Assigment-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing of Cifar-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Preprocessing:\n",
    "    img_size = 32\n",
    "    channels = 3\n",
    "    num_classes = 10\n",
    "    num_files = 5\n",
    "    img_per_file = 10000\n",
    "    _num_train = num_files * img_per_file\n",
    "    \n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def _unpickle(self,file):\n",
    "        with open(file, 'rb') as fo:\n",
    "            dict = pickle.load(fo, encoding='bytes')\n",
    "        return dict\n",
    "\n",
    "    # Load a data from file\n",
    "    def _load(self,file):\n",
    "        data = self._unpickle(file)\n",
    "        raw = data[b'data']\n",
    "        label = np.array(data[b'labels'])\n",
    "        data_norm = np.array(raw, dtype=float) / 255.0\n",
    "        data_reshape = data_norm.reshape([-1, self.channels, self.img_size, self.img_size])\n",
    "        img = data_reshape.transpose([0, 2, 3, 1])\n",
    "        return img, label\n",
    "    \n",
    "    def load_train_data(self):\n",
    "        train_X = np.zeros([self._num_train, self.img_size, self.img_size, self.channels], dtype=float)\n",
    "        train_Y = np.zeros([self._num_train], dtype=int)\n",
    "        temp = 0\n",
    "        for i in range(self.num_files):\n",
    "            img, label = self._load(\"data_batch_\" + str(i + 1))\n",
    "            train_X[temp:temp+img.shape[0], :] = img\n",
    "            train_Y[temp:temp+img.shape[0]] = label\n",
    "            temp += img.shape[0]\n",
    "        return train_X,train_Y,np.eye(self.num_classes, dtype=float)[train_Y]\n",
    "\n",
    "    def load_test_data(self):\n",
    "        img, label = self._load(\"test_batch\")\n",
    "        return img,label,np.eye(self.num_classes, dtype=float)[label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p = Preprocessing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a,b,c = p.load_train_data()\n",
    "d,e,f = p.load_test_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3) (50000,) (50000, 10) (10000, 32, 32, 3) (10000,) (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(a.shape,b.shape,c.shape,d.shape,e.shape,f.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pickle.dump(a,open(\"train.pkl\",'wb'))\n",
    "# pickle.dump(b,open(\"train_label.pkl\",'wb'))\n",
    "# pickle.dump(c,open(\"train_label_1_hot.pkl\",'wb'))\n",
    "# pickle.dump(d,open(\"test.pkl\",'wb'))\n",
    "# pickle.dump(e,open(\"test_label.pkl\",'wb'))\n",
    "# pickle.dump(f,open(\"test_label_1_hot.pkl\",'wb'))\n",
    "\n",
    "train = pickle.load(open(\"train1.pkl\",'rb'))\n",
    "train_label = pickle.load(open(\"train1_label.pkl\",'rb'))\n",
    "train_label_1 = pickle.load(open(\"train1_label_1_hot.pkl\",'rb'))\n",
    "val = pickle.load(open(\"val.pkl\",'rb'))\n",
    "val_label = pickle.load(open(\"val_label.pkl\",'rb'))\n",
    "val_label_1 = pickle.load(open(\"val_label_1_hot.pkl\",'rb'))\n",
    "test = pickle.load(open(\"test.pkl\",'rb'))\n",
    "test_label = pickle.load(open(\"test_label.pkl\",'rb'))\n",
    "test_label_1 = pickle.load(open(\"test_label_1_hot.pkl\",'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting Train set in Train/Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "idx_train = np.random.choice(train.shape[0],45000,replace=False)\n",
    "idx_val = np.delete(np.arange(50000),idx_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "val = train[idx_val]\n",
    "val_label = train_label[idx_val]\n",
    "val_label_1 = train_label_1[idx_val]\n",
    "train = train[idx_train]\n",
    "train_label = train_label[idx_train]\n",
    "train_label_1 = train_label_1[idx_train]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pickle.dump(train,open(\"train1.pkl\",'wb'))\n",
    "pickle.dump(train_label,open(\"train1_label.pkl\",'wb'))\n",
    "pickle.dump(train_label_1,open(\"train1_label_1_hot.pkl\",'wb'))\n",
    "pickle.dump(val,open(\"val.pkl\",'wb'))\n",
    "pickle.dump(val_label,open(\"val_label.pkl\",'wb'))\n",
    "pickle.dump(val_label_1,open(\"val_label_1_hot.pkl\",'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Designing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img_size = 32\n",
    "channels = 3\n",
    "num_classes = 10\n",
    "img_flat = img_size * img_size * channels\n",
    "num_filter = 3\n",
    "conv1_filter = 64\n",
    "conv2_filter = 128\n",
    "conv3_filter = 256\n",
    "conv4_filter = 256\n",
    "fc1_filter = 1024\n",
    "fc2_filter = 1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def var_weight(shape):\n",
    "#     return tf.get_variable(shape=shape,initializer=tf.contrib.layers.xavier_initializer())\n",
    "     return tf.Variable(tf.truncated_normal(shape, stddev=0.1))\n",
    "\n",
    "def var_bias(shape):\n",
    "    return tf.Variable(tf.constant(0.05, shape=[shape]))\n",
    "\n",
    "def max_pool(x):\n",
    "#     x = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]], \"CONSTANT\")\n",
    "    return tf.nn.max_pool(x, [1,2,2,1], [1,2,2,1], padding='VALID')\n",
    "\n",
    "def new_conv_layer(x,infilter,channels,outfilters):\n",
    "    pad_x = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]], \"CONSTANT\")\n",
    "    w = var_weight([infilter, infilter, channels, outfilters])\n",
    "    layer = tf.nn.conv2d(pad_x,w,strides=[1, 1, 1, 1],padding='VALID')\n",
    "    layer = tf.nn.relu(layer + var_bias(outfilters))\n",
    "    return layer, w\n",
    "\n",
    "def flat(x):\n",
    "    features = x.get_shape()[1:4].num_elements()\n",
    "    return tf.reshape(x, [-1, features]),features\n",
    "\n",
    "def new_nn_layer(x,num_in,num_out):\n",
    "    return tf.nn.relu(tf.matmul(x, var_weight([num_in, num_out])) + var_bias(num_out))\n",
    "\n",
    "def new_batch_norm(x,size=10,epsilon=1e-3):\n",
    "    batch_mean, batch_var = tf.nn.moments(x,[0])\n",
    "    scale = tf.Variable(tf.ones([size]))\n",
    "    beta = tf.Variable(tf.zeros([size]))\n",
    "    BN = tf.nn.batch_normalization(x,batch_mean,batch_var,beta,scale,epsilon)\n",
    "    return(tf.nn.sigmoid(BN))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, shape=[None, img_size, img_size, channels], name='x')\n",
    "y = tf.placeholder(tf.float32, shape=[None, num_classes])\n",
    "y_cls = tf.argmax(y, dimension=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Relu_12:0\", shape=(?, 32, 32, 64), dtype=float32)\n",
      "Tensor(\"MaxPool_6:0\", shape=(?, 16, 16, 64), dtype=float32)\n",
      "Tensor(\"Relu_13:0\", shape=(?, 16, 16, 128), dtype=float32)\n",
      "Tensor(\"MaxPool_7:0\", shape=(?, 8, 8, 128), dtype=float32)\n",
      "Tensor(\"Relu_14:0\", shape=(?, 8, 8, 256), dtype=float32)\n",
      "Tensor(\"Relu_15:0\", shape=(?, 8, 8, 256), dtype=float32)\n",
      "Tensor(\"MaxPool_8:0\", shape=(?, 4, 4, 256), dtype=float32)\n",
      "Tensor(\"Reshape_8:0\", shape=(?, 4096), dtype=float32) 4096\n",
      "Tensor(\"Relu_16:0\", shape=(?, 1024), dtype=float32)\n",
      "Tensor(\"Relu_17:0\", shape=(?, 1024), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "conv1,conv1_w = new_conv_layer(x,num_filter,channels,conv1_filter)\n",
    "print(conv1)\n",
    "max1 = max_pool(conv1)\n",
    "print(max1)\n",
    "conv2,conv2_w = new_conv_layer(max1,num_filter,conv1_filter,conv2_filter)\n",
    "print(conv2)\n",
    "max2 = max_pool(conv2)\n",
    "print(max2)\n",
    "# drp1 = tf.nn.dropout(max2, 0.5)\n",
    "# conv3,conv3_w = new_conv_layer(drp1,num_filter,conv2_filter,conv3_filter)\n",
    "conv3,conv3_w = new_conv_layer(max2,num_filter,conv2_filter,conv3_filter)\n",
    "print(conv3)\n",
    "conv4,conv4_w = new_conv_layer(conv3,num_filter,conv3_filter,conv4_filter)\n",
    "print(conv4)\n",
    "max3 = max_pool(conv4)\n",
    "print(max3)\n",
    "# drp2 = tf.nn.dropout(max3, 0.5)\n",
    "# flat1,features = flat(drp2)\n",
    "flat1,features = flat(max3)\n",
    "print(flat1,features)\n",
    "nn1 = new_nn_layer(flat1,features,fc1_filter)\n",
    "print(nn1)\n",
    "nn2 = new_nn_layer(nn1,fc2_filter,fc2_filter)\n",
    "print(nn2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "pred = new_batch_norm(tf.matmul(nn2, var_weight([fc2_filter, num_classes])) + var_bias(num_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute_accuracy(pred,x_t, y_t):\n",
    "    predict = tf.nn.softmax(pred)\n",
    "    y_pre = sess.run(predict, feed_dict={x: x_t})\n",
    "    correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(y_t,1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    result = sess.run([accuracy,cost], feed_dict={x: x_t, y: y_t})\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=pred))\n",
    "opti = tf.train.AdamOptimizer(1e-4).minimize(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def form_batch(x,x_1,batch_size = 50):\n",
    "#     img_index = np.random.choice(x.shape[0],batch_size,replace=False)\n",
    "    img_index = np.random.choice(x.shape[0],batch_size)\n",
    "    return x[img_index, :, :, :], x_1[img_index, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "acc,epo,los = [],[],[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model1\n",
      "0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "batch_size = 52; no_epoch = 2\n",
    "with tf.Session() as sess:\n",
    "    saver = tf.train.Saver()\n",
    "#     sess.run(tf.global_variables_initializer())\n",
    "    saver.restore(sess,'./model')\n",
    "    for i in range(int(train.shape[0]/batch_size) * no_epoch):\n",
    "        x_batch,y_batch = form_batch(train,train_label_1,batch_size)\n",
    "        _,loss = sess.run([opti,cost], feed_dict={x: x_batch, y: y_batch})\n",
    "        if i % int(train.shape[0]/batch_size) == 0: \n",
    "            epk = int(i / int(train.shape[0]/batch_size))\n",
    "            print(epk)\n",
    "            epo.append(epk)\n",
    "            x_val,y_val = form_batch(val,val_label_1,1000)\n",
    "            training,validating = compute_accuracy(pred,x_batch,y_batch),compute_accuracy(pred,x_val,y_val)\n",
    "            acc.append([training[0],validating[0]])\n",
    "            los.append([training[1],validating[1]])\n",
    "            \n",
    "    saver.save(sess,'./model1')\n",
    "#     x_test,y_test = form_batch(test,test_label_1,1000)\n",
    "#     print(compute_accuracy(pred,x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       " [0.86538464, 0.77700001]]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "los"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l72XuqF/Zu0fZ+k8YP//sZuPo1i0uNYGCExbm0ki//rrLrr50qbva27/fzQ/R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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26bf0a41208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.subplot(2,1,1)\n",
    "y1 = np.array(acc)[:,0].tolist()\n",
    "y2 = np.array(acc)[:,1].tolist()\n",
    "plt.plot(epo, y1,\"r\")\n",
    "plt.plot(epo, y2,\"b\")\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Acc')\n",
    "plt.title('Accuracy Cuvre')\n",
    "plt.grid(True)\n",
    "plt.tight_layout(pad=0.5, w_pad=0.6, h_pad=1.5)\n",
    "plt.subplot(2,1,2)\n",
    "y1 = np.array(los)[:,0].tolist()\n",
    "y2 = np.array(los)[:,1].tolist()\n",
    "plt.plot(epo, y1,\"r\")\n",
    "plt.plot(epo, y2,\"b\")\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Loss Curve')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Acc/Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model1\n",
      "[0.77200001, 1.6724793]\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver = tf.train.Saver()\n",
    "    saver.restore(sess,'./model1')\n",
    "    x_test,y_test = form_batch(test,test_label_1,1000)\n",
    "    print(compute_accuracy(pred,x_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deconvolution + Unpooling (not only for 10 neurons but for whole neural layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ls_grp = test_label == 2\n",
    "coll_batch = test[ls_grp]\n",
    "coll_batch_1 = test_label_1[ls_grp]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x26bf2ec1a58>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26bf125b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imageToUse = coll_batch[5]\n",
    "plt.imshow(imageToUse, cmap=plt.cm.RdYlGn)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "active_neuron = np.zeros([conv4_filter,8,8])\n",
    "def getActivations():\n",
    "    with tf.Session() as sess:\n",
    "        saver = tf.train.Saver()\n",
    "        saver.restore(sess,'./model')\n",
    "        for i in range(coll_batch.shape[0]):\n",
    "            con = sess.run(conv4,feed_dict={x:np.reshape(coll_batch[i],[1,32,32,3])})\n",
    "#             plot_F(con)\n",
    "            max_neuron_activated(con)\n",
    "\n",
    "def max_neuron_activated(conv_layer):\n",
    "    global active_neuron\n",
    "    filters = conv_layer.shape[3]\n",
    "    for i in range(filters):\n",
    "        layer = np.array(conv_layer[0,:,:,i])\n",
    "        layer[layer > 0] = 1\n",
    "        active_neuron[i,:,:] += layer\n",
    "#     for i in range(filters):\n",
    "#         id_x,id_y = .nonzero()[0],conv_layer[0,:,:,i].nonzero()[1]\n",
    "#         for x,y in zip(id_x,id_y):\n",
    "            \n",
    "def plot_Filter(units):\n",
    "    filters = int(units.shape[3])\n",
    "    plt.figure(1, figsize=(50,50))\n",
    "    plt.ion() \n",
    "    n_columns = 8\n",
    "    n_rows = math.ceil(filters / n_columns) + 1\n",
    "    for i in range(filters):\n",
    "        plt.subplot(n_rows, n_columns, i+1)\n",
    "        plt.title('FeatureMap ' + str(i))\n",
    "        plt.imshow(units[0,:,:,i], cmap=\"Greys\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model\n"
     ]
    }
   ],
   "source": [
    "getActivations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26c0776c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# active_neuron[6]\n",
    "def plot_frq_kernel(active_neuron):\n",
    "    filters = active_neuron.shape[0]\n",
    "    plt.figure(1, figsize=(50,50))\n",
    "    plt.ion() \n",
    "    n_columns = 8\n",
    "    n_rows = math.ceil(filters / n_columns) + 1\n",
    "    for i in range(filters):\n",
    "        plt.subplot(n_rows, n_columns, i+1)\n",
    "        plt.title('FeatureMap_activeN' + str(i))\n",
    "        plt.imshow(active_neuron[i,:,:], cmap='Blues')\n",
    "plot_frq_kernel(active_neuron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 189"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 804.,  581.,  350.,  534.,  504.,  512.,  355.,   43.],\n",
       "       [ 912.,  797.,  793.,  897.,  867.,  788.,  932.,  233.],\n",
       "       [ 767.,  317.,  484.,  754.,  776.,  693.,  799.,  109.],\n",
       "       [ 783.,  432.,  586.,  850.,  842.,  776.,  864.,  246.],\n",
       "       [ 782.,  422.,  631.,  866.,  876.,  813.,  895.,  247.],\n",
       "       [ 754.,  286.,  453.,  741.,  763.,  632.,  769.,  122.],\n",
       "       [ 544.,  458.,  494.,  745.,  787.,  637.,  843.,  129.],\n",
       "       [ 513.,   21.,   45.,   99.,  126.,   91.,  261.,   12.]])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_neuron[189]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Layer is selected for analysising"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model\n",
      "[[ 3.74534869  2.25498462  1.39463902  3.12187505  0.89676183  1.062814\n",
      "   5.11055326  0.09742226]\n",
      " [ 3.77960181  3.58298612  0.          0.          2.77319813  2.68494248\n",
      "   2.20470166  1.58581197]\n",
      " [ 0.          1.00565946  1.92860723  2.65953684  2.74658895  5.05171394\n",
      "   1.1470238   4.30620766]\n",
      " [ 4.44793892  0.54043573  1.69294786  6.2926383   5.29842472  3.47396469\n",
      "   0.          2.47102475]\n",
      " [ 1.06563485  0.          0.60870683  5.03534126  2.15017319  4.26088762\n",
      "   0.59768397  5.95633411]\n",
      " [ 0.38608104  0.02224236  2.74477506  3.77792454  3.31253695  7.14899778\n",
      "   4.3875351   0.90992951]\n",
      " [ 2.0222075   0.          0.          6.139328    0.          6.01841307\n",
      "   0.          0.22308283]\n",
      " [ 0.94657356  0.          0.          2.10470057  1.04113829  3.88632178\n",
      "   0.48416167  1.95484209]]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26c0b916198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visual_conv4(i):\n",
    "    with tf.Session() as sess:\n",
    "        saver = tf.train.Saver()\n",
    "        saver.restore(sess,'./model')\n",
    "        con = sess.run(conv4,feed_dict={x:np.reshape(coll_batch[i],[1,32,32,3])})\n",
    "        plt.figure(1, figsize=(50,50))\n",
    "        n_columns = 8\n",
    "        n_rows = math.ceil(con.shape[3] / n_columns) + 1\n",
    "        plt.subplot(n_rows, n_columns, 2)\n",
    "        plt.title('FeatureMap ' + str(i))\n",
    "        plt.imshow(con[0,:,:,i], cmap='gray')\n",
    "        print(con[0,:,:,i])\n",
    "visual_conv4(197)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "deconv4 = tf.nn.conv2d_transpose(tf.reshape(conv4[0,:,:,189], [-1, 8,8,1]),tf.reshape(conv4_w[:,:,:,189],[3,3,256,1]),\n",
    "                                 output_shape=tf.stack([1, 8, 8, conv3_filter]),strides=[1, 1, 1, 1],padding='SAME')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### These are not filters but feature maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b085f5ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver = tf.train.Saver()\n",
    "    saver.restore(sess,'./model')\n",
    "    dconvx3,convx4,convx4_w = sess.run([deconv4,conv4,conv4_w],feed_dict={x:np.reshape(coll_batch[5],[1,32,32,3])})\n",
    "    plot_Filter(dconvx3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def unpooling2():\n",
    "    with tf.Session() as sess:\n",
    "        saver = tf.train.Saver()\n",
    "        saver.restore(sess,'./model')\n",
    "        current_conv,mimic_conv = sess.run([deconv3,conv2],feed_dict={x:np.reshape(coll_batch[5],[1,32,32,3])})\n",
    "        approx_conv = np.zeros(mimic_conv.shape)\n",
    "        for k in range(mimic_conv.shape[3]):\n",
    "            conv_filtr = mimic_conv[0,:,:,k]\n",
    "            cord,id_x,id_y = 0,0,0\n",
    "            for i in range(0,mimic_conv.shape[1],2):\n",
    "                for j in range(0,mimic_conv.shape[1],2):\n",
    "#                     print(conv_filtr[i:i+2,j:j+2])\n",
    "                    cord = np.argmax(conv_filtr[i:i+2,j:j+2],axis=0)                \n",
    "                    approx_conv[0,i+cord[0],j+cord[1],k] = current_conv[0,id_x,id_y,k]\n",
    "                    id_y += 1\n",
    "                id_x += 1\n",
    "                id_y = 0\n",
    "        return approx_conv\n",
    "    \n",
    "def unpooling1():\n",
    "    with tf.Session() as sess:\n",
    "        saver = tf.train.Saver()\n",
    "        saver.restore(sess,'./model')\n",
    "        current_conv,mimic_conv = sess.run([deconv2,conv1],feed_dict={x:np.reshape(coll_batch[5],[1,32,32,3])})\n",
    "        approx_conv = np.zeros(mimic_conv.shape)\n",
    "        for k in range(mimic_conv.shape[3]):\n",
    "            conv_filtr = mimic_conv[0,:,:,k]\n",
    "            cord,id_x,id_y = 0,0,0\n",
    "            for i in range(0,mimic_conv.shape[1],2):\n",
    "                for j in range(0,mimic_conv.shape[1],2):\n",
    "                    cord = np.argmax(conv_filtr[i:i+2,j:j+2],axis=0)        \n",
    "#                     print(approx_conv[0,i+cord[0],j+cord[1],k])\n",
    "                    approx_conv[0,i+cord[0],j+cord[1],k] = current_conv[0,id_x,id_y,k]\n",
    "                    id_y += 1\n",
    "                id_x += 1\n",
    "                id_y = 0\n",
    "        return approx_conv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"conv2d_transpose_1:0\", shape=(1, 8, 8, 256), dtype=float32)\n",
      "Tensor(\"conv2d_transpose_2:0\", shape=(1, 8, 8, 128), dtype=float32)\n",
      "INFO:tensorflow:Restoring parameters from ./model\n",
      "Tensor(\"conv2d_transpose_3:0\", shape=(1, 16, 16, 64), dtype=float32)\n",
      "INFO:tensorflow:Restoring parameters from ./model\n",
      "Tensor(\"conv2d_transpose_4:0\", shape=(1, 32, 32, 3), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "deconv4 = tf.nn.conv2d_transpose(tf.reshape(conv4[0,:,:,189], [-1, 8,8,1]),tf.reshape(conv4_w[:,:,:,189],[3,3,256,1]),\n",
    "                                 output_shape=tf.stack([1, 8, 8, conv3_filter]),strides=[1, 1, 1, 1],padding='SAME')\n",
    "print(deconv4)\n",
    "deconv3 = tf.nn.conv2d_transpose(deconv4,conv3_w,output_shape=tf.stack([1, 8, 8, conv2_filter]),\n",
    "                                strides=[1, 1, 1, 1],padding='SAME')\n",
    "print(deconv3)\n",
    "deconv2 = tf.nn.conv2d_transpose(tf.cast(unpooling2(), tf.float32),conv2_w,output_shape=tf.stack([1, 16, 16, conv1_filter]),\n",
    "                                strides=[1, 1, 1, 1],padding='SAME')\n",
    "print(deconv2)\n",
    "deconv1 = tf.nn.conv2d_transpose(tf.cast(unpooling1(), tf.float32),conv1_w,output_shape=tf.stack([1, 32, 32, channels]),\n",
    "                                strides=[1, 1, 1, 1],padding='SAME')\n",
    "print(deconv1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver = tf.train.Saver()\n",
    "    saver.restore(sess,'./model')\n",
    "    image_visual = sess.run(deconv1,feed_dict={x:np.reshape(coll_batch[5],[1,32,32,3])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_image_visual(active_neuron):\n",
    "    filters = active_neuron.shape[3]\n",
    "    plt.figure(1, figsize=(50,50))\n",
    "    plt.ion() \n",
    "    n_columns = 4\n",
    "    n_rows = math.ceil(filters / n_columns) + 1\n",
    "    for i in range(filters):\n",
    "        plt.subplot(n_rows, n_columns, i+1)\n",
    "        plt.title('Image_Channel ' + str(i))\n",
    "        plt.imshow(active_neuron[0,:,:,i], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b1e0159550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_image_visual(image_visual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# print(conv4_w[:,:,:,189])\n",
    "# print(conv4[0,:,:,189])\n",
    "# print(tf.reshape(conv4[0,:,:,189], [-1, 8,8,1]))\n",
    "# print(tf.reshape(conv4_w[:,:,:,189],[3,3,256,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gradunpooling2():\n",
    "    with tf.Session() as sess:\n",
    "        saver = tf.train.Saver()\n",
    "        saver.restore(sess,'./model')\n",
    "        current_conv,mimic_conv = sess.run([gradconv3,conv2],feed_dict={x:np.reshape(coll_batch[5],[1,32,32,3])})\n",
    "        approx_conv = np.zeros(mimic_conv.shape)\n",
    "        for k in range(mimic_conv.shape[3]):\n",
    "            conv_filtr = mimic_conv[0,:,:,k]\n",
    "            cord,id_x,id_y = 0,0,0\n",
    "            for i in range(0,mimic_conv.shape[1],2):\n",
    "                for j in range(0,mimic_conv.shape[1],2):\n",
    "                    cord = np.argmax(conv_filtr[i:i+2,j:j+2],axis=0)                \n",
    "                    approx_conv[0,i+cord[0],j+cord[1],k] = current_conv[0,id_x,id_y,k]\n",
    "                    id_y += 1\n",
    "                id_x += 1\n",
    "                id_y = 0\n",
    "        return approx_conv\n",
    "    \n",
    "def gradunpooling1():\n",
    "    with tf.Session() as sess:\n",
    "        saver = tf.train.Saver()\n",
    "        saver.restore(sess,'./model')\n",
    "        current_conv,mimic_conv = sess.run([gradconv2,conv1],feed_dict={x:np.reshape(coll_batch[5],[1,32,32,3])})\n",
    "        approx_conv = np.zeros(mimic_conv.shape)\n",
    "        for k in range(mimic_conv.shape[3]):\n",
    "            conv_filtr = mimic_conv[0,:,:,k]\n",
    "            cord,id_x,id_y = 0,0,0\n",
    "            for i in range(0,mimic_conv.shape[1],2):\n",
    "                for j in range(0,mimic_conv.shape[1],2):\n",
    "                    cord = np.argmax(conv_filtr[i:i+2,j:j+2],axis=0)        \n",
    "                    approx_conv[0,i+cord[0],j+cord[1],k] = current_conv[0,id_x,id_y,k]\n",
    "                    id_y += 1\n",
    "                id_x += 1\n",
    "                id_y = 0\n",
    "\n",
    "                return approx_conv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Conv2DBackpropInput_15:0\", shape=(1, 8, 8, 256), dtype=float32)\n",
      "Tensor(\"Conv2DBackpropInput_16:0\", shape=(1, 8, 8, 128), dtype=float32)\n",
      "INFO:tensorflow:Restoring parameters from ./model\n",
      "Tensor(\"Conv2DBackpropInput_17:0\", shape=(1, 16, 16, 64), dtype=float32)\n",
      "INFO:tensorflow:Restoring parameters from ./model\n",
      "Tensor(\"Conv2DBackpropInput_18:0\", shape=(1, 32, 32, 3), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "gradconv4 = tf.nn.conv2d_backprop_input(tf.stack([1, 8, 8, conv3_filter]),tf.reshape(conv4_w[:,:,:,120],[3,3,256,1]),\n",
    "                                        out_backprop=tf.reshape(conv4[0,:,:,200], [-1, 8,8,1]),\n",
    "                                        strides=[1, 1, 1, 1],padding='SAME')\n",
    "print(gradconv4)\n",
    "gradconv3 = tf.nn.conv2d_backprop_input(tf.stack([1, 8, 8, conv2_filter]),conv3_w,\n",
    "                                        out_backprop=gradconv4,\n",
    "                                        strides=[1, 1, 1, 1],padding='SAME')\n",
    "print(gradconv3)\n",
    "gradconv2 = tf.nn.conv2d_backprop_input(tf.stack([1, 16, 16, conv1_filter]),conv2_w,\n",
    "                                        out_backprop=tf.cast(gradunpooling2(), tf.float32),\n",
    "                                        strides=[1, 1, 1, 1],padding='SAME')\n",
    "print(gradconv2)\n",
    "gradconv1 = tf.nn.conv2d_backprop_input(tf.stack([1, 32, 32, channels]),conv1_w,\n",
    "                                        out_backprop=tf.cast(gradunpooling1(), tf.float32),\n",
    "                                        strides=[1, 1, 1, 1],padding='SAME')\n",
    "print(gradconv1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver = tf.train.Saver()\n",
    "    saver.restore(sess,'./model')\n",
    "    grad_image_visual = sess.run(gradconv1,feed_dict={x:np.reshape(coll_batch[5],[1,32,32,3])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b1d5062400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_image_visual(grad_image_visual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model\n",
      "[[ 0.          0.          0.26289779  0.20612724  0.          0.\n",
      "   0.15440537  0.        ]\n",
      " [ 0.          0.08365498  0.550345    0.52406633  0.43332034  0.2342803\n",
      "   0.36794949  0.08863012]\n",
      " [ 0.          0.06272166  0.64006323  0.50697392  0.67310554  0.\n",
      "   0.05459101  0.        ]\n",
      " [ 0.          0.          0.34955215  0.          0.          0.\n",
      "   0.19271509  0.        ]\n",
      " [ 0.          0.          0.41934025  0.          0.          0.          0.\n",
      "   0.        ]\n",
      " [ 0.20501722  0.          0.16506442  0.          0.05527518  0.          0.\n",
      "   0.        ]\n",
      " [ 0.28160003  0.14549376  0.36582458  0.1136627   0.10701565  0.\n",
      "   0.28828433  0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.          0.\n",
      "   0.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGQAAAB0CAYAAABzNJfPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOCMEcUYI4owQxBn/AF7attBblTq+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b1dc12eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visual_conv4(120)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8 * 8 Grid for conv1 filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    saver = tf.train.Saver()\n",
    "    saver.restore(sess,'./model')\n",
    "    filter_conv_1 =  sess.run(conv1_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3, 3, 64)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter_conv_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filt1 = np.transpose(filter_conv_1,[3,0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 3, 3, 3)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D4TBdLhebI722w+/3s/FoNErxePyy9Tl8+HBRLdXi+1gsRslk0lafkvd9yJAholrRaFQZ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\nAEZs/Tslfp6iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b1eda28358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "for i in range(64):\n",
    "    plt.subplot(8,8,i+1).set_aspect('equal')\n",
    "    plt.imshow(filt1[i,:,:,0], interpolation=\"nearest\", cmap=\"gray\")\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.subplots_adjust(wspace=0, hspace=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# No of Parameters And  Neurons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'No of Weights'                                           'Not counted'\n",
    "'Conv1        Conv2          Conv3           Conv4           Flatten         NN1             NN2'\n",
    "                                                            '4*4*256' \n",
    "'3*3*3*64' + '3*3*64*128' + '3*3*128*256' + '3*3*128*256'       +       '4*4*256*1024' +  '1024 * 1024'      \"Weights\"\n",
    "\"64\"          \"128\"          \"256\"        +  \"256\"                        \"1024\"           \"1024\"            \"Bias\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'No of Neurons'\n",
    "                                                         'Input neuron'\n",
    "'3*3*3*64' + '3*3*64*128' + '3*3*128*256' + '3*3*128*256' + '4*4*256'  +     \"1024\"    +   '1024'   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"Batch normalization increases the acc by  8-9% on validation Set \"\n",
    "\"Previous acc without batch normalization is 66% after applying it's around 75-76%\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'Small amount of data augmentation done to increase Acc to 80%'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Keras train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 52\n",
    "num_classes = 10\n",
    "epochs = 30\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Activation\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def keras_mod():\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(64, (3, 3),padding='same',input_shape=train.shape[1:]))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Conv2D(128, (3, 3)))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Conv2D(256, (3, 3), padding='same'))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Conv2D(256, (3, 3), padding='same'))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(1024))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dense(1024))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dense(num_classes))\n",
    "    model.add(Activation('softmax'))\n",
    "    opt = keras.optimizers.Adamax(lr=1e-4)\n",
    "    model.compile(loss='categorical_crossentropy',optimizer=opt,metrics=['accuracy'])\n",
    "    return model\n",
    "# opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)\n",
    "cnn_n = keras_mod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45000 samples, validate on 5000 samples\n",
      "Epoch 1/10\n",
      "45000/45000 [==============================] - 35s - loss: 0.3930 - acc: 0.8629 - val_loss: 0.6609 - val_acc: 0.7792\n",
      "Epoch 2/10\n",
      "45000/45000 [==============================] - 33s - loss: 0.3725 - acc: 0.8707 - val_loss: 0.6259 - val_acc: 0.7896\n",
      "Epoch 3/10\n",
      "45000/45000 [==============================] - 36s - loss: 0.3537 - acc: 0.8764 - val_loss: 0.6407 - val_acc: 0.7868\n",
      "Epoch 4/10\n",
      "45000/45000 [==============================] - 36s - loss: 0.3304 - acc: 0.8844 - val_loss: 0.6032 - val_acc: 0.7990\n",
      "Epoch 5/10\n",
      "45000/45000 [==============================] - 36s - loss: 0.3137 - acc: 0.8898 - val_loss: 0.6430 - val_acc: 0.7886\n",
      "Epoch 6/10\n",
      "45000/45000 [==============================] - 36s - loss: 0.3005 - acc: 0.8940 - val_loss: 0.6243 - val_acc: 0.7964\n",
      "Epoch 7/10\n",
      "45000/45000 [==============================] - 31s - loss: 0.2817 - acc: 0.9030 - val_loss: 0.5926 - val_acc: 0.8028\n",
      "Epoch 8/10\n",
      "45000/45000 [==============================] - 32s - loss: 0.2671 - acc: 0.9056 - val_loss: 0.6587 - val_acc: 0.7840\n",
      "Epoch 9/10\n",
      "45000/45000 [==============================] - 32s - loss: 0.2545 - acc: 0.9108 - val_loss: 0.6896 - val_acc: 0.7756\n",
      "Epoch 10/10\n",
      "45000/45000 [==============================] - 31s - loss: 0.2370 - acc: 0.9170 - val_loss: 0.6326 - val_acc: 0.7992\n"
     ]
    }
   ],
   "source": [
    "cnn = cnn_n.fit(train, train_label_1, batch_size=batch_size, epochs=epochs, validation_data=(val,val_label_1),shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cnn_n.save('my_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cnn_n.save('my_model1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cnn_n.save('my_model2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45000 samples, validate on 5000 samples\n",
      "Epoch 1/10\n",
      "45000/45000 [==============================] - 32s - loss: 0.2265 - acc: 0.9207 - val_loss: 0.6573 - val_acc: 0.7934\n",
      "Epoch 2/10\n",
      "45000/45000 [==============================] - 31s - loss: 0.2122 - acc: 0.9257 - val_loss: 0.6472 - val_acc: 0.7944\n",
      "Epoch 3/10\n",
      "45000/45000 [==============================] - 31s - loss: 0.2030 - acc: 0.9291 - val_loss: 0.6280 - val_acc: 0.8046\n",
      "Epoch 4/10\n",
      "45000/45000 [==============================] - 32s - loss: 0.1945 - acc: 0.9322 - val_loss: 0.6869 - val_acc: 0.7954\n",
      "Epoch 5/10\n",
      "45000/45000 [==============================] - 32s - loss: 0.1804 - acc: 0.9364 - val_loss: 0.6536 - val_acc: 0.7956\n",
      "Epoch 6/10\n",
      "45000/45000 [==============================] - 32s - loss: 0.1696 - acc: 0.9405 - val_loss: 0.6580 - val_acc: 0.7978\n",
      "Epoch 7/10\n",
      "45000/45000 [==============================] - 32s - loss: 0.1646 - acc: 0.9426 - val_loss: 0.6519 - val_acc: 0.8060\n",
      "Epoch 8/10\n",
      "45000/45000 [==============================] - 32s - loss: 0.1524 - acc: 0.9476 - val_loss: 0.6658 - val_acc: 0.8016\n",
      "Epoch 9/10\n",
      "45000/45000 [==============================] - 32s - loss: 0.1439 - acc: 0.9498 - val_loss: 0.6617 - val_acc: 0.8058\n",
      "Epoch 10/10\n",
      "45000/45000 [==============================] - 32s - loss: 0.1411 - acc: 0.9504 - val_loss: 0.6506 - val_acc: 0.8036\n"
     ]
    }
   ],
   "source": [
    "cnn = cnn_n.fit(train, train_label_1, batch_size=batch_size, epochs=epochs, validation_data=(val,val_label_1),shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 78.93%\n"
     ]
    }
   ],
   "source": [
    "scores = cnn_n.evaluate(test, test_label_1, verbose=0)\n",
    "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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