74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
'''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
|
|
|
|
import keras
|
|
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
|
|
|
|
batch_size = 32
|
|
num_classes = 10
|
|
epochs = 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 = keras.utils.to_categorical(y_train, num_classes)
|
|
y_test = keras.utils.to_categorical(y_test, num_classes)
|
|
|
|
print('Evaluate IRNN...')
|
|
model = Sequential()
|
|
model.add(SimpleRNN(hidden_units,
|
|
kernel_initializer=initializers.RandomNormal(stddev=0.001),
|
|
recurrent_initializer=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=epochs,
|
|
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])
|