'''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 keras
from keras.utils import plot_model
from keras.datasets import mnist
from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.layers import Conv1D, Conv2D, MaxPooling2D
from keras import backend as K
import numpy as np

batch_size = 512
num_classes = 10
epochs = 50

# 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(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape,
                 padding='same'))
model.add(Conv2D(64, (3, 3), activation='relu',
                 padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(Dropout(0.25))
#model.add(Flatten())
#model.add(Dense(128, activation='relu'))
model.add(Conv2D(128, (14,14), activation='relu', padding='valid'))
#model.add(Dropout(0.5))
#model.add(Dense(num_classes, activation='softmax'))
model.add(Conv2D(num_classes, (1,1), activation='softmax'))
model.add(Flatten())
plot_model(model, 'model.png', show_shapes=True)
model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])

'''
weights = load_model('CNN.h5').get_weights()
model.layers[0].set_weights([weights[0], weights[1]])
model.layers[1].set_weights([weights[2], weights[3]])
model.layers[4].set_weights([weights[4].reshape([14,14,64,128]), weights[5]])
model.layers[6].set_weights([weights[6].reshape([1,1,128,num_classes]), weights[7]])
'''

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)
#model.save('CNN.h5')

print('Test loss:', score[0])
print('Test accuracy:', score[1])
