FinalProject.Keras.GPU.ImageNet.DeepConv.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Load Libraries ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from keras import applications\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras import optimizers\n",
    "from keras.models import Sequential, Model \n",
    "from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D\n",
    "from keras import backend as k \n",
    "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping\n",
    "from keras.backend import backend\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "import os\n",
    "import re\n",
    "\n",
    "import numpy as np\n",
    "import PIL\n",
    "from PIL import Image\n",
    "\n",
    "from scipy.stats import randint as sp_randint\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.cross_validation import StratifiedShuffleSplit\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "from keras.models import Sequential,Model\n",
    "from keras.layers import Dense, Convolution2D, AveragePooling2D, MaxPooling2D, Flatten, Activation, UpSampling2D, Add, merge, Concatenate\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.utils import np_utils\n",
    "from keras.datasets import cifar10\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.applications.vgg16 import preprocess_input\n",
    "from keras.models import load_model\n",
    "from keras.applications.imagenet_utils import decode_predictions\n",
    "from keras.preprocessing import image as im\n",
    "from keras.callbacks import Callback\n",
    "from scipy.ndimage.filters import gaussian_filter\n",
    "\n",
    "from keras.models import model_from_json\n",
    "\n",
    "import tensorflow as tf\n",
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint, ReduceLROnPlateau, TensorBoard, CSVLogger\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "from skimage import color\n",
    "import scipy\n",
    "\n",
    "def preprocess_single_image(img_name, datagen):\n",
    "    image = plt.imread(img_name)\n",
    "    \n",
    "    if image.shape[0] > image.shape[1]:\n",
    "        offset = int((image.shape[0] - image.shape[1]) / 2 + 1)\n",
    "        image = image[offset:image.shape[1]+offset,:,:]\n",
    "    elif image.shape[0] < image.shape[1]:\n",
    "        offset = int((image.shape[1] - image.shape[0]) / 2 + 1)\n",
    "        image = image[:,offset:image.shape[0]+offset,:]\n",
    "\n",
    "    #resize\n",
    "    image = scipy.misc.imresize(image, (224, 224), interp='bilinear')\n",
    "    \n",
    "    #random transform using data generators functions\n",
    "    image = datagen.random_transform(image)\n",
    "    \n",
    "    return image\n",
    "\n",
    "def preprocess_images(image_names, seed, datagen):\n",
    "    np.random.seed(seed)\n",
    "    images=[]\n",
    "    for img_name in image_names:\n",
    "        image = preprocess_single_image(img_name, datagen)\n",
    "        images.append(image)\n",
    "    return images\n",
    "\n",
    "\n",
    "\n",
    "''' Convert color images to gray scales, color images to HUV format,\n",
    "    return grayscale RGB image and UV channels of color image \n",
    "'''\n",
    "def data_generator(image_name_path,batchsize):\n",
    "    image_names = glob.glob(image_name_path)\n",
    "    \n",
    "    datagen_args = dict(rotation_range=10,\n",
    "                        width_shift_range=0.2,\n",
    "                        height_shift_range=0.2,\n",
    "                        #shear_range=0.2,\n",
    "                        zoom_range=0.2,\n",
    "                        horizontal_flip=True,\n",
    "                        \n",
    "                    )\n",
    "    datagen = ImageDataGenerator(**datagen_args)\n",
    "    seed = 1000\n",
    "    \n",
    "    while 1:\n",
    "        batch = np.random.choice(image_names, batchsize,replace=False)\n",
    "        rgbImages = preprocess_images(batch,seed,datagen)\n",
    "        \n",
    "        rgbImages = [ gaussian_filter(i,(.3,.3,0)) for i in rgbImages]\n",
    "        \n",
    "        grayScales = [ color.gray2rgb(color.rgb2gray(i)) for i in rgbImages]\n",
    "        \n",
    "        yuvColor = [ color.rgb2yuv(i) for i in rgbImages]\n",
    "        #yuvGray = [ color.rgb2yuv(i) for i in grayScales]\n",
    "        \n",
    "        grayScale_arr = np.array(grayScales)\n",
    "        rgb_arr = np.array(rgbImages)\n",
    "        \n",
    "        #grayScale_arr = preprocess_input(grayScale_arr)\n",
    "        #yuvGray_arr = np.array(yuvGray)\n",
    "        #yuvGray_arr[:,:,:,1:] = 0\n",
    "        \n",
    "        yuvColor_arr = np.array(yuvColor)[:,:,:,1:]\n",
    "        \n",
    "        \n",
    "        \n",
    "        yield grayScale_arr, yuvColor_arr\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Neural Net ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load Pretrained VGG16 Model #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = applications.VGG16(weights = \"imagenet\", include_top=True, input_shape = (224, 224, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def buildModel_baseVGG16(vgg16_model):\n",
    "    model = vgg16_model\n",
    "    \n",
    "    # add layers in front of vgg16\n",
    "#     x = Convolution2D(filters=32, kernel_size=3, activation='relu', name='c_1')(x)\n",
    "#     x = MaxPooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format=None, name='p_1')(x)\n",
    "    \n",
    "    \n",
    "    layer = model.get_layer(name='block4_conv3')\n",
    "    #layer\n",
    "    x = BatchNormalization(name='n0')(layer.output)\n",
    "    y = Convolution2D(filters=256, kernel_size=1, activation='tanh', name='c1')(x)\n",
    "\n",
    "    y = UpSampling2D(name = 'up1', size=2)(y)\n",
    "    x = BatchNormalization(name='n1')(model.get_layer(name='block3_conv3').output)\n",
    "    z = merge((x,y), mode='concat', name='s1')\n",
    "    #z = Concatenate((x,y))\n",
    "    y = Convolution2D(filters=128, kernel_size=3, strides=1, activation='tanh', name='c2',padding='same')(z)\n",
    "\n",
    "    y = UpSampling2D(name = 'up2', size=2)(y)\n",
    "    x = BatchNormalization(name='n2')(model.get_layer(name='block2_conv2').output)\n",
    "    z = merge((x,y), mode='concat', name='s2')\n",
    "    y = Convolution2D(filters=64, kernel_size=3, strides=1, activation='tanh', name='c3',padding='same')(z)\n",
    "\n",
    "    y = UpSampling2D(name = 'up3', size=2)(y)\n",
    "    x = BatchNormalization(name='n3')(model.get_layer(name='block1_conv2').output)\n",
    "    z = merge((x,y), mode='concat', name='s3')\n",
    "    y = Convolution2D(filters=32, kernel_size=3, strides=1, activation='tanh', name='c4',padding='same')(z)\n",
    "    y = Dropout(0.05)(y)\n",
    "\n",
    "    x = BatchNormalization(name='n4')(model.get_layer(name=model.input_names[0]).output)\n",
    "    z = merge((x,y), mode='concat', name='s4')\n",
    "    y = Convolution2D(filters=8, kernel_size=3, strides=1, activation='tanh', name='c5',padding='same')(z)\n",
    "    y = Dropout(0.05)(y)\n",
    "\n",
    "    y = Convolution2D(filters=2, kernel_size=3, name='output', padding='same', activation='tanh')(y)\n",
    "\n",
    "    model_final = Model(input = model.input, output = y)\n",
    "    \n",
    "    return model_final\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:16: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "  app.launch_new_instance()\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/keras/legacy/layers.py:456: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "  name=name)\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:27: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:32: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_1 (InputLayer)             (None, 224, 224, 3)   0                                            \n",
      "____________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)            (None, 224, 224, 64)  1792                                         \n",
      "____________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)            (None, 224, 224, 64)  36928                                        \n",
      "____________________________________________________________________________________________________\n",
      "block1_pool (MaxPooling2D)       (None, 112, 112, 64)  0                                            \n",
      "____________________________________________________________________________________________________\n",
      "block2_conv1 (Conv2D)            (None, 112, 112, 128) 73856                                        \n",
      "____________________________________________________________________________________________________\n",
      "block2_conv2 (Conv2D)            (None, 112, 112, 128) 147584                                       \n",
      "____________________________________________________________________________________________________\n",
      "block2_pool (MaxPooling2D)       (None, 56, 56, 128)   0                                            \n",
      "____________________________________________________________________________________________________\n",
      "block3_conv1 (Conv2D)            (None, 56, 56, 256)   295168                                       \n",
      "____________________________________________________________________________________________________\n",
      "block3_conv2 (Conv2D)            (None, 56, 56, 256)   590080                                       \n",
      "____________________________________________________________________________________________________\n",
      "block3_conv3 (Conv2D)            (None, 56, 56, 256)   590080                                       \n",
      "____________________________________________________________________________________________________\n",
      "block3_pool (MaxPooling2D)       (None, 28, 28, 256)   0                                            \n",
      "____________________________________________________________________________________________________\n",
      "block4_conv1 (Conv2D)            (None, 28, 28, 512)   1180160                                      \n",
      "____________________________________________________________________________________________________\n",
      "block4_conv2 (Conv2D)            (None, 28, 28, 512)   2359808                                      \n",
      "____________________________________________________________________________________________________\n",
      "block4_conv3 (Conv2D)            (None, 28, 28, 512)   2359808                                      \n",
      "____________________________________________________________________________________________________\n",
      "n0 (BatchNormalization)          (None, 28, 28, 512)   2048                                         \n",
      "____________________________________________________________________________________________________\n",
      "c1 (Conv2D)                      (None, 28, 28, 256)   131328                                       \n",
      "____________________________________________________________________________________________________\n",
      "n1 (BatchNormalization)          (None, 56, 56, 256)   1024                                         \n",
      "____________________________________________________________________________________________________\n",
      "up1 (UpSampling2D)               (None, 56, 56, 256)   0                                            \n",
      "____________________________________________________________________________________________________\n",
      "s1 (Merge)                       (None, 56, 56, 512)   0                                            \n",
      "____________________________________________________________________________________________________\n",
      "c2 (Conv2D)                      (None, 56, 56, 128)   589952                                       \n",
      "____________________________________________________________________________________________________\n",
      "n2 (BatchNormalization)          (None, 112, 112, 128) 512                                          \n",
      "____________________________________________________________________________________________________\n",
      "up2 (UpSampling2D)               (None, 112, 112, 128) 0                                            \n",
      "____________________________________________________________________________________________________\n",
      "s2 (Merge)                       (None, 112, 112, 256) 0                                            \n",
      "____________________________________________________________________________________________________\n",
      "c3 (Conv2D)                      (None, 112, 112, 64)  147520                                       \n",
      "____________________________________________________________________________________________________\n",
      "n3 (BatchNormalization)          (None, 224, 224, 64)  256                                          \n",
      "____________________________________________________________________________________________________\n",
      "up3 (UpSampling2D)               (None, 224, 224, 64)  0                                            \n",
      "____________________________________________________________________________________________________\n",
      "s3 (Merge)                       (None, 224, 224, 128) 0                                            \n",
      "____________________________________________________________________________________________________\n",
      "c4 (Conv2D)                      (None, 224, 224, 32)  36896                                        \n",
      "____________________________________________________________________________________________________\n",
      "n4 (BatchNormalization)          (None, 224, 224, 3)   12                                           \n",
      "____________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)              (None, 224, 224, 32)  0                                            \n",
      "____________________________________________________________________________________________________\n",
      "s4 (Merge)                       (None, 224, 224, 35)  0                                            \n",
      "____________________________________________________________________________________________________\n",
      "c5 (Conv2D)                      (None, 224, 224, 8)   2528                                         \n",
      "____________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)              (None, 224, 224, 8)   0                                            \n",
      "____________________________________________________________________________________________________\n",
      "output (Conv2D)                  (None, 224, 224, 2)   146                                          \n",
      "====================================================================================================\n",
      "Total params: 8,547,486.0\n",
      "Trainable params: 8,545,560.0\n",
      "Non-trainable params: 1,926.0\n",
      "____________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:38: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"ou...)`\n"
     ]
    }
   ],
   "source": [
    "model_final = buildModel_baseVGG16(model)\n",
    "#model.get_layer(name=model.input_names[0]).output\n",
    "model_final.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Visualize Model #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import SVG\n",
    "import logging\n",
    "from keras.utils.vis_utils import model_to_dot #, plot\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "execution_count": 7,
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   "source": [
    "SVG(model_to_dot(model_final, show_shapes=True).create(prog='dot', format='svg'))"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Compile Model #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_final.compile(loss=\"mean_squared_error\", optimizer=\"adam\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save Model ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def saveModelAndWeights(model, filePrefix):\n",
    "    model_json = model.to_json()\n",
    "    with open(filePrefix + \"_model.json\", \"w\") as json_file:\n",
    "        json_file.write(model_json)\n",
    "    \n",
    "    # serialize weights to HDF5\n",
    "    model.save_weights(filePrefix + \"_weights.h5\")\n",
    "    print(\"Saved model to disk\")\n",
    "    \n",
    "def loadModel(filePrefix):\n",
    "    with open(filePrefix + '_model.json', 'r') as json_file:\n",
    "        loaded_model_json = json_file.read()\n",
    "\n",
    "    loaded_model = model_from_json(loaded_model_json)\n",
    "    \n",
    "    # load weights into new model\n",
    "    loaded_model.load_weights(filePrefix + \"_weights.h5\")\n",
    "    \n",
    "    print(\"Loaded model from disk\")\n",
    "    \n",
    "    return(loaded_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "saveModelAndWeights(model_final, filePrefix='tanh_blur_2_deepConv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "#model_final.load_weights('checkpoints_uv_tanh_lessblur_deepConv/backup/3pt_blur_weights.12-0.00375703.hdf5')\n",
    "\n",
    "# for i in data_generator(TRAIN_IMAGE_PATH,BATCH_SIZE):\n",
    "#     print(i)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train Model ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LossHistory(Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        with open('losses.log', 'w') as lossLog:\n",
    "            lossLog.write('')\n",
    "\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        #self.losses.append(logs.get('loss'))\n",
    "        with open('losses.log', 'a') as lossLog:\n",
    "            lossLog.write(logs.get('loss').tostring())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
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   "source": [
    "# Define callback to save model checkpoints\n",
    "if not os.path.exists('checkpoints_uv_tanh_lessblur_deepConv'):\n",
    "    os.makedirs('checkpoints_uv_tanh_lessblur_deepConv')\n",
    "model_checkpoint = ModelCheckpoint(os.path.join('checkpoints_uv_tanh_lessblur_deepConv', '3pt_blur_weights.{epoch:02d}-{val_loss:.8f}.hdf5'),\n",
    "                                  monitor='loss', save_best_only=True)\n",
    "\n",
    "# Define callback to reduce learning rate when learning stagnates\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, epsilon=0.0001, cooldown=1)\n",
    "\n",
    "# TensorBoard visualization\n",
    "#tensorboard = TensorBoard(log_dir='logs/tboard_logs2', histogram_freq=0, write_graph=True, write_images=True)\n",
    "\n",
    "csv_logger = CSVLogger('train_log_uv_tanh_blur_deepConv.csv', separator=',', append=True)\n",
    "lossHistory = LossHistory()\n",
    "# Train the model\n",
    "# model = get_unet(0.001)\n",
    "# model_fin.fit(X_train, Y_train, batch_size=32, epochs=NUM_EPOCHS, verbose=1, shuffle=True,\n",
    "#          callbacks=[model_checkpoint, reduce_lr, tensorboard], validation_data=(X_val, Y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#TRAIN_IMAGE_PATH = '../flickr30k-images/*.jpg'\n",
    "#VALIDATION_IMAGE_NAME_PATH = '../flickr30k-images_validation/*.jpg'\n",
    "\n",
    "TRAIN_IMAGE_PATH = 'data/images/*.JPEG'\n",
    "VALIDATION_IMAGE_NAME_PATH = 'data/validationImages/*.JPEG'\n",
    "\n",
    "\n",
    "\n",
    "BATCH_SIZE = 20\n",
    "VALIDATION_BATCH_SIZE = int(BATCH_SIZE * .1)\n",
    "NUM_EPOCHS = 2000\n",
    "TRAIN_STEPS_PER_EPOCH = len(glob.glob(TRAIN_IMAGE_PATH))// BATCH_SIZE\n",
    "VALIDATION_STEPS_PER_EPOCH = len(glob.glob(VALIDATION_IMAGE_NAME_PATH))// VALIDATION_BATCH_SIZE\n",
    "\n",
    "train_Gen = data_generator(TRAIN_IMAGE_PATH,BATCH_SIZE)\n",
    "validation_dGen = data_generator(VALIDATION_IMAGE_NAME_PATH,VALIDATION_BATCH_SIZE)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in train_Gen:\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 224, 224, 2)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "2250/2250 [==============================] - 5190s - loss: 2.0748e-04 - val_loss: 0.0024\n",
      "Epoch 9/2000\n",
      "2250/2250 [==============================] - 5188s - loss: 1.7714e-04 - val_loss: 0.0022\n",
      "Epoch 11/2000\n",
      "2250/2250 [==============================] - 5181s - loss: 1.5816e-04 - val_loss: 0.0024\n",
      "Epoch 12/2000\n",
      " 755/2250 [=========>....................] - ETA: 3304s - loss: 1.7263e-04"
     ]
    }
   ],
   "source": [
    "history = model_final.fit_generator(\n",
    "generator=train_Gen,\n",
    "steps_per_epoch = TRAIN_STEPS_PER_EPOCH,\n",
    "epochs = NUM_EPOCHS,\n",
    "validation_data = validation_dGen,\n",
    "#nb_val_samples = 20,\n",
    "verbose = 1,\n",
    "validation_steps = VALIDATION_STEPS_PER_EPOCH,\n",
    "callbacks = [model_checkpoint, reduce_lr, csv_logger],\n",
    "workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#dat = plt.imread('../flickr30k-images/1000092795.jpg')\n",
    "\n",
    "colorImg=preprocess_single_image('data/validationImages/frame118000.jpg', ImageDataGenerator())\n",
    "#plt.imshow(dat)\n",
    "\n",
    "grayImg = color.rgb2gray(colorImg)\n",
    "grayImg3Ch = np.zeros(colorImg.shape)\n",
    "grayImg3Ch[:,:,0] = grayImg\n",
    "grayImg3Ch[:,:,1] = grayImg\n",
    "grayImg3Ch[:,:,2] = grayImg\n",
    "\n",
    "yuvColorImg = color.rgb2yuv(colorImg)\n",
    "yuvGrayImg = np.zeros(colorImg.shape)\n",
    "yuvGrayImg[:,:,0] = yuvColorImg[:,:,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "uvResult = model_final.predict(np.expand_dims(grayImg3Ch,0))\n",
    "print(np.min(uvResult),np.max(uvResult))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.hist(yuvColorImg[:,:,1])\n",
    "plt.xlim(-.5,.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.hist(uvResult[0,:,:,1]/10)\n",
    "plt.xlim(-.5,0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.imshow(colorImg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_YUVImg = np.zeros(colorImg.shape)\n",
    "pred_YUVImg[:,:,0] = yuvColorImg[:,:,0]\n",
    "pred_YUVImg[:,:,1] = uvResult[0,:,:,0]/10\n",
    "pred_YUVImg[:,:,2] = uvResult[0,:,:,1]/10\n",
    "\n",
    "pred_YUVImg[pred_YUVImg[:,:,1]>0.5,1] = 0.5\n",
    "pred_YUVImg[pred_YUVImg[:,:,1]<(-0.5),1] = -0.5\n",
    "\n",
    "pred_YUVImg[pred_YUVImg[:,:,2]>0.5,2] = 0.49999\n",
    "pred_YUVImg[pred_YUVImg[:,:,2]<(-0.5),2] = -0.49999\n",
    "\n",
    "plt.imshow(color.yuv2rgb(pred_YUVImg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from twilio.rest import Client\n",
    "\n",
    "# # Find these values at https://twilio.com/user/account\n",
    "# account_sid = \"ACXXXXXXXXXXXXXXXXX\"\n",
    "# auth_token = \"YYYYYYYYYYYYYYYYYY\"\n",
    "# client = Client(account_sid, auth_token)\n",
    "\n",
    "# message = client.api.account.messages.create(to=\"+12316851234\",\n",
    "#                                              from_=\"+15555555555\",\n",
    "#                                              body=\"Hello there!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "j = plt.imread('data/validationImages/frame118000.jpg')\n",
    "k = gaussian_filter(colorImg,(1,1,0))\n",
    "\n",
    "plt.imshow(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.min(colorImg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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