mnist_irnn.py

'''This is a reproduction of the IRNN experiment
with pixel-by-pixel sequential MNIST in
"A Simple Way to Initialize Recurrent Networks of Rectified Linear Units"
by Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton

arXiv:1504.00941v2 [cs.NE] 7 Apr 2015
http://arxiv.org/pdf/1504.00941v2.pdf

Optimizer is replaced with RMSprop which yields more stable and steady
improvement.

Reaches 0.93 train/test accuracy after 900 epochs
(which roughly corresponds to 1687500 steps in the original paper.)
'''

from __future__ import print_function

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.layers import SimpleRNN
from keras import initializers
from keras.optimizers import RMSprop
from keras.utils import np_utils

batch_size = 32
num_classes = 10
epochss = 200
hidden_units = 100

learning_rate = 1e-6
clip_norm = 1.0

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

X_train = X_train.reshape(X_train.shape[0], -1, 1)
X_test = X_test.reshape(X_test.shape[0], -1, 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 = np_utils.to_categorical(y_train, num_classes)
Y_test = np_utils.to_categorical(y_test, num_classes)

print('Evaluate IRNN...')
model = Sequential()
model.add(SimpleRNN(output_dim=hidden_units,
                    init=initializers.RandomNormal(stddev=0.001),
                    inner_init=initializers.Identity(gain=1.0),
                    activation='relu',
                    input_shape=X_train.shape[1:]))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
rmsprop = RMSprop(lr=learning_rate)
model.compile(loss='categorical_crossentropy',
              optimizer=rmsprop,
              metrics=['accuracy'])

model.fit(X_train, Y_train, batch_size=batch_size, epochs=epochss,
          verbose=1, validation_data=(X_test, Y_test))

scores = model.evaluate(X_test, Y_test, verbose=0)
print('IRNN test score:', scores[0])
print('IRNN test accuracy:', scores[1])