pyramid-gan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from nbutil import imshow_multi\n",
    "import os\n",
    "from tensorflow.contrib.layers.python.layers import batch_norm\n",
    "from tensorflow.contrib.layers import xavier_initializer\n",
    "from PIL import Image\n",
    "import urllib, cStringIO\n",
    "# import cv2\n",
    "from tensorflow.contrib import layers\n",
    "from scipy.misc import imresize\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 16\n",
    "\n",
    "PYRAMID_LEVELS = 3\n",
    "LAYERS_PER_LEVEL = 2\n",
    "LEVEL_UPSCALE = 2 ** LAYERS_PER_LEVEL\n",
    "MAX_SIZE = (LEVEL_UPSCALE ** PYRAMID_LEVELS)\n",
    "print MAX_SIZE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "attr_file = '../data/celeba/list_attr_celeba.txt'\n",
    "keys = None\n",
    "attributes_by_image = {}\n",
    "for i, line in enumerate(open(attr_file)):\n",
    "    if i == 1:\n",
    "        keys = line.split()\n",
    "    elif i > 1:\n",
    "        image = os.path.join('../data/celeba/img_align_celeba', line.split()[0])\n",
    "        values = line.split()[1:]\n",
    "        attributes_by_image[image] = {attr: val == '1' for attr, val in zip(keys, values)}\n",
    "\n",
    "image_names = attributes_by_image.keys()\n",
    "attr_vector = np.zeros((len(attributes_by_image), len(keys)))\n",
    "for i, image_name in enumerate(image_names):\n",
    "    attrs = attributes_by_image[image_name]\n",
    "    attr_vector[i] = [(1 if attrs[key] else 0) for key in keys]\n",
    "\n",
    "def create_qs(train):\n",
    "    filename_tensor = tf.convert_to_tensor(image_names, dtype=tf.string)\n",
    "    attr_tensor = tf.convert_to_tensor(attr_vector, dtype=tf.float32)\n",
    "    filename_q, attr_q = tf.train.slice_input_producer([filename_tensor, attr_tensor], num_epochs=None, shuffle=True)\n",
    "\n",
    "    # reader = tf.WholeFileReader()\n",
    "    # filename, image_data = reader.read(filename_q)\n",
    "    image_255 = tf.image.decode_jpeg(tf.read_file(filename_q))\n",
    "    image = tf.reshape(tf.cast(image_255, tf.float32) / 255.0, [218, 178, 3]) # images are 178x218\n",
    "    # image = tf.image.rgb_to_grayscale(image)\n",
    "\n",
    "    def resize_image(image):\n",
    "        # image = tf.random_crop(image, [192, 160, 3])\n",
    "        # return image\n",
    "        image = tf.image.resize_image_with_crop_or_pad(image, 160, 160)\n",
    "        img_reshaped = tf.reshape(image, [1, 160, 160, 3])\n",
    "        image = tf.image.resize_bilinear(img_reshaped, [MAX_SIZE, MAX_SIZE])\n",
    "        return tf.reshape(image, [MAX_SIZE, MAX_SIZE, 3])\n",
    "    image = resize_image(image)\n",
    "\n",
    "    def distort_image(image):\n",
    "        # noise_amt = tf.abs(tf.random_normal([], stddev=0.2))\n",
    "        # distorted_image = image + tf.random_uniform([64, 64, 3], maxval=noise_amt)\n",
    "        distorted_image = tf.image.random_flip_left_right(image)\n",
    "        distorted_image = tf.image.random_brightness(distorted_image, max_delta=0.3)\n",
    "        distorted_image = tf.image.random_contrast(distorted_image, lower=0.6, upper=1.6)\n",
    "        # distorted_image = tf.image.per_image_standardization(distorted_image)\n",
    "        # distorted_image = tf.clip_by_value(distorted_image, 0, 1)\n",
    "        return distorted_image\n",
    "\n",
    "    images_batch, attrs_batch = tf.train.shuffle_batch([distort_image(image), attr_q], batch_size=BATCH_SIZE, capacity=BATCH_SIZE*20, min_after_dequeue=BATCH_SIZE*10)\n",
    "    return images_batch, attrs_batch\n",
    "\n",
    "images_batch, attrs_batch = create_qs(True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "initializer = tf.truncated_normal_initializer(0.00, 0.02)\n",
    "\n",
    "def lrelu(x):\n",
    "    # leaky relu\n",
    "    alpha = 0.1\n",
    "    return tf.maximum(alpha*x,x)\n",
    "\n",
    "def identity(x): return x\n",
    "\n",
    "def l2_loss(y, y_):\n",
    "    return (y - y_) * (y - y_)\n",
    "\n",
    "def resize_images_to_same_size(images, based_on):\n",
    "    if based_on is None:\n",
    "        return None\n",
    "    else:\n",
    "        _,h,w,_ = [d.value for d in based_on.get_shape()]\n",
    "        return tf.image.resize_images(images, [h,w])\n",
    "\n",
    "def avg(list):\n",
    "    return sum(list) * 1.0 / len(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "class Net(object):\n",
    "    def __init__(self):\n",
    "        self.global_step = tf.contrib.framework.get_or_create_global_step()\n",
    "        \n",
    "    session = None\n",
    "    \n",
    "    def setup(self, path=None):\n",
    "        if self.session: self.session.close()\n",
    "        self.session = tf.InteractiveSession()\n",
    "        \n",
    "        init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())\n",
    "        self.session.run(init_op)\n",
    "        tf.train.start_queue_runners(sess=self.session)\n",
    "        \n",
    "        self.saver = None\n",
    "        self.path = path\n",
    "        if path:\n",
    "            if not os.path.exists(path):\n",
    "                os.mkdir(path)\n",
    "            self.saver = tf.train.Saver()\n",
    "            ckpt = tf.train.get_checkpoint_state(path)\n",
    "            if ckpt and ckpt.model_checkpoint_path:\n",
    "                saver.restore(self.session, ckpt.model_checkpoint_path)\n",
    "                print 'Restored from checkpoint', ckpt.model_checkpoint_path\n",
    "            else:\n",
    "                print 'Did not restore from checkpoint'\n",
    "        else:\n",
    "            print 'Will not save progress'\n",
    "    \n",
    "    def save(self):\n",
    "        if self.saver:\n",
    "            step_ = self.session.run(self.global_step)\n",
    "            saver.save(session, self.path + '/model.ckpt', global_step=step_)\n",
    "            print 'Saved'\n",
    "    \n",
    "    def train_step(self, verbose):\n",
    "        # should return the current step\n",
    "        assert False, 'train_step() not implemented'\n",
    "    \n",
    "    def train(self, print_every=2, save_every=100):\n",
    "        while True:\n",
    "            step_ = self.session.run(self.global_step)\n",
    "            verbose = (step_ % print_every == 0)\n",
    "            self.train_step(verbose)\n",
    "            if step_ % save_every == 1:\n",
    "                self.save()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok\n"
     ]
    }
   ],
   "source": [
    "\n",
    "Z_SIZE = 32\n",
    "SCALING_L2_WEIGHT = 2\n",
    "\n",
    "class Upscaler(object):\n",
    "    def __init__(self, inputs, target_outputs):\n",
    "        # upscalers for the same sizes will always share weights        \n",
    "        _, big_h, big_w, c = [d.value for d in target_outputs.get_shape()]\n",
    "        self.name = '{}x{}'.format(big_h,big_w)\n",
    "        self.out_size = (big_h, big_w)\n",
    "        \n",
    "        self.fake_big = self.upscale(inputs)\n",
    "        if inputs is not None:\n",
    "            small_again = resize_images_to_same_size(self.fake_big, inputs)\n",
    "            self.scaling_loss = tf.reduce_mean(l2_loss(inputs, small_again), axis=[1,2,3])\n",
    "        else:\n",
    "            self.scaling_loss = 0\n",
    "        \n",
    "        self.real_crit_score = self.critic(inputs, target_outputs)\n",
    "        self.fake_crit_score = self.critic(inputs, self.fake_big)\n",
    "        \n",
    "    def upscale(self, small):\n",
    "        # small can be None\n",
    "        with tf.variable_scope('gen/' + self.name):\n",
    "            z = tf.random_uniform([BATCH_SIZE, 1, 1, Z_SIZE], minval=-1, maxval=1)\n",
    "            \n",
    "            if small is None:\n",
    "                img = z\n",
    "            else:\n",
    "                _, small_h, small_w, c = [d.value for d in small.get_shape()]\n",
    "                z_tiled = tf.tile(z, [1, small_h, small_w, 1])\n",
    "                img = tf.concat([small, z_tiled], 3)\n",
    "            \n",
    "            for i in xrange(LAYERS_PER_LEVEL):    \n",
    "                n_channels = 32 * 2 ** (LAYERS_PER_LEVEL - 1 - i)\n",
    "                img = layers.conv2d_transpose(img,\n",
    "                          n_channels * 2, \n",
    "                          scope='up_'+str(i), \n",
    "                          kernel_size=4, \n",
    "                          activation_fn=lrelu, \n",
    "                          stride=2,\n",
    "                          normalizer_fn=layers.batch_norm, \n",
    "                          weights_initializer=initializer)\n",
    "            img = layers.conv2d(img, \n",
    "                    3, \n",
    "                    scope='final'+str(i),\n",
    "                    kernel_size=1,\n",
    "                    stride=1,\n",
    "                    activation_fn=tf.tanh,\n",
    "                    weights_initializer=initializer)\n",
    "            return img\n",
    "        \n",
    "    def critic(self, small, big):\n",
    "        # small can be None\n",
    "        with tf.variable_scope('critic/' + self.name):\n",
    "            if small is not None:\n",
    "                # condition the critic on small:\n",
    "                img = tf.concat([big, resize_images_to_same_size(small, big)], 3)\n",
    "            else:\n",
    "                img = big\n",
    "            for i in xrange(LAYERS_PER_LEVEL):\n",
    "                n_channels = 32 * 2**i\n",
    "                norm = None if i == 0 else layers.batch_norm\n",
    "                img = layers.conv2d(img, \n",
    "                                    n_channels, \n",
    "                                    scope='critic_'+str(i),\n",
    "                                    kernel_size=5,\n",
    "                                    stride=2,\n",
    "                                    activation_fn=lrelu,\n",
    "                                    normalizer_fn=norm, \n",
    "                                    weights_initializer=initializer)\n",
    "            # once we've downsampled using convolution, a final layer computes critic scores across the image,\n",
    "            # which are averaged:\n",
    "            img = layers.conv2d(img,1,\n",
    "                                scope='final_'+str(i),\n",
    "                                kernel_size=5,\n",
    "                                stride=1,\n",
    "                                activation_fn=identity,\n",
    "                                weights_initializer=initializer)\n",
    "            return tf.reduce_mean(img, axis=[1,2,3])\n",
    "\n",
    "def true_loss(output, is_true):\n",
    "    return l2_loss(output, tf.ones_like(output) * (1 if is_true else -1))\n",
    "        \n",
    "class Pyramid(Net):\n",
    "    def __init__(self, images_batch, scopename='pg'):\n",
    "        super(Pyramid, self).__init__()\n",
    "        self.critic_losses = []\n",
    "        self.gen_losses = []\n",
    "        with tf.variable_scope(scopename):\n",
    "            self.upscalers = []\n",
    "            for i in xrange(PYRAMID_LEVELS):\n",
    "                in_size = LEVEL_UPSCALE ** i\n",
    "                out_size = LEVEL_UPSCALE ** (i+1)\n",
    "                inputs = None if i == 0 else tf.image.resize_images(images_batch, [in_size, in_size])\n",
    "                outputs = tf.image.resize_images(images_batch, [out_size, out_size])\n",
    "                u = Upscaler(inputs, outputs)\n",
    "                self.upscalers.append(u)\n",
    "\n",
    "            self.critic_loss = tf.reduce_mean([\n",
    "                true_loss(u.fake_crit_score, False) + true_loss(u.real_crit_score, True)\n",
    "                for u in self.upscalers])\n",
    "            self.gen_loss = tf.reduce_mean([\n",
    "                true_loss(u.fake_crit_score, True) + u.scaling_loss * SCALING_L2_WEIGHT\n",
    "                for u in self.upscalers])\n",
    "\n",
    "            critic_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scopename+'/critic')\n",
    "            gen_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scopename+'/gen')\n",
    "\n",
    "            self.train_gen = tf.train.AdamOptimizer(0.001, beta1=0.5).minimize(self.gen_loss, \n",
    "                                                                        var_list=gen_vars, \n",
    "                                                                        global_step=self.global_step)\n",
    "\n",
    "            train_critic_op = tf.train.RMSPropOptimizer(0.0001).minimize(self.critic_loss, var_list=critic_vars)\n",
    "            weight_clip_op = tf.group(*[w.assign(tf.clip_by_value(w, -0.01, 0.01)) for w in critic_vars])\n",
    "            self.train_critic = train_critic_op # tf.group(weight_clip_op, train_critic_op)\n",
    "    \n",
    "    def train_step(self, verbose):\n",
    "        for _ in xrange(1):\n",
    "            _, loss_ = self.session.run([self.train_critic, self.critic_loss])\n",
    "            self.critic_losses.append(loss_)\n",
    "        _, loss_, step_ = self.session.run([self.train_gen, self.gen_loss, self.global_step])\n",
    "        self.gen_losses.append(loss_)\n",
    "        if verbose:\n",
    "            print '{}: gen loss: {}; disc loss: {}'.format(step_, avg(self.gen_losses), avg(self.critic_losses))\n",
    "            self.gen_losses = []\n",
    "            self.critic_losses = []\n",
    "        \n",
    "p = Pyramid(images_batch, scopename='pg16')\n",
    "        \n",
    "print 'ok'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 4, 4, 3)\n",
      "(16, 16, 16, 3)\n",
      "(16, 64, 64, 3)\n"
     ]
    }
   ],
   "source": [
    "for u in p.upscalers:\n",
    "    print u.fake_big.get_shape()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Will not save progress\n"
     ]
    }
   ],
   "source": [
    "p.setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2121: gen loss: 2.26435727645; disc loss: 1.10330996066\n",
      "2161: gen loss: 2.33497138023; disc loss: 1.09000033587\n",
      "2201: gen loss: 2.32054381669; disc loss: 1.08052256405\n",
      "2241: gen loss: 2.31287916005; disc loss: 1.07830102742\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-22-0c20accd0ce3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprint_every\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-5-e4c27a43c3be>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, print_every, save_every)\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0mstep_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_step\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m             \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mstep_\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mprint_every\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstep_\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msave_every\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-11-68c84deedf42>\u001b[0m in \u001b[0;36mtrain_step\u001b[0;34m(self, verbose)\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_critic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcritic_loss\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    121\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcritic_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_gen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_step\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\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    769\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/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\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/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\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   1021\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\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   1023\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   1024\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/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\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-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\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": [
    "p.train(print_every=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aFJgpR3KJaFDcApElotkBD1RX8Io6BCvUHDGFaqF1xKUidWr3xhywjVJN8exI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\n+Ra3uMUtbnGLX0n8H8qi+GNEFJHFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7846e13750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_sample():\n",
    "    outs = []\n",
    "    for u in p.upscalers:\n",
    "        outs.append(tf.image.resize_images(u.fake_big, [MAX_SIZE, MAX_SIZE])[0])\n",
    "    outs.append(images_batch[0])\n",
    "    \n",
    "    results = p.session.run(outs)\n",
    "    \n",
    "    imshow_multi(results)\n",
    "\n",
    "show_sample()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}