image2text.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are parts of this code that are based on the keras example here: https://keras.io/examples/image_ocr/\n",
    "Much of the image generation part was rewritten in Pillow instead of kairo. In hind site I should have tried to write it in keras or something that could be GPU accellerated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install pillow pydot matplotlib editdistance cairocffi editdistance graphviz scipy tensorflow-gpu keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import itertools\n",
    "import codecs\n",
    "import re\n",
    "import datetime\n",
    "import cairocffi as cairo\n",
    "import editdistance\n",
    "import numpy as np\n",
    "from scipy import ndimage\n",
    "import pylab\n",
    "import matplotlib.pyplot as plt\n",
    "from keras import backend as K\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
    "from keras.layers import Input, Dense, Activation\n",
    "from keras.layers import Reshape, Lambda\n",
    "from keras.layers.merge import add, concatenate\n",
    "from keras.models import Model\n",
    "from keras.layers.recurrent import GRU\n",
    "from keras.optimizers import SGD\n",
    "from keras.utils.data_utils import get_file\n",
    "from keras.preprocessing import image\n",
    "import keras.callbacks\n",
    "import random\n",
    "\n",
    "from matplotlib.pyplot import imshow\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 6890037264765727131\n",
      ", name: \"/device:XLA_CPU:0\"\n",
      "device_type: \"XLA_CPU\"\n",
      "memory_limit: 17179869184\n",
      "locality {\n",
      "}\n",
      "incarnation: 16837674082536504141\n",
      "physical_device_desc: \"device: XLA_CPU device\"\n",
      ", name: \"/device:XLA_GPU:0\"\n",
      "device_type: \"XLA_GPU\"\n",
      "memory_limit: 17179869184\n",
      "locality {\n",
      "}\n",
      "incarnation: 2417930563384339738\n",
      "physical_device_desc: \"device: XLA_GPU device\"\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 6652021965\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 13367335053546727382\n",
      "physical_device_desc: \"device: 0, name: GeForce GTX 1080, pci bus id: 0000:0e:00.0, compute capability: 6.1\"\n",
      "]\n",
      "2.3.1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['/job:localhost/replica:0/task:0/device:GPU:0']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List our avaialable devices\n",
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())\n",
    "\n",
    "import keras\n",
    "print(keras.__version__)\n",
    "\n",
    "# Check to make sure we are using a GPU (should show a GPY)\n",
    "K.tensorflow_backend._get_available_gpus()\n",
    "\n",
    "# # confirm TensorFlow sees the GPU\n",
    "# from tensorflow.python.client import device_lib\n",
    "# assert 'GPU' in str(device_lib.list_local_devices())\n",
    "\n",
    "# # confirm Keras sees the GPU\n",
    "# from keras import backend\n",
    "# assert len(backend.tensorflow_backend._get_available_gpus()) > 0\n",
    "\n",
    "# # confirm PyTorch sees the GPU\n",
    "# from torch import cuda\n",
    "# assert cuda.is_available()\n",
    "# assert cuda.device_count() > 0\n",
    "# print(cuda.get_device_name(cuda.current_device()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic configuration\n",
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "OUTPUT_DIR = 'model_weights'\n",
    "\n",
    "# character classes and matching regex filter\n",
    "regex = r'^[a-z ]+$'\n",
    "alphabet = u'abcdefghijklmnopqrstuvwxyz '\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image, ImageDraw, ImageFont\n",
    "\n",
    "def speckle(img):\n",
    "    severity = np.random.uniform(0, 0.6)\n",
    "    blur = ndimage.gaussian_filter(np.random.randn(*img.shape) * severity, 1)\n",
    "    img_speck = (img + blur)\n",
    "    img_speck[img_speck > 1] = 1\n",
    "    img_speck[img_speck <= 0] = 0\n",
    "    return img_speck\n",
    "\n",
    "def draw_text(text, w, h, translate=True, rotate=True, multi_fonts=True, max_rotation=359):\n",
    "    base_image = Image.new('RGB', (w,h), (255,255,255))\n",
    "\n",
    "    #TODO download more fonts from fonts.google.com to support multiple fonts\n",
    "    fnt = ImageFont.truetype('fonts/Aller_Rg.ttf', 20)\n",
    "    text_dim = fnt.getsize(text)\n",
    "    text_image = Image.new('RGBA', text_dim, (0,0,0,0))\n",
    "    \n",
    "    # Draw text\n",
    "    d = ImageDraw.Draw(text_image)\n",
    "    d.text((0,0), text, font=fnt, fill=(0,0,0))\n",
    "    \n",
    "    if(rotate):\n",
    "        rotation_degrees = np.random.randint(-max_rotation, max_rotation)\n",
    "        text_image = text_image.rotate(rotation_degrees, expand=1)\n",
    "        \n",
    "    \n",
    "    if text_image.size[0] > base_image.size[0] or text_image.size[1] > base_image.size[1]:\n",
    "        print( 'Text:' + text)\n",
    "        print( 'Dimensions:' + str(text_image.size) + ', ' + str(base_image.size) )\n",
    "        text_image.show()\n",
    "        base_image.show()\n",
    "        raise IOError('Text will not fit in image size. Increase image size or decrease max text length.')\n",
    "    \n",
    "    max_translate = tuple(t//2 for t in np.subtract(base_image.size, text_image.size))\n",
    "    \n",
    "    location = ((w - text_image.size[0])//2 ,(h - text_image.size[1])//2)\n",
    "    if(translate):\n",
    "        translate_x = np.random.randint(-max_translate[0],max_translate[0])\n",
    "        translate_y = np.random.randint(-max_translate[1],max_translate[1])                                    \n",
    "        location = (location[0] + translate_x, location[1] + translate_y)\n",
    "    \n",
    "    base_image.paste(text_image, location, text_image)\n",
    "    \n",
    "    a = np.array(base_image)\n",
    "    a = a[:, :, 0]\n",
    "    # Convert the 0-255 int value to float from 0-1 \n",
    "    a = a.astype(np.float32) / 255\n",
    "    a = np.expand_dims(a, 0)\n",
    "    a = speckle(a)\n",
    "    \n",
    "    return a\n",
    "\n",
    "\n",
    "def show_single_channel_image(a):\n",
    "    s = a.shape\n",
    "    a = a.reshape((s[1],s[2]))\n",
    "    plt.rcParams[\"figure.figsize\"] = (6,6)\n",
    "    plt.imshow(a, cmap='Greys_r')\n",
    "    plt.show()\n",
    "    \n",
    "test_image = draw_text('parody',256, 256, translate=False, rotate=False)\n",
    "show_single_channel_image(test_image)\n",
    "\n",
    "test_image = draw_text('epstein didnt kill himself', 256, 256, translate=True, rotate=True)\n",
    "show_single_channel_image(test_image)\n",
    "\n",
    "test_image = draw_text('epstein didnt kill himself', 256, 256, translate=True, rotate=True, max_rotation=20)\n",
    "show_single_channel_image(test_image)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is pulled from https://github.com/Tony607/keras-image-ocr\n",
    "def shuffle_mats_or_lists(matrix_list, stop_ind=None):\n",
    "    ret = []\n",
    "    assert all([len(i) == len(matrix_list[0]) for i in matrix_list])\n",
    "    len_val = len(matrix_list[0])\n",
    "    if stop_ind is None:\n",
    "        stop_ind = len_val\n",
    "    assert stop_ind <= len_val\n",
    "\n",
    "    a = list(range(stop_ind))\n",
    "    np.random.shuffle(a)\n",
    "    a += list(range(stop_ind, len_val))\n",
    "    for mat in matrix_list:\n",
    "        if isinstance(mat, np.ndarray):\n",
    "            ret.append(mat[a])\n",
    "        elif isinstance(mat, list):\n",
    "            ret.append([mat[i] for i in a])\n",
    "        else:\n",
    "            raise TypeError('`shuffle_mats_or_lists` only supports '\n",
    "                            'numpy.array and list objects.')\n",
    "    return ret\n",
    "\n",
    "\n",
    "# Translation of characters to unique integer values\n",
    "def text_to_labels(text):\n",
    "    ret = []\n",
    "    for char in text:\n",
    "        ret.append(alphabet.find(char))\n",
    "    return ret\n",
    "\n",
    "\n",
    "# Reverse translation of numerical classes back to characters\n",
    "def labels_to_text(labels):\n",
    "    ret = []\n",
    "    for c in labels:\n",
    "        if c == len(alphabet):  # CTC Blank\n",
    "            ret.append(\"\")\n",
    "        else:\n",
    "            ret.append(alphabet[c])\n",
    "    return \"\".join(ret)\n",
    "\n",
    "\n",
    "# only a-z and space..probably not to difficult\n",
    "# to expand to uppercase and symbols\n",
    "\n",
    "def is_valid_str(in_str):\n",
    "    search = re.compile(regex, re.UNICODE).search\n",
    "    return bool(search(in_str))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataProvider(keras.callbacks.Callback):\n",
    "    def __init__(self, dictionary_file, minibatch_size,\n",
    "                 img_w, img_h, downsample_factor, val_split,\n",
    "                 absolute_max_string_len=16, max_rotation=180):\n",
    "\n",
    "        self.minibatch_size = minibatch_size\n",
    "        self.img_w = img_w\n",
    "        self.img_h = img_h\n",
    "        self.dictionary_file = dictionary_file\n",
    "        self.downsample_factor = downsample_factor\n",
    "        self.val_split = val_split\n",
    "        self.blank_label = self.get_output_size() - 1\n",
    "        self.absolute_max_string_len = absolute_max_string_len\n",
    "        self.max_rotation = max_rotation\n",
    "\n",
    "    def get_output_size(self):\n",
    "        return len(alphabet) + 1\n",
    "\n",
    "    # num_words can be independent of the epoch size due to the use of generators\n",
    "    # as max_string_len grows, num_words can grow\n",
    "    def build_word_list(self, num_words, max_string_len=None):\n",
    "        # TODO this could be much smarter by finding a way to use google ngram api for data\n",
    "        assert max_string_len <= self.absolute_max_string_len\n",
    "        assert num_words % self.minibatch_size == 0\n",
    "        assert (self.val_split * num_words) % self.minibatch_size == 0\n",
    "        self.num_words = num_words\n",
    "        self.string_list = [''] * self.num_words\n",
    "        tmp_string_list = []\n",
    "        \n",
    "        self.max_string_len = max_string_len\n",
    "        self.Y_data = np.ones([self.num_words, self.absolute_max_string_len]) * -1\n",
    "        self.X_text = []\n",
    "        self.Y_len = [0] * self.num_words\n",
    "\n",
    "        \n",
    "        with open(self.dictionary_file, 'r', encoding='utf-8') as f:\n",
    "            lines = f.readlines()\n",
    "        \n",
    "        # Remove newline characters and other characters that are unwanted like punctuation (\\w)\n",
    "        lines = map(lambda s : re.sub(r'[^\\w]+', '', s.strip().lower() ), lines)\n",
    "        \n",
    "        # Filter ouf words over a specific length and words that are not part of our alphabet\n",
    "        lines = [x for x in lines if len(x)<= self.max_string_len and is_valid_str(x) ]\n",
    "        \n",
    "        # Because many dictionaries are in an order(often alphebetical) randomize it.\n",
    "        np.random.shuffle(lines)\n",
    "        self.string_list = lines[:self.num_words]\n",
    "\n",
    "        if len(self.string_list) != self.num_words:\n",
    "            raise IOError('Not enough words in dictionary.')\n",
    "\n",
    "        for i, word in enumerate(self.string_list):\n",
    "            self.Y_len[i] = len(word)\n",
    "            self.Y_data[i, 0:len(word)] = text_to_labels(word)\n",
    "            self.X_text.append(word)\n",
    "        self.Y_len = np.expand_dims(np.array(self.Y_len), 1)\n",
    "\n",
    "        self.cur_val_index = self.val_split\n",
    "        self.cur_train_index = 0\n",
    "\n",
    "    # each time an image is requested from train/val/test, a new random\n",
    "    # painting of the text is performed\n",
    "    def get_batch(self, index, size, train):\n",
    "        # width and height are backwards from typical Keras convention\n",
    "        # because width is the time dimension when it gets fed into the RNN\n",
    "        if K.image_data_format() == 'channels_first':\n",
    "            X_data = np.ones([size, 1, self.img_w, self.img_h])\n",
    "        else:\n",
    "            X_data = np.ones([size, self.img_w, self.img_h, 1])\n",
    "\n",
    "        labels = np.ones([size, self.absolute_max_string_len])\n",
    "        input_length = np.zeros([size, 1])\n",
    "        label_length = np.zeros([size, 1])\n",
    "        source_str = []\n",
    "        for i in range(size):\n",
    "            # Mix in some blank inputs.  This seems to be important for\n",
    "            # achieving translational invariance\n",
    "            if train and i > size - 4:\n",
    "                if K.image_data_format() == 'channels_first':\n",
    "                    X_data[i, 0, 0:self.img_w, :] = self.paint_func('')[0, :, :].T\n",
    "                else:\n",
    "                    X_data[i, 0:self.img_w, :, 0] = self.paint_func('',)[0, :, :].T\n",
    "                labels[i, 0] = self.blank_label\n",
    "                input_length[i] = self.img_w // self.downsample_factor - 2\n",
    "                label_length[i] = 1\n",
    "                source_str.append('')\n",
    "            else:\n",
    "                if K.image_data_format() == 'channels_first':\n",
    "                    X_data[i, 0, 0:self.img_w, :] = self.paint_func(self.X_text[index + i])[0, :, :].T\n",
    "                else:\n",
    "                    X_data[i, 0:self.img_w, :, 0] = self.paint_func(self.X_text[index + i])[0, :, :].T\n",
    "                labels[i, :] = self.Y_data[index + i]\n",
    "                input_length[i] = self.img_w // self.downsample_factor - 2\n",
    "                label_length[i] = self.Y_len[index + i]\n",
    "                source_str.append(self.X_text[index + i])\n",
    "        inputs = {'the_input': X_data,\n",
    "                  'the_labels': labels,\n",
    "                  'input_length': input_length,\n",
    "                  'label_length': label_length,\n",
    "                  'source_str': source_str  # used for visualization only\n",
    "                  }\n",
    "        outputs = {'ctc': np.zeros([size])}  # dummy data for dummy loss function\n",
    "        return (inputs, outputs)\n",
    "\n",
    "    def next_train(self):\n",
    "        while 1:\n",
    "            ret = self.get_batch(self.cur_train_index, self.minibatch_size, train=True)\n",
    "            self.cur_train_index += self.minibatch_size\n",
    "            if self.cur_train_index >= self.val_split:\n",
    "                self.cur_train_index = self.cur_train_index % 32\n",
    "                (self.X_text, self.Y_data, self.Y_len) = shuffle_mats_or_lists(\n",
    "                    [self.X_text, self.Y_data, self.Y_len], self.val_split)\n",
    "            yield ret\n",
    "\n",
    "    def next_val(self):\n",
    "        while 1:\n",
    "            ret = self.get_batch(self.cur_val_index, self.minibatch_size, train=False)\n",
    "            self.cur_val_index += self.minibatch_size\n",
    "            if self.cur_val_index >= self.num_words:\n",
    "                self.cur_val_index = self.val_split + self.cur_val_index % 32\n",
    "            yield ret\n",
    "\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.build_word_list(16000, 6)\n",
    "        self.paint_func = lambda text: draw_text(text, self.img_w, self.img_h,\n",
    "                                                  rotate=False, translate=False, multi_fonts=False)\n",
    "\n",
    "    def on_epoch_begin(self, epoch, logs={}):\n",
    "        # rebind the paint function to implement curriculum learning\n",
    "        if 3 <= epoch < 6:\n",
    "            self.paint_func = lambda text: draw_text(text, self.img_w, self.img_h, rotate=False, translate=True, multi_fonts=False, max_rotation=self.max_rotation)\n",
    "        elif 6 <= epoch < 9:\n",
    "            self.paint_func = lambda text: draw_text(text, self.img_w, self.img_h, rotate=False, translate=True, multi_fonts=True, max_rotation=self.max_rotation)\n",
    "        elif epoch >= 9:\n",
    "            self.paint_func = lambda text: draw_text(text, self.img_w, self.img_h, rotate=True, translate=True, multi_fonts=True, max_rotation=self.max_rotation)\n",
    "        if epoch >= 21 and self.max_string_len < 12:\n",
    "            self.build_word_list(32000, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16000\n",
      "['didos', 'tess', 'havana', 'fart', 'azov', 'seraph', 'chests', 'flatly', 'relief', 'honeys']\n"
     ]
    }
   ],
   "source": [
    "# Exercise the data_provider to make sure its roughly working\n",
    "def test_data_provider():\n",
    "    pool_size = 2\n",
    "    words_per_epoch = 16000\n",
    "    val_split = 0.2\n",
    "    val_words = int(words_per_epoch * (val_split))\n",
    "    data_provider = DataProvider(dictionary_file='./dictionary.txt',\n",
    "                                 minibatch_size=1000,\n",
    "                                 img_w=256,\n",
    "                                 img_h=256,\n",
    "                                 downsample_factor=(pool_size ** 2),\n",
    "                                 val_split=words_per_epoch - val_words)\n",
    "    \n",
    "    data_provider.build_word_list(16000, 6)\n",
    "    print( len(data_provider.string_list) )\n",
    "    print( data_provider.string_list[:10] )\n",
    "    \n",
    "    #print(data_provider.get_batch(0,32,))\n",
    "    \n",
    "    \n",
    "\n",
    "test_data_provider()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the actual loss calc occurs here despite it not being\n",
    "# an internal Keras loss function\n",
    "\n",
    "def ctc_lambda_func(args):\n",
    "    y_pred, labels, input_length, label_length = args\n",
    "    # the 2 is critical here since the first couple outputs of the RNN\n",
    "    # tend to be garbage:\n",
    "    y_pred = y_pred[:, 2:, :]\n",
    "    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)\n",
    "\n",
    "def decode_batch(test_func, word_batch):\n",
    "    out = test_func([word_batch])[0]\n",
    "    ret = []\n",
    "    for j in range(out.shape[0]):\n",
    "        out_best = list(np.argmax(out[j, 2:], 1))\n",
    "        out_best = [k for k, g in itertools.groupby(out_best)]\n",
    "        outstr = labels_to_text(out_best)\n",
    "        ret.append(outstr)\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VizCallback(keras.callbacks.Callback):\n",
    "\n",
    "    def __init__(self, run_name, test_func, text_img_gen, num_display_words=6):\n",
    "        self.test_func = test_func\n",
    "        self.output_dir = os.path.join(\n",
    "            OUTPUT_DIR, run_name)\n",
    "        self.text_img_gen = text_img_gen\n",
    "        self.num_display_words = num_display_words\n",
    "        if not os.path.exists(self.output_dir):\n",
    "            os.makedirs(self.output_dir)\n",
    "\n",
    "    def show_edit_distance(self, num):\n",
    "        num_left = num\n",
    "        mean_norm_ed = 0.0\n",
    "        mean_ed = 0.0\n",
    "        while num_left > 0:\n",
    "            word_batch = next(self.text_img_gen)[0]\n",
    "            num_proc = min(word_batch['the_input'].shape[0], num_left)\n",
    "            decoded_res = decode_batch(self.test_func, word_batch['the_input'][0:num_proc])\n",
    "            for j in range(num_proc):\n",
    "                edit_dist = editdistance.eval(decoded_res[j], word_batch['source_str'][j])\n",
    "                mean_ed += float(edit_dist)\n",
    "                mean_norm_ed += float(edit_dist) / len(word_batch['source_str'][j])\n",
    "            num_left -= num_proc\n",
    "        mean_norm_ed = mean_norm_ed / num\n",
    "        mean_ed = mean_ed / num\n",
    "        print('\\nOut of %d samples:  Mean edit distance: %.3f Mean normalized edit distance: %0.3f'\n",
    "              % (num, mean_ed, mean_norm_ed))\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))\n",
    "        self.show_edit_distance(256)\n",
    "        word_batch = next(self.text_img_gen)[0]\n",
    "        res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])\n",
    "        if word_batch['the_input'][0].shape[0] < 256:\n",
    "            cols = 2\n",
    "        else:\n",
    "            cols = 1\n",
    "        for i in range(self.num_display_words):\n",
    "            plt.subplot(self.num_display_words // cols, cols, i + 1)\n",
    "            if K.image_data_format() == 'channels_first':\n",
    "                the_input = word_batch['the_input'][i, 0, :, :]\n",
    "            else:\n",
    "                the_input = word_batch['the_input'][i, :, :, 0]\n",
    "            plt.imshow(the_input.T, cmap='Greys_r')\n",
    "            plt.xlabel('Truth = \\'%s\\'\\nDecoded = \\'%s\\'' % (word_batch['source_str'][i], res[i]))\n",
    "        fig = pylab.gcf()\n",
    "        fig.set_size_inches(10, 13)\n",
    "        plt.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))\n",
    "        plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(output_size, \n",
    "                absolute_max_string_len,\n",
    "                img_w, img_h,\n",
    "                pool_size,\n",
    "                weight_file=None\n",
    "               ):\n",
    "    \n",
    "    conv_filters = 16\n",
    "    kernel_size = (3, 3)\n",
    "    time_dense_size = 32\n",
    "    rnn_size = 512\n",
    "    \n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        input_shape = (1, img_w, img_h)\n",
    "    else:\n",
    "        input_shape = (img_w, img_h, 1)\n",
    "    print('input_shape='+str(input_shape))\n",
    "    \n",
    "    act = 'relu'\n",
    "    input_data = Input(name='the_input', shape=input_shape, dtype='float32')\n",
    "    inner = Conv2D(conv_filters, kernel_size, padding='same',\n",
    "                   activation=act, kernel_initializer='he_normal',\n",
    "                   name='conv1')(input_data)\n",
    "    inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max1')(inner)\n",
    "    inner = Conv2D(conv_filters, kernel_size, padding='same',\n",
    "                   activation=act, kernel_initializer='he_normal',\n",
    "                   name='conv2')(inner)\n",
    "    inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max2')(inner)\n",
    "\n",
    "    conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_filters)\n",
    "    inner = Reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)\n",
    "\n",
    "    # cuts down input size going into RNN:\n",
    "    inner = Dense(time_dense_size, activation=act, name='dense1')(inner)\n",
    "\n",
    "    # Two layers of bidirectional GRUs\n",
    "    # GRU seems to work as well, if not better than LSTM:\n",
    "    gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)\n",
    "    gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)\n",
    "    gru1_merged = add([gru_1, gru_1b])\n",
    "    gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)\n",
    "    gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)\n",
    "\n",
    "    # transforms RNN output to character activations:\n",
    "    inner = Dense(output_size, kernel_initializer='he_normal',\n",
    "                  name='dense2')(concatenate([gru_2, gru_2b]))\n",
    "    y_pred = Activation('softmax', name='softmax')(inner)\n",
    "    Model(inputs=input_data, outputs=y_pred).summary()\n",
    "\n",
    "    labels = Input(name='the_labels', shape=[absolute_max_string_len], dtype='float32')\n",
    "    input_length = Input(name='input_length', shape=[1], dtype='int64')\n",
    "    label_length = Input(name='label_length', shape=[1], dtype='int64')\n",
    "    # Keras doesn't currently support loss funcs with extra parameters\n",
    "    # so CTC loss is implemented in a lambda layer\n",
    "    loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])\n",
    "\n",
    "    # clipnorm seems to speeds up convergence\n",
    "    sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)\n",
    "\n",
    "    model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)\n",
    "\n",
    "    # the loss calc occurs elsewhere, so use a dummy lambda func for the loss\n",
    "    model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)\n",
    "    \n",
    "    if(weight_file):\n",
    "        model.load_weights(weight_file)\n",
    "    \n",
    "    test_func = K.function([input_data], [y_pred])\n",
    "    \n",
    "    return model, test_func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = None\n",
    "def train(run_name, start_epoch, stop_epoch, img_w, img_h, max_rotation):\n",
    "    words_per_epoch = 16000\n",
    "    val_split = 0.2\n",
    "    val_words = int(words_per_epoch * (val_split))\n",
    "\n",
    "    pool_size = 2\n",
    "    minibatch_size = 32\n",
    "\n",
    "    img_gen = DataProvider(dictionary_file=\"dictionary.txt\",\n",
    "                                 minibatch_size=minibatch_size,\n",
    "                                 img_w=img_w,\n",
    "                                 img_h=img_h,\n",
    "                                 downsample_factor=(pool_size ** 2),\n",
    "                                 val_split=words_per_epoch - val_words,\n",
    "                                max_rotation=max_rotation\n",
    "                                 )\n",
    "    \n",
    "    weight_file = None\n",
    "    if start_epoch > 0:\n",
    "        weight_file = os.path.join(OUTPUT_DIR, os.path.join(run_name, 'weights%02d.h5' % (start_epoch - 1)))\n",
    "    \n",
    "    model,test_func = build_model(output_size=img_gen.get_output_size(), \n",
    "                        absolute_max_string_len=img_gen.absolute_max_string_len,\n",
    "                        img_w=img_w, img_h=img_h,\n",
    "                        pool_size=pool_size,\n",
    "                        weight_file=weight_file\n",
    "                       )\n",
    "    \n",
    "    \n",
    "    # captures output of softmax so we can decode the output during visualization\n",
    "    #test_func = K.function([model.inputs], [model.outputs])\n",
    "\n",
    "    viz_cb = VizCallback(run_name, test_func, img_gen.next_val())\n",
    "    \n",
    "    # TODO temporary debug visual\n",
    "    from keras.utils import plot_model\n",
    "    plot_model(model, to_file='model.png', show_shapes=True)\n",
    "\n",
    "    global history\n",
    "    history = model.fit_generator(generator=img_gen.next_train(),\n",
    "                        steps_per_epoch=(words_per_epoch - val_words) // minibatch_size,\n",
    "                        epochs=stop_epoch,\n",
    "                        validation_data=img_gen.next_val(),\n",
    "                        validation_steps=val_words // minibatch_size,\n",
    "                        callbacks=[viz_cb, img_gen],\n",
    "                        initial_epoch=start_epoch)\n",
    "\n",
    "    # Plot training & validation loss values\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title('Model loss')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['Train', 'Test'], loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_shape=(256, 256, 1)\n",
      "Model: \"model_43\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "the_input (InputLayer)          (None, 256, 256, 1)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 256, 256, 16) 160         the_input[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max1 (MaxPooling2D)             (None, 128, 128, 16) 0           conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2 (Conv2D)                  (None, 128, 128, 16) 2320        max1[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "max2 (MaxPooling2D)             (None, 64, 64, 16)   0           conv2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 64, 1024)     0           max2[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "dense1 (Dense)                  (None, 64, 32)       32800       reshape[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "gru1 (GRU)                      (None, 64, 512)      837120      dense1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gru1_b (GRU)                    (None, 64, 512)      837120      dense1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_18 (Add)                    (None, 64, 512)      0           gru1[0][0]                       \n",
      "                                                                 gru1_b[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gru2 (GRU)                      (None, 64, 512)      1574400     add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gru2_b (GRU)                    (None, 64, 512)      1574400     add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_18 (Concatenate)    (None, 64, 1024)     0           gru2[0][0]                       \n",
      "                                                                 gru2_b[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense2 (Dense)                  (None, 64, 28)       28700       concatenate_18[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "softmax (Activation)            (None, 64, 28)       0           dense2[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 4,887,020\n",
      "Trainable params: 4,887,020\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/20\n",
      "400/400 [==============================] - 116s 290ms/step - loss: 17.0378 - val_loss: 15.8278\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 4.262 Mean normalized edit distance: 0.820\n",
      "Epoch 2/20\n",
      "400/400 [==============================] - 115s 287ms/step - loss: 11.2036 - val_loss: 9.1342\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 2.453 Mean normalized edit distance: 0.478\n",
      "Epoch 3/20\n",
      "400/400 [==============================] - 114s 286ms/step - loss: 6.6205 - val_loss: 5.9477\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.793 Mean normalized edit distance: 0.341\n",
      "Epoch 4/20\n",
      "400/400 [==============================] - 115s 287ms/step - loss: 15.5679 - val_loss: 15.9278\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 4.105 Mean normalized edit distance: 0.792\n",
      "Epoch 5/20\n",
      "400/400 [==============================] - 123s 307ms/step - loss: 12.7537 - val_loss: 13.4946\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 3.672 Mean normalized edit distance: 0.700\n",
      "Epoch 6/20\n",
      "400/400 [==============================] - 124s 309ms/step - loss: 10.5504 - val_loss: 9.5788\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 3.004 Mean normalized edit distance: 0.575\n",
      "Epoch 7/20\n",
      "400/400 [==============================] - 124s 309ms/step - loss: 8.3992 - val_loss: 8.3676\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 2.301 Mean normalized edit distance: 0.436\n",
      "Epoch 8/20\n",
      "400/400 [==============================] - 124s 310ms/step - loss: 6.5859 - val_loss: 6.8102\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 2.039 Mean normalized edit distance: 0.389\n",
      "Epoch 9/20\n",
      "400/400 [==============================] - 122s 305ms/step - loss: 5.3174 - val_loss: 5.0321\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.609 Mean normalized edit distance: 0.305\n",
      "Epoch 10/20\n",
      "400/400 [==============================] - 121s 304ms/step - loss: 4.4118 - val_loss: 4.3727\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.348 Mean normalized edit distance: 0.260\n",
      "Epoch 11/20\n",
      "400/400 [==============================] - 123s 309ms/step - loss: 3.7597 - val_loss: 4.4287\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.176 Mean normalized edit distance: 0.219\n",
      "Epoch 12/20\n",
      "400/400 [==============================] - 121s 302ms/step - loss: 3.2217 - val_loss: 4.5618\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.062 Mean normalized edit distance: 0.198\n",
      "Epoch 13/20\n",
      "400/400 [==============================] - 125s 312ms/step - loss: 2.8159 - val_loss: 2.8015\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 0.816 Mean normalized edit distance: 0.156\n",
      "Epoch 14/20\n",
      "400/400 [==============================] - 122s 305ms/step - loss: 2.5359 - val_loss: 3.9757\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 0.910 Mean normalized edit distance: 0.174\n",
      "Epoch 15/20\n",
      "400/400 [==============================] - 123s 307ms/step - loss: 2.2764 - val_loss: 3.7367\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 0.875 Mean normalized edit distance: 0.163\n",
      "Epoch 16/20\n",
      "400/400 [==============================] - 121s 303ms/step - loss: 2.0441 - val_loss: 1.7708\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 0.758 Mean normalized edit distance: 0.137\n",
      "Epoch 17/20\n",
      "400/400 [==============================] - 121s 303ms/step - loss: 1.8735 - val_loss: 1.9981\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 0.648 Mean normalized edit distance: 0.119\n",
      "Epoch 18/20\n",
      "400/400 [==============================] - 121s 301ms/step - loss: 1.7956 - val_loss: 1.5186\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 0.598 Mean normalized edit distance: 0.111\n",
      "Epoch 19/20\n",
      "400/400 [==============================] - 123s 307ms/step - loss: 1.6655 - val_loss: 2.0156\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 0.523 Mean normalized edit distance: 0.100\n",
      "Epoch 20/20\n",
      "400/400 [==============================] - 124s 310ms/step - loss: 1.5372 - val_loss: 1.3755\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 0.488 Mean normalized edit distance: 0.090\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_shape=(512, 256, 1)\n",
      "Model: \"model_45\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "the_input (InputLayer)          (None, 512, 256, 1)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 512, 256, 16) 160         the_input[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max1 (MaxPooling2D)             (None, 256, 128, 16) 0           conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2 (Conv2D)                  (None, 256, 128, 16) 2320        max1[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "max2 (MaxPooling2D)             (None, 128, 64, 16)  0           conv2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 128, 1024)    0           max2[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "dense1 (Dense)                  (None, 128, 32)      32800       reshape[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "gru1 (GRU)                      (None, 128, 512)     837120      dense1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gru1_b (GRU)                    (None, 128, 512)     837120      dense1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_19 (Add)                    (None, 128, 512)     0           gru1[0][0]                       \n",
      "                                                                 gru1_b[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gru2 (GRU)                      (None, 128, 512)     1574400     add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gru2_b (GRU)                    (None, 128, 512)     1574400     add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_19 (Concatenate)    (None, 128, 1024)    0           gru2[0][0]                       \n",
      "                                                                 gru2_b[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense2 (Dense)                  (None, 128, 28)      28700       concatenate_19[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "softmax (Activation)            (None, 128, 28)      0           dense2[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 4,887,020\n",
      "Trainable params: 4,887,020\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 21/35\n",
      "400/400 [==============================] - 243s 607ms/step - loss: 1.4449 - val_loss: 1.4921\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 0.477 Mean normalized edit distance: 0.091\n",
      "Epoch 22/35\n",
      "400/400 [==============================] - 242s 605ms/step - loss: 6.0659 - val_loss: 5.6916\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.848 Mean normalized edit distance: 0.204\n",
      "Epoch 23/35\n",
      "400/400 [==============================] - 234s 584ms/step - loss: 4.9264 - val_loss: 5.0920\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.750 Mean normalized edit distance: 0.193\n",
      "Epoch 24/35\n",
      "400/400 [==============================] - 247s 619ms/step - loss: 4.3584 - val_loss: 5.1369\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.602 Mean normalized edit distance: 0.177\n",
      "Epoch 25/35\n",
      "400/400 [==============================] - 256s 639ms/step - loss: 3.9676 - val_loss: 5.3036\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.746 Mean normalized edit distance: 0.201\n",
      "Epoch 26/35\n",
      "400/400 [==============================] - 242s 604ms/step - loss: 3.6235 - val_loss: 3.9216\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.527 Mean normalized edit distance: 0.168\n",
      "Epoch 27/35\n",
      "400/400 [==============================] - 243s 608ms/step - loss: 3.3652 - val_loss: 3.2188\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.281 Mean normalized edit distance: 0.140\n",
      "Epoch 28/35\n",
      "400/400 [==============================] - 243s 606ms/step - loss: 3.1121 - val_loss: 3.4045\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.184 Mean normalized edit distance: 0.133\n",
      "Epoch 29/35\n",
      "400/400 [==============================] - 243s 607ms/step - loss: 2.9215 - val_loss: 3.1224\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.105 Mean normalized edit distance: 0.129\n",
      "Epoch 30/35\n",
      "400/400 [==============================] - 235s 588ms/step - loss: 2.7741 - val_loss: 2.1238\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.078 Mean normalized edit distance: 0.120\n",
      "Epoch 31/35\n",
      "400/400 [==============================] - 231s 577ms/step - loss: 2.6101 - val_loss: 2.1561\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.129 Mean normalized edit distance: 0.132\n",
      "Epoch 32/35\n",
      "400/400 [==============================] - 228s 570ms/step - loss: 2.4637 - val_loss: 3.0323\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 1.113 Mean normalized edit distance: 0.121\n",
      "Epoch 33/35\n",
      "400/400 [==============================] - 227s 568ms/step - loss: 2.3841 - val_loss: 3.9751\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 0.984 Mean normalized edit distance: 0.107\n",
      "Epoch 34/35\n",
      "400/400 [==============================] - 225s 563ms/step - loss: 2.3092 - val_loss: 2.6651\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 0.871 Mean normalized edit distance: 0.099\n",
      "Epoch 35/35\n",
      "400/400 [==============================] - 225s 562ms/step - loss: 2.2096 - val_loss: 2.1600\n",
      "\n",
      "Out of 256 samples:  Mean edit distance: 0.914 Mean normalized edit distance: 0.101\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_name = datetime.datetime.now().strftime('%Y:%m:%d:%H:%M:%S')\n",
    "train(run_name, 0, 20, 256,256,max_rotation=3)\n",
    "# increase to wider images and start at epoch 10. The learned weights are reloaded\n",
    "train(run_name, 20, 35, 512,256,max_rotation=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_predict_ctc(out, top_paths = 1):\n",
    "    results = []\n",
    "    beam_width = 5\n",
    "    if beam_width < top_paths:\n",
    "      beam_width = top_paths\n",
    "    for i in range(top_paths):\n",
    "      lables = K.get_value(K.ctc_decode(out, input_length=np.ones(out.shape[0])*out.shape[1],\n",
    "                           greedy=False, beam_width=beam_width, top_paths=top_paths)[0][i])[0]\n",
    "      text = labels_to_text(lables)\n",
    "      results.append(text)\n",
    "    return results\n",
    "  \n",
    "def predit_a_image(model_p, a, top_paths = 1):\n",
    "  c = np.expand_dims(a.T, axis=0)\n",
    "  net_out_value = model_p.predict(c)\n",
    "  top_pred_texts = decode_predict_ctc(net_out_value, top_paths)\n",
    "  return top_pred_texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_shape=(256, 256, 1)\n",
      "Model: \"model_59\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "the_input (InputLayer)          (None, 256, 256, 1)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 256, 256, 16) 160         the_input[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max1 (MaxPooling2D)             (None, 128, 128, 16) 0           conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2 (Conv2D)                  (None, 128, 128, 16) 2320        max1[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "max2 (MaxPooling2D)             (None, 64, 64, 16)   0           conv2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 64, 1024)     0           max2[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "dense1 (Dense)                  (None, 64, 32)       32800       reshape[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "gru1 (GRU)                      (None, 64, 512)      837120      dense1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gru1_b (GRU)                    (None, 64, 512)      837120      dense1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_24 (Add)                    (None, 64, 512)      0           gru1[0][0]                       \n",
      "                                                                 gru1_b[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gru2 (GRU)                      (None, 64, 512)      1574400     add_24[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "gru2_b (GRU)                    (None, 64, 512)      1574400     add_24[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_24 (Concatenate)    (None, 64, 1024)     0           gru2[0][0]                       \n",
      "                                                                 gru2_b[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense2 (Dense)                  (None, 64, 28)       28700       concatenate_24[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "softmax (Activation)            (None, 64, 28)       0           dense2[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 4,887,020\n",
      "Trainable params: 4,887,020\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['tests']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_prediction_model(output_size, \n",
    "                absolute_max_string_len,\n",
    "                img_w, img_h,\n",
    "                pool_size,\n",
    "                weight_file=None\n",
    "               ):\n",
    "    \n",
    "    conv_filters = 16\n",
    "    kernel_size = (3, 3)\n",
    "    time_dense_size = 32\n",
    "    rnn_size = 512\n",
    "    \n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        input_shape = (1, img_w, img_h)\n",
    "    else:\n",
    "        input_shape = (img_w, img_h, 1)\n",
    "    print('input_shape='+str(input_shape))\n",
    "    \n",
    "    act = 'relu'\n",
    "    input_data = Input(name='the_input', shape=input_shape, dtype='float32')\n",
    "    inner = Conv2D(conv_filters, kernel_size, padding='same',\n",
    "                   activation=act, kernel_initializer='he_normal',\n",
    "                   name='conv1')(input_data)\n",
    "    inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max1')(inner)\n",
    "    inner = Conv2D(conv_filters, kernel_size, padding='same',\n",
    "                   activation=act, kernel_initializer='he_normal',\n",
    "                   name='conv2')(inner)\n",
    "    inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max2')(inner)\n",
    "\n",
    "    conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_filters)\n",
    "    inner = Reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)\n",
    "\n",
    "    # cuts down input size going into RNN:\n",
    "    inner = Dense(time_dense_size, activation=act, name='dense1')(inner)\n",
    "\n",
    "    # Two layers of bidirectional GRUs\n",
    "    # GRU seems to work as well, if not better than LSTM:\n",
    "    gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)\n",
    "    gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)\n",
    "    gru1_merged = add([gru_1, gru_1b])\n",
    "    gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)\n",
    "    gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)\n",
    "\n",
    "    # transforms RNN output to character activations:\n",
    "    inner = Dense(output_size, kernel_initializer='he_normal',\n",
    "                  name='dense2')(concatenate([gru_2, gru_2b]))\n",
    "    y_pred = Activation('softmax', name='softmax')(inner)\n",
    "    Model(inputs=input_data, outputs=y_pred).summary()\n",
    "\n",
    "    labels = Input(name='the_labels', shape=[absolute_max_string_len], dtype='float32')\n",
    "    input_length = Input(name='input_length', shape=[1], dtype='int64')\n",
    "    label_length = Input(name='label_length', shape=[1], dtype='int64')\n",
    "    # Keras doesn't currently support loss funcs with extra parameters\n",
    "    # so CTC loss is implemented in a lambda layer\n",
    "    loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])\n",
    "\n",
    "    # clipnorm seems to speeds up convergence\n",
    "    sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)\n",
    "\n",
    "    model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)\n",
    "\n",
    "    # the loss calc occurs elsewhere, so use a dummy lambda func for the loss\n",
    "    model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)\n",
    "    \n",
    "    if(weight_file):\n",
    "        model.load_weights(weight_file)\n",
    "    \n",
    "    test_func = K.function([input_data], [y_pred])\n",
    "    \n",
    "    return Model(inputs=input_data, outputs=y_pred)\n",
    "\n",
    "w = 256\n",
    "h = 256\n",
    "\n",
    "model = build_prediction_model(output_size=len(alphabet) + 1, \n",
    "                        absolute_max_string_len=12,\n",
    "                        img_w=w, img_h=h,\n",
    "                        pool_size=2,\n",
    "                        weight_file=\"best_weights.h5\"\n",
    "                       )\n",
    "\n",
    "# model = Model(inputs=model.inputs[0], outputs=model.outputs[0])\n",
    "\n",
    "a = draw_text('tests',h = h, w = w, max_rotation=3, rotate=False, translate=False)\n",
    "b = a.reshape((h, w))\n",
    "plt.imshow(b, cmap='Greys_r')\n",
    "plt.show()\n",
    "\n",
    "c = np.expand_dims(a.T, axis=0)\n",
    "\n",
    "net_out_value = model.predict(c)\n",
    "pred_texts = decode_predict_ctc(net_out_value)\n",
    "pred_texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tests', 'atests', 'dtests']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predit_a_image(model, a, top_paths = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(28, 32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.imshow(net_out_value[0].T[:,:32], cmap='binary', interpolation='nearest')\n",
    "plt.show()\n",
    "\n",
    "net_out_value[0].T[:,:32].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compute filters 0 to 16\n"
     ]
    },
    {
     "ename": "NotFoundError",
     "evalue": " No algorithm worked!\n\t [[node conv1_23/convolution (defined at /home/james/code/image2text/venv/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py:1751) ]] [Op:__inference_keras_scratch_graph_627151]\n\nFunction call stack:\nkeras_scratch_graph\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-65-e123e190ed01>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m     \u001b[0;31m# example function call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m     \u001b[0mvisualize_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'conv1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m     \u001b[0mvisualize_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'conv2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-65-e123e190ed01>\u001b[0m in \u001b[0;36mvisualize_layer\u001b[0;34m(model, layer_name, step, epochs, upscaling_steps, upscaling_factor, output_dim, filter_range)\u001b[0m\n\u001b[1;32m    229\u001b[0m     \u001b[0mprocessed_filters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilter_lower\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilter_upper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m         \u001b[0mimg_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_generate_filter_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_layer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mimg_loss\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-65-e123e190ed01>\u001b[0m in \u001b[0;36m_generate_filter_image\u001b[0;34m(input_img, layer_output, filter_index)\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0;31m# we run gradient ascent for e.g. 20 steps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m                 \u001b[0mloss_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrads_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miterate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minput_img_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m                 \u001b[0minput_img_data\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mgrads_value\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/code/image2text/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3738\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3739\u001b[0m       \u001b[0mconverted_inputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3740\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mconverted_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3741\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3742\u001b[0m     \u001b[0;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/code/image2text/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1079\u001b[0m       \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFor\u001b[0m \u001b[0minvalid\u001b[0m \u001b[0mpositional\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mkeyword\u001b[0m \u001b[0margument\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1080\u001b[0m     \"\"\"\n\u001b[0;32m-> 1081\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1082\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1083\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/code/image2text/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1119\u001b[0m       raise TypeError(\"Keyword arguments {} unknown. Expected {}.\".format(\n\u001b[1;32m   1120\u001b[0m           list(kwargs.keys()), list(self._arg_keywords)))\n\u001b[0;32m-> 1121\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/code/image2text/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1222\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m       flat_outputs = forward_function.call(\n\u001b[0;32m-> 1224\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[1;32m   1225\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1226\u001b[0m       \u001b[0mgradient_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_delayed_rewrite_functions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/code/image2text/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    509\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"executor_type\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"config_proto\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 511\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    512\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m~/code/image2text/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m     \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     keras_symbolic_tensors = [\n",
      "\u001b[0;32m~/code/image2text/venv/lib/python3.7/site-packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;31mNotFoundError\u001b[0m:  No algorithm worked!\n\t [[node conv1_23/convolution (defined at /home/james/code/image2text/venv/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py:1751) ]] [Op:__inference_keras_scratch_graph_627151]\n\nFunction call stack:\nkeras_scratch_graph\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import time\n",
    "import numpy as np\n",
    "from PIL import Image as pil_image\n",
    "from keras.preprocessing.image import save_img\n",
    "from keras import layers\n",
    "from keras.applications import vgg16\n",
    "from keras import backend as K\n",
    "\n",
    "\n",
    "def normalize(x):\n",
    "    \"\"\"utility function to normalize a tensor.\n",
    "\n",
    "    # Arguments\n",
    "        x: An input tensor.\n",
    "\n",
    "    # Returns\n",
    "        The normalized input tensor.\n",
    "    \"\"\"\n",
    "    return x / (K.sqrt(K.mean(K.square(x))) + K.epsilon())\n",
    "\n",
    "\n",
    "def deprocess_image(x):\n",
    "    \"\"\"utility function to convert a float array into a valid uint8 image.\n",
    "\n",
    "    # Arguments\n",
    "        x: A numpy-array representing the generated image.\n",
    "\n",
    "    # Returns\n",
    "        A processed numpy-array, which could be used in e.g. imshow.\n",
    "    \"\"\"\n",
    "    # normalize tensor: center on 0., ensure std is 0.25\n",
    "    x -= x.mean()\n",
    "    x /= (x.std() + K.epsilon())\n",
    "    x *= 0.25\n",
    "\n",
    "    # clip to [0, 1]\n",
    "    x += 0.5\n",
    "    x = np.clip(x, 0, 1)\n",
    "\n",
    "    # convert to RGB array\n",
    "    x *= 255\n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        x = x.transpose((1, 2, 0))\n",
    "    x = np.clip(x, 0, 255).astype('uint8')\n",
    "    return x\n",
    "\n",
    "\n",
    "def process_image(x, former):\n",
    "    \"\"\"utility function to convert a valid uint8 image back into a float array.\n",
    "       Reverses `deprocess_image`.\n",
    "\n",
    "    # Arguments\n",
    "        x: A numpy-array, which could be used in e.g. imshow.\n",
    "        former: The former numpy-array.\n",
    "                Need to determine the former mean and variance.\n",
    "\n",
    "    # Returns\n",
    "        A processed numpy-array representing the generated image.\n",
    "    \"\"\"\n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        x = x.transpose((2, 0, 1))\n",
    "    return (x / 255 - 0.5) * 4 * former.std() + former.mean()\n",
    "\n",
    "\n",
    "def visualize_layer(model,\n",
    "                    layer_name,\n",
    "                    step=1.,\n",
    "                    epochs=15,\n",
    "                    upscaling_steps=9,\n",
    "                    upscaling_factor=1.2,\n",
    "                    output_dim=(412, 412),\n",
    "                    filter_range=(0, None)):\n",
    "    \"\"\"Visualizes the most relevant filters of one conv-layer in a certain model.\n",
    "\n",
    "    # Arguments\n",
    "        model: The model containing layer_name.\n",
    "        layer_name: The name of the layer to be visualized.\n",
    "                    Has to be a part of model.\n",
    "        step: step size for gradient ascent.\n",
    "        epochs: Number of iterations for gradient ascent.\n",
    "        upscaling_steps: Number of upscaling steps.\n",
    "                         Starting image is in this case (80, 80).\n",
    "        upscaling_factor: Factor to which to slowly upgrade\n",
    "                          the image towards output_dim.\n",
    "        output_dim: [img_width, img_height] The output image dimensions.\n",
    "        filter_range: Tupel[lower, upper]\n",
    "                      Determines the to be computed filter numbers.\n",
    "                      If the second value is `None`,\n",
    "                      the last filter will be inferred as the upper boundary.\n",
    "    \"\"\"\n",
    "\n",
    "    def _generate_filter_image(input_img,\n",
    "                               layer_output,\n",
    "                               filter_index):\n",
    "        \"\"\"Generates image for one particular filter.\n",
    "\n",
    "        # Arguments\n",
    "            input_img: The input-image Tensor.\n",
    "            layer_output: The output-image Tensor.\n",
    "            filter_index: The to be processed filter number.\n",
    "                          Assumed to be valid.\n",
    "\n",
    "        #Returns\n",
    "            Either None if no image could be generated.\n",
    "            or a tuple of the image (array) itself and the last loss.\n",
    "        \"\"\"\n",
    "        s_time = time.time()\n",
    "\n",
    "        # we build a loss function that maximizes the activation\n",
    "        # of the nth filter of the layer considered\n",
    "        if K.image_data_format() == 'channels_first':\n",
    "            loss = K.mean(layer_output[:, filter_index, :, :])\n",
    "        else:\n",
    "            loss = K.mean(layer_output[:, :, :, filter_index])\n",
    "\n",
    "        # we compute the gradient of the input picture wrt this loss\n",
    "        grads = K.gradients(loss, input_img)[0]\n",
    "\n",
    "        # normalization trick: we normalize the gradient\n",
    "        grads = normalize(grads)\n",
    "\n",
    "        # this function returns the loss and grads given the input picture\n",
    "        iterate = K.function([input_img], [loss, grads])\n",
    "\n",
    "        # we start from a gray image with some random noise\n",
    "        intermediate_dim = tuple(\n",
    "            int(x / (upscaling_factor ** upscaling_steps)) for x in output_dim)\n",
    "        if K.image_data_format() == 'channels_first':\n",
    "            input_img_data = np.random.random(\n",
    "                (1, 3, intermediate_dim[0], intermediate_dim[1]))\n",
    "        else:\n",
    "            input_img_data = np.random.random(\n",
    "                (1, intermediate_dim[0], intermediate_dim[1], 3))\n",
    "        input_img_data = (input_img_data - 0.5) * 20 + 128\n",
    "\n",
    "        # Slowly upscaling towards the original size prevents\n",
    "        # a dominating high-frequency of the to visualized structure\n",
    "        # as it would occur if we directly compute the 412d-image.\n",
    "        # Behaves as a better starting point for each following dimension\n",
    "        # and therefore avoids poor local minima\n",
    "        for up in reversed(range(upscaling_steps)):\n",
    "            # we run gradient ascent for e.g. 20 steps\n",
    "            for _ in range(epochs):\n",
    "                loss_value, grads_value = iterate([input_img_data])\n",
    "                input_img_data += grads_value * step\n",
    "\n",
    "                # some filters get stuck to 0, we can skip them\n",
    "                if loss_value <= K.epsilon():\n",
    "                    return None\n",
    "\n",
    "            # Calculate upscaled dimension\n",
    "            intermediate_dim = tuple(\n",
    "                int(x / (upscaling_factor ** up)) for x in output_dim)\n",
    "            # Upscale\n",
    "            img = deprocess_image(input_img_data[0])\n",
    "            img = np.array(pil_image.fromarray(img).resize(intermediate_dim,\n",
    "                                                           pil_image.BICUBIC))\n",
    "            input_img_data = np.expand_dims(\n",
    "                process_image(img, input_img_data[0]), 0)\n",
    "\n",
    "        # decode the resulting input image\n",
    "        img = deprocess_image(input_img_data[0])\n",
    "        e_time = time.time()\n",
    "        print('Costs of filter {:3}: {:5.0f} ( {:4.2f}s )'.format(filter_index,\n",
    "                                                                  loss_value,\n",
    "                                                                  e_time - s_time))\n",
    "        return img, loss_value\n",
    "\n",
    "    def _draw_filters(filters, n=None):\n",
    "        \"\"\"Draw the best filters in a nxn grid.\n",
    "\n",
    "        # Arguments\n",
    "            filters: A List of generated images and their corresponding losses\n",
    "                     for each processed filter.\n",
    "            n: dimension of the grid.\n",
    "               If none, the largest possible square will be used\n",
    "        \"\"\"\n",
    "        if n is None:\n",
    "            n = int(np.floor(np.sqrt(len(filters))))\n",
    "\n",
    "        # the filters that have the highest loss are assumed to be better-looking.\n",
    "        # we will only keep the top n*n filters.\n",
    "        filters.sort(key=lambda x: x[1], reverse=True)\n",
    "        filters = filters[:n * n]\n",
    "\n",
    "        # build a black picture with enough space for\n",
    "        # e.g. our 8 x 8 filters of size 412 x 412, with a 5px margin in between\n",
    "        MARGIN = 5\n",
    "        width = n * output_dim[0] + (n - 1) * MARGIN\n",
    "        height = n * output_dim[1] + (n - 1) * MARGIN\n",
    "        stitched_filters = np.zeros((width, height, 3), dtype='uint8')\n",
    "\n",
    "        # fill the picture with our saved filters\n",
    "        for i in range(n):\n",
    "            for j in range(n):\n",
    "                img, _ = filters[i * n + j]\n",
    "                width_margin = (output_dim[0] + MARGIN) * i\n",
    "                height_margin = (output_dim[1] + MARGIN) * j\n",
    "                stitched_filters[\n",
    "                    width_margin: width_margin + output_dim[0],\n",
    "                    height_margin: height_margin + output_dim[1], :] = img\n",
    "\n",
    "        # save the result to disk\n",
    "        save_img('vgg_{0:}_{1:}x{1:}.png'.format(layer_name, n), stitched_filters)\n",
    "\n",
    "    # this is the placeholder for the input images\n",
    "    assert len(model.inputs) == 1\n",
    "    input_img = model.inputs[0]\n",
    "\n",
    "    # get the symbolic outputs of each \"key\" layer (we gave them unique names).\n",
    "    layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]])\n",
    "\n",
    "    output_layer = layer_dict[layer_name]\n",
    "    assert isinstance(output_layer, layers.Conv2D)\n",
    "\n",
    "    # Compute to be processed filter range\n",
    "    filter_lower = filter_range[0]\n",
    "    filter_upper = (filter_range[1]\n",
    "                    if filter_range[1] is not None\n",
    "                    else len(output_layer.get_weights()[1]))\n",
    "    assert(filter_lower >= 0\n",
    "           and filter_upper <= len(output_layer.get_weights()[1])\n",
    "           and filter_upper > filter_lower)\n",
    "    print('Compute filters {:} to {:}'.format(filter_lower, filter_upper))\n",
    "\n",
    "    # iterate through each filter and generate its corresponding image\n",
    "    processed_filters = []\n",
    "    for f in range(filter_lower, filter_upper):\n",
    "        img_loss = _generate_filter_image(input_img, output_layer.output, f)\n",
    "\n",
    "        if img_loss is not None:\n",
    "            processed_filters.append(img_loss)\n",
    "\n",
    "    print('{} filter processed.'.format(len(processed_filters)))\n",
    "    # Finally draw and store the best filters to disk\n",
    "    _draw_filters(processed_filters)\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    #model.summary()\n",
    "\n",
    "    # example function call\n",
    "    visualize_layer(model, 'conv1')\n",
    "    visualize_layer(model, 'conv2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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