VAE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mlyKPEkaYZbD"
   },
   "source": [
    "## Variational AutoEncoders\n",
    "\n",
    "__Assignment Goals__:\n",
    "\n",
    "- Implementing Fully Connected AutoEncoders\n",
    "- Implementing Convolutional AutoEncoders\n",
    "- Understand Variational Autoncoder intuition\n",
    "\n",
    "\n",
    "__DataSet:__ MNIST handwritten digit dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "dJvVpJS_YyjT",
    "outputId": "3ccc5393-1ecd-437c-f2d6-7b0564972021"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow is already loaded. Please restart the runtime to change versions.\n"
     ]
    }
   ],
   "source": [
    "%tensorflow_version 1.x\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "jELktwWAYZbF",
    "outputId": "9c5940b0-f4cc-4e18-e858-4ecd7105703a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28) (10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data()\n",
    "print(np.shape(x_train),np.shape(x_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "F6aMliypu959"
   },
   "source": [
    "**Fully connected autoencoder**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "25RrinClxPsv",
    "outputId": "a4abfd2c-864b-4c70-8143-0403ed011a8e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784) (10000, 784)\n"
     ]
    }
   ],
   "source": [
    "# Reshaping the inputs\n",
    "\n",
    "train_input = np.reshape(x_train, (-1,784))\n",
    "test_input = np.reshape(x_test, (-1,784))\n",
    "print(np.shape(train_input),np.shape(test_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SNXi4TNL0f6i"
   },
   "outputs": [],
   "source": [
    "# Normalizing the inputs\n",
    "\n",
    "train_input = train_input/255\n",
    "test_input = test_input/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "colab_type": "code",
    "id": "Du9tpuMU0B2f",
    "outputId": "83cc9d44-e33e-4db3-cfbf-37ae20151466"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_10\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           [(None, 784)]             0         \n",
      "_________________________________________________________________\n",
      "enc0 (Dense)                 (None, 300)               235500    \n",
      "_________________________________________________________________\n",
      "enc1 (Dense)                 (None, 75)                22575     \n",
      "_________________________________________________________________\n",
      "enc2 (Dense)                 (None, 15)                1140      \n",
      "_________________________________________________________________\n",
      "bottleneck (Dense)           (None, 2)                 32        \n",
      "_________________________________________________________________\n",
      "dec1 (Dense)                 (None, 15)                45        \n",
      "_________________________________________________________________\n",
      "dec2 (Dense)                 (None, 75)                1200      \n",
      "_________________________________________________________________\n",
      "dec3 (Dense)                 (None, 300)               22800     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 784)               235984    \n",
      "=================================================================\n",
      "Total params: 519,276\n",
      "Trainable params: 519,276\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Model\n",
    "\n",
    "input_layer = tf.keras.layers.Input(shape=(784,), name='input')\n",
    "encoding_layer0 = tf.keras.layers.Dense(300, activation='relu', name='enc0')(input_layer)\n",
    "encoding_layer1 = tf.keras.layers.Dense(75, activation='relu', name='enc1')(encoding_layer0)\n",
    "encoding_layer2 = tf.keras.layers.Dense(15, activation='relu', name='enc2')(encoding_layer1)\n",
    "bottleneck = tf.keras.layers.Dense(2, activation='relu', name='bottleneck')(encoding_layer2)\n",
    "decoding_layer1 = tf.keras.layers.Dense(15, activation='relu', name='dec1')(bottleneck)\n",
    "decoding_layer2 = tf.keras.layers.Dense(75, activation='relu', name='dec2')(decoding_layer1)\n",
    "decoding_layer3 = tf.keras.layers.Dense(300, activation='relu', name='dec3')(decoding_layer2)\n",
    "output_layer = tf.keras.layers.Dense(784, activation='sigmoid', name='output')(decoding_layer3)\n",
    "fcae_model = tf.keras.Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "fcae_model.compile(loss='mse',optimizer='adam')\n",
    "print(fcae_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "pIMFXwwK-gqo",
    "outputId": "21fdbfec-d3cb-451b-918c-7484f8f0c572"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 1s 22us/sample - loss: 0.0870 - val_loss: 0.0686\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0649 - val_loss: 0.0635\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0623 - val_loss: 0.0612\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0606 - val_loss: 0.0601\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0596 - val_loss: 0.0588\n",
      "Epoch 6/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0582 - val_loss: 0.0577\n",
      "Epoch 7/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0570 - val_loss: 0.0563\n",
      "Epoch 8/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0557 - val_loss: 0.0549\n",
      "Epoch 9/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0543 - val_loss: 0.0539\n",
      "Epoch 10/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0534 - val_loss: 0.0531\n",
      "Epoch 11/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0527 - val_loss: 0.0524\n",
      "Epoch 12/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0522 - val_loss: 0.0520\n",
      "Epoch 13/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0518 - val_loss: 0.0516\n",
      "Epoch 14/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0514 - val_loss: 0.0512\n",
      "Epoch 15/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0511 - val_loss: 0.0508\n",
      "Epoch 16/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0507 - val_loss: 0.0505\n",
      "Epoch 17/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0503 - val_loss: 0.0502\n",
      "Epoch 18/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0500 - val_loss: 0.0500\n",
      "Epoch 19/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0497 - val_loss: 0.0496\n",
      "Epoch 20/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0494 - val_loss: 0.0494\n",
      "Epoch 21/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0491 - val_loss: 0.0492\n",
      "Epoch 22/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0490 - val_loss: 0.0489\n",
      "Epoch 23/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0488 - val_loss: 0.0486\n",
      "Epoch 24/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0485 - val_loss: 0.0485\n",
      "Epoch 25/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0483 - val_loss: 0.0484\n",
      "Epoch 26/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0482 - val_loss: 0.0483\n",
      "Epoch 27/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0480 - val_loss: 0.0479\n",
      "Epoch 28/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0477 - val_loss: 0.0478\n",
      "Epoch 29/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0478 - val_loss: 0.0478\n",
      "Epoch 30/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0476 - val_loss: 0.0474\n",
      "Epoch 31/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0474 - val_loss: 0.0473\n",
      "Epoch 32/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0475 - val_loss: 0.0474\n",
      "Epoch 33/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0474 - val_loss: 0.0472\n",
      "Epoch 34/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0471 - val_loss: 0.0469\n",
      "Epoch 35/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0471 - val_loss: 0.0469\n",
      "Epoch 36/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0469 - val_loss: 0.0468\n",
      "Epoch 37/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0468 - val_loss: 0.0466\n",
      "Epoch 38/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0468 - val_loss: 0.0467\n",
      "Epoch 39/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0469 - val_loss: 0.0466\n",
      "Epoch 40/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0465 - val_loss: 0.0464\n",
      "Epoch 41/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0463 - val_loss: 0.0462\n",
      "Epoch 42/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0464 - val_loss: 0.0463\n",
      "Epoch 43/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0461 - val_loss: 0.0461\n",
      "Epoch 44/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0461 - val_loss: 0.0460\n",
      "Epoch 45/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0459 - val_loss: 0.0459\n",
      "Epoch 46/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0458 - val_loss: 0.0460\n",
      "Epoch 47/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0461 - val_loss: 0.0461\n",
      "Epoch 48/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0457 - val_loss: 0.0456\n",
      "Epoch 49/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0459 - val_loss: 0.0458\n",
      "Epoch 50/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0457 - val_loss: 0.0455\n",
      "Epoch 51/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0456 - val_loss: 0.0455\n",
      "Epoch 52/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0454 - val_loss: 0.0454\n",
      "Epoch 53/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0455 - val_loss: 0.0455\n",
      "Epoch 54/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0454 - val_loss: 0.0455\n",
      "Epoch 55/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0454 - val_loss: 0.0454\n",
      "Epoch 56/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0452 - val_loss: 0.0456\n",
      "Epoch 57/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0454 - val_loss: 0.0452\n",
      "Epoch 58/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0454 - val_loss: 0.0454\n",
      "Epoch 59/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0452 - val_loss: 0.0454\n",
      "Epoch 60/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0452 - val_loss: 0.0450\n",
      "Epoch 61/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0450 - val_loss: 0.0452\n",
      "Epoch 62/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0450 - val_loss: 0.0454\n",
      "Epoch 63/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0450 - val_loss: 0.0451\n",
      "Epoch 64/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0449 - val_loss: 0.0454\n",
      "Epoch 65/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0449 - val_loss: 0.0450\n",
      "Epoch 66/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0449 - val_loss: 0.0452\n",
      "Epoch 67/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0450 - val_loss: 0.0452\n",
      "Epoch 68/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0447 - val_loss: 0.0450\n",
      "Epoch 69/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0449 - val_loss: 0.0449\n",
      "Epoch 70/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0448 - val_loss: 0.0449\n",
      "Epoch 71/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0447 - val_loss: 0.0449\n",
      "Epoch 72/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0446 - val_loss: 0.0450\n",
      "Epoch 73/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0447 - val_loss: 0.0448\n",
      "Epoch 74/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0447 - val_loss: 0.0447\n",
      "Epoch 75/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0447 - val_loss: 0.0447\n",
      "Epoch 76/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0445 - val_loss: 0.0447\n",
      "Epoch 77/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0445 - val_loss: 0.0444\n",
      "Epoch 78/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0444 - val_loss: 0.0444\n",
      "Epoch 79/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0443 - val_loss: 0.0444\n",
      "Epoch 80/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0442 - val_loss: 0.0447\n",
      "Epoch 81/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0443 - val_loss: 0.0445\n",
      "Epoch 82/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0442 - val_loss: 0.0445\n",
      "Epoch 83/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0443 - val_loss: 0.0446\n",
      "Epoch 84/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0443 - val_loss: 0.0441\n",
      "Epoch 85/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0441 - val_loss: 0.0441\n",
      "Epoch 86/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0440 - val_loss: 0.0441\n",
      "Epoch 87/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0440 - val_loss: 0.0442\n",
      "Epoch 88/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0440 - val_loss: 0.0441\n",
      "Epoch 89/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0439 - val_loss: 0.0442\n",
      "Epoch 90/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0438 - val_loss: 0.0440\n",
      "Epoch 91/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0438 - val_loss: 0.0439\n",
      "Epoch 92/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0439 - val_loss: 0.0440\n",
      "Epoch 93/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0441 - val_loss: 0.0439\n",
      "Epoch 94/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0437 - val_loss: 0.0441\n",
      "Epoch 95/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0438 - val_loss: 0.0444\n",
      "Epoch 96/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0439 - val_loss: 0.0438\n",
      "Epoch 97/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0438 - val_loss: 0.0438\n",
      "Epoch 98/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0437 - val_loss: 0.0437\n",
      "Epoch 99/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0439 - val_loss: 0.0437\n",
      "Epoch 100/100\n",
      "60000/60000 [==============================] - 1s 15us/sample - loss: 0.0436 - val_loss: 0.0437\n"
     ]
    }
   ],
   "source": [
    "q1 = fcae_model.fit(train_input, train_input, batch_size=500, epochs=100, validation_data=(test_input,test_input)) \n",
    "# Data shuffle is TRUE by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "cj9EujHxB1rh",
    "outputId": "f46c5589-7a50-42a2-94e1-f40d46afa35b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nzrkXIxVrs6Vmw/jpZK2Zzfm9dnHPW6t0FiEi7VZE+yCccy/hNQ+FTvtNyPsK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\n/w9CvcqCUbEVKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loss plots \n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(q1.history['loss'])\n",
    "plt.plot(q1.history['val_loss'])\n",
    "plt.title('Fully connected autoencoder model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper right')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dlg5fJAzFrzD"
   },
   "outputs": [],
   "source": [
    "# Randomly choosing 10 test images\n",
    "np.random.seed(3)\n",
    "rand_index = np.random.randint(10000, size=10)\n",
    "final_test_images = test_input[rand_index]\n",
    "\n",
    "\n",
    "# Getting the intermediate layer output (bottleneck layer encoded)\n",
    "encoding_model = tf.keras.Model(fcae_model.input, fcae_model.get_layer('bottleneck').output)\n",
    "final_test_image_encoding = encoding_model.predict(final_test_images)\n",
    "\n",
    "\n",
    "# Getting the autoencoder output (decoded)\n",
    "decoded_output = fcae_model.predict(final_test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "_Jxza43kTIb1",
    "outputId": "098e56f8-b2e5-4cd2-f7df-cd424a907349"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top row images are original; Bottom row images are the reconstructed ones\n"
     ]
    }
   ],
   "source": [
    "# Visualizing the decoded images (Code borrowed from https://blog.keras.io/building-autoencoders-in-keras.html)\n",
    "\n",
    "n = 10  \n",
    "plt.figure(figsize=(20, 4))\n",
    "\n",
    "for i in range(n):\n",
    "\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(final_test_images[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + 1 + n)\n",
    "    plt.imshow(decoded_output[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()\n",
    "\n",
    "print('Top row images are original; Bottom row images are the reconstructed ones')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xc12S5cppy6v"
   },
   "source": [
    "**Convolutional Autoencoder**\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "yJU-NQNhBjla",
    "outputId": "06c19c89-910d-4564-bbfc-b8e7e0ac8432"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1) (10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "# Preparing the inputs\n",
    "\n",
    "train_input = np.expand_dims(x_train, -1)\n",
    "test_input = np.expand_dims(x_test, -1)\n",
    "\n",
    "# Normalize the inputs\n",
    "train_input = train_input/255\n",
    "test_input = test_input/255\n",
    "\n",
    "print(np.shape(train_input),np.shape(test_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 714
    },
    "colab_type": "code",
    "id": "awL7cLZ1vjV4",
    "outputId": "c98c8cbd-f5bf-4f52-f988-1a7f70e209a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_15\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "e1 (Conv2D)                  (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "e2 (MaxPooling2D)            (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "e3 (Conv2D)                  (None, 11, 11, 8)         2312      \n",
      "_________________________________________________________________\n",
      "e4 (MaxPooling2D)            (None, 6, 6, 8)           0         \n",
      "_________________________________________________________________\n",
      "e5 (Conv2D)                  (None, 4, 4, 3)           219       \n",
      "_________________________________________________________________\n",
      "bottleneck2 (MaxPooling2D)   (None, 2, 2, 3)           0         \n",
      "_________________________________________________________________\n",
      "d1 (Conv2D)                  (None, 2, 2, 4)           112       \n",
      "_________________________________________________________________\n",
      "d2 (UpSampling2D)            (None, 4, 4, 4)           0         \n",
      "_________________________________________________________________\n",
      "d3 (Conv2D)                  (None, 4, 4, 8)           296       \n",
      "_________________________________________________________________\n",
      "d4 (UpSampling2D)            (None, 8, 8, 8)           0         \n",
      "_________________________________________________________________\n",
      "d5 (Conv2D)                  (None, 8, 8, 16)          1168      \n",
      "_________________________________________________________________\n",
      "d6 (UpSampling2D)            (None, 16, 16, 16)        0         \n",
      "_________________________________________________________________\n",
      "d7 (Conv2D)                  (None, 14, 14, 32)        4640      \n",
      "_________________________________________________________________\n",
      "d8 (UpSampling2D)            (None, 28, 28, 32)        0         \n",
      "_________________________________________________________________\n",
      "decoded (Conv2D)             (None, 28, 28, 1)         801       \n",
      "=================================================================\n",
      "Total params: 9,868\n",
      "Trainable params: 9,868\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# np.random.seed(10)\n",
    "\n",
    "input_layer2 = tf.keras.layers.Input(shape=(28, 28, 1), name='input')  \n",
    "e1 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='valid', name='e1')(input_layer2)\n",
    "e2 = tf.keras.layers.MaxPooling2D((2, 2), padding='same', name='e2')(e1)\n",
    "e3 = tf.keras.layers.Conv2D(8, (3, 3), activation='relu', padding='valid', name='e3')(e2)\n",
    "e4 = tf.keras.layers.MaxPooling2D((2, 2), padding='same', name='e4')(e3)\n",
    "e5 = tf.keras.layers.Conv2D(3, (3, 3), activation='relu', padding='valid', name='e5')(e4)\n",
    "bottleneck2 = tf.keras.layers.MaxPooling2D((3, 3), padding='same', name='bottleneck2')(e5)\n",
    "\n",
    "d1 = tf.keras.layers.Conv2D(4, (3, 3), activation='relu', padding='same', name='d1')(bottleneck2)\n",
    "d2 = tf.keras.layers.UpSampling2D((2, 2), name='d2')(d1)\n",
    "d3 = tf.keras.layers.Conv2D(8, (3, 3), activation='relu', padding='same', name='d3')(d2)\n",
    "d4 = tf.keras.layers.UpSampling2D((2, 2), name='d4')(d3)\n",
    "d5 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same', name='d5')(d4)\n",
    "d6 = tf.keras.layers.UpSampling2D((2, 2), name='d6')(d5)\n",
    "d7 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='valid', name='d7')(d6)\n",
    "d8 = tf.keras.layers.UpSampling2D((2, 2), name='d8')(d7)\n",
    "decoded = tf.keras.layers.Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoded')(d8)\n",
    "\n",
    "cae = tf.keras.Model(input_layer2, decoded)\n",
    "print(cae.summary())\n",
    "\n",
    "sgd = tf.keras.optimizers.SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "cae.compile(loss='mse',optimizer=sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "7TSPaKRmDDwj",
    "outputId": "fc2a58e5-2440-49b3-ceb3-6f888e614ce7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 8s 140us/sample - loss: 0.1002 - val_loss: 0.0679\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0649 - val_loss: 0.0635\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 3s 52us/sample - loss: 0.0621 - val_loss: 0.0606\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 3s 53us/sample - loss: 0.0597 - val_loss: 0.0571\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0564 - val_loss: 0.0547\n",
      "Epoch 6/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0538 - val_loss: 0.0531\n",
      "Epoch 7/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0518 - val_loss: 0.0500\n",
      "Epoch 8/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0501 - val_loss: 0.0485\n",
      "Epoch 9/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0486 - val_loss: 0.0486\n",
      "Epoch 10/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0473 - val_loss: 0.0457\n",
      "Epoch 11/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0462 - val_loss: 0.0451\n",
      "Epoch 12/100\n",
      "60000/60000 [==============================] - 3s 52us/sample - loss: 0.0452 - val_loss: 0.0436\n",
      "Epoch 13/100\n",
      "60000/60000 [==============================] - 3s 52us/sample - loss: 0.0441 - val_loss: 0.0429\n",
      "Epoch 14/100\n",
      "60000/60000 [==============================] - 3s 52us/sample - loss: 0.0431 - val_loss: 0.0414\n",
      "Epoch 15/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0421 - val_loss: 0.0431\n",
      "Epoch 16/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0413 - val_loss: 0.0429\n",
      "Epoch 17/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0406 - val_loss: 0.0406\n",
      "Epoch 18/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0400 - val_loss: 0.0386\n",
      "Epoch 19/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0393 - val_loss: 0.0395\n",
      "Epoch 20/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0388 - val_loss: 0.0378\n",
      "Epoch 21/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0383 - val_loss: 0.0383\n",
      "Epoch 22/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0379 - val_loss: 0.0377\n",
      "Epoch 23/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0375 - val_loss: 0.0368\n",
      "Epoch 24/100\n",
      "60000/60000 [==============================] - 3s 52us/sample - loss: 0.0370 - val_loss: 0.0365\n",
      "Epoch 25/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0367 - val_loss: 0.0355\n",
      "Epoch 26/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0363 - val_loss: 0.0357\n",
      "Epoch 27/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0359 - val_loss: 0.0362\n",
      "Epoch 28/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0356 - val_loss: 0.0350\n",
      "Epoch 29/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0354 - val_loss: 0.0345\n",
      "Epoch 30/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0351 - val_loss: 0.0355\n",
      "Epoch 31/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0348 - val_loss: 0.0349\n",
      "Epoch 32/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0346 - val_loss: 0.0336\n",
      "Epoch 33/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0343 - val_loss: 0.0345\n",
      "Epoch 34/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0342 - val_loss: 0.0339\n",
      "Epoch 35/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0339 - val_loss: 0.0338\n",
      "Epoch 36/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0337 - val_loss: 0.0331\n",
      "Epoch 37/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0335 - val_loss: 0.0332\n",
      "Epoch 38/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0334 - val_loss: 0.0326\n",
      "Epoch 39/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0332 - val_loss: 0.0327\n",
      "Epoch 40/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0330 - val_loss: 0.0324\n",
      "Epoch 41/100\n",
      "60000/60000 [==============================] - 3s 52us/sample - loss: 0.0329 - val_loss: 0.0321\n",
      "Epoch 42/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0327 - val_loss: 0.0324\n",
      "Epoch 43/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0325 - val_loss: 0.0321\n",
      "Epoch 44/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0325 - val_loss: 0.0328\n",
      "Epoch 45/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0322 - val_loss: 0.0315\n",
      "Epoch 46/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0322 - val_loss: 0.0315\n",
      "Epoch 47/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0320 - val_loss: 0.0318\n",
      "Epoch 48/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0319 - val_loss: 0.0321\n",
      "Epoch 49/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0318 - val_loss: 0.0318\n",
      "Epoch 50/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0316 - val_loss: 0.0325\n",
      "Epoch 51/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0315 - val_loss: 0.0314\n",
      "Epoch 52/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0314 - val_loss: 0.0308\n",
      "Epoch 53/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0312 - val_loss: 0.0313\n",
      "Epoch 54/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0313 - val_loss: 0.0312\n",
      "Epoch 55/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0310 - val_loss: 0.0308\n",
      "Epoch 56/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0310 - val_loss: 0.0304\n",
      "Epoch 57/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0309 - val_loss: 0.0310\n",
      "Epoch 58/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0308 - val_loss: 0.0309\n",
      "Epoch 59/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0307 - val_loss: 0.0301\n",
      "Epoch 60/100\n",
      "60000/60000 [==============================] - 3s 52us/sample - loss: 0.0306 - val_loss: 0.0312\n",
      "Epoch 61/100\n",
      "60000/60000 [==============================] - 3s 52us/sample - loss: 0.0305 - val_loss: 0.0301\n",
      "Epoch 62/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0304 - val_loss: 0.0298\n",
      "Epoch 63/100\n",
      "60000/60000 [==============================] - 3s 52us/sample - loss: 0.0303 - val_loss: 0.0301\n",
      "Epoch 64/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0302 - val_loss: 0.0297\n",
      "Epoch 65/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0302 - val_loss: 0.0297\n",
      "Epoch 66/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0301 - val_loss: 0.0302\n",
      "Epoch 67/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0300 - val_loss: 0.0296\n",
      "Epoch 68/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0300 - val_loss: 0.0293\n",
      "Epoch 69/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0298 - val_loss: 0.0294\n",
      "Epoch 70/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0297 - val_loss: 0.0291\n",
      "Epoch 71/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0297 - val_loss: 0.0296\n",
      "Epoch 72/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0296 - val_loss: 0.0296\n",
      "Epoch 73/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0296 - val_loss: 0.0292\n",
      "Epoch 74/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0295 - val_loss: 0.0290\n",
      "Epoch 75/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0294 - val_loss: 0.0286\n",
      "Epoch 76/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0293 - val_loss: 0.0286\n",
      "Epoch 77/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0292 - val_loss: 0.0291\n",
      "Epoch 78/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0291 - val_loss: 0.0284\n",
      "Epoch 79/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0291 - val_loss: 0.0293\n",
      "Epoch 80/100\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0290 - val_loss: 0.0288\n",
      "Epoch 81/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0289 - val_loss: 0.0286\n",
      "Epoch 82/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0289 - val_loss: 0.0298\n",
      "Epoch 83/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0288 - val_loss: 0.0285\n",
      "Epoch 84/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0287 - val_loss: 0.0282\n",
      "Epoch 85/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0286 - val_loss: 0.0284\n",
      "Epoch 86/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0286 - val_loss: 0.0279\n",
      "Epoch 87/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0285 - val_loss: 0.0282\n",
      "Epoch 88/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0285 - val_loss: 0.0280\n",
      "Epoch 89/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0284 - val_loss: 0.0279\n",
      "Epoch 90/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0283 - val_loss: 0.0277\n",
      "Epoch 91/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0283 - val_loss: 0.0293\n",
      "Epoch 92/100\n",
      "60000/60000 [==============================] - 3s 52us/sample - loss: 0.0281 - val_loss: 0.0280\n",
      "Epoch 93/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0282 - val_loss: 0.0278\n",
      "Epoch 94/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0281 - val_loss: 0.0277\n",
      "Epoch 95/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0280 - val_loss: 0.0283\n",
      "Epoch 96/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0279 - val_loss: 0.0280\n",
      "Epoch 97/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0279 - val_loss: 0.0278\n",
      "Epoch 98/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0278 - val_loss: 0.0272\n",
      "Epoch 99/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0278 - val_loss: 0.0277\n",
      "Epoch 100/100\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0278 - val_loss: 0.0273\n"
     ]
    }
   ],
   "source": [
    "q2 = cae.fit(train_input,train_input,batch_size=500,epochs=100,validation_data=(test_input,test_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "a9MU1aNBGjcB",
    "outputId": "a1eb9cf0-0307-4569-abf5-7715733d07da"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PP06XLl3o0qULTz755IHtdezYkRtvvJHOnTszfPjwWnvmk91JjXMGYc9iMuYk9v49sPGH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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loss plots \n",
    "\n",
    "plt.plot(q2.history['loss'])\n",
    "plt.plot(q2.history['val_loss'])\n",
    "plt.title('Convolution autoencoder model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper right')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JmxzuZfOvsWT"
   },
   "outputs": [],
   "source": [
    "# Randomly choosing 10 test images\n",
    "np.random.seed(1)\n",
    "rand_index = np.random.randint(10000, size=10)\n",
    "final_test_images = test_input[rand_index]\n",
    "\n",
    "\n",
    "# Getting the intermediate layer output (bottleneck layer encoded)\n",
    "encoding_model2 = tf.keras.Model(cae.input, cae.get_layer('bottleneck2').output)\n",
    "final_test_image_encoding2 = encoding_model2.predict(final_test_images)\n",
    "\n",
    "\n",
    "# Getting the autoencoder output (decoded)\n",
    "decoded_output2 = cae.predict(final_test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "3wOCbGJtu_EC",
    "outputId": "26e38081-8932-4561-c14e-72ae3d23646b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top row images are original; Bottom row images are the reconstructed ones\n"
     ]
    }
   ],
   "source": [
    "# Visualizing the decoded images (Code borrowed from https://blog.keras.io/building-autoencoders-in-keras.html)\n",
    "\n",
    "n = 10  \n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(final_test_images[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + 1 + n)\n",
    "    plt.imshow(decoded_output2[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()\n",
    "\n",
    "print('Top row images are original; Bottom row images are the reconstructed ones')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RTbjpsHcvIs2"
   },
   "source": [
    "**Bottleneck of 12 neurons gives the above readable outputs. Increasing the bottleneck easily gives better quality results, but we try to get the best images from the minimal bottleneck**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8W2_SBgHfjSD"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PFnr4z4Ffqdz"
   },
   "source": [
    "**Generating images from random input to autoencoder model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s4_td6oygBxD"
   },
   "outputs": [],
   "source": [
    "# Preparing a decoding model\n",
    "\n",
    "# input_shape = model.layers[5].get_input_shape_at(0)\n",
    "fcae_enc_input = tf.keras.layers.Input(shape=(2,), name='enc_input')\n",
    "temp = fcae_enc_input\n",
    "for layer in fcae_model.layers[5:]:\n",
    "  temp = layer(temp)\n",
    "fcae_decoder_model = tf.keras.Model(fcae_enc_input, temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "colab_type": "code",
    "id": "d8piTuJGs6b6",
    "outputId": "d7730c3a-0049-4e22-b71e-a71ee74e2dd5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random inputs\n",
      "[ 1.90770723 19.49796981]\n",
      "[10.96023079 18.08662945]\n",
      "[24.4497378  13.46239676]\n",
      "[12.52801159  1.80127833]\n",
      "[ 6.7109745  12.49706252]\n",
      "[16.9807499 20.0934759]\n",
      "[9.52352833 1.64840867]\n",
      "[ 7.20363998 22.73983819]\n",
      "[ 5.33463384 11.30309905]\n",
      "[23.28015049  0.62248069]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the results\n",
    "\n",
    "n = 10\n",
    "plt.figure(figsize=(20, 4))\n",
    "\n",
    "print('Random inputs')\n",
    "\n",
    "# generate random encoded input\n",
    "np.random.seed(7)\n",
    "random_input = np.random.uniform(high=25, size=(10,2))\n",
    "# random_input = np.reshape(final_test_image_encoding[i,:], (1, 2))\n",
    "\n",
    "for i in range(n):\n",
    "\n",
    "    # get model prediction\n",
    "    print(random_input[i])\n",
    "    output = fcae_decoder_model.predict(np.reshape(random_input[i],(1,2)))\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i+1)\n",
    "    plt.imshow(output.reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "88WrzTTSxIJN"
   },
   "source": [
    "**Standard normal distribution constraint**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "P-aBpgETyaxS",
    "outputId": "5d5c0071-7495-4063-d73f-e22d879a91a4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784) (10000, 784)\n"
     ]
    }
   ],
   "source": [
    "# Reshaping the inputs\n",
    "\n",
    "train_input = np.reshape(x_train, (-1,784))\n",
    "test_input = np.reshape(x_test, (-1,784))\n",
    "print(np.shape(train_input),np.shape(test_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T6iogsCd2I5L"
   },
   "outputs": [],
   "source": [
    "# Normalizing the inputs\n",
    "\n",
    "train_input = train_input/255\n",
    "test_input = test_input/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 581
    },
    "colab_type": "code",
    "id": "D6Gg14ad2N01",
    "outputId": "7477869b-528e-4a60-83a2-ff0509089b12"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           [(None, 784)]             0         \n",
      "_________________________________________________________________\n",
      "enc0 (Dense)                 (None, 300)               235500    \n",
      "_________________________________________________________________\n",
      "enc1 (Dense)                 (None, 75)                22575     \n",
      "_________________________________________________________________\n",
      "enc2 (Dense)                 (None, 15)                1140      \n",
      "_________________________________________________________________\n",
      "bottleneck (Dense)           (None, 2)                 32        \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 2)                 8         \n",
      "_________________________________________________________________\n",
      "dec1 (Dense)                 (None, 15)                45        \n",
      "_________________________________________________________________\n",
      "dec2 (Dense)                 (None, 75)                1200      \n",
      "_________________________________________________________________\n",
      "dec3 (Dense)                 (None, 300)               22800     \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 784)               235984    \n",
      "=================================================================\n",
      "Total params: 519,284\n",
      "Trainable params: 519,280\n",
      "Non-trainable params: 4\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Model\n",
    "\n",
    "input_layer = tf.keras.layers.Input(shape=(784,), name='input')\n",
    "encoding_layer0 = tf.keras.layers.Dense(300, activation='relu', name='enc0')(input_layer)\n",
    "encoding_layer1 = tf.keras.layers.Dense(75, activation='relu', name='enc1')(encoding_layer0)\n",
    "encoding_layer2 = tf.keras.layers.Dense(15, activation='relu', name='enc2')(encoding_layer1)\n",
    "bottleneck = tf.keras.layers.Dense(2, activation='relu', name='bottleneck')(encoding_layer2)\n",
    "bottleneck_norm = tf.keras.layers.BatchNormalization()(bottleneck)\n",
    "decoding_layer1 = tf.keras.layers.Dense(15, activation='relu', name='dec1')(bottleneck_norm)\n",
    "decoding_layer2 = tf.keras.layers.Dense(75, activation='relu', name='dec2')(decoding_layer1)\n",
    "decoding_layer3 = tf.keras.layers.Dense(300, activation='relu', name='dec3')(decoding_layer2)\n",
    "output_layer = tf.keras.layers.Dense(784, activation='sigmoid', name='output')(decoding_layer3)\n",
    "normalized_fcae_model = tf.keras.Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "normalized_fcae_model.compile(loss='mse',optimizer='adam')\n",
    "print(normalized_fcae_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "0SYLsuYfEFQH",
    "outputId": "c0adc06f-1224-4087-9b5f-c43eb7b44aee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 9s 154us/sample - loss: 0.0880 - val_loss: 0.0630\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0560 - val_loss: 0.0580\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0527 - val_loss: 0.0518\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0503 - val_loss: 0.0488\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0483 - val_loss: 0.0476\n",
      "Epoch 6/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0472 - val_loss: 0.0462\n",
      "Epoch 7/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0462 - val_loss: 0.0456\n",
      "Epoch 8/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0456 - val_loss: 0.0452\n",
      "Epoch 9/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0449 - val_loss: 0.0444\n",
      "Epoch 10/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0446 - val_loss: 0.0442\n",
      "Epoch 11/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0442 - val_loss: 0.0433\n",
      "Epoch 12/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0436 - val_loss: 0.0432\n",
      "Epoch 13/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0433 - val_loss: 0.0427\n",
      "Epoch 14/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0427 - val_loss: 0.0420\n",
      "Epoch 15/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0425 - val_loss: 0.0421\n",
      "Epoch 16/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0421 - val_loss: 0.0415\n",
      "Epoch 17/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0417 - val_loss: 0.0413\n",
      "Epoch 18/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0416 - val_loss: 0.0408\n",
      "Epoch 19/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0412 - val_loss: 0.0406\n",
      "Epoch 20/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0411 - val_loss: 0.0405\n",
      "Epoch 21/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0409 - val_loss: 0.0402\n",
      "Epoch 22/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0406 - val_loss: 0.0401\n",
      "Epoch 23/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0405 - val_loss: 0.0398\n",
      "Epoch 24/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0403 - val_loss: 0.0398\n",
      "Epoch 25/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0400 - val_loss: 0.0397\n",
      "Epoch 26/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0400 - val_loss: 0.0396\n",
      "Epoch 27/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0398 - val_loss: 0.0393\n",
      "Epoch 28/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0397 - val_loss: 0.0393\n",
      "Epoch 29/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0395 - val_loss: 0.0397\n",
      "Epoch 30/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0395 - val_loss: 0.0393\n",
      "Epoch 31/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0394 - val_loss: 0.0391\n",
      "Epoch 32/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0393 - val_loss: 0.0395\n",
      "Epoch 33/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0390 - val_loss: 0.0388\n",
      "Epoch 34/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0391 - val_loss: 0.0385\n",
      "Epoch 35/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0388 - val_loss: 0.0385\n",
      "Epoch 36/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0387 - val_loss: 0.0385\n",
      "Epoch 37/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0388 - val_loss: 0.0383\n",
      "Epoch 38/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0384 - val_loss: 0.0382\n",
      "Epoch 39/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0385 - val_loss: 0.0394\n",
      "Epoch 40/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0385 - val_loss: 0.0381\n",
      "Epoch 41/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0383 - val_loss: 0.0378\n",
      "Epoch 42/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0383 - val_loss: 0.0377\n",
      "Epoch 43/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0383 - val_loss: 0.0382\n",
      "Epoch 44/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0381 - val_loss: 0.0379\n",
      "Epoch 45/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0381 - val_loss: 0.0378\n",
      "Epoch 46/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0381 - val_loss: 0.0377\n",
      "Epoch 47/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0382 - val_loss: 0.0380\n",
      "Epoch 48/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0381 - val_loss: 0.0376\n",
      "Epoch 49/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0380 - val_loss: 0.0375\n",
      "Epoch 50/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0377 - val_loss: 0.0376\n",
      "Epoch 51/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0376 - val_loss: 0.0375\n",
      "Epoch 52/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0378 - val_loss: 0.0377\n",
      "Epoch 53/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0377 - val_loss: 0.0375\n",
      "Epoch 54/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0374 - val_loss: 0.0372\n",
      "Epoch 55/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0376 - val_loss: 0.0375\n",
      "Epoch 56/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0374 - val_loss: 0.0373\n",
      "Epoch 57/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0374 - val_loss: 0.0369\n",
      "Epoch 58/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0374 - val_loss: 0.0371\n",
      "Epoch 59/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0373 - val_loss: 0.0372\n",
      "Epoch 60/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0372 - val_loss: 0.0372\n",
      "Epoch 61/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0373 - val_loss: 0.0371\n",
      "Epoch 62/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0372 - val_loss: 0.0367\n",
      "Epoch 63/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0371 - val_loss: 0.0370\n",
      "Epoch 64/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0370 - val_loss: 0.0369\n",
      "Epoch 65/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0370 - val_loss: 0.0367\n",
      "Epoch 66/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0371 - val_loss: 0.0367\n",
      "Epoch 67/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0370 - val_loss: 0.0366\n",
      "Epoch 68/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0371 - val_loss: 0.0370\n",
      "Epoch 69/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0368 - val_loss: 0.0367\n",
      "Epoch 70/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0368 - val_loss: 0.0366\n",
      "Epoch 71/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0368 - val_loss: 0.0368\n",
      "Epoch 72/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0371 - val_loss: 0.0365\n",
      "Epoch 73/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0367 - val_loss: 0.0366\n",
      "Epoch 74/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0369 - val_loss: 0.0368\n",
      "Epoch 75/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0369 - val_loss: 0.0366\n",
      "Epoch 76/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0367 - val_loss: 0.0363\n",
      "Epoch 77/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0366 - val_loss: 0.0361\n",
      "Epoch 78/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0364 - val_loss: 0.0361\n",
      "Epoch 79/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0366 - val_loss: 0.0364\n",
      "Epoch 80/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0367 - val_loss: 0.0364\n",
      "Epoch 81/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0366 - val_loss: 0.0366\n",
      "Epoch 82/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0364 - val_loss: 0.0363\n",
      "Epoch 83/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0369 - val_loss: 0.0365\n",
      "Epoch 84/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0367 - val_loss: 0.0364\n",
      "Epoch 85/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0366 - val_loss: 0.0371\n",
      "Epoch 86/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0366 - val_loss: 0.0362\n",
      "Epoch 87/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0364 - val_loss: 0.0368\n",
      "Epoch 88/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0366 - val_loss: 0.0361\n",
      "Epoch 89/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0363 - val_loss: 0.0360\n",
      "Epoch 90/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0363 - val_loss: 0.0362\n",
      "Epoch 91/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0364 - val_loss: 0.0363\n",
      "Epoch 92/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0365 - val_loss: 0.0363\n",
      "Epoch 93/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0364 - val_loss: 0.0360\n",
      "Epoch 94/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0362 - val_loss: 0.0360\n",
      "Epoch 95/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0362 - val_loss: 0.0360\n",
      "Epoch 96/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0362 - val_loss: 0.0359\n",
      "Epoch 97/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0359 - val_loss: 0.0362\n",
      "Epoch 98/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0361 - val_loss: 0.0362\n",
      "Epoch 99/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0360 - val_loss: 0.0359\n",
      "Epoch 100/100\n",
      "60000/60000 [==============================] - 1s 14us/sample - loss: 0.0359 - val_loss: 0.0360\n"
     ]
    }
   ],
   "source": [
    "q3 = normalized_fcae_model.fit(train_input,train_input,batch_size=500,epochs=100,validation_data=(test_input,test_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "BrVm1nSMH8yr",
    "outputId": "9918ab02-cb43-4e70-8163-fa8ba9d5d998"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K+Y4m/ABlz+EbfTOXUkopn9CEH6DCnDWjtXyllFK+oAk/QIW5BECftqeUUson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\nFvgLE/b9yKOHl1oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loss plots \n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(q3.history['loss'])\n",
    "plt.plot(q3.history['val_loss'])\n",
    "plt.title('Fully connected autoencoder (with normalized encoding at bottleneck) model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper right')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hNBgsgnpI0YH"
   },
   "outputs": [],
   "source": [
    "# Preparing a decoding model\n",
    "\n",
    "new_fcae_enc_input = tf.keras.layers.Input(shape=(2,), name='enc_input')\n",
    "temp = new_fcae_enc_input\n",
    "for layer in normalized_fcae_model.layers[6:]:\n",
    "  temp = layer(temp)\n",
    "new_fcae_decoder_model = tf.keras.Model(new_fcae_enc_input, temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "colab_type": "code",
    "id": "iadstTp1Jjt6",
    "outputId": "03eb787d-eac7-4158-b1d9-cc5421135cbc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multivariate random normal inputs\n",
      "[ 1.6905257  -0.46593737]\n",
      "[0.03282016 0.40751628]\n",
      "[-0.78892303  0.00206557]\n",
      "[-8.90385858e-04 -1.75472431e+00]\n",
      "[1.01765801 0.60049852]\n",
      "[-0.62542897 -0.17154826]\n",
      "[ 0.50529937 -0.26135642]\n",
      "[-0.24274908 -1.45324141]\n",
      "[0.55458031 0.12388091]\n",
      "[ 0.27445992 -1.52652453]\n"
     ]
    },
    {
     "data": {
      "image/png": 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zZNnKLGfP+U5ZGkNMKUvKMRoq/56hrAw9ffTRR7s2lvlmOD9/XrX40qZDpa4X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\nERERERGRCeJDGxERERERERGRCfJ/vG/FdaMooTEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the results\n",
    "\n",
    "n = 10\n",
    "plt.figure(figsize=(20, 4))\n",
    "print('Multivariate random normal inputs')\n",
    "\n",
    "# generate normalized random encoded input\n",
    "np.random.seed(7)\n",
    "mean = np.zeros((2))\n",
    "var = np.eye(2)\n",
    "random_input = np.random.multivariate_normal(mean,var, 10)\n",
    "\n",
    "for i in range(n):\n",
    "\n",
    "    # get model prediction\n",
    "    print(random_input[i])\n",
    "    output = new_fcae_decoder_model.predict(np.reshape(random_input[i], (1,2)))\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i+1)\n",
    "    plt.imshow(output.reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b7sAZ6M7T1zC"
   },
   "source": [
    "The output images from the normalized bottleneck are more clear in the sense that they can be more easily recognized than the images generated from the implementation in the first part. This could be due to the fact that in the first case, the encodings could be any real number (since we are not using any activation regularizer; also due to the fact that we are using ReLU instead of sigmoid activation function). Thus, inputting a random value as an input could not match any of the encodings due to the vast domain of possible encodings. However, in the second case, the encodings are normalized and hence would be clusetered near the mean value of normalization ((0,0) in our case). Hence the randomly generated input could more likely match any of the encodings to produce a recognizable output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ah6THxq9tvcx"
   },
   "source": [
    "**Variational AutoEncoder**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "yyVkM9Qy-C9b",
    "outputId": "742d19fe-2464-4c65-dd7a-9cc089e19f59"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784) (10000, 784)\n"
     ]
    }
   ],
   "source": [
    "# Reshaping the inputs\n",
    "\n",
    "train_input = np.reshape(x_train, (-1,784))\n",
    "test_input = np.reshape(x_test, (-1,784))\n",
    "print(np.shape(train_input),np.shape(test_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XpgMBCHJ-HtY"
   },
   "outputs": [],
   "source": [
    "# Normalizing the inputs\n",
    "\n",
    "train_input = train_input/255\n",
    "test_input = test_input/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 561
    },
    "colab_type": "code",
    "id": "3UYIcYXP0LXk",
    "outputId": "ad5dafb1-48f2-4a77-9882-79ef5bdf9f9b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_77\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input (InputLayer)              [(None, 784)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "enc0 (Dense)                    (None, 300)          235500      input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "enc1 (Dense)                    (None, 75)           22575       enc0[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "enc2 (Dense)                    (None, 15)           1140        enc1[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "mu (Dense)                      (None, 2)            32          enc2[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "sigma (Dense)                   (None, 2)            32          enc2[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "lambda_30 (Lambda)              (None, 2)            0           mu[0][0]                         \n",
      "                                                                 sigma[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dec1 (Dense)                    (None, 15)           45          lambda_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dec2 (Dense)                    (None, 75)           1200        dec1[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "dec3 (Dense)                    (None, 300)          22800       dec2[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "output (Dense)                  (None, 784)          235984      dec3[0][0]                       \n",
      "==================================================================================================\n",
      "Total params: 519,308\n",
      "Trainable params: 519,308\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Model\n",
    "\n",
    "input_layer = tf.keras.layers.Input(shape=(784,), name='input')\n",
    "encoding_layer0 = tf.keras.layers.Dense(300, activation='relu', name='enc0')(input_layer)\n",
    "encoding_layer1 = tf.keras.layers.Dense(75, activation='relu', name='enc1')(encoding_layer0)\n",
    "encoding_layer2 = tf.keras.layers.Dense(15, activation='relu', name='enc2')(encoding_layer1)\n",
    "mu = tf.keras.layers.Dense(2, activation='relu', name='mu')(encoding_layer2)\n",
    "sigma = tf.keras.layers.Dense(2, activation='relu', name='sigma')(encoding_layer2)\n",
    "\n",
    "def sampling(args):\n",
    "    mu, sigma = args\n",
    "    reparameter = K.random_normal(shape=(2,), mean=0., stddev=1.)\n",
    "    return mu + (K.exp(sigma))*reparameter\n",
    "\n",
    "bottleneck = tf.keras.layers.Lambda(sampling, output_shape=(2,))([mu, sigma])\n",
    "\n",
    "decoding_layer1 = tf.keras.layers.Dense(15, activation='relu', name='dec1')(bottleneck)\n",
    "decoding_layer2 = tf.keras.layers.Dense(75, activation='relu', name='dec2')(decoding_layer1)\n",
    "decoding_layer3 = tf.keras.layers.Dense(300, activation='relu', name='dec3')(decoding_layer2)\n",
    "output_layer = tf.keras.layers.Dense(784, activation='sigmoid', name='output')(decoding_layer3)\n",
    "\n",
    "vae_model = tf.keras.Model(input_layer, output_layer)\n",
    "print(vae_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ELjZqj5cEpgc"
   },
   "outputs": [],
   "source": [
    "# Preparing the encoder model\n",
    "encoder_vae = tf.keras.Model(input_layer, mu)\n",
    "\n",
    "# Preparing a decoding model\n",
    "decoder_input = tf.keras.layers.Input(shape=(2,))\n",
    "temp = decoder_input\n",
    "for layer in vae_model.layers[7:]:\n",
    "  temp = layer(temp)\n",
    "vae_decoder_model = tf.keras.Model(decoder_input, temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z4UVA5b9EoVM"
   },
   "outputs": [],
   "source": [
    "# Defining the vae loss\n",
    "\n",
    "from keras import backend as K\n",
    "\n",
    "def vae_loss(x, x_decoded_mean):\n",
    "    mse_loss = tf.keras.losses.mse(x, x_decoded_mean)\n",
    "    kl_loss = - 0.5 * K.mean(1 + sigma - K.square(mu) - K.exp(sigma), axis=-1)\n",
    "    return mse_loss + kl_loss\n",
    "\n",
    "vae_model.compile(optimizer='adam', loss=vae_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "1EcG7rNyGZu_",
    "outputId": "8e6ee83f-fe75-49d4-9634-fec691a95050"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 6s 106us/sample - loss: 0.0764 - val_loss: 0.0676\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0674 - val_loss: 0.0676\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0674 - val_loss: 0.0676\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0674 - val_loss: 0.0675\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0677\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0676\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0676\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 3s 51us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 18/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 19/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 20/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 21/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 22/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 23/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 24/50\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 25/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 26/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 27/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 28/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 29/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 30/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 31/50\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 32/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 33/50\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 34/50\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 35/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 36/50\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 37/50\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 38/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 39/50\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 40/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 41/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 42/50\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 43/50\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 44/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 45/50\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 46/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 47/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 48/50\n",
      "60000/60000 [==============================] - 3s 49us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 49/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n",
      "Epoch 50/50\n",
      "60000/60000 [==============================] - 3s 50us/sample - loss: 0.0673 - val_loss: 0.0675\n"
     ]
    }
   ],
   "source": [
    "q4 = vae_model.fit(train_input, train_input, epochs=50, batch_size=100, validation_data=(test_input, test_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "zFCbTgmmG4bO",
    "outputId": "1cf819b1-9cb6-44e2-b7d6-7b402bdb34c3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DROJNgVEktTBEJO4UGEVSYIhI3CkwitTexbkCQ0RiSoFRpMpUkoqyhAJDRGJL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\nUhQFhoiIFEWBISIiRfn/zybwYME/n8EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loss plots \n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(q4.history['loss'])\n",
    "plt.plot(q4.history['val_loss'])\n",
    "plt.title('Fully connected VAE model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper right')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "colab_type": "code",
    "id": "_Ho9XBvGNXUZ",
    "outputId": "955ef775-11ba-4f69-ea2c-9195efa849d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multivariate random normal inputs\n",
      "[0.85099741 0.17775492]\n",
      "[-0.85303971 -0.44389968]\n",
      "[-0.20293796  1.10051557]\n",
      "[-3.03513615 -0.41456145]\n",
      "[-1.34308784 -0.7731739 ]\n",
      "[-1.67520693  0.80527769]\n",
      "[-0.49491418 -0.50388454]\n",
      "[-0.59621701  0.37449326]\n",
      "[ 0.41315697 -0.18360565]\n",
      "[0.65195623 1.07466944]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the results\n",
    "\n",
    "n = 10\n",
    "plt.figure(figsize=(20, 4))\n",
    "print('Multivariate random normal inputs')\n",
    "\n",
    "# generate normalized random encoded input\n",
    "np.random.seed(70)\n",
    "mean = np.zeros((2))\n",
    "var = np.eye(2)\n",
    "random_input = np.random.multivariate_normal(mean,var, 10)\n",
    "# random_input = np.random.uniform(high=100, size=(10,2))\n",
    "\n",
    "for i in range(n):\n",
    "\n",
    "    # get model prediction\n",
    "    print(random_input[i])\n",
    "    output = vae_decoder_model.predict(np.reshape(random_input[i]*1.0, (1,2)))\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i+1)\n",
    "    plt.imshow(output.reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iV3sqElWIcBS"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "AssignmentFour.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": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}