DCGAN_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Dave/anaconda3/envs/deepnet/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import scipy as sp\n",
    "import numpy as np\n",
    "import os \n",
    "import pickle\n",
    "\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "\n",
    "trunc_normal= tf.truncated_normal_initializer(stddev=0.02)\n",
    "normal = tf.random_normal_initializer(stddev=0.02)\n",
    "\n",
    "from NN_architectures import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# some constants\n",
    "\n",
    "LEARNING_RATE = 0.0001\n",
    "BETA1 = 0.5\n",
    "BATCH_SIZE = 128\n",
    "EPOCHS = 5\n",
    "SAVE_SAMPLE_PERIOD = 100\n",
    "\n",
    "#task='TEST'\n",
    "task='TRAIN'\n",
    "\n",
    "PATH='DCGAN_test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "global d_sizes, g_sizes\n",
    "\n",
    "# # Convolutional layers\n",
    "        \n",
    "d_sizes = {\n",
    "         'conv_layers': [(2, 6, 2, False, 1, lrelu, trunc_normal),\n",
    "                         (64, 6, 2, False, 1, lrelu, trunc_normal)],\n",
    "         'dense_layers': [(1024, True, 1, lrelu ,normal)],\n",
    "}\n",
    "g_sizes = {\n",
    "         'z': 100,\n",
    "         'projection': 128,\n",
    "         'bn_after_project': False,\n",
    "         'dense_layers': [(1024, False, 1, tf.nn.relu, normal)],\n",
    "         'conv_layers': [(128, 6, 2, False, 1, tf.nn.relu ,normal),\n",
    "                         (1, 6, 2, False, 1,  tf.sigmoid, normal)],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mnist():\n",
    "    \n",
    "    from tensorflow.examples.tutorials.mnist import input_data\n",
    "    mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=False)\n",
    "\n",
    "    X_train = mnist.train.images\n",
    "\n",
    "    X_train = X_train.reshape(len(X_train),28,28,1)\n",
    "    n_H = X_train.shape[1]\n",
    "    n_W = X_train.shape[2]\n",
    "    n_C = X_train.shape[-1]\n",
    "    \n",
    "    X_test = mnist.test.images\n",
    "    X_test = X_test.reshape(len(X_test),28,28,1)\n",
    "    \n",
    "    tf.reset_default_graph()\n",
    "    \n",
    "    \n",
    "    gan = modular_DCGAN(n_H, n_W, n_C, d_sizes, g_sizes,\n",
    "                lr_g=LEARNING_RATE, lr_d=LEARNING_RATE, beta1=BETA1,\n",
    "                batch_size=BATCH_SIZE, epochs=EPOCHS,\n",
    "                save_sample=SAVE_SAMPLE_PERIOD, path=PATH)\n",
    "    \n",
    "    vars_to_train= tf.trainable_variables()\n",
    "    \n",
    "    \n",
    "    if task == 'TRAIN':\n",
    "        init_op = tf.global_variables_initializer()\n",
    "        \n",
    "    if task == 'TEST':\n",
    "        vars_all = tf.global_variables()\n",
    "        vars_to_init = list(set(vars_all)-set(vars_to_train))\n",
    "        init_op = tf.variables_initializer(vars_to_init)\n",
    "        \n",
    "    # Add ops to save and restore all the variables.\n",
    "    saver = tf.train.Saver()    \n",
    "        \n",
    "    with tf.Session() as sess:\n",
    "        \n",
    "        sess.run(init_op)\n",
    "\n",
    "        if task=='TRAIN':\n",
    "            print('\\n Training...')\n",
    "            \n",
    "            if os.path.exists(PATH+'/'+PATH+'.ckpt.index'):\n",
    "                saver.restore(sess,PATH+'/'+PATH+'.ckpt')\n",
    "                print('Model restored.')\n",
    "            \n",
    "            gan.set_session(sess)\n",
    "            gan.fit(X_train)\n",
    "            \n",
    "            save_path = saver.save(sess, PATH+'/'+PATH+'.ckpt')\n",
    "            print(\"Model saved in path: %s\" % save_path)\n",
    "        \n",
    "        if task=='TEST':\n",
    "            print('\\n Evaluate model on test set...')\n",
    "            saver.restore(sess,PATH+'/'+PATH+'.ckpt')\n",
    "            print('Model restored.')\n",
    "            \n",
    "            gan.set_session(sess) \n",
    "            \n",
    "        done = False\n",
    "        while not done:\n",
    "            \n",
    "            \n",
    "            Z_in = np.random.uniform(-1,1, size=(1, g_sizes['z']))\n",
    "            \n",
    "            im = gan.get_sample(Z_in)\n",
    "            \n",
    "            plt.imshow(im.reshape(28,28), cmap='gray')\n",
    "            plt.show()\n",
    "            \n",
    "            \n",
    "            ans = input(\"Generate another?\")\n",
    "            if ans and ans[0] in ('n' or 'N'):\n",
    "                done = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Convolutional Network architecture detected\n",
      "d_conv_layer_0\n",
      "d_conv_layer_1\n",
      "d_dense_layer_2\n",
      "d_dense_layer_3\n",
      "Convolution\n",
      "Input for convolution shape  (?, 28, 28, 1)\n",
      "(3136, 1024)\n",
      "Logits shape (?, 1)\n",
      "g_dense_layer_0\n",
      "g_dense_layer_1\n",
      "g_conv_layer_2\n",
      "g_conv_layer_3\n",
      "Deconvolution\n",
      "Input for deconvolution shape (?, 100)\n",
      "0\n",
      "Deconvoluted output shape (?, 28, 28, 1)\n",
      "Convolution\n",
      "Input for convolution shape  (?, 28, 28, 1)\n",
      "(3136, 1024)\n",
      "Logits shape (?, 1)\n",
      "Deconvolution\n",
      "Input for deconvolution shape (?, 100)\n",
      "0\n",
      "Deconvoluted output shape (?, 28, 28, 1)\n",
      "\n",
      " Training...\n",
      "\n",
      " ****** \n",
      "\n",
      "Training DCGAN with a total of 55000 samples distributed in batches of size 128\n",
      "\n",
      "The learning rate set for the generator is 0.0001 while for the discriminator is 0.0001, and every 100 epoch a generated sample will be saved to DCGAN_test\n",
      "\n",
      " ****** \n",
      "\n",
      "Epoch: 0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-3d0e91404e9b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m                     \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPATH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m         \u001b[0mmnist\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     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mtask\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'TEST'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-4-e149c4dc7bb0>\u001b[0m in \u001b[0;36mmnist\u001b[0;34m()\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0mgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0mgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0msave_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPATH\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mPATH\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.ckpt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Google Drive (Work)/Università/PhD/Material/Neural Networks/NN_toolkit/NN_architectures.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m   3961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3962\u001b[0m                 _, d_cost, d_acc = self.session.run(\n\u001b[0;32m-> 3963\u001b[0;31m                     \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0md_train_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0md_cost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0md_accuracy\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   3964\u001b[0m                     \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mX_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZ\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mZ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_sz\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3965\u001b[0m                 )\n",
      "\u001b[0;32m~/anaconda3/envs/deepnet/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    887\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 889\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    890\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    891\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~/anaconda3/envs/deepnet/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1119\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1120\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1121\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1122\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~/anaconda3/envs/deepnet/lib/python3.6/site-packages/tensorflow/python/client/session.py\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   1315\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1317\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1318\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1319\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/deepnet/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1321\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   1322\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1323\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   1324\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   1325\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~/anaconda3/envs/deepnet/lib/python3.6/site-packages/tensorflow/python/client/session.py\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   1300\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1301\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-> 1302\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\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": [
    "if __name__=='__main__':\n",
    "\n",
    "    if task == 'TRAIN':\n",
    "        if not os.path.exists(PATH):\n",
    "            os.mkdir(PATH)\n",
    "    \n",
    "        elif os.path.exists(PATH):\n",
    "            if os.path.exists(PATH+'/checkpoint'):\n",
    "                ans = input('A previous checkpoint already exists, choose the action to perform \\n \\n 1) Overwrite the current model saved at '+PATH+'/checkpoint \\n 2) Start training a new model \\n 3) Restore and continue training the previous model \\n ')\n",
    "                \n",
    "                if ans == '1':\n",
    "                    print('Overwriting existing model in '+PATH)\n",
    "                    for file in os.listdir(PATH):\n",
    "                        file_path = os.path.join(PATH, file)\n",
    "                        try:\n",
    "                            if os.path.isfile(file_path):\n",
    "                                os.unlink(file_path)\n",
    "                            #elif os.path.isdir(file_path): shutil.rmtree(file_path)\n",
    "                        except Exception as e:\n",
    "                            print(e)\n",
    "                            \n",
    "                elif ans == '2':\n",
    "                    PATH = input('Specify the name of the model, a new directory will be created.\\n')\n",
    "                    os.mkdir(PATH)    \n",
    "        \n",
    "        mnist()\n",
    "   \n",
    "    elif task == 'TEST': \n",
    "        if not os.path.exists(PATH+'/checkpoint'):\n",
    "            print('No checkpoint to test')\n",
    "        else:\n",
    "            mnist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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