4. MlpNeuralNetworks/MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AI - MLP Project\n",
    "\n",
    "## MNIST digit recognition and noise reduction\n",
    "\n",
    "Ahmad Salimi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At first, import required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EtiOxuOePUwY"
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.datasets import mnist\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "import numpy as np\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load MNIST dataset\n",
    "\n",
    "Load and normalize the dataset, and convert labels to categorical (one-hot encoding)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "48o9F5vNP9qH"
   },
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "-WHiNtgUQKeA",
    "outputId": "27374ea2-acf4-48e1-ceaf-1285138e69aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "s7ZgT8tzQRqA",
    "outputId": "97263507-ccdb-4eb9-bebe-3b2462224bd5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_5wKhSrzQocP"
   },
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(60000, 28 * 28).astype('float32') / 255\n",
    "X_test = X_test.reshape(10000, 28 * 28).astype('float32') / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TJzERvleRng4"
   },
   "outputs": [],
   "source": [
    "y_train = to_categorical(y_train, 10)\n",
    "y_test = to_categorical(y_test, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\S1.$ Digit recognition\n",
    "\n",
    "First, create the model. two hidden layers, two dropout layers to avoid model overfitting, and an output softmax layer which matches with categorical labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s0Po_0fhSWXN"
   },
   "outputs": [],
   "source": [
    "digitClassifier = Sequential()\n",
    "digitClassifier.add(Dense(512, input_dim= 28 * 28, activation='relu'))\n",
    "digitClassifier.add(Dropout(0.2))\n",
    "digitClassifier.add(Dense(512, activation='relu'))\n",
    "digitClassifier.add(Dropout(0.2))\n",
    "digitClassifier.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aSKqjaQ8SjPY"
   },
   "outputs": [],
   "source": [
    "digitClassifier.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 329
    },
    "colab_type": "code",
    "id": "M7QQgaTCSnXl",
    "outputId": "f76154c6-e562-4364-bc32-9225c63dbeea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_7 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 669,706\n",
      "Trainable params: 669,706\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "digitClassifier.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the dataset with below parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 727
    },
    "colab_type": "code",
    "id": "-ZTn-XvNSsvz",
    "outputId": "eb034566-92fb-4485-f61f-16f172bca741"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 54000 samples, validate on 6000 samples\n",
      "Epoch 1/20\n",
      " - 6s - loss: 0.2653 - accuracy: 0.9196 - val_loss: 0.1038 - val_accuracy: 0.9703\n",
      "Epoch 2/20\n",
      " - 6s - loss: 0.1069 - accuracy: 0.9663 - val_loss: 0.0862 - val_accuracy: 0.9745\n",
      "Epoch 3/20\n",
      " - 6s - loss: 0.0745 - accuracy: 0.9768 - val_loss: 0.0744 - val_accuracy: 0.9790\n",
      "Epoch 4/20\n",
      " - 6s - loss: 0.0560 - accuracy: 0.9816 - val_loss: 0.0726 - val_accuracy: 0.9788\n",
      "Epoch 5/20\n",
      " - 6s - loss: 0.0465 - accuracy: 0.9849 - val_loss: 0.0690 - val_accuracy: 0.9803\n",
      "Epoch 6/20\n",
      " - 6s - loss: 0.0385 - accuracy: 0.9873 - val_loss: 0.0625 - val_accuracy: 0.9818\n",
      "Epoch 7/20\n",
      " - 6s - loss: 0.0354 - accuracy: 0.9880 - val_loss: 0.0756 - val_accuracy: 0.9810\n",
      "Epoch 8/20\n",
      " - 6s - loss: 0.0319 - accuracy: 0.9891 - val_loss: 0.0780 - val_accuracy: 0.9792\n",
      "Epoch 9/20\n",
      " - 6s - loss: 0.0254 - accuracy: 0.9919 - val_loss: 0.0772 - val_accuracy: 0.9820\n",
      "Epoch 10/20\n",
      " - 6s - loss: 0.0258 - accuracy: 0.9913 - val_loss: 0.0729 - val_accuracy: 0.9835\n",
      "Epoch 11/20\n",
      " - 6s - loss: 0.0221 - accuracy: 0.9922 - val_loss: 0.0712 - val_accuracy: 0.9812\n",
      "Epoch 12/20\n",
      " - 6s - loss: 0.0229 - accuracy: 0.9928 - val_loss: 0.0724 - val_accuracy: 0.9847\n",
      "Epoch 13/20\n",
      " - 6s - loss: 0.0220 - accuracy: 0.9928 - val_loss: 0.0791 - val_accuracy: 0.9818\n",
      "Epoch 14/20\n",
      " - 6s - loss: 0.0196 - accuracy: 0.9934 - val_loss: 0.0767 - val_accuracy: 0.9823\n",
      "Epoch 15/20\n",
      " - 6s - loss: 0.0201 - accuracy: 0.9928 - val_loss: 0.0706 - val_accuracy: 0.9835\n",
      "Epoch 16/20\n",
      " - 6s - loss: 0.0169 - accuracy: 0.9947 - val_loss: 0.0817 - val_accuracy: 0.9815\n",
      "Epoch 17/20\n",
      " - 6s - loss: 0.0166 - accuracy: 0.9947 - val_loss: 0.0840 - val_accuracy: 0.9828\n",
      "Epoch 18/20\n",
      " - 6s - loss: 0.0159 - accuracy: 0.9944 - val_loss: 0.0964 - val_accuracy: 0.9835\n",
      "Epoch 19/20\n",
      " - 6s - loss: 0.0157 - accuracy: 0.9949 - val_loss: 0.0834 - val_accuracy: 0.9838\n",
      "Epoch 20/20\n",
      " - 6s - loss: 0.0150 - accuracy: 0.9951 - val_loss: 0.0807 - val_accuracy: 0.9857\n"
     ]
    }
   ],
   "source": [
    "history = digitClassifier.fit(X_train, Y_train, batch_size=128, epochs=20, verbose=2, validation_split = 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot of training data and validation accuracy during training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 638
    },
    "colab_type": "code",
    "id": "nvwUsrGKafmS",
    "outputId": "676a9a78-66d6-4858-8b9d-93d57d945138"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5605b7f940>"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "plt.plot(history.history['accuracy'], label=\"accuracy\", color='green')\n",
    "plt.plot(history.history['val_accuracy'], label=\"validation accuracy\", color='red')\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot of training data and validation loss during training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 638
    },
    "colab_type": "code",
    "id": "IQHmgc5_bHPK",
    "outputId": "2f77967f-257f-4491-b062-24254e714c09"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f560628e860>"
      ]
     },
     "execution_count": 43,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "plt.plot(history.history['loss'], label=\"loss\", color='green')\n",
    "plt.plot(history.history['val_loss'], label=\"validation loss\", color='red')\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loss and accuracy of test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iPrafKaaTVEX"
   },
   "outputs": [],
   "source": [
    "loss, accuracy = digitClassifier.evaluate(X_test, Y_test, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "RB0VQPbYV-mt",
    "outputId": "882ec1a8-ce70-4c15-d544-c6adaf466d94"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07656290980688724"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "4Tot8RLDWK-a",
    "outputId": "1fba0fb0-5993-4d92-b450-f43ee7c98569"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9819999933242798"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw some test images\n",
    "\n",
    "This function, draws given images in a grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aECwjKy1WMBY"
   },
   "outputs": [],
   "source": [
    "def draw(images, texts, columns=1, rows=1):\n",
    "    fig = plt.figure(figsize=(3 * columns, 3 * rows))\n",
    "\n",
    "    for i in range(columns * rows):\n",
    "        ax = fig.add_subplot(rows, columns, i + 1)\n",
    "        ax.set_title(texts[i])\n",
    "        ax.set_aspect('equal')\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "        plt.imshow(images[i].reshape(28, 28) * 255)\n",
    "\n",
    "    cax = fig.add_axes([0.12, 0.1, 0.78, 0.8])\n",
    "    cax.get_xaxis().set_visible(False)\n",
    "    cax.get_yaxis().set_visible(False)\n",
    "    cax.set_frame_on(False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 952
    },
    "colab_type": "code",
    "id": "ZICE3nG_eX8Z",
    "outputId": "e5d98c2d-e68d-49df-f038-abe50fbf14f6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "indices = np.random.choice(np.arange(len(X_test)), size=20)\n",
    "images = X_test[indices]\n",
    "probs = digitClassifier.predict(images)\n",
    "predicted_digits = [np.argmax(p) for p in probs]\n",
    "texts = [f'{predicted_digits[i]} - p = {probs[i][predicted_digits[i]]:.3} - expected: {np.argmax(y_test[indices[i]])}' for i in range(len(indices))]\n",
    "\n",
    "draw(images, texts, 5, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $\\S2.$ Digit recognition\n",
    "\n",
    "In this section, we add noise to images and try to build a model that reduces noise of images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "v_yDwmkJrsWH"
   },
   "outputs": [],
   "source": [
    "def add_noise(array:np.ndarray, noise_sigma):\n",
    "    return np.clip(array + np.random.normal(0, noise_sigma, array.shape), 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hpw3LZe3KgS2"
   },
   "outputs": [],
   "source": [
    "def create_noisy_dataset(noise):\n",
    "    return add_noise(X_train, noise), add_noise(X_test, noise)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Autoencoder model\n",
    "\n",
    "An autoencoder has two parts: an encoder and a decoder. The encoder reduces the dimensions of input data so that the original information is compressed.\n",
    "\n",
    "![](Encoder.png)\n",
    "\n",
    "The decoder restores the original information from the compressed data.\n",
    "\n",
    "![](Decoder.png)\n",
    "\n",
    "The autoencoder is a neural network that learns to encode and decode automatically.\n",
    "\n",
    "![](AutoEncoder.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JI52cNhIcEPa"
   },
   "outputs": [],
   "source": [
    "def create_auto_encoder_model():\n",
    "    autoEncoder = Sequential()\n",
    "    autoEncoder.add(Dense(64, input_dim=28*28, activation='relu')) # Encoder\n",
    "    autoEncoder.add(Dense(28*28, activation='sigmoid')) # Decoder\n",
    "    autoEncoder.compile(loss='mse', optimizer='adam')\n",
    "    return autoEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M7XBoBvTcZtb"
   },
   "outputs": [],
   "source": [
    "def draw_results():\n",
    "    indices = np.random.choice(np.arange(len(X_test)), size=20)\n",
    "    images = np.zeros((20, 3, X_test[0].shape[0]))\n",
    "    images[:,0] = X_test[indices]\n",
    "    images[:,1] = noisy_test[indices]\n",
    "    images[:,2] = autoEncoder.predict(images[:,1])\n",
    "    images = images.reshape(20 * 3, X_test[0].shape[0],)\n",
    "    texts = np.zeros((20, 3), dtype=object)\n",
    "    texts[:,0] = np.array(['real' for _ in range(20)])\n",
    "    texts[:,1] = np.array(['noisy' for _ in range(20)])\n",
    "    texts[:,2] = np.array(['corrected' for _ in range(20)])\n",
    "    texts = texts.reshape(20 * 3)\n",
    "    draw(images, texts, 6, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BISFA-whh1pv"
   },
   "source": [
    "#### Noise = 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "I81LPnw9c19c",
    "outputId": "a7eaa9f3-f16f-4544-9b28-e9025a877172",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/100\n",
      " - 1s - loss: 0.0879 - val_loss: 0.0582\n",
      "Epoch 2/100\n",
      " - 1s - loss: 0.0474 - val_loss: 0.0390\n",
      "Epoch 3/100\n",
      " - 1s - loss: 0.0348 - val_loss: 0.0313\n",
      "Epoch 4/100\n",
      " - 2s - loss: 0.0290 - val_loss: 0.0271\n",
      "Epoch 5/100\n",
      " - 2s - loss: 0.0253 - val_loss: 0.0236\n",
      "Epoch 6/100\n",
      " - 1s - loss: 0.0222 - val_loss: 0.0210\n",
      "Epoch 7/100\n",
      " - 1s - loss: 0.0199 - val_loss: 0.0191\n",
      "Epoch 8/100\n",
      " - 1s - loss: 0.0182 - val_loss: 0.0176\n",
      "Epoch 9/100\n",
      " - 1s - loss: 0.0169 - val_loss: 0.0164\n",
      "Epoch 10/100\n",
      " - 1s - loss: 0.0158 - val_loss: 0.0155\n",
      "Epoch 11/100\n",
      " - 1s - loss: 0.0149 - val_loss: 0.0147\n",
      "Epoch 12/100\n",
      " - 1s - loss: 0.0142 - val_loss: 0.0140\n",
      "Epoch 13/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0135\n",
      "Epoch 14/100\n",
      " - 1s - loss: 0.0130 - val_loss: 0.0130\n",
      "Epoch 15/100\n",
      " - 1s - loss: 0.0125 - val_loss: 0.0126\n",
      "Epoch 16/100\n",
      " - 1s - loss: 0.0121 - val_loss: 0.0122\n",
      "Epoch 17/100\n",
      " - 1s - loss: 0.0118 - val_loss: 0.0119\n",
      "Epoch 18/100\n",
      " - 1s - loss: 0.0115 - val_loss: 0.0116\n",
      "Epoch 19/100\n",
      " - 1s - loss: 0.0112 - val_loss: 0.0114\n",
      "Epoch 20/100\n",
      " - 1s - loss: 0.0109 - val_loss: 0.0111\n",
      "Epoch 21/100\n",
      " - 1s - loss: 0.0107 - val_loss: 0.0109\n",
      "Epoch 22/100\n",
      " - 1s - loss: 0.0105 - val_loss: 0.0107\n",
      "Epoch 23/100\n",
      " - 1s - loss: 0.0104 - val_loss: 0.0106\n",
      "Epoch 24/100\n",
      " - 1s - loss: 0.0102 - val_loss: 0.0105\n",
      "Epoch 25/100\n",
      " - 1s - loss: 0.0100 - val_loss: 0.0103\n",
      "Epoch 26/100\n",
      " - 1s - loss: 0.0099 - val_loss: 0.0102\n",
      "Epoch 27/100\n",
      " - 1s - loss: 0.0098 - val_loss: 0.0101\n",
      "Epoch 28/100\n",
      " - 1s - loss: 0.0097 - val_loss: 0.0100\n",
      "Epoch 29/100\n",
      " - 1s - loss: 0.0095 - val_loss: 0.0098\n",
      "Epoch 30/100\n",
      " - 1s - loss: 0.0094 - val_loss: 0.0097\n",
      "Epoch 31/100\n",
      " - 1s - loss: 0.0093 - val_loss: 0.0097\n",
      "Epoch 32/100\n",
      " - 1s - loss: 0.0093 - val_loss: 0.0096\n",
      "Epoch 33/100\n",
      " - 1s - loss: 0.0092 - val_loss: 0.0095\n",
      "Epoch 34/100\n",
      " - 1s - loss: 0.0091 - val_loss: 0.0095\n",
      "Epoch 35/100\n",
      " - 1s - loss: 0.0091 - val_loss: 0.0095\n",
      "Epoch 36/100\n",
      " - 1s - loss: 0.0090 - val_loss: 0.0094\n",
      "Epoch 37/100\n",
      " - 1s - loss: 0.0090 - val_loss: 0.0094\n",
      "Epoch 38/100\n",
      " - 1s - loss: 0.0090 - val_loss: 0.0093\n",
      "Epoch 39/100\n",
      " - 1s - loss: 0.0089 - val_loss: 0.0093\n",
      "Epoch 40/100\n",
      " - 1s - loss: 0.0089 - val_loss: 0.0093\n",
      "Epoch 41/100\n",
      " - 1s - loss: 0.0089 - val_loss: 0.0093\n",
      "Epoch 42/100\n",
      " - 1s - loss: 0.0088 - val_loss: 0.0092\n",
      "Epoch 43/100\n",
      " - 1s - loss: 0.0088 - val_loss: 0.0092\n",
      "Epoch 44/100\n",
      " - 1s - loss: 0.0088 - val_loss: 0.0092\n",
      "Epoch 45/100\n",
      " - 1s - loss: 0.0088 - val_loss: 0.0092\n",
      "Epoch 46/100\n",
      " - 1s - loss: 0.0087 - val_loss: 0.0092\n",
      "Epoch 47/100\n",
      " - 1s - loss: 0.0087 - val_loss: 0.0091\n",
      "Epoch 48/100\n",
      " - 1s - loss: 0.0087 - val_loss: 0.0091\n",
      "Epoch 49/100\n",
      " - 1s - loss: 0.0087 - val_loss: 0.0091\n",
      "Epoch 50/100\n",
      " - 1s - loss: 0.0087 - val_loss: 0.0091\n",
      "Epoch 51/100\n",
      " - 1s - loss: 0.0086 - val_loss: 0.0090\n",
      "Epoch 52/100\n",
      " - 1s - loss: 0.0086 - val_loss: 0.0091\n",
      "Epoch 53/100\n",
      " - 1s - loss: 0.0086 - val_loss: 0.0090\n",
      "Epoch 54/100\n",
      " - 1s - loss: 0.0086 - val_loss: 0.0090\n",
      "Epoch 55/100\n",
      " - 1s - loss: 0.0085 - val_loss: 0.0089\n",
      "Epoch 56/100\n",
      " - 1s - loss: 0.0085 - val_loss: 0.0089\n",
      "Epoch 57/100\n",
      " - 1s - loss: 0.0085 - val_loss: 0.0089\n",
      "Epoch 58/100\n",
      " - 1s - loss: 0.0085 - val_loss: 0.0089\n",
      "Epoch 59/100\n",
      " - 1s - loss: 0.0084 - val_loss: 0.0089\n",
      "Epoch 60/100\n",
      " - 1s - loss: 0.0084 - val_loss: 0.0089\n",
      "Epoch 61/100\n",
      " - 1s - loss: 0.0084 - val_loss: 0.0088\n",
      "Epoch 62/100\n",
      " - 1s - loss: 0.0084 - val_loss: 0.0088\n",
      "Epoch 63/100\n",
      " - 1s - loss: 0.0084 - val_loss: 0.0088\n",
      "Epoch 64/100\n",
      " - 1s - loss: 0.0084 - val_loss: 0.0088\n",
      "Epoch 65/100\n",
      " - 1s - loss: 0.0084 - val_loss: 0.0088\n",
      "Epoch 66/100\n",
      " - 1s - loss: 0.0084 - val_loss: 0.0088\n",
      "Epoch 67/100\n",
      " - 1s - loss: 0.0083 - val_loss: 0.0088\n",
      "Epoch 68/100\n",
      " - 1s - loss: 0.0083 - val_loss: 0.0088\n",
      "Epoch 69/100\n",
      " - 1s - loss: 0.0083 - val_loss: 0.0087\n",
      "Epoch 70/100\n",
      " - 1s - loss: 0.0083 - val_loss: 0.0087\n",
      "Epoch 71/100\n",
      " - 1s - loss: 0.0082 - val_loss: 0.0087\n",
      "Epoch 72/100\n",
      " - 1s - loss: 0.0082 - val_loss: 0.0086\n",
      "Epoch 73/100\n",
      " - 1s - loss: 0.0082 - val_loss: 0.0086\n",
      "Epoch 74/100\n",
      " - 1s - loss: 0.0082 - val_loss: 0.0086\n",
      "Epoch 75/100\n",
      " - 1s - loss: 0.0082 - val_loss: 0.0086\n",
      "Epoch 76/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 77/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 78/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 79/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 80/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 81/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 82/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 83/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 84/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 85/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 86/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 87/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 88/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0085\n",
      "Epoch 89/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 90/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0085\n",
      "Epoch 91/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 92/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 93/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0085\n",
      "Epoch 94/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0085\n",
      "Epoch 95/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 96/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 97/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0086\n",
      "Epoch 98/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0085\n",
      "Epoch 99/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0085\n",
      "Epoch 100/100\n",
      " - 1s - loss: 0.0081 - val_loss: 0.0085\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7f5604067f98>"
      ]
     },
     "execution_count": 150,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisy_train, noisy_test = create_noisy_dataset(0.3)\n",
    "autoEncoder = create_auto_encoder_model()\n",
    "autoEncoder.fit(noisy_train, X_train, batch_size=512, epochs=100, validation_split=0.2, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error of test dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "Dr_Xz-MjdAWD",
    "outputId": "f85e3ec3-c973-4a42-a97b-ffd3bf14be9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 22us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.008249689091742039"
      ]
     },
     "execution_count": 151,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoEncoder.evaluate(noisy_test, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "QUXMQmwmd9YM",
    "outputId": "a857a615-4c2b-4999-fc9f-2e4d433358c6"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1728x2880 with 61 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5rSzFViKh8Rk"
   },
   "source": [
    "#### Noise = 0.45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "JPh_2mKteFE8",
    "outputId": "03a3492a-7f93-4806-e1d2-0f7e167f5273",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/100\n",
      " - 1s - loss: 0.0859 - val_loss: 0.0636\n",
      "Epoch 2/100\n",
      " - 1s - loss: 0.0556 - val_loss: 0.0474\n",
      "Epoch 3/100\n",
      " - 1s - loss: 0.0426 - val_loss: 0.0386\n",
      "Epoch 4/100\n",
      " - 1s - loss: 0.0358 - val_loss: 0.0334\n",
      "Epoch 5/100\n",
      " - 1s - loss: 0.0313 - val_loss: 0.0297\n",
      "Epoch 6/100\n",
      " - 1s - loss: 0.0280 - val_loss: 0.0269\n",
      "Epoch 7/100\n",
      " - 1s - loss: 0.0257 - val_loss: 0.0250\n",
      "Epoch 8/100\n",
      " - 1s - loss: 0.0239 - val_loss: 0.0234\n",
      "Epoch 9/100\n",
      " - 1s - loss: 0.0226 - val_loss: 0.0222\n",
      "Epoch 10/100\n",
      " - 1s - loss: 0.0214 - val_loss: 0.0212\n",
      "Epoch 11/100\n",
      " - 1s - loss: 0.0204 - val_loss: 0.0203\n",
      "Epoch 12/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0196\n",
      "Epoch 13/100\n",
      " - 1s - loss: 0.0189 - val_loss: 0.0189\n",
      "Epoch 14/100\n",
      " - 1s - loss: 0.0182 - val_loss: 0.0183\n",
      "Epoch 15/100\n",
      " - 1s - loss: 0.0177 - val_loss: 0.0180\n",
      "Epoch 16/100\n",
      " - 1s - loss: 0.0174 - val_loss: 0.0176\n",
      "Epoch 17/100\n",
      " - 1s - loss: 0.0170 - val_loss: 0.0173\n",
      "Epoch 18/100\n",
      " - 1s - loss: 0.0167 - val_loss: 0.0170\n",
      "Epoch 19/100\n",
      " - 1s - loss: 0.0164 - val_loss: 0.0168\n",
      "Epoch 20/100\n",
      " - 1s - loss: 0.0162 - val_loss: 0.0166\n",
      "Epoch 21/100\n",
      " - 1s - loss: 0.0160 - val_loss: 0.0163\n",
      "Epoch 22/100\n",
      " - 1s - loss: 0.0157 - val_loss: 0.0161\n",
      "Epoch 23/100\n",
      " - 1s - loss: 0.0155 - val_loss: 0.0160\n",
      "Epoch 24/100\n",
      " - 1s - loss: 0.0154 - val_loss: 0.0158\n",
      "Epoch 25/100\n",
      " - 1s - loss: 0.0152 - val_loss: 0.0157\n",
      "Epoch 26/100\n",
      " - 1s - loss: 0.0151 - val_loss: 0.0156\n",
      "Epoch 27/100\n",
      " - 1s - loss: 0.0150 - val_loss: 0.0156\n",
      "Epoch 28/100\n",
      " - 1s - loss: 0.0149 - val_loss: 0.0154\n",
      "Epoch 29/100\n",
      " - 1s - loss: 0.0148 - val_loss: 0.0154\n",
      "Epoch 30/100\n",
      " - 1s - loss: 0.0147 - val_loss: 0.0153\n",
      "Epoch 31/100\n",
      " - 1s - loss: 0.0147 - val_loss: 0.0152\n",
      "Epoch 32/100\n",
      " - 1s - loss: 0.0146 - val_loss: 0.0152\n",
      "Epoch 33/100\n",
      " - 1s - loss: 0.0145 - val_loss: 0.0151\n",
      "Epoch 34/100\n",
      " - 1s - loss: 0.0145 - val_loss: 0.0151\n",
      "Epoch 35/100\n",
      " - 1s - loss: 0.0145 - val_loss: 0.0151\n",
      "Epoch 36/100\n",
      " - 1s - loss: 0.0144 - val_loss: 0.0150\n",
      "Epoch 37/100\n",
      " - 1s - loss: 0.0143 - val_loss: 0.0149\n",
      "Epoch 38/100\n",
      " - 1s - loss: 0.0143 - val_loss: 0.0149\n",
      "Epoch 39/100\n",
      " - 1s - loss: 0.0142 - val_loss: 0.0149\n",
      "Epoch 40/100\n",
      " - 1s - loss: 0.0142 - val_loss: 0.0148\n",
      "Epoch 41/100\n",
      " - 1s - loss: 0.0142 - val_loss: 0.0148\n",
      "Epoch 42/100\n",
      " - 1s - loss: 0.0141 - val_loss: 0.0148\n",
      "Epoch 43/100\n",
      " - 1s - loss: 0.0141 - val_loss: 0.0148\n",
      "Epoch 44/100\n",
      " - 1s - loss: 0.0141 - val_loss: 0.0147\n",
      "Epoch 45/100\n",
      " - 1s - loss: 0.0140 - val_loss: 0.0147\n",
      "Epoch 46/100\n",
      " - 1s - loss: 0.0140 - val_loss: 0.0147\n",
      "Epoch 47/100\n",
      " - 1s - loss: 0.0140 - val_loss: 0.0147\n",
      "Epoch 48/100\n",
      " - 1s - loss: 0.0140 - val_loss: 0.0147\n",
      "Epoch 49/100\n",
      " - 1s - loss: 0.0140 - val_loss: 0.0147\n",
      "Epoch 50/100\n",
      " - 1s - loss: 0.0139 - val_loss: 0.0146\n",
      "Epoch 51/100\n",
      " - 1s - loss: 0.0139 - val_loss: 0.0146\n",
      "Epoch 52/100\n",
      " - 1s - loss: 0.0139 - val_loss: 0.0146\n",
      "Epoch 53/100\n",
      " - 1s - loss: 0.0139 - val_loss: 0.0146\n",
      "Epoch 54/100\n",
      " - 1s - loss: 0.0139 - val_loss: 0.0146\n",
      "Epoch 55/100\n",
      " - 1s - loss: 0.0138 - val_loss: 0.0145\n",
      "Epoch 56/100\n",
      " - 1s - loss: 0.0138 - val_loss: 0.0145\n",
      "Epoch 57/100\n",
      " - 1s - loss: 0.0138 - val_loss: 0.0145\n",
      "Epoch 58/100\n",
      " - 1s - loss: 0.0138 - val_loss: 0.0145\n",
      "Epoch 59/100\n",
      " - 1s - loss: 0.0138 - val_loss: 0.0145\n",
      "Epoch 60/100\n",
      " - 1s - loss: 0.0138 - val_loss: 0.0145\n",
      "Epoch 61/100\n",
      " - 1s - loss: 0.0137 - val_loss: 0.0145\n",
      "Epoch 62/100\n",
      " - 1s - loss: 0.0137 - val_loss: 0.0144\n",
      "Epoch 63/100\n",
      " - 1s - loss: 0.0137 - val_loss: 0.0144\n",
      "Epoch 64/100\n",
      " - 1s - loss: 0.0137 - val_loss: 0.0144\n",
      "Epoch 65/100\n",
      " - 1s - loss: 0.0137 - val_loss: 0.0144\n",
      "Epoch 66/100\n",
      " - 1s - loss: 0.0137 - val_loss: 0.0144\n",
      "Epoch 67/100\n",
      " - 1s - loss: 0.0137 - val_loss: 0.0144\n",
      "Epoch 68/100\n",
      " - 1s - loss: 0.0137 - val_loss: 0.0144\n",
      "Epoch 69/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0144\n",
      "Epoch 70/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0144\n",
      "Epoch 71/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0144\n",
      "Epoch 72/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0144\n",
      "Epoch 73/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0144\n",
      "Epoch 74/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0143\n",
      "Epoch 75/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0143\n",
      "Epoch 76/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0143\n",
      "Epoch 77/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0144\n",
      "Epoch 78/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0143\n",
      "Epoch 79/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0143\n",
      "Epoch 80/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0143\n",
      "Epoch 81/100\n",
      " - 1s - loss: 0.0136 - val_loss: 0.0143\n",
      "Epoch 82/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n",
      "Epoch 83/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n",
      "Epoch 84/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n",
      "Epoch 85/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n",
      "Epoch 86/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n",
      "Epoch 87/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n",
      "Epoch 88/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n",
      "Epoch 89/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n",
      "Epoch 90/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n",
      "Epoch 91/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n",
      "Epoch 92/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n",
      "Epoch 93/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n",
      "Epoch 94/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0142\n",
      "Epoch 95/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0142\n",
      "Epoch 96/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0142\n",
      "Epoch 97/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0142\n",
      "Epoch 98/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0142\n",
      "Epoch 99/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0142\n",
      "Epoch 100/100\n",
      " - 1s - loss: 0.0135 - val_loss: 0.0143\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7f56062e4cf8>"
      ]
     },
     "execution_count": 153,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisy_train, noisy_test = create_noisy_dataset(0.45)\n",
    "autoEncoder = create_auto_encoder_model()\n",
    "autoEncoder.fit(noisy_train, X_train, batch_size=512, epochs=100, validation_split=0.2, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error of test dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "qJAbVdBdeUr1",
    "outputId": "8939a5ba-aa08-4977-8978-9e267d95778a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 23us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.01381909001171589"
      ]
     },
     "execution_count": 154,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoEncoder.evaluate(noisy_test, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "B0fMst53e1If",
    "outputId": "614aaea1-db4b-470f-bde9-f5b523c72ab5"
   },
   "outputs": [
    {
     "data": {
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MH1x9Xpjb/q2js/HX58fvZQw9bkc2fv9vxfe8qY3x/HpXa/436OlTvx/WpFPjVF/6we4jw9ySN+P73r+9eFI2PuPiX77lMQ121c6u1P3Khvpqnn0+Tk6bmg2v+rt5YcmpXzw9zC257tZs/Jnr8/H/kp8v687++7Di7Mmzw9z0+fl1UHlvvI5+4Cf3Z+MvdO4Pa/7oy58Jc0u+XOvvzRv/o5Vhbt5jG7PxC9ecFdZ0XHBsNv61b9wS1sxvjZ93btvRlo3f8a34927cd/LrncrvnJIveNbzcKSyfWeYG/H9fG8kpZR+Pit/n7pw+OthTbkX7xb+9Ge/FeZmfi//3PuNto+GNX96aX7dPXvqK2HNvkpTmCuP7czGG6fG6/jNraPCXPOOfA+r6c34d2brifn5VemusWZY3RzmmrZsy8arPYdmPeyNVAAAAACAAhqpAAAAAAAFNFIBAAAAAApopAIAAAAAFNBIBQAAAAAoUKr24a6vI0pjqvNK7+mz4w0EDaNHZ+NDfxTv8veDYx4Jcy925XdDXLrvqLDm+Jb8zoVzmuMx9LXnO+Md2T59xVXZePPDSw7WcA6ZR6v3La1Wq/20/2vfGN4+qTr75kuyuSFn53dIX3dDvENp9IvRfnu8K3b3upfDXGnuO7Lxlz4R79o764u/ycZf+9CMsOaGP413Q93Und/p+5IR+V0XU0rp6lfzO7z+r7b87vIppbSpEu8wvzvYcfDYpuFhzfxlF4a5p2ffl43X2vk1UmppCXPdC04Mc5tPbs3Gd82Kf0/aL382G28YNTIbX7TrX9PO7s01tp8fmAb8/bKU/0jLQ+J5+dIN+bl873l/F9Yc1RhfC53B+qQ1GFtKKY0s56+5coprGkp9+2/Ka7reDHMf+sqfZ+Pj78xf9ymlVO3ufstj6m+Lq4+lXdVtg2peDvQ52TB+fDa+48z8TtUppfTG/Hx8ymP53YFTSmnIT38V5qr74x2969XYNrlGMl7frv9gfr28Z2rlrQ7p//GF9/0wzJ0xZG2fnuv6jedk4xvmx78l9RqMczKl2vNy01ULsvGd8+Jdqcub8ztCH/XT+Pppfij+fd7//rnZeMtP4pqHX1uWja/siteI11x4RZirPvt8mItEn92Em/O7zqeU0r5zTwtzrQ88k42v/dv4eaLtZ/G9reXB/Oe37vr4eCs/dms2fswjnwhr1r4vfjaYc92V2fi42xeFNV1nzcnGmx5dmo0Pxnk5EO6VtZ5L9r735Gz8ym/cG9acOXRDNn73zneGNY9fnH8OTCml6vKV+USN9WZ5RP55rzplYlize+aIeAzl+i+rkcs2x8mt27PhUmt+3Z1SSp3TJ+Tjo/O/wymlNGztzngMr+fHV9leo6an/rXBgfZ7vJEKAAAAAFBAIxUAAAAAoIBGKgAAAABAAY1UAAAAAIACGqkAAAAAAAU0UgEAAAAACpSq1WqfHWxEaUx1Xuk9fXa8gazxqClh7qtP3hfmTmxqPhjDOehO+frVYW7SN5/qx5EcWo9W71tarVZPPdTjqEdv5uXWT54e5sZ+d9FbHdJb9qmVa7Px77QfE9bc8vIvwtyxTcOz8a9tOzas+fMxa8Jcfznn/IvDXHXJ8rqP95sv57/3aZ/v3XdemvuObLxhy66wpmvSqPyxFv0qG19cfSztqm4r1T+6Q2uw3i9LjY1hrnrKrGz8rDvje8SnRsXX6fBSSzbeUOq/fwOuVHuy8Re79oc1l1z/mTA3/ntLs/FqV2d9AxvgBuO8HKxzEg7EYJyTKaU0dPxR1eM/8Olsbt+4/J8z9c5V8QHL+ZoNF88IS478Rv8859y4bnGY+9R1C8Pc6Lv6Z13eOKUtzK24Nv9c3LKlIaxpipeC6cib+uczf/i1ZWHu7Mmz6z5eeejQbHzNnfnra8Nnb0v71rw6qOblgL9XlvIfZ+PECWHJKx/NP++V8kvAlFJKbfesDnOVTZvziVp9t2Dcqcaat9QUr8lLwfGq3d1hTbVSCXPheZrj3lY0H6Lf4ZRSqu7dF+c682vlmuPuRa/zQPs93kgFAAAAACigkQoAAAAAUEAjFQAAAACggEYqAAAAAEABjVQAAAAAgAKlai92sooM+F3c+smqm+fFuQ/c2o8jyTvu5x/Pxsf/uDWsGXnvkjBXa/e3w82B7uI2kIwY3lY9bfaV2VxlSH63v8bH8jtL17Lt0vyO7ymltOP4uK5pV37nvs4TO8KasQ8NyccfjHdQbH9oe5j71pHx9R055v7Ls/G1v397WHPXrnjHyPten5ONb/nu0WHNrqPjfws76iv5HU/X3BPvQtp2T37nxS3vjHeFnHJ9/+ysGhmsOxH32/0y2gU0pV7tZFnreOWWlvxp3jEzrNn51/E8/8sZD2fjxzVtCmsaSvm/aVslvr892dEe5m7/zzOy8Rk31dj19LkVYS711L8j6mA0GOelNSyHs8E4J1MaGPOy633xsr/pkfrXj+u/uCAbn7ikK6yZ89fxunz5nBpbiwcqv31KNt7wxHN1H2ugWHdD/jlk+rWL+vQ8Gz6b//5SSmn4hvwapDv/2JJe+udvpo7NrwyqeTkQ5mRfKzXln3/Kw4f16niVnbvyib5eA9Za40d62/sLzlVqbKpxrvxvU7Wnl2MIjtfrvylwoP0eb6QCAAAAABTQSAUAAAAAKKCRCgAAAABQQCMVAAAAAKCARioAAAAAQAGNVAAAAACAAo2HegCHo5lXLQ5z77/qlH4cSd6xaVndNdWDMA76yZ69qfT08myqsadS9+GqC07Oxsfc/UxYM6YX5+mNzR8/PcxV5iwKc++f9cF8zYpVYc3x41bnj/VX747HsGNnmEtpYzY6Moj/V65+x15U//yf8kCc2/GH8Wd+45duz8a/8JlPhjXbjs/fltpufCoeBP2nGt8NevbvzyeW/jqsGXl+Q5i7rXxiNl5uqXEfLZfy8Z7e3cVmVvK/AT1798VF/fR7B8D/b8vl8bpk3O35tWD3mXP6dAy7PjI/zI1dnr9HtPzbs2HNo5fNCnMTFzRn4x2TW8OaN+bm36VqOmNBWNP2xN4wV37yl2EuUvnt+F7e8MRzdR/vvPfmn7+XXxvXXPDrzWHu2z/4vWx88pPBWiel1Pj40mx8/+/OzcYbOj1hDwTVrs5svGd3T1hTHjkizDVOGBecpysew56OfLy7O66ptbatBmMvBevkIqVevH8Z1JTK8edaaozbkz2dwedXPTTrbm+kAgAAAAAU0EgFAAAAACigkQoAAAAAUEAjFQAAAACggEYqAAAAAEABjVQAAAAAgAKNh3oAwME1/qT96VM/XJXNfesvP5KN77hkd3i86uJh2fjItlPDmpbt3WGu6dGlYa5erTt6elVXWZH/fGrWbNmaje+5cF5YM+y+xWGuYdbMbLx865vxGK4ZGea6Rw7JH+/JX4Y1pTknZuP7JgwNa0au6QhzN3z4o/nE5LAktd34VJykftXqoT9XtRKX9MS5SGX//rprAHh7GHf7ojBXPnlWNt668vWwZsW1U8LczEfy8RH/5+mwpjemXL0rzHVveDUb3/9Ae1hzzLkrs/HVN80Pa2qtHyO7LoqPN+Ke+DPqfnRqNt541vqwZvmc/DPA9j86Paz54QnxtTI15dejex+eHtY0Pp6Ptzz4bDZeqsZraA69aqXGGrVGrufICflEY/wOY3lrfo5Xd8Zzv9b4Ss3N+bF1xNdctSt+Xi+VS2EuPF4wvnJrS3yeI4bHB3xjU91jOJi8kQoAAAAAUEAjFQAAAACggEYqAAAAAEABjVQAAAAAgAIaqQAAAAAABRoP9QCAg2vz8pb0nfZjsrmhKb+L/J5J8Q6XjXvzO3MPvzfekX79FxaEuamP5uMNM+JdMSur12XjQ++Px9Bfht0Xj6Fx0sQw171iVTa++e74u0inxqkxd+Z3Ii3PPiGs6Vn6Qja+8SvxGIaetDvMTfi9F7PxN74cAigG4AAABiRJREFUH+8dfzYuG999xpawBgB4e9l33mnZeOuPnwlr1vzBqGx8+udWhDUzr361T8fQccG8bLyxI96Be8fEpjA3dml+l+sx574U1uy8eH423nDk3rCmN944Pf/MkFJKI+6J67bf15aNj0/r6x7D6Lvz6+GUUlp5a/77Syml9ivz3+GQs/PPICmldMf6X2Tjl019V1jDAFaNr9+eN/eEudLa/HUa7WKfUkqVKFeq8d5juRTnorHXGEOq9sSp7uB4pfrH0NPREdfUyg0w3kgFAAAAACigkQoAAAAAUEAjFQAAAACggEYqAAAAAEABjVQAAAAAgAIaqQAAAAAABRoP9QCAgWf8rYviZLmh7uMd/eOdYa4axCur19V9nlq2fvL0MDdybWc23vj40rBmw2cXZONTvvpUWPPiX0wPc9MemJKNT3jk5bAmVSpxbtrUbPjXfzI0LBm1NP83tW6OTzP58u1hrjuIT/t8fH298KX8GKamLfEgAIC3lfdf/7Ns/B+nvjesmf65eI0W2f6xeP3Y05SPt9Y43rBX9mTj5d9sDGuat2wNczVWgqGR//B0EO/FwVJKWy/Lf0bjn4lrSk3NYW77O/N/1d5gjZhSSu/57/k1+6q5+8Oa9ivjAW6+IvibbovXsFf+t4uCzPqwhsGp2h095dTO9Zfo+brvT9RvZxpwvJEKAAAAAFBAIxUAAAAAoIBGKgAAAABAAY1UAAAAAIACGqkAAAAAAAUaD/UAgINrf9uwtPaa/M6TC89/IBv/0Qljw+OVh+T3In39YyeHNRNurn+X1JXfPTXMtX9ySTbeeOSksGbsd+NdNiMb/0e8O+iUr+b/psa2yWHN0I3xv12t/VBDNt5+xWthTec5c8Nc18L8Dq/lLfH+rhNuqf97SpMm1l9Tw9Qv5cew79zTsvHqv9f/vQIAg9vPP3xKNj5p3bKwZuOf5Nd1Rz65I6wZfVe8zmgM1kD7zpwT1uwd35SNH7Ekv24rUj55Vjbe86sVdR+rYdTIMFfZsTPMbZvdk43PvGpxWFNrr+9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      "text/plain": [
       "<Figure size 1728x2880 with 61 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hS9L2zQ2iByE"
   },
   "source": [
    "### Noise = 0.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "alhqX8hke3__",
    "outputId": "e67071e2-028b-4ce7-b726-993b733b6784",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/100\n",
      " - 1s - loss: 0.0866 - val_loss: 0.0613\n",
      "Epoch 2/100\n",
      " - 1s - loss: 0.0544 - val_loss: 0.0478\n",
      "Epoch 3/100\n",
      " - 1s - loss: 0.0438 - val_loss: 0.0405\n",
      "Epoch 4/100\n",
      " - 1s - loss: 0.0382 - val_loss: 0.0365\n",
      "Epoch 5/100\n",
      " - 1s - loss: 0.0351 - val_loss: 0.0341\n",
      "Epoch 6/100\n",
      " - 1s - loss: 0.0330 - val_loss: 0.0323\n",
      "Epoch 7/100\n",
      " - 1s - loss: 0.0313 - val_loss: 0.0307\n",
      "Epoch 8/100\n",
      " - 1s - loss: 0.0298 - val_loss: 0.0294\n",
      "Epoch 9/100\n",
      " - 1s - loss: 0.0285 - val_loss: 0.0283\n",
      "Epoch 10/100\n",
      " - 1s - loss: 0.0274 - val_loss: 0.0273\n",
      "Epoch 11/100\n",
      " - 1s - loss: 0.0265 - val_loss: 0.0264\n",
      "Epoch 12/100\n",
      " - 1s - loss: 0.0257 - val_loss: 0.0257\n",
      "Epoch 13/100\n",
      " - 1s - loss: 0.0249 - val_loss: 0.0250\n",
      "Epoch 14/100\n",
      " - 1s - loss: 0.0243 - val_loss: 0.0244\n",
      "Epoch 15/100\n",
      " - 1s - loss: 0.0237 - val_loss: 0.0239\n",
      "Epoch 16/100\n",
      " - 1s - loss: 0.0233 - val_loss: 0.0236\n",
      "Epoch 17/100\n",
      " - 1s - loss: 0.0229 - val_loss: 0.0234\n",
      "Epoch 18/100\n",
      " - 1s - loss: 0.0226 - val_loss: 0.0231\n",
      "Epoch 19/100\n",
      " - 1s - loss: 0.0224 - val_loss: 0.0229\n",
      "Epoch 20/100\n",
      " - 1s - loss: 0.0222 - val_loss: 0.0227\n",
      "Epoch 21/100\n",
      " - 1s - loss: 0.0220 - val_loss: 0.0225\n",
      "Epoch 22/100\n",
      " - 1s - loss: 0.0218 - val_loss: 0.0224\n",
      "Epoch 23/100\n",
      " - 1s - loss: 0.0216 - val_loss: 0.0222\n",
      "Epoch 24/100\n",
      " - 1s - loss: 0.0215 - val_loss: 0.0221\n",
      "Epoch 25/100\n",
      " - 1s - loss: 0.0214 - val_loss: 0.0220\n",
      "Epoch 26/100\n",
      " - 1s - loss: 0.0213 - val_loss: 0.0219\n",
      "Epoch 27/100\n",
      " - 1s - loss: 0.0211 - val_loss: 0.0218\n",
      "Epoch 28/100\n",
      " - 1s - loss: 0.0210 - val_loss: 0.0217\n",
      "Epoch 29/100\n",
      " - 1s - loss: 0.0210 - val_loss: 0.0216\n",
      "Epoch 30/100\n",
      " - 1s - loss: 0.0209 - val_loss: 0.0216\n",
      "Epoch 31/100\n",
      " - 1s - loss: 0.0208 - val_loss: 0.0216\n",
      "Epoch 32/100\n",
      " - 1s - loss: 0.0208 - val_loss: 0.0215\n",
      "Epoch 33/100\n",
      " - 1s - loss: 0.0207 - val_loss: 0.0214\n",
      "Epoch 34/100\n",
      " - 1s - loss: 0.0206 - val_loss: 0.0214\n",
      "Epoch 35/100\n",
      " - 1s - loss: 0.0206 - val_loss: 0.0213\n",
      "Epoch 36/100\n",
      " - 1s - loss: 0.0205 - val_loss: 0.0213\n",
      "Epoch 37/100\n",
      " - 1s - loss: 0.0205 - val_loss: 0.0213\n",
      "Epoch 38/100\n",
      " - 1s - loss: 0.0205 - val_loss: 0.0213\n",
      "Epoch 39/100\n",
      " - 1s - loss: 0.0204 - val_loss: 0.0212\n",
      "Epoch 40/100\n",
      " - 1s - loss: 0.0204 - val_loss: 0.0213\n",
      "Epoch 41/100\n",
      " - 1s - loss: 0.0204 - val_loss: 0.0212\n",
      "Epoch 42/100\n",
      " - 1s - loss: 0.0204 - val_loss: 0.0212\n",
      "Epoch 43/100\n",
      " - 1s - loss: 0.0203 - val_loss: 0.0212\n",
      "Epoch 44/100\n",
      " - 1s - loss: 0.0203 - val_loss: 0.0211\n",
      "Epoch 45/100\n",
      " - 1s - loss: 0.0203 - val_loss: 0.0211\n",
      "Epoch 46/100\n",
      " - 1s - loss: 0.0202 - val_loss: 0.0210\n",
      "Epoch 47/100\n",
      " - 1s - loss: 0.0202 - val_loss: 0.0210\n",
      "Epoch 48/100\n",
      " - 1s - loss: 0.0201 - val_loss: 0.0210\n",
      "Epoch 49/100\n",
      " - 1s - loss: 0.0201 - val_loss: 0.0210\n",
      "Epoch 50/100\n",
      " - 1s - loss: 0.0201 - val_loss: 0.0210\n",
      "Epoch 51/100\n",
      " - 1s - loss: 0.0201 - val_loss: 0.0209\n",
      "Epoch 52/100\n",
      " - 1s - loss: 0.0201 - val_loss: 0.0209\n",
      "Epoch 53/100\n",
      " - 1s - loss: 0.0200 - val_loss: 0.0209\n",
      "Epoch 54/100\n",
      " - 1s - loss: 0.0200 - val_loss: 0.0209\n",
      "Epoch 55/100\n",
      " - 1s - loss: 0.0200 - val_loss: 0.0209\n",
      "Epoch 56/100\n",
      " - 1s - loss: 0.0200 - val_loss: 0.0209\n",
      "Epoch 57/100\n",
      " - 1s - loss: 0.0200 - val_loss: 0.0208\n",
      "Epoch 58/100\n",
      " - 1s - loss: 0.0199 - val_loss: 0.0208\n",
      "Epoch 59/100\n",
      " - 1s - loss: 0.0199 - val_loss: 0.0208\n",
      "Epoch 60/100\n",
      " - 1s - loss: 0.0199 - val_loss: 0.0208\n",
      "Epoch 61/100\n",
      " - 1s - loss: 0.0199 - val_loss: 0.0208\n",
      "Epoch 62/100\n",
      " - 1s - loss: 0.0199 - val_loss: 0.0208\n",
      "Epoch 63/100\n",
      " - 1s - loss: 0.0199 - val_loss: 0.0208\n",
      "Epoch 64/100\n",
      " - 1s - loss: 0.0199 - val_loss: 0.0207\n",
      "Epoch 65/100\n",
      " - 1s - loss: 0.0198 - val_loss: 0.0207\n",
      "Epoch 66/100\n",
      " - 1s - loss: 0.0198 - val_loss: 0.0207\n",
      "Epoch 67/100\n",
      " - 1s - loss: 0.0198 - val_loss: 0.0207\n",
      "Epoch 68/100\n",
      " - 1s - loss: 0.0198 - val_loss: 0.0207\n",
      "Epoch 69/100\n",
      " - 1s - loss: 0.0198 - val_loss: 0.0207\n",
      "Epoch 70/100\n",
      " - 1s - loss: 0.0198 - val_loss: 0.0207\n",
      "Epoch 71/100\n",
      " - 1s - loss: 0.0197 - val_loss: 0.0206\n",
      "Epoch 72/100\n",
      " - 1s - loss: 0.0197 - val_loss: 0.0207\n",
      "Epoch 73/100\n",
      " - 1s - loss: 0.0197 - val_loss: 0.0206\n",
      "Epoch 74/100\n",
      " - 1s - loss: 0.0197 - val_loss: 0.0206\n",
      "Epoch 75/100\n",
      " - 1s - loss: 0.0197 - val_loss: 0.0206\n",
      "Epoch 76/100\n",
      " - 1s - loss: 0.0197 - val_loss: 0.0206\n",
      "Epoch 77/100\n",
      " - 1s - loss: 0.0197 - val_loss: 0.0206\n",
      "Epoch 78/100\n",
      " - 1s - loss: 0.0197 - val_loss: 0.0206\n",
      "Epoch 79/100\n",
      " - 1s - loss: 0.0197 - val_loss: 0.0206\n",
      "Epoch 80/100\n",
      " - 1s - loss: 0.0197 - val_loss: 0.0206\n",
      "Epoch 81/100\n",
      " - 1s - loss: 0.0197 - val_loss: 0.0206\n",
      "Epoch 82/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0206\n",
      "Epoch 83/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 84/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 85/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0206\n",
      "Epoch 86/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 87/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 88/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 89/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 90/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 91/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0206\n",
      "Epoch 92/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 93/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 94/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 95/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 96/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0206\n",
      "Epoch 97/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 98/100\n",
      " - 1s - loss: 0.0196 - val_loss: 0.0205\n",
      "Epoch 99/100\n",
      " - 1s - loss: 0.0195 - val_loss: 0.0205\n",
      "Epoch 100/100\n",
      " - 1s - loss: 0.0195 - val_loss: 0.0205\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7f560346df28>"
      ]
     },
     "execution_count": 156,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisy_train, noisy_test = create_noisy_dataset(0.6)\n",
    "autoEncoder = create_auto_encoder_model()\n",
    "autoEncoder.fit(noisy_train, X_train, batch_size=512, epochs=100, validation_split=0.2, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error of test dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "e-cKZDmLfBQz",
    "outputId": "e0509383-5fc1-4bca-c484-c157eea11953"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 23us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.02000248035490513"
      ]
     },
     "execution_count": 157,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoEncoder.evaluate(noisy_test, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "h2ybXTTTfFhk",
    "outputId": "913577d9-2044-4bc4-9f13-af6bb4396a08"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1728x2880 with 61 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_results()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "TPU",
  "colab": {
   "authorship_tag": "ABX9TyNxbxfVmlQdQC29gtg9THFx",
   "name": "MNISTClassification.ipynb",
   "provenance": []
  },
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}