dog_app.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Artificial Intelligence Nanodegree\n",
    "\n",
    "## Convolutional Neural Networks\n",
    "\n",
    "## Project: Write an Algorithm for a Dog Identification App \n",
    "\n",
    "---\n",
    "\n",
    "In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with **'(IMPLEMENTATION)'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully! \n",
    "\n",
    "> **Note**: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission.\n",
    "\n",
    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.\n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut.  Markdown cells can be edited by double-clicking the cell to enter edit mode.\n",
    "\n",
    "The rubric contains _optional_ \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. If you decide to pursue the \"Stand Out Suggestions\", you should include the code in this IPython notebook.\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "### Why We're Here \n",
    "\n",
    "In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app.  At the end of this project, your code will accept any user-supplied image as input.  If a dog is detected in the image, it will provide an estimate of the dog's breed.  If a human is detected, it will provide an estimate of the dog breed that is most resembling.  The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!). \n",
    "\n",
    "![Sample Dog Output](images/sample_dog_output.png)\n",
    "\n",
    "In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed.  There are many points of possible failure, and no perfect algorithm exists.  Your imperfect solution will nonetheless create a fun user experience!\n",
    "\n",
    "### The Road Ahead\n",
    "\n",
    "We break the notebook into separate steps.  Feel free to use the links below to navigate the notebook.\n",
    "\n",
    "* [Step 0](#step0): Import Datasets\n",
    "* [Step 1](#step1): Detect Humans\n",
    "* [Step 2](#step2): Detect Dogs\n",
    "* [Step 3](#step3): Create a CNN to Classify Dog Breeds (from Scratch)\n",
    "* [Step 4](#step4): Use a CNN to Classify Dog Breeds (using Transfer Learning)\n",
    "* [Step 5](#step5): Create a CNN to Classify Dog Breeds (using Transfer Learning)\n",
    "* [Step 6](#step6): Write your Algorithm\n",
    "* [Step 7](#step7): Test Your Algorithm\n",
    "\n",
    "---\n",
    "<a id='step0'></a>\n",
    "## Step 0: Import Datasets\n",
    "\n",
    "### Import Dog Dataset\n",
    "\n",
    "In the code cell below, we import a dataset of dog images.  We populate a few variables through the use of the `load_files` function from the scikit-learn library:\n",
    "- `train_files`, `valid_files`, `test_files` - numpy arrays containing file paths to images\n",
    "- `train_targets`, `valid_targets`, `test_targets` - numpy arrays containing onehot-encoded classification labels \n",
    "- `dog_names` - list of string-valued dog breed names for translating labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 133 total dog categories.\n",
      "There are 8351 total dog images.\n",
      "\n",
      "There are 6680 training dog images.\n",
      "There are 835 validation dog images.\n",
      "There are 836 test dog images.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_files       \n",
    "from keras.utils import np_utils\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "\n",
    "# define function to load train, test, and validation datasets\n",
    "def load_dataset(path):\n",
    "    data = load_files(path)\n",
    "    dog_files = np.array(data['filenames'])\n",
    "    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)\n",
    "    return dog_files, dog_targets\n",
    "\n",
    "# load train, test, and validation datasets\n",
    "train_files, train_targets = load_dataset('dogImages/train')\n",
    "valid_files, valid_targets = load_dataset('dogImages/valid')\n",
    "test_files, test_targets = load_dataset('dogImages/test')\n",
    "\n",
    "# load list of dog names\n",
    "dog_names = [item[20:-1] for item in sorted(glob(\"dogImages/train/*/\"))]\n",
    "\n",
    "# print statistics about the dataset\n",
    "print('There are %d total dog categories.' % len(dog_names))\n",
    "print('There are %s total dog images.\\n' % len(np.hstack([train_files, valid_files, test_files])))\n",
    "print('There are %d training dog images.' % len(train_files))\n",
    "print('There are %d validation dog images.' % len(valid_files))\n",
    "print('There are %d test dog images.'% len(test_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Human Dataset\n",
    "\n",
    "In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array `human_files`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 13233 total human images.\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "random.seed(8675309)\n",
    "\n",
    "# load filenames in shuffled human dataset\n",
    "human_files = np.array(glob(\"lfw/*/*\"))\n",
    "random.shuffle(human_files)\n",
    "\n",
    "# print statistics about the dataset\n",
    "print('There are %d total human images.' % len(human_files))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step1'></a>\n",
    "## Step 1: Detect Humans\n",
    "\n",
    "We use OpenCV's implementation of [Haar feature-based cascade classifiers](http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html) to detect human faces in images.  OpenCV provides many pre-trained face detectors, stored as XML files on [github](https://github.com/opencv/opencv/tree/master/data/haarcascades).  We have downloaded one of these detectors and stored it in the `haarcascades` directory.\n",
    "\n",
    "In the next code cell, we demonstrate how to use this detector to find human faces in a sample image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of faces detected: 1\n"
     ]
    },
    {
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XtOHX+HOdC0TKvUYQlIqDrVvsA2Zhf1qjStCKGxSJApSSlsA/w1BusiF+mnP1B8mxUBYh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FTzzWcReulfVFVD34sL2nn7mMgJyS03J7Tz20zKKE0L1HVNVVVMrGRabS8ObF2xV4CLV/9dJbHL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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4de3f437f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2                \n",
    "import matplotlib.pyplot as plt                        \n",
    "%matplotlib inline                               \n",
    "\n",
    "# extract pre-trained face detector\n",
    "face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')\n",
    "\n",
    "# load color (BGR) image\n",
    "img = cv2.imread(human_files[3])\n",
    "# convert BGR image to grayscale\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# find faces in image\n",
    "faces = face_cascade.detectMultiScale(gray)\n",
    "\n",
    "# print number of faces detected in the image\n",
    "print('Number of faces detected:', len(faces))\n",
    "\n",
    "# get bounding box for each detected face\n",
    "for (x, y, w, h) in faces:\n",
    "    # add bounding box to color image\n",
    "    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)\n",
    "    \n",
    "# convert BGR image to RGB for plotting\n",
    "cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# display the image, along with bounding box\n",
    "plt.imshow(cv_rgb)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before using any of the face detectors, it is standard procedure to convert the images to grayscale.  The `detectMultiScale` function executes the classifier stored in `face_cascade` and takes the grayscale image as a parameter.  \n",
    "\n",
    "In the above code, `faces` is a numpy array of detected faces, where each row corresponds to a detected face.  Each detected face is a 1D array with four entries that specifies the bounding box of the detected face.  The first two entries in the array (extracted in the above code as `x` and `y`) specify the horizontal and vertical positions of the top left corner of the bounding box.  The last two entries in the array (extracted here as `w` and `h`) specify the width and height of the box.\n",
    "\n",
    "### Write a Human Face Detector\n",
    "\n",
    "We can use this procedure to write a function that returns `True` if a human face is detected in an image and `False` otherwise.  This function, aptly named `face_detector`, takes a string-valued file path to an image as input and appears in the code block below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# returns \"True\" if face is detected in image stored at img_path\n",
    "def face_detector(img_path):\n",
    "    img = cv2.imread(img_path)\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_cascade.detectMultiScale(gray)\n",
    "    return len(faces) > 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Assess the Human Face Detector\n",
    "\n",
    "__Question 1:__ Use the code cell below to test the performance of the `face_detector` function.  \n",
    "- What percentage of the first 100 images in `human_files` have a detected human face?  \n",
    "- What percentage of the first 100 images in `dog_files` have a detected human face? \n",
    "\n",
    "Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face.  You will see that our algorithm falls short of this goal, but still gives acceptable performance.  We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays `human_files_short` and `dog_files_short`.\n",
    "\n",
    "__Answer:__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of detected human faces on human_files_short: 98\n",
      "Percent of detected human faces on dog_files_short: 11\n"
     ]
    }
   ],
   "source": [
    "human_files_short = human_files[:100]\n",
    "dog_files_short = train_files[:100]\n",
    "# Do NOT modify the code above this line.\n",
    "\n",
    "## Test the performance of the face_detector algorithm \n",
    "## on the images in human_files_short and dog_files_short.\n",
    "\n",
    "percent_hfs_detected_human_face = sum([1 if face_detector(hfs) else 0 for hfs in human_files_short])\n",
    "percent_dfs_detected_human_face = sum([1 if face_detector(dfs) else 0 for dfs in dog_files_short])\n",
    "\n",
    "print('Percent of detected human faces on human_files_short:', percent_hfs_detected_human_face)\n",
    "print('Percent of detected human faces on dog_files_short:', percent_dfs_detected_human_face)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Question 2:__ This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?\n",
    "\n",
    "\n",
    "__Answer:__\n",
    "\n",
    "Its depends of how sofisticate we want to make our app, as a first version of the product we can start with the face dectector provided by OpenCV, after all done is better than perfect. If our app gets popular and we want to get paid users, having this validation will be necessary and then we can use a Multilayer Perceptron (MLP) or a Convolutional Neural Network (CNN) to detect the presense of human faces, which is not hard using Keras and a pre-trained model like ImageNet or any other well known model that has been trained with human faces, and then we can extend it using the technic Transfer Learning.\n",
    "\n",
    "\n",
    "---\n",
    "We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :).  Please use the code cell below to design and test your own face detection algorithm.  If you decide to pursue this _optional_ task, report performance on each of the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## (Optional) TODO: Report the performance of another  \n",
    "## face detection algorithm on the LFW dataset\n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step2'></a>\n",
    "## Step 2: Detect Dogs\n",
    "\n",
    "In this section, we use a pre-trained [ResNet-50](http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006) model to detect dogs in images.  Our first line of code downloads the ResNet-50 model, along with weights that have been trained on [ImageNet](http://www.image-net.org/), a very large, very popular dataset used for image classification and other vision tasks.  ImageNet contains over 10 million URLs, each linking to an image containing an object from one of [1000 categories](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a).  Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.resnet50 import ResNet50\n",
    "\n",
    "# define ResNet50 model\n",
    "ResNet50_model = ResNet50(weights='imagenet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-process the Data\n",
    "\n",
    "When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape\n",
    "\n",
    "$$\n",
    "(\\text{nb_samples}, \\text{rows}, \\text{columns}, \\text{channels}),\n",
    "$$\n",
    "\n",
    "where `nb_samples` corresponds to the total number of images (or samples), and `rows`, `columns`, and `channels` correspond to the number of rows, columns, and channels for each image, respectively.  \n",
    "\n",
    "The `path_to_tensor` function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN.  The function first loads the image and resizes it to a square image that is $224 \\times 224$ pixels.  Next, the image is converted to an array, which is then resized to a 4D tensor.  In this case, since we are working with color images, each image has three channels.  Likewise, since we are processing a single image (or sample), the returned tensor will always have shape\n",
    "\n",
    "$$\n",
    "(1, 224, 224, 3).\n",
    "$$\n",
    "\n",
    "The `paths_to_tensor` function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape \n",
    "\n",
    "$$\n",
    "(\\text{nb_samples}, 224, 224, 3).\n",
    "$$\n",
    "\n",
    "Here, `nb_samples` is the number of samples, or number of images, in the supplied array of image paths.  It is best to think of `nb_samples` as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image                  \n",
    "from tqdm import tqdm\n",
    "\n",
    "def path_to_tensor(img_path):\n",
    "    # loads RGB image as PIL.Image.Image type\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)\n",
    "    x = image.img_to_array(img)\n",
    "    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor\n",
    "    return np.expand_dims(x, axis=0)\n",
    "\n",
    "def paths_to_tensor(img_paths):\n",
    "    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]\n",
    "    return np.vstack(list_of_tensors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions with ResNet-50\n",
    "\n",
    "Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing.  First, the RGB image is converted to BGR by reordering the channels.  All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image.  This is implemented in the imported function `preprocess_input`.  If you're curious, you can check the code for `preprocess_input` [here](https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py).\n",
    "\n",
    "Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions.  This is accomplished with the `predict` method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category.  This is implemented in the `ResNet50_predict_labels` function below.\n",
    "\n",
    "By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this [dictionary](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "\n",
    "def ResNet50_predict_labels(img_path):\n",
    "    # returns prediction vector for image located at img_path\n",
    "    img = preprocess_input(path_to_tensor(img_path))\n",
    "    return np.argmax(ResNet50_model.predict(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write a Dog Detector\n",
    "\n",
    "While looking at the [dictionary](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a), you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from `'Chihuahua'` to `'Mexican hairless'`.  Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the `ResNet50_predict_labels` function above returns a value between 151 and 268 (inclusive).\n",
    "\n",
    "We use these ideas to complete the `dog_detector` function below, which returns `True` if a dog is detected in an image (and `False` if not)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "### returns \"True\" if a dog is detected in the image stored at img_path\n",
    "def dog_detector(img_path):\n",
    "    prediction = ResNet50_predict_labels(img_path)\n",
    "    return ((prediction <= 268) & (prediction >= 151)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Assess the Dog Detector\n",
    "\n",
    "__Question 3:__ Use the code cell below to test the performance of your `dog_detector` function.  \n",
    "- What percentage of the images in `human_files_short` have a detected dog?  \n",
    "- What percentage of the images in `dog_files_short` have a detected dog?\n",
    "\n",
    "__Answer:__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of detected dogs on human_files_short: 2\n",
      "Percent of detected dogs on dog_files_short: 100\n"
     ]
    }
   ],
   "source": [
    "### Test the performance of the dog_detector function\n",
    "### on the images in human_files_short and dog_files_short.\n",
    "\n",
    "percent_hfs_detected_dog = sum([1 if dog_detector(hfs) else 0 for hfs in human_files_short])\n",
    "percent_dfs_detected_dog = sum([1 if dog_detector(dfs) else 0 for dfs in dog_files_short])\n",
    "\n",
    "print('Percent of detected dogs on human_files_short:', percent_hfs_detected_dog)\n",
    "print('Percent of detected dogs on dog_files_short:', percent_dfs_detected_dog)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step3'></a>\n",
    "## Step 3: Create a CNN to Classify Dog Breeds (from Scratch)\n",
    "\n",
    "Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images.  In this step, you will create a CNN that classifies dog breeds.  You must create your CNN _from scratch_ (so, you can't use transfer learning _yet_!), and you must attain a test accuracy of at least 1%.  In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.\n",
    "\n",
    "Be careful with adding too many trainable layers!  More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process.  Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train. \n",
    "\n",
    "We mention that the task of assigning breed to dogs from images is considered exceptionally challenging.  To see why, consider that *even a human* would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.  \n",
    "\n",
    "Brittany | Welsh Springer Spaniel\n",
    "- | - \n",
    "<img src=\"images/Brittany_02625.jpg\" width=\"100\"> | <img src=\"images/Welsh_springer_spaniel_08203.jpg\" width=\"200\">\n",
    "\n",
    "It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).  \n",
    "\n",
    "Curly-Coated Retriever | American Water Spaniel\n",
    "- | -\n",
    "<img src=\"images/Curly-coated_retriever_03896.jpg\" width=\"200\"> | <img src=\"images/American_water_spaniel_00648.jpg\" width=\"200\">\n",
    "\n",
    "\n",
    "Likewise, recall that labradors come in yellow, chocolate, and black.  Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.  \n",
    "\n",
    "Yellow Labrador | Chocolate Labrador | Black Labrador\n",
    "- | -\n",
    "<img src=\"images/Labrador_retriever_06457.jpg\" width=\"150\"> | <img src=\"images/Labrador_retriever_06455.jpg\" width=\"240\"> | <img src=\"images/Labrador_retriever_06449.jpg\" width=\"220\">\n",
    "\n",
    "We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.  \n",
    "\n",
    "Remember that the practice is far ahead of the theory in deep learning.  Experiment with many different architectures, and trust your intuition.  And, of course, have fun! \n",
    "\n",
    "### Pre-process the Data\n",
    "\n",
    "We rescale the images by dividing every pixel in every image by 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6680/6680 [00:55<00:00, 121.00it/s]\n",
      "100%|██████████| 835/835 [00:06<00:00, 133.95it/s]\n",
      "100%|██████████| 836/836 [00:06<00:00, 133.05it/s]\n"
     ]
    }
   ],
   "source": [
    "from PIL import ImageFile                            \n",
    "ImageFile.LOAD_TRUNCATED_IMAGES = True                 \n",
    "\n",
    "# pre-process the data for Keras\n",
    "train_tensors = paths_to_tensor(train_files).astype('float32')/255\n",
    "valid_tensors = paths_to_tensor(valid_files).astype('float32')/255\n",
    "test_tensors = paths_to_tensor(test_files).astype('float32')/255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Model Architecture\n",
    "\n",
    "Create a CNN to classify dog breed.  At the end of your code cell block, summarize the layers of your model by executing the line:\n",
    "    \n",
    "        model.summary()\n",
    "\n",
    "We have imported some Python modules to get you started, but feel free to import as many modules as you need.  If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:\n",
    "\n",
    "![Sample CNN](images/sample_cnn.png)\n",
    "           \n",
    "__Question 4:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.\n",
    "\n",
    "__Answer:__\n",
    "\n",
    "Traditional neural networks (or Multilayer Perceptron MLP) only accept vectors as input that means that all the spatial information contained on images will be mostly ignored versus CNNs that accept matrices which allow to \"learn\" a set of filters for each layer, also these layers are not fully connected which is better compared to fully connected layers of MLPs. For this exercise the hinted architecture will work fine because with three convolutional layers it will detect a good number of features or patterns, for example first layer will \"learn\" simple things like borders, lines orientation, the second layer using the first one will store filters to detect shapes like como regions, circuferences, rectanges; the third one based on the previous layer will be able to differenciate between some kind of dogs. Adding a max pooling layer between each convolutional layer will help to reduce dimensionality. I also added dropout layers to avoid overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 224, 224, 32)      416       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 112, 112, 64)      8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 56, 56, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 56, 56, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 56, 56, 80)        20560     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 28, 28, 80)        0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 28, 28, 80)        0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_1 ( (None, 80)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 133)               10773     \n",
      "=================================================================\n",
      "Total params: 40,005.0\n",
      "Trainable params: 40,005.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "from keras.models import Sequential\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "### Define your architecture.\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu', \n",
    "                        input_shape=(224, 224, 3)))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.18))\n",
    "\n",
    "model.add(Conv2D(filters=80, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.1))\n",
    "\n",
    "model.add(GlobalAveragePooling2D())\n",
    "model.add(Dense(133, activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Train the Model\n",
    "\n",
    "Train your model in the code cell below.  Use model checkpointing to save the model that attains the best validation loss.\n",
    "\n",
    "You are welcome to [augment the training data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html), but this is not a requirement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "### specify the number of epochs that you would like to use to train the model.\n",
    "\n",
    "epochs = 50\n",
    "\n",
    "# shift images horizontally 15% of total width and height\n",
    "# zoom images 5%\n",
    "# flip images horizontally\n",
    "\n",
    "datagen_train = ImageDataGenerator(\n",
    "    width_shift_range=0.15,  \n",
    "    height_shift_range=0.15,\n",
    "    zoom_range=0.05,\n",
    "    horizontal_flip=True)\n",
    "\n",
    "datagen_valid = ImageDataGenerator(\n",
    "    width_shift_range=0.15,\n",
    "    height_shift_range=0.15,\n",
    "    zoom_range=0.05,\n",
    "    horizontal_flip=True)\n",
    "\n",
    "datagen_train.fit(train_tensors)\n",
    "datagen_valid.fit(valid_tensors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.8826 - acc: 0.0081Epoch 00000: val_loss improved from inf to 4.86854, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 38s - loss: 4.8831 - acc: 0.0081 - val_loss: 4.8685 - val_acc: 0.0108\n",
      "Epoch 2/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.8390 - acc: 0.0138Epoch 00001: val_loss improved from 4.86854 to 4.81811, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.8388 - acc: 0.0138 - val_loss: 4.8181 - val_acc: 0.0228\n",
      "Epoch 3/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.7927 - acc: 0.0186Epoch 00002: val_loss improved from 4.81811 to 4.79503, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.7926 - acc: 0.0186 - val_loss: 4.7950 - val_acc: 0.0156\n",
      "Epoch 4/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.7602 - acc: 0.0186Epoch 00003: val_loss improved from 4.79503 to 4.76358, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.7603 - acc: 0.0186 - val_loss: 4.7636 - val_acc: 0.0144\n",
      "Epoch 5/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.7292 - acc: 0.0212Epoch 00004: val_loss improved from 4.76358 to 4.74525, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.7293 - acc: 0.0213 - val_loss: 4.7452 - val_acc: 0.0251\n",
      "Epoch 6/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.7015 - acc: 0.0254Epoch 00005: val_loss improved from 4.74525 to 4.72487, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.7022 - acc: 0.0253 - val_loss: 4.7249 - val_acc: 0.0323\n",
      "Epoch 7/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.6691 - acc: 0.0309Epoch 00006: val_loss improved from 4.72487 to 4.68883, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 38s - loss: 4.6688 - acc: 0.0310 - val_loss: 4.6888 - val_acc: 0.0251\n",
      "Epoch 8/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.6365 - acc: 0.0341Epoch 00007: val_loss improved from 4.68883 to 4.67448, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.6373 - acc: 0.0340 - val_loss: 4.6745 - val_acc: 0.0275\n",
      "Epoch 9/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.6061 - acc: 0.0384Epoch 00008: val_loss improved from 4.67448 to 4.64622, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.6057 - acc: 0.0385 - val_loss: 4.6462 - val_acc: 0.0287\n",
      "Epoch 10/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.5769 - acc: 0.0377Epoch 00009: val_loss improved from 4.64622 to 4.61983, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.5767 - acc: 0.0376 - val_loss: 4.6198 - val_acc: 0.0347\n",
      "Epoch 11/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.5453 - acc: 0.0416Epoch 00010: val_loss improved from 4.61983 to 4.61152, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.5450 - acc: 0.0415 - val_loss: 4.6115 - val_acc: 0.0395\n",
      "Epoch 12/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.5128 - acc: 0.0459Epoch 00011: val_loss improved from 4.61152 to 4.59835, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.5139 - acc: 0.0458 - val_loss: 4.5983 - val_acc: 0.0323\n",
      "Epoch 13/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.4889 - acc: 0.0497Epoch 00012: val_loss improved from 4.59835 to 4.57210, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.4889 - acc: 0.0500 - val_loss: 4.5721 - val_acc: 0.0383\n",
      "Epoch 14/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.4587 - acc: 0.0563Epoch 00013: val_loss improved from 4.57210 to 4.56518, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.4584 - acc: 0.0563 - val_loss: 4.5652 - val_acc: 0.0419\n",
      "Epoch 15/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.4310 - acc: 0.0551Epoch 00014: val_loss improved from 4.56518 to 4.53631, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.4314 - acc: 0.0551 - val_loss: 4.5363 - val_acc: 0.0311\n",
      "Epoch 16/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.4033 - acc: 0.0554Epoch 00015: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 4.4035 - acc: 0.0552 - val_loss: 4.5848 - val_acc: 0.0335\n",
      "Epoch 17/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.3814 - acc: 0.0605Epoch 00016: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 4.3815 - acc: 0.0606 - val_loss: 4.5471 - val_acc: 0.0311\n",
      "Epoch 18/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.3635 - acc: 0.0608Epoch 00017: val_loss improved from 4.53631 to 4.49262, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.3632 - acc: 0.0606 - val_loss: 4.4926 - val_acc: 0.0479\n",
      "Epoch 19/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.3399 - acc: 0.0644Epoch 00018: val_loss improved from 4.49262 to 4.47237, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.3396 - acc: 0.0644 - val_loss: 4.4724 - val_acc: 0.0395\n",
      "Epoch 20/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.3195 - acc: 0.0665Epoch 00019: val_loss improved from 4.47237 to 4.44255, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.3191 - acc: 0.0665 - val_loss: 4.4425 - val_acc: 0.0455\n",
      "Epoch 21/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.2960 - acc: 0.0695Epoch 00020: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 4.2960 - acc: 0.0696 - val_loss: 4.4455 - val_acc: 0.0599\n",
      "Epoch 22/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.2793 - acc: 0.0758Epoch 00021: val_loss improved from 4.44255 to 4.39838, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.2791 - acc: 0.0760 - val_loss: 4.3984 - val_acc: 0.0539\n",
      "Epoch 23/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.2550 - acc: 0.0740Epoch 00022: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 4.2559 - acc: 0.0741 - val_loss: 4.4613 - val_acc: 0.0479\n",
      "Epoch 24/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.2376 - acc: 0.0782Epoch 00023: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 4.2377 - acc: 0.0784 - val_loss: 4.4110 - val_acc: 0.0479\n",
      "Epoch 25/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.2196 - acc: 0.0799Epoch 00024: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 4.2190 - acc: 0.0799 - val_loss: 4.4134 - val_acc: 0.0563\n",
      "Epoch 26/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.1931 - acc: 0.0868Epoch 00025: val_loss improved from 4.39838 to 4.38508, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 38s - loss: 4.1932 - acc: 0.0868 - val_loss: 4.3851 - val_acc: 0.0527\n",
      "Epoch 27/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.1754 - acc: 0.0836Epoch 00026: val_loss improved from 4.38508 to 4.35398, saving model to saved_models/weights.best.from_scratch.hdf5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6680/6680 [==============================] - 37s - loss: 4.1754 - acc: 0.0837 - val_loss: 4.3540 - val_acc: 0.0587\n",
      "Epoch 28/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.1511 - acc: 0.0880Epoch 00027: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 4.1517 - acc: 0.0877 - val_loss: 4.4116 - val_acc: 0.0539\n",
      "Epoch 29/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.1361 - acc: 0.0914Epoch 00028: val_loss improved from 4.35398 to 4.34650, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.1370 - acc: 0.0915 - val_loss: 4.3465 - val_acc: 0.0719\n",
      "Epoch 30/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.1140 - acc: 0.0937Epoch 00029: val_loss improved from 4.34650 to 4.28692, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.1136 - acc: 0.0942 - val_loss: 4.2869 - val_acc: 0.0695\n",
      "Epoch 31/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.0967 - acc: 0.0929Epoch 00030: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 4.0974 - acc: 0.0928 - val_loss: 4.3180 - val_acc: 0.0695\n",
      "Epoch 32/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.0782 - acc: 0.0949Epoch 00031: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 4.0784 - acc: 0.0949 - val_loss: 4.3362 - val_acc: 0.0695\n",
      "Epoch 33/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.0583 - acc: 0.0995Epoch 00032: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 4.0596 - acc: 0.0994 - val_loss: 4.3164 - val_acc: 0.0695\n",
      "Epoch 34/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.0378 - acc: 0.0973Epoch 00033: val_loss improved from 4.28692 to 4.24887, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.0381 - acc: 0.0973 - val_loss: 4.2489 - val_acc: 0.0695\n",
      "Epoch 35/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.0217 - acc: 0.1026Epoch 00034: val_loss improved from 4.24887 to 4.23975, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 4.0204 - acc: 0.1030 - val_loss: 4.2397 - val_acc: 0.0802\n",
      "Epoch 36/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 4.0148 - acc: 0.1032Epoch 00035: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 4.0148 - acc: 0.1028 - val_loss: 4.2873 - val_acc: 0.0838\n",
      "Epoch 37/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.9898 - acc: 0.1062Epoch 00036: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 3.9886 - acc: 0.1063 - val_loss: 4.3059 - val_acc: 0.0659\n",
      "Epoch 38/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.9676 - acc: 0.1119Epoch 00037: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 3.9676 - acc: 0.1115 - val_loss: 4.2709 - val_acc: 0.0731\n",
      "Epoch 39/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.9521 - acc: 0.1092Epoch 00038: val_loss improved from 4.23975 to 4.22699, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 3.9534 - acc: 0.1091 - val_loss: 4.2270 - val_acc: 0.0802\n",
      "Epoch 40/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.9377 - acc: 0.1087Epoch 00039: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 3.9380 - acc: 0.1088 - val_loss: 4.2408 - val_acc: 0.0743\n",
      "Epoch 41/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.9203 - acc: 0.1180Epoch 00040: val_loss improved from 4.22699 to 4.20214, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 3.9219 - acc: 0.1177 - val_loss: 4.2021 - val_acc: 0.0802\n",
      "Epoch 42/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.9093 - acc: 0.1207Epoch 00041: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 3.9088 - acc: 0.1207 - val_loss: 4.3180 - val_acc: 0.0719\n",
      "Epoch 43/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.8958 - acc: 0.1228Epoch 00042: val_loss improved from 4.20214 to 4.18238, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 3.8952 - acc: 0.1232 - val_loss: 4.1824 - val_acc: 0.0994\n",
      "Epoch 44/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.8768 - acc: 0.1234Epoch 00043: val_loss improved from 4.18238 to 4.14017, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 38s - loss: 3.8768 - acc: 0.1234 - val_loss: 4.1402 - val_acc: 0.0850\n",
      "Epoch 45/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.8667 - acc: 0.1225Epoch 00044: val_loss improved from 4.14017 to 4.12776, saving model to saved_models/weights.best.from_scratch.hdf5\n",
      "6680/6680 [==============================] - 37s - loss: 3.8663 - acc: 0.1226 - val_loss: 4.1278 - val_acc: 0.1114\n",
      "Epoch 46/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.8518 - acc: 0.1276Epoch 00045: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 3.8512 - acc: 0.1275 - val_loss: 4.1470 - val_acc: 0.1006\n",
      "Epoch 47/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.8403 - acc: 0.1302Epoch 00046: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 3.8408 - acc: 0.1302 - val_loss: 4.1847 - val_acc: 0.0874\n",
      "Epoch 48/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.8232 - acc: 0.1315Epoch 00047: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 3.8235 - acc: 0.1314 - val_loss: 4.1544 - val_acc: 0.0838\n",
      "Epoch 49/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.7995 - acc: 0.1318Epoch 00048: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 3.8022 - acc: 0.1319 - val_loss: 4.1714 - val_acc: 0.1006\n",
      "Epoch 50/50\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 3.8008 - acc: 0.1360Epoch 00049: val_loss did not improve\n",
      "6680/6680 [==============================] - 37s - loss: 3.7998 - acc: 0.1368 - val_loss: 4.1744 - val_acc: 0.1066\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f7a6169f5f8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Do NOT modify the code below this line.\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "\n",
    "model.fit(train_tensors, train_targets, \n",
    "          validation_data=(valid_tensors, valid_targets),\n",
    "          epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Model with the Best Validation Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('saved_models/weights.best.from_scratch.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Model\n",
    "\n",
    "Try out your model on the test dataset of dog images.  Ensure that your test accuracy is greater than 1%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 10.6459%\n"
     ]
    }
   ],
   "source": [
    "# get index of predicted dog breed for each image in test set\n",
    "dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]\n",
    "\n",
    "# report test accuracy\n",
    "test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step4'></a>\n",
    "## Step 4: Use a CNN to Classify Dog Breeds\n",
    "\n",
    "To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning.  In the following step, you will get a chance to use transfer learning to train your own CNN.\n",
    "\n",
    "### Obtain Bottleneck Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')\n",
    "train_VGG16 = bottleneck_features['train']\n",
    "valid_VGG16 = bottleneck_features['valid']\n",
    "test_VGG16 = bottleneck_features['test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Architecture\n",
    "\n",
    "The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model.  We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "global_average_pooling2d_2 ( (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 133)               68229     \n",
      "=================================================================\n",
      "Total params: 68,229.0\n",
      "Trainable params: 68,229.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n",
      "(6680, 7, 7, 512)\n",
      "(7, 7, 512)\n"
     ]
    }
   ],
   "source": [
    "VGG16_model = Sequential()\n",
    "VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))\n",
    "VGG16_model.add(Dense(133, activation='softmax'))\n",
    "\n",
    "VGG16_model.summary()\n",
    "\n",
    "print(train_VGG16.shape)\n",
    "print(train_VGG16.shape[1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/20\n",
      "6500/6680 [============================>.] - ETA: 0s - loss: 13.1959 - acc: 0.1022Epoch 00000: val_loss improved from inf to 12.17720, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 13.1651 - acc: 0.1034 - val_loss: 12.1772 - val_acc: 0.1557\n",
      "Epoch 2/20\n",
      "6500/6680 [============================>.] - ETA: 0s - loss: 11.5995 - acc: 0.2215Epoch 00001: val_loss improved from 12.17720 to 11.57390, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 11.5826 - acc: 0.2226 - val_loss: 11.5739 - val_acc: 0.2132\n",
      "Epoch 3/20\n",
      "6500/6680 [============================>.] - ETA: 0s - loss: 11.1265 - acc: 0.2686Epoch 00002: val_loss improved from 11.57390 to 11.21165, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 11.1252 - acc: 0.2689 - val_loss: 11.2117 - val_acc: 0.2419\n",
      "Epoch 4/20\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 10.7830 - acc: 0.3002Epoch 00003: val_loss improved from 11.21165 to 11.03807, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 10.7796 - acc: 0.3004 - val_loss: 11.0381 - val_acc: 0.2599\n",
      "Epoch 5/20\n",
      "6480/6680 [============================>.] - ETA: 0s - loss: 10.6382 - acc: 0.3114Epoch 00004: val_loss improved from 11.03807 to 10.99515, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 10.6386 - acc: 0.3109 - val_loss: 10.9952 - val_acc: 0.2479\n",
      "Epoch 6/20\n",
      "6440/6680 [===========================>..] - ETA: 0s - loss: 10.4950 - acc: 0.3283Epoch 00005: val_loss improved from 10.99515 to 10.80441, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 10.4949 - acc: 0.3281 - val_loss: 10.8044 - val_acc: 0.2874\n",
      "Epoch 7/20\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 10.3994 - acc: 0.3375Epoch 00006: val_loss improved from 10.80441 to 10.78404, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 10.4063 - acc: 0.3371 - val_loss: 10.7840 - val_acc: 0.2671\n",
      "Epoch 8/20\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 10.2063 - acc: 0.3444Epoch 00007: val_loss improved from 10.78404 to 10.55817, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 10.2007 - acc: 0.3449 - val_loss: 10.5582 - val_acc: 0.2970\n",
      "Epoch 9/20\n",
      "6460/6680 [============================>.] - ETA: 0s - loss: 10.0177 - acc: 0.3553Epoch 00008: val_loss improved from 10.55817 to 10.40852, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 10.0213 - acc: 0.3554 - val_loss: 10.4085 - val_acc: 0.2946\n",
      "Epoch 10/20\n",
      "6480/6680 [============================>.] - ETA: 0s - loss: 9.8257 - acc: 0.3716Epoch 00009: val_loss improved from 10.40852 to 10.23808, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 9.8397 - acc: 0.3710 - val_loss: 10.2381 - val_acc: 0.3126\n",
      "Epoch 11/20\n",
      "6480/6680 [============================>.] - ETA: 0s - loss: 9.7501 - acc: 0.3827Epoch 00010: val_loss improved from 10.23808 to 10.17048, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 9.7707 - acc: 0.3814 - val_loss: 10.1705 - val_acc: 0.3305\n",
      "Epoch 12/20\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 9.7468 - acc: 0.3877Epoch 00011: val_loss did not improve\n",
      "6680/6680 [==============================] - 1s - loss: 9.7466 - acc: 0.3877 - val_loss: 10.2930 - val_acc: 0.3054\n",
      "Epoch 13/20\n",
      "6480/6680 [============================>.] - ETA: 0s - loss: 9.7103 - acc: 0.3912Epoch 00012: val_loss did not improve\n",
      "6680/6680 [==============================] - 1s - loss: 9.7337 - acc: 0.3895 - val_loss: 10.2071 - val_acc: 0.3281\n",
      "Epoch 14/20\n",
      "6440/6680 [===========================>..] - ETA: 0s - loss: 9.6771 - acc: 0.3911Epoch 00013: val_loss improved from 10.17048 to 10.12960, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 9.6630 - acc: 0.3921 - val_loss: 10.1296 - val_acc: 0.3234\n",
      "Epoch 15/20\n",
      "6440/6680 [===========================>..] - ETA: 0s - loss: 9.5451 - acc: 0.3989Epoch 00014: val_loss improved from 10.12960 to 10.00438, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 9.5529 - acc: 0.3987 - val_loss: 10.0044 - val_acc: 0.3234\n",
      "Epoch 16/20\n",
      "6480/6680 [============================>.] - ETA: 0s - loss: 9.4151 - acc: 0.4026Epoch 00015: val_loss improved from 10.00438 to 9.89912, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 9.4204 - acc: 0.4019 - val_loss: 9.8991 - val_acc: 0.3257\n",
      "Epoch 17/20\n",
      "6440/6680 [===========================>..] - ETA: 0s - loss: 9.2319 - acc: 0.4132Epoch 00016: val_loss improved from 9.89912 to 9.74901, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 9.2446 - acc: 0.4123 - val_loss: 9.7490 - val_acc: 0.3365\n",
      "Epoch 18/20\n",
      "6460/6680 [============================>.] - ETA: 0s - loss: 9.1781 - acc: 0.4203Epoch 00017: val_loss improved from 9.74901 to 9.67895, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 9.1544 - acc: 0.4219 - val_loss: 9.6789 - val_acc: 0.3365\n",
      "Epoch 19/20\n",
      "6500/6680 [============================>.] - ETA: 0s - loss: 9.0525 - acc: 0.4272Epoch 00018: val_loss improved from 9.67895 to 9.48935, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 9.0405 - acc: 0.4277 - val_loss: 9.4893 - val_acc: 0.3533\n",
      "Epoch 20/20\n",
      "6500/6680 [============================>.] - ETA: 0s - loss: 8.8368 - acc: 0.4402Epoch 00019: val_loss improved from 9.48935 to 9.35133, saving model to saved_models/weights.best.VGG16.hdf5\n",
      "6680/6680 [==============================] - 1s - loss: 8.8450 - acc: 0.4400 - val_loss: 9.3513 - val_acc: 0.3605\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f7a3c2bfda0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', \n",
    "                               verbose=1, save_best_only=True)\n",
    "\n",
    "VGG16_model.fit(train_VGG16, train_targets, \n",
    "          validation_data=(valid_VGG16, valid_targets),\n",
    "          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Model with the Best Validation Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Model\n",
    "\n",
    "Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images.  We print the test accuracy below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 36.1244%\n"
     ]
    }
   ],
   "source": [
    "# get index of predicted dog breed for each image in test set\n",
    "VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]\n",
    "\n",
    "# report test accuracy\n",
    "test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict Dog Breed with the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from extract_bottleneck_features import *\n",
    "\n",
    "def VGG16_predict_breed(img_path):\n",
    "    # extract bottleneck features\n",
    "    bottleneck_feature = extract_VGG16(path_to_tensor(img_path))\n",
    "    # obtain predicted vector\n",
    "    predicted_vector = VGG16_model.predict(bottleneck_feature)\n",
    "    # return dog breed that is predicted by the model\n",
    "    return dog_names[np.argmax(predicted_vector)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step5'></a>\n",
    "## Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)\n",
    "\n",
    "You will now use transfer learning to create a CNN that can identify dog breed from images.  Your CNN must attain at least 60% accuracy on the test set.\n",
    "\n",
    "In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features.  In this section, you must use the bottleneck features from a different pre-trained model.  To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:\n",
    "- [VGG-19](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz) bottleneck features\n",
    "- [ResNet-50](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz) bottleneck features\n",
    "- [Inception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz) bottleneck features\n",
    "- [Xception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz) bottleneck features\n",
    "\n",
    "The files are encoded as such:\n",
    "\n",
    "    Dog{network}Data.npz\n",
    "    \n",
    "where `{network}`, in the above filename, can be one of `VGG19`, `Resnet50`, `InceptionV3`, or `Xception`.  Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the `bottleneck_features/` folder in the repository.\n",
    "\n",
    "### (IMPLEMENTATION) Obtain Bottleneck Features\n",
    "\n",
    "In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:\n",
    "\n",
    "    bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')\n",
    "    train_{network} = bottleneck_features['train']\n",
    "    valid_{network} = bottleneck_features['valid']\n",
    "    test_{network} = bottleneck_features['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6680, 7, 7, 2048)\n",
      "(7, 7, 2048)\n"
     ]
    }
   ],
   "source": [
    "### Obtain bottleneck features from another pre-trained CNN.\n",
    "bottleneck_features = np.load('bottleneck_features/DogXceptionData.npz')\n",
    "\n",
    "train_Xception = bottleneck_features['train']\n",
    "valid_Xception = bottleneck_features['valid']\n",
    "test_Xception = bottleneck_features['test']\n",
    "\n",
    "print(train_Xception.shape)\n",
    "print(train_Xception.shape[1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Model Architecture\n",
    "\n",
    "Create a CNN to classify dog breed.  At the end of your code cell block, summarize the layers of your model by executing the line:\n",
    "    \n",
    "        <your model's name>.summary()\n",
    "   \n",
    "__Question 5:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  Describe why you think the architecture is suitable for the current problem.\n",
    "\n",
    "\n",
    "__Answer:__ \n",
    "\n",
    "According with what we studied in classes there are four main cases when we want to use Transfer Learning:\n",
    "\n",
    "1. new data set is small, new data is similar to original training data\n",
    "2. new data set is small, new data is different from original training data\n",
    "3. new data set is large, new data is similar to original training data\n",
    "4. new data set is large, new data is different from original training data\n",
    "\n",
    "In this case (Xception) we're in presense of the case number 3, we have new large data set, more than 3 GB, and we know that has been trained with dog images, so is similiar to our dog training data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "global_average_pooling2d_3 ( (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 133)               272517    \n",
      "=================================================================\n",
      "Total params: 272,517.0\n",
      "Trainable params: 272,517.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "### Define your architecture.\n",
    "\n",
    "Xception_model = Sequential()\n",
    "Xception_model.add(GlobalAveragePooling2D(input_shape=train_Xception.shape[1:]))\n",
    "Xception_model.add(Dense(133, activation='softmax'))\n",
    "\n",
    "Xception_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Compile the model.\n",
    "Xception_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Train the Model\n",
    "\n",
    "Train your model in the code cell below.  Use model checkpointing to save the model that attains the best validation loss.  \n",
    "\n",
    "You are welcome to [augment the training data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html), but this is not a requirement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6680 samples, validate on 835 samples\n",
      "Epoch 1/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 1.0596 - acc: 0.7347Epoch 00000: val_loss improved from inf to 0.55611, saving model to saved_models/weights.best.Xception.hdf5\n",
      "6680/6680 [==============================] - 4s - loss: 1.0581 - acc: 0.7350 - val_loss: 0.5561 - val_acc: 0.8096\n",
      "Epoch 2/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.3972 - acc: 0.8715Epoch 00001: val_loss improved from 0.55611 to 0.48812, saving model to saved_models/weights.best.Xception.hdf5\n",
      "6680/6680 [==============================] - 4s - loss: 0.3990 - acc: 0.8714 - val_loss: 0.4881 - val_acc: 0.8419\n",
      "Epoch 3/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.3187 - acc: 0.8995Epoch 00002: val_loss improved from 0.48812 to 0.46789, saving model to saved_models/weights.best.Xception.hdf5\n",
      "6680/6680 [==============================] - 4s - loss: 0.3178 - acc: 0.8999 - val_loss: 0.4679 - val_acc: 0.8515\n",
      "Epoch 4/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.2792 - acc: 0.9093Epoch 00003: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.2784 - acc: 0.9094 - val_loss: 0.5082 - val_acc: 0.8515\n",
      "Epoch 5/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.2455 - acc: 0.9252Epoch 00004: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.2448 - acc: 0.9254 - val_loss: 0.4893 - val_acc: 0.8659\n",
      "Epoch 6/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.2177 - acc: 0.9326Epoch 00005: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.2181 - acc: 0.9320 - val_loss: 0.5467 - val_acc: 0.8575\n",
      "Epoch 7/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.1936 - acc: 0.9378Epoch 00006: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.1931 - acc: 0.9380 - val_loss: 0.5562 - val_acc: 0.8455\n",
      "Epoch 8/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.1819 - acc: 0.9450Epoch 00007: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.1826 - acc: 0.9448 - val_loss: 0.5616 - val_acc: 0.8563\n",
      "Epoch 9/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.1628 - acc: 0.9492Epoch 00008: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.1634 - acc: 0.9493 - val_loss: 0.5433 - val_acc: 0.8635\n",
      "Epoch 10/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.1492 - acc: 0.9550Epoch 00009: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.1487 - acc: 0.9549 - val_loss: 0.5659 - val_acc: 0.8551\n",
      "Epoch 11/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.1350 - acc: 0.9591Epoch 00010: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.1348 - acc: 0.9591 - val_loss: 0.5752 - val_acc: 0.8611\n",
      "Epoch 12/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.1245 - acc: 0.9628Epoch 00011: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.1268 - acc: 0.9623 - val_loss: 0.6140 - val_acc: 0.8515\n",
      "Epoch 13/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.1172 - acc: 0.9668Epoch 00012: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.1164 - acc: 0.9671 - val_loss: 0.6167 - val_acc: 0.8575\n",
      "Epoch 14/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.1125 - acc: 0.9666Epoch 00013: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.1116 - acc: 0.9669 - val_loss: 0.6134 - val_acc: 0.8611\n",
      "Epoch 15/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0986 - acc: 0.9702Epoch 00014: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0988 - acc: 0.9699 - val_loss: 0.6390 - val_acc: 0.8551\n",
      "Epoch 16/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0966 - acc: 0.9731Epoch 00015: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0965 - acc: 0.9731 - val_loss: 0.6349 - val_acc: 0.8623\n",
      "Epoch 17/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0902 - acc: 0.9729Epoch 00016: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0895 - acc: 0.9731 - val_loss: 0.6570 - val_acc: 0.8503\n",
      "Epoch 18/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0790 - acc: 0.9756Epoch 00017: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0807 - acc: 0.9756 - val_loss: 0.6890 - val_acc: 0.8611\n",
      "Epoch 19/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0784 - acc: 0.9772Epoch 00018: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0780 - acc: 0.9772 - val_loss: 0.6862 - val_acc: 0.8515\n",
      "Epoch 20/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0745 - acc: 0.9794Epoch 00019: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0739 - acc: 0.9795 - val_loss: 0.6769 - val_acc: 0.8563\n",
      "Epoch 21/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0699 - acc: 0.9793Epoch 00020: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0700 - acc: 0.9792 - val_loss: 0.7123 - val_acc: 0.8527\n",
      "Epoch 22/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0649 - acc: 0.9807Epoch 00021: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0650 - acc: 0.9805 - val_loss: 0.6837 - val_acc: 0.8623\n",
      "Epoch 23/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0626 - acc: 0.9826Epoch 00022: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0625 - acc: 0.9826 - val_loss: 0.7152 - val_acc: 0.8551\n",
      "Epoch 24/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0581 - acc: 0.9830Epoch 00023: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0581 - acc: 0.9829 - val_loss: 0.7158 - val_acc: 0.8611\n",
      "Epoch 25/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0507 - acc: 0.9859Epoch 00024: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0541 - acc: 0.9855 - val_loss: 0.7752 - val_acc: 0.8551\n",
      "Epoch 26/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0510 - acc: 0.9860Epoch 00025: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0509 - acc: 0.9861 - val_loss: 0.7519 - val_acc: 0.8563\n",
      "Epoch 27/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0500 - acc: 0.9856Epoch 00026: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0497 - acc: 0.9856 - val_loss: 0.7605 - val_acc: 0.8611\n",
      "Epoch 28/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0470 - acc: 0.9868Epoch 00027: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0470 - acc: 0.9867 - val_loss: 0.7545 - val_acc: 0.8551\n",
      "Epoch 29/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0460 - acc: 0.9877Epoch 00028: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0458 - acc: 0.9877 - val_loss: 0.7875 - val_acc: 0.8527\n",
      "Epoch 30/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0425 - acc: 0.9891Epoch 00029: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0429 - acc: 0.9891 - val_loss: 0.7879 - val_acc: 0.8599\n",
      "Epoch 31/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0407 - acc: 0.9893Epoch 00030: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0405 - acc: 0.9894 - val_loss: 0.7950 - val_acc: 0.8587\n",
      "Epoch 32/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0369 - acc: 0.9896Epoch 00031: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0370 - acc: 0.9894 - val_loss: 0.8106 - val_acc: 0.8611\n",
      "Epoch 33/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0380 - acc: 0.9889Epoch 00032: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0378 - acc: 0.9889 - val_loss: 0.8203 - val_acc: 0.8599\n",
      "Epoch 34/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0359 - acc: 0.9898Epoch 00033: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0383 - acc: 0.9897 - val_loss: 0.8352 - val_acc: 0.8491\n",
      "Epoch 35/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0366 - acc: 0.9904Epoch 00034: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0365 - acc: 0.9904 - val_loss: 0.8412 - val_acc: 0.8611\n",
      "Epoch 36/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0353 - acc: 0.9900Epoch 00035: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0353 - acc: 0.9900 - val_loss: 0.8644 - val_acc: 0.8491\n",
      "Epoch 37/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0326 - acc: 0.9914Epoch 00036: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0324 - acc: 0.9915 - val_loss: 0.8544 - val_acc: 0.8611\n",
      "Epoch 38/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0310 - acc: 0.9920Epoch 00037: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0308 - acc: 0.9921 - val_loss: 0.8765 - val_acc: 0.8515\n",
      "Epoch 39/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0305 - acc: 0.9923Epoch 00038: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0301 - acc: 0.9924 - val_loss: 0.9018 - val_acc: 0.8539\n",
      "Epoch 40/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0296 - acc: 0.9916Epoch 00039: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0293 - acc: 0.9918 - val_loss: 0.8788 - val_acc: 0.8503\n",
      "Epoch 41/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0288 - acc: 0.9926Epoch 00040: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0286 - acc: 0.9927 - val_loss: 0.8946 - val_acc: 0.8563\n",
      "Epoch 42/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0284 - acc: 0.9928Epoch 00041: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0283 - acc: 0.9928 - val_loss: 0.9215 - val_acc: 0.8491\n",
      "Epoch 43/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0264 - acc: 0.9929Epoch 00042: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0264 - acc: 0.9927 - val_loss: 0.9468 - val_acc: 0.8467\n",
      "Epoch 44/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0274 - acc: 0.9932Epoch 00043: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0275 - acc: 0.9931 - val_loss: 0.9116 - val_acc: 0.8563\n",
      "Epoch 45/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0259 - acc: 0.9944Epoch 00044: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0257 - acc: 0.9945 - val_loss: 0.9318 - val_acc: 0.8539\n",
      "Epoch 46/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0232 - acc: 0.9946Epoch 00045: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0230 - acc: 0.9946 - val_loss: 0.9495 - val_acc: 0.8515\n",
      "Epoch 47/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0217 - acc: 0.9945Epoch 00046: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0239 - acc: 0.9945 - val_loss: 0.9777 - val_acc: 0.8455\n",
      "Epoch 48/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0234 - acc: 0.9946Epoch 00047: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0234 - acc: 0.9945 - val_loss: 0.9774 - val_acc: 0.8539\n",
      "Epoch 49/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0229 - acc: 0.9947Epoch 00048: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0227 - acc: 0.9948 - val_loss: 0.9621 - val_acc: 0.8479\n",
      "Epoch 50/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0233 - acc: 0.9944Epoch 00049: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0230 - acc: 0.9945 - val_loss: 0.9599 - val_acc: 0.8503\n",
      "Epoch 51/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0228 - acc: 0.9945Epoch 00050: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0228 - acc: 0.9945 - val_loss: 0.9899 - val_acc: 0.8443\n",
      "Epoch 52/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0228 - acc: 0.9953Epoch 00051: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0225 - acc: 0.9954 - val_loss: 0.9681 - val_acc: 0.8563\n",
      "Epoch 53/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0208 - acc: 0.9950Epoch 00052: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0207 - acc: 0.9951 - val_loss: 0.9567 - val_acc: 0.8515\n",
      "Epoch 54/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0215 - acc: 0.9956Epoch 00053: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0214 - acc: 0.9957 - val_loss: 1.0116 - val_acc: 0.8515\n",
      "Epoch 55/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0195 - acc: 0.9955Epoch 00054: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0212 - acc: 0.9952 - val_loss: 1.0310 - val_acc: 0.8467\n",
      "Epoch 56/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0212 - acc: 0.9953Epoch 00055: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0210 - acc: 0.9954 - val_loss: 1.0350 - val_acc: 0.8479\n",
      "Epoch 57/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0202 - acc: 0.9947Epoch 00056: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0202 - acc: 0.9948 - val_loss: 1.0293 - val_acc: 0.8491\n",
      "Epoch 58/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0200 - acc: 0.9956Epoch 00057: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0198 - acc: 0.9957 - val_loss: 1.0374 - val_acc: 0.8431\n",
      "Epoch 59/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0195 - acc: 0.9956Epoch 00058: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0193 - acc: 0.9957 - val_loss: 1.0393 - val_acc: 0.8515\n",
      "Epoch 60/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0185 - acc: 0.9960Epoch 00059: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0183 - acc: 0.9961 - val_loss: 1.0519 - val_acc: 0.8455\n",
      "Epoch 61/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0177 - acc: 0.9965Epoch 00060: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0175 - acc: 0.9966 - val_loss: 1.0356 - val_acc: 0.8443\n",
      "Epoch 62/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0172 - acc: 0.9968Epoch 00061: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0182 - acc: 0.9967 - val_loss: 1.0802 - val_acc: 0.8455\n",
      "Epoch 63/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0172 - acc: 0.9958Epoch 00062: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0170 - acc: 0.9958 - val_loss: 1.0739 - val_acc: 0.8467\n",
      "Epoch 64/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0178 - acc: 0.9967Epoch 00063: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0180 - acc: 0.9966 - val_loss: 1.0507 - val_acc: 0.8479\n",
      "Epoch 65/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0177 - acc: 0.9964Epoch 00064: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0175 - acc: 0.9964 - val_loss: 1.0779 - val_acc: 0.8479\n",
      "Epoch 66/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0161 - acc: 0.9962Epoch 00065: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0159 - acc: 0.9963 - val_loss: 1.0834 - val_acc: 0.8455\n",
      "Epoch 67/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0171 - acc: 0.9962Epoch 00066: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0169 - acc: 0.9963 - val_loss: 1.0788 - val_acc: 0.8479\n",
      "Epoch 68/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0176 - acc: 0.9961Epoch 00067: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0175 - acc: 0.9961 - val_loss: 1.1180 - val_acc: 0.8467\n",
      "Epoch 69/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0168 - acc: 0.9960Epoch 00068: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0165 - acc: 0.9961 - val_loss: 1.1088 - val_acc: 0.8503\n",
      "Epoch 70/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0165 - acc: 0.9971Epoch 00069: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0165 - acc: 0.9972 - val_loss: 1.1211 - val_acc: 0.8455\n",
      "Epoch 71/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0169 - acc: 0.9964Epoch 00070: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0167 - acc: 0.9964 - val_loss: 1.1422 - val_acc: 0.8455\n",
      "Epoch 72/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0168 - acc: 0.9968Epoch 00071: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0165 - acc: 0.9969 - val_loss: 1.1318 - val_acc: 0.8467\n",
      "Epoch 73/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0165 - acc: 0.9970Epoch 00072: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0162 - acc: 0.9970 - val_loss: 1.1320 - val_acc: 0.8455\n",
      "Epoch 74/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0157 - acc: 0.9973Epoch 00073: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0156 - acc: 0.9973 - val_loss: 1.1149 - val_acc: 0.8455\n",
      "Epoch 75/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0150 - acc: 0.9973Epoch 00074: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0149 - acc: 0.9973 - val_loss: 1.1352 - val_acc: 0.8491\n",
      "Epoch 76/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0141 - acc: 0.9974Epoch 00075: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0140 - acc: 0.9975 - val_loss: 1.1226 - val_acc: 0.8443\n",
      "Epoch 77/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0151 - acc: 0.9968Epoch 00076: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0151 - acc: 0.9969 - val_loss: 1.1222 - val_acc: 0.8431\n",
      "Epoch 78/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0152 - acc: 0.9967Epoch 00077: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0150 - acc: 0.9967 - val_loss: 1.1422 - val_acc: 0.8347\n",
      "Epoch 79/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0148 - acc: 0.9970Epoch 00078: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0147 - acc: 0.9970 - val_loss: 1.1561 - val_acc: 0.8467\n",
      "Epoch 80/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0147 - acc: 0.9973Epoch 00079: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0146 - acc: 0.9973 - val_loss: 1.1599 - val_acc: 0.8443\n",
      "Epoch 81/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0138 - acc: 0.9973Epoch 00080: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0136 - acc: 0.9973 - val_loss: 1.1791 - val_acc: 0.8443\n",
      "Epoch 82/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0142 - acc: 0.9977Epoch 00081: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0141 - acc: 0.9978 - val_loss: 1.1987 - val_acc: 0.8407\n",
      "Epoch 83/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0139 - acc: 0.9977Epoch 00082: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0139 - acc: 0.9978 - val_loss: 1.2018 - val_acc: 0.8383\n",
      "Epoch 84/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0144 - acc: 0.9979Epoch 00083: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0144 - acc: 0.9979 - val_loss: 1.2040 - val_acc: 0.8395\n",
      "Epoch 85/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0123 - acc: 0.9973Epoch 00084: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0133 - acc: 0.9972 - val_loss: 1.1981 - val_acc: 0.8431\n",
      "Epoch 86/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0130 - acc: 0.9976Epoch 00085: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0129 - acc: 0.9976 - val_loss: 1.1990 - val_acc: 0.8431\n",
      "Epoch 87/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0119 - acc: 0.9980Epoch 00086: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0118 - acc: 0.9981 - val_loss: 1.1886 - val_acc: 0.8455\n",
      "Epoch 88/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0130 - acc: 0.9980Epoch 00087: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0129 - acc: 0.9981 - val_loss: 1.1869 - val_acc: 0.8407\n",
      "Epoch 89/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0128 - acc: 0.9983Epoch 00088: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0133 - acc: 0.9981 - val_loss: 1.2016 - val_acc: 0.8443\n",
      "Epoch 90/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0132 - acc: 0.9982Epoch 00089: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0132 - acc: 0.9982 - val_loss: 1.1768 - val_acc: 0.8467\n",
      "Epoch 91/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0125 - acc: 0.9980Epoch 00090: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0123 - acc: 0.9981 - val_loss: 1.2557 - val_acc: 0.8311\n",
      "Epoch 92/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0132 - acc: 0.9983Epoch 00091: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0131 - acc: 0.9984 - val_loss: 1.2522 - val_acc: 0.8371\n",
      "Epoch 93/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0130 - acc: 0.9980Epoch 00092: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0130 - acc: 0.9981 - val_loss: 1.2309 - val_acc: 0.8359\n",
      "Epoch 94/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0137 - acc: 0.9980Epoch 00093: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0135 - acc: 0.9981 - val_loss: 1.2457 - val_acc: 0.8419\n",
      "Epoch 95/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0139 - acc: 0.9982Epoch 00094: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0137 - acc: 0.9982 - val_loss: 1.2467 - val_acc: 0.8395\n",
      "Epoch 96/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0125 - acc: 0.9983Epoch 00095: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0126 - acc: 0.9982 - val_loss: 1.2364 - val_acc: 0.8347\n",
      "Epoch 97/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0128 - acc: 0.9985Epoch 00096: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0127 - acc: 0.9985 - val_loss: 1.2371 - val_acc: 0.8443\n",
      "Epoch 98/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0130 - acc: 0.9979Epoch 00097: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0129 - acc: 0.9979 - val_loss: 1.2418 - val_acc: 0.8395\n",
      "Epoch 99/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0132 - acc: 0.9980Epoch 00098: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0132 - acc: 0.9981 - val_loss: 1.2368 - val_acc: 0.8395\n",
      "Epoch 100/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0130 - acc: 0.9980Epoch 00099: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0129 - acc: 0.9981 - val_loss: 1.2701 - val_acc: 0.8395\n",
      "Epoch 101/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0122 - acc: 0.9980Epoch 00100: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0130 - acc: 0.9979 - val_loss: 1.2623 - val_acc: 0.8371\n",
      "Epoch 102/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0130 - acc: 0.9980Epoch 00101: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0128 - acc: 0.9981 - val_loss: 1.2771 - val_acc: 0.8347\n",
      "Epoch 103/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0134 - acc: 0.9986Epoch 00102: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0133 - acc: 0.9987 - val_loss: 1.2655 - val_acc: 0.8371\n",
      "Epoch 104/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0131 - acc: 0.9982Epoch 00103: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0130 - acc: 0.9982 - val_loss: 1.2767 - val_acc: 0.8323\n",
      "Epoch 105/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0131 - acc: 0.9982Epoch 00104: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0131 - acc: 0.9982 - val_loss: 1.2682 - val_acc: 0.8323\n",
      "Epoch 106/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0134 - acc: 0.9980Epoch 00105: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0132 - acc: 0.9981 - val_loss: 1.2979 - val_acc: 0.8311\n",
      "Epoch 107/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0119 - acc: 0.9982Epoch 00106: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0118 - acc: 0.9982 - val_loss: 1.2816 - val_acc: 0.8371\n",
      "Epoch 108/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0121 - acc: 0.9983Epoch 00107: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0121 - acc: 0.9984 - val_loss: 1.2703 - val_acc: 0.8383\n",
      "Epoch 109/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9985Epoch 00108: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0125 - acc: 0.9982 - val_loss: 1.3045 - val_acc: 0.8359\n",
      "Epoch 110/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0129 - acc: 0.9983Epoch 00109: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0127 - acc: 0.9984 - val_loss: 1.3022 - val_acc: 0.8347\n",
      "Epoch 111/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0121 - acc: 0.9986Epoch 00110: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0124 - acc: 0.9985 - val_loss: 1.3045 - val_acc: 0.8359\n",
      "Epoch 112/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0121 - acc: 0.9979Epoch 00111: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0120 - acc: 0.9979 - val_loss: 1.3220 - val_acc: 0.8347\n",
      "Epoch 113/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0136 - acc: 0.9983Epoch 00112: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0134 - acc: 0.9984 - val_loss: 1.3282 - val_acc: 0.8251\n",
      "Epoch 114/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0127 - acc: 0.9982Epoch 00113: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0125 - acc: 0.9982 - val_loss: 1.3250 - val_acc: 0.8323\n",
      "Epoch 115/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0127 - acc: 0.9980Epoch 00114: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0126 - acc: 0.9981 - val_loss: 1.3460 - val_acc: 0.8359\n",
      "Epoch 116/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0123 - acc: 0.9983Epoch 00115: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0122 - acc: 0.9984 - val_loss: 1.3290 - val_acc: 0.8347\n",
      "Epoch 117/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0122 - acc: 0.9982Epoch 00116: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0121 - acc: 0.9982 - val_loss: 1.3441 - val_acc: 0.8299\n",
      "Epoch 118/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0127 - acc: 0.9983Epoch 00117: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0126 - acc: 0.9984 - val_loss: 1.3079 - val_acc: 0.8359\n",
      "Epoch 119/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0122 - acc: 0.9983Epoch 00118: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0121 - acc: 0.9984 - val_loss: 1.3411 - val_acc: 0.8287\n",
      "Epoch 120/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0115 - acc: 0.9985Epoch 00119: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0114 - acc: 0.9985 - val_loss: 1.3183 - val_acc: 0.8323\n",
      "Epoch 121/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0121 - acc: 0.9983Epoch 00120: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0119 - acc: 0.9984 - val_loss: 1.3215 - val_acc: 0.8323\n",
      "Epoch 122/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0121 - acc: 0.9983Epoch 00121: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0120 - acc: 0.9984 - val_loss: 1.3280 - val_acc: 0.8299\n",
      "Epoch 123/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0118 - acc: 0.9985Epoch 00122: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0118 - acc: 0.9985 - val_loss: 1.3492 - val_acc: 0.8335\n",
      "Epoch 124/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0117 - acc: 0.9985Epoch 00123: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0116 - acc: 0.9985 - val_loss: 1.3560 - val_acc: 0.8275\n",
      "Epoch 125/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0124 - acc: 0.9983Epoch 00124: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0123 - acc: 0.9984 - val_loss: 1.3241 - val_acc: 0.8299\n",
      "Epoch 126/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0114 - acc: 0.9986Epoch 00125: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0113 - acc: 0.9987 - val_loss: 1.3432 - val_acc: 0.8311\n",
      "Epoch 127/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0119 - acc: 0.9983Epoch 00126: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0119 - acc: 0.9984 - val_loss: 1.3500 - val_acc: 0.8347\n",
      "Epoch 128/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0114 - acc: 0.9986Epoch 00127: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0113 - acc: 0.9987 - val_loss: 1.3319 - val_acc: 0.8431\n",
      "Epoch 129/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0120 - acc: 0.9985Epoch 00128: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0119 - acc: 0.9985 - val_loss: 1.3601 - val_acc: 0.8371\n",
      "Epoch 130/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0121 - acc: 0.9985Epoch 00129: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0120 - acc: 0.9985 - val_loss: 1.3597 - val_acc: 0.8347\n",
      "Epoch 131/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0117 - acc: 0.9985Epoch 00130: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0116 - acc: 0.9985 - val_loss: 1.3717 - val_acc: 0.8335\n",
      "Epoch 132/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0114 - acc: 0.9985Epoch 00131: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0113 - acc: 0.9985 - val_loss: 1.3604 - val_acc: 0.8299\n",
      "Epoch 133/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0110 - acc: 0.9982Epoch 00132: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0108 - acc: 0.9982 - val_loss: 1.3732 - val_acc: 0.8311\n",
      "Epoch 134/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0113 - acc: 0.9983Epoch 00133: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0112 - acc: 0.9984 - val_loss: 1.3729 - val_acc: 0.8335\n",
      "Epoch 135/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0113 - acc: 0.9986Epoch 00134: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0112 - acc: 0.9987 - val_loss: 1.3830 - val_acc: 0.8311\n",
      "Epoch 136/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0113 - acc: 0.9988Epoch 00135: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0112 - acc: 0.9988 - val_loss: 1.3733 - val_acc: 0.8311\n",
      "Epoch 137/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0115 - acc: 0.9986Epoch 00136: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0115 - acc: 0.9987 - val_loss: 1.3951 - val_acc: 0.8359\n",
      "Epoch 138/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0112 - acc: 0.9986Epoch 00137: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0112 - acc: 0.9987 - val_loss: 1.3763 - val_acc: 0.8311\n",
      "Epoch 139/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9989Epoch 00138: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9990 - val_loss: 1.3959 - val_acc: 0.8335\n",
      "Epoch 140/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0114 - acc: 0.9985Epoch 00139: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0112 - acc: 0.9985 - val_loss: 1.3667 - val_acc: 0.8311\n",
      "Epoch 141/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0110 - acc: 0.9985Epoch 00140: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0108 - acc: 0.9985 - val_loss: 1.3847 - val_acc: 0.8371\n",
      "Epoch 142/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0110 - acc: 0.9985Epoch 00141: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0109 - acc: 0.9985 - val_loss: 1.3747 - val_acc: 0.8359\n",
      "Epoch 143/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0118 - acc: 0.9988Epoch 00142: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0118 - acc: 0.9988 - val_loss: 1.3882 - val_acc: 0.8299\n",
      "Epoch 144/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0108 - acc: 0.9986Epoch 00143: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0107 - acc: 0.9987 - val_loss: 1.4182 - val_acc: 0.8323\n",
      "Epoch 145/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9985Epoch 00144: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0105 - acc: 0.9985 - val_loss: 1.4164 - val_acc: 0.8323\n",
      "Epoch 146/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0109 - acc: 0.9985Epoch 00145: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0116 - acc: 0.9984 - val_loss: 1.4326 - val_acc: 0.8311\n",
      "Epoch 147/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9989Epoch 00146: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9990 - val_loss: 1.4138 - val_acc: 0.8347\n",
      "Epoch 148/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9983Epoch 00147: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9984 - val_loss: 1.4381 - val_acc: 0.8335\n",
      "Epoch 149/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9986Epoch 00148: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0105 - acc: 0.9987 - val_loss: 1.4065 - val_acc: 0.8287\n",
      "Epoch 150/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0108 - acc: 0.9986Epoch 00149: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0108 - acc: 0.9987 - val_loss: 1.4224 - val_acc: 0.8323\n",
      "Epoch 151/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9989Epoch 00150: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0105 - acc: 0.9990 - val_loss: 1.4221 - val_acc: 0.8335\n",
      "Epoch 152/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9985Epoch 00151: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0105 - acc: 0.9985 - val_loss: 1.4129 - val_acc: 0.8311\n",
      "Epoch 153/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0107 - acc: 0.9988Epoch 00152: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0106 - acc: 0.9988 - val_loss: 1.4165 - val_acc: 0.8299\n",
      "Epoch 154/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0114 - acc: 0.9986Epoch 00153: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0112 - acc: 0.9987 - val_loss: 1.4071 - val_acc: 0.8311\n",
      "Epoch 155/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0110 - acc: 0.9985Epoch 00154: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0109 - acc: 0.9985 - val_loss: 1.4138 - val_acc: 0.8299\n",
      "Epoch 156/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0108 - acc: 0.9983Epoch 00155: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0108 - acc: 0.9984 - val_loss: 1.4332 - val_acc: 0.8287\n",
      "Epoch 157/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9986Epoch 00156: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9987 - val_loss: 1.4373 - val_acc: 0.8323\n",
      "Epoch 158/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0110 - acc: 0.9989Epoch 00157: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0108 - acc: 0.9990 - val_loss: 1.4358 - val_acc: 0.8311\n",
      "Epoch 159/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0107 - acc: 0.9988Epoch 00158: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0106 - acc: 0.9988 - val_loss: 1.4233 - val_acc: 0.8263\n",
      "Epoch 160/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0110 - acc: 0.9983Epoch 00159: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0108 - acc: 0.9984 - val_loss: 1.4199 - val_acc: 0.8359\n",
      "Epoch 161/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0111 - acc: 0.9986Epoch 00160: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0110 - acc: 0.9987 - val_loss: 1.4452 - val_acc: 0.8287\n",
      "Epoch 162/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0110 - acc: 0.9986Epoch 00161: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0109 - acc: 0.9987 - val_loss: 1.4273 - val_acc: 0.8311\n",
      "Epoch 163/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9986Epoch 00162: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0104 - acc: 0.9987 - val_loss: 1.4462 - val_acc: 0.8335\n",
      "Epoch 164/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00163: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.4461 - val_acc: 0.8347\n",
      "Epoch 165/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0109 - acc: 0.9988Epoch 00164: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0108 - acc: 0.9988 - val_loss: 1.4319 - val_acc: 0.8323\n",
      "Epoch 166/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9988Epoch 00165: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.4671 - val_acc: 0.8323\n",
      "Epoch 167/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0107 - acc: 0.9985Epoch 00166: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0106 - acc: 0.9985 - val_loss: 1.4436 - val_acc: 0.8311\n",
      "Epoch 168/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9985Epoch 00167: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0102 - acc: 0.9985 - val_loss: 1.4585 - val_acc: 0.8347\n",
      "Epoch 169/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00168: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.4495 - val_acc: 0.8299\n",
      "Epoch 170/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9986Epoch 00169: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0102 - acc: 0.9987 - val_loss: 1.4565 - val_acc: 0.8287\n",
      "Epoch 171/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0113 - acc: 0.9982Epoch 00170: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0112 - acc: 0.9982 - val_loss: 1.4643 - val_acc: 0.8311\n",
      "Epoch 172/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00171: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.4471 - val_acc: 0.8335\n",
      "Epoch 173/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9985Epoch 00172: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0103 - acc: 0.9985 - val_loss: 1.4577 - val_acc: 0.8311\n",
      "Epoch 174/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0080 - acc: 0.9986Epoch 00173: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0103 - acc: 0.9985 - val_loss: 1.4686 - val_acc: 0.8299\n",
      "Epoch 175/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0108 - acc: 0.9985Epoch 00174: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0107 - acc: 0.9985 - val_loss: 1.4504 - val_acc: 0.8347\n",
      "Epoch 176/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9988Epoch 00175: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0106 - acc: 0.9988 - val_loss: 1.4532 - val_acc: 0.8323\n",
      "Epoch 177/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9985Epoch 00176: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0104 - acc: 0.9985 - val_loss: 1.4578 - val_acc: 0.8323\n",
      "Epoch 178/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0108 - acc: 0.9988Epoch 00177: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0108 - acc: 0.9988 - val_loss: 1.4513 - val_acc: 0.8347\n",
      "Epoch 179/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00178: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0103 - acc: 0.9988 - val_loss: 1.4799 - val_acc: 0.8311\n",
      "Epoch 180/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00179: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.4606 - val_acc: 0.8311\n",
      "Epoch 181/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00180: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.4508 - val_acc: 0.8323\n",
      "Epoch 182/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9989Epoch 00181: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0098 - acc: 0.9990 - val_loss: 1.4733 - val_acc: 0.8287\n",
      "Epoch 183/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00182: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.4699 - val_acc: 0.8347\n",
      "Epoch 184/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9986Epoch 00183: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9987 - val_loss: 1.4611 - val_acc: 0.8347\n",
      "Epoch 185/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9985Epoch 00184: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9985 - val_loss: 1.4592 - val_acc: 0.8335\n",
      "Epoch 186/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9985Epoch 00185: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0099 - acc: 0.9985 - val_loss: 1.4752 - val_acc: 0.8311\n",
      "Epoch 187/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9985Epoch 00186: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0097 - acc: 0.9985 - val_loss: 1.4708 - val_acc: 0.8299\n",
      "Epoch 188/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00187: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.4791 - val_acc: 0.8311\n",
      "Epoch 189/800\n",
      "6560/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9991Epoch 00188: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0099 - acc: 0.9990 - val_loss: 1.4659 - val_acc: 0.8311\n",
      "Epoch 190/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9988Epoch 00189: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0100 - acc: 0.9988 - val_loss: 1.4678 - val_acc: 0.8335\n",
      "Epoch 191/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9986Epoch 00190: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.4711 - val_acc: 0.8287\n",
      "Epoch 192/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9988Epoch 00191: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.4842 - val_acc: 0.8311\n",
      "Epoch 193/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00192: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.4997 - val_acc: 0.8299\n",
      "Epoch 194/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00193: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.4708 - val_acc: 0.8323\n",
      "Epoch 195/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9989Epoch 00194: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0103 - acc: 0.9990 - val_loss: 1.4838 - val_acc: 0.8323\n",
      "Epoch 196/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00195: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.4781 - val_acc: 0.8311\n",
      "Epoch 197/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0110 - acc: 0.9986Epoch 00196: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0108 - acc: 0.9987 - val_loss: 1.4776 - val_acc: 0.8347\n",
      "Epoch 198/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9988Epoch 00197: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0103 - acc: 0.9988 - val_loss: 1.4817 - val_acc: 0.8347\n",
      "Epoch 199/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00198: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.4891 - val_acc: 0.8287\n",
      "Epoch 200/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00199: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.4830 - val_acc: 0.8335\n",
      "Epoch 201/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9986Epoch 00200: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0104 - acc: 0.9987 - val_loss: 1.4897 - val_acc: 0.8299\n",
      "Epoch 202/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00201: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.4909 - val_acc: 0.8311\n",
      "Epoch 203/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9985Epoch 00202: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0100 - acc: 0.9985 - val_loss: 1.5017 - val_acc: 0.8323\n",
      "Epoch 204/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9988Epoch 00203: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0105 - acc: 0.9988 - val_loss: 1.5053 - val_acc: 0.8299\n",
      "Epoch 205/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9985Epoch 00204: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9985 - val_loss: 1.4897 - val_acc: 0.8287\n",
      "Epoch 206/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9983Epoch 00205: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9984 - val_loss: 1.4928 - val_acc: 0.8311\n",
      "Epoch 207/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00206: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.4892 - val_acc: 0.8323\n",
      "Epoch 208/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00207: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5002 - val_acc: 0.8299\n",
      "Epoch 209/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9989Epoch 00208: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9990 - val_loss: 1.4910 - val_acc: 0.8299\n",
      "Epoch 210/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00209: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5079 - val_acc: 0.8311\n",
      "Epoch 211/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9988Epoch 00210: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.4872 - val_acc: 0.8311\n",
      "Epoch 212/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9988Epoch 00211: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5054 - val_acc: 0.8311\n",
      "Epoch 213/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00212: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.4863 - val_acc: 0.8311\n",
      "Epoch 214/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9988Epoch 00213: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9988 - val_loss: 1.5023 - val_acc: 0.8299\n",
      "Epoch 215/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00214: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.4928 - val_acc: 0.8311\n",
      "Epoch 216/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00215: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5089 - val_acc: 0.8287\n",
      "Epoch 217/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9989Epoch 00216: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0100 - acc: 0.9990 - val_loss: 1.5177 - val_acc: 0.8287\n",
      "Epoch 218/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9989Epoch 00217: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.4993 - val_acc: 0.8287\n",
      "Epoch 219/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9986Epoch 00218: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5073 - val_acc: 0.8263\n",
      "Epoch 220/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00219: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0097 - acc: 0.9985 - val_loss: 1.5000 - val_acc: 0.8299\n",
      "Epoch 221/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9986Epoch 00220: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5064 - val_acc: 0.8299\n",
      "Epoch 222/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00221: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5028 - val_acc: 0.8263\n",
      "Epoch 223/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00222: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5126 - val_acc: 0.8287\n",
      "Epoch 224/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0107 - acc: 0.9988Epoch 00223: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0105 - acc: 0.9988 - val_loss: 1.5034 - val_acc: 0.8287\n",
      "Epoch 225/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9988Epoch 00224: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9988 - val_loss: 1.4990 - val_acc: 0.8323\n",
      "Epoch 226/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00225: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5111 - val_acc: 0.8275\n",
      "Epoch 227/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00226: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5118 - val_acc: 0.8311\n",
      "Epoch 228/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00227: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5074 - val_acc: 0.8275\n",
      "Epoch 229/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9991Epoch 00228: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9990 - val_loss: 1.5079 - val_acc: 0.8263\n",
      "Epoch 230/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00229: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5109 - val_acc: 0.8287\n",
      "Epoch 231/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9986Epoch 00230: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5133 - val_acc: 0.8263\n",
      "Epoch 232/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9986Epoch 00231: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0103 - acc: 0.9987 - val_loss: 1.5080 - val_acc: 0.8275\n",
      "Epoch 233/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9985Epoch 00232: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0099 - acc: 0.9985 - val_loss: 1.5149 - val_acc: 0.8251\n",
      "Epoch 234/800\n",
      "6560/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9988Epoch 00233: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5104 - val_acc: 0.8275\n",
      "Epoch 235/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9989Epoch 00234: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0096 - acc: 0.9990 - val_loss: 1.5114 - val_acc: 0.8287\n",
      "Epoch 236/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00235: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5064 - val_acc: 0.8299\n",
      "Epoch 237/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9989Epoch 00236: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0103 - acc: 0.9990 - val_loss: 1.5107 - val_acc: 0.8299\n",
      "Epoch 238/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9988Epoch 00237: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.4979 - val_acc: 0.8287\n",
      "Epoch 239/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00238: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5218 - val_acc: 0.8287\n",
      "Epoch 240/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9986Epoch 00239: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0102 - acc: 0.9987 - val_loss: 1.5147 - val_acc: 0.8275\n",
      "Epoch 241/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9985Epoch 00240: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0100 - acc: 0.9985 - val_loss: 1.5195 - val_acc: 0.8263\n",
      "Epoch 242/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00241: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5143 - val_acc: 0.8263\n",
      "Epoch 243/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9988Epoch 00242: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0098 - acc: 0.9988 - val_loss: 1.5083 - val_acc: 0.8311\n",
      "Epoch 244/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00243: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5281 - val_acc: 0.8275\n",
      "Epoch 245/800\n",
      "6560/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00244: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5193 - val_acc: 0.8287\n",
      "Epoch 246/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9988Epoch 00245: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5141 - val_acc: 0.8275\n",
      "Epoch 247/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00246: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5382 - val_acc: 0.8263\n",
      "Epoch 248/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9986Epoch 00247: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5157 - val_acc: 0.8263\n",
      "Epoch 249/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00248: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5182 - val_acc: 0.8287\n",
      "Epoch 250/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9988Epoch 00249: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0104 - acc: 0.9988 - val_loss: 1.5254 - val_acc: 0.8251\n",
      "Epoch 251/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00250: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5191 - val_acc: 0.8251\n",
      "Epoch 252/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9989Epoch 00251: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0104 - acc: 0.9990 - val_loss: 1.5135 - val_acc: 0.8299\n",
      "Epoch 253/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00252: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5238 - val_acc: 0.8263\n",
      "Epoch 254/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00253: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5191 - val_acc: 0.8263\n",
      "Epoch 255/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9989Epoch 00254: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0100 - acc: 0.9990 - val_loss: 1.5096 - val_acc: 0.8275\n",
      "Epoch 256/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00255: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5056 - val_acc: 0.8311\n",
      "Epoch 257/800\n",
      "6560/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00256: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5190 - val_acc: 0.8287\n",
      "Epoch 258/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00257: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5265 - val_acc: 0.8263\n",
      "Epoch 259/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00258: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5181 - val_acc: 0.8275\n",
      "Epoch 260/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9988Epoch 00259: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5183 - val_acc: 0.8263\n",
      "Epoch 261/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00260: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5168 - val_acc: 0.8287\n",
      "Epoch 262/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9983Epoch 00261: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0100 - acc: 0.9984 - val_loss: 1.5247 - val_acc: 0.8275\n",
      "Epoch 263/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00262: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5148 - val_acc: 0.8287\n",
      "Epoch 264/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00263: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5151 - val_acc: 0.8275\n",
      "Epoch 265/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00264: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5198 - val_acc: 0.8275\n",
      "Epoch 266/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9988Epoch 00265: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5142 - val_acc: 0.8263\n",
      "Epoch 267/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00266: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0103 - acc: 0.9988 - val_loss: 1.5215 - val_acc: 0.8287\n",
      "Epoch 268/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9989Epoch 00267: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9990 - val_loss: 1.5216 - val_acc: 0.8287\n",
      "Epoch 269/800\n",
      "6560/6680 [============================>.] - ETA: 0s - loss: 0.0107 - acc: 0.9989Epoch 00268: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0105 - acc: 0.9990 - val_loss: 1.5255 - val_acc: 0.8263\n",
      "Epoch 270/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0107 - acc: 0.9988Epoch 00269: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0106 - acc: 0.9988 - val_loss: 1.5358 - val_acc: 0.8251\n",
      "Epoch 271/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0111 - acc: 0.9989Epoch 00270: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0111 - acc: 0.9990 - val_loss: 1.5258 - val_acc: 0.8263\n",
      "Epoch 272/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9989Epoch 00271: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0105 - acc: 0.9990 - val_loss: 1.5329 - val_acc: 0.8251\n",
      "Epoch 273/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9988Epoch 00272: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0100 - acc: 0.9988 - val_loss: 1.5409 - val_acc: 0.8251\n",
      "Epoch 274/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00273: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5281 - val_acc: 0.8287\n",
      "Epoch 275/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9989Epoch 00274: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0105 - acc: 0.9990 - val_loss: 1.5310 - val_acc: 0.8287\n",
      "Epoch 276/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9988Epoch 00275: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9988 - val_loss: 1.5270 - val_acc: 0.8263\n",
      "Epoch 277/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9988Epoch 00276: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0105 - acc: 0.9988 - val_loss: 1.5374 - val_acc: 0.8251\n",
      "Epoch 278/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00277: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5342 - val_acc: 0.8251\n",
      "Epoch 279/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9986Epoch 00278: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9987 - val_loss: 1.5321 - val_acc: 0.8263\n",
      "Epoch 280/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9988Epoch 00279: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5248 - val_acc: 0.8275\n",
      "Epoch 281/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00280: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5234 - val_acc: 0.8287\n",
      "Epoch 282/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9986Epoch 00281: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0105 - acc: 0.9987 - val_loss: 1.5269 - val_acc: 0.8275\n",
      "Epoch 283/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00282: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5338 - val_acc: 0.8275\n",
      "Epoch 284/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9988Epoch 00283: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5237 - val_acc: 0.8275\n",
      "Epoch 285/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00284: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5340 - val_acc: 0.8275\n",
      "Epoch 286/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9989Epoch 00285: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0103 - acc: 0.9990 - val_loss: 1.5334 - val_acc: 0.8263\n",
      "Epoch 287/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9989Epoch 00286: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5370 - val_acc: 0.8251\n",
      "Epoch 288/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9989Epoch 00287: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0099 - acc: 0.9990 - val_loss: 1.5429 - val_acc: 0.8251\n",
      "Epoch 289/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9989Epoch 00288: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0103 - acc: 0.9990 - val_loss: 1.5382 - val_acc: 0.8275\n",
      "Epoch 290/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9988Epoch 00289: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0103 - acc: 0.9988 - val_loss: 1.5289 - val_acc: 0.8251\n",
      "Epoch 291/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9989Epoch 00290: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0103 - acc: 0.9990 - val_loss: 1.5402 - val_acc: 0.8263\n",
      "Epoch 292/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9989Epoch 00291: val_loss did not improve\n",
      "6680/6680 [==============================] - 3s - loss: 0.0100 - acc: 0.9988 - val_loss: 1.5408 - val_acc: 0.8251\n",
      "Epoch 293/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9985Epoch 00292: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9985 - val_loss: 1.5314 - val_acc: 0.8275\n",
      "Epoch 294/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00293: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5333 - val_acc: 0.8275\n",
      "Epoch 295/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9989Epoch 00294: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9990 - val_loss: 1.5289 - val_acc: 0.8299\n",
      "Epoch 296/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9985Epoch 00295: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9985 - val_loss: 1.5398 - val_acc: 0.8263\n",
      "Epoch 297/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00296: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5374 - val_acc: 0.8240\n",
      "Epoch 298/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00297: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5302 - val_acc: 0.8263\n",
      "Epoch 299/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9985Epoch 00298: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9985 - val_loss: 1.5387 - val_acc: 0.8251\n",
      "Epoch 300/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9988Epoch 00299: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9988 - val_loss: 1.5293 - val_acc: 0.8275\n",
      "Epoch 301/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00300: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5334 - val_acc: 0.8263\n",
      "Epoch 302/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9983Epoch 00301: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9984 - val_loss: 1.5437 - val_acc: 0.8251\n",
      "Epoch 303/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9988Epoch 00302: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5418 - val_acc: 0.8251\n",
      "Epoch 304/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9985Epoch 00303: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9985 - val_loss: 1.5383 - val_acc: 0.8263\n",
      "Epoch 305/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9985Epoch 00304: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9985 - val_loss: 1.5334 - val_acc: 0.8287\n",
      "Epoch 306/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00305: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5288 - val_acc: 0.8275\n",
      "Epoch 307/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9988Epoch 00306: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9988 - val_loss: 1.5355 - val_acc: 0.8251\n",
      "Epoch 308/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00307: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5328 - val_acc: 0.8263\n",
      "Epoch 309/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9989Epoch 00308: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9990 - val_loss: 1.5435 - val_acc: 0.8275\n",
      "Epoch 310/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9988Epoch 00309: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5317 - val_acc: 0.8251\n",
      "Epoch 311/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00310: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5505 - val_acc: 0.8240\n",
      "Epoch 312/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00311: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5457 - val_acc: 0.8275\n",
      "Epoch 313/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9988Epoch 00312: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9988 - val_loss: 1.5431 - val_acc: 0.8251\n",
      "Epoch 314/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9989Epoch 00313: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9990 - val_loss: 1.5460 - val_acc: 0.8263\n",
      "Epoch 315/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00314: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5436 - val_acc: 0.8275\n",
      "Epoch 316/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9988Epoch 00315: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5470 - val_acc: 0.8251\n",
      "Epoch 317/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00316: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5390 - val_acc: 0.8263\n",
      "Epoch 318/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00317: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5397 - val_acc: 0.8263\n",
      "Epoch 319/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00318: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5351 - val_acc: 0.8263\n",
      "Epoch 320/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00319: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5378 - val_acc: 0.8263\n",
      "Epoch 321/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9988Epoch 00320: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5442 - val_acc: 0.8263\n",
      "Epoch 322/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00321: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9988 - val_loss: 1.5466 - val_acc: 0.8251\n",
      "Epoch 323/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00322: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5471 - val_acc: 0.8263\n",
      "Epoch 324/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00323: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5437 - val_acc: 0.8251\n",
      "Epoch 325/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9989Epoch 00324: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9990 - val_loss: 1.5373 - val_acc: 0.8251\n",
      "Epoch 326/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9989Epoch 00325: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9990 - val_loss: 1.5530 - val_acc: 0.8263\n",
      "Epoch 327/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9985Epoch 00326: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9985 - val_loss: 1.5400 - val_acc: 0.8275\n",
      "Epoch 328/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00327: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9985 - val_loss: 1.5410 - val_acc: 0.8263\n",
      "Epoch 329/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9989Epoch 00328: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9990 - val_loss: 1.5495 - val_acc: 0.8251\n",
      "Epoch 330/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9988Epoch 00329: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5432 - val_acc: 0.8275\n",
      "Epoch 331/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9989Epoch 00330: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9990 - val_loss: 1.5414 - val_acc: 0.8275\n",
      "Epoch 332/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9988Epoch 00331: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9988 - val_loss: 1.5481 - val_acc: 0.8275\n",
      "Epoch 333/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9989Epoch 00332: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9990 - val_loss: 1.5509 - val_acc: 0.8263\n",
      "Epoch 334/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9988Epoch 00333: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0105 - acc: 0.9988 - val_loss: 1.5502 - val_acc: 0.8251\n",
      "Epoch 335/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0108 - acc: 0.9989Epoch 00334: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0106 - acc: 0.9990 - val_loss: 1.5474 - val_acc: 0.8263\n",
      "Epoch 336/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9989Epoch 00335: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9990 - val_loss: 1.5465 - val_acc: 0.8275\n",
      "Epoch 337/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9986Epoch 00336: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9987 - val_loss: 1.5481 - val_acc: 0.8275\n",
      "Epoch 338/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00337: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5426 - val_acc: 0.8275\n",
      "Epoch 339/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9988Epoch 00338: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9988 - val_loss: 1.5530 - val_acc: 0.8275\n",
      "Epoch 340/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00339: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5493 - val_acc: 0.8263\n",
      "Epoch 341/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00340: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5593 - val_acc: 0.8251\n",
      "Epoch 342/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9986Epoch 00341: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9987 - val_loss: 1.5488 - val_acc: 0.8251\n",
      "Epoch 343/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00342: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9987 - val_loss: 1.5509 - val_acc: 0.8251\n",
      "Epoch 344/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9988Epoch 00343: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0105 - acc: 0.9988 - val_loss: 1.5462 - val_acc: 0.8263\n",
      "Epoch 345/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9986Epoch 00344: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9987 - val_loss: 1.5543 - val_acc: 0.8240\n",
      "Epoch 346/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9988Epoch 00345: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9988 - val_loss: 1.5439 - val_acc: 0.8275\n",
      "Epoch 347/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00346: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5506 - val_acc: 0.8263\n",
      "Epoch 348/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9988Epoch 00347: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9988 - val_loss: 1.5513 - val_acc: 0.8251\n",
      "Epoch 349/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9986Epoch 00348: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9987 - val_loss: 1.5490 - val_acc: 0.8275\n",
      "Epoch 350/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00349: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5452 - val_acc: 0.8275\n",
      "Epoch 351/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9988Epoch 00350: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5513 - val_acc: 0.8275\n",
      "Epoch 352/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00351: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5399 - val_acc: 0.8251\n",
      "Epoch 353/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9989Epoch 00352: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9990 - val_loss: 1.5422 - val_acc: 0.8263\n",
      "Epoch 354/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9986Epoch 00353: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9987 - val_loss: 1.5427 - val_acc: 0.8275\n",
      "Epoch 355/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9989Epoch 00354: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9990 - val_loss: 1.5522 - val_acc: 0.8251\n",
      "Epoch 356/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9989Epoch 00355: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9990 - val_loss: 1.5548 - val_acc: 0.8251\n",
      "Epoch 357/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9988Epoch 00356: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9988 - val_loss: 1.5502 - val_acc: 0.8263\n",
      "Epoch 358/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9989Epoch 00357: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9990 - val_loss: 1.5493 - val_acc: 0.8251\n",
      "Epoch 359/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9986Epoch 00358: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9987 - val_loss: 1.5503 - val_acc: 0.8275\n",
      "Epoch 360/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00359: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5503 - val_acc: 0.8263\n",
      "Epoch 361/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9988Epoch 00360: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0105 - acc: 0.9988 - val_loss: 1.5451 - val_acc: 0.8275\n",
      "Epoch 362/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9988Epoch 00361: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9988 - val_loss: 1.5442 - val_acc: 0.8263\n",
      "Epoch 363/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00362: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5463 - val_acc: 0.8263\n",
      "Epoch 364/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00363: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9987 - val_loss: 1.5499 - val_acc: 0.8263\n",
      "Epoch 365/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9985Epoch 00364: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9985 - val_loss: 1.5534 - val_acc: 0.8263\n",
      "Epoch 366/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9989Epoch 00365: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9990 - val_loss: 1.5573 - val_acc: 0.8263\n",
      "Epoch 367/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00366: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5466 - val_acc: 0.8263\n",
      "Epoch 368/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9988Epoch 00367: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9988 - val_loss: 1.5563 - val_acc: 0.8263\n",
      "Epoch 369/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9988Epoch 00368: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9988 - val_loss: 1.5506 - val_acc: 0.8251\n",
      "Epoch 370/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9988Epoch 00369: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9988 - val_loss: 1.5517 - val_acc: 0.8251\n",
      "Epoch 371/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9985Epoch 00370: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9985 - val_loss: 1.5567 - val_acc: 0.8263\n",
      "Epoch 372/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00371: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5502 - val_acc: 0.8263\n",
      "Epoch 373/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9988Epoch 00372: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5515 - val_acc: 0.8275\n",
      "Epoch 374/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9986Epoch 00373: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0105 - acc: 0.9987 - val_loss: 1.5485 - val_acc: 0.8263\n",
      "Epoch 375/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00374: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5512 - val_acc: 0.8251\n",
      "Epoch 376/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9989Epoch 00375: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9990 - val_loss: 1.5628 - val_acc: 0.8240\n",
      "Epoch 377/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9989Epoch 00376: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5510 - val_acc: 0.8263\n",
      "Epoch 378/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9985Epoch 00377: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9985 - val_loss: 1.5514 - val_acc: 0.8263\n",
      "Epoch 379/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00378: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5487 - val_acc: 0.8263\n",
      "Epoch 380/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9989Epoch 00379: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9990 - val_loss: 1.5499 - val_acc: 0.8263\n",
      "Epoch 381/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00380: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5515 - val_acc: 0.8251\n",
      "Epoch 382/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00381: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5511 - val_acc: 0.8251\n",
      "Epoch 383/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00382: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5584 - val_acc: 0.8251\n",
      "Epoch 384/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00383: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9987 - val_loss: 1.5537 - val_acc: 0.8251\n",
      "Epoch 385/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00384: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5544 - val_acc: 0.8251\n",
      "Epoch 386/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0076 - acc: 0.9988Epoch 00385: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5527 - val_acc: 0.8275\n",
      "Epoch 387/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0108 - acc: 0.9989Epoch 00386: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0107 - acc: 0.9990 - val_loss: 1.5502 - val_acc: 0.8275\n",
      "Epoch 388/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9989Epoch 00387: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9990 - val_loss: 1.5613 - val_acc: 0.8251\n",
      "Epoch 389/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9988Epoch 00388: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9988 - val_loss: 1.5507 - val_acc: 0.8263\n",
      "Epoch 390/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9986Epoch 00389: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9987 - val_loss: 1.5567 - val_acc: 0.8263\n",
      "Epoch 391/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9986Epoch 00390: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9987 - val_loss: 1.5518 - val_acc: 0.8251\n",
      "Epoch 392/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00391: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9988 - val_loss: 1.5527 - val_acc: 0.8263\n",
      "Epoch 393/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9985Epoch 00392: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9985 - val_loss: 1.5492 - val_acc: 0.8263\n",
      "Epoch 394/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9985Epoch 00393: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0105 - acc: 0.9985 - val_loss: 1.5487 - val_acc: 0.8251\n",
      "Epoch 395/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00394: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5558 - val_acc: 0.8251\n",
      "Epoch 396/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9985Epoch 00395: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9985 - val_loss: 1.5541 - val_acc: 0.8263\n",
      "Epoch 397/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9988Epoch 00396: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9988 - val_loss: 1.5654 - val_acc: 0.8240\n",
      "Epoch 398/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00397: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5632 - val_acc: 0.8251\n",
      "Epoch 399/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9988Epoch 00398: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9988 - val_loss: 1.5593 - val_acc: 0.8251\n",
      "Epoch 400/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9989Epoch 00399: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9990 - val_loss: 1.5524 - val_acc: 0.8251\n",
      "Epoch 401/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00400: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5581 - val_acc: 0.8251\n",
      "Epoch 402/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00401: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5520 - val_acc: 0.8263\n",
      "Epoch 403/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00402: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5516 - val_acc: 0.8263\n",
      "Epoch 404/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00403: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5608 - val_acc: 0.8263\n",
      "Epoch 405/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9988Epoch 00404: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9988 - val_loss: 1.5622 - val_acc: 0.8263\n",
      "Epoch 406/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9988Epoch 00405: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9988 - val_loss: 1.5566 - val_acc: 0.8275\n",
      "Epoch 407/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9988Epoch 00406: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9988 - val_loss: 1.5585 - val_acc: 0.8263\n",
      "Epoch 408/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9988Epoch 00407: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9988 - val_loss: 1.5585 - val_acc: 0.8263\n",
      "Epoch 409/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00408: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5523 - val_acc: 0.8263\n",
      "Epoch 410/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00409: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5525 - val_acc: 0.8263\n",
      "Epoch 411/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9989Epoch 00410: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0106 - acc: 0.9990 - val_loss: 1.5581 - val_acc: 0.8263\n",
      "Epoch 412/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00411: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5527 - val_acc: 0.8275\n",
      "Epoch 413/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00412: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5575 - val_acc: 0.8275\n",
      "Epoch 414/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9989Epoch 00413: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9990 - val_loss: 1.5549 - val_acc: 0.8263\n",
      "Epoch 415/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9988Epoch 00414: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9988 - val_loss: 1.5635 - val_acc: 0.8251\n",
      "Epoch 416/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9986Epoch 00415: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5602 - val_acc: 0.8263\n",
      "Epoch 417/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9988Epoch 00416: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5541 - val_acc: 0.8263\n",
      "Epoch 418/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0090 - acc: 0.9989Epoch 00417: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9988 - val_loss: 1.5532 - val_acc: 0.8263\n",
      "Epoch 419/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00418: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5601 - val_acc: 0.8263\n",
      "Epoch 420/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9989Epoch 00419: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9990 - val_loss: 1.5594 - val_acc: 0.8263\n",
      "Epoch 421/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9989Epoch 00420: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9990 - val_loss: 1.5523 - val_acc: 0.8263\n",
      "Epoch 422/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9988Epoch 00421: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5517 - val_acc: 0.8263\n",
      "Epoch 423/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0107 - acc: 0.9989Epoch 00422: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0105 - acc: 0.9990 - val_loss: 1.5558 - val_acc: 0.8263\n",
      "Epoch 424/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9986Epoch 00423: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0104 - acc: 0.9987 - val_loss: 1.5566 - val_acc: 0.8263\n",
      "Epoch 425/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00424: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5552 - val_acc: 0.8263\n",
      "Epoch 426/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9989Epoch 00425: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9990 - val_loss: 1.5569 - val_acc: 0.8263\n",
      "Epoch 427/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9991Epoch 00426: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9990 - val_loss: 1.5596 - val_acc: 0.8263\n",
      "Epoch 428/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00427: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5669 - val_acc: 0.8251\n",
      "Epoch 429/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9989Epoch 00428: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9990 - val_loss: 1.5601 - val_acc: 0.8263\n",
      "Epoch 430/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9985Epoch 00429: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9985 - val_loss: 1.5629 - val_acc: 0.8263\n",
      "Epoch 431/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00430: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5546 - val_acc: 0.8263\n",
      "Epoch 432/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9986Epoch 00431: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5617 - val_acc: 0.8251\n",
      "Epoch 433/800\n",
      "6560/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9989Epoch 00432: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5635 - val_acc: 0.8251\n",
      "Epoch 434/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00433: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9985 - val_loss: 1.5588 - val_acc: 0.8263\n",
      "Epoch 435/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9988Epoch 00434: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5586 - val_acc: 0.8263\n",
      "Epoch 436/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9986Epoch 00435: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5529 - val_acc: 0.8263\n",
      "Epoch 437/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9986Epoch 00436: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5552 - val_acc: 0.8263\n",
      "Epoch 438/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00437: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5583 - val_acc: 0.8263\n",
      "Epoch 439/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9989Epoch 00438: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9990 - val_loss: 1.5541 - val_acc: 0.8251\n",
      "Epoch 440/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9985Epoch 00439: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9985 - val_loss: 1.5576 - val_acc: 0.8263\n",
      "Epoch 441/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00440: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5588 - val_acc: 0.8263\n",
      "Epoch 442/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9988Epoch 00441: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5628 - val_acc: 0.8263\n",
      "Epoch 443/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9989Epoch 00442: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9990 - val_loss: 1.5636 - val_acc: 0.8263\n",
      "Epoch 444/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00443: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5578 - val_acc: 0.8263\n",
      "Epoch 445/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00444: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5572 - val_acc: 0.8263\n",
      "Epoch 446/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00445: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5687 - val_acc: 0.8251\n",
      "Epoch 447/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9989Epoch 00446: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5608 - val_acc: 0.8263\n",
      "Epoch 448/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00447: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5593 - val_acc: 0.8263\n",
      "Epoch 449/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00448: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5573 - val_acc: 0.8263\n",
      "Epoch 450/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00449: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5600 - val_acc: 0.8263\n",
      "Epoch 451/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00450: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5605 - val_acc: 0.8263\n",
      "Epoch 452/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9988Epoch 00451: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9988 - val_loss: 1.5593 - val_acc: 0.8263\n",
      "Epoch 453/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9985Epoch 00452: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9985 - val_loss: 1.5619 - val_acc: 0.8263\n",
      "Epoch 454/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9988Epoch 00453: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9988 - val_loss: 1.5617 - val_acc: 0.8251\n",
      "Epoch 455/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9989Epoch 00454: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5600 - val_acc: 0.8263\n",
      "Epoch 456/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00455: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5597 - val_acc: 0.8263\n",
      "Epoch 457/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00456: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5612 - val_acc: 0.8263\n",
      "Epoch 458/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00457: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5598 - val_acc: 0.8263\n",
      "Epoch 459/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9989Epoch 00458: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9990 - val_loss: 1.5622 - val_acc: 0.8263\n",
      "Epoch 460/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00459: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5680 - val_acc: 0.8251\n",
      "Epoch 461/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00460: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5646 - val_acc: 0.8263\n",
      "Epoch 462/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00461: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5610 - val_acc: 0.8263\n",
      "Epoch 463/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9985Epoch 00462: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9985 - val_loss: 1.5611 - val_acc: 0.8263\n",
      "Epoch 464/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9989Epoch 00463: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9990 - val_loss: 1.5590 - val_acc: 0.8275\n",
      "Epoch 465/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00464: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5594 - val_acc: 0.8263\n",
      "Epoch 466/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00465: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5691 - val_acc: 0.8251\n",
      "Epoch 467/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9989Epoch 00466: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9990 - val_loss: 1.5600 - val_acc: 0.8263\n",
      "Epoch 468/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00467: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5658 - val_acc: 0.8263\n",
      "Epoch 469/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00468: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5694 - val_acc: 0.8263\n",
      "Epoch 470/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9989Epoch 00469: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9990 - val_loss: 1.5641 - val_acc: 0.8263\n",
      "Epoch 471/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9988Epoch 00470: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5676 - val_acc: 0.8263\n",
      "Epoch 472/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00471: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5658 - val_acc: 0.8263\n",
      "Epoch 473/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00472: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5572 - val_acc: 0.8275\n",
      "Epoch 474/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00473: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5652 - val_acc: 0.8263\n",
      "Epoch 475/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0089 - acc: 0.9989Epoch 00474: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9988 - val_loss: 1.5568 - val_acc: 0.8275\n",
      "Epoch 476/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9985Epoch 00475: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9985 - val_loss: 1.5583 - val_acc: 0.8263\n",
      "Epoch 477/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00476: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5610 - val_acc: 0.8263\n",
      "Epoch 478/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9985Epoch 00477: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9985 - val_loss: 1.5602 - val_acc: 0.8263\n",
      "Epoch 479/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00478: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5613 - val_acc: 0.8263\n",
      "Epoch 480/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9985Epoch 00479: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9985 - val_loss: 1.5584 - val_acc: 0.8275\n",
      "Epoch 481/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00480: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5600 - val_acc: 0.8263\n",
      "Epoch 482/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9989Epoch 00481: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9990 - val_loss: 1.5625 - val_acc: 0.8263\n",
      "Epoch 483/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00482: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9987 - val_loss: 1.5652 - val_acc: 0.8263\n",
      "Epoch 484/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00483: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5664 - val_acc: 0.8263\n",
      "Epoch 485/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9985Epoch 00484: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9985 - val_loss: 1.5647 - val_acc: 0.8263\n",
      "Epoch 486/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00485: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5680 - val_acc: 0.8263\n",
      "Epoch 487/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9988Epoch 00486: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5614 - val_acc: 0.8275\n",
      "Epoch 488/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9989Epoch 00487: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9990 - val_loss: 1.5703 - val_acc: 0.8263\n",
      "Epoch 489/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00488: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9988 - val_loss: 1.5680 - val_acc: 0.8263\n",
      "Epoch 490/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9986Epoch 00489: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5716 - val_acc: 0.8263\n",
      "Epoch 491/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00490: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5630 - val_acc: 0.8275\n",
      "Epoch 492/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00491: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5603 - val_acc: 0.8263\n",
      "Epoch 493/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9989Epoch 00492: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9990 - val_loss: 1.5650 - val_acc: 0.8263\n",
      "Epoch 494/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00493: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5713 - val_acc: 0.8263\n",
      "Epoch 495/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9986Epoch 00494: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5717 - val_acc: 0.8263\n",
      "Epoch 496/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0088 - acc: 0.9989Epoch 00495: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9988 - val_loss: 1.5662 - val_acc: 0.8263\n",
      "Epoch 497/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9988Epoch 00496: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0101 - acc: 0.9988 - val_loss: 1.5658 - val_acc: 0.8263\n",
      "Epoch 498/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9985Epoch 00497: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9985 - val_loss: 1.5668 - val_acc: 0.8263\n",
      "Epoch 499/800\n",
      "6560/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00498: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9988 - val_loss: 1.5760 - val_acc: 0.8263\n",
      "Epoch 500/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00499: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5679 - val_acc: 0.8263\n",
      "Epoch 501/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9989Epoch 00500: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9990 - val_loss: 1.5694 - val_acc: 0.8263\n",
      "Epoch 502/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00501: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5643 - val_acc: 0.8263\n",
      "Epoch 503/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00502: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5615 - val_acc: 0.8263\n",
      "Epoch 504/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00503: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5625 - val_acc: 0.8263\n",
      "Epoch 505/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00504: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5648 - val_acc: 0.8263\n",
      "Epoch 506/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00505: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5740 - val_acc: 0.8263\n",
      "Epoch 507/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00506: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5693 - val_acc: 0.8263\n",
      "Epoch 508/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00507: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5709 - val_acc: 0.8263\n",
      "Epoch 509/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00508: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9987 - val_loss: 1.5702 - val_acc: 0.8263\n",
      "Epoch 510/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9985Epoch 00509: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9985 - val_loss: 1.5680 - val_acc: 0.8263\n",
      "Epoch 511/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9989Epoch 00510: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9990 - val_loss: 1.5691 - val_acc: 0.8263\n",
      "Epoch 512/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00511: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5703 - val_acc: 0.8275\n",
      "Epoch 513/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00512: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5697 - val_acc: 0.8263\n",
      "Epoch 514/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9985Epoch 00513: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9985 - val_loss: 1.5715 - val_acc: 0.8275\n",
      "Epoch 515/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00514: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5735 - val_acc: 0.8275\n",
      "Epoch 516/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9985Epoch 00515: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9985 - val_loss: 1.5756 - val_acc: 0.8263\n",
      "Epoch 517/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9985Epoch 00516: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9985 - val_loss: 1.5663 - val_acc: 0.8263\n",
      "Epoch 518/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9989Epoch 00517: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5720 - val_acc: 0.8263\n",
      "Epoch 519/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00518: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5697 - val_acc: 0.8263\n",
      "Epoch 520/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0076 - acc: 0.9989Epoch 00519: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9988 - val_loss: 1.5704 - val_acc: 0.8263\n",
      "Epoch 521/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00520: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5704 - val_acc: 0.8275\n",
      "Epoch 522/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00521: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5729 - val_acc: 0.8263\n",
      "Epoch 523/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9986Epoch 00522: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0102 - acc: 0.9987 - val_loss: 1.5702 - val_acc: 0.8275\n",
      "Epoch 524/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9989Epoch 00523: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0100 - acc: 0.9990 - val_loss: 1.5679 - val_acc: 0.8275\n",
      "Epoch 525/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9985Epoch 00524: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9985 - val_loss: 1.5669 - val_acc: 0.8287\n",
      "Epoch 526/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9989Epoch 00525: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0103 - acc: 0.9990 - val_loss: 1.5711 - val_acc: 0.8275\n",
      "Epoch 527/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00526: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5710 - val_acc: 0.8275\n",
      "Epoch 528/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9985Epoch 00527: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9985 - val_loss: 1.5716 - val_acc: 0.8275\n",
      "Epoch 529/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00528: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5738 - val_acc: 0.8275\n",
      "Epoch 530/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00529: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5701 - val_acc: 0.8275\n",
      "Epoch 531/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00530: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5699 - val_acc: 0.8275\n",
      "Epoch 532/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00531: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5717 - val_acc: 0.8275\n",
      "Epoch 533/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9989Epoch 00532: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9988 - val_loss: 1.5729 - val_acc: 0.8275\n",
      "Epoch 534/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9989Epoch 00533: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9990 - val_loss: 1.5706 - val_acc: 0.8275\n",
      "Epoch 535/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9986Epoch 00534: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5751 - val_acc: 0.8275\n",
      "Epoch 536/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9986Epoch 00535: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9987 - val_loss: 1.5731 - val_acc: 0.8275\n",
      "Epoch 537/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9985Epoch 00536: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9985 - val_loss: 1.5647 - val_acc: 0.8275\n",
      "Epoch 538/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00537: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9987 - val_loss: 1.5639 - val_acc: 0.8275\n",
      "Epoch 539/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9989Epoch 00538: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5673 - val_acc: 0.8275\n",
      "Epoch 540/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00539: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5700 - val_acc: 0.8275\n",
      "Epoch 541/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9986Epoch 00540: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5674 - val_acc: 0.8275\n",
      "Epoch 542/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00541: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5652 - val_acc: 0.8275\n",
      "Epoch 543/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9985Epoch 00542: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9985 - val_loss: 1.5672 - val_acc: 0.8275\n",
      "Epoch 544/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9989Epoch 00543: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9990 - val_loss: 1.5656 - val_acc: 0.8275\n",
      "Epoch 545/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9985Epoch 00544: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9985 - val_loss: 1.5737 - val_acc: 0.8275\n",
      "Epoch 546/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00545: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5708 - val_acc: 0.8275\n",
      "Epoch 547/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9985Epoch 00546: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9985 - val_loss: 1.5648 - val_acc: 0.8275\n",
      "Epoch 548/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00547: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5637 - val_acc: 0.8287\n",
      "Epoch 549/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00548: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5623 - val_acc: 0.8275\n",
      "Epoch 550/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00549: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5657 - val_acc: 0.8275\n",
      "Epoch 551/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00550: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5682 - val_acc: 0.8275\n",
      "Epoch 552/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00551: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5729 - val_acc: 0.8275\n",
      "Epoch 553/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00552: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5729 - val_acc: 0.8275\n",
      "Epoch 554/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9988Epoch 00553: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0099 - acc: 0.9988 - val_loss: 1.5636 - val_acc: 0.8275\n",
      "Epoch 555/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9986Epoch 00554: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5659 - val_acc: 0.8287\n",
      "Epoch 556/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00555: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5689 - val_acc: 0.8275\n",
      "Epoch 557/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00556: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5692 - val_acc: 0.8275\n",
      "Epoch 558/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00557: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5684 - val_acc: 0.8287\n",
      "Epoch 559/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9985Epoch 00558: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9985 - val_loss: 1.5739 - val_acc: 0.8275\n",
      "Epoch 560/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00559: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5759 - val_acc: 0.8275\n",
      "Epoch 561/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00560: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5732 - val_acc: 0.8275\n",
      "Epoch 562/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9985Epoch 00561: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9985 - val_loss: 1.5726 - val_acc: 0.8275\n",
      "Epoch 563/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00562: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5705 - val_acc: 0.8287\n",
      "Epoch 564/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9988Epoch 00563: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5741 - val_acc: 0.8275\n",
      "Epoch 565/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9988Epoch 00564: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5720 - val_acc: 0.8275\n",
      "Epoch 566/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00565: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5715 - val_acc: 0.8287\n",
      "Epoch 567/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9989Epoch 00566: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9990 - val_loss: 1.5723 - val_acc: 0.8287\n",
      "Epoch 568/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9989Epoch 00567: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9990 - val_loss: 1.5719 - val_acc: 0.8287\n",
      "Epoch 569/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9986Epoch 00568: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9987 - val_loss: 1.5722 - val_acc: 0.8287\n",
      "Epoch 570/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9989Epoch 00569: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9988 - val_loss: 1.5734 - val_acc: 0.8287\n",
      "Epoch 571/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00570: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5708 - val_acc: 0.8299\n",
      "Epoch 572/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0068 - acc: 0.9991Epoch 00571: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5692 - val_acc: 0.8287\n",
      "Epoch 573/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00572: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5704 - val_acc: 0.8275\n",
      "Epoch 574/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9989Epoch 00573: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9990 - val_loss: 1.5729 - val_acc: 0.8275\n",
      "Epoch 575/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00574: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5674 - val_acc: 0.8287\n",
      "Epoch 576/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9986Epoch 00575: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0098 - acc: 0.9987 - val_loss: 1.5673 - val_acc: 0.8275\n",
      "Epoch 577/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9985Epoch 00576: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9985 - val_loss: 1.5706 - val_acc: 0.8275\n",
      "Epoch 578/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9988Epoch 00577: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5694 - val_acc: 0.8287\n",
      "Epoch 579/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00578: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5678 - val_acc: 0.8287\n",
      "Epoch 580/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00579: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5700 - val_acc: 0.8299\n",
      "Epoch 581/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9985Epoch 00580: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9985 - val_loss: 1.5677 - val_acc: 0.8299\n",
      "Epoch 582/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00581: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5756 - val_acc: 0.8287\n",
      "Epoch 583/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00582: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9988 - val_loss: 1.5754 - val_acc: 0.8287\n",
      "Epoch 584/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9988Epoch 00583: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9988 - val_loss: 1.5713 - val_acc: 0.8287\n",
      "Epoch 585/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0070 - acc: 0.9986Epoch 00584: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9985 - val_loss: 1.5747 - val_acc: 0.8287\n",
      "Epoch 586/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9985Epoch 00585: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9985 - val_loss: 1.5712 - val_acc: 0.8287\n",
      "Epoch 587/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00586: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5691 - val_acc: 0.8299\n",
      "Epoch 588/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00587: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5717 - val_acc: 0.8287\n",
      "Epoch 589/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9989Epoch 00588: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5742 - val_acc: 0.8287\n",
      "Epoch 590/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0091 - acc: 0.9985Epoch 00589: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9985 - val_loss: 1.5700 - val_acc: 0.8287\n",
      "Epoch 591/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00590: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5676 - val_acc: 0.8299\n",
      "Epoch 592/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00591: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5675 - val_acc: 0.8299\n",
      "Epoch 593/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00592: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5679 - val_acc: 0.8299\n",
      "Epoch 594/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0073 - acc: 0.9988Epoch 00593: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5744 - val_acc: 0.8287\n",
      "Epoch 595/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0065 - acc: 0.9988Epoch 00594: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5731 - val_acc: 0.8287\n",
      "Epoch 596/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00595: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9988 - val_loss: 1.5726 - val_acc: 0.8299\n",
      "Epoch 597/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0090 - acc: 0.9989Epoch 00596: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5696 - val_acc: 0.8299\n",
      "Epoch 598/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00597: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9987 - val_loss: 1.5715 - val_acc: 0.8299\n",
      "Epoch 599/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00598: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5709 - val_acc: 0.8299\n",
      "Epoch 600/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00599: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5775 - val_acc: 0.8287\n",
      "Epoch 601/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0089 - acc: 0.9988Epoch 00600: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5760 - val_acc: 0.8287\n",
      "Epoch 602/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00601: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5709 - val_acc: 0.8287\n",
      "Epoch 603/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9985Epoch 00602: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9985 - val_loss: 1.5732 - val_acc: 0.8299\n",
      "Epoch 604/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00603: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5751 - val_acc: 0.8299\n",
      "Epoch 605/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00604: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5725 - val_acc: 0.8311\n",
      "Epoch 606/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00605: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9985 - val_loss: 1.5741 - val_acc: 0.8311\n",
      "Epoch 607/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00606: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5731 - val_acc: 0.8311\n",
      "Epoch 608/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00607: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5717 - val_acc: 0.8311\n",
      "Epoch 609/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00608: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5746 - val_acc: 0.8299\n",
      "Epoch 610/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00609: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5733 - val_acc: 0.8299\n",
      "Epoch 611/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00610: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5711 - val_acc: 0.8311\n",
      "Epoch 612/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00611: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5707 - val_acc: 0.8299\n",
      "Epoch 613/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9985Epoch 00612: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9985 - val_loss: 1.5707 - val_acc: 0.8311\n",
      "Epoch 614/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9985Epoch 00613: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0097 - acc: 0.9985 - val_loss: 1.5726 - val_acc: 0.8311\n",
      "Epoch 615/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00614: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5706 - val_acc: 0.8311\n",
      "Epoch 616/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9985Epoch 00615: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9985 - val_loss: 1.5665 - val_acc: 0.8311\n",
      "Epoch 617/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00616: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5672 - val_acc: 0.8311\n",
      "Epoch 618/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9986Epoch 00617: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9985 - val_loss: 1.5733 - val_acc: 0.8299\n",
      "Epoch 619/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00618: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5768 - val_acc: 0.8299\n",
      "Epoch 620/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00619: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5725 - val_acc: 0.8299\n",
      "Epoch 621/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00620: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5723 - val_acc: 0.8311\n",
      "Epoch 622/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9985Epoch 00621: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9985 - val_loss: 1.5713 - val_acc: 0.8311\n",
      "Epoch 623/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9988Epoch 00622: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5714 - val_acc: 0.8311\n",
      "Epoch 624/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00623: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5731 - val_acc: 0.8287\n",
      "Epoch 625/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9985Epoch 00624: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9985 - val_loss: 1.5738 - val_acc: 0.8311\n",
      "Epoch 626/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00625: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5765 - val_acc: 0.8311\n",
      "Epoch 627/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9985Epoch 00626: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9985 - val_loss: 1.5743 - val_acc: 0.8299\n",
      "Epoch 628/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00627: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5702 - val_acc: 0.8311\n",
      "Epoch 629/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9985Epoch 00628: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9985 - val_loss: 1.5747 - val_acc: 0.8311\n",
      "Epoch 630/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9989Epoch 00629: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9990 - val_loss: 1.5736 - val_acc: 0.8299\n",
      "Epoch 631/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00630: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5740 - val_acc: 0.8311\n",
      "Epoch 632/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00631: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5763 - val_acc: 0.8311\n",
      "Epoch 633/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00632: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5738 - val_acc: 0.8311\n",
      "Epoch 634/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00633: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5773 - val_acc: 0.8299\n",
      "Epoch 635/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00634: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5741 - val_acc: 0.8299\n",
      "Epoch 636/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00635: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5758 - val_acc: 0.8299\n",
      "Epoch 637/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00636: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5744 - val_acc: 0.8311\n",
      "Epoch 638/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00637: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5744 - val_acc: 0.8299\n",
      "Epoch 639/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00638: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5747 - val_acc: 0.8311\n",
      "Epoch 640/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9988Epoch 00639: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5739 - val_acc: 0.8299\n",
      "Epoch 641/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00640: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5709 - val_acc: 0.8311\n",
      "Epoch 642/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00641: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5747 - val_acc: 0.8299\n",
      "Epoch 643/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00642: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5750 - val_acc: 0.8311\n",
      "Epoch 644/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00643: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5762 - val_acc: 0.8311\n",
      "Epoch 645/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00644: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5754 - val_acc: 0.8311\n",
      "Epoch 646/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00645: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9985 - val_loss: 1.5782 - val_acc: 0.8299\n",
      "Epoch 647/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00646: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5756 - val_acc: 0.8311\n",
      "Epoch 648/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00647: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5763 - val_acc: 0.8311\n",
      "Epoch 649/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00648: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0096 - acc: 0.9988 - val_loss: 1.5734 - val_acc: 0.8311\n",
      "Epoch 650/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0070 - acc: 0.9989Epoch 00649: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5713 - val_acc: 0.8311\n",
      "Epoch 651/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0088 - acc: 0.9988Epoch 00650: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5752 - val_acc: 0.8311\n",
      "Epoch 652/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9986Epoch 00651: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9985 - val_loss: 1.5773 - val_acc: 0.8311\n",
      "Epoch 653/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00652: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5759 - val_acc: 0.8311\n",
      "Epoch 654/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0090 - acc: 0.9988Epoch 00653: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5769 - val_acc: 0.8299\n",
      "Epoch 655/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9986Epoch 00654: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5762 - val_acc: 0.8299\n",
      "Epoch 656/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00655: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5763 - val_acc: 0.8299\n",
      "Epoch 657/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00656: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5765 - val_acc: 0.8299\n",
      "Epoch 658/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00657: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5721 - val_acc: 0.8311\n",
      "Epoch 659/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00658: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5761 - val_acc: 0.8311\n",
      "Epoch 660/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00659: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5736 - val_acc: 0.8311\n",
      "Epoch 661/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00660: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5746 - val_acc: 0.8311\n",
      "Epoch 662/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0090 - acc: 0.9988Epoch 00661: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5722 - val_acc: 0.8311\n",
      "Epoch 663/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00662: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5809 - val_acc: 0.8299\n",
      "Epoch 664/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9985Epoch 00663: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5782 - val_acc: 0.8299\n",
      "Epoch 665/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9989Epoch 00664: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9990 - val_loss: 1.5779 - val_acc: 0.8299\n",
      "Epoch 666/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0068 - acc: 0.9988Epoch 00665: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5757 - val_acc: 0.8299\n",
      "Epoch 667/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00666: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5745 - val_acc: 0.8311\n",
      "Epoch 668/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9985Epoch 00667: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9985 - val_loss: 1.5778 - val_acc: 0.8299\n",
      "Epoch 669/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00668: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5750 - val_acc: 0.8311\n",
      "Epoch 670/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9989Epoch 00669: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9990 - val_loss: 1.5747 - val_acc: 0.8311\n",
      "Epoch 671/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00670: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5745 - val_acc: 0.8311\n",
      "Epoch 672/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00671: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5769 - val_acc: 0.8299\n",
      "Epoch 673/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00672: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5763 - val_acc: 0.8311\n",
      "Epoch 674/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00673: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5771 - val_acc: 0.8311\n",
      "Epoch 675/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00674: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5767 - val_acc: 0.8311\n",
      "Epoch 676/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00675: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9988 - val_loss: 1.5744 - val_acc: 0.8311\n",
      "Epoch 677/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00676: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5752 - val_acc: 0.8311\n",
      "Epoch 678/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00677: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5747 - val_acc: 0.8323\n",
      "Epoch 679/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00678: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5760 - val_acc: 0.8311\n",
      "Epoch 680/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00679: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5751 - val_acc: 0.8311\n",
      "Epoch 681/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00680: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5756 - val_acc: 0.8311\n",
      "Epoch 682/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9989Epoch 00681: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9990 - val_loss: 1.5757 - val_acc: 0.8311\n",
      "Epoch 683/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0068 - acc: 0.9986Epoch 00682: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9985 - val_loss: 1.5738 - val_acc: 0.8311\n",
      "Epoch 684/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00683: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5740 - val_acc: 0.8311\n",
      "Epoch 685/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00684: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5746 - val_acc: 0.8311\n",
      "Epoch 686/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00685: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5783 - val_acc: 0.8299\n",
      "Epoch 687/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00686: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5747 - val_acc: 0.8311\n",
      "Epoch 688/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9989Epoch 00687: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9990 - val_loss: 1.5722 - val_acc: 0.8311\n",
      "Epoch 689/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9986Epoch 00688: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5741 - val_acc: 0.8311\n",
      "Epoch 690/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00689: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5753 - val_acc: 0.8311\n",
      "Epoch 691/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00690: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5765 - val_acc: 0.8311\n",
      "Epoch 692/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00691: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5699 - val_acc: 0.8311\n",
      "Epoch 693/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00692: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5754 - val_acc: 0.8299\n",
      "Epoch 694/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00693: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5749 - val_acc: 0.8299\n",
      "Epoch 695/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00694: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5728 - val_acc: 0.8311\n",
      "Epoch 696/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00695: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5706 - val_acc: 0.8311\n",
      "Epoch 697/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9989Epoch 00696: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9990 - val_loss: 1.5733 - val_acc: 0.8311\n",
      "Epoch 698/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0091 - acc: 0.9991Epoch 00697: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9990 - val_loss: 1.5706 - val_acc: 0.8323\n",
      "Epoch 699/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0089 - acc: 0.9988Epoch 00698: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5728 - val_acc: 0.8311\n",
      "Epoch 700/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9985Epoch 00699: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9985 - val_loss: 1.5748 - val_acc: 0.8311\n",
      "Epoch 701/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00700: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5742 - val_acc: 0.8299\n",
      "Epoch 702/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9989Epoch 00701: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9990 - val_loss: 1.5739 - val_acc: 0.8323\n",
      "Epoch 703/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00702: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5756 - val_acc: 0.8299\n",
      "Epoch 704/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9986Epoch 00703: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5742 - val_acc: 0.8311\n",
      "Epoch 705/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00704: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5729 - val_acc: 0.8311\n",
      "Epoch 706/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00705: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5754 - val_acc: 0.8299\n",
      "Epoch 707/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0071 - acc: 0.9988Epoch 00706: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5758 - val_acc: 0.8311\n",
      "Epoch 708/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00707: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5758 - val_acc: 0.8299\n",
      "Epoch 709/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00708: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5754 - val_acc: 0.8311\n",
      "Epoch 710/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00709: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5759 - val_acc: 0.8311\n",
      "Epoch 711/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00710: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5770 - val_acc: 0.8299\n",
      "Epoch 712/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00711: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9985 - val_loss: 1.5738 - val_acc: 0.8311\n",
      "Epoch 713/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00712: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5746 - val_acc: 0.8311\n",
      "Epoch 714/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00713: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9987 - val_loss: 1.5751 - val_acc: 0.8311\n",
      "Epoch 715/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9989Epoch 00714: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9990 - val_loss: 1.5756 - val_acc: 0.8311\n",
      "Epoch 716/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00715: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5741 - val_acc: 0.8299\n",
      "Epoch 717/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00716: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5774 - val_acc: 0.8299\n",
      "Epoch 718/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00717: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5780 - val_acc: 0.8299\n",
      "Epoch 719/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9988Epoch 00718: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9988 - val_loss: 1.5782 - val_acc: 0.8299\n",
      "Epoch 720/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00719: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9988 - val_loss: 1.5733 - val_acc: 0.8311\n",
      "Epoch 721/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9986Epoch 00720: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5783 - val_acc: 0.8299\n",
      "Epoch 722/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9986Epoch 00721: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5756 - val_acc: 0.8323\n",
      "Epoch 723/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00722: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5726 - val_acc: 0.8335\n",
      "Epoch 724/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9988Epoch 00723: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9988 - val_loss: 1.5726 - val_acc: 0.8311\n",
      "Epoch 725/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00724: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5749 - val_acc: 0.8311\n",
      "Epoch 726/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6560/6680 [============================>.] - ETA: 0s - loss: 0.0089 - acc: 0.9988Epoch 00725: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5741 - val_acc: 0.8299\n",
      "Epoch 727/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9983Epoch 00726: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9984 - val_loss: 1.5726 - val_acc: 0.8335\n",
      "Epoch 728/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9985Epoch 00727: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9985 - val_loss: 1.5719 - val_acc: 0.8323\n",
      "Epoch 729/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00728: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5742 - val_acc: 0.8311\n",
      "Epoch 730/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9985Epoch 00729: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9985 - val_loss: 1.5753 - val_acc: 0.8311\n",
      "Epoch 731/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0069 - acc: 0.9988Epoch 00730: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5769 - val_acc: 0.8323\n",
      "Epoch 732/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00731: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5754 - val_acc: 0.8311\n",
      "Epoch 733/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00732: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5716 - val_acc: 0.8335\n",
      "Epoch 734/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00733: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5730 - val_acc: 0.8335\n",
      "Epoch 735/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9986Epoch 00734: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9987 - val_loss: 1.5710 - val_acc: 0.8323\n",
      "Epoch 736/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00735: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5754 - val_acc: 0.8311\n",
      "Epoch 737/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00736: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5762 - val_acc: 0.8335\n",
      "Epoch 738/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00737: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5741 - val_acc: 0.8335\n",
      "Epoch 739/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00738: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5755 - val_acc: 0.8323\n",
      "Epoch 740/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9986Epoch 00739: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5757 - val_acc: 0.8311\n",
      "Epoch 741/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00740: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9988 - val_loss: 1.5740 - val_acc: 0.8335\n",
      "Epoch 742/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00741: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5750 - val_acc: 0.8323\n",
      "Epoch 743/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00742: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5762 - val_acc: 0.8323\n",
      "Epoch 744/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00743: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5755 - val_acc: 0.8323\n",
      "Epoch 745/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00744: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5770 - val_acc: 0.8299\n",
      "Epoch 746/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00745: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5767 - val_acc: 0.8299\n",
      "Epoch 747/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00746: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5721 - val_acc: 0.8323\n",
      "Epoch 748/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00747: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9987 - val_loss: 1.5747 - val_acc: 0.8335\n",
      "Epoch 749/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00748: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5792 - val_acc: 0.8311\n",
      "Epoch 750/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00749: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5751 - val_acc: 0.8323\n",
      "Epoch 751/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9988Epoch 00750: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9988 - val_loss: 1.5761 - val_acc: 0.8335\n",
      "Epoch 752/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9985Epoch 00751: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9985 - val_loss: 1.5761 - val_acc: 0.8335\n",
      "Epoch 753/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00752: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5771 - val_acc: 0.8311\n",
      "Epoch 754/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0088 - acc: 0.9988Epoch 00753: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5784 - val_acc: 0.8311\n",
      "Epoch 755/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9989Epoch 00754: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9990 - val_loss: 1.5756 - val_acc: 0.8299\n",
      "Epoch 756/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00755: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5756 - val_acc: 0.8323\n",
      "Epoch 757/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00756: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5754 - val_acc: 0.8335\n",
      "Epoch 758/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9989Epoch 00757: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9990 - val_loss: 1.5730 - val_acc: 0.8335\n",
      "Epoch 759/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9989Epoch 00758: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9990 - val_loss: 1.5729 - val_acc: 0.8347\n",
      "Epoch 760/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9989Epoch 00759: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9990 - val_loss: 1.5711 - val_acc: 0.8335\n",
      "Epoch 761/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00760: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5717 - val_acc: 0.8335\n",
      "Epoch 762/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9985Epoch 00761: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5719 - val_acc: 0.8335\n",
      "Epoch 763/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9988Epoch 00762: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5732 - val_acc: 0.8323\n",
      "Epoch 764/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00763: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5752 - val_acc: 0.8311\n",
      "Epoch 765/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9989Epoch 00764: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9990 - val_loss: 1.5737 - val_acc: 0.8323\n",
      "Epoch 766/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00765: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5726 - val_acc: 0.8347\n",
      "Epoch 767/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00766: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9988 - val_loss: 1.5718 - val_acc: 0.8323\n",
      "Epoch 768/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00767: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5725 - val_acc: 0.8335\n",
      "Epoch 769/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00768: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5760 - val_acc: 0.8335\n",
      "Epoch 770/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00769: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5774 - val_acc: 0.8323\n",
      "Epoch 771/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00770: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5771 - val_acc: 0.8323\n",
      "Epoch 772/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9986Epoch 00771: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5739 - val_acc: 0.8323\n",
      "Epoch 773/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00772: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5752 - val_acc: 0.8311\n",
      "Epoch 774/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00773: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5748 - val_acc: 0.8323\n",
      "Epoch 775/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00774: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5756 - val_acc: 0.8323\n",
      "Epoch 776/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00775: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5734 - val_acc: 0.8335\n",
      "Epoch 777/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9986Epoch 00776: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9987 - val_loss: 1.5745 - val_acc: 0.8335\n",
      "Epoch 778/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9986Epoch 00777: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9987 - val_loss: 1.5735 - val_acc: 0.8335\n",
      "Epoch 779/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00778: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5750 - val_acc: 0.8335\n",
      "Epoch 780/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00779: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5764 - val_acc: 0.8323\n",
      "Epoch 781/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9988Epoch 00780: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0095 - acc: 0.9988 - val_loss: 1.5745 - val_acc: 0.8335\n",
      "Epoch 782/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9986Epoch 00781: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9987 - val_loss: 1.5736 - val_acc: 0.8335\n",
      "Epoch 783/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00782: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5757 - val_acc: 0.8335\n",
      "Epoch 784/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0091 - acc: 0.9988Epoch 00783: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9988 - val_loss: 1.5756 - val_acc: 0.8323\n",
      "Epoch 785/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00784: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5794 - val_acc: 0.8323\n",
      "Epoch 786/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0071 - acc: 0.9989Epoch 00785: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9988 - val_loss: 1.5754 - val_acc: 0.8347\n",
      "Epoch 787/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0088 - acc: 0.9986Epoch 00786: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5777 - val_acc: 0.8335\n",
      "Epoch 788/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0095 - acc: 0.9989Epoch 00787: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0094 - acc: 0.9990 - val_loss: 1.5782 - val_acc: 0.8347\n",
      "Epoch 789/800\n",
      "6640/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00788: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5779 - val_acc: 0.8347\n",
      "Epoch 790/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0091 - acc: 0.9986Epoch 00789: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5768 - val_acc: 0.8347\n",
      "Epoch 791/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9988Epoch 00790: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9988 - val_loss: 1.5784 - val_acc: 0.8335\n",
      "Epoch 792/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00791: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5801 - val_acc: 0.8335\n",
      "Epoch 793/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9985Epoch 00792: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5771 - val_acc: 0.8323\n",
      "Epoch 794/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00793: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5746 - val_acc: 0.8347\n",
      "Epoch 795/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9985Epoch 00794: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5760 - val_acc: 0.8335\n",
      "Epoch 796/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9988Epoch 00795: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9988 - val_loss: 1.5767 - val_acc: 0.8335\n",
      "Epoch 797/800\n",
      "6660/6680 [============================>.] - ETA: 0s - loss: 0.0091 - acc: 0.9983Epoch 00796: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9984 - val_loss: 1.5754 - val_acc: 0.8335\n",
      "Epoch 798/800\n",
      "6620/6680 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9986Epoch 00797: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0091 - acc: 0.9987 - val_loss: 1.5744 - val_acc: 0.8335\n",
      "Epoch 799/800\n",
      "6580/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9985Epoch 00798: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0092 - acc: 0.9985 - val_loss: 1.5749 - val_acc: 0.8335\n",
      "Epoch 800/800\n",
      "6600/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9986Epoch 00799: val_loss did not improve\n",
      "6680/6680 [==============================] - 4s - loss: 0.0093 - acc: 0.9987 - val_loss: 1.5748 - val_acc: 0.8335\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f9e1c67cb00>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs = 800\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Xception.hdf5',\n",
    "                               verbose=1, save_best_only=True)\n",
    "\n",
    "Xception_model.fit(train_Xception, train_targets,\n",
    "          validation_data=(valid_Xception, valid_targets),\n",
    "          epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Load the Model with the Best Validation Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Load the model weights with the best validation loss.\n",
    "Xception_model.load_weights('saved_models/weights.best.Xception.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Test the Model\n",
    "\n",
    "Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy for Xception predictions: 84.2105%\n"
     ]
    }
   ],
   "source": [
    "### Calculate classification accuracy on the test dataset.\n",
    "\n",
    "#\n",
    "Xception_predictions = [np.argmax(Xception_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Xception]\n",
    "\n",
    "#\n",
    "test_accuracy = 100*np.sum(np.array(Xception_predictions) == np.argmax(test_targets, axis=1)) / len(Xception_predictions)\n",
    "\n",
    "print('Test accuracy for Xception predictions: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (IMPLEMENTATION) Predict Dog Breed with the Model\n",
    "\n",
    "Write a function that takes an image path as input and returns the dog breed (`Affenpinscher`, `Afghan_hound`, etc) that is predicted by your model.  \n",
    "\n",
    "Similar to the analogous function in Step 5, your function should have three steps:\n",
    "1. Extract the bottleneck features corresponding to the chosen CNN model.\n",
    "2. Supply the bottleneck features as input to the model to return the predicted vector.  Note that the argmax of this prediction vector gives the index of the predicted dog breed.\n",
    "3. Use the `dog_names` array defined in Step 0 of this notebook to return the corresponding breed.\n",
    "\n",
    "The functions to extract the bottleneck features can be found in `extract_bottleneck_features.py`, and they have been imported in an earlier code cell.  To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function\n",
    "\n",
    "    extract_{network}\n",
    "    \n",
    "where `{network}`, in the above filename, should be one of `VGG19`, `Resnet50`, `InceptionV3`, or `Xception`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Write a function that takes a path to an image as input\n",
    "### and returns the dog breed that is predicted by the model.\n",
    "\n",
    "def Xception_predict_breed(img_path):\n",
    "    # extract bottleneck features\n",
    "    bottleneck_feature = extract_Xception(path_to_tensor(img_path))\n",
    "    \n",
    "    # obtain predicted vector\n",
    "    predicted_vector = Xception_model.predict(bottleneck_feature)\n",
    "    \n",
    "    # return dog breed that is predicted by the model\n",
    "    return dog_names[np.argmax(predicted_vector)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step6'></a>\n",
    "## Step 6: Write your Algorithm\n",
    "\n",
    "Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither.  Then,\n",
    "- if a __dog__ is detected in the image, return the predicted breed.\n",
    "- if a __human__ is detected in the image, return the resembling dog breed.\n",
    "- if __neither__ is detected in the image, provide output that indicates an error.\n",
    "\n",
    "You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the `face_detector` and `dog_detector` functions developed above.  You are __required__ to use your CNN from Step 5 to predict dog breed.  \n",
    "\n",
    "Some sample output for our algorithm is provided below, but feel free to design your own user experience!\n",
    "\n",
    "![Sample Human Output](images/sample_human_output.png)\n",
    "\n",
    "\n",
    "### (IMPLEMENTATION) Write your Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Write your algorithm.\n",
    "### Feel free to use as many code cells as needed.\n",
    "from IPython.display import Image\n",
    "\n",
    "def show_image(path):\n",
    "    img = cv2.imread(path)\n",
    "    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    plt.imshow(cv_rgb)\n",
    "    plt.show()\n",
    "\n",
    "def dog_or_human(file_path):\n",
    "\n",
    "    if dog_detector(file_path):\n",
    "        breed = Xception_predict_breed(file_path)\n",
    "        show_image(file_path)\n",
    "        print('Breed: %s' % breed)\n",
    "        return\n",
    "        \n",
    "    if face_detector(file_path):\n",
    "        print('\\nHello fellow human!')\n",
    "        breed = Xception_predict_breed(file_path)\n",
    "        show_image(file_path)\n",
    "        print('you look like a: %s' % breed)\n",
    "        return\n",
    "    \n",
    "    raise RuntimeError(\"Image doesn't contain a dog or a human face :(\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a id='step7'></a>\n",
    "## Step 7: Test Your Algorithm\n",
    "\n",
    "In this section, you will take your new algorithm for a spin!  What kind of dog does the algorithm think that __you__ look like?  If you have a dog, does it predict your dog's breed accurately?  If you have a cat, does it mistakenly think that your cat is a dog?\n",
    "\n",
    "### (IMPLEMENTATION) Test Your Algorithm on Sample Images!\n",
    "\n",
    "Test your algorithm at least six images on your computer.  Feel free to use any images you like.  Use at least two human and two dog images.  \n",
    "\n",
    "__Question 6:__ Is the output better than you expected :) ?  Or worse :( ?  Provide at least three possible points of improvement for your algorithm.\n",
    "\n",
    "\n",
    "__Answer:__\n",
    "\n",
    "The output is actually better than I expected. First way to improve our algorithms is using more data for training, other traditional supervised algorithms tend to reach a point where more data is not necessary, but neural networks have this advantage of improvement in presence of more training data, also increasing the number of epochs improves the prediction and for that we'll need more computational power (better GPUs) and more time as well. Other way to get improvement is testing with other optimizers and with other loss functions and changing hyperparameters like batch size, learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Execute your algorithm from Step 6 on\n",
    "## at least 6 images on your computer.\n",
    "## Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R1VD2FFOcimAUDZfQ5JCbg0Pt2Qs5KXLxdks05kGFBKIW2ljULhEwY0W9JftSXCCXl+PL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\nyqL3ErYENFiy2RpsxVavHu2ppC217Rar5TtFeaNp8BoKKTb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vMS+1HK1mBmVfb8tDA2ww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uLPofSkBNSpSEt37a1GW4u2Vv7dZChlkjLRzJNgpRN5P6kq5yWXEjcxtLBCpv3TWYYeISdnmS25\nK2+4Wd2yMivWy2tsVHTHHb474fsTTbPDe4dVGcvFmq490jZH8rKkHR3aGKq85MtXL+m6gaubF4yx\n5NRHPvj4+9SbFfEMqnMsN7dgCr589Ypje2J1d00zjHz+6is+uL2ivNqwWa9pj+dZyaYRwUIYBxZ1\nLeKMqmIY3NwxT+b1kw3kJDg5HA5z8Zg6uL5vOfVSQN3YA4HjfifdkK3ozEn8OcLI2AeR78pBp23F\nVS/Lcg6HI3d3dxRFgRoDdV3TdudUvKQrPJ/P4rW92wrLYeh59eoVH330kQziBkNVLYhK1JP9ONB0\nLe04EJXi2apivVxR2oxVWVOXFTc3NwIhqBRTloaLq6UkmA/DANHTnFryejnT3XxUnPoe4wJK5xgz\nsCgNeRYZ+4bl1Q0v375CWcXPbOT2+pox3ZAGHyjKGqXa2fq0bVuWy+X885AGg1kmOZgTO0duTHaG\njVycMgA1PkT0xY2rbZ8yJEGgk3EcyTNxhBR2lZehdnDkZQnRC2NKBfREjAgpyRtk7kQqwmpaKT9R\nX5/obn8NBTdGMVeKgjzP6uU0BPRKVss6DS4DMYUbTCyxBHUkYoOZVmAR8a2OERdjMnWSfZf69P7+\nvBeo+ztu34lCDULJk4y1tBQKAu6HxK2eVYQXE9356wKrnvHpy4M3Fcdkpj8XRdLyZ8pMvNgfWSJJ\n16z1dHMQzrQQhORENNpKsdaaaAEjlLyopSOeCPZzwTZ6LtIheZTo9060ZEYeL3ZIgbYZhanYLG/4\naPMxN6sb1tWCoqhEgHEe6E5HVGiJTgJjbWZx3uIjPOx27JsTh6bl1PXcra+oqoq6qHnc7gmmw+Qr\nhrHFYYgmkpcFnXNomxMVFHXG2/t3ZMaSZ5ablXSXr1694j/46U8YRj93VqggBu/O4dx0ogoTYhgG\n7u/v3wuUXSyKGZ/d78WnoijEwS0vC3Evcx6dF6jgWV/VnM9HgtcpnDUQwwh4ggOtM/KqnCl5MYrU\n3Cef5RxmXBZ4jzq43W7BBrpxoFzUXN3e8Ljf8emnn6IzKXB929A5TzcOdOOQ6G4bbq5X2KgYz0du\nnt2R24xllRHCQFHUuF7SXaYb07Ku2Q8jOgXUHk4nmmGc08SP+wcKW+KGntFFghupc8tXDw8sN2te\nv90RgmO1qKjzjPVK8iTbMTCMgVVt5+tmxvyT9N8FN69qps7eWnl8kGkcJv3sQ2BIxys3yXEwZS9O\ncErXdbPr3PSe3ntsluN9T0wc7iwpAHVMqy2V5jfqKWB3gkKeGrKkHmGiYU7XZ5xl5JdY/FTAtZKb\nT5hs+WKUp4UpIIDZj3p+K56UzxPkMWXbEgWQDukzSnKYJ6bK9JDpsd9gdv9u23ejUEe5SwldWLIR\nhdUh5vfhIvNsfko6KDMX2iTnorSprxfqaZl00Ykr5hwAgZSmhzMV5TALcURgIS522hhJ71YCb2gt\nQxRvkW7aCNzhURhtUzFO3UESvsSL95wGoIINzkdZ/r/A3/O8REdNmZXkKkM5zag9oW0Zu5bj9h27\n7VvevH3J7rADb1mvKuoyIxjF7YvnfFAt2Z3OrKsr7l9/xV/+/BcMw8hnP/oJfdOzeXaLMoGx7+hG\noTD2Y0dRlxzePWCLShahIXJ9e8P+8ZGf/egHPDzcs1rfCKvDB5q2JRpNzDKWyzp1c2vu7+9nOKIo\nitm7o+8lAGC/36OU4t27d2IXulzi2l6Su53DavBJzLFcLlFBhB7OR6zVqZgIr3u3281Dr+kmMBUX\n1DBDAnIzEdOiaZh5/WzDdrtlHMf5fLu/v2e1WqGVJVpN07R0XTd3THlZkmvQIVCWOQtrCMFhoscY\njbGKYXRoA2WV89Xrl5idYMSZzoR6qTVX6w1RwbmVbEOnPcFCbiy2O3O1rjmeT+wevsJkG5qm4auv\nXvHi9prrq+es12v8scMUFUqd5sR0YA4cjjHOv39RFGIXm2Uzfn04NiglaszVajUXPsGkn/y+J/+P\noijkPHVP3axg1NKZyuMM9mt1y5i0go4KnxIFtEo854s1bwDxGUelZinNj+LEt37/up/1EtOocLqu\nlAhdZDiYHsv7PtgouS9IFy2Fmij2ygTweqINqzTmUkl5HFOnnd7qm5Xud96+E4U6IndeH/yFui99\nMf05PfZygv01BGgqajL/f39Tc7H+lpGkVu99qOryObNlarrbazMv9Wwi9j/xv6fBYlI1IayOyWR8\nijRCwRhDUkSl3yNy8XvKCTqtFEIQypExhqqqWK3W1HaBVZYxunSx5bLErjNubteMfqRvPI8Pb/j8\ni1/y8PiGMUT2Xce5i+Qh4+7mmucvPmYYOpz3LNdXnLszXTMSlOZwGHh43EvXpTz1subQQNe2fHB7\nyxdffMHf+/GPAVJK+JNwp6oqdBRFIEixvL+/J8aY5NX9LBFvmobVqpJDobWIQ9JS/HA4SP68NqCj\nhAsUhciWs57r9QvOzRY/DCyXS1E0FvJaZSl+IBNLQWs9mw8FLQOiLMtn3ByYGShTERL5uMAG06Bx\n6B1khjFEmqbBReECy7njWFYVanBkWo6hjo7Hxx2rK1kdTq9V1/XsmXI+nymqBX3fcTg11MuFDDi3\njdiBqgxjbWIdRK42C94+HFhtPhTHu8OW/X7P3e0LFosFL++3qNFTX+kZ6iiKguPxKEyVriMv63kg\nOH1GU+qN+InLEHi320mHn4r4VIRDCMlxT353ay3evQ85juNI0E9GW9NAM9rJ0F9k1wT/VGRJ1+Nv\nAHgnym364b16cVmo52ZOllRExIJhGiLO9SJ+s148qRkVGUlQFVMiTBS0XClJgprnZHF2Ipl/Vn+g\ncv2dKNQqRrRz2FFk4NoDGFzwKD/OJi2yaplSuGPyg5aprQpg02JGA16lZXVM4a5IsslTGZYCZ9V8\nWjzh31hQAaMkYBMVEtcjUpBj0n+gUdYQjcEpRa4SHJLk3Yoo9cUo3HSHT4kQUyqFAkY1LcHkZx88\nQU0e2ZHKZORoFrrkpt5QKvGs8L7Hdyfa3VseX33O7uGe/nwSscliiWXkZrlEvfiE47tHhtOB//Cz\nzzidTpx7TwyON69e8vzFhzSnnhhOrMoFlbL4EDiGUWKwFhWn8wETFHWhcG3gsN/z6Y9/io+Gfgwo\nI2o65wasMRRFhtUiKJEhXUOdLTBZhus7srKgKDLxN14W4rvgHEoZzs2AR5EXlmr9jBAHrMlnYcbx\neGaxfo7znqZrubp+wfF45nDoqMprhjFiLQydpyhyiXRyDlNabFbgIrhg8TFjaEcwBW4c6GLk7AbO\nbsAGjbUlw9DR9T1V8o4OMdDHlvNZjOaVzSm0ovYd18bh+xaXrbHFhvuh5+Z6QbCa86Hnn/3z/42s\nqnHIDfvjDz9iWVaUmaHr9zT3X1FkNWH0DJ0FZcg3d8RDy+m8Y9AtXRzBJpx8OFCXGdvtmZMx/OWv\nvqC+uuPu7jlXC8Ovf/0r6uJTjNao0WNQku04DhSZpetbyrKkbUXMMo7iPa7QaXYgXW3TnFChR4XI\nsjDsmpasyIl5To8UrrHtybWhyhJVUitcYkd4P6Kj4zor0IXBZwFnPLkXY6dReUYVqOITBCHX7AWM\nEYQD7QSrlCIatSiElWDX0tVIPYlB8k1DgjWFyqzw0+xKMftgiz4BlHdYpIOWSqEwUYtqMU0GI8xs\nXQUpIES0GDpIooszKWQEke8/ERJ+9+07UahjFPwxJCA/hKTo8WHuMuXOGN/7ecKwpkL7NCMmQQ2K\nyYFufuwMiZiv/cys1VeJ8aGVm3EmCdZ8ymK7/P51GWu4eL0IuBieJsM6LdTeg17e7+e1lpNUKcHe\nDIYiq1jVS/Ei7nt8P+CGkTg0dKcjRiuuNyu6TDMMJcdzQ9edaVuxrPyTP/kTdrtH9vs96/Wapc75\ni7/4S2yueHj3lrJakJuK/eM9RX3Nw/ZAef2M2+sNx7bjxd0zHt69RQfFMR4J44hRCtcPqLJitVhB\nFK6sNbJE9kpUXs5JQneWT+ZTJcMw4nxDtVwQo2f0gmcrXaL1iPeBofc05yPWKPI80g9SqIde8gaN\nVTTNwPG45+bmjqLIaNoz69VGTJyMYRg7lus1MUZOzVm40DGQl2WyIBB+tzEZmbYUtqA5nQgeijJj\nTnZ3bnbZK8qaPnQURZkgKUuVbSiLgkMP2g48v1pTFIZ/9r//X3z1GOjY8GavGcZHyirDmJG/+Pe/\nIsNT55aqtPz0Rz/m9VdfsKpq6q7nxYfil5FVGSu74nQeKataIJlBsblez9TC8/mM1Rmff/451uZ8\n+OGHOBfY7vZcX1/R96M0N8lC1zlZUYzjOMMi0/cYSHCJpSzkhqqisGKW9YLb6xsOzYmuaakWNcM4\n4NsenxeoxGTKFGlYnGiqGDJrIJOVpUsNS4jSteZITuF70MW0uibijSwvTXKeJKQk8zQAZIrDS/Mt\nDczt7wwnvg9HqEl+OA0JE+SiUanTf5KaT/DKBFnOiCtCPojJmC0CwUkSlfeDhFr/XSnUJPHJdBdT\nQbAeSQUOhGSMdAl/AL+9UM8d61SM1fsnARdDxelApedM9okaS/SACmkZpHEhSBT95DOQ/AwmLnWE\nOSbo6ST4mk/2hOGlAz53DmnfQ4gJs5YblTYS7mlUzqpcUWclikgwCp1FYmNpvJj1HA87tDUCORSW\nly+/5Hg8cm6WXF9v5mX+q7fvuLlZ8uzZM06nEy8+/Ijj8cjjw2uqUfxLTAzc3lyz9J7dbifHxzuK\nzGC1hMLWhWEYDLG2xBDwwQOS3u2c0OiqWiCI/X5LUS8AGMaRqlowDIJXK6UZnaLpOsGRPWSZQE3W\n6vnYZ5mhrDJOpwMMgUwJF/t43M8ud/3QYaxka+Z5hg/jbLcaQuBwOrJzPbbI0dYmX42avm+5ub7m\nfHrBq7dvaLoBrcVnGqVwMVDUFaN3rBcWF0n4suyjC56oC4wpGLuWt9tHXr478BCfMdYf8rN/+Ecs\nas2f/j//B333yIura9RwpusbfAv/75/9OX/yR39EcCNGwdg21KsamxXkhWIYc867gNaWGEV8st1u\nubm5pe97nHNstwKBXK83XF1dsW8bnPNYa6TY2NQhjiMK4bDneZnYLpJynmdFcsrzmCR20YRZLLQo\nl5R5wbvxEX8SDHwKJmhHh3KKrLDYxASZnq+9JhqFNwlvVgGln/JEp+J4eV3GdL24tKINKKHjXVbc\nKGZKU4YizFY5ydkyznDH5dOmIaGCZCosJdVGhb1gfUwRX5CA7IRjxxDTIFKGiVNmo1EWH6RI+xCZ\n0qZ+n+07UqhlwgxTyoea75IhBnyQXzhwEZ1z0Qk/Hdgw31V/0/ZUrCfzHDmY0hnb1FGLtJnpfabu\nNwjEMt8ELnCwbzWHUYmnTRp6xjgPKaa4+zgVdd4v1GJkLl3/slqyzFcsixWFrRm6Du96VPRkwfHr\nX/4VD/evOG7f8cMf/gCTWf70X/4rxlPDarXks88+4xe//Dnj2Itgoeuoq4KrzYrXr19jrWX38JYY\nPWFsyJR4jWQmSnvlHEPbUVcVw3AiU7CqK2JwiTkRaJoGFYXVQXIbq+uavmvp+lYuxCxnu91SLxbi\nJ41m6Aa63pFnNU3T0rfdzEzIskzYGP1AiJ7zec9Xpz2LZc7ptCNEj6adMxiHsWO5WBPCQAiaLCvI\nci3p1SHg3ID3kdVyw+m4xflIneeU9ZLzcYtRlsLmrOolzr3CGDkn1uuSYexmA6JxEHx9kfDrKclc\nW8mvVGgW6w1//vN/A8WCn/7sH/CP/9t/wpe/fM2nH21w7sBf/dt/QZ2d2Vxf0x5h/9hTJsw405rT\n4Ygf5UYx6oHVasHqasPLN1/iA3TjQEBiyNbrDeM4zjfP8/nMOI7yuWgrg7M0mNba4ryEDMToUkdd\nzApJa62YPGmZe4AU2vZ8lNmM0pyPJ6KCqih48/CO9XrN4SQBD0VuqIqSfhxRefJlJ3Xr0eEQ2uEQ\nR4nQUgqCJ1OQiXhciuL7jTCZklXuGOU61BFMnBqrZIik4pOtRrourWKec034OPq4rPYAACAASURB\nVKRrOIpc3SqFTV20jnECNWepuU5DxpAaLp0650nwNgUIhImxEgSWUSBd9W+TSv8Nt+9EoVZcUHFS\nGrd8TQfsCatC/eZf+tLuMDBlo70/BZ63iw9Pq2QmPrfBXHxPJ49KZ00aEMkAOJmPzz/L7k3CpGmA\nMXXNEVAhzCKb+TnfMnC4HJioqDDKYlXO2DtC7xi7E11/IjRnrNF89snHZN//hDdvv0KNGpspfviz\nn3F3d8vLly+5u7tjtVrMgoft9oFhGMhzEVQoPMM4cHe1JuqAiYHMKraHR4pqw831FbvHLVYr1suV\neIePDqsF5siMYVmv6LoGQmS1XtA0Z7xzWKs5HA5ktsaHgPeRwQXG/sx6dQXAdncAoF5Kxx2DYxg7\nia7SYyqQDd71DJ1IrruuI8sjp9ORm5sbhmFkHHtub59R5AJTdJ1jc30rFz05wyAqwsVqTdt35IXI\np8uiwCvoI9xe35AluXg39ATEK8Ynmmi9XDG0Db7vWJQrQvRcXV2hTA7FiqEfeNwdsdWatz9/yX/5\n3/yn/K//8//An/6LP6c7vcGM71hmZz775IrNKmfYGH7tDrx4dscf/70/4tnVFX3X8vnnn1MuK0zw\nfPHlrymKjKqsOTenpPr0nE7CPJl46X3f8/DwwPi9z/A+EgL0oyczgULn5EVB23hxgNQCS7RtS4xS\n9Ke0m9ELJ96HgLV65rtro6jygkMrQ9nX7+45nKVwd8OAtQVj8NhocEmVGkJA1aV4ekSFw+OIhGml\nSpyx4a83YDFdC3YypkvPmH6SoinZhdNjJ4aHYMhxduWLPMV0zZ1yqjOTBcT06hM9b3qsni7HxBI0\nMXXLSJEPUwGPzN7x0t3rPwj94ztRqIGEOSUjJC+8zYlU/oRL81sLtVJPJe8S+pj+7RLu+Dpu9F4h\njxfUH62QwSOCGStPUE+2hnYm5b8PvUSECaACKQhA/t4jSeTGCK/6crI9oWjeP9EOtdb0p45gFXhN\nYUuUG/EEdHBE79gsSrr2wGF3YLu9p+l6fvSTH1IGSdSetvP5zGKx4PHxcVbJBTfy0YfPefvmK+6u\nF5R5wVf7PuUz9rx99ZIf/GRDUS046wPPb24Zx5G3b99RVwXn5sRieYOLPnWckmt5OOzBB4IXZd9q\ntUDpgtgNDN6xXq5gCIxOmAFZLZ4h2/0jeWZwbpwzANt2L+wP72nbnmHI6bqetu3oR8kuPDdHlos1\n4yiCmrHoMSbDu562KwU/D8IwyLKcU3tKwbYdmVbzcNhoTZGVMxR0OOxkqG0s/SjsBQZHlWcoIiYG\nvAuocomPUPuBQZXUVx+w/eUv+eSjD/mf/rt/wuEUsfWS5bjjxSpwU3s+XjRcLQ1DrtlXlj/+2Y/4\n8Nkt5+OZ4+lMuVxTLlac91+grWJ0gVPTEkKChLQmz734rlxf83D/SJFYLG/fvuXTTz9jTPJ2VRVk\nUaCluq7pug43tqnAixBHfLw9wUdGN1KWUvybpqEuUxZmkNa8Liv6s+P6+pq3D+8kFSd4WjcIJDdK\ngcuM5F92TUtZZHitcMELrVQylDBKY5U0JJfX6yX0Ybx0vy5dZSoADvBR6IvTajr4VDBToVbwvoHp\nE1ZtE+fbqCm5PMwma3MduSjsUyfNlCIT00yNQPQB7+Wz1iaxc6KWoOM/QEf9+6Pcf4AtpuWM9x58\nmM1wpugfecxvhjOeXud90vu0XWLTv0nF9M2O+/Kj0Uwf1bc9/3K7VDhOvOjL/ZpoTZd/Ny07p6+v\nE/dBlGHGWPp2oD03sxm9UZqmaTgej+z3e25ubvjZz35ClmVcXV3hnJNJv/d88sknPD4+cjgcuLm5\nYb1csFwuxWs6eqqi4PHxYVYONscTu90OkGRuBXSddF9FJkGtk7H8+Xye07eni2wYJRCgroWOt9vt\nWK9lANY07Zyv2PcDp5PETOVVTkA6saY5cTodOJ3ly/mBGAUvFw9pceKbDPBP5wPns1izDsPAOPaz\nef/kH7JYLBgGYQR1XTfHgbVtS/SBRVXPfhyTAdH11Y18Xus16/WaPBN14bIuZ+Mi5nAJgQrGEDkc\nT3z22ad89tEzrovIOnN8cF3ybJNzu8pYF7DI4fDwhh//8PuUeYYfJVHl8XFHiIqf/9UvxSI2y9jt\ndmzW1/M5McEx4zjO9LtpiT9RIOWzbjgcDnRdx7t37wBmY6bp/JuoedP5OXl1K6WSXatQUicMeBzH\nmf448atjjIzBz6/ho3yf4CEQSM9PfGMl15NR7+cLfts1ptNjTADtI8pH8JLiM19T/kkePmPSXxv8\nf+N1E8QxFfcYJbQA3i/Sl9uc9pK+y83NE51YFrgL8ZR38pn8vtt3oqOW+2iOMwqvAw4n9pREQkxZ\nb2HiVIvk84n4HueOVnCqJ7z/vff4GusjTkYraUigg0yU5S6Z7E3xiBlM6m9VxI7SZU9DSm3EKEks\nU8XTQwUJDcgAguDMXRTrVJOWbaMPM6/axRKxYwqgnCDaUQj5MUTW1Yab1TVVZnDdka7ZEXwDOMLQ\n0IVA5w1tyLlZv+BwOGCV5uXugaJa87g78Mn3foAL4FFsbm5ZrpdsHx+FltULpzZETbVYkBU1QRtC\nJ5hj8CPLZYaxHmNrmtMRbyIYh9aBsW/ZrFYcHiW/TyfjqXM3EpQIgwYXWdVLus5hs5J+kAAGpXOU\nzcnsQNSGoY9EJ7awp+OOt/vXYAbyomRoe3Kt8Cbg/Eg3jpgiZ3QB17UYkzH0HVnfEw8HXmxuyKsM\nHSe588AwRoq6QnvxvXjcnXl2fU2WV+jYMYYWk3tqa3m22eCaMw9fvebFB88oEw87oMnrDLuoOA8D\nq+WG5XI1e7KU9YJiYaA98frf/zt+8IOPuPvxkod3AU3LJ5uaupSydb890gyWbKj4+MOf4ukIxUC2\nKnjz7i1t23E4PmBMRj869vcPWG0J0WDIMMozdqJ29Cm55HzccjzeU2WOm8WKX7zbYtYrdqeWItfs\n9u8IfsRYYQet12tRiRqBCpbLmrFt5iCF437L8+fPpRjmGTiDiy4ZkYknSKYVwY/gMmIesV5jCWht\ncHokzzIGMxAywQ2U6qi0wgZD6YyoOQuHSYVyErP5BDEGpQQ2THBCRLyhg4U85mmW5ecbyTw7UrIy\n90rqh7J6Hg5OcKZ2wpGecHAAH/oU6mGJUad9SE1l+hpjwEfwBrrgcMYTtNARlQ4oHbDKz4X/99m+\nE4U6RqE/hSgKn8nMhxCJKkXukLrRVIQFCgrzAZ08M/4QWwiJz621FP9UzIWpITcDMwEdwc/Nd4wq\n4XAiKo1MjuJCQQoXbJXoHcEjU/BgSKcjqAS5KC22pjbj+d0Lbm9uyEOGC8P8+ZzPR3TX8fiww409\nt7e37PdHtJZusSrqZCok6rqmadisrymrnDqz7DzkpYxwdJZTVQVlUdB6g/eR9XotirNkFVnYjKxY\ncNhtCSHQdfK6ZVmmRGphFwhMFRKboJ0TxoPK8C7gfYfNChZVTgwyV+iAzXLF0A00xyOH05HjcSed\nmu4hRnJr8eMgajZjCKMo1YwRuEgUigGlDDEobhdrQhCtTN8PszdHrWq81qwWS8ZR0rk3i4qm6Snz\nkhg9xmQp61AK1EPyZa6qhXSBNtAcT6zXawjyZ6UysqKeBTs//uFP+Iu/+Jf8+pe/4mc/+T7f+96I\nChUfPVtioqMqb/g///m/4txE/tHf/ymZNhx3B7a7Bx7v36AVLJeWfqjpeulglYu0XQNJBGSNoQ1p\nhYXG+Ug3DtzfP3C1WmOtpqqKeZhalSsx+o+e9aaaU+QncU/TNFRVhU1/ruuaw+nE4Bw2z3FiDE+u\nLUEJCyc6icLTUXbLDRZVlOK1nSWb3+jE7TlGxhCIie0hsINBB2SgN0EPUZANEO+YMdU6g5qpriFd\n+yo8rUBnj4+LrnwSpIjNaWJ3ROYQEoNEJKvE2kiMbEje19MwMiQsWrgOcbpicR5c8DgvfO4xeOmk\n3cgwjPi/K8PEGFN8EooxBlzw+BTcMCjBn5wPOCXJC4GYUoLl+X8dHPG33SSBI7n5JWz7yVdkGiZO\nfiEeIug0ZIiaNNhIOHeaMAb1BHdMNxThj4fkJTIl02hMzMgpKVTJptrwwfoDMgf9+Qx9R3c68vb+\nJd6PrHPL3d0dbhzZ77eslyva7kyWFTw+vkMpxd3dXTJDKlmt1pxOJ0xUvLh7MTMmuuY824qGpmdV\nVLQh5+rqShRlIfLxhx/x1aGbl5Kb9YbVasVisZDEj3npHGm6ltO5Z1GvKKuKPM8p6xtyayVVZuhx\nQ4/zHSE4clMS3Ejfd4ToOB73nJojNoM8E7GLR3O1XND1bbJPHYlKvCSIEedE9adMQVYNnJsBbMv1\nVY0bR0JAUmcIEGTw5voBU1V0ozQK0WSUqys+zpccDgfquub29pZ//a//PxRGEtUjPG7fJH4xFFmO\ntRltMxKCp+86tO15dnXH3//j/4i3b77k7Rf3HPf3PL81vP6rLzEK7m4+47/4z/5rVqtnuKDo24Zm\n90izv2e9EE76/vjAqekJAe4fD5iiIoyO4AM6KDrXY6xlezyx2VxzOJ9YVAWv393z7MUdN7dXAj35\nQJ4LPTHPMtwQeHx8pKqq2QDrfD4DSEBxGia+e/eOq6srXr9+zWYj9M5FuaAfB4KO5LkV+uTosSnq\nTSfrhaqSkNyssAzeMRiPV2FWcepIyiQUPDkfUyxWYltppEDJjF9DDGL8j3iFxESR86lhS6yEp+V0\nlDIf51E+GCxWyeDSBtkHg8LGC6hDgUsU3RAVLkTQgnQ7BVFrPMiwFJk/RXTaj+nPjtEHAvoPAjB/\nRwp1pB8dXoFXSpboKuKiuFJ5JKZnMkIJCfQI4ZvOWRMONR28GKdctScWBfCEXV2wSi73R6lJsJJm\nzSm3MHLB2uDiZAueaBQ44YgmSSKJG4L3gvfFyYCcNLuOEJxgjD5KBqCOWqK17JIXiw+oTYUOoOLI\ndrelbzvac8v19RXXawmi/eLtW54/f44fB+pqyX77gNKRu7vbNMzbiG9wK0t1G0WFtqhXtH3D9d0L\ntEm8Wg83z+44DXruljWKMi/IMs+LFy/4xS9+wTAMHI9HqkKEEs4PZLZgHAfGwZNnJUVRUeS1+EWo\nnLLMGbqerj3hxx7ciDEK5yHPSzKj6FVgCCMuylK32Z1Yr1aU5imlJNeaqKOYG3lHjHI8y7Km6XrK\nwXHuB9Y643g8Utciz767u5NCpA3L5ZKj2wucZjKq1Ya22bPZrKBruNrc8Lh9h1I919c3ODfy5vU9\nmdUUZU6eC7Z9dXVD07S0bc+qFLvWw3aHivDs6o48RN69fUnhVvzgwz/mxYfPef78Oev1FTEqHh+2\nqKyW1VlwqOhxKVjBaBk8b3dboeMdZAhqlKz2tM3pxg6lYIzgUDTdwGa1lO++J8s1+92J6+trhq4l\nTuEJZfWe/HuSzZ/PZ6qqwIeAtoZ+HMSga+jJCnm/wY9gkkhMg3MDmS6wWm5ccuV4Wd1ED1pEK2E6\n/5WEHE/qWw/kPmCjShCk4NIjUVZJJl0zUYbzT9O+JGqb8GWlhM+f5lxiIS8iG7neBFIUCq40FXM6\ny2UtMWauPQFwYeqgNVHLPoxK4aLHR6lZgZj41akrjwofPSp+XaD+t9++G4UaGFXEIb9sT2BM/rGe\nqVhPRZoEhVzQ9i5eh/jN4j2/z8Xy6Hfe10Ren3yyY/RykiT+NSEQtUWlAUK4wMLixf1CEec7rVZR\niP9RDJ6KWLHO13x0/RGfPv+UjJxm+8jYnNEhctofuF5vuNlcoZXn4eGeIi9pmoZFJcINYzIWdcZq\nteTt2/t0Mk/iHo02inKxQGc5BiiWFcpAqTV2ucZ5GZJ0XUeRiaKwLkv0Uahg0xJzGkrl1uDHkSx7\nmrCvVhuqqubm9hlaW4auJ7hRsMw0ZPI4BufIy5qiyHh4eGC336WkFkRNiLxP048URmOzclbURT/i\nvTixeS9ZmuiMYXTs9geur2+IMYqJf5ahYyA3mj5CVWR0aRjXDxkZChc1gxeefQiesqjld6+3adgb\nGNuzQFzJ36Q7N7hhlAxDAs3pSL18xqcf3aBj5Ecff8L5+AM+2PyEH/7kh1SLisNpy9vHlzTnLUo7\nhqHheNpxbk/kuaXph8T7FdzZFjm+b9DW0nYDhbEQYPROvECIdH2CfrQ0Pm3X0w+tFN0HcQW8Wq8I\nwbNYrPBRViXrpNycnPOstbR9N8em9eMwe1VPQ8DBDwzdSD90En6QhoIqBoyGLJ9c6zwxgleBnkBM\nMvwp5i6g8MnWVIIKnwz6I09whPZxhkQur+VLH6DLv5+v86/Nq6bhoUJqzUSn9RN1L72BkACEfz51\n70EZAlG43ErUlV5pQkD+nN5ofq5Cslf15R78btt3olAHoA1CewtoRhROR4IXKo8nJsFLxKcPwIUn\nPGpKapkUf9+2TV3y37ZYT9DH/PqpIyCdSGEyIleKEEe0tmgcHk0M0lEEpRPjT3CumcsXUpimTks+\nrclsyUIvuVre8L27T3i2vGX3bkt37uj2B7rzkTzL2KxXjP3IGHuxBu073OjJN/ns47xaC0d4vV6J\n9LkoyDJhdAxDR7FY4rVC5znFcsXgerKiwJaBN2/foUyRoBGIPlDWFVdXVzSnI4Dgoota2DrWMAxy\ncQtromC1XnO1ucWFSNv0bCqL855j24joxzlxGCxKNIrmdCZGkR1nmWFwYHJDFgVy0D6CEyGHGUfa\nocUaJfkLthAxhQuEYGYYpB8GysT42O/34p+sRf0qnXbN6XTGxUhucurlinJRczy39L0MgJfLJev1\n1SyF788FTbsnL+xFyons45jEPufjieJ6Qd92bG6uUOWCpnnH6zcZi+trHrbvGGOPC4HzeUvTKxaL\nik+//z36vuXV65ds9zvaLvB42ILOsEXO2Ea0trKkDl5Ca41hdAHftVilscoSgMGNKBUYXI+1hsfH\nBxZVCUpR5gatzDww/LqxkTGG0+mEtXa2pp28pkNwtENPN3RJoObEYiFBGAZFmeUQPN4rWSkazRg9\nIkoLBO9ROhKTkO2pOF8asl0wMRIWPV13XFzLk/Pkk8JXaoIoByVJ6Ym4q2ZOtITpCgvFqGS9OtEI\nUk6XR24eXgm0MaYh4txEqmSgGp72NQTBrAUHDbO47vfZvhOFOhLpoydGjVOOAQHoJzWfhN1O3GUl\nHXUUkrtsE6MjmSNeLo2m94hydNRvKebftn29UAsE9iRJBekyrNaEoPBxJEaJ3QpBlmxaKjmT5VPU\nT4rFSPICBhRy4b+4e8Gnm4+5Xl5z3p75xb/7K/rjnsrCsspxXc+bN29YLpdUhVhpxqhYrZZoLQ57\nVmnKUmCfoihozj3L5Tp1hRGV5yw34jZnrSJbVBx24k0c+54xCC/62bNnSWYsA7KVztkm3myei5Pd\nlJu4XC6FFtaNVIs1RVnjfcTanAFPe9zjY8AoUEYRlOF8aOmGnrEV5sLPf/krlFVE7QlqoCgtdS5J\n19/78GOqzNIPHRlw7s5k2uBcgydyPvdk1YLj4UQZZJr/9uGR73/6Cd3QU+UFWW5xw4gxliwr6LuR\noq7SMlku07YTk6LFYkGWGbqu49mzZ7O5FKsFv/78xJASXsgUq2WFd45iUVHbmq7TrJcbmqAp84oX\n19c87v8Njes5Pb7B5CtcH7BlxVX+nDLBX914whEwWUlRrTmd99SrJfvDEa1ztIW+HVAmQ6ExWU6I\nkX7ssUqRVzVVXVMtavKyZrGsOLwRdk9d1xyPRz56/lxsTYsnN7wYBWKZnPbQZqYtPj4+kuf5/O9N\n39APA6fzMQ1eDSqtrDKd49wwaxpCnIyerPivpw5XTUM9/aReVEYTfWRIMV8RUnMW5457uvYmZohH\nVIHfRmlVSs0r3yn9aZKCTySEQMQomShJ0RbCwjwyUhKM65xjCFGYHsaIoCUGVMKjfWKAgMIFh3fS\nhVubi7/877n9tYVaKfUp8D8CL5DP+L+PMf5TpdQN8L8A3wd+BfxXMcatksr2T4H/HGiAfxxj/LPf\n9h4hwuDNrNTzswVopGVaKk3FVQ6qUebJWnIaAoR0V1cKP5sfMRPhlU46QKWIXkqjTqkWIEMCAB0H\nSGkTfuJAqpCGhgZwEquoYPz/uXuzV9vW9Lzv9zWjnd1aa+999tn7NKUqSWWrsyJjSVEUsI1lkAkh\nF4bchRACvohDCOQiIX9BcpPbBEMSEggYQxKSCxMCFiFOSByrw7JVKklVpapz9tndamY72q/JxfuN\nsebadaqRy4iiBkzWXHPNOeZcc4zxfu/7vM/7PElz1weNSgc8eHFQB40LDmcMUcVkbKsIPhJtOjm1\nImQlNiuo8hXr/JIPlx/zfPE+/c2B65ev2V9/i8v1hqLMOZwavPPkVU3nPKOXrK9cXXDoemytqZdL\nbk8vWebr2fLK1ganoahqjscjHkM7BKpayt4wWkq9xHeem7uBsrwCbWhOB9abHGVgP7R4J8yJ6901\nzz94TH1R4sNAqa2MHGuLrZZg17RD5GKtMaqhLlo6nc38bxGV6iii42qz5K66Y+VXHI+PuL29pTl1\neDNgTUY3OMpFzWdvxIx2ud7Q9yOnDhgbehdxQRgo3alhWZRYrRlxRBXItKbUhuW6Fh2NTNMFoWbm\ndc3hcBLta2vxQbjsi2pJCB4XIuViyYUR7FaZjH7/hiKviQGKvMRYjc0My2XJoYXgewyWrr3DhYAu\nK3pd4sNjDjdHyrInKyL90KIWpVQWQ6TrOo7tln5sODR3tH5g0C1jb8jsitOxgyGSYcSVREesrgSa\nqJOoVXSEriELC2oVKDHkWgZ8qqLmsN0RHkcWixWjl+nOuq7na8AYmVb0AYa+RWHIS8PgeoLrwSr6\nYWDoe4auBx/IckMwVppqqiArlvgxkOUZTgVcFokF9PaYrl357nXQJOcA4WKHODcSfYI0vFZEbeij\nxmlDjEriAQrwGK8ZRAR7XgDONxeEwmuC9DCGGHBC9cAGGTt3IZJCybwwkMzBpqVhRBN0JHrNEKRp\nKM3KkF7jiSpBgSoQbGpwRo3RP3hG/f30Ix3wH8UYfxr4l4G/rZT6aeA/Af5BjPEngX+Qfgf4G8BP\nptvfAv7L7+eDyIoUcCE1D+N9Q+A73ibsiPvV8Vw74/vZPi+7nvbx4AZnK3yiBabPAEys63l/Pt5/\nlmn1jmdZwIMyU4NFU6qcTbXianWJCoH99o67t2+4uroSyKFp+Oyzz6iqas6AYlQsFguMsVxfXwu7\noiwpy5rleoPNC9CGsl4QUJT1AhekfNbaMgySjYOm70eappPAbi1N07Fabma36Uknoa5r/DiyWa0Y\nu14my6xNnNqY7J4G6rpCKUXfj4yj4LtZluHdgMHxaLPkyaMLLpYlzy4vqYzn4w8v+OCDNYuVJbiR\nsQu0o+PQtGyPe7bHPV/56u/z4uVn3G7vuDvu6dzI6Bz9OIgnnlVJpF5xebFBKZWGORIEZk36vHLL\n85zJGd27KBWHUsLkaFuGXuRXJ6f0q0dPyPKSoqrFeaesktvHvZKi9yOn0wEfRl68+ISmPYpqX56n\nBt0422ItFgtCcPR9K7Kwo3z/fd8nWIXk8i0nttYqmf8q+q7FB0f0gcE59KztEedewqwTDuhMFu5h\nkAV+Gj0H0eOIUfTCJ7MFIMEbAiW1bcs4jvK9DEIVdeeWXrk0jI21OITBFVWQbPp7XYtofFLHC1Gm\nEH1g1vsW+q5U0xJE9QyHusDn3iZjY5+cfkJ6PHgeDJ5NySHpOp+GdeS1796XEXEXIt4JI20MAe/l\ndp7RTxpCP+j2PfcQY3wJvEz3D0qprwAfAP8G8FfS0/474P8A/uP0+H8f5dP+v0qpC6XUs7Sf7/Ae\nMjc/szsS5WYMXnzU4uSccA856EkQhLNx07Py57uNmst7SpPjXc7l99qkSy4HioRxBSVCLjE1OYlS\nmpGygvPx8ZnyMf0fKgHu0WMLxSqrKZWh2R2I48jFZk20jv1uP3N58zxnGLoU5EWms2l2PHv2jOVi\njTGK9XrNYrHg7k6aYBPWPNkwoS2LxYLXr1+zWq1mY9fJldqaDOcObDYbtvsDVbkkMxat9CwgX5h7\nVxJrLN5F8iqnbU9oI16H7aljUeVktqD3nus3r3i0WfL44hKip7CG42GHjp5FmXO7f0u9Nqz7kn4c\n2B0askIRu+ZBaeuQhdIwoLUnIMFvvakpi5ysKCAElst6Frfvug6bZ1RFxdANRC8NN43BDSOEkjwT\ntb1hcFxcrGlbcQ4/NfLzyZOnHG9fU06wjtFYOy2OCzwe5wLBu1kadbfbCi3OCLxhdYZKjdiY9DmG\nscOHkanm7vuetumFMmchBI9SwsIROViR+awKgwvQx4hyoo0+GfwOw4DOBIbrxoHjoZ0VBJu2pSiz\nByYJU/Kx3+/xAayV9/N4jo2o9bV9NzeZh2GgzPLUTBazAaulqeiVFx12Ik4j+uoT5S2NuAUlsMf0\nu1fn84mCBc/kASX+hA4x0QhB4Qk4HiZScwU8X+eTdHKYWVxigJ2GXoTvKw4zPgVtlWJJUHg8Udl5\noRDJYulNhZRIPhjQIc7wikLkcyc/yR9k+1Mx/JRSPwb8AvCPgKdnwfcVAo2ABPFPzl72aXrsO26R\nyIjwpH2Qn450MaYMNnzO/alb69P989+/1/bdcOqQIJgw3T+/nWFbU3YM9yfLvP/03GmE/PwzhdT2\n8KnpkduMwhYssoqLeo0eQbtAYaCuTBIXmsaxRadhkrW0eUnb9yiTYYsCTyQrKlabS9CWw6mlrIWq\nFdC0/SgzkCFibcZ2u6Ntu0Qv6zDGUpXSIPQukmUFMTC7eLuUWasIYRgp85yuaRNrIEOr5LkXHMZO\nuiqG/f7I9fUtT64uuVovqUrLalFwd7ijD47gFcpYnn70EeX6gkfPnvH0+Ufk+YouBEalUFmOrQqU\nNSiryHLDGMBHMJmlrCrKsiDPLXWZs6hKwuggCDUyz3MCirbrZfrO9VxcD1AXbwAAIABJREFUXMzj\n5eMg+L1WoqK42x24uLgghMh6dcGkvuajIi8X9KPDZCV3uxN3u5N8t4FZHOny6oK+bwUnPtzR+RGn\noiwWC5FntdZy3O9ZLOSz90PLdntH17fCX8cwuh6lRI/bhxFtJNGAQCZjLjKQVJXzcb68vOJ4PNKP\nwzyRezjJ7/04zOd/18kY+lQJTFUDCJ9apAH8jE9Po+Ln8g4Txc8YQ2EsKgYxdy5zRhtxJjKqacow\nXRtnNy9CdjitRAtEKxzCWR4Vc4B2SCbtUHgtpX5QyIxFuk1Tg2cS1fO17GOizqXHfdqn7Ff40mOI\njD5l6eo+e5cgLXMcMSjJ1KcsXWlQk1CqmSGPSf3yz7SZqJRaAv8j8B/GGPfnWWiMMSr17kT899zf\n30KgEcrKMJC+XGD0YS4xvJbR0JiwKzVNAark15JOwnfn+M/Lj/O/z6OlWqeQHud9h4mIrx7u4/x1\nPgYMEz0wzTBFhdaeLJnZph4oaKHBRZWkThNDRWktDuTKoKzBeU9uYF1veLS8QjvwQ8+pO9B3R/pe\nZERliKDidNwzjiObzQalLfv9jsvLS6zVGJuB0mwuLvn0m3+IsRk2y2najqbtyPKCw/GExpBnJ6zJ\nubvd8ejRI4ia4GG5XLPb7aQJOIxJBD3diLx8+ZI4OpZFxSqvWNQ1i3rJqRlkElCLd6H3Y2Jb7PGD\n4+pyw6qyjEPLi7u3HLsGb0v+6Bvf4pNXt7y+2dKpnKpesNvf8nM/9WUef/wxNzefMI49UzN4URVY\nrUTmtCooi4o6NTEZBi4vH5HnGTFoutOe6skTvB8lxylKXIShO5Ebzdi3aBSLckGIDh0N+DBP5u3C\ngb5vuby8JLMFbdOzWj/GB8ujx++z290RsewPW+L1jufPn2N0Rp6VHA47bKbRxrNYFBy7lryuOLaN\nnGdR8N2maTgcr9kddxzbPU3T0PQDY1CpiacZh0iWwdALj7yqKmEZtXu0trT9SB9En4PHhmEcef/y\nCU3TcLfbslgu8fE1LgTaXlggEycdmJuIMcpE6vEkC8WUmfejZPed6ymtVCiT9kdEtGAWiwW1zVAq\nchhOBGWwm4poPQN+FicSIViF1kYYHyjGGAEjkqJ+uvZ1yqaFKTWqic6XKm4fGLx7IN727mYTuUCD\n9Imm+BOEcaKjwCBjEKOOkIZYJOeWOOOd4NRB6ZlTLQuADLY5J1UUCbuOPmKMTfBZTp4Vf5rQ+Lnb\n9xWolVIZEqT/hxjj/5Qefj1BGkqpZ8Cb9PgL4KOzl3+YHnuwxRj/DvB3ANYXeZwI417JFyH5gpoz\n6JlxkURg3h34ie/8nCQTvzOscc/3VWnf01Pj51KF7p/viSgfQIVZYHx+PNGH9DzNeMa5TkwPEill\nWjz64MgTNu9cmLG/qCPKSCm63W7ZXKx58eIFFxuBKsqy5G67Z7XcTN+plN1TmZ+EeiaH6aZp5qwo\nuDjjs5OZwHRhTs7h4tFoybLjvO/FxWa+SHWIjMMAqRmV5yUmy7jZ3qEzK7ZHUSQyu1NHbFoaFNub\ntzgcxzHyjZtrvvXiht1oeN3V9KcM6orcPOb/+ydf51d+6Sd5//3n7G5v6LuGqswIQyssG6coFyuC\nkww/N5b1ekkOxF4YIFkumWAMitV6wRhBZwXWDOSLGu+grFacTj19L/ZMfT8Sy8hqtcJay3q95nAQ\n090sy8l0xuNH2azlXBQV4hKjOB6aGXeuqnLWeB4Gh64ysiKnKgU7fvPZC/LC0p4aMB3OjYnT3dL3\njlM3ok0pUqtBKGMxqtQzGPHBYYcOh0HrEqUM7amZs/osy6hXS+Lb3YyPT+dGXmR4d+9MPsmYThQ8\nyaClgpAJULmynHOMUadpRsGmJ+1wGQmH0Xu6MKKKjBBHBiJ9GJPGuoyJiyyozB/oqc8TIzKkK9Ci\nD5GQhktU9PhEjfMhzLi16FP7+fx/N1iHyP1AyxlJTy5YaUkGL9etThm7jzLXQNSpL4WwQRIk66ZQ\nEKQ2niqKIMEEpUzSVM++4wLyp92+H9aHAv5r4Csxxv/i7E//K/BvA/9Z+vm/nD3+7yul/i7wy8Du\nu+HTcA/eTw24kOgaE3wQZtxHnqeTZvW729Q0ETPZe/x6OgG//V3n//HB/fMVehZ5mYI2UToSiKOE\nCy6NjWuUCyibYbRO3MoA5t4KbF5M4kTtkQGZoizJbI5WFjeKXvMYRkY/METH7e0tIE4jTdNgjSws\nbdsyuJFFtmQYe+qslkaS0tzttpjM0ruR6+trwSK947AVzeHgRrqumzFKGTEvcM6l5t9IVEJDyvOS\n67e3PHnvOfvE2rDaUGQ5YXS4fuAUoKovZtVDnWlilEzD9U5ogJni+s0dRWkZneLQHvnqN15wdxq4\nCRk//0t/nX/vP/jP+ZOvf4Pf+cf/O7/1//zP/MHX/5gfv1hyeXlJd8ro24Y6L8iMEVEoY9Emoy5K\nghFhHdcJnqy1prCSMWotI9nRGJTN8K7FZJL5TE7jWZZxPB5xbiD4gboWqy1tZBFbLpfc3d1hTMbd\nXYO1mm4YyYqK995/zvXNm7kx2XVCdayqCq0NWRbBGqJWjN4x9gNXV1fsD1uMMWyPe3rXY63QKW1e\nMvgjp6bDVAUiIgZFkSUoIk1iFhnBaXExH8D5yO1uy/ure33q6fzOy4K2adFVIYYDdTWfm30vlMQJ\n4pjZH97TDz1FJf+Xcw7fu/k8VlpR19WcjftxlFncUqGtlcTrDLZUwohGRxh8nP0Kp2CmIWWmghVP\nkr8xphHuEBiCZOdjUiyMkwnH2fV6fpnHKJmzUkkpb0rglJYgDphE+/UIRKIj0gRFFkcZvPMCdUyV\nQaIIK2VS4phMEgz3sMeZvdgPsn0/GfWvAv8W8HtKqd9Nj/2nSID+e0qpfxf4JvBvpr/9fYSa98cI\nPe/f+V5vEImMWqQnhWzu7gNllO5bRKaEtLYzFqSSL6KcVCRbn4hSkSxKw2tyjIgpWM8ZslIYLWl0\nSGXO3Jx0kZB0PrRKXI8UpH28x/dCVGmc1IF31IhoetQKZWSlJng04MhAxxn28NHLYqKhCgULu8Ri\nsIjt1ugsuS/ZvrlB+cB6vebm5oYnT57M+gs+GHKbMQ4dWoM1irHvWFQl12/f8PFHX+Ttm1cYXZOp\nglx52r5jsVpyGltEdMZgbUnfy6AJQBwiyimUNXR9R1VVNF2LzjRDH0Q3ol6wPx54cnnJ3d0dzz/8\niNbvCF4R4kDfa+qqpI0do3Lkj5e02xtummtWyw/45MbxO1+54/XBkNUr/rW/9Df5ib/wC+y7W/7b\nv/tfcf3JV7nKFU8vPmI8fhOzvGRRPMbGkSzzqHDCx4ai1GTG4r0jKwxjGCmKHBccqm/Y7zUXF1dU\nZY42iovVhnH0aF2gAnRdQ1bkeN/R9yPaRKLrKYv3cM5R1xVVVbLfbynySFWuOB12lFlOjJHNcoNz\njsPhwMXyEV7LPECIjuWFjO8PzjE4TxYtwXkGpdAmAxOJOtL6A0ZbTscd2mq8S3jp6KmqhSjj+YjH\nEIww8utiiSVwjC0Yj223FF5TlhXXhxPleknMRswYyLWhD0qU4JQmt5lIcnpYlzVdO5AXYuRsrJXK\ntdC4NnDqB7KspOk7okHO4UGBkalJW+eYrJAGddSEfsBlEbcpaWqPLwVDVmiiGu8veqWJMYVtpVFa\nkTsBkEMMEvL81DyGYDVDiAw+4LVhiB4XZfHT4/nwi56vc6UUYUyQhrpvImklubX1Tqi1OpHsEmQZ\nE249dygRmHPOjoOepxuF063IEAouIWLsvZ/r53mq/vNs3w/r4//i28ZH5u2vfc7zI/C3/7QfJMaI\nS7P5IcEA3217V7Z0gi8mHHPGMab7EZTSAp3IWJPo200Qh1azHOGMJ8cofOp43zjx4Z55EGN80GVu\n1IDBoKJHOQcquZVPnysmXQIS1h4CeBFBt5VhUy9ZLZYMbw50Xctut+NwOPD48WPevn1LXdc0TTND\nFKfTibKUjDEEaf5oveH6+hprsjmj8t6TJO9n9sdE/1osRA1uyipjjByPp8SlFTutw6lJdL2GpvX0\n7SD+eUHwy8WiIriR1p1wDobBU+Qy/ZjnlmFo+ebX/oTKKnRp2e9u+erv/yHtfuBqlRNVyx985e/x\nG//3f0PbZ6wuVyz0HY8Xgb/w5eecjh/yD//Pf8Zf+sWfZfFkhcYzOoUhJ4+B3ErZLpl7KxeLMWSZ\nYn+4JcaPqBc53g10/QmbSlJg9uubRKvi2XdTL0q01tzc3HBxIcNCu90OFyJZVuKSJnOz21GvL8nG\nkcPhjuAdi3rN17/2TR49eiR0N1uSWTGVlSCi6TrJdhf1ijbCs2fPaPuW12/f0PWDKNcdutR7EDld\nF1TyLuwFAnAwtD1aGxbVgr3XjGPPsRtBF/fnrRsheIpMxsOrskiaHiVVUTINgUznRuf9POTU9z1R\nx5kdMihxGXIJv861Yswyxrbh1PUs3rtIbBOB2XyIKK/Q2ZT5CgfaJ9uyaQuTxEGCJCbGRowR7zyD\ndwJ1EMWej0gYwbyDUU+BGsBMpuNnsWISFZsE1aZM/H7kPJ4F6Pjg9nmbVPKpJxYBk/YVtZhZmx8R\nc9uIlDQBAfhdFHby51HnpJx4N0CrOVDL7dstuCb6EWm/4v5972UYpScoWwhnkEfSo37ngH3eQRsR\nzEyHCEoye6OlrIoJCxQDTX0/VekDykXUGDBBE4ae5rDneNhxOh2IUQYh2rZlvV7PgXriAIcQcKN8\nzhhlLFqaGCJ6Pwm+WyPCNUoVCb/O7818tfCgz/3kpgpiEo+fxPn75igNwkHGqaMWPK9tT2R1xjC0\nuMERQ8Pm8gqlVLLMMrx8fc2zDzfsdzsO2y06wLOLHJPD8djyM194zuiXHO6uKYucL14YProo+K3r\nHasVfPqtr/PLv/yrHI9HgiqJWEJ7RGeW7XZLmVzOJ9H7pj2gteWTT78hjUKVcbGJXFxcEZzi5uaG\nzeYS58Rgt6oWnBpp9MmCJt9nSAtSlmVcXV3RJVlNmwT9ddvStG3C9wMhkCiTHeP4hg8++EBodaGn\nrGqcCzTtCaXFD1SbnBiFjXJsjmcQXpoHQbDhwQM6A+w9JRXLarXhcDjgxkCWVfT9yPE0MAShhhkN\nZWbJtSIgi+/xsKfKS47HI8t6QcQnKVQnuo/qPnh578lygTbGcUy0uUBZ5izKirosUN6JwqF2VIUW\nUS3nU4UhueckwCTZqUoMDM8kJ+pVJKYgqyMyiThfb8I4CkqkXCcfSO/cHGzfDdRKKVRIQTnex4bp\n8zg1NTfvKYNTX8lGPd+PEXzSM5GY8vC6F5roWbxJTCel1Vz1/6DbD0WgBhE1Oecigqxshs8P1NP9\n85/n9ycN2ungkA7WBKGQnL5Vem5MzQRIKzX3QX1qJE5TSmcV0YOyJimayvOUcIFEFjVCGpE3CYQ3\nCS9TCvJYkIWMLEAcB/ruRNM0EmizjLbvWa1WMx9zCtJCpUpSVkFjDIyjx1qN1vk8mHDuxDE5gkz3\nvfezmezUdBRbpgZlDW3XYTIZEc/znDIvGTvJz4cwcjj1LKpHBN9jMQQnC5tWkeZ4ICsLlouKu9vI\nYrkiRvHcu1rK53i8hLLWrD/4Sb75zWt08KzyjM1iw5977wJ9agm7E/kAP/Xlj1lFzeXVe7y9fcvt\n4Q6LZhwDRVESksPGqEeyzFAUGbe3t2Leu71lUW9ouwNsIbfSifdemD+jcxSFNNKGYcAqT9+2LBYL\nxlE45pKBVgxJe8SNAzE4tBIOeV0viWPPRF+bzsfdbpcYS1OFE1OgininEh87R9HPEgAYzf4kDcY8\nzwlRGrxZkROjQBTCMNC0/YDVhs6NoKAfHHenntMQ6EZHXRTsTyeMCtSVOL9U9YIwjDMObayoyU2B\nOhg1U/AkgMe5/zAEjwWqomRRlxRGQ4jY3NBn4HJDFx1jcPNKIwFyGhQTKEEl56QpwA6JUzdl0tN9\ngDDKsY1aWFLCGIn4waOM+txADdIc/rxYoZTCz4Fb4kKYqmvEw3G6/s/393lB1xgjVl6pYTniU+AR\nmDP+KGl9TA28iR8NCByR2AeThkdI0EUIQfi0Z1+81pKFvBvMp+xjfr9peU0d3mkt8CmLloxyakrI\n6jsdrHOce9rXjIcJYJXKnhS0g3SrC6tQUZNF0TDOtEUFTW5yCp+xMguWWUXoZLTauyF143NObTtj\nXaI/kc1u4lmWMQyixeHGgaqWhuAkpD81h7QxrJZLTqeD2Gqlk2dqHAmMUqZpR/mf2qYhL0tM0oNu\nmoaqKNis17x8+U2pgnyHsprh2KCKjIinLHKIGqsNNrF4CmsYjUW5QJ2VfPj0A96+ecVFWfDRR49x\n44n6i5rT9g2ZgkebBYvFicP2FfVY8eOPH7HxGf7tgfzC8LNf/Cm++vU/pBv3ZEYWmlMvWK9zA1Ul\nutxTc+94PEoPGIXRBSpq1us1bdtTLxfYXBanSdAqxshms+F0OuGSFOt0HuW5fB/TfqfjYa2lqgpi\nlID8Yz/2MXd3d9zcvCUEx8df/JAYNMPgMDbHhx40xBF04dke9qKXkWRjIx6baZr2yDAGUBn90GGs\nDK1oJRmuCgP4EUNO1zXkVcmrt9cMQSqh1VLsxZxz7PZSkU2snmEYRLBLZWRG2B/BuZnRUVUVwzBw\nbPfzAk+UQZ8yt9R5RpHl7NsD+aKiXUTGStPHHh+FHw9aaI/aAFEYrGlKUK6npMSIJzjh6RuliP6s\nkk2Ml+icYMjOJYwbok/XaZw43ffXfxLkE1cmJZ9l1gRCEebFYCIR8OA2TSGGEDFmeu078SvGOXEL\nMWKy+/6ZwvzZTCb+WW1CzZsafffkue+2Fj0MlnAOpauEQ82BfP6DSqWOHBwmCFudQx8KbSXDnBeQ\nKTgrOdmmnHo6uCrhzpPhfUxZfKYNyliKmGGNpYwldV6jPZhoqSjZ1I/48gdfYmULdnc3DO2JEMaU\nmYU5aEwZzjSyO5Wqi4VoLYcQ09BGwOd+xp2ttZgEZ0wQR5bJJNnkSj4FGu89Q9fP73V3d8ezDz5M\nwd9TmCUXmyucc3z26gUfPX/Cze1rdO8IuSGMDh8iJlOE4CBIo3e9WRK6PcuiwuiKX/8rv4Ixhn/8\nW3+fN9/c8uUvr7n6YIUKjuA8b9/e8K0XDf/09wfqaFkVFT/+wZ/nYr1izDUvXr/hp37659jt33B3\ne83hsBfvQgImyUoaXXBsdwS/Z7nUNKcRayquLs1Mm8sygblMZmeamvjeDZyaw6wGOI1vN02DHsQb\nsixLCqvwrufRxUqc1nO4LJdst8LWiYxcPVoRwsCnLz/j6XvvkxUFw+CAHEUkOo/WlkePHnP6dE/b\ntmijMUbMUcuyxGaRoC2jl3PauYEw9GSlwTUO5wPojrKq8QPcHPf89u//Hh/90i/w0cWGly9f8mi5\nEOaQ05isQLuRYehFm7oQXB6EeTGOaew/yBDMod2J9rcbKbVmXVTU1mKC6JC3caBYrzEbTaMco/Io\nowlhRCUrK7zmPsm59yEVq737YarovPCnhWKRAuEUIGXIRJp5ksA9mJEI9wkZSKastCaGCReV+0rr\n5DSeMuoYxSZvxqjvexjTzwcwa5w+TdJHSawSyahFokGIDhna/shk1Al+CA+D9PfS7Pi2LPksUH8n\n4P+8hBH5Q0jHZ8aexHRTC1Y9FUOJKkjSqJ0yfK2N0PCUkmHYCEonzmYSY9GAwVDoEhMNla0wGKL3\nXFQbPnjyAZvFkjAOuKFHxUiZ54kq18/wRJPw03M9aO/ivFBJli0ZU3PqyLLFPNAAzPDHdLFMAXra\n/0xhS99RlmWoXi7kSSXPZBUxyIDDoTlxOpUYV/D+5gLnA1opHB6rNUPfs1hW4ovIgiFvybD8zJ//\nObRZ8YWPv8ijxxm/+dv/kK/+9msW1Z6n7zkO+yNf+cMTndvwF3/+X+fp4wW/+Bd/gdA2XL99zea9\nC27DyGe3txQE+k6czquy5nTa0TZNqiygKCqsyenagaurJ1hTysh3ns3Z8egdKnHDTcL9TZaBCqkh\nK03Ruq7nDH0YPGPfzcaywY1kRqMyGWAZXU9Z5Sgt2hld17E7HtmsOxa1SM2O44hFegn73RGTGy4u\nLrjdXRNU+jxGMY69FONGmpB5bsmNpfcDPSNRe0EYLBAl0zSZ5uWb17NOR1VKA7rKc2KWEXRGaI5z\n9TAMA3VyHc+LgixmxKgYu2FODO4XayN63IOjjScckfyigiLDaY+bJIuTYFgMQqmNU99nxqnjPAE7\nVcrSEkqZrowqIDE+oNH3HAHMg0Rs2t5lWGh1T9md/j4HXbKzz3M+MyGpnBAT7vd53+/iQaDWWj8I\n1NqASfRQaSj+qGTUZ/CBFCfp4RAxupoB4USekFUvRoIRzY9oEEcGpUCb5DqRyqkomSkavBKFKwwY\nnc1QC6Rx1pDybOulnFKBoA0Ek9DpQEySpPIxJsqgnAyxcIxjwGLFlkhFsmjQQTGoQKZhEQzLYMiR\ncesrU3O1XBJHz+56KxoUCT8bwsC+P1KXOafDAW00ShtsVtAOgoWuDcRxpLQyuhuGEZNJmTs2ThgD\nxjC4EWMCMWqUzmZ/PNGi2M0nvTGGduwF8w2aPK9pTh0//qWP8e5IVtVo63nv2Yd8+uKbHHvDotR4\nNXBqejJboVUGeEIQPQ2lkl/fyqJUzqE7UFaa1jX8zM/9NT78+Bf5jd/4+7x+/Zrf/MNP+NrXjrz3\n9Cf44p/7In/jb/46Hzz9mJeffYvWOZ7+5E+QFRl/4fIxv/U7v4t3kUV9SdMMBA/Wlhit8G4kz2rq\nKqfvRup6xXJxQZYV5FmF1mCM4ORGGZSDTGmMzgjK4dRIliRiM23o+xE/yHlXLytaP5Blljw3KRCL\nE3xRSdP4g+cfsd3vaIeRoR85tkdcP/DqxWe89yxQVQuqxRJPpIiRMrO8evWKGCsW1SNubt9QGsuj\nRc1NI6yUOqvo/IAePcEp8mxFN7biu2lTlu89PmpOVGxPijf7E0/blsePr+j7F6yWJUPjMVYzVLVk\n72WFznO6KHzxg/OUdkWmR5zb40MvqhoGmrYlyzWRgmZssdrSKcfqyQX7wtGrlqjjDGlMwW2IXlxf\nUmMw+EiM4m3pkpO4cgEVAkRxOQrJTzMGhUkyAEpJf0cgxgleNHOy8i5BLWbpIS0NP6OdBN+YfE/1\nPUQyQR1j8KgUqIHEvbby/KjR0Uk2p4VDbbRJ2b00ka0Sho6OsiBYnfGDbj8UgVpYCva+EXi28ir/\n7c3E85+f9/icNSpEYlAn258JWnnndXP2nfC36XEJ0n5+TkCjVHiQ6Uc9U02ISLmqTSalTzRyIJVB\nm5JMZ1S2pigqci/ldFEUs5PGVE77ceTYnB40pLTSdH3PxeWV6H6kUl1by0QPGP0ARuOShZBke9I0\nq6pKyuXEPplYHJNPYt/3s7nANOxAUpuTjLri5cuXfGDXPH3vPZqu52t/9AeEsJoHZYqiRqEYB8fo\nTizXl0KTKwq6vkUVJYqM7XbLysEnn36T06nl6uoxv/ZX/ypvb2/4+te/wa/92q/x3vtP+ejDLwCw\nvXtL1x5Z1Dldc2C7GxhCpMgsf/LZGxZ1IaL9vWDSMUBZrvFBUZY137p7QVUtqes6OW73KOxcYVhr\nOR4aQghsNpdn50VI4kNjYnHIGH+WstAQAtamyccombMPIuX58uVLynrB08dPpNlnLXc31xwPJ16+\nfMnTp8/I8hKTZ9R1TVHnDMPAy1ffEquxPOfU7ACo6wpjNP3QkmU5TXPC2pzVckN36Ah+TI1gM+vT\nTBZULz57xZ/70of4CIvFAq96sq5j9J66rESzxXl633FxcSH/R1Xje+mR1HXN4Nq5AjPGUNqMsigI\n48DoHbrOQCmGOH5nIi/nmevD+9/+nLPfw3fZ4dlrzq/nB7EhZdNKydCc1gkw0WpuAE77mNzOiUmS\neI4FzIuOTpCNOjMrmXpiUSdxY3Wftf+L2n4oAvX9l/nt0MV3Up4SCtPDA3P++kBMX5hCGS0uyUrP\n/GjBkZFsXquZvC77MWKfk+b/JZs2KCWi4efb+efWKsNmkpVlWDSW3BbkJkeFAusyqrLCUpBhyLOM\nqlgKZmxEzc73HW9fv54nxWyR0+4OaBVp25aq7lHqnqXRO/EWnGsxoxm8ZAN938+jwdvtlqurK5wb\n5vHfScchxjirqskFKXhlVAXLxZq73S3LVc3rtyOn457HV4/pJ05t19G1FcY6skowVx9k3Nw5hw8j\n/VEab/VySd+PDEPHH/3RV/nyl3+aKi84GEXbeFRU/OW//K8yjp5T07K9e8vloyv2d29wvQgfjU5c\nRaIxhLHhbntDWTwVNksPRV5BtNS10Pe6duDZs2dcXj6iLPN5ZL7vAuNYzoppxogM6nYrIvsxaIyN\nDL1jHAfyvJirvkkXQ26pWZuC2vWtUP6y5MC+vdtRlqXYXXmHGz2H5jQvjFVmk5vKnnq1ZNFsCHFg\nvH5DWS/Z7e449vvUFJZm3qPHG4ia02k3UwK9D2gjzi8+KR9prfnGi8/AZCLziRL9aKtxYyAEl5rv\nkWHo2e+31HVN244irpQqikktUSlFbnKyJLPaRceIo1pXjEboqe9uD4JoTNdbEJ5y9AHQ9xZbMU4U\nLEASBTXxu1MDkIdXKucrgzqLI3PCZuRxo8FGhVVgtPgy2jN8eyYLEHAaohN9vWnhmJrvKqlVCuQ9\nwSoZQemziUfzIBb9yNDzlBLxmXcDdYyROHz7c0EayA851A9v4swQ5waDcLTvB2l0cnw4X7DjRNVQ\nifWhIlGJOa3SEUgH6MEHSieaUhhVkOuCTGcJk86ps5rMWMq4RgdYmg1lyFA+kmc1y8VmDrpd13Hc\n3knnP8vQVj2wvarrmv1+z3K5ngP5qWvT9+UT5pzj/ZiGYqDtJZArbXlzfct6vSQrcoa+mwPMarWa\n3/8ejzP0vaNtBpwLHA8NRVHh+w7fd7z35DE//qUvcfvmBX0/4E5YRDUPAAAgAElEQVRH1qmSCF4o\nVDYTsf31xZrt9ojSkffee583b97w6NGK0/GG7rjluNkQogS/168+YbFYsj8cMHnBmzefYqKn2e/Y\nba/xQ09QmryouLnZcnFxIeyL056qqIGIMQWHfcv1zZanT59S18sZmzwcdknY6jAvYtKIhSzLZypc\nnzLKosxYb5Yc9qeZOdH3PUWZzUF70oz23vP86XPatuV46qhrkaQ9nlqapuHy8pJFvWR/OvLmzTU+\nQLi94f33n1NVC8axFy73KfDo0XsIzTGnuXmVphM9RZFxOu2pqorVumbQsvAcuwFHxhisjGwryey+\ndbvlD775GT//Ez/GciUBXueB/bGjd8nKS2s0EasVXXPi8vISg8KFkbHrqcsKjcYNAaMsyywjywy+\nrNDlEn1R0NmAV6QG3dk1dRao58Z8mGCGdyrkoBI8kqYWEwxC5Lv6Dk6884cMMAnYXgeM0hgVsUpR\nKIXVGqs0+Zl+eIxBRNliSBZbeo4JXgrrGerwSmFV8kRUidWR/hbVPSVw+v9/hAL1w8z0vOQXfmd8\npwyRYRL1zhcyvea+ISBiKngE9piafErdC6ikKUVR4zNnnGojDsJYVFCCbaswiy1NmgE6yoHRWpNh\nKHWJVZbS5BQUFDEnJ6eiEFik8+giozCFnCxGDsHpdJpF2YuiwGSWV29egpHvpSxL0fVYLERXQQv1\nSkS7BL9rh4asLHABdIDMihZxPwhWra2h6XqKqp4pZhPNbxpyEYW+Bfud0M76/kRV1RijKRcZn/3J\nt/jggw+IJuf9955y++YFTdPx0XtXaCvUs+ADISia5jRTJi8uLujbkf1+i4oBqwPt8RaNwo8HHr/3\nBAX48cT1mx2jj2lIxGONIuqBLNcsqgVNN9L2HavVgm7nZpH7PM/RBprjgRhhvb5gvb6YOeiT71/T\nHHl0dUU/SPDLs2kyL8ySniE4jCnwfsQ5kxqu4jafJTW0GOO8YA5DlwwVOryP5CZjWS84NR2ZyVkv\nMw7HO/KsYLFY8P77lu3ugIuB29tbLjcL0e6oarI8x+YZd3c39INnuVwzjp5xFKfzSZel6xq6TgwD\nfNA4pXEqR5uYFOgCxeKSf/S7/4TnTy5ZP3+KtTm27ymMFk6yk0EVpdQMvXVdx7IqMUqzWtY0XUth\nC6wW6GPwju64RW8qilWNzw0uunlIbYKFJphtCqI6ceVSQi1QQRRaXAgRrTNikP2olLFqLXoZXt9P\nAGt1ts932F1TYJwft5I5GyVKejoEMjSZtpRKpjSCEw0PqyzKCC3Sc888cYCbFwo9kwTQSUNbTXof\nQvmb4pBSajY/+UG3H4pADWrm78LDzq1BPwjU8O2MjvO/T0MbYf5iJyaJKGSRFOwmQ3AAZfQ8+CJW\nWjKkopUV7FlH8HKCTPP8imQpFCI24dClKgTUCIZC5ZQqp6YgizllakbYAGr0FAsDBMnEkqLZpDmt\n85zdzTXAPedVG0p7b0Y6KZFZZTh1I2WV4wePH0YZ3PGBoi7SdxmFKhclAxzHkTIZBAxpwnCy7AJp\nzmRZxuCktSsnm+dyvZYgOA5kWSEeiQmvdT7imoYYFd4ptBFOdtueGMc1d3c3WF0yoLi7vWFRFlys\n1mRajv2rT79FvVqyWl+wXFQMbqTILVFn9GOHi4itVl2ToxkYCdHw6NEj2lNDWZbkeYFSkasrWXwO\nx0a0pTEMvaPtxHLryZMnHI/7hMVbIn6u6iYOfVVVCYMucG5kuaoZh7N+xRmvfppa7LoOFXpxowea\nQ8Mw+vkcm8bwu3GgLGWxxLtkIpuyShXYHQ5cXl4SlSEqw6evPiUGjVZZ8qWUse7dbgdmBcAYxR17\nQCQ7fQBCwNkFX//kJX/4x3/Clz54nrB8R5VZlDbEIGrMwyDDO8ELvKJihrWGfhxmBcFjm4awzMAp\nDJT5ErXM6Un6N9ELTTBt77Il7odPmKcBUVKtKkAFhVEWoX6kKzRK9Rv1fQ/rQfCf8OfP6VnJ9ymm\n0ZaIiYrSGnItHP9snoozJMYzOrHOvJaFyymFiTHJ6oqhLUl0DW3QSrw/Q6riVTJOPq/u/0y0Pv4s\ntumfOR8euf+bcJbvM+mJHvcwcM+z+vAg6MtUIDAH6/Sacyoe9xBIJAXuoIiTwFLQUtP5MHecdSpx\nMqXIjZUDT05BjsVQxozMK2wwZFFhDeSJCWJihCCmnyhPSIGxKAqG5kSfMpzLy0tOSQnOmmy2AJvY\nMTFGCJIR6CgCTcFFjFHiYjJ6jAkJRsk4HveCv6Jxrp+biJMF05RVOSeCQBHDYrG47/THSNeceP3y\nM548/wKresFysWZstrRtS7Gw9J3jsG+p6kjftyk4Hdntdrz3eIEKgQ8/fI7vO0bXYqzIQX788YfC\n6z41eKdRUWF0xAUv0pZEVGbxIZBVNZtFRtuNIuAVHM5psqT5sVrWxLilTdSyvu9p25a8kIu9aRqy\nlIHPF5QWtT9SOeyjp+t6tJagWtc1e7eXHoQtQU3HIp8XQ+ccNgRcDGQmp+06imoxZ+DaB6qyZnvY\nczo1WJuB0RgT58lPrTV5VnI6NRSFuMY8fvQ+wWvutjc0p4Hbmy1FmWFtjvATtIy5B5koDEqa3igY\nm5bN5oLf/b3f41f+pZ/l6XotIlYpQ50cfab707Xo/UiZGUYUPgQWVc2yWnKMDSaLVEVOtq7EvSWC\nigEdFfFMLXK6NqdAZSYtj4mmPEEcamqYW4ISO7Hz61lE+OODqvthNs3nBkWlFEGF+b5ByRCW1mRK\n2oJGTfMPAmFqLyYBJupJkTr1tRCGSJSsWSXP9ZDU8bTWBKPSZ/Xftqj8oNsPRaBGAZlGJ5YBoxZx\nI6USR/Q+iHvvz/iYnkkdT6uEMaNRGCyTw/LEJJFsep5YMj0qlTGSHYl4iooaEwNOQTBJSElpTAAb\n7TwmTlCYoFkVKwpbYFDkKpI7g/U5djDkKkMXBq00hJ4Ye9ZlhQ6e2LcUixWjKTEx4McRFzzN0DO6\ngc1mw/X1G3SM5FXFOHqCDxhj8T7SN02CBrokpA9NP6DLiiqvwEOZ28SjTiJFa9GS9kOPsZJpZHlB\nRNF2HTpJfspFEhmGliKvcL4n9IbD3vDRl36GGBVxOPHp65eoiyd84/UrTG24soauH7GZxvuW6zev\nWF4+5fjyyLMPv8zt4USzP1AXR/Z3N3zpC88wpSWqwDDsQVc8frLh+uaWgGJ/e0CbDKXE7spRgqlp\njh2bTY3KHEPcYkpxvQko8rIiqJxieYFqenRecep6MUN2kb7rBb7KR5wbePToEUUh03fL5Up4xx5c\n46nzArRl6EZCOKAwyRJKJ1syBeOAQppUy8rixgbf96ACy0UOqofQAwFTZmRZwdoGdGHpBw8ucNrv\nCb3Ioj569IgyBezFYkEYA60eubJPcFb4zm0INGmysNcZI+C1JkRw0QFRNFhUQEXDYYh0Ef633/yn\n/Pqv/iKXRaT0I+MgcrRvT6L9stms6NoTZWExqfpSWhKUsspZLkvAcRMannz4Pq70OOvRwWOiBQ/e\nnsOUk1XcxKwSvWmljYjrB4OKRgJ3ki/NokJFgUd6BK6MWlNqhTIBbUQCWCuLsRnGZHyeZ8kUuK3P\nMApKpcmIFDGQBcm7CiUxR8Uk5+CFsidriEtiZgqrAl6fOdHkAtlElVxm9P009eQyNSWSE/T3g27/\nAnbxL2b7Tk1BzXe+qZhWQVT6KZ1X/R32db4anxPc5/ePn/d5jDQj0sJh0ntYFDYKRp2hKXROriwq\naqIP8yhs9AE/uhmvmgZKuqGfmQPnixBI4/BwOMyUxfMqY8L+JuPR85+T6a1SQvubVvMJn52eJwMb\ngtcaY+b3mgYaJpx2mmScaHyT4NNqs5wx7mmycXqfyZNxEpI/Ho+UZclut+P6+pqmPbLdbjFG8erV\nS3aHLfv9HmUEenn5+hU+BrbbLYvFAm3u/+dJm2S1WrHZbKiqIhn7mnm0XqYIhXK42Wzm78paGVhZ\nLAQLLvJy3l/TNCyX63vOrjEsl8uZOrlaLeb9yv9mUvUhDdAsNwnX9vNz5PsMaGWTS4tBeUdzPOBH\nR2EFj82t4WqzZnN5CVqzPx5pug4fI/vjkagUm3rJsqh49t5TXD9QVZVUQ+MwywXIaawTHjod9/s8\nLCr4+p98g7vdjtGLVC0kWMCoGT44t+VSSs3HcjKh0Fqz2qyFBom4fIuehVBVP4/t8PDaM6jEYdYk\nzZt0nU3n2sPXTBnp/X6NzubJP+lTfefbu9f9lFkrpTDzhKO4nxsier7J57MxMUS0JkOnxqSepSrO\nh1/kTcKDePMubPvPu/1wZNSkEiHdV0GMO+Xx6YCQglk8++IDMlM/lUb3M/33tD6VJg0j586FKsEh\nKnUuNNOkokoZuVBxdJQyXCvp9KoQ0MEkUr4ic4rMGnIsVqwgiC4SXQSbnGBiJBpNGB2jGYlasNGy\nLHHDSEjMgT6J8scoinmLRfXg+5GL38+c3Rgj2ppZaGkaAZ8YIiH4xCCRYDG6PjW/cnKbcTq1rFYr\nwWmT/xsJFjmnLUmgsRyOR9bLis1mQ5YZTseGrhtkoKOBTSGLjp4hFIfz4kRzs93ifE9mFcEH8jKD\nMPLy1SsuLte8ePlS9DbalsViibY5L158wnK9Ic9KyjJnu90Tg6K6WtB1DcPQQVKDizHSJNnQ6fhP\n31mRW8aun9k0VSW+irIw6iTur9OUpwTuRbmQoKdGirzCx5GATF6GGJNwv0Ur0YDQEUKwOCfZdEiW\nZH3f0/Uysm+sIlNAdLSnFpzDkxZHNGVdYYwRT8xxSBzmERU9hbF0PvDs2TM+e/2KMXiKskTFEu/H\neSJRLul7bq/kfJLpvXj5ij/62td5/rNfBmvRYUg6zIJFiNluICSB/ExJia+VSCEsFwv5bKYRE2qV\nYEIFMRnTKvUQmz7Hk42X/tAUeJUS27oJnRS/TdH8UCkZkjEKk56nICb4UQv9lajTcMrDbbqSJTCn\nRSOKUYFOcKdNLzMxEf2Umg0DYiBReFNfSslniUpE9uUVcTYR0ARiNJL5ni1W8n/9qEAfTAc14Voq\n3LMQkGGPaVBFo5KZAOhUqMwMDCbcycyNiwmL1upeSwQgai3j35ztO0pWIAdLYdO6qpWRrrECG6w0\nE4NCBwnUdhRsy0Yt4jEhTSzG9Al9wA8jxMCYMtaiKkSn1oti22RgG71LDZ9hxo6nLEmww4DWahbL\nGZywRIC5qTVlz9OKPmXQOonuTBz0CRc1Rgx0p4tqCvxTQ22qBoqioKwqBudQKnJsTnS9Z/SBthnp\n637OwPq+FyuvxYpxHJN33w2bdc2iynHBI1eDph0dfXsAc0JrA6al67cpa5chk91uK/+HG2maI6vV\nhnpR0bUOhTRY67pGm8kGSc/c5uZ0wA0D6/US0uBG2w/JLqtCYfABBjdKc8tIZpfnOUGpRHdMixmA\nyYmJhz5l1sYYVuuKcVCM4zAvnF3XyALsIcZeggUKvMP3TdJYEc9Q7wNFVbKqc7bbLW/u3nJxcUF3\nK03QLGWj6/Wa3o20buB4PDI6cF6BMmgjWhaRkLTTdXJIApMX/O4/+wr/ys//DEVtqG1HPwiTKcsM\n3g24sQdjKM09XXa6ZvI8Z7lYsI29FPcKMaw1ChMEuzXq3mVlOp+ma1kppN8TJUsKgI7CtDLAkLjL\nOlWvPnKfiEUAh4rS4McpMVEICmPyz4soMAGdinQlp4UkvfvUTFSIrKqJEici4ITkJdedEnuxqJO8\nhRFoY6J3y3yGzFmEyAN7PlQyKPkBtx+KQH1eLk2BRccpUE/kcaFPiRLVlAVIyTY7vKQvRSedDmkS\nTbxqaQBOgc8YkYn0MR1OrWcy+1RyETW4iAkKGzTGR0pvUlsYlFcU2pIrTWYVmc/ET00ZyFXCVrXo\ndwTJhAcnF/Ai6SpEHzgcDmy3W6YmYVmWUhamoOOcY7lcMgwDSmn6TgK7tRbLfQN2uVzOdLFxHCkS\nS6Rt24RVh5mlgLGUhTAZtNaMg8Au5zrXGJl0E29AEZEvKsnMu95jsgLGAecVg4vzxGOVZFUng9D9\nfg/Acr2i68VBvS4zqjKn7UYaF8m0x/mBuqw49dJMjQR0Ksu1tqm6MvTDCdMIS2NyD7fWYoOYT2SZ\nlgZo0jlZLBboesEwdJJJWUuxEPgmy3PG0eMCZEVamIzFWqnSJpGfCQfN8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BmM+kZ3ZqQ3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\nyEEOcsnlYKgPcpCDHOSSy/8P5ArbqMbKlNYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4adb96a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breed: Labrador_retriever\n"
     ]
    },
    {
     "data": {
      "image/png": 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oS9P0mM5wfdWzLtYc3rzHi3cPSYVH7EuGds37D9+jKFYM5RqFoDMWt+mI4xhh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ad9c7bb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breed: Brittany\n"
     ]
    },
    {
     "data": {
      "image/png": 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N1CmA0pS6eHZa4YwFXVfqHjxSz0A9WF/dK+Nxghbbdzni5qKJaXmGVzi78RC3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hEcYORhje/eg+O7ubjCfnVJvXeOvtP0P3NacXp8yXKRv7t7icnRP5Fk/P73Pv3j2WZUnoxMzz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\ngvwMZ3iH4/8Apl5kSsKK9msAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ad7feaf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breed: Labrador_retriever\n"
     ]
    }
   ],
   "source": [
    "# Testing with dog images:\n",
    "#\n",
    "try:\n",
    "    dog_or_human('images/Labrador_retriever_06455.jpg')\n",
    "    dog_or_human('images/Brittany_02625.jpg')\n",
    "    dog_or_human('images/Labrador_retriever_06457.jpg')\n",
    "except RuntimeError as err:\n",
    "    print(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Hello fellow human!\n"
     ]
    },
    {
     "data": {
      "image/png": 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I7O3coUiqqohzF+qiPiIcAoZhQL65QUmC/e4AJsIgO7OaCHIuta9U5fF7BsCM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\n/k3bxvO0FMZ2LrtInHPvOue+5B9PgD8Cb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ad62eebe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "you look like a: English_toy_spaniel\n",
      "\n",
      "Hello fellow human!\n"
     ]
    },
    {
     "data": {
      "image/png": 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+gbg3jaIt2OsGd+q/2pjPQa6JqpIXMRwTOQug8c6SunKzO9WqUhl/dNWDOqeD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FAAAg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ad44b0780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "you look like a: Petit_basset_griffon_vendeen\n"
     ]
    }
   ],
   "source": [
    "# Testing with human faces:\n",
    "try:\n",
    "    dog_or_human('lfw/Miguel_Contreras/Miguel_Contreras_0001.jpg')\n",
    "    dog_or_human('lfw/Jose_Jose/Jose_Jose_0001.jpg')\n",
    "except RuntimeError as err:\n",
    "    print(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}