 - mae: 0.0029 - val_loss: 0.0034 - val_mae: 0.0034\nEpoch 281/500\n10/10 [==============================] - 0s 8ms/step - loss: 0.0042 - mae: 0.0042 - val_loss: 0.0033 - val_mae: 0.0033\nEpoch 282/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 0.0012 - val_mae: 0.0012\nEpoch 283/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0063 - val_mae: 0.0063\nEpoch 284/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 7.5979e-04 - val_mae: 7.5979e-04\nEpoch 285/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0076 - val_mae: 0.0076\nEpoch 286/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0022 - val_mae: 0.0022\nEpoch 287/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0026 - val_mae: 0.0026\nEpoch 288/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 289/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0022 - mae: 0.0022 - val_loss: 0.0031 - val_mae: 0.0031\nEpoch 290/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0023 - mae: 0.0023 - val_loss: 0.0027 - val_mae: 0.0027\nEpoch 291/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0026 - val_mae: 0.0026\nEpoch 292/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0056 - val_mae: 0.0056\nEpoch 293/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 294/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0027 - val_mae: 0.0027\nEpoch 295/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0019 - val_mae: 0.0019\nEpoch 296/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0024 - mae: 0.0024 - val_loss: 0.0025 - val_mae: 0.0025\nEpoch 297/500\n10/10 [==============================] - 0s 8ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 0.0060 - val_mae: 0.0060\nEpoch 298/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0064 - val_mae: 0.0064\nEpoch 299/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0021 - mae: 0.0021 - val_loss: 0.0024 - val_mae: 0.0024\nEpoch 300/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 6.4063e-04 - val_mae: 6.4063e-04\nEpoch 301/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0047 - val_mae: 0.0047\nEpoch 302/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0048 - val_mae: 0.0048\nEpoch 303/500\n10/10 [==============================] - 0s 6ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 0.0074 - val_mae: 0.0074\nEpoch 304/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0040 - val_mae: 0.0040\nEpoch 305/500\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "10/10 [==============================] - 0s 2ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 5.7092e-04 - val_mae: 5.7092e-04\nEpoch 306/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 5.1990e-04 - val_mae: 5.1990e-04\nEpoch 307/500\n10/10 [==============================] - 0s 5ms/step - loss: 9.5164e-04 - mae: 9.5164e-04 - val_loss: 5.6682e-04 - val_mae: 5.6682e-04\nEpoch 308/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0015 - val_mae: 0.0015\nEpoch 309/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 310/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0028 - mae: 0.0028 - val_loss: 0.0116 - val_mae: 0.0116\nEpoch 311/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0022 - mae: 0.0022 - val_loss: 0.0085 - val_mae: 0.0085\nEpoch 312/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0025 - mae: 0.0025 - val_loss: 0.0018 - val_mae: 0.0018\nEpoch 313/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0030 - mae: 0.0030 - val_loss: 0.0083 - val_mae: 0.0083\nEpoch 314/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0034 - mae: 0.0034 - val_loss: 0.0042 - val_mae: 0.0042\nEpoch 315/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0038 - mae: 0.0038 - val_loss: 0.0100 - val_mae: 0.0100\nEpoch 316/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 0.0070 - val_mae: 0.0070\nEpoch 317/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 4.3535e-04 - val_mae: 4.3535e-04\nEpoch 318/500\n10/10 [==============================] - 0s 3ms/step - loss: 9.8803e-04 - mae: 9.8803e-04 - val_loss: 0.0038 - val_mae: 0.0038\nEpoch 319/500\n10/10 [==============================] - 0s 3ms/step - loss: 7.0575e-04 - mae: 7.0575e-04 - val_loss: 0.0017 - val_mae: 0.0017\nEpoch 320/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0037 - val_mae: 0.0037\nEpoch 321/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 5.5804e-04 - val_mae: 5.5804e-04\nEpoch 322/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0072 - val_mae: 0.0072\nEpoch 323/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0031 - mae: 0.0031 - val_loss: 0.0016 - val_mae: 0.0016\nEpoch 324/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 7.1535e-04 - val_mae: 7.1535e-04\nEpoch 325/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0070 - val_mae: 0.0070\nEpoch 326/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0029 - mae: 0.0029 - val_loss: 0.0054 - val_mae: 0.0054\nEpoch 327/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 7.3166e-04 - val_mae: 7.3166e-04\nEpoch 328/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0033 - val_mae: 0.0033\nEpoch 329/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0064 - val_mae: 0.0064\nEpoch 330/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0023 - mae: 0.0023 - val_loss: 0.0024 - val_mae: 0.0024\nEpoch 331/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0038 - val_mae: 0.0038\nEpoch 332/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0028 - mae: 0.0028 - val_loss: 0.0014 - val_mae: 0.0014\nEpoch 333/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0037 - val_mae: 0.0037\nEpoch 334/500\n10/10 [==============================] - 0s 4ms/step - loss: 9.3845e-04 - mae: 9.3845e-04 - val_loss: 8.3842e-04 - val_mae: 8.3842e-04\nEpoch 335/500\n10/10 [==============================] - 0s 2ms/step - loss: 3.8228e-04 - mae: 3.8228e-04 - val_loss: 0.0029 - val_mae: 0.0029\nEpoch 336/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0045 - val_mae: 0.0045\nEpoch 337/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0031 - val_mae: 0.0031\nEpoch 338/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 8.6927e-04 - val_mae: 8.6927e-04\nEpoch 339/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0018 - val_mae: 0.0018\nEpoch 340/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0026 - mae: 0.0026 - val_loss: 0.0031 - val_mae: 0.0031\nEpoch 341/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 0.0011 - val_mae: 0.0011\nEpoch 342/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0041 - val_mae: 0.0041\nEpoch 343/500\n10/10 [==============================] - 0s 2ms/step - loss: 7.6289e-04 - mae: 7.6289e-04 - val_loss: 0.0045 - val_mae: 0.0045\nEpoch 344/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0040 - val_mae: 0.0040\nEpoch 345/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 346/500\n10/10 [==============================] - 0s 6ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 7.8974e-04 - val_mae: 7.8974e-04\nEpoch 347/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 9.1190e-04 - val_mae: 9.1190e-04\nEpoch 348/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0058 - val_mae: 0.0058\nEpoch 349/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 4.0684e-04 - val_mae: 4.0684e-04\nEpoch 350/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0024 - val_mae: 0.0024\nEpoch 351/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0012 - val_mae: 0.0012\nEpoch 352/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 6.1412e-04 - val_mae: 6.1412e-04\nEpoch 353/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 0.0078 - val_mae: 0.0078\nEpoch 354/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0036 - mae: 0.0036 - val_loss: 0.0041 - val_mae: 0.0041\nEpoch 355/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0066 - val_mae: 0.0066\nEpoch 356/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0051 - val_mae: 0.0051\nEpoch 357/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0029 - mae: 0.0029 - val_loss: 0.0034 - val_mae: 0.0034\nEpoch 358/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 0.0022 - val_mae: 0.0022\nEpoch 359/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0026 - val_mae: 0.0026\nEpoch 360/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 9.4972e-04 - val_mae: 9.4972e-04\nEpoch 361/500\n10/10 [==============================] - 0s 3ms/step - loss: 9.3862e-04 - mae: 9.3862e-04 - val_loss: 4.2973e-04 - val_mae: 4.2973e-04\nEpoch 362/500\n10/10 [==============================] - 0s 3ms/step - loss: 7.9938e-04 - mae: 7.9938e-04 - val_loss: 4.4537e-04 - val_mae: 4.4537e-04\nEpoch 363/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0011 - val_mae: 0.0011\nEpoch 364/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0015 - val_mae: 0.0015\nEpoch 365/500\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "10/10 [==============================] - 0s 3ms/step - loss: 9.4932e-04 - mae: 9.4932e-04 - val_loss: 0.0043 - val_mae: 0.0043\nEpoch 366/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0031 - mae: 0.0031 - val_loss: 0.0034 - val_mae: 0.0034\nEpoch 367/500\n10/10 [==============================] - 0s 3ms/step - loss: 7.3417e-04 - mae: 7.3417e-04 - val_loss: 0.0011 - val_mae: 0.0011\nEpoch 368/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 9.1820e-04 - val_mae: 9.1820e-04\nEpoch 369/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 370/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 371/500\n10/10 [==============================] - 0s 3ms/step - loss: 7.8549e-04 - mae: 7.8549e-04 - val_loss: 0.0016 - val_mae: 0.0016\nEpoch 372/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0025 - mae: 0.0025 - val_loss: 0.0019 - val_mae: 0.0019\nEpoch 373/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0030 - mae: 0.0030 - val_loss: 0.0075 - val_mae: 0.0075\nEpoch 374/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0022 - mae: 0.0022 - val_loss: 0.0029 - val_mae: 0.0029\nEpoch 375/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0092 - val_mae: 0.0092\nEpoch 376/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0036 - mae: 0.0036 - val_loss: 0.0051 - val_mae: 0.0051\nEpoch 377/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0036 - mae: 0.0036 - val_loss: 0.0114 - val_mae: 0.0114\nEpoch 378/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0022 - mae: 0.0022 - val_loss: 0.0027 - val_mae: 0.0027\nEpoch 379/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0025 - mae: 0.0025 - val_loss: 0.0044 - val_mae: 0.0044\nEpoch 380/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0024 - val_mae: 0.0024\nEpoch 381/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 0.0076 - val_mae: 0.0076\nEpoch 382/500\n10/10 [==============================] - 0s 6ms/step - loss: 0.0023 - mae: 0.0023 - val_loss: 0.0047 - val_mae: 0.0047\nEpoch 383/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0099 - val_mae: 0.0099\nEpoch 384/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 385/500\n10/10 [==============================] - 0s 6ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0018 - val_mae: 0.0018\nEpoch 386/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0019 - val_mae: 0.0019\nEpoch 387/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0020 - val_mae: 0.0020\nEpoch 388/500\n10/10 [==============================] - 0s 3ms/step - loss: 5.0942e-04 - mae: 5.0942e-04 - val_loss: 0.0016 - val_mae: 0.0016\nEpoch 389/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0035 - val_mae: 0.0035\nEpoch 390/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0091 - val_mae: 0.0091\nEpoch 391/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0089 - val_mae: 0.0089\nEpoch 392/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0066 - val_mae: 0.0066\nEpoch 393/500\n10/10 [==============================] - 0s 6ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0052 - val_mae: 0.0052\nEpoch 394/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 0.0022 - val_mae: 0.0022\nEpoch 395/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 0.0024 - val_mae: 0.0024\nEpoch 396/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0081 - val_mae: 0.0081\nEpoch 397/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0021 - mae: 0.0021 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 398/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0037 - val_mae: 0.0037\nEpoch 399/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0044 - val_mae: 0.0044\nEpoch 400/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 0.0024 - val_mae: 0.0024\nEpoch 401/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0022 - mae: 0.0022 - val_loss: 0.0033 - val_mae: 0.0033\nEpoch 402/500\n10/10 [==============================] - 0s 6ms/step - loss: 0.0022 - mae: 0.0022 - val_loss: 0.0010 - val_mae: 0.0010\nEpoch 403/500\n10/10 [==============================] - 0s 7ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0021 - val_mae: 0.0021\nEpoch 404/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0091 - val_mae: 0.0091\nEpoch 405/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0021 - mae: 0.0021 - val_loss: 0.0039 - val_mae: 0.0039\nEpoch 406/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0010 - val_mae: 0.0010\nEpoch 407/500\n10/10 [==============================] - 0s 5ms/step - loss: 8.6223e-04 - mae: 8.6223e-04 - val_loss: 0.0022 - val_mae: 0.0022\nEpoch 408/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0024 - mae: 0.0024 - val_loss: 0.0014 - val_mae: 0.0014\nEpoch 409/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0037 - mae: 0.0037 - val_loss: 0.0092 - val_mae: 0.0092\nEpoch 410/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0043 - mae: 0.0043 - val_loss: 0.0018 - val_mae: 0.0018\nEpoch 411/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0030 - mae: 0.0030 - val_loss: 0.0019 - val_mae: 0.0019\nEpoch 412/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0043 - mae: 0.0043 - val_loss: 0.0039 - val_mae: 0.0039\nEpoch 413/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0024 - mae: 0.0024 - val_loss: 5.9819e-04 - val_mae: 5.9819e-04\nEpoch 414/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 0.0082 - val_mae: 0.0082\nEpoch 415/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0021 - mae: 0.0021 - val_loss: 0.0101 - val_mae: 0.0101\nEpoch 416/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 5.5294e-04 - val_mae: 5.5294e-04\nEpoch 417/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 0.0028 - val_mae: 0.0028\nEpoch 418/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 0.0068 - val_mae: 0.0068\nEpoch 419/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0089 - val_mae: 0.0089\nEpoch 420/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0023 - mae: 0.0023 - val_loss: 0.0056 - val_mae: 0.0056\nEpoch 421/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0020 - mae: 0.0020 - val_loss: 0.0050 - val_mae: 0.0050\nEpoch 422/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0014 - val_mae: 0.0014\nEpoch 423/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0023 - mae: 0.0023 - val_loss: 0.0050 - val_mae: 0.0050\nEpoch 424/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0051 - val_mae: 0.0051\nEpoch 425/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0026 - val_mae: 0.0026\nEpoch 426/500\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "10/10 [==============================] - 0s 3ms/step - loss: 0.0032 - mae: 0.0032 - val_loss: 0.0011 - val_mae: 0.0011\nEpoch 427/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0010 - mae: 0.0010 - val_loss: 0.0077 - val_mae: 0.0077\nEpoch 428/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 0.0032 - val_mae: 0.0032\nEpoch 429/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0091 - val_mae: 0.0091\nEpoch 430/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0040 - mae: 0.0040 - val_loss: 0.0059 - val_mae: 0.0059\nEpoch 431/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0034 - mae: 0.0034 - val_loss: 0.0076 - val_mae: 0.0076\nEpoch 432/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0011 - val_mae: 0.0011\nEpoch 433/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0028 - val_mae: 0.0028\nEpoch 434/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0036 - mae: 0.0036 - val_loss: 0.0076 - val_mae: 0.0076\nEpoch 435/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0071 - val_mae: 0.0071\nEpoch 436/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0028 - mae: 0.0028 - val_loss: 0.0066 - val_mae: 0.0066\nEpoch 437/500\n10/10 [==============================] - 0s 5ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0020 - val_mae: 0.0020\nEpoch 438/500\n10/10 [==============================] - 0s 3ms/step - loss: 8.4509e-04 - mae: 8.4509e-04 - val_loss: 9.4972e-04 - val_mae: 9.4972e-04\nEpoch 439/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0022 - mae: 0.0022 - val_loss: 0.0041 - val_mae: 0.0041\nEpoch 440/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0032 - val_mae: 0.0032\nEpoch 441/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0065 - val_mae: 0.0065\nEpoch 442/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 9.4314e-04 - val_mae: 9.4314e-04\nEpoch 443/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0026 - val_mae: 0.0026\nEpoch 444/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 445/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0032 - val_mae: 0.0032\nEpoch 446/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0012 - val_mae: 0.0012\nEpoch 447/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0092 - val_mae: 0.0092\nEpoch 448/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0024 - mae: 0.0024 - val_loss: 0.0046 - val_mae: 0.0046\nEpoch 449/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0038 - mae: 0.0038 - val_loss: 0.0066 - val_mae: 0.0066\nEpoch 450/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0024 - mae: 0.0024 - val_loss: 0.0043 - val_mae: 0.0043\nEpoch 451/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0083 - val_mae: 0.0083\nEpoch 452/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0021 - mae: 0.0021 - val_loss: 0.0092 - val_mae: 0.0092\nEpoch 453/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 4.7588e-04 - val_mae: 4.7588e-04\nEpoch 454/500\n10/10 [==============================] - 0s 3ms/step - loss: 8.1295e-04 - mae: 8.1295e-04 - val_loss: 0.0024 - val_mae: 0.0024\nEpoch 455/500\n10/10 [==============================] - 0s 3ms/step - loss: 5.7171e-04 - mae: 5.7171e-04 - val_loss: 0.0025 - val_mae: 0.0025\nEpoch 456/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0084 - val_mae: 0.0084\nEpoch 457/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0022 - mae: 0.0022 - val_loss: 0.0043 - val_mae: 0.0043\nEpoch 458/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0019 - mae: 0.0019 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 459/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0015 - mae: 0.0015 - val_loss: 0.0037 - val_mae: 0.0037\nEpoch 460/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0014 - val_mae: 0.0014\nEpoch 461/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0032 - val_mae: 0.0032\nEpoch 462/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0053 - val_mae: 0.0053\nEpoch 463/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0024 - val_mae: 0.0024\nEpoch 464/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0010 - mae: 0.0010 - val_loss: 0.0047 - val_mae: 0.0047\nEpoch 465/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 9.9301e-04 - val_mae: 9.9301e-04\nEpoch 466/500\n10/10 [==============================] - 0s 3ms/step - loss: 7.1894e-04 - mae: 7.1894e-04 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 467/500\n10/10 [==============================] - 0s 3ms/step - loss: 9.9074e-04 - mae: 9.9074e-04 - val_loss: 0.0013 - val_mae: 0.0013\nEpoch 468/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0010 - mae: 0.0010 - val_loss: 0.0059 - val_mae: 0.0059\nEpoch 469/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0021 - mae: 0.0021 - val_loss: 4.8466e-04 - val_mae: 4.8466e-04\nEpoch 470/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 0.0030 - val_mae: 0.0030\nEpoch 471/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 0.0072 - val_mae: 0.0072\nEpoch 472/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 1.4510e-04 - val_mae: 1.4510e-04\nEpoch 473/500\n10/10 [==============================] - 0s 3ms/step - loss: 9.4587e-04 - mae: 9.4587e-04 - val_loss: 6.2141e-04 - val_mae: 6.2141e-04\nEpoch 474/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0045 - val_mae: 0.0045\nEpoch 475/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0013 - mae: 0.0013 - val_loss: 0.0024 - val_mae: 0.0024\nEpoch 476/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 0.0038 - val_mae: 0.0038\nEpoch 477/500\n10/10 [==============================] - 0s 3ms/step - loss: 9.1282e-04 - mae: 9.1282e-04 - val_loss: 0.0012 - val_mae: 0.0012\nEpoch 478/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0018 - mae: 0.0018 - val_loss: 4.8523e-04 - val_mae: 4.8523e-04\nEpoch 479/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0023 - mae: 0.0023 - val_loss: 0.0029 - val_mae: 0.0029\nEpoch 480/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0028 - mae: 0.0028 - val_loss: 0.0034 - val_mae: 0.0034\nEpoch 481/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0024 - mae: 0.0024 - val_loss: 0.0025 - val_mae: 0.0025\nEpoch 482/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0048 - mae: 0.0048 - val_loss: 0.0125 - val_mae: 0.0125\nEpoch 483/500\n10/10 [==============================] - 0s 4ms/step - loss: 0.0034 - mae: 0.0034 - val_loss: 0.0021 - val_mae: 0.0021\nEpoch 484/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0021 - mae: 0.0021 - val_loss: 0.0087 - val_mae: 0.0087\nEpoch 485/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0016 - mae: 0.0016 - val_loss: 0.0033 - val_mae: 0.0033\nEpoch 486/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0010 - val_mae: 0.0010\n",
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": "Epoch 487/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0014 - mae: 0.0014 - val_loss: 0.0011 - val_mae: 0.0011\nEpoch 488/500\n10/10 [==============================] - 0s 3ms/step - loss: 6.8047e-04 - mae: 6.8047e-04 - val_loss: 7.7386e-04 - val_mae: 7.7386e-04\nEpoch 489/500\n10/10 [==============================] - 0s 2ms/step - loss: 9.0363e-04 - mae: 9.0363e-04 - val_loss: 0.0018 - val_mae: 0.0018\nEpoch 490/500\n10/10 [==============================] - 0s 2ms/step - loss: 9.8231e-04 - mae: 9.8231e-04 - val_loss: 0.0057 - val_mae: 0.0057\nEpoch 491/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0017 - mae: 0.0017 - val_loss: 0.0067 - val_mae: 0.0067\nEpoch 492/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0028 - mae: 0.0028 - val_loss: 5.8417e-04 - val_mae: 5.8417e-04\nEpoch 493/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 0.0094 - val_mae: 0.0094\nEpoch 494/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0027 - mae: 0.0027 - val_loss: 0.0037 - val_mae: 0.0037\nEpoch 495/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0010 - mae: 0.0010 - val_loss: 0.0017 - val_mae: 0.0017\nEpoch 496/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0011 - mae: 0.0011 - val_loss: 0.0010 - val_mae: 0.0010\nEpoch 497/500\n10/10 [==============================] - 0s 3ms/step - loss: 0.0012 - mae: 0.0012 - val_loss: 0.0018 - val_mae: 0.0018\nEpoch 498/500\n10/10 [==============================] - 0s 2ms/step - loss: 5.0030e-04 - mae: 5.0030e-04 - val_loss: 8.1325e-04 - val_mae: 8.1325e-04\nEpoch 499/500\n10/10 [==============================] - 0s 2ms/step - loss: 0.0010 - mae: 0.0010 - val_loss: 0.0020 - val_mae: 0.0020\nEpoch 500/500\n10/10 [==============================] - 0s 3ms/step - loss: 8.2627e-04 - mae: 8.2627e-04 - val_loss: 0.0014 - val_mae: 0.0014\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\n\nerr = hist.history['mae']\nval_err = hist.history['val_mae']\nepochs = range(1, len(err) + 1)\n\nplt.plot(epochs, err, '-', label='Training MAE')\nplt.plot(epochs, val_err, ':', label='Validation MAE')\nplt.title('Training and Validation Accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Mean Absolute Error')\nplt.legend(loc='upper right')\nplt.plot()",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "[]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "print(model.get_weights())",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": "[array([[-0.14091301, -0.15582293,  1.039766  ],\n       [-0.566372  , -0.102009  ,  1.0398471 ]], dtype=float32), array([ 0.        ,  0.        , -0.00045502], dtype=float32), array([[ 0.51683176],\n       [-0.95711666],\n       [ 0.96160114]], dtype=float32), array([-0.00050538], dtype=float32)]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\n\nmodel.predict(np.array([[2, 2]]))",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "array([[3.998574]], dtype=float32)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "weights = model.get_weights()\n\nwac = weights[0][0][0]\nwad = weights[0][0][1]\nwae = weights[0][0][2]\nwbc = weights[0][1][0]\nwbd = weights[0][1][1]\nwbe = weights[0][1][2]\n\nbc = weights[1][0]\nbd = weights[1][1]\nbe = weights[1][2]\n\nwcf = weights[2][0][0]\nwdf = weights[2][1][0]\nwef = weights[2][2][0]\n\nbf = weights[3][0]\n\ndef relu(x):\n    return max(0, x)\n\ndef add(a, b):\n    c = (a * wac) + (b * wbc) + bc\n    d = (a * wad) + (b * wbd) + bd\n    e = (a * wae) + (b * wbe) + be\n    f = (relu(c) * wcf) + (relu(d) * wdf) + (relu(e) * wef) + bf\n    return f",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "add(2, 2)",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "3.9985736963631173"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python36",
      "display_name": "Python 3.6",
      "language": "python"
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    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
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      "file_extension": ".py",
      "codemirror_mode": {
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