CAE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "INFO:tensorflow:Restoring parameters from saver/CAE/CAE-1000\n",
      "restored from checkpoint CAE-1000\n"
     ]
    },
    {
     "data": {
      "image/png": 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4AADqM9p0AAAoAAUWAIACUGABACgABRYAgAJQYAEAKAAFFgCAAlBgAQAoAAUWAIACUGABACgABRYAgAJQYAEAKAAFFgCAAlBgAQAoQLVX0ynCrFmzwnz33XfPzVauXBmO3WCD+KE++uijYb733nuHeaRJkyZhPnXq1Grfdrt27cJ88ODBYd6vX79q37ckNWrUKDf75JNPwrEdOnQI8xdeeCHMe/XqlZt98MEH4diUzz77LMzfeeed3Cy1JONzzz0X5i+99FKY9+7dO8wjX375ZZgvWLAgzI877rgwj56PTz31VFn3nfLxxx/nZqnn2qeffhrmqdfZueeem5udccYZ4diU5cuXh/lBBx0U5rfddltutscee4Rjn3nmmTDv2rVrmEc233zzMH/99dfDfNtttw3zHXfcMTdLPa4WLVqEeVWxBwsAQAEosAAAFIACCwBAASiwAAAUgAILAEABKLAAABSAAgsAQAHM3dfanVVUVHjUI7jFFluE46Ne1xkzZoRjly1bFuY777xzmJfTo9e0adMw32STTcI86ge75JJLwrFdunQJ81/84hdhnvLTn/40N0v1wb755pth3rZt2zAfPXp0bhb151bF888/H+bXX399bnbssceGY0844YQwT/Umvvzyy2Ee6dSpU5hPmjQpzFNzj3rZ11sv/nv+xRdfDPOU6HW24YYbhmNTvcvDhw8P84022ig3K7cn+8MPPwzzb775Jsyj1/gdd9wRjr3sssvCfNiwYWEeSfWaN27cOMyjbS5JzZo1y81Sr7GRI0eG+aBBgya5e7fwSmIPFgCAQlBgAQAoAAUWAIACUGABACgABRYAgAJQYAEAKAAFFgCAAqxT68H27NkzzEeNGpWbpdZ7TK0tmOp73H777cM8klrvdeONNw7zaG3CU045JRx7xRVXhPmcOXPCPGXEiBG52Y033hiOTfUep9Z7TPUXl2Pp0qVhHvXwHnnkkeHYVI/u4sWLw7wcN998c5jPnj07zFPbPOpdTvV7l2u//fbLzcaNGxeOTfV7nnTSSWF+ww035GYVFRXh2JQzzzwzzF977bUw/4//+I/c7Je//GU4dsmSJWFejtT3HkyYMCHMDznkkDCP1oN97LHHwrE1JbkHa2Z3mtkCM5tS6bItzGysmb2f/R+vnAsAwPdMVQ4R3y3p4NUuO1/S0+6+naSns/MAACCTLLDuPl7SotUu7iPpnuz0PZKOqOF5AQBQp1X3Q07N3X1udnqepOZ5VzSz08xsoplNTL2vBQBAfVH2p4i9tFpA7ooB7j7U3bu5e7eGDRuWe3cAANQJ1S2w882shSRl/1d/qRkAAOqh6hbYUZL6Zaf7SfpLzUwHAID6IdkHa2YPSuolaUszmy3pEklXSXrYzE6RNFNSvABmFX355ZdhPnPmzNzsySefDMcecUT8Oazdd989zMePHx/mkVTf41577RXm0bqGDRo0qNacasqee+6Zm6XWFu3Ro0eYp3qbzzjjjNzs5JNPDsemRNtcktq3b5+bjRkzJhy7YsWKMH/66afDvFu35DKUuY4++ugwf+aZZ8I81bs4cODA3CzqY68J9913X242ffr0cOz+++8f5qm+ya5du+Zm8+bNC8em/P3vfw/zVq1ahfmDDz6Ym6V61d94440w79y5c5hHov5+SWrePPejPZLS601HPbxTpkzJzSRpp512CvOqShZYd++bE8XPSAAAvsf4qkQAAApAgQUAoAAUWAAACkCBBQCgABRYAAAKsE4tV3fxxReH+axZs3KzsWPHhmMvv/zyMH/rrbfCvF27dmEeOeCAA8L8oIMOCvNjj83vgkotR3fRRReFeTnL8EnxEmEPPfRQOLZNmzZhHi2FJ8WPrdxl+Hbdddcwj9oXDj549bUxvm3ZsmVh/sUXX4R5OVKtUalWudSyjr/61a9ys9Tru3v37mGesscee+RmX331VTg29TWuDz/8cJhHrTTXXnttODYl1TKS+t0X/UxSy/Cllvkrx+DBg8O8S5cuYb7NNtuE+dChQ3Oz3XbbLRxb+oLC8rEHCwBAASiwAAAUgAILAEABKLAAABSAAgsAQAEosAAAFIACCwBAAaym+n2qoqKiwgcMGLDW7g8AgJo2aNCgSe6eXDeSPVgAAApAgQUAoAAUWAAACkCBBQCgABRYAAAKQIEFAKAAFFgAAApAgQUAoAAUWAAACkCBBQCgABRYAAAKQIEFAKAAFFgAAApAgQUAoAAUWAAACrBBbU+gsqOPPjrMZ86cmZtttdVW4dh99903zBcsWBDm11xzTZhHPvjggzA/8cQTw7xVq1a52ZAhQ8KxJ510Upj/7W9/C/OUY445Jjf78Y9/HI5t3bp1mLdv3z7Mr7766tzshhtuCMempJ4v0XY7/vjjw7FDhw4N84ULF4b5LrvsEuaRTp06hXmjRo3C/Pzzzw/zE044ITfbZ599wrFjxowJ85RFixblZv/4xz/Csd26xUt7br311mFeUVFR7ftOeeyxx8J84MCBYT5u3LjcLNpmkrT//vuHeeq5Glm+fHmYb7zxxmHeuHHjat93z549w3zUqFHVvu3KknuwZnanmS0wsymVLrvUzOaY2eTs3yE1MhsAAOqJqhwivlvSwWu4/Hp375L9K+9PTwAA6plkgXX38ZLi4wgAAOBbyvmQ01lm9mZ2CHnzvCuZ2WlmNtHMJi5durSMuwMAoO6oboEdIqm9pC6S5kq6Nu+K7j7U3bu5e7eGDRtW8+4AAKhbqlVg3X2+u69w95WS/iipe81OCwCAuq1aBdbMWlQ6e6SkKXnXBQDg+yjZB2tmD0rqJWlLM5st6RJJvcysiySXNEPS6TUxmYsuuijMd9xxx9xs2bJl4dhHHnkkzMePHx/m5Tj33HPLyrfbbrvcLNUH27dv3zDfYYcdwjzlP//zP3OzCy64IBx77733hnmqb/Ljjz8O83KMHj06zAcMGJCbvfDCC+HYVL/nrrvuGublGDZsWJinng/vvfdemE+fPj03mzFjRjh2yy23DPOUJk2a5GZz5swJx5522mlh/sknn4R5kc/Fww47LMwfeuihMO/fv39uluolvfvuu8M8+p2cssEGcflJ9cnPmjUrzFu2bJmbpX6eNSVZYN19Tb+h41cpAADfc3xVIgAABaDAAgBQAAosAAAFoMACAFAACiwAAAVYp5arSy3bduWVV+ZmRxxxRDj2jTfeCPNyPm6esnjx4jD/xS9+Eeb33XdfbrbeevHfSB07dgzzFStWhHlKtN1SS3z9+te/DvMJEyaE+cSJE3Ozpk2bhmNTotYGKW5B2nPPPcOx0fKDUtwCJEmTJ08O88h5551XVp5aii9qlenTp084ttylE6Ol8m6++eZw7PDhw8M81RIS3Xe5vv766zBPtXVFz8dUG1/37sV9h9CSJUvCvFmzZmH+6aefhrm752YXX3xxOPZHP/pRmFcVe7AAABSAAgsAQAEosAAAFIACCwBAASiwAAAUgAILAEABKLAAABTAol6hmlZRUeFRj9+0adPC8d26dcvNUn2sc+fODfPU+McffzzMI6+88kqY77XXXmH+5Zdf5mapJZ023njjME8tIZYSLdNVUVERjk0tMbj55puH+RZbbJGbffDBB+HYlGibS9KHH36Ym7Vv3z4cm/qZnXXWWWF+4YUXhnlkypR46eZUX3WjRo3C/NBDD83NZs+eHY4tdwmx6PfD1KlTw7Hrr79+mHfo0CHMb7/99tysc+fO4diUwYMHh3mbNm3CfI899sjNRo0aFY5NPR9SPduR1OvbzMI8eg1KUteuXXOzH/7wh+HY6LsHJGnQoEGT3D3/CZdhDxYAgAJQYAEAKAAFFgCAAlBgAQAoAAUWAIACUGABACgABRYAgAKsU+vB9ujRI8yjHr733nsvHLvpppuG+T333BPm++yzT5hHbr311jBP9QeOGDEiN/vmm2/CseWu95oSrZua+plce+21Yb7bbruF+amnnpqbldsHm+r33GGHHXKzli1bhmNTPZmDBg0K86+++irMI6+++mqYH3XUUWGeWoPzyCOPzM1GjhwZji23D/bzzz/PzVLzPvnkk8M8td5r7969c7PUazRlyJAhYT5mzJgwj14L55xzTji23D75SJcuXcL8iiuuCPPU74eFCxfmZgcccEA4tl+/fmFeVezBAgBQAAosAAAFoMACAFAACiwAAAWgwAIAUAAKLAAABaDAAgBQgOR6sGbWWtK9kppLcklD3f1GM9tC0v9IaitphqRj3T1sZEutBwsAwLquJteDXS7pN+7eUVIPSWeaWUdJ50t62t23k/R0dh4AAKgKBdbd57r7a9npJZLekdRSUh9Jq77+6B5JRxQ1SQAA6prv9B6smbWVtJukVyQ1d/e5WTRPpUPIAABA36HAmlkjSX+SNNDdP6uceemN3DW+mWtmp5nZRDObuHTp0rImCwBAXVGlAmtmG6pUXIe7+5+zi+ebWYssbyFpwZrGuvtQd+/m7t0aNmxYE3MGAGCdlyywZmaShkl6x92vqxSNkrRqyYF+kv5S89MDAKBuqspydXtJOknSW2Y2Obvsd5KukvSwmZ0iaaakY4uZIgAAdU+ywLr785IsJ96/ZqcDAED9wDc5AQBQAAosAAAFoMACAFAACiwAAAWgwAIAUAAKLAAABahKH+xa07Zt2zAfMmRIbnbAAQeEY+fOnRvmffr0CfPXX389zCPPP/98mPfu3TvM99lnn9zswgsvDMdeeumlYf7kk0+GeUrnzp1zsy5duoRjf//734d5q1atwjxaanGTTTYJx6Y8++yzYb7DDjvkZnvvvXc4tkOHDmE+fvz4MP/qq6/CPHLggQeG+YsvvhjmHTt2DPPodXb44YeHY2+55ZYwT9l5552rlUnSU089FeYrVqwI8yeeeCI369GjRzg2pX379mH+yiuvhPmMGTNys9S36/Xs2TPM//d//zfMI8uWLQvzXr16hfl+++0X5lFNuPzyy8OxY8eODfOqYg8WAIACUGABACgABRYAgAJQYAEAKAAFFgCAAlBgAQAoAAUWAIACrFN9sKm+yalTp+ZmN910Uzg21d93++23h3n37t3DPHL22WeH+aRJk8J84MCBuVnqcaf62M4777wwT3nrrbdys+HDh4djU/2gU6ZMCfNmzZrlZm3atAnHpjz++ONh/uGHH+ZmjzzySDg29bjXX3/9MC9Ham6nnnpqmKd6LqPn48KFC8Ox5dpwww1zs1Qf/OzZs8O8U6dOYX700UdX+7ZTXn755TA/+eSTw3zMmDG52f333x+OHTp0aJjfcMMNYR75+uuvwzz1fBkwYECYRz36xx13XFm3XVXswQIAUAAKLAAABaDAAgBQAAosAAAFoMACAFAACiwAAAWgwAIAUACL1tSsaRUVFR71F91xxx3h+KjvceXKleHY1BqaqX7QWbNmhXkkNbfUmor9+vXLzaK1HqV0D95nn30W5ilbbbVVbvbuu++GY1Pr5P74xz8O80MOOSQ3GzVqVDg2JbX+74MPPpibPfzww+HY1HPpqKOOCvPU7UdS6+Sm1otN9ehGP9OmTZuGY99///0wT4nWF031sTZu3DjMo15SKe6L7tq1azg2JfU6Sf3uip6rO+20U7XmtMrSpUurPfass84K85EjR4Z5qp88+r26yy67hGOjbSZJgwYNmuTu3cIriT1YAAAKQYEFAKAAFFgAAApAgQUAoAAUWAAACkCBBQCgABRYAAAKkFwP1sxaS7pXUnNJLmmou99oZpdKOlXSqkX7fufucbNYQuvWrcP89NNPz81SfU3Tp08P81SfbDl69OgR5qnHfcQRR+RmxxxzTDg21Vu46667hnnK5MmTc7Np06aFY++8884wT/WiPvroo7lZgwYNwrEpqd7FaO3i3//+9+HY+fPnh/mCBQvCvBwfffRRmKd6Ve+9994wv/XWW3OzTTfdNBwb9XtXRfQzmTdvXjh2//33D/OJEyeG+ZIlS8K8SIMGDQrzn//857lZw4YNy7rtww8/PMwjqd+5EyZMCPPUetG77bZbbta3b99wbDmPq7KqLLi+XNJv3P01M2ssaZKZjc2y6939mhqZCQAA9UiywLr7XElzs9NLzOwdSS2LnhgAAHXZd3oP1szaStoXYqrLAAAF20lEQVRN0ivZRWeZ2ZtmdqeZbZ4z5jQzm2hmE8v5Wi0AAOqSKhdYM2sk6U+SBrr7Z5KGSGovqYtKe7jXrmmcuw91927u3i11vB8AgPqiSgXWzDZUqbgOd/c/S5K7z3f3Fe6+UtIfJXUvbpoAANQtyQJrZiZpmKR33P26Spe3qHS1IyXFH+kCAOB7pCqfIt5L0kmS3jKzVT0Zv5PU18y6qNS6M0NSfg9NFX366adhHi3b9NBDD4VjW7aMP5e1fPnyMN92223DPLJw4cIwnzNnTphHy1FFS/hJ0qJFi8K8XG3bts3N9ttvv3DsH/7whzAvp9UmtZRVSvPmzcP8rrvuys1Gjx4djo22mZTeLv/2b/8W5pEVK1aE+dNPPx3mqeUtn3vuudxso402CsfefffdYZ7yxBNP5GbHH398OPaqq64K84qKijBv0aJFmJfjzDPPDPMXXnghzO+///7crH///uHYVFtXOVKPq1evXmEetWVJ0ptvvpmbdezYMRxbU6ryKeLnJdkaorJ6XgEAqM/4JicAAApAgQUAoAAUWAAACkCBBQCgABRYAAAKQIEFAKAAluprq0kVFRU+YMCAtXZ/AADUtEGDBk1y926p67EHCwBAASiwAAAUgAILAEABKLAAABSAAgsAQAEosAAAFIACCwBAAdZqH6yZLZQ0s9JFW0r6eK1NoH5gm1UP26162G7fHduseurSdmvj7vFi3FrLBfZf7txsYlWadfFPbLPqYbtVD9vtu2ObVU993G4cIgYAoAAUWAAAClDbBXZoLd9/XcQ2qx62W/Ww3b47tln11LvtVqvvwQIAUF/V9h4sAAD1EgUWAIAC1EqBNbODzez/N7MPzOz82phDXWBmd5rZAjObUumyLcxsrJm9n/2/eW3OcV1kZq3N7Fkz+4eZvW1mZ2eXs+1ymNnGZvaqmb2RbbPLssu3NbNXstfq/5hZg9qe67rIzNY3s9fN7K/ZebZbgpnNMLO3zGyymU3MLqtXr9G1XmDNbH1Jt0jqLamjpL5m1nFtz6OOuFvSwatddr6kp919O0lPZ+fxbcsl/cbdO0rqIenM7DnGtsv3taT93L2zpC6SDjazHpKulnS9u3eQ9ImkU2pxjuuysyW9U+k8261q/t3du1Tqf61Xr9Ha2IPtLukDd5/m7t9IekhSn1qYxzrP3cdLWrTaxX0k3ZOdvkfSEWt1UnWAu89199ey00tU+sXXUmy7XF7yeXZ2w+yfS9pP0qPZ5WyzNTCzVpIOlXRHdt7EdquuevUarY0C21LSrErnZ2eXoWqau/vc7PQ8Sc1rczLrOjNrK2k3Sa+IbRfKDnNOlrRA0lhJUyUtdvfl2VV4ra7ZDZLOk7QyO/8Dsd2qwiU9aWaTzOy07LJ69RrdoLYngOpzdzcz+qxymFkjSX+SNNDdPyvtWJSw7f6Vu6+Q1MXMNpM0QtKOtTyldZ6ZHSZpgbtPMrNetT2fOmZvd59jZltJGmtm71YO68NrtDb2YOdIal3pfKvsMlTNfDNrIUnZ/wtqeT7rJDPbUKXiOtzd/5xdzLarAndfLOlZSXtK2szMVv0hzmv1X+0l6XAzm6HS2137SbpRbLckd5+T/b9ApT/ouquevUZro8BOkLRd9im7BpKOlzSqFuZRV42S1C873U/SX2pxLuuk7D2wYZLecffrKkVsuxxm1izbc5WZbSLpRyq9d/2spKOzq7HNVuPuF7h7K3dvq9Lvsmfc/Sdiu4XMbFMza7zqtKQDJU1RPXuN1so3OZnZISq9b7G+pDvd/Yq1Pok6wMwelNRLpWWc5ku6RNJISQ9L2kalpf+OdffVPwj1vWZme0t6TtJb+uf7Yr9T6X1Ytt0amFknlT5Usr5Kf3g/7O7/ZWbtVNoz20LS65JOdPeva2+m667sEPG57n4Y2y2WbZ8R2dkNJD3g7leY2Q9Uj16jfFUiAAAF4JucAAAoAAUWAIACUGABACgABRYAgAJQYAEAKAAFFgCAAlBgAQAowP8DHgZdIj7m9SAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/training/input.py:187: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/training/input.py:187: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n",
      "WARNING:tensorflow:From <ipython-input-1-cb46bdabd9b5>:146: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "To construct input pipelines, use the `tf.data` module.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from models import *\n",
    "import os\n",
    "\n",
    "%pylab inline\n",
    "\n",
    "class ConvolutionalAutoencoder(object):\n",
    "    \"\"\"\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        \"\"\"\n",
    "        build the graph\n",
    "        \"\"\"\n",
    "        # place holder of input data\n",
    "        x = tf.placeholder(tf.float32, shape=[None, 20, 20, 1])  # [#batch, img_height, img_width, #channels]\n",
    "\n",
    "        # encode\n",
    "        conv1 = Convolution2D([5, 5, 1, 32], activation=tf.nn.relu, scope='conv_1')(x)\n",
    "        pool1 = MaxPooling(kernel_shape=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', scope='pool_1')(conv1)\n",
    "        conv2 = Convolution2D([5, 5, 32, 32], activation=tf.nn.relu, scope='conv_2')(pool1)\n",
    "        pool2 = MaxPooling(kernel_shape=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', scope='pool_2')(conv2)\n",
    "        unfold = Unfold(scope='unfold')(pool2)\n",
    "        encoded = FullyConnected(20, activation=tf.nn.relu, scope='encode')(unfold)\n",
    "        # decode\n",
    "        decoded = FullyConnected(5*5*32, activation=tf.nn.relu, scope='decode')(encoded)\n",
    "        fold = Fold([-1, 5, 5, 32], scope='fold')(decoded)\n",
    "        unpool1 = UnPooling((2, 2), output_shape=tf.shape(conv2), scope='unpool_1')(fold)\n",
    "        deconv1 = DeConvolution2D([5, 5, 32, 32], output_shape=tf.shape(pool1), activation=tf.nn.relu, scope='deconv_1')(unpool1)\n",
    "        unpool2 = UnPooling((2, 2), output_shape=tf.shape(conv1), scope='unpool_2')(deconv1)\n",
    "        reconstruction = DeConvolution2D([5, 5, 1, 32], output_shape=tf.shape(x), activation=tf.nn.sigmoid, scope='deconv_2')(unpool2)\n",
    "\n",
    "        # loss function\n",
    "        loss = tf.nn.l2_loss(x - reconstruction)  # L2 loss\n",
    "\n",
    "        # training\n",
    "        training = tf.train.AdamOptimizer(1e-4).minimize(loss)\n",
    "\n",
    "        #\n",
    "        self.x = x\n",
    "        self.reconstruction = reconstruction\n",
    "        self.loss = loss\n",
    "        self.training = training\n",
    "\n",
    "    def train(self, passes, new_training=True):\n",
    "        \"\"\"\n",
    "\n",
    "        :param batch_size:\n",
    "        :param passes:\n",
    "        :param new_training:\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        pwd = os.getcwd()\n",
    "        tfrecord_file_path = pwd + \"/SAE_input2.tfrecord\"\n",
    "        filename_queue = tf.train.string_input_producer(\n",
    "                                      tf.train.match_filenames_once(tfrecord_file_path),\n",
    "                                      shuffle=True, num_epochs=None)\n",
    "\n",
    "        reader = tf.TFRecordReader()\n",
    "        _, serialized_example = reader.read(filename_queue)\n",
    "        features = tf.parse_single_example(serialized_example,\n",
    "                                               features={'data': tf.FixedLenFeature([], tf.string)}) \n",
    "        img = tf.decode_raw(features['data'], tf.uint8)\n",
    "        img = tf.image.convert_image_dtype(img,tf.float32)\n",
    "        img = tf.reshape(img, [20, 20, 1])\n",
    "        input_batch = tf.train.shuffle_batch([img],batch_size=10,capacity=500,min_after_dequeue=250,num_threads=1)\n",
    "\n",
    "        with tf.Session() as sess:\n",
    "            # prepare session\n",
    "            if new_training:\n",
    "                saver, global_step = Model.start_new_session(sess)\n",
    "            else:\n",
    "                saver, global_step = Model.continue_previous_session(sess, ckpt_file='saver/CAE/checkpoint')\n",
    "\n",
    "            tf.local_variables_initializer().run()\n",
    "            tf.global_variables_initializer().run()\n",
    "            coord = tf.train.Coordinator()\n",
    "            thread = tf.train.start_queue_runners(sess=sess,coord=coord) \n",
    "            \n",
    "            # start training\n",
    "            for step in range(1+global_step, 1+passes+global_step):\n",
    "                x = input_batch.eval()\n",
    "                self.training.run(feed_dict={self.x: x})\n",
    "\n",
    "                if step % 100 == 0:\n",
    "                    loss = self.loss.eval(feed_dict={self.x: x})\n",
    "                    print(\"pass {}, training loss {}\".format(step, loss))\n",
    "\n",
    "                if step % 1000 == 0:  # save weights\n",
    "                    saver.save(sess, 'saver/CAE/CAE', global_step=step)\n",
    "                    print('checkpoint saved')\n",
    "            \n",
    "            coord.request_stop()\n",
    "            coord.join(thread)\n",
    "\n",
    "    def reconstruct(self):\n",
    "        \"\"\"\n",
    "\n",
    "        \"\"\"\n",
    "        def weights_to_grid(weights, rows, cols):\n",
    "            \"\"\"convert the weights tensor into a grid for visualization\"\"\"\n",
    "            height, width, in_channel, out_channel = weights.shape\n",
    "            padded = np.pad(weights, [(1, 1), (1, 1), (0, 0), (0, rows * cols - out_channel)],\n",
    "                            mode='constant', constant_values=0)\n",
    "            transposed = padded.transpose((3, 1, 0, 2))\n",
    "            reshaped = transposed.reshape((rows, -1))\n",
    "            grid_rows = [row.reshape((-1, height + 2, in_channel)).transpose((1, 0, 2)) for row in reshaped]\n",
    "            grid = np.concatenate(grid_rows, axis=0)\n",
    "\n",
    "            return grid.squeeze()\n",
    "\n",
    "        with tf.Session() as sess:\n",
    "            saver, global_step = Model.continue_previous_session(sess, ckpt_file='saver/CAE/checkpoint')\n",
    "\n",
    "            # visualize weights\n",
    "            first_layer_weights = tf.get_default_graph().get_tensor_by_name(\"conv_1/kernel:0\").eval()\n",
    "            grid_image = weights_to_grid(first_layer_weights, 4, 8)\n",
    "\n",
    "            fig, ax0 = plt.subplots(ncols=1, figsize=(8, 4))\n",
    "            ax0.imshow(grid_image, cmap=plt.cm.gray, interpolation='nearest')\n",
    "            ax0.set_title('first conv layers weights')\n",
    "            plt.show()\n",
    "\n",
    "            # visualize results\n",
    "            pwd = os.getcwd()\n",
    "            tfrecord_file_path = pwd + \"/SAE_input2.tfrecord\"\n",
    "            filename_queue = tf.train.string_input_producer(\n",
    "                                          tf.train.match_filenames_once(tfrecord_file_path),\n",
    "                                          shuffle=True, num_epochs=None)\n",
    "\n",
    "            reader = tf.TFRecordReader()\n",
    "            _, serialized_example = reader.read(filename_queue)\n",
    "            features = tf.parse_single_example(serialized_example,\n",
    "                                                   features={'data': tf.FixedLenFeature([], tf.string)}) \n",
    "            img = tf.decode_raw(features['data'], tf.uint8)\n",
    "            img = tf.image.convert_image_dtype(img,tf.float32)\n",
    "            img = tf.reshape(img, [20, 20, 1])\n",
    "            input_batch = tf.train.batch([img],batch_size=36)\n",
    "            \n",
    "            with tf.Session() as sess:\n",
    "                tf.local_variables_initializer().run()\n",
    "                tf.global_variables_initializer().run()\n",
    "                coord = tf.train.Coordinator()\n",
    "                thread = tf.train.start_queue_runners(sess=sess,coord=coord) \n",
    "                \n",
    "                x = input_batch.eval()\n",
    "                org, recon = sess.run((self.x, self.reconstruction), feed_dict={self.x: x})\n",
    "\n",
    "                input_images = weights_to_grid(org.transpose((1, 2, 3, 0)), 6, 6)\n",
    "                recon_images = weights_to_grid(recon.transpose((1, 2, 3, 0)), 6, 6)\n",
    "\n",
    "                fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(20, 10))\n",
    "                ax0.imshow(input_images, cmap=plt.cm.gray, interpolation='nearest')\n",
    "                ax0.set_title('input images')\n",
    "                ax1.imshow(recon_images, cmap=plt.cm.gray, interpolation='nearest')\n",
    "                ax1.set_title('reconstructed images')\n",
    "                plt.show()\n",
    "\n",
    "                coord.request_stop()\n",
    "                coord.join(thread)\n",
    "            \n",
    "\n",
    "def main():\n",
    "    conv_autoencoder = ConvolutionalAutoencoder()\n",
    "    #conv_autoencoder.train(passes=10000, new_training=True)\n",
    "    conv_autoencoder.reconstruct()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  }
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