'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import tensorflow
KERAS_BACKEND = tensorflow
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from sys import exit

batch_size = 128
num_classes = 10
epochs = 15

# input image dimensions
img_rows, img_cols = 28, 28

# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()


if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)


x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(filters = 32, kernel_size = (3, 3), activation = 'relu', input_shape = input_shape))     # output shape (None, 26, 26, 32)
model.add(Conv2D(filters = 64, kernel_size = (3, 3), activation = 'relu'))                                # output shape (None, 24, 24, 64)
model.add(MaxPooling2D(pool_size=(2, 2)))                                                                 # output shape (None, 12, 12, 64)
model.add(Dropout(rate = 0.25))                                                                           # output shape (None, 12, 12, 64)
model.add(Flatten())                                                                                      # output shape (None, 9216)
model.add(Dense(units = 128, activation='relu'))                                                          # output shape (None, 128)
model.add(Dropout(rate = 0.5))                                                                            # output shape (None, 128)
model.add(Dense(units = num_classes, activation='softmax'))                                               # output shape (None, 10)

model.compile(loss = keras.losses.categorical_crossentropy, optimizer = keras.optimizers.Adadelta(), metrics = ['accuracy'])

model.fit(x_train, y_train, batch_size = batch_size, epochs = epochs, verbose = 1, validation_data = (x_test, y_test))
score = model.evaluate(x_test, y_test, verbose = 0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])