letter_digit_generator_v2_CVAE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conditional VAE on EMNIST\n",
    "Data source: [The EMNIST Dataset](https://www.nist.gov/node/1298471/emnist-dataset)\n",
    "\n",
    "EMNIST ByClass: \n",
    "* segmented digits and characters arranged by class\n",
    "* 62 classes comprising [0-9], [a-z] and [A-Z]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import module and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Lambda,Input,Dense,Flatten,Conv2D,Conv2DTranspose\n",
    "from keras.layers import Activation,BatchNormalization,Reshape,Concatenate,Dropout\n",
    "from keras.models import Model\n",
    "from keras.losses import mse, binary_crossentropy\n",
    "from keras.callbacks import Callback,ModelCheckpoint,ReduceLROnPlateau\n",
    "from keras.utils import to_categorical\n",
    "from keras import backend as K\n",
    "from keras.optimizers import Adam\n",
    "from keras import initializers\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "import cv2\n",
    "from mnist import MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (697932, 784)\n",
      "X_test shape: (116323, 784)\n",
      "y_train shape: (697932,)\n",
      "y_test shape: (116323,)\n"
     ]
    }
   ],
   "source": [
    "emndata = MNIST('emnist_data')\n",
    "img_rows = 28\n",
    "img_cols = 28\n",
    "img_channel = 1\n",
    "orig_dimension = img_rows*img_cols\n",
    "\n",
    "X_train,y_train = emndata.load('emnist_data/emnist-byclass-train-images-idx3-ubyte',\n",
    "                               'emnist_data/emnist-byclass-train-labels-idx1-ubyte')\n",
    "X_test,y_test = emndata.load('emnist_data/emnist-byclass-test-images-idx3-ubyte',\n",
    "                             'emnist_data/emnist-byclass-test-labels-idx1-ubyte')\n",
    "X_train = np.array(X_train)\n",
    "y_train = np.array(y_train)\n",
    "X_test = np.array(X_test)\n",
    "y_test = np.array(y_test)\n",
    "\n",
    "print('X_train shape: {}'.format(X_train.shape))\n",
    "print('X_test shape: {}'.format(X_test.shape))\n",
    "print('y_train shape: {}'.format(y_train.shape))\n",
    "print('y_test shape: {}'.format(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_str = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'\n",
    "lebel_dict = {i:v for i,v in enumerate(label_str)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "n_samples = 5\n",
    "n_cls = 5\n",
    "n_label = len(np.unique(y_train))\n",
    "fig, rows = plt.subplots(n_cls,n_samples,figsize=(4*n_samples,3*n_cls))\n",
    "\n",
    "for row,cls in zip(rows,np.random.choice(list(range(n_label)),n_cls)):\n",
    "    row[int(np.floor(n_samples/2))].set_title(lebel_dict[cls],fontsize=25)\n",
    "    for col_ax,img_idx in zip(row,[i for i,v in enumerate(y_train) if v==cls][:n_samples]):\n",
    "        img = X_train[img_idx].reshape(img_rows,img_cols)\n",
    "        img = np.transpose(img)\n",
    "        col_ax.imshow(img,cmap='gray')\n",
    "        col_ax.axis('off')\n",
    "plt.subplots_adjust(left=0.2, wspace=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (697932, 62)\n",
      "y_test shape: (116323, 62)\n"
     ]
    }
   ],
   "source": [
    "X_train = X_train.astype('float32')/(255/2)-1 # for tanh\n",
    "X_test = X_test.astype('float32')/(255/2)-1\n",
    "y_train = to_categorical(y_train,n_label)\n",
    "y_test = to_categorical(y_test,n_label)\n",
    "print('y_train shape: {}'.format(y_train.shape))\n",
    "print('y_test shape: {}'.format(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sampling(arg):\n",
    "    arg = [z_mean,z_log_var]\n",
    "    dim = K.int_shape(z_mean)[1]\n",
    "    epsilon = K.random_normal(shape=(K.shape(z_mean)[0], dim),mean=0.0, stddev=1.0) # reparameterization trick\n",
    "    return z_mean + K.exp(0.5 * z_log_var) * epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "latent_dim = 10\n",
    "epochs = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditional VAE - Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "image_input (InputLayer)        (None, 784)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "label_input (InputLayer)        (None, 62)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 846)          0           image_input[0][0]                \n",
      "                                                                 label_input[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 512)          433664      concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 256)          131328      dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "z_mean (Dense)                  (None, 10)           2570        dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "z_log_var (Dense)               (None, 10)           2570        dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, 10)           0           z_mean[0][0]                     \n",
      "                                                                 z_log_var[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 570,132\n",
      "Trainable params: 570,132\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "w_init = initializers.random_normal(stddev=0.02)\n",
    "gamma_init = initializers.random_normal(mean=1.0,stddev=0.02)\n",
    "\n",
    "img_inputs = Input(shape=(orig_dimension,),name='image_input')\n",
    "label_inputs = Input(shape=(n_label,),name='label_input')\n",
    "encoder_inputs = Concatenate()([img_inputs,label_inputs])\n",
    "\n",
    "x = Dense(512,kernel_initializer=w_init,activation='relu')(encoder_inputs)\n",
    "x = Dense(256,kernel_initializer=w_init,activation='relu')(x)\n",
    "\n",
    "z_mean = Dense(latent_dim, name='z_mean')(x)\n",
    "z_log_var = Dense(latent_dim, name='z_log_var')(x)\n",
    "\n",
    "z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])\n",
    "\n",
    "encoder = Model([img_inputs,label_inputs],[z_mean,z_log_var,z],name='encoder')\n",
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditional VAE - Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "latent_inputs (InputLayer)      (None, 10)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "label_input (InputLayer)        (None, 62)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 72)           0           latent_inputs[0][0]              \n",
      "                                                                 label_input[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 256)          18688       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 512)          131584      dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 784)          402192      dense_4[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 552,464\n",
      "Trainable params: 552,464\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "latent_inputs = Input(shape=(latent_dim,),name='latent_inputs')\n",
    "decoder_inputs = Concatenate()([latent_inputs,label_inputs])\n",
    "\n",
    "x = Dense(256,kernel_initializer=w_init,activation='relu')(decoder_inputs)\n",
    "x = Dense(512,kernel_initializer=w_init,activation='relu')(x)\n",
    "outputs = Dense(orig_dimension,kernel_initializer=w_init,activation='tanh')(x)\n",
    "\n",
    "# instantiate decoder model\n",
    "decoder = Model([latent_inputs,label_inputs],outputs,name='decoder')\n",
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditional VAE - complete model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = decoder([encoder([img_inputs,label_inputs])[2],label_inputs])\n",
    "vae = Model([img_inputs,label_inputs], outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta = 1 # 1 --> regular VAE\n",
    "reconstruction_loss = mse(img_inputs,outputs)\n",
    "reconstruction_loss *= orig_dimension\n",
    "\n",
    "kl_loss = -0.5*beta*K.sum(1+z_log_var-K.square(z_mean)-K.exp(z_log_var),axis=-1)\n",
    "vae_loss = K.mean(reconstruction_loss+kl_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "image_input (InputLayer)        (None, 784)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "label_input (InputLayer)        (None, 62)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "encoder (Model)                 [(None, 10), (None,  570132      image_input[0][0]                \n",
      "                                                                 label_input[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "decoder (Model)                 (None, 784)          552464      encoder[1][2]                    \n",
      "                                                                 label_input[0][0]                \n",
      "==================================================================================================\n",
      "Total params: 1,122,596\n",
      "Trainable params: 1,122,596\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "learning_rate = 0.001\n",
    "vae.add_loss(vae_loss) ## in this case, set y value to NONE during the fit\n",
    "vae.compile(optimizer=Adam(lr=learning_rate))\n",
    "vae.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Callback functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NEpochPrint(Callback):\n",
    "    def __init__(self, display_step):\n",
    "        self.epoch = 0\n",
    "        self.display_step = display_step\n",
    "    def on_epoch_end(self,epoch,logs={}):\n",
    "        self.epoch += 1        \n",
    "        if self.epoch == 1 or self.epoch % self.display_step == 0:\n",
    "            print('Epoch: {}/{} ..... {}: {:.4f} - {}: {:.4f}'.format(self.epoch,\n",
    "                                                                      self.params['epochs'],\n",
    "                                                                      self.params['metrics'][0], \n",
    "                                                                      logs.get(self.params['metrics'][0]),\n",
    "                                                                      self.params['metrics'][1],               \n",
    "                                                                      logs.get(self.params['metrics'][1])))\n",
    "NEpochPrinter = NEpochPrint(display_step=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate_reduction = ReduceLROnPlateau(monitor='val_loss',patience=5,verbose=1,factor=0.5,min_lr=0.00001)\n",
    "\n",
    "saving_folder = 'best_weight_ldg_v2_cvae'\n",
    "file_name = 'ldg_v2_cvae'\n",
    "os.makedirs(saving_folder,exist_ok=True)\n",
    "best_model_weight_path = os.path.join(saving_folder,file_name+'-best-wiehgts'+'-{epoch:03d}-{loss:.3f}-{val_loss:.3f}.h5')\n",
    "save_best_model = ModelCheckpoint(best_model_weight_path,monitor='val_loss',verbose=0,save_weights_only=True,save_best_only=True,mode='min')\n",
    "def train_or_load_weights(flag,weight_name=None):\n",
    "    if flag == 'train':\n",
    "        hist = vae.fit([X_train,y_train],\n",
    "                       epochs=epochs,\n",
    "                       batch_size=batch_size,\n",
    "                       validation_data=([X_test,y_test],None),\n",
    "                       verbose=0,\n",
    "                       callbacks=[save_best_model,learning_rate_reduction,NEpochPrinter])\n",
    "        return hist\n",
    "    if flag == 'load':\n",
    "        vae.load_weights(os.path.join(saving_folder,weight_name)) # load all the weights for encoder and decoder when loading for vae"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/100 ..... loss: 97.1905 - val_loss: 76.4282\n",
      "Epoch: 2/100 ..... loss: 73.6711 - val_loss: 71.7335\n",
      "Epoch: 4/100 ..... loss: 68.3842 - val_loss: 67.9752\n",
      "Epoch: 6/100 ..... loss: 66.2330 - val_loss: 66.5499\n",
      "Epoch: 8/100 ..... loss: 64.9775 - val_loss: 64.8543\n",
      "Epoch: 10/100 ..... loss: 64.1831 - val_loss: 64.0960\n",
      "Epoch: 12/100 ..... loss: 63.5560 - val_loss: 63.5303\n",
      "Epoch: 14/100 ..... loss: 63.0984 - val_loss: 63.0068\n",
      "Epoch: 16/100 ..... loss: 62.7093 - val_loss: 62.8645\n",
      "Epoch: 18/100 ..... loss: 62.4020 - val_loss: 62.7260\n",
      "Epoch: 20/100 ..... loss: 62.1364 - val_loss: 62.3846\n",
      "Epoch: 22/100 ..... loss: 61.9400 - val_loss: 62.2605\n",
      "Epoch: 24/100 ..... loss: 61.7324 - val_loss: 62.1788\n",
      "Epoch: 26/100 ..... loss: 61.5913 - val_loss: 62.0848\n",
      "Epoch: 28/100 ..... loss: 61.4375 - val_loss: 61.9849\n",
      "Epoch: 30/100 ..... loss: 61.3275 - val_loss: 61.8468\n",
      "Epoch: 32/100 ..... loss: 61.2086 - val_loss: 61.5122\n",
      "Epoch: 34/100 ..... loss: 61.0855 - val_loss: 61.3751\n",
      "Epoch: 36/100 ..... loss: 60.9956 - val_loss: 61.5753\n",
      "Epoch: 38/100 ..... loss: 60.9076 - val_loss: 61.5407\n",
      "Epoch: 40/100 ..... loss: 60.8223 - val_loss: 61.1163\n",
      "Epoch: 42/100 ..... loss: 60.7424 - val_loss: 61.9018\n",
      "Epoch: 44/100 ..... loss: 60.6860 - val_loss: 61.3629\n",
      "\n",
      "Epoch 00046: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch: 46/100 ..... loss: 60.6230 - val_loss: 61.1228\n",
      "Epoch: 48/100 ..... loss: 58.6909 - val_loss: 59.0883\n",
      "Epoch: 50/100 ..... loss: 58.6008 - val_loss: 59.1040\n",
      "Epoch: 52/100 ..... loss: 58.5359 - val_loss: 59.0626\n",
      "Epoch: 54/100 ..... loss: 58.4944 - val_loss: 59.5241\n",
      "Epoch: 56/100 ..... loss: 58.4373 - val_loss: 59.0856\n",
      "\n",
      "Epoch 00057: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch: 58/100 ..... loss: 57.4988 - val_loss: 58.2125\n",
      "Epoch: 60/100 ..... loss: 57.4320 - val_loss: 58.0939\n",
      "Epoch: 62/100 ..... loss: 57.3908 - val_loss: 57.9535\n",
      "Epoch: 64/100 ..... loss: 57.3686 - val_loss: 58.0842\n",
      "Epoch: 66/100 ..... loss: 57.3355 - val_loss: 58.0557\n",
      "\n",
      "Epoch 00067: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Epoch: 68/100 ..... loss: 56.8375 - val_loss: 57.5418\n",
      "Epoch: 70/100 ..... loss: 56.8016 - val_loss: 57.6705\n",
      "Epoch: 72/100 ..... loss: 56.7767 - val_loss: 57.5515\n",
      "Epoch: 74/100 ..... loss: 56.7545 - val_loss: 57.4692\n",
      "Epoch: 76/100 ..... loss: 56.7494 - val_loss: 57.4807\n",
      "Epoch: 78/100 ..... loss: 56.7242 - val_loss: 57.4948\n",
      "Epoch: 80/100 ..... loss: 56.7154 - val_loss: 57.5942\n",
      "Epoch: 82/100 ..... loss: 56.7119 - val_loss: 57.4330\n",
      "Epoch: 84/100 ..... loss: 56.6966 - val_loss: 57.4217\n",
      "Epoch: 86/100 ..... loss: 56.6877 - val_loss: 57.4714\n",
      "Epoch: 88/100 ..... loss: 56.6676 - val_loss: 57.4099\n",
      "Epoch: 90/100 ..... loss: 56.6592 - val_loss: 57.4581\n",
      "\n",
      "Epoch 00092: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "Epoch: 92/100 ..... loss: 56.6537 - val_loss: 57.3731\n",
      "Epoch: 94/100 ..... loss: 56.3851 - val_loss: 57.1834\n",
      "Epoch: 96/100 ..... loss: 56.3686 - val_loss: 57.1920\n",
      "Epoch: 98/100 ..... loss: 56.3689 - val_loss: 57.1763\n",
      "Epoch: 100/100 ..... loss: 56.3642 - val_loss: 57.1566\n"
     ]
    }
   ],
   "source": [
    "hist = train_or_load_weights(flag='train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def training_plot(hist,model_name):\n",
    "    loss = [hist.history['loss'],hist.history['val_loss']]\n",
    "    plt.plot(loss[0],color='b',label='training')\n",
    "    plt.plot(loss[1],color='r',label='valid')\n",
    "    plt.title(model_name,fontsize=18)\n",
    "    plt.legend(loc='best',fontsize=13)\n",
    "    plt.xlabel('epoch',fontsize=15)\n",
    "    plt.ylabel('loss',fontsize=15)\n",
    "    plt.tick_params(axis='both',labelsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "training_plot(hist,'ldg_CVAE_v2 training')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traing set reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_five_reconstruction(input_set,label_set,random_idx,encoder,decoder):\n",
    "    fig, axes = plt.subplots(2,5,figsize=(15,5))\n",
    "    for row,flag in zip(axes,['in_img','out_img']): \n",
    "        for col,idx in zip(row,random_idx):\n",
    "            original = input_set[idx]\n",
    "            if flag == 'in_img':\n",
    "                original_img = original.reshape(img_rows,img_cols)\n",
    "                original_img = np.transpose(original_img)\n",
    "                col.imshow(1-original_img,cmap='bone')\n",
    "                col.set_title('idx = {}\\nlabel = {}\\noriginal'.format(idx,lebel_dict[np.argmax(label_set[idx])]))\n",
    "            if flag == 'out_img':\n",
    "                encoded_z = encoder.predict([np.expand_dims(original,0),np.expand_dims(label_set[idx],0)],batch_size=1)[2]\n",
    "                predict = decoder.predict([encoded_z,np.expand_dims(label_set[idx],0)])\n",
    "                predict = (0.5*predict) + 0.5 # image back to 0-1 from -1 - 1\n",
    "                decoded = predict.reshape(img_rows,img_cols)\n",
    "                decoded = np.transpose(decoded)\n",
    "                col.imshow(1-decoded,cmap='bone')\n",
    "                col.set_title('reconstructed')\n",
    "            col.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "random_idx = np.random.randint(len(X_train),size=5)\n",
    "plot_five_reconstruction(X_train,y_train,random_idx,encoder,decoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test set reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(100)\n",
    "random_idx = np.random.randint(len(X_test),size=5)\n",
    "plot_five_reconstruction(X_test,y_test,random_idx,encoder,decoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating arbitrary handwritten letters/digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_idx_from_label_dict(search_str):\n",
    "    for i,val in lebel_dict.items():\n",
    "        if val == search_str:\n",
    "            return i\n",
    "def letter_digit_gen(input_str,th=None):\n",
    "    gap = 20\n",
    "    img = np.zeros((img_cols,gap*len(input_str)+(img_cols-gap)))\n",
    "    for idx,l in enumerate(input_str):\n",
    "        if l == ' ':\n",
    "            img[:,gap*idx:gap*idx+img_cols] += np.zeros((img_cols,img_cols))\n",
    "        elif l not in label_str:\n",
    "            pass\n",
    "        else:\n",
    "            cls_idx = find_idx_from_label_dict(l)\n",
    "            latent = np.random.randn(latent_dim)\n",
    "            latent = np.expand_dims(np.random.randn(latent_dim),0)\n",
    "            generated = decoder.predict([latent,np.expand_dims(to_categorical(cls_idx,n_label),0)])\n",
    "            generated = (0.5*generated) + 0.5\n",
    "            generated = generated.reshape(img_rows,img_cols)\n",
    "            generated = np.transpose(generated)\n",
    "            img[:,gap*idx:gap*idx+img_cols] += generated \n",
    "    if th != None:\n",
    "        for i,v in np.ndenumerate(img):\n",
    "            if v >= th:\n",
    "                img[i] = 1\n",
    "            else:\n",
    "                img[i] = 0\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "img = letter_digit_gen('TesT 123?9')\n",
    "plt.imshow(1-img,cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "img = letter_digit_gen('TesT 123?9',th=0.2)\n",
    "plt.imshow(1-img,cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_images_gen(input_str,rows=5,cols=3,th=None):\n",
    "    images = []\n",
    "    for _ in range(rows):\n",
    "        current = []\n",
    "        for _ in range(cols):\n",
    "            current.append(letter_digit_gen(input_str,th=th))\n",
    "            current.append(np.ones(shape=(img_rows,10)))\n",
    "        images.append(np.concatenate(current, axis=1))\n",
    "    combined_img = np.concatenate(images)\n",
    "    plt.figure(figsize=(15,20))\n",
    "    plt.imshow(1-combined_img,cmap='gray')\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_text = 'HeLlo 2?468'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_images_gen(sample_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_images_gen(sample_text,th=0.2)"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}