Bengali_OCR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Project: Write an Algorithm for Bengali Alphabet Recognition App \n",
    "\n",
    "\n",
    "---\n",
    "### Overview \n",
    "\n",
    "In this notebook, we will be developing an algorithm that could be used for recognition of handwritten Bengali alphabets. There are 50 basic bengali alphabets; 11 vowels and 39 consonants.\n",
    "\n",
    "\n",
    "### Import Bengali Alphabet Dataset\n",
    "\n",
    "In the code cell below, we import a dataset of bengali character 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",
    "- `alphabets` - numerical representations of the 50 Bengali alphabets from 0 to 49"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 50 total alphabets\n",
      "There are 15000 total alphabet images.\n",
      "\n",
      "There are 12000 training alphabet images.\n",
      "There are 3000 test alphabet 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",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# define function to load train and test datasets \n",
    "# the function returns 3 numpy arrays with filenames, one-hot-encoded values and raw outputs\n",
    "def load_dataset(path):\n",
    "    data = load_files(path)\n",
    "    alphabet_files = np.array(data['filenames'])\n",
    "    targets = np.array(data['target'])\n",
    "    alphabet_targets = np_utils.to_categorical(targets, 50)\n",
    "    \n",
    "    return alphabet_files, alphabet_targets, targets\n",
    "\n",
    "# load train and test datasets\n",
    "train_files, train_targets, raw_train_targets = load_dataset('BasicFinalDatabase/Train')\n",
    "test_files, test_targets, raw_test_targets = load_dataset('BasicFinalDatabase/Test')\n",
    "\n",
    "# load list of characters\n",
    "alphabets = [item[25:-1] for item in sorted(glob(\"BasicFinalDatabase/Train/*/\"))]\n",
    "\n",
    "# print statistics about the dataset\n",
    "print('There are %d total alphabets' % len(alphabets))\n",
    "print('There are %s total alphabet images.\\n' % len(np.hstack([train_files, test_files])))\n",
    "print('There are %d training alphabet images.' % len(train_files))\n",
    "print('There are %d test alphabet images.'% len(test_files))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the Dataset\n",
    "\n",
    "In the code cell below, we analyze the dataset using some statistical tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of the training data in the dataset\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1237d5cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of the test data in the dataset\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fc96320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to display the distribution of data in the training and test sets by alphabet classes\n",
    "def plot_dist(target_set):\n",
    "    plt.figure(figsize=(6,4))\n",
    "    labels, values = zip(*target_set.items())\n",
    "    #indexes = np.arange(len(labels))\n",
    "    width = 0.8\n",
    "    plt.bar(labels, values, width)\n",
    "    plt.xlabel('Alphabets')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.show()\n",
    "\n",
    "print('Distribution of the training data in the dataset')    \n",
    "plot_dist(Counter(raw_train_targets))\n",
    "print('Distribution of the test data in the dataset') \n",
    "plot_dist(Counter(raw_test_targets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EAAAa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\n3HgrSZIkSZIkSRH6/wF/Fh7nXmfEkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12001aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Show sample images \n",
    "from keras.preprocessing import image                  \n",
    "from keras.preprocessing.image import img_to_array, load_img\n",
    "from PIL import ImageFile                            \n",
    "ImageFile.LOAD_TRUNCATED_IMAGES = True \n",
    "\n",
    "#Scan through the training dataset to pick up one random character each folder\n",
    "alphabet_folders = sorted(glob(\"BasicFinalDatabase/Train/*/\"))\n",
    "sample_files = []\n",
    "for folder in alphabet_folders:\n",
    "    sample_files.append(glob(folder + \"*.bmp\")[np.random.randint(0,49)])\n",
    "sample_files = np.array(sample_files)\n",
    "\n",
    "#Display the characters\n",
    "fig = plt.figure(figsize=(25,10))\n",
    "for i in range(50):\n",
    "    ax = fig.add_subplot(5, 10, i + 1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(load_img(sample_files[i], target_size=(50,50), grayscale=True)), cmap = 'gray')\n",
    "    ax.set_title(\"{}\".format(alphabets[i]),\n",
    "                 color=\"green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xSZKUNTND0pC+9nRCCBx88MH7n0Y/eML8Kh1yyCGAmaEu8z460mw8ikmSpKyZ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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fda5e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#View random characters in folders\n",
    "sample_files = []\n",
    "folder = 'BasicFinalDatabase/Train/202/'\n",
    "for i in range(10):    \n",
    "    sample_files.append(glob(folder + \"*.bmp\")[np.random.randint(0, 49)])\n",
    "\n",
    "sample_files = np.array(sample_files)\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "for i in range(10):\n",
    "    ax = fig.add_subplot(2, 5, i + 1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(load_img(sample_files[i], target_size=(50,50), grayscale=True)), cmap = 'gray')\n",
    "    ax.set_title(\"{}\".format(sample_files[i][29:-4]),\n",
    "                 color=\"green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 50 total alphabets\n",
      "There are 15000 total alphabet images.\n",
      "\n",
      "There are 9600 training alphabet images.\n",
      "There are 2400 validation alphabet images.\n",
      "There are 3000 test alphabet images.\n",
      "\n",
      "Distribution of the validation data in the dataset\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12000ad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Splitting the Training set into the Training set and Validation set\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_files, valid_files, train_targets, valid_targets = train_test_split(train_files, train_targets, test_size = 0.2, random_state = 0, stratify = raw_train_targets)\n",
    "\n",
    "# print statistics about the dataset post split\n",
    "print('There are %d total alphabets' % len(alphabets))\n",
    "print('There are %s total alphabet images.\\n' % len(np.hstack([train_files, valid_files, test_files])))\n",
    "print('There are %d training alphabet images.' % len(train_files))\n",
    "print('There are %d validation alphabet images.' % len(valid_files))\n",
    "print('There are %d test alphabet images.\\n'% len(test_files))\n",
    "\n",
    "# Show distribution of data in validation set\n",
    "raw_valid_targets = []\n",
    "for i in range(len(valid_targets)):\n",
    "    raw_valid_targets.append(np.argmax(valid_targets[i]))\n",
    "    \n",
    "raw_valid_targets = np.array(raw_valid_targets)\n",
    "print('Distribution of the validation data in the dataset')  \n",
    "plot_dist(Counter(raw_valid_targets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the First 36 Validation Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x124d5e278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,5))\n",
    "for i in range(36):\n",
    "    ax = fig.add_subplot(3, 12, i + 1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(load_img(valid_files[i], target_size=(64,64), grayscale=True)), cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the Training, Validation and Test Datasets\n",
    "\n",
    "In the code cell below, we prepare the training, validation and test datasets for CNN in Keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9600 train samples\n",
      "2400 valid samples\n",
      "3000 test samples\n"
     ]
    }
   ],
   "source": [
    "### Prepare the Training, Validation and Test Datasets\n",
    "def path_to_tensor(img_path):\n",
    "    # loads RGB image as PIL.Image.Image type\n",
    "    img = load_img(img_path, target_size=(64, 64), grayscale=True)\n",
    "    # convert PIL.Image.Image type to 3D tensor with shape (64, 64, 3)\n",
    "    x = img_to_array(img)\n",
    "    # convert 3D tensor to 4D tensor with shape (1, 64, 64, 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 img_paths]\n",
    "    return np.vstack(list_of_tensors)\n",
    "\n",
    "# pre-process the data for Keras. We rescale the images by dividing every pixel in every image by 255. \n",
    "# So the scale is now 0-1 instead of 0-255.\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\n",
    "\n",
    "# print number of training, validation, and test images\n",
    "print(train_tensors.shape[0], 'train samples')\n",
    "print(valid_tensors.shape[0], 'valid samples')\n",
    "print(test_tensors.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benchmark Architecture\n",
    "\n",
    "Build a simple CNN model with 2 convolutional layers using relu activation and 1 fully connected layer terminating in 50 softmax classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 63, 63, 16)        208       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 31, 31, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 30, 30, 32)        2080      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_1 ( (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 50)                1650      \n",
      "=================================================================\n",
      "Total params: 3,938\n",
      "Trainable params: 3,938\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Importing the Keras libraries and packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "\n",
    "# Initialising the CNN\n",
    "model = Sequential()\n",
    "\n",
    "# First Convolution Layer with Pooling\n",
    "model.add(Conv2D(filters=16, kernel_size=2, padding='valid', activation='relu', input_shape=(train_tensors.shape[1:])))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "\n",
    "# Adding a second convolutional layer with Pooling\n",
    "model.add(Conv2D(filters=32, kernel_size=2, padding='valid', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size =2))\n",
    "\n",
    "model.add(GlobalAveragePooling2D())\n",
    "\n",
    "# Full connection Dense Layers\n",
    "model.add(Dense(50, activation = 'softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 2.0000%\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "# Accuracy with random prediction is expected to be 1 in 50 so roughly 2%\n",
    "alphabet_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(alphabet_predictions)==np.argmax(test_targets, axis=1))/len(alphabet_predictions)\n",
    "print('Test accuracy: %.4f%%' % test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Model\n",
    "\n",
    "The benchmark model is trained below.  Used model checkpointing to save the model that attains the best validation loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9600 samples, validate on 2400 samples\n",
      "Epoch 1/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.9336 - acc: 0.1833Epoch 00000: val_loss improved from inf to 2.91677, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 33s - loss: 2.9329 - acc: 0.1831 - val_loss: 2.9168 - val_acc: 0.2071\n",
      "Epoch 2/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.9146 - acc: 0.1952Epoch 00001: val_loss improved from 2.91677 to 2.90175, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 32s - loss: 2.9157 - acc: 0.1953 - val_loss: 2.9017 - val_acc: 0.2067\n",
      "Epoch 3/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.9019 - acc: 0.1909Epoch 00002: val_loss did not improve\n",
      "9600/9600 [==============================] - 31s - loss: 2.9024 - acc: 0.1909 - val_loss: 2.9038 - val_acc: 0.1938\n",
      "Epoch 4/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.8894 - acc: 0.1952Epoch 00003: val_loss improved from 2.90175 to 2.88434, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.8896 - acc: 0.1949 - val_loss: 2.8843 - val_acc: 0.2092\n",
      "Epoch 5/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.8742 - acc: 0.1981Epoch 00004: val_loss did not improve\n",
      "9600/9600 [==============================] - 31s - loss: 2.8740 - acc: 0.1981 - val_loss: 2.9063 - val_acc: 0.2071\n",
      "Epoch 6/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.8618 - acc: 0.2026Epoch 00005: val_loss improved from 2.88434 to 2.85726, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.8615 - acc: 0.2027 - val_loss: 2.8573 - val_acc: 0.2096\n",
      "Epoch 7/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.8502 - acc: 0.2096Epoch 00006: val_loss improved from 2.85726 to 2.84390, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.8497 - acc: 0.2096 - val_loss: 2.8439 - val_acc: 0.2179\n",
      "Epoch 8/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.8418 - acc: 0.2066Epoch 00007: val_loss improved from 2.84390 to 2.83365, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.8410 - acc: 0.2069 - val_loss: 2.8336 - val_acc: 0.2096\n",
      "Epoch 9/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.8238 - acc: 0.2124Epoch 00008: val_loss improved from 2.83365 to 2.83233, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.8246 - acc: 0.2120 - val_loss: 2.8323 - val_acc: 0.2121\n",
      "Epoch 10/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.8129 - acc: 0.2177Epoch 00009: val_loss improved from 2.83233 to 2.82713, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.8122 - acc: 0.2178 - val_loss: 2.8271 - val_acc: 0.2046\n",
      "Epoch 11/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.8023 - acc: 0.2161Epoch 00010: val_loss improved from 2.82713 to 2.79125, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.8025 - acc: 0.2160 - val_loss: 2.7912 - val_acc: 0.2183\n",
      "Epoch 12/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.7914 - acc: 0.2202Epoch 00011: val_loss improved from 2.79125 to 2.78713, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.7915 - acc: 0.2200 - val_loss: 2.7871 - val_acc: 0.2288\n",
      "Epoch 13/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.7793 - acc: 0.2252Epoch 00012: val_loss improved from 2.78713 to 2.78365, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.7793 - acc: 0.2251 - val_loss: 2.7837 - val_acc: 0.2429\n",
      "Epoch 14/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.7723 - acc: 0.2250- ETA: 0s - loss: 2.7726 - acc: 0.Epoch 00013: val_loss improved from 2.78365 to 2.75599, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 32s - loss: 2.7720 - acc: 0.2248 - val_loss: 2.7560 - val_acc: 0.2346\n",
      "Epoch 15/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.7577 - acc: 0.2226Epoch 00014: val_loss did not improve\n",
      "9600/9600 [==============================] - 33s - loss: 2.7577 - acc: 0.2227 - val_loss: 2.7617 - val_acc: 0.2283\n",
      "Epoch 16/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.7481 - acc: 0.2331Epoch 00015: val_loss improved from 2.75599 to 2.74285, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 34s - loss: 2.7482 - acc: 0.2331 - val_loss: 2.7429 - val_acc: 0.2346\n",
      "Epoch 17/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.7368 - acc: 0.2323Epoch 00016: val_loss did not improve\n",
      "9600/9600 [==============================] - 34s - loss: 2.7370 - acc: 0.2321 - val_loss: 2.7525 - val_acc: 0.2296\n",
      "Epoch 18/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.7271 - acc: 0.2364Epoch 00017: val_loss did not improve\n",
      "9600/9600 [==============================] - 33s - loss: 2.7271 - acc: 0.2366 - val_loss: 2.7454 - val_acc: 0.2275\n",
      "Epoch 19/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.7193 - acc: 0.2375Epoch 00018: val_loss improved from 2.74285 to 2.73702, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.7190 - acc: 0.2374 - val_loss: 2.7370 - val_acc: 0.2504\n",
      "Epoch 20/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.7055 - acc: 0.2417Epoch 00019: val_loss improved from 2.73702 to 2.72235, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.7070 - acc: 0.2412 - val_loss: 2.7224 - val_acc: 0.2408\n",
      "Epoch 21/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6991 - acc: 0.2386Epoch 00020: val_loss improved from 2.72235 to 2.70611, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.6993 - acc: 0.2381 - val_loss: 2.7061 - val_acc: 0.2458\n",
      "Epoch 22/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6911 - acc: 0.2403Epoch 00021: val_loss did not improve\n",
      "9600/9600 [==============================] - 31s - loss: 2.6916 - acc: 0.2401 - val_loss: 2.7199 - val_acc: 0.2462\n",
      "Epoch 23/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6774 - acc: 0.2426Epoch 00022: val_loss improved from 2.70611 to 2.67644, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.6786 - acc: 0.2425 - val_loss: 2.6764 - val_acc: 0.2558\n",
      "Epoch 24/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6716 - acc: 0.2480Epoch 00023: val_loss did not improve\n",
      "9600/9600 [==============================] - 31s - loss: 2.6710 - acc: 0.2481 - val_loss: 2.6784 - val_acc: 0.2417\n",
      "Epoch 25/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6634 - acc: 0.2443Epoch 00024: val_loss improved from 2.67644 to 2.66758, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.6636 - acc: 0.2442 - val_loss: 2.6676 - val_acc: 0.2562\n",
      "Epoch 26/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6552 - acc: 0.2460Epoch 00025: val_loss improved from 2.66758 to 2.64966, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.6552 - acc: 0.2457 - val_loss: 2.6497 - val_acc: 0.2542\n",
      "Epoch 27/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6451 - acc: 0.2528Epoch 00026: val_loss did not improve\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9600/9600 [==============================] - 31s - loss: 2.6464 - acc: 0.2525 - val_loss: 2.6652 - val_acc: 0.2525\n",
      "Epoch 28/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6385 - acc: 0.2552Epoch 00027: val_loss improved from 2.64966 to 2.64009, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 29s - loss: 2.6390 - acc: 0.2550 - val_loss: 2.6401 - val_acc: 0.2642\n",
      "Epoch 29/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6302 - acc: 0.2542Epoch 00028: val_loss did not improve\n",
      "9600/9600 [==============================] - 29s - loss: 2.6309 - acc: 0.2541 - val_loss: 2.6590 - val_acc: 0.2500\n",
      "Epoch 30/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6223 - acc: 0.2540Epoch 00029: val_loss improved from 2.64009 to 2.63894, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 29s - loss: 2.6239 - acc: 0.2537 - val_loss: 2.6389 - val_acc: 0.2517\n",
      "Epoch 31/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6165 - acc: 0.2578Epoch 00030: val_loss improved from 2.63894 to 2.61731, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 29s - loss: 2.6169 - acc: 0.2577 - val_loss: 2.6173 - val_acc: 0.2646\n",
      "Epoch 32/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6084 - acc: 0.2628Epoch 00031: val_loss did not improve\n",
      "9600/9600 [==============================] - 29s - loss: 2.6093 - acc: 0.2624 - val_loss: 2.6249 - val_acc: 0.2450\n",
      "Epoch 33/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.6053 - acc: 0.2585Epoch 00032: val_loss improved from 2.61731 to 2.60041, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 30s - loss: 2.6043 - acc: 0.2586 - val_loss: 2.6004 - val_acc: 0.2679\n",
      "Epoch 34/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5936 - acc: 0.2656Epoch 00033: val_loss did not improve\n",
      "9600/9600 [==============================] - 29s - loss: 2.5928 - acc: 0.2655 - val_loss: 2.6062 - val_acc: 0.2600\n",
      "Epoch 35/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5859 - acc: 0.2637Epoch 00034: val_loss improved from 2.60041 to 2.59705, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 29s - loss: 2.5855 - acc: 0.2636 - val_loss: 2.5970 - val_acc: 0.2658\n",
      "Epoch 36/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5806 - acc: 0.2648Epoch 00035: val_loss did not improve\n",
      "9600/9600 [==============================] - 29s - loss: 2.5822 - acc: 0.2648 - val_loss: 2.6040 - val_acc: 0.2587\n",
      "Epoch 37/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5768 - acc: 0.2681Epoch 00036: val_loss improved from 2.59705 to 2.58523, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 30s - loss: 2.5761 - acc: 0.2682 - val_loss: 2.5852 - val_acc: 0.2742\n",
      "Epoch 38/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5710 - acc: 0.2649Epoch 00037: val_loss improved from 2.58523 to 2.57709, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 29s - loss: 2.5722 - acc: 0.2649 - val_loss: 2.5771 - val_acc: 0.2758\n",
      "Epoch 39/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5602 - acc: 0.2691Epoch 00038: val_loss did not improve\n",
      "9600/9600 [==============================] - 30s - loss: 2.5600 - acc: 0.2689 - val_loss: 2.5935 - val_acc: 0.2600\n",
      "Epoch 40/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5552 - acc: 0.2724Epoch 00039: val_loss did not improve\n",
      "9600/9600 [==============================] - 29s - loss: 2.5555 - acc: 0.2721 - val_loss: 2.5782 - val_acc: 0.2654\n",
      "Epoch 41/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5525 - acc: 0.2746Epoch 00040: val_loss improved from 2.57709 to 2.55763, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 29s - loss: 2.5526 - acc: 0.2746 - val_loss: 2.5576 - val_acc: 0.2696\n",
      "Epoch 42/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5473 - acc: 0.2684Epoch 00041: val_loss did not improve\n",
      "9600/9600 [==============================] - 29s - loss: 2.5453 - acc: 0.2690 - val_loss: 2.5611 - val_acc: 0.2687\n",
      "Epoch 43/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5368 - acc: 0.2768Epoch 00042: val_loss did not improve\n",
      "9600/9600 [==============================] - 33s - loss: 2.5374 - acc: 0.2768 - val_loss: 2.5824 - val_acc: 0.2621\n",
      "Epoch 44/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5323 - acc: 0.2744Epoch 00043: val_loss improved from 2.55763 to 2.55073, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 41s - loss: 2.5312 - acc: 0.2744 - val_loss: 2.5507 - val_acc: 0.2692\n",
      "Epoch 45/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5253 - acc: 0.2807Epoch 00044: val_loss improved from 2.55073 to 2.53350, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 37s - loss: 2.5256 - acc: 0.2807 - val_loss: 2.5335 - val_acc: 0.2838\n",
      "Epoch 46/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5180 - acc: 0.2795Epoch 00045: val_loss did not improve\n",
      "9600/9600 [==============================] - 40s - loss: 2.5187 - acc: 0.2794 - val_loss: 2.5665 - val_acc: 0.2683\n",
      "Epoch 47/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5139 - acc: 0.2782Epoch 00046: val_loss did not improve\n",
      "9600/9600 [==============================] - 41s - loss: 2.5142 - acc: 0.2781 - val_loss: 2.5517 - val_acc: 0.2800\n",
      "Epoch 48/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5114 - acc: 0.2839Epoch 00047: val_loss improved from 2.53350 to 2.51672, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 42s - loss: 2.5104 - acc: 0.2842 - val_loss: 2.5167 - val_acc: 0.2783\n",
      "Epoch 49/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4997 - acc: 0.2824Epoch 00048: val_loss did not improve\n",
      "9600/9600 [==============================] - 42s - loss: 2.5009 - acc: 0.2822 - val_loss: 2.5436 - val_acc: 0.2658\n",
      "Epoch 50/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.5011 - acc: 0.2839Epoch 00049: val_loss did not improve\n",
      "9600/9600 [==============================] - 40s - loss: 2.5018 - acc: 0.2839 - val_loss: 2.5229 - val_acc: 0.2717\n",
      "Epoch 51/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4899 - acc: 0.2881Epoch 00050: val_loss improved from 2.51672 to 2.51103, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 42s - loss: 2.4909 - acc: 0.2884 - val_loss: 2.5110 - val_acc: 0.2833\n",
      "Epoch 52/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4855 - acc: 0.2864Epoch 00051: val_loss did not improve\n",
      "9600/9600 [==============================] - 40s - loss: 2.4860 - acc: 0.2865 - val_loss: 2.5192 - val_acc: 0.2804\n",
      "Epoch 53/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4810 - acc: 0.2845Epoch 00052: val_loss did not improve\n",
      "9600/9600 [==============================] - 47s - loss: 2.4801 - acc: 0.2846 - val_loss: 2.5367 - val_acc: 0.2712\n",
      "Epoch 54/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4751 - acc: 0.2927Epoch 00053: val_loss did not improve\n",
      "9600/9600 [==============================] - 37s - loss: 2.4761 - acc: 0.2926 - val_loss: 2.5120 - val_acc: 0.2871\n",
      "Epoch 55/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4713 - acc: 0.2901Epoch 00054: val_loss improved from 2.51103 to 2.50563, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 40s - loss: 2.4715 - acc: 0.2901 - val_loss: 2.5056 - val_acc: 0.2850\n",
      "Epoch 56/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4699 - acc: 0.2889Epoch 00055: val_loss did not improve\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9600/9600 [==============================] - 39s - loss: 2.4689 - acc: 0.2895 - val_loss: 2.5183 - val_acc: 0.2792\n",
      "Epoch 57/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4623 - acc: 0.2910Epoch 00056: val_loss did not improve\n",
      "9600/9600 [==============================] - 31s - loss: 2.4630 - acc: 0.2909 - val_loss: 2.5345 - val_acc: 0.2729\n",
      "Epoch 58/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4588 - acc: 0.2964Epoch 00057: val_loss improved from 2.50563 to 2.49559, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 34s - loss: 2.4594 - acc: 0.2964 - val_loss: 2.4956 - val_acc: 0.2833\n",
      "Epoch 59/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4520 - acc: 0.2938Epoch 00058: val_loss improved from 2.49559 to 2.47967, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.4510 - acc: 0.2942 - val_loss: 2.4797 - val_acc: 0.2963\n",
      "Epoch 60/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4457 - acc: 0.2997Epoch 00059: val_loss did not improve\n",
      "9600/9600 [==============================] - 31s - loss: 2.4462 - acc: 0.2997 - val_loss: 2.4812 - val_acc: 0.2992\n",
      "Epoch 61/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4453 - acc: 0.2995Epoch 00060: val_loss improved from 2.47967 to 2.45973, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 30s - loss: 2.4455 - acc: 0.2992 - val_loss: 2.4597 - val_acc: 0.3038\n",
      "Epoch 62/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4386 - acc: 0.2989Epoch 00061: val_loss did not improve\n",
      "9600/9600 [==============================] - 32s - loss: 2.4390 - acc: 0.2986 - val_loss: 2.4926 - val_acc: 0.2963\n",
      "Epoch 63/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4371 - acc: 0.2994Epoch 00062: val_loss improved from 2.45973 to 2.45329, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 34s - loss: 2.4370 - acc: 0.2993 - val_loss: 2.4533 - val_acc: 0.3000\n",
      "Epoch 64/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4285 - acc: 0.2997Epoch 00063: val_loss did not improve\n",
      "9600/9600 [==============================] - 35s - loss: 2.4292 - acc: 0.2995 - val_loss: 2.4661 - val_acc: 0.2967\n",
      "Epoch 65/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4233 - acc: 0.3058Epoch 00064: val_loss did not improve\n",
      "9600/9600 [==============================] - 32s - loss: 2.4231 - acc: 0.3057 - val_loss: 2.5194 - val_acc: 0.2642\n",
      "Epoch 66/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4240 - acc: 0.3042Epoch 00065: val_loss improved from 2.45329 to 2.44505, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 32s - loss: 2.4227 - acc: 0.3044 - val_loss: 2.4450 - val_acc: 0.2925\n",
      "Epoch 67/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4158 - acc: 0.2993Epoch 00066: val_loss did not improve\n",
      "9600/9600 [==============================] - 32s - loss: 2.4160 - acc: 0.2992 - val_loss: 2.4493 - val_acc: 0.2983\n",
      "Epoch 68/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4116 - acc: 0.3016Epoch 00067: val_loss did not improve\n",
      "9600/9600 [==============================] - 32s - loss: 2.4107 - acc: 0.3017 - val_loss: 2.4555 - val_acc: 0.2829\n",
      "Epoch 69/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4081 - acc: 0.3056Epoch 00068: val_loss did not improve\n",
      "9600/9600 [==============================] - 31s - loss: 2.4094 - acc: 0.3055 - val_loss: 2.4459 - val_acc: 0.2950\n",
      "Epoch 70/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4037 - acc: 0.3050Epoch 00069: val_loss did not improve\n",
      "9600/9600 [==============================] - 32s - loss: 2.4051 - acc: 0.3050 - val_loss: 2.4488 - val_acc: 0.2892\n",
      "Epoch 71/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.4033 - acc: 0.3085Epoch 00070: val_loss improved from 2.44505 to 2.44008, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 32s - loss: 2.4043 - acc: 0.3081 - val_loss: 2.4401 - val_acc: 0.2892\n",
      "Epoch 72/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3965 - acc: 0.3059Epoch 00071: val_loss improved from 2.44008 to 2.41861, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.3971 - acc: 0.3055 - val_loss: 2.4186 - val_acc: 0.3050\n",
      "Epoch 73/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3890 - acc: 0.3045Epoch 00072: val_loss did not improve\n",
      "9600/9600 [==============================] - 31s - loss: 2.3907 - acc: 0.3042 - val_loss: 2.4343 - val_acc: 0.3054\n",
      "Epoch 74/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3880 - acc: 0.3083Epoch 00073: val_loss did not improve\n",
      "9600/9600 [==============================] - 33s - loss: 2.3879 - acc: 0.3084 - val_loss: 2.4550 - val_acc: 0.2950\n",
      "Epoch 75/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3839 - acc: 0.3101Epoch 00074: val_loss did not improve\n",
      "9600/9600 [==============================] - 30s - loss: 2.3843 - acc: 0.3105 - val_loss: 2.4217 - val_acc: 0.3100\n",
      "Epoch 76/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3762 - acc: 0.3139Epoch 00075: val_loss did not improve\n",
      "9600/9600 [==============================] - 30s - loss: 2.3770 - acc: 0.3135 - val_loss: 2.4338 - val_acc: 0.3050\n",
      "Epoch 77/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3752 - acc: 0.3159Epoch 00076: val_loss did not improve\n",
      "9600/9600 [==============================] - 31s - loss: 2.3785 - acc: 0.3154 - val_loss: 2.4292 - val_acc: 0.2958\n",
      "Epoch 78/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3697 - acc: 0.3114Epoch 00077: val_loss improved from 2.41861 to 2.40424, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.3707 - acc: 0.3108 - val_loss: 2.4042 - val_acc: 0.3129\n",
      "Epoch 79/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3670 - acc: 0.3126Epoch 00078: val_loss did not improve\n",
      "9600/9600 [==============================] - 30s - loss: 2.3669 - acc: 0.3125 - val_loss: 2.4180 - val_acc: 0.3033\n",
      "Epoch 80/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3668 - acc: 0.3109Epoch 00079: val_loss did not improve\n",
      "9600/9600 [==============================] - 31s - loss: 2.3663 - acc: 0.3107 - val_loss: 2.4065 - val_acc: 0.3079\n",
      "Epoch 81/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3602 - acc: 0.3153Epoch 00080: val_loss did not improve\n",
      "9600/9600 [==============================] - 33s - loss: 2.3615 - acc: 0.3149 - val_loss: 2.4656 - val_acc: 0.2913\n",
      "Epoch 82/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3538 - acc: 0.3153Epoch 00081: val_loss did not improve\n",
      "9600/9600 [==============================] - 31s - loss: 2.3541 - acc: 0.3150 - val_loss: 2.4508 - val_acc: 0.2946\n",
      "Epoch 83/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3554 - acc: 0.3190Epoch 00082: val_loss improved from 2.40424 to 2.40304, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 30s - loss: 2.3561 - acc: 0.3189 - val_loss: 2.4030 - val_acc: 0.2950\n",
      "Epoch 84/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3515 - acc: 0.3145Epoch 00083: val_loss did not improve\n",
      "9600/9600 [==============================] - 31s - loss: 2.3510 - acc: 0.3147 - val_loss: 2.4210 - val_acc: 0.2988\n",
      "Epoch 85/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3436 - acc: 0.3195Epoch 00084: val_loss improved from 2.40304 to 2.38825, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 31s - loss: 2.3446 - acc: 0.3196 - val_loss: 2.3882 - val_acc: 0.3050\n",
      "Epoch 86/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3454 - acc: 0.3196Epoch 00085: val_loss did not improve\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9600/9600 [==============================] - 31s - loss: 2.3456 - acc: 0.3196 - val_loss: 2.4164 - val_acc: 0.2833\n",
      "Epoch 87/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3413 - acc: 0.3230Epoch 00086: val_loss did not improve\n",
      "9600/9600 [==============================] - 30s - loss: 2.3424 - acc: 0.3228 - val_loss: 2.3896 - val_acc: 0.3046\n",
      "Epoch 88/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3373 - acc: 0.3203Epoch 00087: val_loss did not improve\n",
      "9600/9600 [==============================] - 30s - loss: 2.3376 - acc: 0.3204 - val_loss: 2.4100 - val_acc: 0.3067\n",
      "Epoch 89/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3383 - acc: 0.3230Epoch 00088: val_loss improved from 2.38825 to 2.37686, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 30s - loss: 2.3383 - acc: 0.3227 - val_loss: 2.3769 - val_acc: 0.3217\n",
      "Epoch 90/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3316 - acc: 0.3254Epoch 00089: val_loss did not improve\n",
      "9600/9600 [==============================] - 30s - loss: 2.3310 - acc: 0.3255 - val_loss: 2.3774 - val_acc: 0.3183\n",
      "Epoch 91/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3272 - acc: 0.3227Epoch 00090: val_loss did not improve\n",
      "9600/9600 [==============================] - 28s - loss: 2.3283 - acc: 0.3223 - val_loss: 2.3912 - val_acc: 0.3096\n",
      "Epoch 92/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3212 - acc: 0.3317Epoch 00091: val_loss improved from 2.37686 to 2.35880, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 28s - loss: 2.3231 - acc: 0.3310 - val_loss: 2.3588 - val_acc: 0.3179\n",
      "Epoch 93/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3183 - acc: 0.3295Epoch 00092: val_loss did not improve\n",
      "9600/9600 [==============================] - 28s - loss: 2.3198 - acc: 0.3294 - val_loss: 2.3797 - val_acc: 0.3117\n",
      "Epoch 94/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3198 - acc: 0.3211Epoch 00093: val_loss did not improve\n",
      "9600/9600 [==============================] - 28s - loss: 2.3198 - acc: 0.3216 - val_loss: 2.3699 - val_acc: 0.3038\n",
      "Epoch 95/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3193 - acc: 0.3231Epoch 00094: val_loss did not improve\n",
      "9600/9600 [==============================] - 28s - loss: 2.3191 - acc: 0.3233 - val_loss: 2.3726 - val_acc: 0.3150\n",
      "Epoch 96/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3143 - acc: 0.3284Epoch 00095: val_loss improved from 2.35880 to 2.35243, saving model to saved_models/weights.best.from_simplebm.hdf5\n",
      "9600/9600 [==============================] - 29s - loss: 2.3143 - acc: 0.3289 - val_loss: 2.3524 - val_acc: 0.3229\n",
      "Epoch 97/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3099 - acc: 0.3287Epoch 00096: val_loss did not improve\n",
      "9600/9600 [==============================] - 29s - loss: 2.3112 - acc: 0.3281 - val_loss: 2.3579 - val_acc: 0.3033\n",
      "Epoch 98/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3078 - acc: 0.3306Epoch 00097: val_loss did not improve\n",
      "9600/9600 [==============================] - 28s - loss: 2.3077 - acc: 0.3304 - val_loss: 2.3798 - val_acc: 0.3063\n",
      "Epoch 99/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.3060 - acc: 0.3305Epoch 00098: val_loss did not improve\n",
      "9600/9600 [==============================] - 28s - loss: 2.3064 - acc: 0.3306 - val_loss: 2.3619 - val_acc: 0.3212\n",
      "Epoch 100/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 2.2996 - acc: 0.3307Epoch 00099: val_loss did not improve\n",
      "9600/9600 [==============================] - 28s - loss: 2.2998 - acc: 0.3306 - val_loss: 2.3629 - val_acc: 0.3088\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x12000e8d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Train the model and save the weights for the best one\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 = 100\n",
    "batch_size = 32\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_simplebm.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=batch_size, callbacks=[checkpointer], verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the Benchmark Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test F1 accuracy: 32.5667%\n"
     ]
    }
   ],
   "source": [
    "# get index of predicted alphabetnfor each image in test set\n",
    "alphabet_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]\n",
    "\n",
    "# report test accuracy\n",
    "from sklearn import metrics\n",
    "y_true = [np.argmax(y_test) for y_test in test_targets]\n",
    "f1_accuracy = 100* metrics.f1_score(y_true,alphabet_predictions, average = 'micro')\n",
    "print('Test F1 accuracy: %.4f%%' % f1_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Model\n",
    "Build the best model as per experiments and further fine tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 63, 63, 16)        80        \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 31, 31, 16)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 31, 31, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 30, 30, 32)        2080      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 14, 14, 64)        8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 6, 6, 128)         32896     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 3, 3, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 3, 3, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 2, 2, 256)         131328    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 1, 1, 256)         0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 1, 1, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 512)               131584    \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 50)                25650     \n",
      "=================================================================\n",
      "Total params: 397,666\n",
      "Trainable params: 397,666\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Importing the Keras libraries and packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "\n",
    "\n",
    "# Initialising the CNN\n",
    "model = Sequential()\n",
    "\n",
    "# First Convolution Layer with Pooling\n",
    "model.add(Conv2D(filters=16, kernel_size=2, padding='valid', activation='relu', input_shape=(train_tensors.shape[1:])))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# Adding a second convolutional layer with Pooling\n",
    "model.add(Conv2D(filters=32, kernel_size=2, padding='valid', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size =2))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# Adding a third convolutional layer with Pooling\n",
    "model.add(Conv2D(filters=64, kernel_size=2, padding='valid', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size =2))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "# Adding a fourth convolutional layer with Pooling\n",
    "model.add(Conv2D(filters=128, kernel_size=2, padding='valid', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size =2))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "\n",
    "# Adding a fifth convolutional layer with Pooling\n",
    "model.add(Conv2D(filters=256, kernel_size=2, padding='valid', activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size =2))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "\n",
    "\n",
    "#model.add(GlobalAveragePooling2D())\n",
    "model.add(Flatten())\n",
    "\n",
    "# Full connection Dense Layers\n",
    "model.add(Dense(256, activation='relu'))\n",
    "#model.add(Dropout(0.2))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(50, activation = 'softmax'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "\n",
    "### Compile the model\n",
    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9600 samples, validate on 2400 samples\n",
      "Epoch 1/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2110 - acc: 0.9355Epoch 00000: val_loss improved from inf to 0.15763, saving model to saved_models/weights.best.from_deepcnnwithDO.hdf5\n",
      "9600/9600 [==============================] - 52s - loss: 0.2108 - acc: 0.9356 - val_loss: 0.1576 - val_acc: 0.9550\n",
      "Epoch 2/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2500 - acc: 0.9298Epoch 00001: val_loss did not improve\n",
      "9600/9600 [==============================] - 53s - loss: 0.2500 - acc: 0.9298 - val_loss: 0.1883 - val_acc: 0.9417\n",
      "Epoch 3/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2396 - acc: 0.9338Epoch 00002: val_loss did not improve\n",
      "9600/9600 [==============================] - 47s - loss: 0.2398 - acc: 0.9339 - val_loss: 0.1789 - val_acc: 0.9492\n",
      "Epoch 4/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2190 - acc: 0.9403Epoch 00003: val_loss did not improve\n",
      "9600/9600 [==============================] - 48s - loss: 0.2188 - acc: 0.9403 - val_loss: 0.1696 - val_acc: 0.9496\n",
      "Epoch 5/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2245 - acc: 0.9383Epoch 00004: val_loss improved from 0.15763 to 0.14946, saving model to saved_models/weights.best.from_deepcnnwithDO.hdf5\n",
      "9600/9600 [==============================] - 51s - loss: 0.2240 - acc: 0.9384 - val_loss: 0.1495 - val_acc: 0.9683\n",
      "Epoch 6/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2318 - acc: 0.9409Epoch 00005: val_loss did not improve\n",
      "9600/9600 [==============================] - 48s - loss: 0.2314 - acc: 0.9410 - val_loss: 0.1827 - val_acc: 0.9529\n",
      "Epoch 7/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2411 - acc: 0.9377Epoch 00006: val_loss did not improve\n",
      "9600/9600 [==============================] - 48s - loss: 0.2407 - acc: 0.9378 - val_loss: 0.1537 - val_acc: 0.9613\n",
      "Epoch 8/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2347 - acc: 0.9401Epoch 00007: val_loss did not improve\n",
      "9600/9600 [==============================] - 51s - loss: 0.2359 - acc: 0.9400 - val_loss: 0.1939 - val_acc: 0.9463\n",
      "Epoch 9/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2213 - acc: 0.9411Epoch 00008: val_loss did not improve\n",
      "9600/9600 [==============================] - 55s - loss: 0.2211 - acc: 0.9411 - val_loss: 0.1787 - val_acc: 0.9575\n",
      "Epoch 10/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2472 - acc: 0.9402Epoch 00009: val_loss did not improve\n",
      "9600/9600 [==============================] - 52s - loss: 0.2472 - acc: 0.9402 - val_loss: 0.1781 - val_acc: 0.9487\n",
      "Epoch 11/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2322 - acc: 0.9438Epoch 00010: val_loss did not improve\n",
      "9600/9600 [==============================] - 57s - loss: 0.2338 - acc: 0.9438 - val_loss: 0.2743 - val_acc: 0.9321\n",
      "Epoch 12/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2422 - acc: 0.9392Epoch 00011: val_loss did not improve\n",
      "9600/9600 [==============================] - 53s - loss: 0.2429 - acc: 0.9389 - val_loss: 0.2092 - val_acc: 0.9383\n",
      "Epoch 13/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2447 - acc: 0.9377Epoch 00012: val_loss did not improve\n",
      "9600/9600 [==============================] - 51s - loss: 0.2440 - acc: 0.9379 - val_loss: 0.1852 - val_acc: 0.9558\n",
      "Epoch 14/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2498 - acc: 0.9415Epoch 00013: val_loss did not improve\n",
      "9600/9600 [==============================] - 51s - loss: 0.2500 - acc: 0.9414 - val_loss: 0.1886 - val_acc: 0.9537\n",
      "Epoch 15/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2208 - acc: 0.9449Epoch 00014: val_loss improved from 0.14946 to 0.14819, saving model to saved_models/weights.best.from_deepcnnwithDO.hdf5\n",
      "9600/9600 [==============================] - 51s - loss: 0.2203 - acc: 0.9450 - val_loss: 0.1482 - val_acc: 0.9675\n",
      "Epoch 16/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2580 - acc: 0.9376Epoch 00015: val_loss did not improve\n",
      "9600/9600 [==============================] - 51s - loss: 0.2598 - acc: 0.9374 - val_loss: 0.1764 - val_acc: 0.9546\n",
      "Epoch 17/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2355 - acc: 0.9398Epoch 00016: val_loss did not improve\n",
      "9600/9600 [==============================] - 48s - loss: 0.2356 - acc: 0.9398 - val_loss: 0.1566 - val_acc: 0.9625\n",
      "Epoch 18/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2352 - acc: 0.9444Epoch 00017: val_loss did not improve\n",
      "9600/9600 [==============================] - 52s - loss: 0.2349 - acc: 0.9444 - val_loss: 0.2357 - val_acc: 0.9546\n",
      "Epoch 19/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2590 - acc: 0.9391Epoch 00018: val_loss did not improve\n",
      "9600/9600 [==============================] - 50s - loss: 0.2601 - acc: 0.9389 - val_loss: 0.2280 - val_acc: 0.9463\n",
      "Epoch 20/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2391 - acc: 0.9405Epoch 00019: val_loss did not improve\n",
      "9600/9600 [==============================] - 53s - loss: 0.2393 - acc: 0.9405 - val_loss: 0.1787 - val_acc: 0.9533\n",
      "Epoch 21/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2354 - acc: 0.9443Epoch 00020: val_loss did not improve\n",
      "9600/9600 [==============================] - 51s - loss: 0.2354 - acc: 0.9443 - val_loss: 0.1991 - val_acc: 0.9487\n",
      "Epoch 22/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2944 - acc: 0.9368Epoch 00021: val_loss did not improve\n",
      "9600/9600 [==============================] - 51s - loss: 0.2945 - acc: 0.9368 - val_loss: 0.2482 - val_acc: 0.9550\n",
      "Epoch 23/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2606 - acc: 0.9427Epoch 00022: val_loss did not improve\n",
      "9600/9600 [==============================] - 50s - loss: 0.2598 - acc: 0.9429 - val_loss: 0.1809 - val_acc: 0.9571\n",
      "Epoch 24/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2916 - acc: 0.9385Epoch 00023: val_loss did not improve\n",
      "9600/9600 [==============================] - 51s - loss: 0.2906 - acc: 0.9388 - val_loss: 0.2422 - val_acc: 0.9529\n",
      "Epoch 25/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2549 - acc: 0.9406Epoch 00024: val_loss did not improve\n",
      "9600/9600 [==============================] - 52s - loss: 0.2547 - acc: 0.9406 - val_loss: 0.1817 - val_acc: 0.9550\n",
      "Epoch 26/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2680 - acc: 0.9391Epoch 00025: val_loss did not improve\n",
      "9600/9600 [==============================] - 50s - loss: 0.2679 - acc: 0.9389 - val_loss: 0.2044 - val_acc: 0.9496\n",
      "Epoch 27/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2527 - acc: 0.9424Epoch 00026: val_loss did not improve\n",
      "9600/9600 [==============================] - 54s - loss: 0.2523 - acc: 0.9424 - val_loss: 0.2192 - val_acc: 0.9558\n",
      "Epoch 28/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2961 - acc: 0.9420Epoch 00027: val_loss did not improve\n",
      "9600/9600 [==============================] - 58s - loss: 0.2962 - acc: 0.9418 - val_loss: 0.2401 - val_acc: 0.9396\n",
      "Epoch 29/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2867 - acc: 0.9380Epoch 00028: val_loss did not improve\n",
      "9600/9600 [==============================] - 58s - loss: 0.2869 - acc: 0.9380 - val_loss: 0.1990 - val_acc: 0.9454\n",
      "Epoch 30/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2699 - acc: 0.9408Epoch 00029: val_loss did not improve\n",
      "9600/9600 [==============================] - 59s - loss: 0.2703 - acc: 0.9407 - val_loss: 0.3119 - val_acc: 0.9483\n",
      "Epoch 31/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2927 - acc: 0.9398Epoch 00030: val_loss did not improve\n",
      "9600/9600 [==============================] - 60s - loss: 0.2921 - acc: 0.9399 - val_loss: 0.1923 - val_acc: 0.9579\n",
      "Epoch 32/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2967 - acc: 0.9373Epoch 00031: val_loss did not improve\n",
      "9600/9600 [==============================] - 69s - loss: 0.2957 - acc: 0.9375 - val_loss: 0.1994 - val_acc: 0.9558\n",
      "Epoch 33/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3037 - acc: 0.9386Epoch 00032: val_loss did not improve\n",
      "9600/9600 [==============================] - 51s - loss: 0.3027 - acc: 0.9389 - val_loss: 0.2069 - val_acc: 0.9525\n",
      "Epoch 34/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2552 - acc: 0.9423Epoch 00033: val_loss did not improve\n",
      "9600/9600 [==============================] - 50s - loss: 0.2557 - acc: 0.9422 - val_loss: 0.2187 - val_acc: 0.9479\n",
      "Epoch 35/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3082 - acc: 0.9428Epoch 00034: val_loss did not improve\n",
      "9600/9600 [==============================] - 54s - loss: 0.3076 - acc: 0.9429 - val_loss: 0.2258 - val_acc: 0.9421\n",
      "Epoch 36/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2785 - acc: 0.9412Epoch 00035: val_loss did not improve\n",
      "9600/9600 [==============================] - 53s - loss: 0.2795 - acc: 0.9410 - val_loss: 0.2308 - val_acc: 0.9371\n",
      "Epoch 37/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2720 - acc: 0.9398Epoch 00036: val_loss did not improve\n",
      "9600/9600 [==============================] - 52s - loss: 0.2714 - acc: 0.9399 - val_loss: 0.2550 - val_acc: 0.9437\n",
      "Epoch 38/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2873 - acc: 0.9418Epoch 00037: val_loss did not improve\n",
      "9600/9600 [==============================] - 53s - loss: 0.2873 - acc: 0.9418 - val_loss: 0.2568 - val_acc: 0.9337\n",
      "Epoch 39/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3019 - acc: 0.9402Epoch 00038: val_loss did not improve\n",
      "9600/9600 [==============================] - 51s - loss: 0.3013 - acc: 0.9402 - val_loss: 0.2145 - val_acc: 0.9508\n",
      "Epoch 40/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2899 - acc: 0.9413Epoch 00039: val_loss did not improve\n",
      "9600/9600 [==============================] - 53s - loss: 0.2918 - acc: 0.9410 - val_loss: 0.3336 - val_acc: 0.9146\n",
      "Epoch 41/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2904 - acc: 0.9393Epoch 00040: val_loss did not improve\n",
      "9600/9600 [==============================] - 55s - loss: 0.2905 - acc: 0.9393 - val_loss: 0.2420 - val_acc: 0.9400\n",
      "Epoch 42/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2965 - acc: 0.9371Epoch 00041: val_loss did not improve\n",
      "9600/9600 [==============================] - 57s - loss: 0.2962 - acc: 0.9371 - val_loss: 0.2256 - val_acc: 0.9413\n",
      "Epoch 43/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2967 - acc: 0.9382Epoch 00042: val_loss did not improve\n",
      "9600/9600 [==============================] - 61s - loss: 0.2968 - acc: 0.9381 - val_loss: 0.2456 - val_acc: 0.9358\n",
      "Epoch 44/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2849 - acc: 0.9422Epoch 00043: val_loss did not improve\n",
      "9600/9600 [==============================] - 54s - loss: 0.2844 - acc: 0.9422 - val_loss: 0.2393 - val_acc: 0.9450\n",
      "Epoch 45/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2928 - acc: 0.9322Epoch 00044: val_loss did not improve\n",
      "9600/9600 [==============================] - 55s - loss: 0.2936 - acc: 0.9321 - val_loss: 0.2489 - val_acc: 0.9350\n",
      "Epoch 46/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2837 - acc: 0.9395Epoch 00045: val_loss did not improve\n",
      "9600/9600 [==============================] - 56s - loss: 0.2830 - acc: 0.9396 - val_loss: 0.2503 - val_acc: 0.9413\n",
      "Epoch 47/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2731 - acc: 0.9390Epoch 00046: val_loss did not improve\n",
      "9600/9600 [==============================] - 52s - loss: 0.2728 - acc: 0.9391 - val_loss: 0.2280 - val_acc: 0.9483\n",
      "Epoch 48/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2806 - acc: 0.9382Epoch 00047: val_loss did not improve\n",
      "9600/9600 [==============================] - 52s - loss: 0.2801 - acc: 0.9383 - val_loss: 0.2485 - val_acc: 0.9467\n",
      "Epoch 49/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3129 - acc: 0.9368Epoch 00048: val_loss did not improve\n",
      "9600/9600 [==============================] - 57s - loss: 0.3124 - acc: 0.9368 - val_loss: 0.2183 - val_acc: 0.9500\n",
      "Epoch 50/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2669 - acc: 0.9451Epoch 00049: val_loss did not improve\n",
      "9600/9600 [==============================] - 62s - loss: 0.2668 - acc: 0.9450 - val_loss: 0.2225 - val_acc: 0.9479\n",
      "Epoch 51/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2893 - acc: 0.9416Epoch 00050: val_loss did not improve\n",
      "9600/9600 [==============================] - 56s - loss: 0.2908 - acc: 0.9416 - val_loss: 0.3678 - val_acc: 0.9254\n",
      "Epoch 52/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3031 - acc: 0.9390Epoch 00051: val_loss did not improve\n",
      "9600/9600 [==============================] - 50s - loss: 0.3032 - acc: 0.9390 - val_loss: 0.1993 - val_acc: 0.9458\n",
      "Epoch 53/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2889 - acc: 0.9415Epoch 00052: val_loss did not improve\n",
      "9600/9600 [==============================] - 49s - loss: 0.2894 - acc: 0.9415 - val_loss: 0.2357 - val_acc: 0.9425\n",
      "Epoch 54/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2763 - acc: 0.9413Epoch 00053: val_loss did not improve\n",
      "9600/9600 [==============================] - 48s - loss: 0.2766 - acc: 0.9410 - val_loss: 0.2373 - val_acc: 0.9408\n",
      "Epoch 55/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2934 - acc: 0.9381Epoch 00054: val_loss did not improve\n",
      "9600/9600 [==============================] - 49s - loss: 0.2929 - acc: 0.9382 - val_loss: 0.3547 - val_acc: 0.9062\n",
      "Epoch 56/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2791 - acc: 0.9425Epoch 00055: val_loss did not improve\n",
      "9600/9600 [==============================] - 49s - loss: 0.2793 - acc: 0.9425 - val_loss: 0.2731 - val_acc: 0.9379\n",
      "Epoch 57/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3277 - acc: 0.9357Epoch 00056: val_loss did not improve\n",
      "9600/9600 [==============================] - 48s - loss: 0.3288 - acc: 0.9356 - val_loss: 0.2450 - val_acc: 0.9350\n",
      "Epoch 58/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2724 - acc: 0.9390Epoch 00057: val_loss did not improve\n",
      "9600/9600 [==============================] - 50s - loss: 0.2717 - acc: 0.9391 - val_loss: 0.2597 - val_acc: 0.9454\n",
      "Epoch 59/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2953 - acc: 0.9426Epoch 00058: val_loss did not improve\n",
      "9600/9600 [==============================] - 49s - loss: 0.2943 - acc: 0.9428 - val_loss: 0.2155 - val_acc: 0.9496\n",
      "Epoch 60/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3149 - acc: 0.9356Epoch 00059: val_loss did not improve\n",
      "9600/9600 [==============================] - 47s - loss: 0.3152 - acc: 0.9353 - val_loss: 0.2875 - val_acc: 0.9275\n",
      "Epoch 61/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3073 - acc: 0.9356Epoch 00060: val_loss did not improve\n",
      "9600/9600 [==============================] - 50s - loss: 0.3074 - acc: 0.9356 - val_loss: 0.2892 - val_acc: 0.9187\n",
      "Epoch 62/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2948 - acc: 0.9369Epoch 00061: val_loss did not improve\n",
      "9600/9600 [==============================] - 50s - loss: 0.2941 - acc: 0.9370 - val_loss: 0.2543 - val_acc: 0.9350\n",
      "Epoch 63/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3153 - acc: 0.9342Epoch 00062: val_loss did not improve\n",
      "9600/9600 [==============================] - 48s - loss: 0.3152 - acc: 0.9343 - val_loss: 0.2331 - val_acc: 0.9479\n",
      "Epoch 64/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3082 - acc: 0.9341Epoch 00063: val_loss did not improve\n",
      "9600/9600 [==============================] - 48s - loss: 0.3073 - acc: 0.9343 - val_loss: 0.2465 - val_acc: 0.9304\n",
      "Epoch 65/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3096 - acc: 0.9320Epoch 00064: val_loss did not improve\n",
      "9600/9600 [==============================] - 47s - loss: 0.3101 - acc: 0.9318 - val_loss: 0.2137 - val_acc: 0.9342\n",
      "Epoch 66/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2793 - acc: 0.9371Epoch 00065: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.2801 - acc: 0.9372 - val_loss: 0.2245 - val_acc: 0.9425\n",
      "Epoch 67/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3372 - acc: 0.9346Epoch 00066: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3373 - acc: 0.9346 - val_loss: 0.2424 - val_acc: 0.9396\n",
      "Epoch 68/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2984 - acc: 0.9425Epoch 00067: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.2979 - acc: 0.9426 - val_loss: 0.2755 - val_acc: 0.9329\n",
      "Epoch 69/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3043 - acc: 0.9394Epoch 00068: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3036 - acc: 0.9394 - val_loss: 0.2885 - val_acc: 0.9429\n",
      "Epoch 70/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2972 - acc: 0.9402Epoch 00069: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.2973 - acc: 0.9401 - val_loss: 0.2939 - val_acc: 0.9279\n",
      "Epoch 71/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.2867 - acc: 0.9377Epoch 00070: val_loss did not improve\n",
      "9600/9600 [==============================] - 45s - loss: 0.2867 - acc: 0.9376 - val_loss: 0.3112 - val_acc: 0.9233\n",
      "Epoch 72/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3267 - acc: 0.9376Epoch 00071: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3285 - acc: 0.9374 - val_loss: 0.2716 - val_acc: 0.9246\n",
      "Epoch 73/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3153 - acc: 0.9371Epoch 00072: val_loss did not improve\n",
      "9600/9600 [==============================] - 45s - loss: 0.3145 - acc: 0.9372 - val_loss: 0.2773 - val_acc: 0.9267\n",
      "Epoch 74/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3043 - acc: 0.9386Epoch 00073: val_loss did not improve\n",
      "9600/9600 [==============================] - 45s - loss: 0.3036 - acc: 0.9388 - val_loss: 0.2234 - val_acc: 0.9550\n",
      "Epoch 75/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3125 - acc: 0.9372Epoch 00074: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3114 - acc: 0.9374 - val_loss: 0.2305 - val_acc: 0.9475\n",
      "Epoch 76/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3118 - acc: 0.9372Epoch 00075: val_loss did not improve\n",
      "9600/9600 [==============================] - 47s - loss: 0.3125 - acc: 0.9371 - val_loss: 0.3474 - val_acc: 0.9104\n",
      "Epoch 77/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3253 - acc: 0.9353Epoch 00076: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3243 - acc: 0.9355 - val_loss: 0.2874 - val_acc: 0.9525\n",
      "Epoch 78/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3401 - acc: 0.9357Epoch 00077: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3395 - acc: 0.9358 - val_loss: 0.2726 - val_acc: 0.9417\n",
      "Epoch 79/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3388 - acc: 0.9401Epoch 00078: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3400 - acc: 0.9399 - val_loss: 0.2561 - val_acc: 0.9321\n",
      "Epoch 80/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3012 - acc: 0.9409Epoch 00079: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3003 - acc: 0.9411 - val_loss: 0.3587 - val_acc: 0.9254\n",
      "Epoch 81/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3025 - acc: 0.9354Epoch 00080: val_loss did not improve\n",
      "9600/9600 [==============================] - 45s - loss: 0.3020 - acc: 0.9354 - val_loss: 0.2303 - val_acc: 0.9413\n",
      "Epoch 82/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3462 - acc: 0.9343Epoch 00081: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3469 - acc: 0.9343 - val_loss: 0.3160 - val_acc: 0.9492\n",
      "Epoch 83/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3326 - acc: 0.9316Epoch 00082: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3326 - acc: 0.9316 - val_loss: 0.2394 - val_acc: 0.9437\n",
      "Epoch 84/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3499 - acc: 0.9333Epoch 00083: val_loss did not improve\n",
      "9600/9600 [==============================] - 45s - loss: 0.3491 - acc: 0.9334 - val_loss: 0.3271 - val_acc: 0.9279\n",
      "Epoch 85/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3263 - acc: 0.9404Epoch 00084: val_loss did not improve\n",
      "9600/9600 [==============================] - 45s - loss: 0.3252 - acc: 0.9406 - val_loss: 0.2358 - val_acc: 0.9446\n",
      "Epoch 86/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3358 - acc: 0.9379Epoch 00085: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3359 - acc: 0.9378 - val_loss: 0.2534 - val_acc: 0.9442\n",
      "Epoch 87/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3171 - acc: 0.9379Epoch 00086: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3164 - acc: 0.9380 - val_loss: 0.2392 - val_acc: 0.9367\n",
      "Epoch 88/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3267 - acc: 0.9400Epoch 00087: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3259 - acc: 0.9401 - val_loss: 0.2495 - val_acc: 0.9508\n",
      "Epoch 89/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3224 - acc: 0.9399Epoch 00088: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3215 - acc: 0.9401 - val_loss: 0.2359 - val_acc: 0.9437\n",
      "Epoch 90/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3206 - acc: 0.9372Epoch 00089: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3239 - acc: 0.9370 - val_loss: 0.3380 - val_acc: 0.9358\n",
      "Epoch 91/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3226 - acc: 0.9381Epoch 00090: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3255 - acc: 0.9378 - val_loss: 0.3408 - val_acc: 0.9142\n",
      "Epoch 92/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3432 - acc: 0.9376Epoch 00091: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3428 - acc: 0.9377 - val_loss: 0.3756 - val_acc: 0.9292\n",
      "Epoch 93/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3498 - acc: 0.9371Epoch 00092: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3489 - acc: 0.9373 - val_loss: 0.2210 - val_acc: 0.9492\n",
      "Epoch 94/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3623 - acc: 0.9360Epoch 00093: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3629 - acc: 0.9360 - val_loss: 0.3292 - val_acc: 0.9171\n",
      "Epoch 95/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3215 - acc: 0.9389Epoch 00094: val_loss did not improve\n",
      "9600/9600 [==============================] - 47s - loss: 0.3209 - acc: 0.9389 - val_loss: 0.2387 - val_acc: 0.9446\n",
      "Epoch 96/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3261 - acc: 0.9350Epoch 00095: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3258 - acc: 0.9350 - val_loss: 0.2654 - val_acc: 0.9350\n",
      "Epoch 97/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3450 - acc: 0.9339Epoch 00096: val_loss did not improve\n",
      "9600/9600 [==============================] - 45s - loss: 0.3456 - acc: 0.9340 - val_loss: 0.2520 - val_acc: 0.9475\n",
      "Epoch 98/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3595 - acc: 0.9322Epoch 00097: val_loss did not improve\n",
      "9600/9600 [==============================] - 46s - loss: 0.3583 - acc: 0.9324 - val_loss: 0.2434 - val_acc: 0.9433\n",
      "Epoch 99/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3115 - acc: 0.9396Epoch 00098: val_loss did not improve\n",
      "9600/9600 [==============================] - 45s - loss: 0.3116 - acc: 0.9397 - val_loss: 0.2314 - val_acc: 0.9496\n",
      "Epoch 100/100\n",
      "9568/9600 [============================>.] - ETA: 0s - loss: 0.3558 - acc: 0.9359Epoch 00099: val_loss did not improve\n",
      "9600/9600 [==============================] - 45s - loss: 0.3566 - acc: 0.9359 - val_loss: 0.2642 - val_acc: 0.9367\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x123931438>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Train the model and save the weights for the best one\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 = 100\n",
    "batch_size = 32\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_deepcnnwithDO.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=batch_size, callbacks=[checkpointer], verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Model with the Best Validation Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('saved_models/weights.best.from_deepcnnwithDO.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": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test F1 accuracy: 95.9333%\n"
     ]
    }
   ],
   "source": [
    "# get index of predicted alphabetnfor each image in test set\n",
    "alphabet_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]\n",
    "\n",
    "from sklearn import metrics\n",
    "y_true = [np.argmax(y_test) for y_test in test_targets]\n",
    "f1_accuracy = 100* metrics.f1_score(y_true,alphabet_predictions, average = 'micro')\n",
    "print('Test F1 accuracy: %.4f%%' % f1_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and test the Best model with augmented data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Augment the training data \n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# create and configure augmented image generator\n",
    "datagen_train = ImageDataGenerator(\n",
    "    width_shift_range=0.2,  # randomly shift images horizontally (10% of total width)\n",
    "    height_shift_range=0.2,  # randomly shift images vertically (10% of total height)\n",
    "    rotation_range=15, \n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    fill_mode='nearest') # randomly rotate images by 15 degrees\n",
    "\n",
    "# create and configure augmented image generator\n",
    "datagen_valid = ImageDataGenerator(\n",
    "    width_shift_range=0.2,  # randomly shift images horizontally (10% of total width)\n",
    "    height_shift_range=0.2,  # randomly shift images vertically (10% of total height)\n",
    "    rotation_range=15, \n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    fill_mode='nearest')\n",
    "\n",
    "# fit augmented image generator on data\n",
    "datagen_train.fit(train_tensors)\n",
    "datagen_valid.fit(valid_tensors)\n",
    "\n",
    "from keras.callbacks import ModelCheckpoint   \n",
    "\n",
    "batch_size = 256\n",
    "epochs = 500\n",
    "\n",
    "# train the model\n",
    "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.with_augmentation_new.hdf5', verbose=1, \n",
    "                               save_best_only=True)\n",
    "model.fit_generator(datagen_train.flow(train_tensors, train_targets, batch_size=batch_size),\n",
    "                    steps_per_epoch=train_tensors.shape[0] // batch_size,\n",
    "                    epochs=epochs, verbose=2, callbacks=[checkpointer],\n",
    "                    validation_data=datagen_valid.flow(valid_tensors, valid_targets, batch_size=batch_size),\n",
    "                    validation_steps=valid_tensors.shape[0] // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test F1 accuracy: 96.3333%\n"
     ]
    }
   ],
   "source": [
    "#load the model with best weights\n",
    "model.load_weights('saved_models/weights.best.with_augmentation_new.hdf5')\n",
    "#model.load_weights('saved_models/weights.best.with_augmentation.hdf5')\n",
    "# get index of predicted alphabetnfor each image in test set\n",
    "alphabet_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]\n",
    "\n",
    "from sklearn import metrics\n",
    "y_true = [np.argmax(y_test) for y_test in test_targets]\n",
    "f1_accuracy = 100* metrics.f1_score(y_true,alphabet_predictions, average = 'micro')\n",
    "print('Test F1 accuracy: %.4f%%' % f1_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizations and Error Analysis\n",
    "This may give you some insight into why the network is misclassifying certain objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vjfc1ddHzh58BHovZ3SXrHkX0SrjHEr3OTbqmfOMn5Rt/KTZ+U3z8pfrGX4qNv1Sm+Uv5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\n9Q3dFmij3TVLB2OL/9CxCckA7fdJxSakclb5pn818l+l2NQ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mu7sbIyMj2Llz5+SCw3F8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PTbqpTG6so9J1tHJ2Gcz+tMQ3Gvu05TpcKYVK4QRcV0SxRd3vklbR2bdE2fqJslFFF+xN1ivCzy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SJwcuuOACnHXWWXj77bdRKpUwMjICKSUOP/xwDAwMRPa5a9euxWc+8xlceeWVmDhxIp599ll0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\nj7YV1xd4QdL94yF1G5J+T9JJks4L/WU/y2Yiqy8w8uMKDxGMR9nUIb5NK3t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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1249afe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#from sklearn.metrics import confusion_matrix\n",
    "#confusion_matrix(y_true, alphabet_predictions)\n",
    "\n",
    "### Error Analysis\n",
    "## Find all the test cases where the prediction does not match the actual.\n",
    "## Trace back to file names and analyse \n",
    "## Loop over test tensors, predict and if doesnt match with actual add the file\n",
    "## name to an array\n",
    "\n",
    "error_files = []\n",
    "error_images = []\n",
    "for i, idx in enumerate(np.random.choice(test_tensors.shape[0], size=3000, replace=False)):\n",
    "    pred_idx = alphabet_predictions[idx]\n",
    "    true_idx = np.argmax(test_targets[idx])\n",
    "    if pred_idx != true_idx:\n",
    "         error_files.append(test_files[idx])\n",
    "         error_images.append(idx)\n",
    "\n",
    "error_files = np.array(error_files) \n",
    "error_images = np.array(error_images) \n",
    "errors=np.stack((error_images,error_files),axis=-1)\n",
    "         \n",
    "\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "i = 0 \n",
    "for idx, pic in errors:\n",
    "    idx = int(idx)\n",
    "    ax = fig.add_subplot(10, 11, i + 1, xticks=[], yticks=[])\n",
    "    \n",
    "    #print ('Misclassified the file: ' + pic + ' As: ' + str (alphabet_predictions[idx] ) + ' instead of ' + str(np.argmax(test_targets[idx])) + ' Index: ' + str(idx))\n",
    "\n",
    "    ax.imshow(np.squeeze(load_img(pic, target_size=(100,100), grayscale=True)), cmap = 'gray')\n",
    "    ax.set_title(\"{} ({})\".format(str (alphabet_predictions[idx] ), str(np.argmax(test_targets[idx]))),\n",
    "                 color=(\"red\"))\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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