keras_gan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from skimage import io\n",
    "from skimage import transform\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.optimizers import Adam\n",
    "import os\n",
    "from keras.layers import *\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Reshape\n",
    "from keras.layers.core import Activation\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.convolutional import UpSampling2D\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
    "from keras.layers.core import Flatten\n",
    "from keras.optimizers import SGD\n",
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from PIL import Image\n",
    "import argparse\n",
    "import math\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"http://bamos.github.io/data/2016-08-09/gen-architecture.png\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "Image(url= \"http://bamos.github.io/data/2016-08-09/gen-architecture.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def generator_model():\n",
    "    model = Sequential([\n",
    "        Dense(1024*4*4, input_dim=100, activation=LeakyReLU(0.2)),\n",
    "        BatchNormalization(),\n",
    "        Reshape((4,4,1024)),\n",
    "        UpSampling2D(),\n",
    "        Conv2D(512, (5, 5), padding='same', activation=LeakyReLU(0.2)),\n",
    "        BatchNormalization(),\n",
    "        UpSampling2D(),\n",
    "        Conv2D(256,(5, 5), padding='same', activation='tanh'),\n",
    "        BatchNormalization(),\n",
    "        UpSampling2D(),\n",
    "        Conv2D(128, (5, 5), padding='same', activation='tanh'),\n",
    "        BatchNormalization(),\n",
    "        UpSampling2D(),\n",
    "        Conv2D(3,(5, 5), padding='same', activation='tanh')\n",
    "    ])\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_4 (Dense)              (None, 16384)             1654784   \n",
      "_________________________________________________________________\n",
      "batch_normalization_10 (Batc (None, 16384)             65536     \n",
      "_________________________________________________________________\n",
      "reshape_4 (Reshape)          (None, 4, 4, 1024)        0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_10 (UpSampling (None, 8, 8, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 8, 8, 512)         13107712  \n",
      "_________________________________________________________________\n",
      "batch_normalization_11 (Batc (None, 8, 8, 512)         2048      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_11 (UpSampling (None, 16, 16, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 16, 16, 256)       3277056   \n",
      "_________________________________________________________________\n",
      "batch_normalization_12 (Batc (None, 16, 16, 256)       1024      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_12 (UpSampling (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 32, 32, 128)       819328    \n",
      "_________________________________________________________________\n",
      "batch_normalization_13 (Batc (None, 32, 32, 128)       512       \n",
      "_________________________________________________________________\n",
      "up_sampling2d_13 (UpSampling (None, 64, 64, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 64, 64, 3)         9603      \n",
      "=================================================================\n",
      "Total params: 18,937,603.0\n",
      "Trainable params: 18,903,043.0\n",
      "Non-trainable params: 34,560.0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "m1=generator_model().summary()\n",
    "print(m1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"http://bamos.github.io/data/2016-08-09/discrim-architecture.png\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"http://bamos.github.io/data/2016-08-09/discrim-architecture.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def discriminator_model():\n",
    "    model = Sequential([\n",
    "        Conv2D(64, (5, 5), input_shape=(64,64,3), padding='same', activation=LeakyReLU(0.2)),\n",
    "        MaxPool2D(pool_size=(2,2)),\n",
    "        Dropout(0.3),\n",
    "        Convolution2D(128, (5, 5), padding='same', activation=LeakyReLU(0.2)),\n",
    "        MaxPool2D(pool_size=(2,2)),\n",
    "        Dropout(0.3),\n",
    "        Convolution2D(256, (5, 5), padding='same', activation=LeakyReLU(0.2)),\n",
    "        MaxPool2D(pool_size=(2,2)),\n",
    "        Dropout(0.3),\n",
    "        Convolution2D(512, (5, 5), padding='same', activation=LeakyReLU(0.2)),\n",
    "        MaxPool2D(pool_size=(2,2)),\n",
    "        Dropout(0.3),\n",
    "        Flatten(),\n",
    "        Dense(1, activation='sigmoid')\n",
    "    ])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_17 (Conv2D)           (None, 64, 64, 64)        4864      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_18 (Conv2D)           (None, 32, 32, 128)       204928    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_19 (Conv2D)           (None, 16, 16, 256)       819456    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_20 (Conv2D)           (None, 8, 8, 512)         3277312   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 8193      \n",
      "=================================================================\n",
      "Total params: 4,314,753.0\n",
      "Trainable params: 4,314,753.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(discriminator_model().summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generator_containing_discriminator(g, d):\n",
    "    model = Sequential()\n",
    "    model.add(g)\n",
    "    d.trainable = False\n",
    "    model.add(d)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shubham/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "ename": "IOError",
     "evalue": "cannot identify image file <open file '/home/shubham/DCGAN/img_align_celeba_64/Legends of Tomorrow 3.desktop', mode 'rb' at 0x7fb17d856f60>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-a7cfae164483>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/home/shubham/DCGAN/img_align_celeba_64/'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mx_complete\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_complete\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/shubham/anaconda2/lib/python2.7/site-packages/skimage/io/_io.pyc\u001b[0m in \u001b[0;36mimread\u001b[0;34m(fname, as_grey, plugin, flatten, **plugin_args)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mfile_or_url_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_plugin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'imread'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplugin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplugin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplugin_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ndim'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/shubham/anaconda2/lib/python2.7/site-packages/skimage/io/manage_plugins.pyc\u001b[0m in \u001b[0;36mcall_plugin\u001b[0;34m(kind, *args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                                (plugin, kind))\n\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/shubham/anaconda2/lib/python2.7/site-packages/skimage/io/_plugins/pil_plugin.pyc\u001b[0m in \u001b[0;36mimread\u001b[0;34m(fname, dtype, img_num, **kwargs)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m             \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mpil_to_ndarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_num\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimg_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/shubham/anaconda2/lib/python2.7/site-packages/PIL/Image.pyc\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2318\u001b[0m     raise IOError(\"cannot identify image file %r\"\n\u001b[0;32m-> 2319\u001b[0;31m                   % (filename if filename else fp))\n\u001b[0m\u001b[1;32m   2320\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2321\u001b[0m \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: cannot identify image file <open file '/home/shubham/DCGAN/img_align_celeba_64/Legends of Tomorrow 3.desktop', mode 'rb' at 0x7fb17d856f60>"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.cross_validation import train_test_split\n",
    "imgs=os.listdir('/home/shubham/DCGAN/img_align_celeba_64')\n",
    "l=[]\n",
    "for j,i in enumerate(imgs):\n",
    "    l.append(io.imread('/home/shubham/DCGAN/img_align_celeba_64/'+i))\n",
    "x_complete=np.array(l)\n",
    "plt.imshow(x_complete[0])\n",
    "#(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa970678110>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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53MzSKvjYNd2M1+7e8B9Jl2jsHf+XJf1tET2U6etkSU9mP88U3ZukOzX2NHBYY++NXCXp\nw5K6Jb0o6SFJ7U3U23clPSVpt8aC1llQb8s19pR+t6Rd2c8lRR+7RF+FHDc+4QcExRt+QFCEHwiK\n8ANBEX4gKMIPBEX4gaAIPxAU4QeC+n8DZI6NXofNrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa9a8655050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train=X_train.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "<type 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(type(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train=(X_train-127.5)/127.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa978a653d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa97065bb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  -1.         -1.        ]\n",
      " [-1.         -1.         -1.         -1.         -1.         -1.         -1.\n",
      "  -1.         -1.         -1.         -1.         -1.         -1.         -1.\n",
      "  -1.         -1.         -1.         -1.         -1.         -1.         -1.\n",
      "  -1.         -1.         -1.         -1.         -1.         -1.         -1.        ]\n",
      " [-1.         -1.         -1.         -1.         -1.         -1.         -1.\n",
      "  -1.         -1.         -1.         -1.         -1.         -1.         -1.\n",
      "  -1.         -1.         -1.         -1.         -1.         -1.         -1.\n",
      "  -1.         -1.         -1.         -1.         -1.         -1.         -1.        ]\n",
      " [-1.         -1.         -1.         -1.         -1.         -1.         -1.\n",
      "  -1.         -1.         -1.         -1.         -1.         -1.         -1.\n",
      "  -1.         -1.         -1.         -1.         -1.         -1.         -1.\n",
      "  -1.         -1.         -1.         -1.         -1.         -1.         -1.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = X_train[:, :, :, None]\n",
    "X_test = X_test[:, :, :, None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def combine_images(generated_images):\n",
    "    num = generated_images.shape[0]\n",
    "    width = int(math.sqrt(num))\n",
    "    height = int(math.ceil(float(num)/width))\n",
    "    shape = generated_images.shape[1:3]\n",
    "    image = np.zeros((height*shape[0], width*shape[1]),\n",
    "                     dtype=generated_images.dtype)\n",
    "    for index, img in enumerate(generated_images):\n",
    "        i = int(index/width)\n",
    "        j = index % width\n",
    "        image[i*shape[0]:(i+1)*shape[0], j*shape[1]:(j+1)*shape[1]] = \\\n",
    "            img[:, :, 0]\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FqKJ7Fzw3fzraa6cQbaLYRJQXsMheEoq/Q28TdQBwsH0T4iy0yeUvHCpMMip5zPGtQyw4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\nQ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P87Od2ERj/RPZJ61ogbbWoT7U65V7HwurSPoMb4a1DdUG0MHADuHGiDI51+s5IY2kN+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa978a14d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img=combine_images(X_train[0:1000])\n",
    "plt.imshow(img)\n",
    "from PIL import Image\n",
    "Image.fromarray(img.astype(np.uint8)).save(\n",
    "                   \"sample\"+\".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1]*12 + [0]*9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mask=np.ones((64,64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mask[24:40,24:40]=0.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(mask)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def custom_loss(y_true,y_pred):\n",
    "    loss=0.\n",
    "    res1=K.variable(value=y_true)\n",
    "    res2=K.variable(value=y_pred)\n",
    "    for i in range(int(y_true.shape[0])):\n",
    "        res1=np.multiply(mask,y_true[i,:,:,0])\n",
    "        res2=np.multiply(mask,y_pred[i,:,:,0])\n",
    "        los=abs(res1-res2)\n",
    "        loss+=sum(sum(abs(los)))\n",
    "    return 0.1*loss\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def train(BATCH_SIZE):\n",
    "    d = discriminator_model()\n",
    "    g= generator_model()\n",
    "    d_on_g = generator_containing_discriminator(g, d)\n",
    "    d_optim = SGD(lr=0.0005, momentum=0.9, nesterov=True)\n",
    "    g_optim = SGD(lr=0.0005, momentum=0.9, nesterov=True)\n",
    "    g.compile(loss= 'hinge_loss', optimizer=\"adam\")\n",
    "    d_on_g.compile(loss='binary_crossentropy', optimizer='adam')\n",
    "    d.trainable = True\n",
    "    d.compile(loss='binary_crossentropy', optimizer='adam')\n",
    "    for epoch in range(10):\n",
    "        print(\"Epoch is\", epoch)\n",
    "        print(\"Number of batches\", int(X_train.shape[0]/BATCH_SIZE))\n",
    "        for index in range(int(X_train.shape[0]/BATCH_SIZE)):\n",
    "            noise = np.random.uniform(-1, 1, size=(BATCH_SIZE, 100))\n",
    "            image_batch = X_train[index*BATCH_SIZE:(index+1)*BATCH_SIZE]\n",
    "            generated_images = g.predict(noise, verbose=0)\n",
    "            if index % 20 == 0:\n",
    "                image = combine_images(generated_images)\n",
    "                image = image*127.5+127.5\n",
    "                Image.fromarray(image.astype(np.uint8)).save(\n",
    "                    str(epoch)+\"_\"+str(index)+\".png\")\n",
    "            X = np.concatenate((image_batch, generated_images))\n",
    "            y = [1] * BATCH_SIZE + [0] * BATCH_SIZE\n",
    "            d_loss = d.train_on_batch(X, y)\n",
    "            print(\"batch %d d_loss : %f\" % (index, d_loss))\n",
    "            noise = np.random.uniform(-1, 1, (BATCH_SIZE, 100))\n",
    "            d.trainable = False\n",
    "            g_loss = d_on_g.train_on_batch(noise, [1] * BATCH_SIZE)\n",
    "            g1_loss= g.train_on_batch(noise,generated_images) #for mG(z)-m(Y)\n",
    "            print(\"batch %d g1_loss : %f\" % (index, g1_loss))\n",
    "            d.trainable = True\n",
    "            print(\"batch %d g_loss : %f\" % (index, g_loss))\n",
    "            if index % 10 == 9:\n",
    "                g.save_weights('generator', True)\n",
    "                d.save_weights('discriminator', True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Epoch is', 0)\n",
      "('Number of batches', 468)\n",
      "batch 0 d_loss : 0.730649\n",
      "batch 0 g1_loss : 0.240129\n",
      "batch 0 g_loss : 0.776239\n",
      "batch 1 d_loss : 0.723530\n",
      "batch 1 g1_loss : 0.232085\n",
      "batch 1 g_loss : 0.767185\n",
      "batch 2 d_loss : 0.708389\n",
      "batch 2 g1_loss : 0.230493\n",
      "batch 2 g_loss : 0.753768\n",
      "batch 3 d_loss : 0.690191\n",
      "batch 3 g1_loss : 0.226204\n",
      "batch 3 g_loss : 0.742533\n",
      "batch 4 d_loss : 0.671703\n",
      "batch 4 g1_loss : 0.222564\n",
      "batch 4 g_loss : 0.736073\n",
      "batch 5 d_loss : 0.657287\n",
      "batch 5 g1_loss : 0.219599\n",
      "batch 5 g_loss : 0.721227\n",
      "batch 6 d_loss : 0.636199\n",
      "batch 6 g1_loss : 0.220284\n",
      "batch 6 g_loss : 0.708528\n",
      "batch 7 d_loss : 0.611801\n",
      "batch 7 g1_loss : 0.214050\n",
      "batch 7 g_loss : 0.697963\n",
      "batch 8 d_loss : 0.591273\n",
      "batch 8 g1_loss : 0.213718\n",
      "batch 8 g_loss : 0.682710\n",
      "batch 9 d_loss : 0.584175\n",
      "batch 9 g1_loss : 0.207315\n",
      "batch 9 g_loss : 0.671675\n",
      "batch 10 d_loss : 0.569356\n",
      "batch 10 g1_loss : 0.208265\n",
      "batch 10 g_loss : 0.659580\n",
      "batch 11 d_loss : 0.543587\n",
      "batch 11 g1_loss : 0.203303\n",
      "batch 11 g_loss : 0.649322\n",
      "batch 12 d_loss : 0.531239\n",
      "batch 12 g1_loss : 0.199812\n",
      "batch 12 g_loss : 0.639255\n",
      "batch 13 d_loss : 0.515156\n",
      "batch 13 g1_loss : 0.200598\n",
      "batch 13 g_loss : 0.630324\n",
      "batch 14 d_loss : 0.503583\n",
      "batch 14 g1_loss : 0.197611\n",
      "batch 14 g_loss : 0.625343\n",
      "batch 15 d_loss : 0.503055\n",
      "batch 15 g1_loss : 0.193182\n",
      "batch 15 g_loss : 0.614032\n",
      "batch 16 d_loss : 0.488347\n",
      "batch 16 g1_loss : 0.191720\n",
      "batch 16 g_loss : 0.603249\n",
      "batch 17 d_loss : 0.477046\n",
      "batch 17 g1_loss : 0.189975\n",
      "batch 17 g_loss : 0.598188\n",
      "batch 18 d_loss : 0.471756\n",
      "batch 18 g1_loss : 0.187789\n",
      "batch 18 g_loss : 0.593104\n",
      "batch 19 d_loss : 0.464956\n",
      "batch 19 g1_loss : 0.185278\n",
      "batch 19 g_loss : 0.588745\n",
      "batch 20 d_loss : 0.464550\n",
      "batch 20 g1_loss : 0.183888\n",
      "batch 20 g_loss : 0.580203\n",
      "batch 21 d_loss : 0.457614\n",
      "batch 21 g1_loss : 0.179074\n",
      "batch 21 g_loss : 0.574774\n",
      "batch 22 d_loss : 0.454221\n",
      "batch 22 g1_loss : 0.176968\n",
      "batch 22 g_loss : 0.570276\n",
      "batch 23 d_loss : 0.447448\n",
      "batch 23 g1_loss : 0.179687\n",
      "batch 23 g_loss : 0.567088\n",
      "batch 24 d_loss : 0.445485\n",
      "batch 24 g1_loss : 0.179552\n",
      "batch 24 g_loss : 0.562463\n",
      "batch 25 d_loss : 0.444041\n",
      "batch 25 g1_loss : 0.173329\n",
      "batch 25 g_loss : 0.564656\n",
      "batch 26 d_loss : 0.444435\n",
      "batch 26 g1_loss : 0.173544\n",
      "batch 26 g_loss : 0.560328\n",
      "batch 27 d_loss : 0.443929\n",
      "batch 27 g1_loss : 0.168360\n",
      "batch 27 g_loss : 0.560249\n",
      "batch 28 d_loss : 0.449639\n",
      "batch 28 g1_loss : 0.169645\n",
      "batch 28 g_loss : 0.556881\n",
      "batch 29 d_loss : 0.445967\n",
      "batch 29 g1_loss : 0.167624\n",
      "batch 29 g_loss : 0.555634\n",
      "batch 30 d_loss : 0.439992\n",
      "batch 30 g1_loss : 0.165866\n",
      "batch 30 g_loss : 0.555305\n",
      "batch 31 d_loss : 0.445335\n",
      "batch 31 g1_loss : 0.164276\n",
      "batch 31 g_loss : 0.555610\n",
      "batch 32 d_loss : 0.443979\n",
      "batch 32 g1_loss : 0.162413\n",
      "batch 32 g_loss : 0.556024\n",
      "batch 33 d_loss : 0.446424\n",
      "batch 33 g1_loss : 0.159884\n",
      "batch 33 g_loss : 0.552338\n",
      "batch 34 d_loss : 0.443920\n",
      "batch 34 g1_loss : 0.158421\n",
      "batch 34 g_loss : 0.556920\n",
      "batch 35 d_loss : 0.449869\n",
      "batch 35 g1_loss : 0.158355\n",
      "batch 35 g_loss : 0.559480\n",
      "batch 36 d_loss : 0.455320\n",
      "batch 36 g1_loss : 0.158010\n",
      "batch 36 g_loss : 0.556821\n",
      "batch 37 d_loss : 0.455556\n",
      "batch 37 g1_loss : 0.153133\n",
      "batch 37 g_loss : 0.557459\n",
      "batch 38 d_loss : 0.452769\n",
      "batch 38 g1_loss : 0.156529\n",
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      "batch 39 d_loss : 0.452524\n",
      "batch 39 g1_loss : 0.151621\n",
      "batch 39 g_loss : 0.560421\n",
      "batch 40 d_loss : 0.454453\n",
      "batch 40 g1_loss : 0.149836\n",
      "batch 40 g_loss : 0.563149\n",
      "batch 41 d_loss : 0.458241\n",
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      "batch 42 d_loss : 0.461766\n",
      "batch 42 g1_loss : 0.149597\n",
      "batch 42 g_loss : 0.566238\n",
      "batch 43 d_loss : 0.460224\n",
      "batch 43 g1_loss : 0.144972\n",
      "batch 43 g_loss : 0.569618\n",
      "batch 44 d_loss : 0.459191\n",
      "batch 44 g1_loss : 0.144047\n",
      "batch 44 g_loss : 0.570938\n",
      "batch 45 d_loss : 0.454170\n",
      "batch 45 g1_loss : 0.144836\n",
      "batch 45 g_loss : 0.571259\n",
      "batch 46 d_loss : 0.465805\n",
      "batch 46 g1_loss : 0.145162\n",
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      "batch 47 d_loss : 0.476007\n",
      "batch 47 g1_loss : 0.141023\n",
      "batch 47 g_loss : 0.579266\n",
      "batch 48 d_loss : 0.467697\n",
      "batch 48 g1_loss : 0.138963\n",
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      "batch 49 g_loss : 0.587911\n",
      "batch 50 d_loss : 0.464410\n",
      "batch 50 g1_loss : 0.138146\n",
      "batch 50 g_loss : 0.590446\n",
      "batch 51 d_loss : 0.468914\n",
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      "batch 51 g_loss : 0.597009\n",
      "batch 52 d_loss : 0.463217\n",
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      "batch 53 d_loss : 0.464329\n",
      "batch 53 g1_loss : 0.132452\n",
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-80-b12e920899c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-79-b8003345f442>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(BATCH_SIZE)\u001b[0m\n\u001b[1;32m     28\u001b[0m             \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m             \u001b[0mg_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md_on_g\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoise\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m             \u001b[0mg1_loss\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoise\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgenerated_images\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#for mG(z)-m(Y)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m             \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"batch %d g1_loss : %f\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg1_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m             \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/shubham/anaconda2/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, class_weight, sample_weight)\u001b[0m\n\u001b[1;32m    931\u001b[0m         return self.model.train_on_batch(x, y,\n\u001b[1;32m    932\u001b[0m                                          \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 933\u001b[0;31m                                          class_weight=class_weight)\n\u001b[0m\u001b[1;32m    934\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m     def test_on_batch(self, x, y,\n",
      "\u001b[0;32m/home/shubham/anaconda2/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1618\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1619\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1620\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1621\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1622\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/shubham/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2071\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2072\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2073\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2074\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2075\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/shubham/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/shubham/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    980\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/shubham/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1030\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/home/shubham/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1037\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1040\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/shubham/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train(128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "kdf=np.arange(0,12)\n",
    "print(kdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0]\n",
      " [ 1]\n",
      " [ 2]\n",
      " [ 3]\n",
      " [ 4]\n",
      " [ 5]\n",
      " [ 6]\n",
      " [ 7]\n",
      " [ 8]\n",
      " [ 9]\n",
      " [10]\n",
      " [11]]\n"
     ]
    }
   ],
   "source": [
    "kdf.resize(12,1)\n",
    "print(kdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "cvb=np.zeros(10,)\n",
    "print(cvb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "y1=X_train[0]\n",
    "print(y1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  ..., \n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]]\n",
      "\n",
      " [[ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  ..., \n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]]\n",
      "\n",
      " [[ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  ..., \n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]]\n",
      "\n",
      " ..., \n",
      " [[ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  ..., \n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]]\n",
      "\n",
      " [[ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  ..., \n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]]\n",
      "\n",
      " [[ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  ..., \n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]\n",
      "  [ 0.  0.  0. ...,  0.  0.  0.]]]\n"
     ]
    }
   ],
   "source": [
    "cv2=np.zeros((10,) + X_train.shape[1:3], dtype=np.float32)\n",
    "print(cv2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(cv2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate(BATCH_SIZE, nice=False):\n",
    "    g = generator_model()\n",
    "    g.compile(loss='hinge_loss', optimizer=\"adam\")\n",
    "    g.load_weights('generator')\n",
    "    if nice:\n",
    "        d = discriminator_model()\n",
    "        d.compile(loss='binary_crossentropy', optimizer=\"adam\")\n",
    "        d.load_weights('discriminator')\n",
    "        noise = np.random.uniform(-1, 1, (BATCH_SIZE*20, 100))\n",
    "        generated_images = g.predict(noise, verbose=1)\n",
    "        d_pret = d.predict(generated_images, verbose=1)\n",
    "        index = np.arange(0, BATCH_SIZE*20)\n",
    "        index.resize((BATCH_SIZE*20, 1))\n",
    "        pre_with_index = list(np.append(d_pret, index, axis=1))\n",
    "        pre_with_index.sort(key=lambda x: x[0], reverse=True)\n",
    "        nice_images = np.zeros((BATCH_SIZE,) + generated_images.shape[1:3], dtype=np.float32)\n",
    "        nice_images = nice_images[:, :, :, None]\n",
    "        for i in range(BATCH_SIZE):\n",
    "            idx = int(pre_with_index[i][1])\n",
    "            nice_images[i, :, :, 0] = generated_images[idx, :, :, 0]\n",
    "        image = combine_images(nice_images)\n",
    "    else:\n",
    "        noise = np.random.uniform(-1, 1, (BATCH_SIZE, 100))\n",
    "        generated_images = g.predict(noise, verbose=1)\n",
    "        image = combine_images(generated_images)\n",
    "    image = image*127.5+127.5\n",
    "    Image.fromarray(image.astype(np.uint8)).save(\n",
    "        \"generated_image.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128/128 [==============================] - 0s     \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
     ]
    }
   ],
   "source": [
    "generate(128,nice=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate(BATCH_SIZE, nice=False):\n",
    "    g = generator_model()\n",
    "    g.compile(loss='hinge_loss', optimizer=\"adam\")\n",
    "    g.load_weights('generator')\n",
    "    if nice:\n",
    "        d = discriminator_model()\n",
    "        d.compile(loss='binary_crossentropy', optimizer=\"adam\")\n",
    "        d.load_weights('discriminator')\n",
    "        noise = np.random.uniform(-1, 1, (BATCH_SIZE*20, 100))\n",
    "        generated_images = g.predict(noise, verbose=1)\n",
    "        d_pret = d.predict(generated_images, verbose=1)\n",
    "        index = np.arange(0, BATCH_SIZE*20)\n",
    "        index.resize((BATCH_SIZE*20, 1))\n",
    "        pre_with_index = list(np.append(d_pret, index, axis=1))\n",
    "        pre_with_index.sort(key=lambda x: x[0], reverse=True)\n",
    "        nice_images = np.zeros((BATCH_SIZE,) + generated_images.shape[1:3], dtype=np.float32)\n",
    "        nice_images = nice_images[:, :, :, None]\n",
    "        for i in range(BATCH_SIZE):\n",
    "            idx = int(pre_with_index[i][1])\n",
    "            nice_images[i, :, :, 0] = generated_images[idx, :, :, 0]\n",
    "        image = combine_images(nice_images)\n",
    "    else:\n",
    "        noise = np.random.uniform(-1, 1, (BATCH_SIZE, 100))\n",
    "        generated_images = g.predict(noise, verbose=1)\n",
    "        image = combine_images(generated_images)\n",
    "    image = image*127.5+127.5\n",
    "    Image.fromarray(image.astype(np.uint8)).save(\n",
    "        \"nice_image.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 19s     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      "2560/2560 [==============================] - 6s     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     ]
    }
   ],
   "source": [
    "generate(128,nice=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "#masking and completion portion starts \n",
    "exmpl=X_train[0,:,:,0]\n",
    "print(exmpl.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa977ebb4d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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53MzSKvjYNd2M1+7e8B9Jl2jsHf+XJf1tET2U6etkSU9mP88U3ZukOzX2NHBYY++NXCXp\nw5K6Jb0o6SFJ7U3U23clPSVpt8aC1llQb8s19pR+t6Rd2c8lRR+7RF+FHDc+4QcExRt+QFCEHwiK\n8ANBEX4gKMIPBEX4gaAIPxAU4QeC+n8DZI6NXofNrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa97811f650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(exmpl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa97861c610>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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53MzSKvjYNd2M1+7e8B9Jl2jsHf+XJf1tET2U6etkSU9mP88U3ZukOzX2NHBYY++NXCXp\nw5K6Jb0o6SFJ7U3U23clPSVpt8aC1llQb8s19pR+t6Rd2c8lRR+7RF+FHDc+4QcExRt+QFCEHwiK\n8ANBEX4gKMIPBEX4gaAIPxAU4QeC+n8DZI6NXofNrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa977e275d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exmpl=(exmpl*127.5)+127.5\n",
    "plt.imshow(exmpl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "exmpl2=X_train[0,:,:,0]\n",
    "mask=np.ones((28,28))\n",
    "print(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5 12]\n",
      " [21 32]]\n"
     ]
    }
   ],
   "source": [
    "a=np.array([[1,2],[3,4]])\n",
    "b=np.array([[5,6],[7,8]])\n",
    "print(np.multiply(a,b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "mask[10:17,10:17]=0.0\n",
    "print(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa978431950>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa978604310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sampl3=X_train[0,:,:,0]\n",
    "plt.imshow(np.multiply(mask,sampl3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s\n"
     ]
    }
   ],
   "source": [
    "g1 = generator_model()\n",
    "g1.compile(loss='binary_crossentropy', optimizer=\"SGD\")\n",
    "g1.load_weights('generator')\n",
    "noise1 = np.random.uniform(-1, 1, (1, 100))\n",
    "gen_image = g1.predict(noise1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "mask2=np.zeros((64,64))\n",
    "print(mask2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.  1.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.  1.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.  1.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.  1.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.  1.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.  1.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.  1.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "   0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "mask2[24:40,24:40]=1.0\n",
    "print(mask2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "res1=np.multiply(mask,sampl3)\n",
    "print(res1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "print(gen_image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "gen_image.reshape(28,28)\n",
    "print(gen_image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "gen_image2=gen_image[0,:,:,0]\n",
    "print(gen_image2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa977c47050>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/i5L+uogeyvR1gaQnsp8DRfcm6S5Nvgwc0+S5kRskvVtSr6TnJT0sqaOFevuGpCcl7ddk\n0LoK6m21Jl/S75e0L/u5uujnLtFXIc8bV/gBQXHCDwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxA\nUP8PqrpmnvYA2loAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa97060e410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res2=np.multiply(mask2,gen_image2)\n",
    "res=np.add(res1,res2)\n",
    "plt.imshow(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}