word_classifier_seq2seqX.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training: Extended Sequence to Sequence Model - Word Classification\n",
    "Training extended sequence to sequnce model for recognition of words. This is my personal experimental model, which should improve accuracy of regular sequence to sequence model\n",
    "## TODO\n",
    "```\n",
    "Testing imageStandardization on whole images, removed from TF model\n",
    "Remove random border\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import tensorflow as tf\n",
    "import tensorflow.contrib.seq2seq as seq2seq\n",
    "from tensorflow.python.layers import core as layers_core\n",
    "from tensorflow.python.ops import math_ops\n",
    "import time\n",
    "import math\n",
    "import unidecode\n",
    "import cv2\n",
    "\n",
    "sys.path.append('src')\n",
    "from ocr.datahelpers import load_words_data, corresponding_shuffle, char2idx\n",
    "from ocr.helpers import implt, img_extend, resize\n",
    "from ocr.mlhelpers import TrainingPlot\n",
    "from ocr.normalization import letter_normalization, image_standardization\n",
    "from ocr.tfhelpers import create_cell\n",
    "\n",
    "\n",
    "%matplotlib notebook\n",
    "plt.rcParams['figure.figsize'] = (9.0, 5.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "LANG = 'en'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading words...\n",
      " |████████████████████████████████████████| 100.0% \n",
      "-> Number of words: 5069\n"
     ]
    }
   ],
   "source": [
    "images, labels, gaplines = load_words_data(\n",
    "    ['data/processed/breta/words_gaplines/'], load_gaplines=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "CHARS = 82 if LANG =='cz' else 52\n",
    "\n",
    "PAD = 0   # Padding\n",
    "EOS = 1   # End of seq\n",
    "LETTER_PAD = -1.0\n",
    "\n",
    "# Testing partialy separated dataset\n",
    "sep_words = 500\n",
    "is_sep_words = False\n",
    "\n",
    "num_buckets = 20\n",
    "slider_size = (60, 1)\n",
    "step_size = 1\n",
    "N_INPUT = slider_size[0]*slider_size[1]\n",
    "char_size = CHARS + 2\n",
    "letter_size = 64*64\n",
    "\n",
    "encoder_layers = 2\n",
    "encoder_residual_layers = 1        # HAVE TO be smaller than encoder_layers\n",
    "encoder_units = 128                # 256\n",
    "\n",
    "decoder_layers = 2*encoder_layers  # 2* is due to the bidirectional encoder\n",
    "decoder_residual_layers = 2*encoder_residual_layers\n",
    "decoder_units = encoder_units\n",
    "\n",
    "wordRNN_layers = 2\n",
    "wordRNN_residual_layers = 1\n",
    "wordRNN_units = 128\n",
    "\n",
    "attention_size = 256\n",
    "\n",
    "add_output_length = 0 # 4\n",
    "\n",
    "learning_rate = 1e-3               # 1e-4\n",
    "max_gradient_norm = 5.0            # For gradient clipping\n",
    "dropout = 0.4\n",
    "train_per = 0.8                    # Percentage of training data\n",
    "\n",
    "TRAIN_STEPS = 1000000               # Number of training steps!\n",
    "TEST_ITER = 150\n",
    "LOSS_ITER = 50\n",
    "SAVE_ITER = 1000\n",
    "BATCH_SIZE = 25                    # 20\n",
    "EPOCH = 1000                       # Number of batches in epoch - not accurate\n",
    "save_location = 'models/word-clas/' + LANG + '/SeqRNN/Classifier4'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training images: 4055\n",
      "Testing images: 1014\n"
     ]
    }
   ],
   "source": [
    "# Shuffle data for later splitting\n",
    "images, labels, gaplines = corresponding_shuffle([images, labels, gaplines])\n",
    "\n",
    "# TODO: for testing, can be removed\n",
    "if slider_size[1] > 2:\n",
    "    for i in range(len(images)):\n",
    "        images[i] = cv2.copyMakeBorder(\n",
    "            images[i],\n",
    "            0, 0, slider_size[1]//2, slider_size[1]//2,\n",
    "            cv2.BORDER_CONSTANT,\n",
    "            value=[0, 0, 0])\n",
    "\n",
    "labels_idx = np.empty(len(labels), dtype=object)\n",
    "for i, label in enumerate(labels):\n",
    "    labels_idx[i] = [char2idx(c, True) for c in label]\n",
    "    labels_idx[i].append(EOS)    \n",
    "    \n",
    "# Split data on train and test dataset\n",
    "div = int(train_per * len(images))\n",
    "\n",
    "trainImages = images[0:div]\n",
    "testImages = images[div:]\n",
    "\n",
    "trainGaplines = gaplines[0:div]\n",
    "testGaplines = gaplines[div:]\n",
    "\n",
    "trainLabels_idx = labels_idx[0:div]\n",
    "testLabels_idx = labels_idx[div:]\n",
    "\n",
    "print(\"Training images:\", div)\n",
    "print(\"Testing images:\", len(images) - div)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total train images 4055\n"
     ]
    }
   ],
   "source": [
    "def stackImage(img, a, b):\n",
    "    \"\"\" Add blank columns (lenght a, b) at start and end of image \"\"\"\n",
    "    return np.concatenate(\n",
    "        (np.zeros((img.shape[0], a)),\n",
    "         np.concatenate((img, np.zeros((img.shape[0], b))), axis=1)),\n",
    "        axis=1)\n",
    "    \n",
    "# Dont mix train and test images\n",
    "num_new_images = 0 # 2\n",
    "trainImagesF = np.empty(len(trainImages) * (num_new_images+1), dtype=object)\n",
    "trainGaplinesF = np.empty(len(trainImages) * (num_new_images+1), dtype=object)\n",
    "trainLabelsF_idx = np.empty(len(trainImages)*(num_new_images+1), dtype=object)\n",
    "for idx, img in enumerate(trainImages):\n",
    "    add_idx = idx*(num_new_images+1)\n",
    "    trainImagesF[add_idx] = img\n",
    "    trainGaplinesF[add_idx] = trainGaplines[idx]\n",
    "    trainLabelsF_idx[add_idx] = trainLabels_idx[idx]\n",
    "    for i in range(num_new_images):\n",
    "        a, b = np.random.randint(1, 16, size=2)\n",
    "        trainImagesF[add_idx + (i+1)] = stackImage(img, a, b)\n",
    "        trainGaplinesF[add_idx + (i+1)] = trainGaplines[idx] + a\n",
    "        trainLabelsF_idx[add_idx + (i+1)] = trainLabels_idx[idx]\n",
    "        \n",
    "print(\"Total train images\", len(trainImagesF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BucketDataIterator():\n",
    "    \"\"\" Iterator for feeding seq2seq model during training \"\"\"\n",
    "    def __init__(self,\n",
    "                 images,\n",
    "                 targets,\n",
    "                 gaplines,\n",
    "                 num_buckets=5,\n",
    "                 slider=(60, 30),\n",
    "                 slider_step=2,\n",
    "                 train=True):\n",
    "        \n",
    "        self.train = train\n",
    "        self.slider = slider\n",
    "        images4letters = images.copy()   # Just for 1px slider testing\n",
    "        \n",
    "        # PADDING of images to slider size; -(a // b) =  ceil(a/b)\n",
    "        for i in range(len(images)):\n",
    "            images[i] = image_standardization(\n",
    "                img_extend(\n",
    "                    images[i],\n",
    "                    (images[i].shape[0],\n",
    "                     max(-(-images[i].shape[1] // slider_step) * slider_step, 60))))\n",
    "        in_length = [(image.shape[1] + 1 - slider[1])//slider_step for image in images]\n",
    "        \n",
    "        # Split images to sequence of vectors\n",
    "        img_seq = np.empty(len(images), dtype=object)\n",
    "        for i, img in enumerate(images):\n",
    "            img_seq[i] = [img[:, loc * slider_step: loc*slider_step + slider[1]].flatten()\n",
    "                          for loc in range(in_length[i])]\n",
    "            \n",
    "        end_letter = np.ones(letter_size) # * LETTER_PAD # End letter is full white\n",
    "        np.put(end_letter, [0], [1])\n",
    "        letter_seq = np.empty(len(images), dtype=object)\n",
    "        for i, img in enumerate(images4letters):\n",
    "            # Removed imageStandardization\n",
    "            letter_seq[i] = [imageStandardization(    \n",
    "                letter_normalization(img[:, gaplines[i][x]:gaplines[i][x+1]])).flatten()\n",
    "                             for x in range(len(gaplines[i])-1)]\n",
    "#             letter_seq[i] = [letter_normalization(img[:, gaplines[i][x]:gaplines[i][x+1]]).flatten()\n",
    "#                              for x in range(len(gaplines[i])-1)]\n",
    "            letter_seq[i].append(end_letter)\n",
    "\n",
    "        # Create pandas dataFrame and sort it by images width (length)\n",
    "        # letters_length is num_letter + EOS\n",
    "        self.dataFrame = pd.DataFrame({'in_length': in_length,\n",
    "                                       'letters_length': [len(g) for g in gaplines],\n",
    "                                       'words_length': [len(t) for t in targets],\n",
    "                                       'in_images': img_seq,\n",
    "                                       'letters': letter_seq,\n",
    "                                       'words': targets\n",
    "                                      }).sort_values('in_length').reset_index(drop=True)\n",
    "\n",
    "        bsize = int(len(images) / num_buckets)\n",
    "        self.num_buckets = num_buckets\n",
    "        \n",
    "        # Create buckets by slicing parts by indexes\n",
    "        self.buckets = []\n",
    "        for bucket in range(num_buckets-1):\n",
    "            self.buckets.append(self.dataFrame.iloc[bucket * bsize: (bucket+1) * bsize])\n",
    "        self.buckets.append(self.dataFrame.iloc[(num_buckets-1) * bsize:])        \n",
    "        \n",
    "        self.buckets_size = [len(bucket) for bucket in self.buckets]\n",
    "\n",
    "        # cursor[i] will be the cursor for the ith bucket\n",
    "        self.cursor = np.array([0] * num_buckets)\n",
    "        self.bucket_order = np.random.permutation(num_buckets)\n",
    "        self.bucket_cursor = 0\n",
    "        self.shuffle()\n",
    "        print(\"Iterator created.\")\n",
    "\n",
    "        \n",
    "    def shuffle(self, idx=None):\n",
    "        \"\"\" Shuffle idx bucket or each bucket separately \"\"\"\n",
    "        for i in [idx] if idx is not None else range(self.num_buckets):\n",
    "            self.buckets[i] = self.buckets[i].sample(frac=1).reset_index(drop=True)\n",
    "            self.cursor[i] = 0\n",
    "\n",
    "\n",
    "    def next_batch(self, batch_size):\n",
    "        \"\"\"\n",
    "        Creates next training batch of size: batch_size\n",
    "        Retruns: (in_images, letters, words,\n",
    "                  in_length, letter_length, word_length)\n",
    "        \"\"\"\n",
    "        i_bucket = self.bucket_order[self.bucket_cursor]\n",
    "        # Increment cursor and shuffle in case of new round\n",
    "        self.bucket_cursor = (self.bucket_cursor + 1) % self.num_buckets\n",
    "        if self.bucket_cursor == 0:\n",
    "            self.bucket_order = np.random.permutation(self.num_buckets)\n",
    "            \n",
    "        if self.cursor[i_bucket] + batch_size > self.buckets_size[i_bucket]:\n",
    "            self.shuffle(i_bucket)\n",
    "\n",
    "        # Handle too big batch sizes\n",
    "        if (batch_size > self.buckets_size[i_bucket]):\n",
    "            batch_size = self.buckets_size[i_bucket]\n",
    "\n",
    "        res = self.buckets[i_bucket].iloc[self.cursor[i_bucket]:\n",
    "                                          self.cursor[i_bucket]+batch_size]\n",
    "        self.cursor[i_bucket] += batch_size\n",
    "        \n",
    "        # Check correct length of later prediction of sequences\n",
    "        assert np.all(res['in_length'] + add_output_length >= res['letters_length'])\n",
    "\n",
    "        input_max = max(res['in_length'])\n",
    "        letters_max = max(res['letters_length'])\n",
    "        words_max = max(res['words_length'])        \n",
    "        \n",
    "        input_seq = np.ones((batch_size, input_max, N_INPUT), dtype=np.float32) * LETTER_PAD\n",
    "        for i, img in enumerate(res['in_images']):\n",
    "            input_seq[i][:res['in_length'].values[i]] = img\n",
    "        input_seq = input_seq.swapaxes(0, 1)    # Time major\n",
    "        \n",
    "        letters = np.ones((batch_size, letters_max, letter_size), dtype=np.float32) * LETTER_PAD\n",
    "        for i, img in enumerate(res['letters']):\n",
    "            letters[i][:res['letters_length'].values[i]] = img\n",
    "        \n",
    "        # Need to pad according to the maximum length output sequence\n",
    "        words = np.zeros([batch_size, words_max], dtype=np.int32)\n",
    "        for i, word in enumerate(res['words']):\n",
    "            words[i][:res['words_length'].values[i]] = word\n",
    "        \n",
    "        return (input_seq, letters, words,\n",
    "                res['in_length'].values, res['letters_length'].values, res['words_length'].values)\n",
    "\n",
    "\n",
    "    def next_feed(self, size, words=True, train=None):\n",
    "        \"\"\" Create feed directly for model training \"\"\"\n",
    "        if train is None:\n",
    "            train = self.train\n",
    "        (encoder_inputs_,\n",
    "         letter_targets_,\n",
    "         word_targets_,\n",
    "         encoder_inputs_length_,\n",
    "         letter_targets_length_,\n",
    "         word_targets_length_) = self.next_batch(size)\n",
    "        return {\n",
    "            encoder_inputs: encoder_inputs_,\n",
    "            encoder_inputs_length: encoder_inputs_length_,\n",
    "            letter_targets: letter_targets_,\n",
    "            letter_targets_length: letter_targets_length_,\n",
    "            word_targets: word_targets_,\n",
    "            word_targets_length: word_targets_length_,\n",
    "            keep_prob: (1.0 - dropout) if self.train else 1.0,\n",
    "            is_training: train,\n",
    "            is_words: words\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iterator created.\n",
      "Iterator created.\n"
     ]
    }
   ],
   "source": [
    "# Create iterator for feeding RNN\n",
    "# Create only once, it modifies: labels_idx\n",
    "train_iterator = BucketDataIterator(\n",
    "    trainImagesF,\n",
    "    trainLabelsF_idx,\n",
    "    trainGaplinesF,\n",
    "    num_buckets,\n",
    "    slider_size,\n",
    "    step_size,\n",
    "    train=True)\n",
    "\n",
    "if is_sep_words:\n",
    "    train_letters_iterator = BucketDataIterator(\n",
    "        trainImagesF[:sep_words],\n",
    "        trainLabelsF_idx[:sep_words],\n",
    "        trainGaplinesF[:sep_words],\n",
    "        1,\n",
    "        slider_size,\n",
    "        step_size,\n",
    "        train=True)\n",
    "\n",
    "test_iterator = BucketDataIterator(\n",
    "    testImages,\n",
    "    testLabels_idx,\n",
    "    testGaplines,\n",
    "    num_buckets,\n",
    "    slider_size,\n",
    "    step_size,\n",
    "    train=False)\n",
    "\n",
    "del images\n",
    "del labels\n",
    "del gaplines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Placeholders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only encoder inputs are time major\n",
    "# Encoder inputs shape (max_seq_length, batch_size, vec_size)\n",
    "encoder_inputs = tf.placeholder(shape=(None, None, N_INPUT),\n",
    "                                dtype=tf.float32,\n",
    "                                name='encoder_inputs')\n",
    "encoder_inputs_length = tf.placeholder(shape=(None,),\n",
    "                                       dtype=tf.int32,\n",
    "                                       name='encoder_inputs_length')\n",
    "\n",
    "# Required for letter sep. training\n",
    "# Contains EOS symbol\n",
    "letter_targets = tf.placeholder(shape=(None, None, letter_size),\n",
    "                                dtype=tf.float32,\n",
    "                                name='letter_targets')\n",
    "letter_targets_length = tf.placeholder(shape=(None,),\n",
    "                                       dtype=tf.int32,\n",
    "                                       name='letter_targets_length')\n",
    "\n",
    "# Required for word training\n",
    "word_targets = tf.placeholder(shape=(None, None),\n",
    "                              dtype=tf.int32,\n",
    "                              name='word_targets')\n",
    "word_targets_length = tf.placeholder(shape=(None,),\n",
    "                                     dtype=tf.int32,\n",
    "                                     name='word_targets_length')\n",
    "# Dropout value\n",
    "keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n",
    "# Testing control\n",
    "is_training = tf.placeholder(tf.bool, shape=None, name=\"is_training\")\n",
    "is_words = tf.placeholder(tf.bool, shape=None, name=\"is_words\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decoder Train Feeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence_size, batch_size, _ = tf.unstack(tf.shape(encoder_inputs)) # letter_targets\n",
    "\n",
    "EOS_SLICE = tf.cast(tf.fill([batch_size, 1, letter_size], EOS), tf.float32)\n",
    "PAD_SLICE = tf.cast(tf.fill([batch_size, 1, letter_size], LETTER_PAD), tf.float32) # PAD\n",
    "\n",
    "# Train inputs with EOS symbol at start of seq\n",
    "letter_train_inputs = tf.concat([PAD_SLICE, letter_targets], axis=1) #EOS_SLICE\n",
    "letter_train_length = letter_targets_length\n",
    "\n",
    "# Length of infer (test) letter output\n",
    "# TODO: will have to make shorte...\n",
    "output_length = tf.minimum(\n",
    "    tf.reduce_max(encoder_inputs_length) * step_size // 15 + add_output_length,\n",
    "    23)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "enc_cell_fw = create_cell(encoder_units,\n",
    "                          encoder_layers,\n",
    "                          encoder_residual_layers,\n",
    "                          is_dropout=True,\n",
    "                          keep_prob=keep_prob)\n",
    "enc_cell_bw = create_cell(encoder_units,\n",
    "                          encoder_layers,\n",
    "                          encoder_residual_layers,\n",
    "                          is_dropout=True,\n",
    "                          keep_prob=keep_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "### CNN ###\n",
    "SCALE = 0.001 # 0.1\n",
    "# Functions for initializing convulation and pool layers\n",
    "def weights(name, shape):\n",
    "    return tf.get_variable(name, shape=shape,\n",
    "                           initializer=tf.contrib.layers.xavier_initializer(),\n",
    "                           regularizer=tf.contrib.layers.l2_regularizer(scale=SCALE))\n",
    "\n",
    "def bias(const, shape, name=None):\n",
    "    return tf.Variable(tf.constant(const, shape=shape), name=name)\n",
    "\n",
    "def conv2d2(x, W, name=None):\n",
    "    return tf.nn.conv2d(x, W, strides=[1, 2, 1, 1], padding='SAME', name=name)\n",
    "\n",
    "\n",
    "# W_conv1 = weights('W_conv1', shape=[2, 1, 1, 1])\n",
    "# b_conv1 = bias(0.1, shape=[1], name='b_conv1')\n",
    "\n",
    "\n",
    "# def CNN_1(x):\n",
    "#     x = tf.reshape(x, [slider_size[0], slider_size[1], 1])\n",
    "#     x = tf.image.per_image_standardization(x)\n",
    "#     img = tf.reshape(x, [1, slider_size[0], slider_size[1], 1])  \n",
    "#     h_conv1 = tf.nn.relu(conv2d2(img, W_conv1) + b_conv1, name='h_conv1')    \n",
    "#     return h_conv1\n",
    "\n",
    "def CNN_1(x):\n",
    "    x = tf.image.per_image_standardization(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inputs = tf.map_fn(\n",
    "#     lambda seq: tf.map_fn(\n",
    "#         lambda img:\n",
    "#             tf.reshape(\n",
    "#                 CNN_1(tf.reshape(img, [slider_size[0], slider_size[1], 1])), [-1]),\n",
    "#         seq),\n",
    "#     encoder_inputs,\n",
    "#     dtype=tf.float32)\n",
    "inputs = encoder_inputs\n",
    "\n",
    "# Bidirectional RNN, gibe fw and bw outputs separately\n",
    "enc_outputs, enc_state = tf.nn.bidirectional_dynamic_rnn(\n",
    "    cell_fw = enc_cell_fw,\n",
    "    cell_bw = enc_cell_bw,\n",
    "    inputs = inputs,\n",
    "    sequence_length = encoder_inputs_length,\n",
    "    dtype = tf.float32,\n",
    "    time_major = True)\n",
    "\n",
    "encoder_outputs = tf.concat(enc_outputs, -1)\n",
    "\n",
    "if encoder_layers == 1:\n",
    "    encoder_state = enc_state\n",
    "else:\n",
    "    encoder_state = []\n",
    "    for layer_id in range(encoder_layers):\n",
    "        encoder_state.append(enc_state[0][layer_id])  # forward\n",
    "        encoder_state.append(enc_state[1][layer_id])  # backward\n",
    "    encoder_state = tuple(encoder_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# attention_states: size [batch_size, max_time, num_units]\n",
    "attention_states = tf.transpose(encoder_outputs, [1, 0, 2])\n",
    "\n",
    "# Create an attention mechanism\n",
    "attention_mechanism = seq2seq.BahdanauAttention(\n",
    "    attention_size, attention_states,              # decoder_units instead of attention_size\n",
    "    memory_sequence_length=encoder_inputs_length)\n",
    "\n",
    "decoder_cell = create_cell(decoder_units,\n",
    "                           decoder_layers,\n",
    "                           decoder_residual_layers,\n",
    "                           is_dropout=True,\n",
    "                           keep_prob=keep_prob)\n",
    "\n",
    "decoder_cell = seq2seq.AttentionWrapper(\n",
    "    decoder_cell, attention_mechanism,\n",
    "    attention_layer_size=attention_size)\n",
    "\n",
    "decoder_initial_state = decoder_cell.zero_state(batch_size, tf.float32).clone(\n",
    "    cell_state=encoder_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TRAIN DECODER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper\n",
    "helper = seq2seq.TrainingHelper(\n",
    "    letter_train_inputs, letter_targets_length)\n",
    "\n",
    "# Decoder\n",
    "projection_layer = layers_core.Dense(\n",
    "    letter_size, activation=tf.tanh, use_bias=True)\n",
    "\n",
    "decoder = seq2seq.BasicDecoder(\n",
    "    decoder_cell, helper, decoder_initial_state,\n",
    "    output_layer=projection_layer)\n",
    "\n",
    "# Dynamic decoding\n",
    "outputs, final_context_state, _ = seq2seq.dynamic_decode(\n",
    "    decoder)\n",
    "\n",
    "letter_logits_train = outputs.rnn_output\n",
    "letter_prediction_train = outputs.sample_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### INFERENCE DECODER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "### CNN ###\n",
    "def conv2d(x, W, name=None):\n",
    "    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME', name=name)\n",
    "\n",
    "def max_pool_2x2(x, name=None):\n",
    "    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)\n",
    "\n",
    "W_conv1_ = weights('W_conv1_', shape=[16, 16, 1, 4])\n",
    "b_conv1_ = bias(0.1, shape=[4], name='b_conv1_')\n",
    "W_conv2_ = weights('W_conv2_', shape=[5, 5, 4, 12])\n",
    "b_conv2_ = bias(0.1, shape=[12], name='b_conv2_')\n",
    "W_conv3_ = weights('W_conv3_', shape=[3, 3, 12, 20])\n",
    "b_conv3_ = bias(0.1, shape=[20], name='b_conv3_')\n",
    "W_fc1_ = weights('W_fc2_', shape=[8*8*20, char_size])\n",
    "b_fc1_ = bias(0.1, shape=[char_size], name='b_fc2_')\n",
    "\n",
    "\n",
    "def CNN_2(x, clas=True):\n",
    "    if clas:\n",
    "        b_size, seq_size, _ = tf.unstack(tf.shape(x))\n",
    "    else:\n",
    "        b_size, _ = tf.unstack(tf.shape(x))\n",
    "        \n",
    "    imgs = tf.reshape(x, [-1, 64, 64, 1])\n",
    "    x_imgs = tf.map_fn(\n",
    "        lambda img: tf.image.per_image_standardization(img), imgs)    \n",
    "    # 1. Layer - Convulation\n",
    "    h_conv1 = tf.nn.relu(conv2d(x_imgs, W_conv1_) + b_conv1_, name='h_conv1_')\n",
    "    # 2. Layer - Max Pool\n",
    "    h_pool1 = max_pool_2x2(h_conv1, name='h_pool1_')\n",
    "    # 3. Layer - Convulation\n",
    "    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2_) + b_conv2_, name='h_conv2_')\n",
    "    # Reshape filters into flat arraty\n",
    "    h_pool2 = max_pool_2x2(h_conv2, name='h_pool2_')\n",
    "    h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3_) + b_conv3_, name='h_conv3_')\n",
    "    # Reshape filters into flat arraty\n",
    "    h_pool3 = max_pool_2x2(h_conv3, name='h_pool3_')\n",
    "    h_flat = tf.reshape(h_pool3, [-1, 8*8*20])\n",
    "    # 6. Dropout\n",
    "    h_flat_drop = tf.nn.dropout(h_flat, keep_prob)\n",
    "    # 7. Output layer\n",
    "    out = tf.matmul(h_flat_drop, W_fc1_) + b_fc1_\n",
    "    if clas:\n",
    "        return tf.reshape(out, [b_size, seq_size, -1])\n",
    "    else:\n",
    "        return tf.reshape(out, [b_size, -1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper without embedding, can add param: 'next_inputs_fn'\n",
    "helper_infer = seq2seq.InferenceHelper(\n",
    "    sample_fn=(lambda x: x),\n",
    "    sample_shape=[letter_size],\n",
    "    sample_dtype=tf.float32,\n",
    "    start_inputs=tf.cast(tf.fill([batch_size, letter_size], LETTER_PAD), tf.float32), # PAD <- EOS, need flaot32\n",
    "    end_fn=(lambda sample_ids:\n",
    "            tf.equal(tf.argmax(CNN_2(sample_ids, False), axis=-1, output_type=tf.int32), 1)))\n",
    "            \n",
    "\n",
    "decoder_infer = seq2seq.BasicDecoder(\n",
    "    decoder_cell, helper_infer, decoder_initial_state,\n",
    "    output_layer=projection_layer)\n",
    "\n",
    "# Dynamic decoding\n",
    "outputs_infer, final_context_state, final_seq_lengths = seq2seq.dynamic_decode(\n",
    "    decoder_infer,\n",
    "    impute_finished=True,\n",
    "    maximum_iterations=output_length)\n",
    "\n",
    "letter_prediction_infer = tf.identity(outputs_infer.rnn_output, # sample_id\n",
    "                                      name='letter_prediction_infer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_inputs = tf.cond(is_training,\n",
    "                      lambda: letter_targets,\n",
    "                      lambda: letter_prediction_infer)\n",
    "word_inputs_length_ = tf.cond(is_training,\n",
    "                              lambda: letter_targets_length,\n",
    "                              lambda: final_seq_lengths)\n",
    "\n",
    "word_inputs_length = word_inputs_length_    # tf.subtract(word_inputs_length_, 1)\n",
    "\n",
    "# Input images CNN\n",
    "word_outputs = CNN_2(word_inputs)\n",
    "\n",
    "word_logits = word_outputs\n",
    "word_prediction = tf.argmax(\n",
    "    word_logits, axis=-1, output_type=tf.int32,    # word_logits = tf.layers.dense(\n",
    "    name='word_prediction')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weights + Paddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pad test accuracy\n",
    "letter_test_targets = tf.pad(\n",
    "    letter_targets,\n",
    "    [[0, 0],\n",
    "     [0, output_length - tf.reduce_max(letter_targets_length)],\n",
    "     [0, 0]],\n",
    "    constant_values=LETTER_PAD,\n",
    "    mode='CONSTANT')\n",
    "\n",
    "# Pad prediction to match lengths\n",
    "letter_pred_infer_pad = tf.pad(\n",
    "    letter_prediction_infer,\n",
    "    [[0, 0],\n",
    "     [0, output_length - tf.reduce_max(word_inputs_length_)],\n",
    "     [0, 0]],\n",
    "    constant_values=LETTER_PAD,\n",
    "    mode='CONSTANT')\n",
    "\n",
    "\n",
    "word_pad_lenght = tf.maximum(\n",
    "    tf.reduce_max(word_inputs_length_),\n",
    "    tf.reduce_max(word_targets_length))\n",
    "\n",
    "word_logits_pad = tf.pad(\n",
    "    word_logits,\n",
    "    [[0, 0],\n",
    "     [0, word_pad_lenght - tf.reduce_max(word_inputs_length_)],\n",
    "     [0, 0]],\n",
    "    constant_values=PAD,\n",
    "    mode='CONSTANT')\n",
    "word_pred_pad = tf.pad(\n",
    "    word_prediction,\n",
    "    [[0, 0],\n",
    "     [0, word_pad_lenght - tf.reduce_max(word_inputs_length_)]],\n",
    "    constant_values=PAD,\n",
    "    mode='CONSTANT')\n",
    "word_targets_pad = tf.pad(\n",
    "    word_targets,\n",
    "    [[0, 0],\n",
    "     [0, word_pad_lenght - tf.reduce_max(word_targets_length)]],\n",
    "    constant_values=PAD,\n",
    "    mode='CONSTANT')\n",
    "\n",
    "\n",
    "# Weights\n",
    "letter_loss_weights = tf.sequence_mask(\n",
    "    letter_train_length,\n",
    "    tf.reduce_max(letter_train_length),\n",
    "    dtype=tf.float32)\n",
    "\n",
    "letter_test_weights = tf.sequence_mask(\n",
    "    letter_train_length,\n",
    "    output_length,\n",
    "    dtype=tf.float32)\n",
    "\n",
    "word_loss_weights = tf.sequence_mask(\n",
    "    word_targets_length,    # word_inputs_length, try max(targets, inputs)\n",
    "    word_pad_lenght,\n",
    "    dtype=tf.float32)\n",
    "\n",
    "word_acc_weights = tf.sequence_mask(\n",
    "    tf.subtract(final_seq_lengths, 1),    # word_inputs_length, try max(targets, inputs)\n",
    "    word_pad_lenght,\n",
    "    dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Loss\n",
    "letter_loss = tf.losses.mean_squared_error(\n",
    "    predictions=letter_logits_train,\n",
    "    labels=letter_targets,\n",
    "    weights=tf.stack([letter_loss_weights for i in range(letter_size)], axis=-1))\n",
    "\n",
    "word_seq_loss = seq2seq.sequence_loss(\n",
    "    logits=word_logits_pad,\n",
    "    targets=word_targets_pad,\n",
    "    weights=word_loss_weights,\n",
    "    name='word_loss')\n",
    "regularization = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\n",
    "word_loss = word_seq_loss + sum(regularization)\n",
    "\n",
    "\n",
    "loss = tf.cond(is_words,\n",
    "               lambda: word_loss,\n",
    "               lambda: letter_loss)\n",
    "\n",
    "# learning_rate_ = learning_rate\n",
    "learning_rate_ = tf.cond(tf.logical_and(is_words, tf.logical_not(is_training)),\n",
    "                         lambda: learning_rate, # * 0.1,\n",
    "                         lambda: learning_rate)\n",
    "\n",
    "## Calculate and clip gradients\n",
    "params = tf.trainable_variables()\n",
    "gradients = tf.gradients(loss, params)\n",
    "clipped_gradients, _ = tf.clip_by_global_norm(\n",
    "    gradients, max_gradient_norm)\n",
    "\n",
    "### Optimization\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate_)\n",
    "train_step = optimizer.apply_gradients(\n",
    "    zip(clipped_gradients, params),\n",
    "    name='train_step')\n",
    "\n",
    "### Evaluate model\n",
    "correct_prediction = tf.equal(\n",
    "    word_pred_pad,\n",
    "    word_targets_pad)\n",
    "## Advanced accuracy only the elements of seq including EOS symbol\n",
    "# accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "accuracy = (tf.reduce_sum(tf.cast(correct_prediction, tf.float32) * word_acc_weights) \\\n",
    "            / tf.reduce_sum(word_acc_weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch 0 - loss: 4.1433916\n",
      "    expected  > [22 46 28 34 32  1  0  0]\n",
      "    predicted > [25 25 25 25 25 25 25]\n",
      "    expected  > [31 32 28 39  1  0  0  0]\n",
      "    predicted > [25 25 25 25 25 25 39]\n",
      "\n",
      "batch 1000 - loss: 3.9913728\n",
      "    expected  > [17 45 49 32 38  1  0  0]\n",
      "    predicted > [25 39 25 25 25 25 25 25]\n",
      "    expected  > [30 28 46 47  1  0  0  0]\n",
      "    predicted > [25 25 25 25 25 25 25 25]\n",
      "\n",
      "batch 2000 - loss: 2.3057177\n",
      "    expected  > [10  1  0  0  0]\n",
      "    predicted > [11  1  1  1]\n",
      "    expected  > [52  1  0  0  0]\n",
      "    predicted > [11  1  1  1]\n",
      "\n",
      "batch 3000 - loss: 1.8378663\n",
      "    expected  > [37 36  1  0  0]\n",
      "    predicted > [37 36 47  1]\n",
      "    expected  > [19  1  0  0  0]\n",
      "    predicted > [33  1  1  1]\n",
      "\n",
      "batch 4000 - loss: 1.9805311\n",
      "    expected  > [45 42 39 39  1  0  0  0]\n",
      "    predicted > [45 32 39 39  1  1]\n",
      "    expected  > [27 42 41 32  1  0  0  0]\n",
      "    predicted > [21 28 40 32  1  1]\n",
      "\n",
      "batch 5000 - loss: 2.1887887\n",
      "    expected  > [49 28 46 36  1  0  0  0  0]\n",
      "    predicted > [41 42 46 28  1  1  1  1]\n",
      "    expected  > [40  1  0  0  0  0  0  0  0]\n",
      "    predicted > [41  1  1  1  1  1  1  1]\n",
      "\n",
      "batch 6000 - loss: 1.872894\n",
      "    expected  > [17 45 36 41 28 46 32 37 36  1]\n",
      "    predicted > [20 36 41 36 36 41 41  1]\n",
      "    expected  > [43 39 28 41 32 47  1  0  0  0]\n",
      "    predicted > [43 39 42 40 42 28 47  1]\n",
      "\n",
      "batch 7000 - loss: 1.7666719\n",
      "    expected  > [46 47 28 47  1  0  0  0]\n",
      "    predicted > [46 47 28 47  1  1]\n",
      "    expected  > [32 28 30 35  1  0  0  0]\n",
      "    predicted > [28 28 28  1  1  1]\n",
      "\n",
      "batch 8000 - loss: 1.7898357\n",
      "    expected  > [45 28 31  1  0  0  0]\n",
      "    predicted > [45 28 31  1  1  1]\n",
      "    expected  > [38 52 47 38 28  1  0]\n",
      "    predicted > [47 52 29 39 39 39]\n",
      "\n",
      "batch 9000 - loss: 2.1676068\n",
      "    expected  > [46 36 47 48 28 30 32  1  0  0  0  0]\n",
      "    predicted > [46 36 47 45 42 32  1  1  1  1  1]\n",
      "    expected  > [49 30 32 45 28  1  0  0  0  0  0  0]\n",
      "    predicted > [41 32 32 42  1  1  1  1  1  1  1]\n",
      "\n",
      "batch 10000 - loss: 1.7424697\n",
      "    expected  > [49 52 38 45 52 46 47 28 39 36 53 42 49 28 39 28  1]\n",
      "    predicted > [49 52 39 32 42 42 47 28 42  1]\n",
      "    expected  > [53 32 41 28  1  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      "    predicted > [45 32 41 28  1  1  1  1  1  1]\n",
      "\n",
      "batch 11000 - loss: 1.4472994\n",
      "    expected  > [53 42 39 36 38  1  0  0  0]\n",
      "    predicted > [53 30 39 36 38  1  1  1]\n",
      "    expected  > [43 45 42 30 32 46  1  0  0]\n",
      "    predicted > [43 45 45 46  1  1  1  1]\n",
      "\n",
      "batch 12000 - loss: 1.5876625\n",
      "    expected  > [42 35 32 41  1  0  0  0]\n",
      "    predicted > [42 35 32 36  1  1  1]\n",
      "    expected  > [43 42 47 45 32 39 36  1]\n",
      "    predicted > [43 42 47 36 32 39  1]\n",
      "\n",
      "batch 13000 - loss: 1.1057456\n",
      "    expected  > [40  1  0  0  0  0  0  0]\n",
      "    predicted > [41  1  1  1  1  1  1]\n",
      "    expected  > [29 52 49 28 47  1  0  0]\n",
      "    predicted > [29 52 49 28 47  1  1]\n",
      "\n",
      "batch 14000 - loss: 1.254143\n",
      "    expected  > [30 32 46 47 28  1  0  0  0]\n",
      "    predicted > [42 32 47 47 28  1  1  1  1]\n",
      "    expected  > [29 45 42 48 34 35 47  1  0]\n",
      "    predicted > [29 42 40 42 34 34 47 47  1]\n",
      "\n",
      "batch 15000 - loss: 1.5632484\n",
      "    expected  > [35 32 28 49 52  1  0]\n",
      "    predicted > [38 45 28 41 52]\n",
      "    expected  > [10 41 33 42  1  0  0]\n",
      "    predicted > [17 28 33 32  1]\n",
      "\n",
      "batch 16000 - loss: 1.454107\n",
      "    expected  > [49  1  0  0  0  0  0  0  0  0]\n",
      "    predicted > [49  1  1  1  1  1  1  1  1]\n",
      "    expected  > [24 32 45 41 32 45  1  0  0  0]\n",
      "    predicted > [23 28 45 41 45  1  1  1  1]\n",
      "\n",
      "batch 17000 - loss: 1.2110069\n",
      "    expected  > [37 36 41 52  1  0]\n",
      "    predicted > [37 36 28  1  1]\n",
      "    expected  > [19 36 43  1  0  0]\n",
      "    predicted > [19 36 43  1  1]\n",
      "\n",
      "batch 18000 - loss: 1.3091056\n",
      "    expected  > [21 36 43  1  0  0]\n",
      "    predicted > [27 28 43  1]\n",
      "    expected  > [23  1  0  0  0  0]\n",
      "    predicted > [23  1  1  1]\n",
      "\n",
      "batch 19000 - loss: 1.0000377\n",
      "    expected  > [45  1  0  0  0]\n",
      "    predicted > [27  1  1  1]\n",
      "    expected  > [ 8 42  1  0  0]\n",
      "    predicted > [ 8 28  1  1]\n",
      "\n",
      "batch 20000 - loss: 1.087924\n",
      "    expected  > [ 8 32 41  1  0  0]\n",
      "    predicted > [26 48 41  1  1]\n",
      "    expected  > [53 36 43  1  0  0]\n",
      "    predicted > [46 45 43  1  1]\n",
      "\n",
      "batch 21000 - loss: 2.0892587\n",
      "    expected  > [ 5 28 29  1  0  0  0]\n",
      "    predicted > [ 5 28 29  1  1]\n",
      "    expected  > [29 42 45  1  0  0  0]\n",
      "    predicted > [29 42 45  1  1]\n",
      "\n",
      "batch 22000 - loss: 1.8513888\n",
      "    expected  > [49 30 32 45 28  1  0  0  0  0  0]\n",
      "    predicted > [49 30 45 28  1  1  1  1  1  1  1]\n",
      "    expected  > [36 41 30 39 48 31 32  1  0  0  0]\n",
      "    predicted > [36 41 30 39 28 31 32  1  1  1  1]\n",
      "\n",
      "batch 23000 - loss: 1.4094399\n",
      "    expected  > [52 32 39 39 42 50  1  0  0]\n",
      "    predicted > [52 32 39 42 50  1  1  1  1]\n",
      "    expected  > [52 42 48 45  1  0  0  0  0]\n",
      "    predicted > [52 42 46  1  1  1  1  1  1]\n",
      "\n",
      "batch 24000 - loss: 1.4057717\n",
      "    expected  > [33 36 28 39 42 49 28  1  0]\n",
      "    predicted > [33 36 28 39 42 49  1  1]\n",
      "    expected  > [28 46  1  0  0  0  0  0  0]\n",
      "    predicted > [28 45  1  1  1  1  1  1]\n",
      "\n",
      "batch 25000 - loss: 2.255497\n",
      "    expected  > [15 36 38 31 42  1  0  0  0  0]\n",
      "    predicted > [15 36 38 31 42  1  1  1  1]\n",
      "    expected  > [16 47 28 38 28 45  1  0  0  0]\n",
      "    predicted > [42 31 38 28 45  1  1  1  1]\n",
      "\n",
      "batch 26000 - loss: 2.1663573\n",
      "    expected  > [14 42 41 36 38 28  1  0  0  0  0  0  0]\n",
      "    predicted > [14 42 40 38 42  1  1  1  1  1]\n",
      "    expected  > [40 28 47 47 32 45  1  0  0  0  0  0  0]\n",
      "    predicted > [40 42 47 47 32 45  1  1  1  1]\n",
      "\n",
      "batch 27000 - loss: 1.1713476\n",
      "    expected  > [47 35 32 45 32  1  0  0  0]\n",
      "    predicted > [47 35 32 45 32  1  1  1]\n",
      "    expected  > [50 36 46 35  1  0  0  0  0]\n",
      "    predicted > [50 36 46 35  1  1  1  1]\n",
      "\n",
      "batch 28000 - loss: 1.4209536\n",
      "    expected  > [33 28 30 32  1  0  0]\n",
      "    predicted > [33 42 30 32  1  1]\n",
      "    expected  > [46 48 43 28  1  0  0]\n",
      "    predicted > [46 42 43 42  1  1]\n",
      "\n",
      "batch 29000 - loss: 1.3666291\n",
      "    expected  > [45 52 47  1  0  0]\n",
      "    predicted > [45 52 47  1]\n",
      "    expected  > [4 1 0 0 0 0]\n",
      "    predicted > [4 1 1 1]\n",
      "\n",
      "batch 30000 - loss: 1.3133948\n",
      "    expected  > [ 7 48 32 39  1  0  0  0]\n",
      "    predicted > [ 7 32 48 39  1  1  1]\n",
      "    expected  > [19 52 30 35 39 32  1  0]\n",
      "    predicted > [ 3 52 30 35 39 32  1]\n",
      "\n",
      "batch 31000 - loss: 1.6442363\n",
      "    expected  > [ 8 39 42 45 52  1]\n",
      "    predicted > [ 8 32 41 52  1]\n",
      "    expected  > [33 32 32 39  1  0]\n",
      "    predicted > [33 32 39  1  1]\n",
      "\n",
      "batch 32000 - loss: 1.2510978\n",
      "    expected  > [42 29 30 28 46  1  0  0  0]\n",
      "    predicted > [42 38 32 28  1  1  1  1]\n",
      "    expected  > [43 45 32 46 47 42  1  0  0]\n",
      "    predicted > [43 45 32 46 46 47 42  1]\n",
      "\n",
      "batch 33000 - loss: 1.1826903\n",
      "    expected  > [20 43 45 36 41 34  1  0]\n",
      "    predicted > [20 42 45 36 41 34  1]\n",
      "    expected  > [41 28 46 47 45 42 37  1]\n",
      "    predicted > [41 28 46 47 45 34 37]\n",
      "\n",
      "Training interrupted, model saved.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'models/word-clas/en/SeqRNN/Classifier4'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "# Creat plot for live stats ploting\n",
    "trainPlot = TrainingPlot(TRAIN_STEPS, TEST_ITER, LOSS_ITER)\n",
    "\n",
    "try:\n",
    "    for i_batch in range(TRAIN_STEPS):\n",
    "        # Three steps (can overflow): 1. train letters, 2. train words on known letter 3. combine\n",
    "        is_words_, is_train_ = (True, True)\n",
    "        if is_sep_words:\n",
    "            is_words_, is_train_ = (True, False)   # (True, True) - without partial sep letters\n",
    "        if i_batch < 1000 or i_batch % 2 == 0:    # 2000 # 5000\n",
    "            is_words_, is_train_ = (False, True)\n",
    "#         elif i_batch < 3000:                      # 12000\n",
    "#             is_words_, is_train_ = (True, True)\n",
    "        elif i_batch % 4 == 3:\n",
    "            is_words_, is_train_ = (True, False)\n",
    "\n",
    "        fd = train_iterator.next_feed(BATCH_SIZE, words=is_words_, train=is_train_)\n",
    "        \n",
    "        if is_train_ and is_sep_words:\n",
    "            train_step.run(train_letters_iterator.next_feed(BATCH_SIZE, words=is_words_, train=is_train_))\n",
    "        else:\n",
    "            train_step.run(fd)\n",
    "        \n",
    "        if i_batch % LOSS_ITER == 0:\n",
    "            # Plotting loss\n",
    "            tmpLoss = loss.eval(fd)\n",
    "            trainPlot.updateCost(tmpLoss, i_batch // LOSS_ITER)\n",
    "    \n",
    "        if i_batch % TEST_ITER == 0:\n",
    "            # Plotting accuracy\n",
    "            fd_test = test_iterator.next_feed(BATCH_SIZE)\n",
    "            fd = train_iterator.next_feed(BATCH_SIZE, words=True, train=False)\n",
    "            accTest = accuracy.eval(fd_test)\n",
    "            accTrain = accuracy.eval(fd)\n",
    "            trainPlot.updateAcc(accTest, accTrain, i_batch // TEST_ITER)\n",
    "\n",
    "        if i_batch % SAVE_ITER == 0:\n",
    "            saver.save(sess, save_location)\n",
    "        \n",
    "        if i_batch % EPOCH == 0:\n",
    "            fd_test = test_iterator.next_feed(BATCH_SIZE)\n",
    "            print('batch %r - loss: %r' % (i_batch, sess.run(loss, fd_test)))\n",
    "            predict_, target_ = sess.run([word_prediction, word_targets], fd_test)\n",
    "            for i, (inp, pred) in enumerate(zip(target_, predict_)):\n",
    "                print('    expected  > {}'.format(inp))\n",
    "                print('    predicted > {}'.format(pred))\n",
    "                if i >= 1:\n",
    "                    break\n",
    "            print()\n",
    "\n",
    "except KeyboardInterrupt:\n",
    "    print('Training interrupted, model saved.')\n",
    "    \n",
    "saver.save(sess, save_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected images: 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADG5JREFUeJzt3W+MHPV9x/H3pzYuaQIyhoAsDDVIVkoeBBNZlIioIjSJXBoFHkBFlEpOhHpPUomolRJopbapVKk8CfRBVckCGj9oA5Q0MeJBieWA0kcG868xcRyTlIJlF7cClKQPUA3fPti59nAPdn03M2v4vV+SdbuTuZuvsvfemd1bZlJVSGrLL817AEnjM3ypQYYvNcjwpQYZvtQgw5caZPhSg1YVfpLtSQ4leT7JbX0NJWlYWekHeJKsAX4MfAo4AjwBfK6qftjfeJKGsHYV33sl8HxV/RQgyX3A9cDbhp/EjwlKA6uqTFtnNYf6FwIvLbl/pFsm6TS3mj3+cs8q/2+PnmQBWFjFdiT1bDXhHwEuWnJ/E3D05JWqaiewEzzUl04XqznUfwLYkuSSJOuAm4GH+hlL0pBWvMevqhNJfh94BFgD3FtVz/U2maTBrPjPeSvamIf60uCGfldf0ruU4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2rQ1PCT3JvkeJIDS5ZtSLInyeHu6znDjimpT7Ps8b8BbD9p2W3A3qraAuzt7kt6l5gaflV9H3jlpMXXA7u627uAG3qeS9KAVvoa/4KqOgbQfT2/v5EkDW3Fl8meVZIFYGHo7Uia3Ur3+C8n2QjQfT3+ditW1c6q2lZV21a4LUk9W2n4DwE7uts7gN39jCNpDKmqd14h+SZwDXAe8DLwp8B3gAeAi4EXgZuq6uQ3AJf7We+8MUmrVlWZts7U8Ptk+NLwZgnfT+5JDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2qQ4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDDF9qkOFLDZoafpKLkjya5GCS55Lc2i3fkGRPksPd13OGH1dSH2a5dt5GYGNVPZXkLOBJ4AbgC8ArVfWXSW4Dzqmqr075WV5CSxpYL5fQqqpjVfVUd/vnwEHgQuB6YFe32i4mTwaS3gVO6TV+ks3AFcA+4IKqOgaTJwfg/L6HkzSMtbOumOQDwLeAL1fVz5KpRxOL37cALKxsPElDmOky2UnOAB4GHqmqr3fLDgHXVNWx7n2Ax6rqQ1N+jq/xpYH18ho/k137PcDBxeg7DwE7uts7gN0rGVLS+GZ5V//jwD8DPwDe7Bb/EZPX+Q8AFwMvAjdV1StTfpZ7fGlgs+zxZzrU74vhS8Pr5VBf0nuP4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2rQLNfOOzPJ40meTfJckq91yy9Jsi/J4ST3J1k3/LiS+jDLHv914NqquhzYCmxPchVwB3BnVW0BXgVuGW5MSX2aGn5N/KK7e0b3r4BrgQe75buAGwaZUFLvZnqNn2RNkmeA48Ae4CfAa1V1olvlCHDhMCNK6ttM4VfVG1W1FdgEXAlcttxqy31vkoUk+5PsX/mYkvp0Su/qV9VrwGPAVcD6JGu7/2kTcPRtvmdnVW2rqm2rGVRSf2Z5V/+DSdZ3t98HfBI4CDwK3NittgPYPdSQkvqVqmWP0P9vheQjTN68W8PkieKBqvrzJJcC9wEbgKeB362q16f8rHfemKRVq6pMW2dq+H0yfGl4s4TvJ/ekBhm+1CDDlxpk+FKDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2qQ4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBs0cfnep7KeTPNzdvyTJviSHk9yfZN1wY0rq06ns8W9lcrHMRXcAd1bVFuBV4JY+B5M0nJnCT7IJ+G3g7u5+gGuBB7tVdgE3DDGgpP7Nuse/C/gK8GZ3/1zgtao60d0/AlzY82ySBjI1/CSfAY5X1ZNLFy+z6rJXwk2ykGR/kv0rnFFSz9bOsM7VwGeTXAecCZzN5AhgfZK13V5/E3B0uW+uqp3ATvAy2dLpYuoev6pur6pNVbUZuBn4XlV9HngUuLFbbQewe7ApJfVqNX/H/yrwB0meZ/Ka/55+RpI0tFSNd/Ttob40vKpa7j24t/CTe1KDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2qQ4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDZrloJkleAH4OvAGcqKptSTYA9wObgReA36mqV4cZU1KfTmWP/4mq2lpV27r7twF7q2oLsLe7L+ldYDWH+tcDu7rbu4AbVj+OpDHMGn4B303yZJKFbtkFVXUMoPt6/hADSurfTK/xgaur6miS84E9SX406wa6J4qFqStKGs0pXyY7yZ8BvwB+D7imqo4l2Qg8VlUfmvK9XiZbGlgvl8lO8v4kZy3eBj4NHAAeAnZ0q+0Adq98VEljmrrHT3Ip8O3u7lrg76vqL5KcCzwAXAy8CNxUVa9M+Vnu8aWBzbLHP+VD/dUwfGl4vRzqS3rvMXypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2qQ4UsNmin8JOuTPJjkR0kOJvlYkg1J9iQ53H09Z+hhJfVj1j3+XwH/VFW/BlwOHARuA/ZW1RZgb3df0rvALBfNPBt4Fri0lqyc5BBeJls67fR17bxLgf8A/jbJ00nu7i6XfUFVHes2dAw4f1XTShrNLOGvBT4K/E1VXQH8F6dwWJ9kIcn+JPtXOKOkns0S/hHgSFXt6+4/yOSJ4OXuEJ/u6/HlvrmqdlbVtqra1sfAklZvavhV9e/AS0kWX7//JvBD4CFgR7dsB7B7kAkl9W7qm3sASbYCdwPrgJ8CX2TypPEAcDHwInBTVb0y5ef45p40sFne3Jsp/L4YvjS8vt7Vl/QeY/hSgwxfapDhSw0yfKlBhi81yPClBq0deXv/CfwbcF53e55OhxnAOU7mHG91qnP86iwrjfoBnv/daLJ/3p/dPx1mcA7nmNccHupLDTJ8qUHzCn/nnLa71OkwAzjHyZzjrQaZYy6v8SXNl4f6UoNGDT/J9iSHkjyfZLSz8ia5N8nxJAeWLBv99OBJLkryaHeK8ueS3DqPWZKcmeTxJM92c3ytW35Jkn3dHPcnWTfkHEvmWdOdz/Hhec2R5IUkP0jyzOJp4ub0OzLKqexHCz/JGuCvgd8CPgx8LsmHR9r8N4DtJy2bx+nBTwB/WFWXAVcBX+r+Pxh7lteBa6vqcmArsD3JVcAdwJ3dHK8Ctww8x6JbmZyyfdG85vhEVW1d8uezefyOjHMq+6oa5R/wMeCRJfdvB24fcfubgQNL7h8CNna3NwKHxpplyQy7gU/NcxbgV4CngF9n8kGRtcs9XgNuf1P3y3wt8DCQOc3xAnDeSctGfVyAs4F/pXvvbcg5xjzUvxB4acn9I92yeZnr6cGTbAauAPbNY5bu8PoZJidJ3QP8BHitqk50q4z1+NwFfAV4s7t/7pzmKOC7SZ5MstAtG/txGe1U9mOGv9zpgJr8k0KSDwDfAr5cVT+bxwxV9UZVbWWyx70SuGy51YacIclngONV9eTSxWPP0bm6qj7K5KXol5L8xgjbPNmqTmV/KsYM/whw0ZL7m4CjI27/ZDOdHrxvSc5gEv3fVdU/znMWgKp6DXiMyXsO65Ms/vcbYzw+VwOfTfICcB+Tw/275jAHVXW0+3oc+DaTJ8OxH5dVncr+VIwZ/hPAlu4d23XAzUxO0T0vo58ePEmAe4CDVfX1ec2S5INJ1ne33wd8ksmbSI8CN441R1XdXlWbqmozk9+H71XV58eeI8n7k5y1eBv4NHCAkR+XGvNU9kO/aXLSmxTXAT9m8nryj0fc7jeBY8B/M3lWvYXJa8m9wOHu64YR5vg4k8PWfwGe6f5dN/YswEeAp7s5DgB/0i2/FHgceB74B+CXR3yMrgEenscc3fae7f49t/i7Oaffka3A/u6x+Q5wzhBz+Mk9qUF+ck9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoP+B5DjZ1E5WbVBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted images: 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADG5JREFUeJzt3W+MHPV9x/H3pzYuaQIyhoAsDDVIVkoeBBNZlIioIjSJXBoFHkBFlEpOhHpPUomolRJopbapVKk8CfRBVckCGj9oA5Q0MeJBieWA0kcG868xcRyTlIJlF7cClKQPUA3fPti59nAPdn03M2v4vV+SdbuTuZuvsvfemd1bZlJVSGrLL817AEnjM3ypQYYvNcjwpQYZvtQgw5caZPhSg1YVfpLtSQ4leT7JbX0NJWlYWekHeJKsAX4MfAo4AjwBfK6qftjfeJKGsHYV33sl8HxV/RQgyX3A9cDbhp/EjwlKA6uqTFtnNYf6FwIvLbl/pFsm6TS3mj3+cs8q/2+PnmQBWFjFdiT1bDXhHwEuWnJ/E3D05JWqaiewEzzUl04XqznUfwLYkuSSJOuAm4GH+hlL0pBWvMevqhNJfh94BFgD3FtVz/U2maTBrPjPeSvamIf60uCGfldf0ruU4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2rQ1PCT3JvkeJIDS5ZtSLInyeHu6znDjimpT7Ps8b8BbD9p2W3A3qraAuzt7kt6l5gaflV9H3jlpMXXA7u627uAG3qeS9KAVvoa/4KqOgbQfT2/v5EkDW3Fl8meVZIFYGHo7Uia3Ur3+C8n2QjQfT3+ditW1c6q2lZV21a4LUk9W2n4DwE7uts7gN39jCNpDKmqd14h+SZwDXAe8DLwp8B3gAeAi4EXgZuq6uQ3AJf7We+8MUmrVlWZts7U8Ptk+NLwZgnfT+5JDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2qQ4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDDF9qkOFLDZoafpKLkjya5GCS55Lc2i3fkGRPksPd13OGH1dSH2a5dt5GYGNVPZXkLOBJ4AbgC8ArVfWXSW4Dzqmqr075WV5CSxpYL5fQqqpjVfVUd/vnwEHgQuB6YFe32i4mTwaS3gVO6TV+ks3AFcA+4IKqOgaTJwfg/L6HkzSMtbOumOQDwLeAL1fVz5KpRxOL37cALKxsPElDmOky2UnOAB4GHqmqr3fLDgHXVNWx7n2Ax6rqQ1N+jq/xpYH18ho/k137PcDBxeg7DwE7uts7gN0rGVLS+GZ5V//jwD8DPwDe7Bb/EZPX+Q8AFwMvAjdV1StTfpZ7fGlgs+zxZzrU74vhS8Pr5VBf0nuP4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2rQLNfOOzPJ40meTfJckq91yy9Jsi/J4ST3J1k3/LiS+jDLHv914NqquhzYCmxPchVwB3BnVW0BXgVuGW5MSX2aGn5N/KK7e0b3r4BrgQe75buAGwaZUFLvZnqNn2RNkmeA48Ae4CfAa1V1olvlCHDhMCNK6ttM4VfVG1W1FdgEXAlcttxqy31vkoUk+5PsX/mYkvp0Su/qV9VrwGPAVcD6JGu7/2kTcPRtvmdnVW2rqm2rGVRSf2Z5V/+DSdZ3t98HfBI4CDwK3NittgPYPdSQkvqVqmWP0P9vheQjTN68W8PkieKBqvrzJJcC9wEbgKeB362q16f8rHfemKRVq6pMW2dq+H0yfGl4s4TvJ/ekBhm+1CDDlxpk+FKDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2qQ4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBs0cfnep7KeTPNzdvyTJviSHk9yfZN1wY0rq06ns8W9lcrHMRXcAd1bVFuBV4JY+B5M0nJnCT7IJ+G3g7u5+gGuBB7tVdgE3DDGgpP7Nuse/C/gK8GZ3/1zgtao60d0/AlzY82ySBjI1/CSfAY5X1ZNLFy+z6rJXwk2ykGR/kv0rnFFSz9bOsM7VwGeTXAecCZzN5AhgfZK13V5/E3B0uW+uqp3ATvAy2dLpYuoev6pur6pNVbUZuBn4XlV9HngUuLFbbQewe7ApJfVqNX/H/yrwB0meZ/Ka/55+RpI0tFSNd/Ttob40vKpa7j24t/CTe1KDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2qQ4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDZrloJkleAH4OvAGcqKptSTYA9wObgReA36mqV4cZU1KfTmWP/4mq2lpV27r7twF7q2oLsLe7L+ldYDWH+tcDu7rbu4AbVj+OpDHMGn4B303yZJKFbtkFVXUMoPt6/hADSurfTK/xgaur6miS84E9SX406wa6J4qFqStKGs0pXyY7yZ8BvwB+D7imqo4l2Qg8VlUfmvK9XiZbGlgvl8lO8v4kZy3eBj4NHAAeAnZ0q+0Adq98VEljmrrHT3Ip8O3u7lrg76vqL5KcCzwAXAy8CNxUVa9M+Vnu8aWBzbLHP+VD/dUwfGl4vRzqS3rvMXypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2qQ4UsNmin8JOuTPJjkR0kOJvlYkg1J9iQ53H09Z+hhJfVj1j3+XwH/VFW/BlwOHARuA/ZW1RZgb3df0rvALBfNPBt4Fri0lqyc5BBeJls67fR17bxLgf8A/jbJ00nu7i6XfUFVHes2dAw4f1XTShrNLOGvBT4K/E1VXQH8F6dwWJ9kIcn+JPtXOKOkns0S/hHgSFXt6+4/yOSJ4OXuEJ/u6/HlvrmqdlbVtqra1sfAklZvavhV9e/AS0kWX7//JvBD4CFgR7dsB7B7kAkl9W7qm3sASbYCdwPrgJ8CX2TypPEAcDHwInBTVb0y5ef45p40sFne3Jsp/L4YvjS8vt7Vl/QeY/hSgwxfapDhSw0yfKlBhi81yPClBq0deXv/CfwbcF53e55OhxnAOU7mHG91qnP86iwrjfoBnv/daLJ/3p/dPx1mcA7nmNccHupLDTJ8qUHzCn/nnLa71OkwAzjHyZzjrQaZYy6v8SXNl4f6UoNGDT/J9iSHkjyfZLSz8ia5N8nxJAeWLBv99OBJLkryaHeK8ueS3DqPWZKcmeTxJM92c3ytW35Jkn3dHPcnWTfkHEvmWdOdz/Hhec2R5IUkP0jyzOJp4ub0OzLKqexHCz/JGuCvgd8CPgx8LsmHR9r8N4DtJy2bx+nBTwB/WFWXAVcBX+r+Pxh7lteBa6vqcmArsD3JVcAdwJ3dHK8Ctww8x6JbmZyyfdG85vhEVW1d8uezefyOjHMq+6oa5R/wMeCRJfdvB24fcfubgQNL7h8CNna3NwKHxpplyQy7gU/NcxbgV4CngF9n8kGRtcs9XgNuf1P3y3wt8DCQOc3xAnDeSctGfVyAs4F/pXvvbcg5xjzUvxB4acn9I92yeZnr6cGTbAauAPbNY5bu8PoZJidJ3QP8BHitqk50q4z1+NwFfAV4s7t/7pzmKOC7SZ5MstAtG/txGe1U9mOGv9zpgJr8k0KSDwDfAr5cVT+bxwxV9UZVbWWyx70SuGy51YacIclngONV9eTSxWPP0bm6qj7K5KXol5L8xgjbPNmqTmV/KsYM/whw0ZL7m4CjI27/ZDOdHrxvSc5gEv3fVdU/znMWgKp6DXiMyXsO65Ms/vcbYzw+VwOfTfICcB+Tw/275jAHVXW0+3oc+DaTJ8OxH5dVncr+VIwZ/hPAlu4d23XAzUxO0T0vo58ePEmAe4CDVfX1ec2S5INJ1ne33wd8ksmbSI8CN441R1XdXlWbqmozk9+H71XV58eeI8n7k5y1eBv4NHCAkR+XGvNU9kO/aXLSmxTXAT9m8nryj0fc7jeBY8B/M3lWvYXJa8m9wOHu64YR5vg4k8PWfwGe6f5dN/YswEeAp7s5DgB/0i2/FHgceB74B+CXR3yMrgEenscc3fae7f49t/i7Oaffka3A/u6x+Q5wzhBz+Mk9qUF+ck9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoP+B5DjZ1E5WbVBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected images: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADG5JREFUeJzt3W+MHPV9x/H3pzYuaQIyhoAsDDVIVkoeBBNZlIioIjSJXBoFHkBFlEpOhHpPUomolRJopbapVKk8CfRBVckCGj9oA5Q0MeJBieWA0kcG868xcRyTlIJlF7cClKQPUA3fPti59nAPdn03M2v4vV+SdbuTuZuvsvfemd1bZlJVSGrLL817AEnjM3ypQYYvNcjwpQYZvtQgw5caZPhSg1YVfpLtSQ4leT7JbX0NJWlYWekHeJKsAX4MfAo4AjwBfK6qftjfeJKGsHYV33sl8HxV/RQgyX3A9cDbhp/EjwlKA6uqTFtnNYf6FwIvLbl/pFsm6TS3mj3+cs8q/2+PnmQBWFjFdiT1bDXhHwEuWnJ/E3D05JWqaiewEzzUl04XqznUfwLYkuSSJOuAm4GH+hlL0pBWvMevqhNJfh94BFgD3FtVz/U2maTBrPjPeSvamIf60uCGfldf0ruU4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2rQ1PCT3JvkeJIDS5ZtSLInyeHu6znDjimpT7Ps8b8BbD9p2W3A3qraAuzt7kt6l5gaflV9H3jlpMXXA7u627uAG3qeS9KAVvoa/4KqOgbQfT2/v5EkDW3Fl8meVZIFYGHo7Uia3Ur3+C8n2QjQfT3+ditW1c6q2lZV21a4LUk9W2n4DwE7uts7gN39jCNpDKmqd14h+SZwDXAe8DLwp8B3gAeAi4EXgZuq6uQ3AJf7We+8MUmrVlWZts7U8Ptk+NLwZgnfT+5JDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2qQ4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDDF9qkOFLDZoafpKLkjya5GCS55Lc2i3fkGRPksPd13OGH1dSH2a5dt5GYGNVPZXkLOBJ4AbgC8ArVfWXSW4Dzqmqr075WV5CSxpYL5fQqqpjVfVUd/vnwEHgQuB6YFe32i4mTwaS3gVO6TV+ks3AFcA+4IKqOgaTJwfg/L6HkzSMtbOumOQDwLeAL1fVz5KpRxOL37cALKxsPElDmOky2UnOAB4GHqmqr3fLDgHXVNWx7n2Ax6rqQ1N+jq/xpYH18ho/k137PcDBxeg7DwE7uts7gN0rGVLS+GZ5V//jwD8DPwDe7Bb/EZPX+Q8AFwMvAjdV1StTfpZ7fGlgs+zxZzrU74vhS8Pr5VBf0nuP4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2rQLNfOOzPJ40meTfJckq91yy9Jsi/J4ST3J1k3/LiS+jDLHv914NqquhzYCmxPchVwB3BnVW0BXgVuGW5MSX2aGn5N/KK7e0b3r4BrgQe75buAGwaZUFLvZnqNn2RNkmeA48Ae4CfAa1V1olvlCHDhMCNK6ttM4VfVG1W1FdgEXAlcttxqy31vkoUk+5PsX/mYkvp0Su/qV9VrwGPAVcD6JGu7/2kTcPRtvmdnVW2rqm2rGVRSf2Z5V/+DSdZ3t98HfBI4CDwK3NittgPYPdSQkvqVqmWP0P9vheQjTN68W8PkieKBqvrzJJcC9wEbgKeB362q16f8rHfemKRVq6pMW2dq+H0yfGl4s4TvJ/ekBhm+1CDDlxpk+FKDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2qQ4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBs0cfnep7KeTPNzdvyTJviSHk9yfZN1wY0rq06ns8W9lcrHMRXcAd1bVFuBV4JY+B5M0nJnCT7IJ+G3g7u5+gGuBB7tVdgE3DDGgpP7Nuse/C/gK8GZ3/1zgtao60d0/AlzY82ySBjI1/CSfAY5X1ZNLFy+z6rJXwk2ykGR/kv0rnFFSz9bOsM7VwGeTXAecCZzN5AhgfZK13V5/E3B0uW+uqp3ATvAy2dLpYuoev6pur6pNVbUZuBn4XlV9HngUuLFbbQewe7ApJfVqNX/H/yrwB0meZ/Ka/55+RpI0tFSNd/Ttob40vKpa7j24t/CTe1KDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2qQ4UsNMnypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDZrloJkleAH4OvAGcqKptSTYA9wObgReA36mqV4cZU1KfTmWP/4mq2lpV27r7twF7q2oLsLe7L+ldYDWH+tcDu7rbu4AbVj+OpDHMGn4B303yZJKFbtkFVXUMoPt6/hADSurfTK/xgaur6miS84E9SX406wa6J4qFqStKGs0pXyY7yZ8BvwB+D7imqo4l2Qg8VlUfmvK9XiZbGlgvl8lO8v4kZy3eBj4NHAAeAnZ0q+0Adq98VEljmrrHT3Ip8O3u7lrg76vqL5KcCzwAXAy8CNxUVa9M+Vnu8aWBzbLHP+VD/dUwfGl4vRzqS3rvMXypQYYvNcjwpQYZvtQgw5caZPhSgwxfapDhSw0yfKlBhi81yPClBhm+1CDDlxpk+FKDDF9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoMMX2qQ4UsNmin8JOuTPJjkR0kOJvlYkg1J9iQ53H09Z+hhJfVj1j3+XwH/VFW/BlwOHARuA/ZW1RZgb3df0rvALBfNPBt4Fri0lqyc5BBeJls67fR17bxLgf8A/jbJ00nu7i6XfUFVHes2dAw4f1XTShrNLOGvBT4K/E1VXQH8F6dwWJ9kIcn+JPtXOKOkns0S/hHgSFXt6+4/yOSJ4OXuEJ/u6/HlvrmqdlbVtqra1sfAklZvavhV9e/AS0kWX7//JvBD4CFgR7dsB7B7kAkl9W7qm3sASbYCdwPrgJ8CX2TypPEAcDHwInBTVb0y5ef45p40sFne3Jsp/L4YvjS8vt7Vl/QeY/hSgwxfapDhSw0yfKlBhi81yPClBq0deXv/CfwbcF53e55OhxnAOU7mHG91qnP86iwrjfoBnv/daLJ/3p/dPx1mcA7nmNccHupLDTJ8qUHzCn/nnLa71OkwAzjHyZzjrQaZYy6v8SXNl4f6UoNGDT/J9iSHkjyfZLSz8ia5N8nxJAeWLBv99OBJLkryaHeK8ueS3DqPWZKcmeTxJM92c3ytW35Jkn3dHPcnWTfkHEvmWdOdz/Hhec2R5IUkP0jyzOJp4ub0OzLKqexHCz/JGuCvgd8CPgx8LsmHR9r8N4DtJy2bx+nBTwB/WFWXAVcBX+r+Pxh7lteBa6vqcmArsD3JVcAdwJ3dHK8Ctww8x6JbmZyyfdG85vhEVW1d8uezefyOjHMq+6oa5R/wMeCRJfdvB24fcfubgQNL7h8CNna3NwKHxpplyQy7gU/NcxbgV4CngF9n8kGRtcs9XgNuf1P3y3wt8DCQOc3xAnDeSctGfVyAs4F/pXvvbcg5xjzUvxB4acn9I92yeZnr6cGTbAauAPbNY5bu8PoZJidJ3QP8BHitqk50q4z1+NwFfAV4s7t/7pzmKOC7SZ5MstAtG/txGe1U9mOGv9zpgJr8k0KSDwDfAr5cVT+bxwxV9UZVbWWyx70SuGy51YacIclngONV9eTSxWPP0bm6qj7K5KXol5L8xgjbPNmqTmV/KsYM/whw0ZL7m4CjI27/ZDOdHrxvSc5gEv3fVdU/znMWgKp6DXiMyXsO65Ms/vcbYzw+VwOfTfICcB+Tw/275jAHVXW0+3oc+DaTJ8OxH5dVncr+VIwZ/hPAlu4d23XAzUxO0T0vo58ePEmAe4CDVfX1ec2S5INJ1ne33wd8ksmbSI8CN441R1XdXlWbqmozk9+H71XV58eeI8n7k5y1eBv4NHCAkR+XGvNU9kO/aXLSmxTXAT9m8nryj0fc7jeBY8B/M3lWvYXJa8m9wOHu64YR5vg4k8PWfwGe6f5dN/YswEeAp7s5DgB/0i2/FHgceB74B+CXR3yMrgEenscc3fae7f49t/i7Oaffka3A/u6x+Q5wzhBz+Mk9qUF+ck9qkOFLDTJ8qUGGLzXI8KUGGb7UIMOXGmT4UoP+B5DjZ1E5WbVBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted images: 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "def evalLetters(feed):\n",
    "    predict_, target_, predict_lengths_, target_lengths_ = sess.run(\n",
    "        [letter_prediction_infer,\n",
    "         letter_targets,\n",
    "         final_seq_lengths,\n",
    "         letter_targets_length],\n",
    "        feed)\n",
    "    for i, (inp, pred) in enumerate(zip(target_, predict_)):\n",
    "        print(\"Expected images:\", target_lengths_[i])\n",
    "        for x in range(len(inp)):\n",
    "            implt(inp[x].reshape((64, 64)), 'gray')\n",
    "        print(\"Predicted images:\", predict_lengths_[i])\n",
    "        for x in range(len(pred)):\n",
    "            implt(pred[x].reshape((64, 64)), 'gray')\n",
    "        if i >= 0:\n",
    "            break\n",
    "            \n",
    "fd_test = test_iterator.next_feed(BATCH_SIZE)\n",
    "fd = train_iterator.next_feed(BATCH_SIZE, words=False, train=False)\n",
    "\n",
    "evalLetters(fd_test)\n",
    "evalLetters(fd)"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}