U2.05 - Network Architectures - Autoencoders.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget --no-cache -O init.py -q https://raw.githubusercontent.com/rramosp/2020.deeplearning/master/init.py\n",
    "from init import init; init(force_download=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "    print (\"setting tensorflow version in colab\")\n",
    "    %tensorflow_version 2.x\n",
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dimension de las imagenes y las clases (1500, 784) (1500,)\n"
     ]
    }
   ],
   "source": [
    "mnist = pd.read_csv(\"local/data/mnist1.5k.csv.gz\", compression=\"gzip\", header=None).values\n",
    "X=mnist[:,1:785]/255.\n",
    "y=mnist[:,0]\n",
    "print(\"dimension de las imagenes y las clases\", X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 50 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "perm = np.random.permutation(list(range(X.shape[0])))[0:50]\n",
    "random_imgs   = X[perm]\n",
    "random_labels = y[perm] \n",
    "fig = plt.figure(figsize=(10,6))\n",
    "for i in range(random_imgs.shape[0]):\n",
    "    ax=fig.add_subplot(5,10,i+1)\n",
    "    plt.imshow(random_imgs[i].reshape(28,28), interpolation=\"nearest\", cmap = plt.cm.Greys_r)\n",
    "    ax.set_title(int(random_labels[i]))\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Autoencoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Input\n",
    "from tensorflow.keras.backend import clear_session\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model(input_dim, code_size):\n",
    "\n",
    "    inputs = Input(shape=input_dim)\n",
    "    encoder = Dense(code_size, activation='relu')(inputs)\n",
    "    avg_latent_activations = tf.reduce_mean(encoder, axis=0)\n",
    "    outputs = Dense(input_dim, activation='sigmoid')(encoder)\n",
    "\n",
    "    model = Model([inputs], [outputs])\n",
    "    model.compile(optimizer='adam', loss='mse')\n",
    "    return model, encoder, avg_latent_activations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "porqué sigmoide en la última capa. qué pasaría si ponemos tanh o linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 784)]             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 50)                39250     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 784)               39984     \n",
      "=================================================================\n",
      "Total params: 79,234\n",
      "Trainable params: 79,234\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model, encoder, avg_latent_activations = get_model(input_dim=X.shape[1], code_size=50)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "try with larger layer, try with more layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1200 samples\n",
      "Epoch 1/100\n",
      "1200/1200 [==============================] - 0s 220us/sample - loss: 0.1031\n",
      "Epoch 2/100\n",
      "1200/1200 [==============================] - 0s 96us/sample - loss: 0.0602\n",
      "Epoch 3/100\n",
      "1200/1200 [==============================] - 0s 96us/sample - loss: 0.0492\n",
      "Epoch 4/100\n",
      "1200/1200 [==============================] - 0s 66us/sample - loss: 0.0431\n",
      "Epoch 5/100\n",
      "1200/1200 [==============================] - 0s 65us/sample - loss: 0.0386\n",
      "Epoch 6/100\n",
      "1200/1200 [==============================] - 0s 68us/sample - loss: 0.0349\n",
      "Epoch 7/100\n",
      "1200/1200 [==============================] - 0s 63us/sample - loss: 0.0317\n",
      "Epoch 8/100\n",
      "1200/1200 [==============================] - 0s 67us/sample - loss: 0.0291\n",
      "Epoch 9/100\n",
      "1200/1200 [==============================] - 0s 70us/sample - loss: 0.0269\n",
      "Epoch 10/100\n",
      "1200/1200 [==============================] - 0s 74us/sample - loss: 0.0250\n",
      "Epoch 11/100\n",
      "1200/1200 [==============================] - 0s 73us/sample - loss: 0.0234\n",
      "Epoch 12/100\n",
      "1200/1200 [==============================] - 0s 75us/sample - loss: 0.0220\n",
      "Epoch 13/100\n",
      "1200/1200 [==============================] - 0s 68us/sample - loss: 0.0208\n",
      "Epoch 14/100\n",
      "1200/1200 [==============================] - 0s 77us/sample - loss: 0.0196\n",
      "Epoch 15/100\n",
      "1200/1200 [==============================] - 0s 67us/sample - loss: 0.0186\n",
      "Epoch 16/100\n",
      "1200/1200 [==============================] - 0s 71us/sample - loss: 0.0176\n",
      "Epoch 17/100\n",
      "1200/1200 [==============================] - 0s 69us/sample - loss: 0.0167\n",
      "Epoch 18/100\n",
      "1200/1200 [==============================] - 0s 79us/sample - loss: 0.0160\n",
      "Epoch 19/100\n",
      "1200/1200 [==============================] - 0s 76us/sample - loss: 0.0153\n",
      "Epoch 20/100\n",
      "1200/1200 [==============================] - 0s 67us/sample - loss: 0.0146\n",
      "Epoch 21/100\n",
      "1200/1200 [==============================] - 0s 82us/sample - loss: 0.0140\n",
      "Epoch 22/100\n",
      "1200/1200 [==============================] - 0s 65us/sample - loss: 0.0135\n",
      "Epoch 23/100\n",
      "1200/1200 [==============================] - 0s 64us/sample - loss: 0.0130\n",
      "Epoch 24/100\n",
      "1200/1200 [==============================] - 0s 65us/sample - loss: 0.0124\n",
      "Epoch 25/100\n",
      "1200/1200 [==============================] - 0s 62us/sample - loss: 0.0120\n",
      "Epoch 26/100\n",
      "1200/1200 [==============================] - 0s 71us/sample - loss: 0.0116\n",
      "Epoch 27/100\n",
      "1200/1200 [==============================] - 0s 66us/sample - loss: 0.0112\n",
      "Epoch 28/100\n",
      "1200/1200 [==============================] - 0s 85us/sample - loss: 0.0109\n",
      "Epoch 29/100\n",
      "1200/1200 [==============================] - 0s 64us/sample - loss: 0.0105\n",
      "Epoch 30/100\n",
      "1200/1200 [==============================] - 0s 58us/sample - loss: 0.0102\n",
      "Epoch 31/100\n",
      "1200/1200 [==============================] - 0s 58us/sample - loss: 0.0099\n",
      "Epoch 32/100\n",
      "1200/1200 [==============================] - 0s 57us/sample - loss: 0.0096\n",
      "Epoch 33/100\n",
      "1200/1200 [==============================] - 0s 60us/sample - loss: 0.0094\n",
      "Epoch 34/100\n",
      "1200/1200 [==============================] - 0s 67us/sample - loss: 0.0091\n",
      "Epoch 35/100\n",
      "1200/1200 [==============================] - 0s 76us/sample - loss: 0.0089\n",
      "Epoch 36/100\n",
      "1200/1200 [==============================] - 0s 74us/sample - loss: 0.0087\n",
      "Epoch 37/100\n",
      "1200/1200 [==============================] - 0s 74us/sample - loss: 0.0085\n",
      "Epoch 38/100\n",
      "1200/1200 [==============================] - 0s 69us/sample - loss: 0.0083\n",
      "Epoch 39/100\n",
      "1200/1200 [==============================] - 0s 77us/sample - loss: 0.0081\n",
      "Epoch 40/100\n",
      "1200/1200 [==============================] - 0s 78us/sample - loss: 0.0079\n",
      "Epoch 41/100\n",
      "1200/1200 [==============================] - 0s 60us/sample - loss: 0.0078\n",
      "Epoch 42/100\n",
      "1200/1200 [==============================] - 0s 63us/sample - loss: 0.0076\n",
      "Epoch 43/100\n",
      "1200/1200 [==============================] - 0s 62us/sample - loss: 0.0075\n",
      "Epoch 44/100\n",
      "1200/1200 [==============================] - 0s 59us/sample - loss: 0.0073\n",
      "Epoch 45/100\n",
      "1200/1200 [==============================] - 0s 59us/sample - loss: 0.0072\n",
      "Epoch 46/100\n",
      "1200/1200 [==============================] - 0s 68us/sample - loss: 0.0070\n",
      "Epoch 47/100\n",
      "1200/1200 [==============================] - 0s 61us/sample - loss: 0.0069\n",
      "Epoch 48/100\n",
      "1200/1200 [==============================] - 0s 59us/sample - loss: 0.0068\n",
      "Epoch 49/100\n",
      "1200/1200 [==============================] - 0s 60us/sample - loss: 0.0067\n",
      "Epoch 50/100\n",
      "1200/1200 [==============================] - 0s 62us/sample - loss: 0.0066\n",
      "Epoch 51/100\n",
      "1200/1200 [==============================] - 0s 77us/sample - loss: 0.0065\n",
      "Epoch 52/100\n",
      "1200/1200 [==============================] - 0s 77us/sample - loss: 0.0064\n",
      "Epoch 53/100\n",
      "1200/1200 [==============================] - 0s 75us/sample - loss: 0.0063\n",
      "Epoch 54/100\n",
      "1200/1200 [==============================] - 0s 61us/sample - loss: 0.0062\n",
      "Epoch 55/100\n",
      "1200/1200 [==============================] - 0s 61us/sample - loss: 0.0061\n",
      "Epoch 56/100\n",
      "1200/1200 [==============================] - 0s 59us/sample - loss: 0.0060\n",
      "Epoch 57/100\n",
      "1200/1200 [==============================] - 0s 58us/sample - loss: 0.0060\n",
      "Epoch 58/100\n",
      "1200/1200 [==============================] - 0s 61us/sample - loss: 0.0059\n",
      "Epoch 59/100\n",
      "1200/1200 [==============================] - 0s 79us/sample - loss: 0.0058\n",
      "Epoch 60/100\n",
      "1200/1200 [==============================] - 0s 79us/sample - loss: 0.0057\n",
      "Epoch 61/100\n",
      "1200/1200 [==============================] - 0s 78us/sample - loss: 0.0057\n",
      "Epoch 62/100\n",
      "1200/1200 [==============================] - 0s 75us/sample - loss: 0.0056\n",
      "Epoch 63/100\n",
      "1200/1200 [==============================] - 0s 63us/sample - loss: 0.0056\n",
      "Epoch 64/100\n",
      "1200/1200 [==============================] - 0s 61us/sample - loss: 0.0055\n",
      "Epoch 65/100\n",
      "1200/1200 [==============================] - 0s 63us/sample - loss: 0.0054\n",
      "Epoch 66/100\n",
      "1200/1200 [==============================] - 0s 58us/sample - loss: 0.0054\n",
      "Epoch 67/100\n",
      "1200/1200 [==============================] - 0s 69us/sample - loss: 0.0053\n",
      "Epoch 68/100\n",
      "1200/1200 [==============================] - 0s 69us/sample - loss: 0.0053\n",
      "Epoch 69/100\n",
      "1200/1200 [==============================] - 0s 76us/sample - loss: 0.0052\n",
      "Epoch 70/100\n",
      "1200/1200 [==============================] - 0s 80us/sample - loss: 0.0052\n",
      "Epoch 71/100\n",
      "1200/1200 [==============================] - 0s 71us/sample - loss: 0.0052\n",
      "Epoch 72/100\n",
      "1200/1200 [==============================] - 0s 58us/sample - loss: 0.0051\n",
      "Epoch 73/100\n",
      "1200/1200 [==============================] - 0s 64us/sample - loss: 0.0051\n",
      "Epoch 74/100\n",
      "1200/1200 [==============================] - 0s 67us/sample - loss: 0.0050\n",
      "Epoch 75/100\n",
      "1200/1200 [==============================] - 0s 64us/sample - loss: 0.0050\n",
      "Epoch 76/100\n",
      "1200/1200 [==============================] - 0s 79us/sample - loss: 0.0049\n",
      "Epoch 77/100\n",
      "1200/1200 [==============================] - 0s 71us/sample - loss: 0.0049\n",
      "Epoch 78/100\n",
      "1200/1200 [==============================] - 0s 64us/sample - loss: 0.0049\n",
      "Epoch 79/100\n",
      "1200/1200 [==============================] - 0s 61us/sample - loss: 0.0049\n",
      "Epoch 80/100\n",
      "1200/1200 [==============================] - 0s 60us/sample - loss: 0.0048\n",
      "Epoch 81/100\n",
      "1200/1200 [==============================] - 0s 61us/sample - loss: 0.0048\n",
      "Epoch 82/100\n",
      "1200/1200 [==============================] - 0s 68us/sample - loss: 0.0047\n",
      "Epoch 83/100\n",
      "1200/1200 [==============================] - 0s 79us/sample - loss: 0.0047\n",
      "Epoch 84/100\n",
      "1200/1200 [==============================] - 0s 65us/sample - loss: 0.0047\n",
      "Epoch 85/100\n",
      "1200/1200 [==============================] - 0s 60us/sample - loss: 0.0047\n",
      "Epoch 86/100\n",
      "1200/1200 [==============================] - 0s 61us/sample - loss: 0.0046\n",
      "Epoch 87/100\n",
      "1200/1200 [==============================] - 0s 61us/sample - loss: 0.0046\n",
      "Epoch 88/100\n",
      "1200/1200 [==============================] - 0s 65us/sample - loss: 0.0046\n",
      "Epoch 89/100\n",
      "1200/1200 [==============================] - 0s 63us/sample - loss: 0.0046\n",
      "Epoch 90/100\n",
      "1200/1200 [==============================] - 0s 72us/sample - loss: 0.0045\n",
      "Epoch 91/100\n",
      "1200/1200 [==============================] - 0s 79us/sample - loss: 0.0045\n",
      "Epoch 92/100\n",
      "1200/1200 [==============================] - 0s 75us/sample - loss: 0.0045\n",
      "Epoch 93/100\n",
      "1200/1200 [==============================] - 0s 66us/sample - loss: 0.0045\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 94/100\n",
      "1200/1200 [==============================] - 0s 62us/sample - loss: 0.0045\n",
      "Epoch 95/100\n",
      "1200/1200 [==============================] - 0s 74us/sample - loss: 0.0044\n",
      "Epoch 96/100\n",
      "1200/1200 [==============================] - 0s 63us/sample - loss: 0.0044\n",
      "Epoch 97/100\n",
      "1200/1200 [==============================] - 0s 59us/sample - loss: 0.0044\n",
      "Epoch 98/100\n",
      "1200/1200 [==============================] - 0s 61us/sample - loss: 0.0044\n",
      "Epoch 99/100\n",
      "1200/1200 [==============================] - 0s 60us/sample - loss: 0.0043\n",
      "Epoch 100/100\n",
      "1200/1200 [==============================] - 0s 59us/sample - loss: 0.0043\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1516b8780>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, X_train, epochs=100, batch_size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observe the average activation of each latent neuron for all train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 7.821822 ,  7.236865 ,  6.947421 ,  5.5829062,  9.733132 ,\n",
       "         5.3420925,  5.6889577,  4.6146173,  5.6246243,  2.756387 ,\n",
       "         5.263189 ,  5.7526317,  3.3460698,  6.508909 ,  5.6402445,\n",
       "         5.226904 ,  5.562851 ,  5.232005 ,  6.6963797,  5.827833 ,\n",
       "         5.4785905,  5.1644454,  3.891168 ,  5.441585 ,  4.9872856,\n",
       "         5.5402126,  6.745936 ,  4.788079 ,  3.8945162,  5.1530633,\n",
       "         7.1550336,  6.552982 ,  7.3555093,  6.0033793,  6.0774965,\n",
       "         6.000663 ,  6.185482 ,  5.227069 ,  6.505696 ,  6.821496 ,\n",
       "         5.205787 ,  5.802242 ,  5.285625 , 11.446103 ,  8.927542 ,\n",
       "         8.3400545,  5.401397 ,  5.1066194,  7.4206576,  4.431855 ],\n",
       "       dtype=float32)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras import backend as K\n",
    "fl = K.function([model.input], [avg_latent_activations])\n",
    "fl(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### show reconstruction on sampled test images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sample = np.random.permutation(X_test)[:10]\n",
    "X_pred   = model.predict(X_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "for i in range(len(X_sample)):\n",
    "    plt.subplot(2,len(X_sample),i+1)\n",
    "    plt.imshow(X_sample[i].reshape(28,28), cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\")\n",
    "    plt.subplot(2,len(X_sample),len(X_sample)+i+1)\n",
    "    plt.imshow(X_pred[i].reshape(28,28), cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### show weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784, 50)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = model.get_weights()\n",
    "w[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 50 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "for k,i in enumerate(np.random.permutation(w[0].shape[1])[:100]):\n",
    "    plt.subplot(10,10,k+1)\n",
    "    plt.imshow(w[0][:,i].reshape(28,28), cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### see data in latent space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "encoded data size (200, 50)\n",
      "mean activation at encoder 6.047\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idxs = np.random.permutation(len(X_test))[:200]\n",
    "idxs = idxs[np.argsort(y_test[idxs])]\n",
    "y_sample = y_test[idxs]\n",
    "X_sample = X_test[idxs]\n",
    "\n",
    "import tensorflow.keras.backend as K\n",
    "l0_output = K.function([model.input], [encoder])\n",
    "X_sample_encoded = l0_output([X_sample])[0]\n",
    "print(\"encoded data size\", X_sample_encoded.shape)\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.imshow(X_sample_encoded.T, cmap=plt.cm.Greys_r, origin=\"bottom\")\n",
    "plt.ylabel(\"component\")\n",
    "plt.xlabel(\"sample class number\")\n",
    "plt.xticks(range(len(y_sample))[::5], y_sample[::5]);\n",
    "print (\"mean activation at encoder %.3f\"%np.mean(X_sample_encoded))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### observe distribution of activations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(X_sample_encoded.flatten(), bins=30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### avg representative of each class in latent space, reconstructed\n",
    "\n",
    "for each class:\n",
    "- take all train samples\n",
    "- obtain their representation in latent space\n",
    "- average all latent space representations\n",
    "- propage the averaged vector through the decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_encoded = l0_output([X_train])[0]\n",
    "\n",
    "fdecode = K.function([encoder], [model.outputs])\n",
    "plt.figure(figsize=(15,3))\n",
    "for k, i in enumerate(np.unique(y_train)):\n",
    "    plt.subplot(1,10,k+1)\n",
    "    plt.imshow(fdecode(X_train_encoded[y_train==i].mean(axis=0).reshape(1,-1))[0][0][0].reshape(28,28), cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\"); plt.title(\"%d\"%i);\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### most representative neuron in latent space for each class\n",
    "\n",
    "for each class:\n",
    "- take all train samples\n",
    "- obtain their representation in latent space\n",
    "- average all latent space representations\n",
    "- build a one hot vector in latent space with the activated neuron in average\n",
    "- propage the averaged vector through the decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class 0, most avg activated neuron is 44, with value 16.96\n",
      "class 1, most avg activated neuron is 43, with value 9.20\n",
      "class 2, most avg activated neuron is 44, with value 11.49\n",
      "class 3, most avg activated neuron is 43, with value 14.78\n",
      "class 4, most avg activated neuron is 43, with value 12.14\n",
      "class 5, most avg activated neuron is 43, with value 11.80\n",
      "class 6, most avg activated neuron is 0, with value 16.54\n",
      "class 7, most avg activated neuron is 43, with value 13.64\n",
      "class 8, most avg activated neuron is 43, with value 15.46\n",
      "class 9, most avg activated neuron is 43, with value 15.58\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_encoded = l0_output([X_train])[0]\n",
    "plt.figure(figsize=(15,3))\n",
    "for k, i in enumerate(np.unique(y_train)):\n",
    "    plt.subplot(1,10,k+1)\n",
    "    k = np.zeros(50)\n",
    "    most_activated_neuron = np.argmax(X_train_encoded[y_train==i].mean(axis=0))\n",
    "    k[np.argmax(X_train_encoded[y_train==i].mean(axis=0))] = 1\n",
    "    print (\"class %d, most avg activated neuron is %d, with value %.2f\"%(i, most_activated_neuron, np.max(X_train_encoded[y_train==i].mean(axis=0))))\n",
    "    plt.imshow(fdecode(k.reshape(1,-1))[0][0][0].reshape(28,28), cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\"); plt.title(\"%d\"%i);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Custom loss, unsupervised `.fit(X)` call\n",
    "\n",
    "given:\n",
    "\n",
    "- $\\mathbf{x}^{(i)} \\in \\mathbb{R}^{784}$\n",
    "- $e(\\mathbf{x}^{(i)}) \\in \\mathbb{R}^{50}$, the encoder\n",
    "- $d(e(\\mathbf{x}^{(i)})) \\in \\mathbb{R}^{784}$, the decoder\n",
    "\n",
    "we define the loss function as (MSE):\n",
    "\n",
    "$$\\text{loss}(\\mathbf{x}^{(i)}) = \\frac{1}{m}\\sum_m \\big(\\mathbf{x}^{(i)} - d(e(\\mathbf{x}^{(i)}))\\big)^2$$\n",
    "\n",
    "and implement it by hand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.losses import mse\n",
    "import tensorflow as tf\n",
    "\n",
    "def get_model_U(input_dim, code_size):\n",
    "    inputs = Input(shape=input_dim)\n",
    "    encoder = Dense(code_size, activation='relu')(inputs)\n",
    "    outputs = Dense(input_dim, activation='sigmoid')(encoder)\n",
    "\n",
    "    loss = tf.reduce_mean( (inputs-outputs)**2)\n",
    "    model = Model([inputs], [outputs])    \n",
    "    model.add_loss(loss)\n",
    "    \n",
    "    model.compile(optimizer='adam')\n",
    "    return model, encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0220 14:29:26.162351 4663383488 training_utils.py:1444] Output dense_3 missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to dense_3.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 784)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 40)           31400       input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 784)          32144       dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_sub (TensorFlowOpLa [(None, 784)]        0           input_2[0][0]                    \n",
      "                                                                 dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_pow (TensorFlowOpLa [(None, 784)]        0           tf_op_layer_sub[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Mean_1 (TensorFlowO [()]                 0           tf_op_layer_pow[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_loss (AddLoss)              ()                   0           tf_op_layer_Mean_1[0][0]         \n",
      "==================================================================================================\n",
      "Total params: 63,544\n",
      "Trainable params: 63,544\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model, encoder = get_model_U(input_dim=X.shape[1], code_size=40)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observe `.fit` call is unsupervised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1200 samples\n",
      "Epoch 1/100\n",
      "1200/1200 [==============================] - 0s 167us/sample - loss: 0.1481\n",
      "Epoch 2/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0705\n",
      "Epoch 3/100\n",
      "1200/1200 [==============================] - 0s 37us/sample - loss: 0.0632\n",
      "Epoch 4/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0562\n",
      "Epoch 5/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0507\n",
      "Epoch 6/100\n",
      "1200/1200 [==============================] - 0s 46us/sample - loss: 0.0467\n",
      "Epoch 7/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0434\n",
      "Epoch 8/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0406\n",
      "Epoch 9/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0381\n",
      "Epoch 10/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0361\n",
      "Epoch 11/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0344\n",
      "Epoch 12/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0328\n",
      "Epoch 13/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0314\n",
      "Epoch 14/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0301\n",
      "Epoch 15/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0289\n",
      "Epoch 16/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0278\n",
      "Epoch 17/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0268\n",
      "Epoch 18/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0259\n",
      "Epoch 19/100\n",
      "1200/1200 [==============================] - 0s 37us/sample - loss: 0.0250\n",
      "Epoch 20/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0242\n",
      "Epoch 21/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0235\n",
      "Epoch 22/100\n",
      "1200/1200 [==============================] - 0s 49us/sample - loss: 0.0227\n",
      "Epoch 23/100\n",
      "1200/1200 [==============================] - 0s 48us/sample - loss: 0.0220\n",
      "Epoch 24/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0213\n",
      "Epoch 25/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0207\n",
      "Epoch 26/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0202\n",
      "Epoch 27/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0195\n",
      "Epoch 28/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0191\n",
      "Epoch 29/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0185\n",
      "Epoch 30/100\n",
      "1200/1200 [==============================] - 0s 37us/sample - loss: 0.0180\n",
      "Epoch 31/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0175\n",
      "Epoch 32/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0172\n",
      "Epoch 33/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0167\n",
      "Epoch 34/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0163\n",
      "Epoch 35/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0160\n",
      "Epoch 36/100\n",
      "1200/1200 [==============================] - 0s 46us/sample - loss: 0.0157\n",
      "Epoch 37/100\n",
      "1200/1200 [==============================] - 0s 49us/sample - loss: 0.0153\n",
      "Epoch 38/100\n",
      "1200/1200 [==============================] - 0s 47us/sample - loss: 0.0149\n",
      "Epoch 39/100\n",
      "1200/1200 [==============================] - 0s 48us/sample - loss: 0.0146\n",
      "Epoch 40/100\n",
      "1200/1200 [==============================] - 0s 49us/sample - loss: 0.0143\n",
      "Epoch 41/100\n",
      "1200/1200 [==============================] - 0s 49us/sample - loss: 0.0141\n",
      "Epoch 42/100\n",
      "1200/1200 [==============================] - 0s 52us/sample - loss: 0.0138\n",
      "Epoch 43/100\n",
      "1200/1200 [==============================] - 0s 45us/sample - loss: 0.0135\n",
      "Epoch 44/100\n",
      "1200/1200 [==============================] - 0s 43us/sample - loss: 0.0132\n",
      "Epoch 45/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0130\n",
      "Epoch 46/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0128\n",
      "Epoch 47/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0126\n",
      "Epoch 48/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0124\n",
      "Epoch 49/100\n",
      "1200/1200 [==============================] - 0s 36us/sample - loss: 0.0121\n",
      "Epoch 50/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0119\n",
      "Epoch 51/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0117\n",
      "Epoch 52/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0115\n",
      "Epoch 53/100\n",
      "1200/1200 [==============================] - 0s 47us/sample - loss: 0.0113\n",
      "Epoch 54/100\n",
      "1200/1200 [==============================] - 0s 46us/sample - loss: 0.0111\n",
      "Epoch 55/100\n",
      "1200/1200 [==============================] - 0s 47us/sample - loss: 0.0110\n",
      "Epoch 56/100\n",
      "1200/1200 [==============================] - 0s 48us/sample - loss: 0.0109\n",
      "Epoch 57/100\n",
      "1200/1200 [==============================] - 0s 45us/sample - loss: 0.0107\n",
      "Epoch 58/100\n",
      "1200/1200 [==============================] - 0s 48us/sample - loss: 0.0105\n",
      "Epoch 59/100\n",
      "1200/1200 [==============================] - 0s 52us/sample - loss: 0.0103\n",
      "Epoch 60/100\n",
      "1200/1200 [==============================] - 0s 49us/sample - loss: 0.0102\n",
      "Epoch 61/100\n",
      "1200/1200 [==============================] - 0s 47us/sample - loss: 0.0101\n",
      "Epoch 62/100\n",
      "1200/1200 [==============================] - 0s 46us/sample - loss: 0.0100\n",
      "Epoch 63/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0099\n",
      "Epoch 64/100\n",
      "1200/1200 [==============================] - 0s 36us/sample - loss: 0.0097\n",
      "Epoch 65/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0096\n",
      "Epoch 66/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0095\n",
      "Epoch 67/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0094\n",
      "Epoch 68/100\n",
      "1200/1200 [==============================] - 0s 37us/sample - loss: 0.0093\n",
      "Epoch 69/100\n",
      "1200/1200 [==============================] - 0s 47us/sample - loss: 0.0092\n",
      "Epoch 70/100\n",
      "1200/1200 [==============================] - 0s 48us/sample - loss: 0.0091\n",
      "Epoch 71/100\n",
      "1200/1200 [==============================] - 0s 49us/sample - loss: 0.0090\n",
      "Epoch 72/100\n",
      "1200/1200 [==============================] - 0s 48us/sample - loss: 0.0089\n",
      "Epoch 73/100\n",
      "1200/1200 [==============================] - 0s 48us/sample - loss: 0.0088\n",
      "Epoch 74/100\n",
      "1200/1200 [==============================] - 0s 46us/sample - loss: 0.0088\n",
      "Epoch 75/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0087\n",
      "Epoch 76/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0086\n",
      "Epoch 77/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0085\n",
      "Epoch 78/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0084\n",
      "Epoch 79/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0084\n",
      "Epoch 80/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0083\n",
      "Epoch 81/100\n",
      "1200/1200 [==============================] - 0s 47us/sample - loss: 0.0082\n",
      "Epoch 82/100\n",
      "1200/1200 [==============================] - 0s 49us/sample - loss: 0.0082\n",
      "Epoch 83/100\n",
      "1200/1200 [==============================] - 0s 48us/sample - loss: 0.0081\n",
      "Epoch 84/100\n",
      "1200/1200 [==============================] - 0s 47us/sample - loss: 0.0080\n",
      "Epoch 85/100\n",
      "1200/1200 [==============================] - 0s 43us/sample - loss: 0.0080\n",
      "Epoch 86/100\n",
      "1200/1200 [==============================] - 0s 37us/sample - loss: 0.0079\n",
      "Epoch 87/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0079\n",
      "Epoch 88/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0078\n",
      "Epoch 89/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0078\n",
      "Epoch 90/100\n",
      "1200/1200 [==============================] - 0s 36us/sample - loss: 0.0077\n",
      "Epoch 91/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0077\n",
      "Epoch 92/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0076\n",
      "Epoch 93/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0076\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 94/100\n",
      "1200/1200 [==============================] - 0s 43us/sample - loss: 0.0075\n",
      "Epoch 95/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0075\n",
      "Epoch 96/100\n",
      "1200/1200 [==============================] - 0s 37us/sample - loss: 0.0074\n",
      "Epoch 97/100\n",
      "1200/1200 [==============================] - 0s 46us/sample - loss: 0.0074\n",
      "Epoch 98/100\n",
      "1200/1200 [==============================] - 0s 47us/sample - loss: 0.0073\n",
      "Epoch 99/100\n",
      "1200/1200 [==============================] - 0s 45us/sample - loss: 0.0073\n",
      "Epoch 100/100\n",
      "1200/1200 [==============================] - 0s 46us/sample - loss: 0.0073\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x152df55f8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, epochs=100, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AAAAAAECiePADAAAAAACQKB78AAAAAAAAJIoHPwAAAAAAAIniwQ8AAAAAAECiePADAAAAAACQKB78AAAAAAAAJIoHPwAAAAAAAIniwQ8AAAAAAECiePADAAAAAACQKB78AAAAAAAAJIoHPwAAAAAAAIniwQ8AAAAAAECiePADAAAAAACQKB78AAAAAAAAJIoHPwAAAAAAAIniwQ8AAAAAAECiePADAAAAAACQKB78AAAAAAAAJIoHPwAAAAAAAIniwQ8AAAAAAECiePADAAAAAACQKB78AAAAAAAAJOr/AeW1uhn/FlqKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_sample = np.random.permutation(X_test)[:10]\n",
    "X_pred   = model.predict(X_sample)\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "for i in range(len(X_sample)):\n",
    "    plt.subplot(2,len(X_sample),i+1)\n",
    "    plt.imshow(X_sample[i].reshape(28,28), cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\")\n",
    "    plt.subplot(2,len(X_sample),len(X_sample)+i+1)\n",
    "    plt.imshow(X_pred[i].reshape(28,28), cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Autoencoder for image denoising\n",
    "\n",
    "observe reconstruction when fed with noisy data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_noise(x, noise_level=.2):\n",
    "    return x + np.random.normal(size=x.shape)*noise_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sample = np.random.permutation(X_test)[:10]\n",
    "X_pred   = model.predict(X_sample)\n",
    "X_sample_noisy = add_noise(X_sample)\n",
    "X_pred_noisy   = model.predict(X_sample_noisy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "for i in range(len(X_sample_noisy)):\n",
    "    plt.subplot(2,len(X_sample_noisy),i+1)\n",
    "    plt.imshow(X_sample_noisy[i].reshape(28,28), cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\")\n",
    "    plt.subplot(2,len(X_sample_noisy),len(X_sample_noisy)+i+1)\n",
    "    plt.imshow(X_pred_noisy[i].reshape(28,28), cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### in a more real scenario we only have noisy data to train the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1200 samples\n",
      "Epoch 1/100\n",
      "1200/1200 [==============================] - 0s 170us/sample - loss: 0.1832\n",
      "Epoch 2/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.1103\n",
      "Epoch 3/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.1040\n",
      "Epoch 4/100\n",
      "1200/1200 [==============================] - 0s 43us/sample - loss: 0.0974\n",
      "Epoch 5/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0918\n",
      "Epoch 6/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0872\n",
      "Epoch 7/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0835\n",
      "Epoch 8/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0807\n",
      "Epoch 9/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0785\n",
      "Epoch 10/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0766\n",
      "Epoch 11/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0748\n",
      "Epoch 12/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0731\n",
      "Epoch 13/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0715\n",
      "Epoch 14/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0700\n",
      "Epoch 15/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0686\n",
      "Epoch 16/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0673\n",
      "Epoch 17/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0661\n",
      "Epoch 18/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0649\n",
      "Epoch 19/100\n",
      "1200/1200 [==============================] - 0s 37us/sample - loss: 0.0639\n",
      "Epoch 20/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0630\n",
      "Epoch 21/100\n",
      "1200/1200 [==============================] - 0s 46us/sample - loss: 0.0621\n",
      "Epoch 22/100\n",
      "1200/1200 [==============================] - 0s 43us/sample - loss: 0.0613\n",
      "Epoch 23/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0605\n",
      "Epoch 24/100\n",
      "1200/1200 [==============================] - 0s 45us/sample - loss: 0.0597\n",
      "Epoch 25/100\n",
      "1200/1200 [==============================] - 0s 51us/sample - loss: 0.0590\n",
      "Epoch 26/100\n",
      "1200/1200 [==============================] - 0s 47us/sample - loss: 0.0584\n",
      "Epoch 27/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0577\n",
      "Epoch 28/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0571\n",
      "Epoch 29/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0565\n",
      "Epoch 30/100\n",
      "1200/1200 [==============================] - 0s 46us/sample - loss: 0.0560\n",
      "Epoch 31/100\n",
      "1200/1200 [==============================] - 0s 43us/sample - loss: 0.0555\n",
      "Epoch 32/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0550\n",
      "Epoch 33/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0545\n",
      "Epoch 34/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0541\n",
      "Epoch 35/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0536\n",
      "Epoch 36/100\n",
      "1200/1200 [==============================] - 0s 49us/sample - loss: 0.0533\n",
      "Epoch 37/100\n",
      "1200/1200 [==============================] - 0s 47us/sample - loss: 0.0528\n",
      "Epoch 38/100\n",
      "1200/1200 [==============================] - 0s 48us/sample - loss: 0.0525\n",
      "Epoch 39/100\n",
      "1200/1200 [==============================] - 0s 46us/sample - loss: 0.0522\n",
      "Epoch 40/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0519\n",
      "Epoch 41/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0515\n",
      "Epoch 42/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0513\n",
      "Epoch 43/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0510\n",
      "Epoch 44/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0507\n",
      "Epoch 45/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0505\n",
      "Epoch 46/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0503\n",
      "Epoch 47/100\n",
      "1200/1200 [==============================] - 0s 43us/sample - loss: 0.0500\n",
      "Epoch 48/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0498\n",
      "Epoch 49/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0496\n",
      "Epoch 50/100\n",
      "1200/1200 [==============================] - 0s 47us/sample - loss: 0.0494\n",
      "Epoch 51/100\n",
      "1200/1200 [==============================] - 0s 48us/sample - loss: 0.0492\n",
      "Epoch 52/100\n",
      "1200/1200 [==============================] - 0s 48us/sample - loss: 0.0490\n",
      "Epoch 53/100\n",
      "1200/1200 [==============================] - 0s 48us/sample - loss: 0.0489\n",
      "Epoch 54/100\n",
      "1200/1200 [==============================] - 0s 43us/sample - loss: 0.0487\n",
      "Epoch 55/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0485\n",
      "Epoch 56/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0484\n",
      "Epoch 57/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0483\n",
      "Epoch 58/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0481\n",
      "Epoch 59/100\n",
      "1200/1200 [==============================] - 0s 37us/sample - loss: 0.0480\n",
      "Epoch 60/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0479\n",
      "Epoch 61/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0477\n",
      "Epoch 62/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0476\n",
      "Epoch 63/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0475\n",
      "Epoch 64/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0474\n",
      "Epoch 65/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0473\n",
      "Epoch 66/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0471\n",
      "Epoch 67/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0470\n",
      "Epoch 68/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0469\n",
      "Epoch 69/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0468\n",
      "Epoch 70/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0467\n",
      "Epoch 71/100\n",
      "1200/1200 [==============================] - 0s 44us/sample - loss: 0.0466\n",
      "Epoch 72/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0466\n",
      "Epoch 73/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0464\n",
      "Epoch 74/100\n",
      "1200/1200 [==============================] - 0s 43us/sample - loss: 0.0464\n",
      "Epoch 75/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0463\n",
      "Epoch 76/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0462\n",
      "Epoch 77/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0462\n",
      "Epoch 78/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0461\n",
      "Epoch 79/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0460\n",
      "Epoch 80/100\n",
      "1200/1200 [==============================] - 0s 42us/sample - loss: 0.0459\n",
      "Epoch 81/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0458\n",
      "Epoch 82/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0458\n",
      "Epoch 83/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0457\n",
      "Epoch 84/100\n",
      "1200/1200 [==============================] - 0s 37us/sample - loss: 0.0457\n",
      "Epoch 85/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0456\n",
      "Epoch 86/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0455\n",
      "Epoch 87/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0454\n",
      "Epoch 88/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0454\n",
      "Epoch 89/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0453\n",
      "Epoch 90/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0453\n",
      "Epoch 91/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0452\n",
      "Epoch 92/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0452\n",
      "Epoch 93/100\n",
      "1200/1200 [==============================] - 0s 38us/sample - loss: 0.0451\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 94/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0451\n",
      "Epoch 95/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0450\n",
      "Epoch 96/100\n",
      "1200/1200 [==============================] - 0s 36us/sample - loss: 0.0449\n",
      "Epoch 97/100\n",
      "1200/1200 [==============================] - 0s 41us/sample - loss: 0.0449\n",
      "Epoch 98/100\n",
      "1200/1200 [==============================] - 0s 39us/sample - loss: 0.0449\n",
      "Epoch 99/100\n",
      "1200/1200 [==============================] - 0s 36us/sample - loss: 0.0448\n",
      "Epoch 100/100\n",
      "1200/1200 [==============================] - 0s 40us/sample - loss: 0.0448\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1531e03c8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_model, n_encoder,_ = get_model(input_dim=X.shape[1], code_size=40)\n",
    "X_train_noisy = add_noise(X_train)\n",
    "\n",
    "n_model.fit(X_train_noisy, X_train_noisy, epochs=100, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_sample_noisy = add_noise(X_sample)\n",
    "X_pred_noisy   = n_model.predict(X_sample_noisy)\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "for i in range(len(X_sample_noisy)):\n",
    "    plt.subplot(2,len(X_sample_noisy),i+1)\n",
    "    plt.imshow(X_sample_noisy[i].reshape(28,28), cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\")\n",
    "    plt.subplot(2,len(X_sample_noisy),len(X_sample_noisy)+i+1)\n",
    "    plt.imshow(X_pred_noisy[i].reshape(28,28), cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": 2
}