# -*- coding: utf-8 -*-
"""
Created on Tue Oct  2 19:39:51 2018

@author: murata
"""

'''Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''



import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers.core import Activation
from keras.optimizers import RMSprop

# setting GPU
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto(
    gpu_options=tf.GPUOptions(
        visible_device_list="2", # specify GPU number
        allow_growth=True
    )
)
set_session(tf.Session(config=config))

batch_size = 128
num_classes = 10
epochs = 20

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

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape, 'train samples')
print(x_test.shape, '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)
print(y_train.shape, 'train gts')
print(y_test.shape, 'test gts')

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
#model.add(Dense(num_classes, activation='softmax'))
model.add(Dense(num_classes))
model.add(Activation('softmax'))

model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

history = 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])