lab-11-0-cnn-basics-keras-eager.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.12.0\n",
      "2.1.6-tf\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)\n",
    "print(keras.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.enable_eager_execution()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 3, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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UxPCQ1MTwkNTE8JDUxPCQ1MTwkNTkfwBRARJelRPLdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = tf.constant([[[[1],[2],[3]],\n",
    "                   [[4],[5],[6]], \n",
    "                   [[7],[8],[9]]]], dtype=np.float32)\n",
    "print(image.shape)\n",
    "plt.imshow(image.numpy().reshape(3,3), cmap='Greys')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 filter (2,2,1,1) with padding: VALID\n",
    "\n",
    "weight.shape = 1 filter (2 , 2 , 1, 1)\n",
    "![image](https://cloud.githubusercontent.com/assets/901975/24833375/c0d9c262-1cf9-11e7-9efc-5dd6fe0fedb0.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image.shape (1, 3, 3, 1)\n",
      "weight.shape (2, 2, 1, 1)\n",
      "conv2d.shape (1, 2, 2, 1)\n",
      "[[12. 16.]\n",
      " [24. 28.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"image.shape\", image.shape)\n",
    "weight = np.array([[[[1.]],[[1.]]],\n",
    "                   [[[1.]],[[1.]]]])\n",
    "print(\"weight.shape\", weight.shape)\n",
    "weight_init = tf.constant_initializer(weight)\n",
    "conv2d = keras.layers.Conv2D(filters=1, kernel_size=2, padding='VALID', \n",
    "                             kernel_initializer=weight_init)(image)\n",
    "print(\"conv2d.shape\", conv2d.shape)\n",
    "print(conv2d.numpy().reshape(2,2))\n",
    "plt.imshow(conv2d.numpy().reshape(2,2), cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 filter (2,2,1,1) with padding:SAME\n",
    "![image](https://cloud.githubusercontent.com/assets/901975/24833381/fd01869e-1cf9-11e7-9d59-df08c7c6e5c4.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image.shape (1, 3, 3, 1)\n",
      "weight.shape (2, 2, 1, 1)\n",
      "conv2d.shape (1, 3, 3, 1)\n",
      "[[12. 16.  9.]\n",
      " [24. 28. 15.]\n",
      " [15. 17.  9.]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"image.shape\", image.shape)\n",
    "weight = np.array([[[[1.]],[[1.]]],\n",
    "                   [[[1.]],[[1.]]]])\n",
    "print(\"weight.shape\", weight.shape)\n",
    "weight_init = tf.constant_initializer(weight)\n",
    "conv2d = keras.layers.Conv2D(filters=1, kernel_size=2, padding='SAME', \n",
    "                             kernel_initializer=weight_init)(image)\n",
    "print(\"conv2d.shape\", conv2d.shape)\n",
    "print(conv2d.numpy().reshape(3,3))\n",
    "plt.imshow(conv2d.numpy().reshape(3,3), cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 filters (2,2,1,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image.shape (1, 3, 3, 1)\n",
      "weight.shape (2, 2, 1, 3)\n",
      "conv2d.shape (1, 3, 3, 3)\n",
      "[[12. 16.  9.]\n",
      " [24. 28. 15.]\n",
      " [15. 17.  9.]]\n",
      "[[120. 160.  90.]\n",
      " [240. 280. 150.]\n",
      " [150. 170.  90.]]\n",
      "[[-12. -16.  -9.]\n",
      " [-24. -28. -15.]\n",
      " [-15. -17.  -9.]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAACFCAYAAAB7VhJ6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAB29JREFUeJzt3cFrXXUaxvHnmSbtohoaOrOQa5k4VITulNtsBCmuOm7c6iLdCF0FFGbjH1HcdVOwlIAoQ3XhQpBZWGRArHeKA+0Eh47tYFBwWlsiXVQC7yxyGcJYyYk55/zO+7vfDwRy08s5z+0THk4uNzeOCAEA8vhN6QAAgL1huAEgGYYbAJJhuAEgGYYbAJJhuAEgGYYbAJJhuAEgGYYbAJKZ6+Sgc3MxPz/fxaEbO3z4cNHzS9Ldu3dLR1BEuK1j0eu22npdXFyM0WjU1uF+lQcPHhQ9vyQdPXq06Plv376tO3fuNOq1k+Gen5/X0tJSF4dubHl5uej5JWltba10hFbR67baeh2NRrp8+XLRDFevXi16fkk6c+ZM0fOPx+PG9+WpEgBIhuEGgGQYbgBIhuEGgGQYbgBIhuEGgGQYbgBIhuEGgGQYbgBIhuEGgGQYbgBIptFw2z5t+yvbN22/2XUo9INe60Sv9dt1uG0fkHRe0h8lnZD0qu0TXQdDt+i1TvQ6G5pccS9LuhkRX0fET5Lek/Ryt7HQA3qtE73OgCbDPZL0zY7bG9OvITd6rRO9zoAmw/2oN/aOn93JPmt7YnuytbW1/2ToGr3Wac+93rt3r4dYaFOT4d6QdGzH7Sclffv/d4qICxExjojx3Fwnf58B7aLXOu2518XFxd7CoR1NhvsLSU/bfsr2QUmvSPqw21joAb3WiV5nwK6XUBGxZXtV0seSDki6GBE3Ok+GTtFrneh1NjT62TciPpL0UcdZ0DN6rRO91o/fnASAZBhuAEiG4QaAZBhuAEiG4QaAZBhuAEiG4QaAZBhuAEiG4QaAZBhuAEiG4QaAZDp5n86lpSWtra11cejGTp48WfT8krS5uVn0/FeuXGn1ePS6rbZeb926pZWVlVaPuVeTyaTo+SVpYWGh6Pnv37/f+L5ccQNAMgw3ACTDcANAMgw3ACTDcANAMgw3ACTDcANAMgw3ACTDcANAMgw3ACTDcANAMgw3ACSz63Dbvmj7e9vX+wiEftBrvei2fk2uuC9JOt1xDvTvkui1VpdEt1Xbdbgj4lNJP/SQBT2i13rRbf14jhsAkmltuG2ftT2xPdnLG4Jj2Oi1Tjt73draKh0He9TacEfEhYgYR8T4yJEjbR0WhdFrnXb2OjfXyR/CQod4qgQAkmnycsB3JX0m6RnbG7Zf6z4Wukav9aLb+u36M1JEvNpHEPSLXutFt/XjqRIASIbhBoBkGG4ASIbhBoBkGG4ASIbhBoBkGG4ASIbhBoBkGG4ASIbhBoBkGG4ASMYR0fpBFxcX49SpU60fdy9Go1HR80vS+fPnS0dQRLitY9Hrttp6PX78eJw7d66tw/0qGxsbRc8vSaurq0XPPx6PNZlMGvXKFTcAJMNwA0AyDDcAJMNwA0AyDDcAJMNwA0AyDDcAJMNwA0AyDDcAJMNwA0AyDDcAJMNwA0Ayuw637WO2P7G9bvuG7df7CIZu0Wud6HU2zDW4z5akP0XENduPS/qb7b9ExD86zoZu0Wud6HUG7HrFHRHfRcS16ec/SlqXVP69NbEv9Fonep0Ne3qO2/aSpGclff6Ifztre2J78vDhw3bSoRf0WqemvW5ubvYdDfvUeLhtPybpfUlvRMTPmo6ICxExjojxoUOH2syIDtFrnfbS68LCQv8BsS+Nhtv2vLa/Cd6JiA+6jYS+0Gud6LV+TV5VYklvS1qPiLe6j4Q+0Gud6HU2NLnifl7SiqQXbX85/Xip41zoHr3WiV5nwK4vB4yIv0pq7Q+TYhjotU70Ohv4zUkASIbhBoBkGG4ASIbhBoBkGG4ASIbhBoBkGG4ASIbhBoBkGG4ASIbhBoBkGG4ASMYR0f5B7f9I+vc+DvFbSXdaijPLGX4fEb9rKwy9DiYDvdaZoXGvnQz3ftmeRMSYDOUztGkIj4cM7RvC45m1DDxVAgDJMNwAkMxQh/tC6QAiQxeG8HjI0L4hPJ6ZyjDI57gBAL9sqFfcAIBfMKjhtn3a9le2b9p+s1CGi7a/t3290PmP2f7E9rrtG7ZfL5GjbaW7pdduzHqv0wz9dxsRg/iQdEDSvyT9QdJBSX+XdKJAjhckPSfpeqH/hyckPTf9/HFJ/yzx/1Bbt/RKrzV1O6Qr7mVJNyPi64j4SdJ7kl7uO0REfCrph77Pu+P830XEtennP0palzQqlaclxbul107MfK/TDL13O6ThHkn6ZsftDeX/xt4X20uSnpX0edkk+0a3O9BrvfrqdkjD7Ud8bWZf8mL7MUnvS3ojIjZL59knup2i13r12e2QhntD0rEdt5+U9G2hLEXZntf2N8A7EfFB6TwtoFvRa8367nZIw/2FpKdtP2X7oKRXJH1YOFPvbFvS25LWI+Kt0nlaMvPd0mu9SnQ7mOGOiC1Jq5I+1vaT+3+OiBt957D9rqTPJD1je8P2az1HeF7SiqQXbX85/Xip5wytGkK39No+ev2f3rvlNycBIJnBXHEDAJphuAEgGYYbAJJhuAEgGYYbAJJhuAEgGYYbAJJhuAEgmf8C4CcOAm45hJMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print(\"imag:\\n\", image)\n",
    "print(\"image.shape\", image.shape)\n",
    "\n",
    "weight = np.array([[[[1.,10.,-1.]],[[1.,10.,-1.]]],\n",
    "                   [[[1.,10.,-1.]],[[1.,10.,-1.]]]])\n",
    "print(\"weight.shape\", weight.shape)\n",
    "weight_init = tf.constant_initializer(weight)\n",
    "conv2d = keras.layers.Conv2D(filters=3, kernel_size=2, padding='SAME',\n",
    "                             kernel_initializer=weight_init)(image)\n",
    "print(\"conv2d.shape\", conv2d.shape)\n",
    "feature_maps = np.swapaxes(conv2d, 0, 3)\n",
    "for i, feature_map in enumerate(feature_maps):\n",
    "    print(feature_map.reshape(3,3))\n",
    "    plt.subplot(1,3,i+1), plt.imshow(feature_map.reshape(3,3), cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MAX POOLING\n",
    "![image](https://cloud.githubusercontent.com/assets/901975/23337676/bd154da2-fc30-11e6-888c-d86bc2206066.png)\n",
    "\n",
    "![image](https://cloud.githubusercontent.com/assets/901975/23340355/a4bd3c08-fc6f-11e6-8a99-1e3bbbe86733.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 1, 1)\n",
      "[[[[4.]]]]\n"
     ]
    }
   ],
   "source": [
    "image = tf.constant([[[[4],[3]],\n",
    "                    [[2],[1]]]], dtype=np.float32)\n",
    "pool = keras.layers.MaxPool2D(pool_size=(2,2), strides=1, padding='VALID')(image)\n",
    "print(pool.shape)\n",
    "print(pool.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SAME: Zero paddings\n",
    "\n",
    "![image](https://cloud.githubusercontent.com/assets/901975/23340337/71b27652-fc6f-11e6-96ef-760998755f77.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 2, 1)\n",
      "[[[[4.]\n",
      "   [3.]]\n",
      "\n",
      "  [[2.]\n",
      "   [1.]]]]\n"
     ]
    }
   ],
   "source": [
    "image = tf.constant([[[[4],[3]],\n",
    "                    [[2],[1]]]], dtype=np.float32)\n",
    "pool = keras.layers.MaxPool2D(pool_size=(2,2), strides=1, padding='SAME')(image)\n",
    "print(pool.shape)\n",
    "print(pool.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = keras.datasets.mnist\n",
    "class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
    "#mnist = keras.datasets.fashion_mnist\n",
    "#class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images.astype(np.float32) / 255.\n",
    "test_images = test_images.astype(np.float32) / 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = train_images[0]\n",
    "plt.imshow(img, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 14, 14, 5)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = img.reshape(-1,28,28,1)\n",
    "img = tf.convert_to_tensor(img)\n",
    "weight_init = keras.initializers.RandomNormal(stddev=0.01)\n",
    "conv2d = keras.layers.Conv2D(filters=5, kernel_size=3, strides=(2, 2), padding='SAME', \n",
    "                             kernel_initializer=weight_init)(img)\n",
    "print(conv2d.shape)\n",
    "feature_maps = np.swapaxes(conv2d, 0, 3)\n",
    "for i, feature_map in enumerate(feature_maps):\n",
    "    plt.subplot(1,5,i+1), plt.imshow(feature_map.reshape(14,14), cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 7, 7, 5)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAABcCAYAAABOZ1+dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAACglJREFUeJzt3UtoVGkWB/D/SWKZWElItLVD2p5goFsJPmCUkKAZmE3b4kJEEQdduHJj40IE3agLQUfF6CiCxKE3StsIIrho7A6oCC6k00HR+BgzEkeJEh95mBjyPLOw7JRW5ftuVeXWvV/y/4Fo6ty+3/FP1eny1r11RVVBRETuyAm6ASIiSg0HNxGRYzi4iYgcw8FNROQYDm4iIsdwcBMROYaDm4jIMRzcRESO4eAmInJMnh87LSkp0fLycj92HRrt7e3o6uoSr9uLyFS5RPW1qs72smFBQYEWFxf73U/gOjo6PGcSiUQ0Pz/f75YC9+7dO8+ZAEA0GtXS0lI/WwpcZ2cn+vr6PM0UT4NbRL4H8C8AuQD+rar/NG1fXl6Os2fPetm1s9auXQsReQSPmUwhRSLSCg+ZFBcXY9OmTVlqKzjHjh3r9Ppcyc/PR01NTfaaC0hjY6PnTACgtLQU27Zty05zATl16pTnba2HSkQkF8ApAKsAVAH4h4hUpd3dJDAyMoKXL18CzCSZ/4CZ/Gl0dBQA/gI+V/4U+34kZpIBL8e4qwG0quoTVR0E8DOANf62FW4tLS2IRCJgJkkNMpMxsf/BD/C5Mqa7uxtgJhnxMri/AvAs7ufnscc+ISJbRaRJRJo6Ozsnqr9Q6ujoQF7eJ0eZrJlkrbnwsGbS398fQFvZ1dvbCwCDcQ8l5BKfydDQUDbbC8TAwABgyQT4NJe+vr5stecEL4M72cHyhA/aVLVBVZep6rLJ/iHCOIyZBNFQCBgzKSgoCKKnMPgkl/hMpk2bFlRPQTM+V6LRaBA9hZaXwf0cwNdxP88F0O5PO26YM2cOhoeH4x+a8pkkwUwAFBYWAkAk7qEpn8v06dMBZpIRL4P7dwDfiMg8EYkA2Ajgsr9thVtVVRUGBwfBTJKKMJMxZWVlAJDP58qY2CmgzCQD1tMBVXVYRH4A8Cs+nLrzo6q2ZLJoJBKxbmM7pnXo0CFjffny5ca6l396VVdXJ308Ly8PZWVlePbs2YRlsmaN/bOZnTt3GusLFiww1k+fPm2sd3V1WXs4evSobZNvATzABGTi5VqAyspKY/3x48fG+u7du411L6fm1dbWjlvLyckBgP9hAl8/No2Njcb66tWrjfULFy4Y6/v27bP2cOfOnXFrfmQSexdvtGrVKmO9oqLCWF+/fr2x3tPTY+3Blr1Xns7jVtVfAPwyIStOEoWFhVDVb4PuI4TuTeFj+uPpZiYJmEkGeMk7EZFjOLiJiBzDwU1E5BgObiIix3BwExE5hoObiMgxHNxERI7x5UYKNvPmzbNu09bWZqwfP37cWH/9+nUqLQXu0aNH1m1WrFhhrNfX1xvrV69eNdavXbtm7SGb2tvtV0HbLkq6fv26sX748GFj/cWLF9Yewmb//v3G+vz58431M2fOGOumi2uCEvviKqOLFy8a67bnW11dXUo9+YnvuImIHMPBTUTkGA5uIiLHcHATETmGg5uIyDEc3EREjuHgJiJyTCDncXs5Z3nx4sXGuu3efLG7a4/rs5v9Bu7hw4fWbRYtWmSs287fvXzZfJORoqIiaw9hs2PHDmPdds5y2J4HE+HGjRvGekuL+Z4F69atm8h2QiM3N9dYb25uNtZnzZplrM+YMSPlntLFd9xERI7h4CYicgwHNxGRYzi4iYgcw8FNROQYDm4iIsdwcBMROSa0J7E+ePDAWLedf7tlyxZj/dy5c6m2FLiamhpjvaSkxFi3fR93QUGBtYf+/n7rNtk0e/bsjP77jRs3GutHjhzJaP9hdOXKFWP9/PnzxnpDQ8NEtpM1GzZsMNZXrlxprC9dutRYP3DgQMo9pYvvuImIHMPBTUTkGA5uIiLHcHATETmGg5uIyDEc3EREjuHgJiJyjC/ncefk5Bi/m3ZwcNC6jyVLlhjrN2/eNNbDdp52NBrFwoULx63bvlcaACoqKoz127dvG+snTpww1rN9jnZRURHq6urGrd+9e9e6j/r6emN95syZxrqX74YPk1evXlm32b59u7G+d+9eY33Xrl0p9ZQNOTk5iEaj49Ztf2cA2LNnj7He29trrGfzPG0bT4NbRNoAvAMwAmBYVZf52ZQLWltbISJ3wUw+t4i5JGAmiZhJBlJ5x/13VX3tWyduYibJMZdEzCQRM0kTj3ETETnG6+BWAL+JyB8isjXZBiKyVUSaRKTp7du3E9dhuHnOZGhoKNu9BWncXOIz6enpCaK3oHjKhM+TMfG52I4/TzVeB/dyVf0rgFUAtonI3z7fQFUbVHWZqi6zfSA0GVRUVCCVTGw3N55EHppyic+kuLg4mA6zz3MmfJ6Mic+lsLAw+x2GmKfBrartsd87AFwCUO1nUy74+AJjJgmGAObyGWaSiJlkwDq4RSQqIkUf/wzgOwD3/G4szPr7+zEyMgKAmSSRAzCXj2KHPphJnNhrh5lkwMtZJV8CuCQiH7f/SVXNX+g7yb158wZPnz6FiNwBM/ncAuYypq+vD2AmnxgYGACYSUasg1tVnwAwXw3zmUgkYrxYJDc317oP240UYi+IQMydOxeVlZW4f/++51xGRkbQ3d09bv3kyZPWfbx//95Yb25u9tqOn+57PSe3r68Pt27dGre+efNm6z66urq8dxaA2M0tPGdiU1tba92msbHRWLfdKMFvsYvzUspkdHTU+Jo/ePCg13XTrocJTwckInIMBzcRkWM4uImIHMPBTUTkGA5uIiLHcHATETmGg5uIyDGiqhO/U5FXAJ7GPfQFgLB/fWOqPVao6myvG0+RTIAUcmEmiZJkku6a2cbXTyLfMvFlcCcsItIU9i9Kz3aPzCT49dIRRI/MJfj10uFnjzxUQkTkGA5uIiLHZGtwN2RpnUxku0dmEvx66QiiR+YS/Hrp8K3HrBzjJiKiicNDJUREjvF1cIvI9yLySERaRWS3n2tlQkTaROSuiNwWkSaf12ImydcLfS7MJBEzSc73XFTVl18AcgH8F0AlgAiAOwCq/Fovw17bAHyRhXWYicO5MBNmEpZc/HzHXQ2gVVWfqOoggJ8BrPFxPRcwk+SYSyJmkoiZxPg5uL8C8Czu5+exx8JIAfwmIn+IyFYf12EmybmSCzNJxEyS8zUXL/ecTJckeSysp7AsV9V2EZkDoFFEHqrqDR/WYSbJuZILM0nETJLzNRc/33E/B/B13M9zAbT7uF7aVLU99nsHgEv48E8yPzCT5JzIhZkkYibJ+Z2Ln4P7dwDfiMg8EYkA2Ajgso/rpUVEoiJS9PHPAL4DcM+n5ZhJcqHPhZkkYibJZSMX3w6VqOqwiPwA4Fd8+DT4R1Vt8Wu9DHwJ4JKIAB/y+ElVr/ixEDNJzpFcmEkiZpKc77nwykkiIsfwykkiIsdwcBMROYaDm4jIMRzcRESO4eAmInIMBzcRkWM4uImIHMPBTUTkmP8D7XN/kdOwICAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pool = keras.layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2), padding='SAME')(conv2d)\n",
    "print(pool.shape)\n",
    "\n",
    "feature_maps = np.swapaxes(pool, 0, 3)\n",
    "for i, feature_map in enumerate(feature_maps):\n",
    "    plt.subplot(1,5,i+1), plt.imshow(feature_map.reshape(7, 7), cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}