from __future__ import absolute_import
from __future__ import print_function
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.regularizers import l2, l1
from keras.constraints import maxnorm
from keras.optimizers import SGD, Adam, RMSprop
from keras.utils import np_utils
import numpy as np

'''
    Train a simple deep NN on the MNIST dataset.
'''

batch_size = 128
nb_classes = 10
nb_epoch = 20

np.random.seed(1337) # for reproducibility

# the data, shuffled and split between tran 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[0], 'train samples')
print(X_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

model = Sequential()
model.add(Dense(784, 128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(128, 128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(128, 10))
model.add(Activation('softmax'))

rms = RMSprop()
model.compile(loss='categorical_crossentropy', optimizer=rms)

model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=2, validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])