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Update IRNN example

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This commit is contained in:
Francois Chollet 2015-11-29 13:08:45 -08:00
parent 361a7cfe41
commit df860fdb94
1 changed files with 4 additions and 6 deletions
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10
examples/mnist_irnn.py
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@ -23,8 +23,8 @@ from keras.utils import np_utils
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.)
Reaches 0.93 train/test accuracy after 900 epochs
(which roughly corresponds to 1687500 steps in the original paper.)
'''
batch_size = 32
@ -34,7 +34,6 @@ hidden_units = 100
learning_rate = 1e-6
clip_norm = 1.0
BPTT_truncate = 28*28
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
@ -58,8 +57,7 @@ model = Sequential()
model.add(SimpleRNN(output_dim=hidden_units,
init=lambda shape: normal(shape, scale=0.001),
inner_init=lambda shape: identity(shape, scale=1.0),
activation='relu', truncate_gradient=BPTT_truncate,
input_shape=(None, 1)))
activation='relu', input_shape=X_train.shape[1:]))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
rmsprop = RMSprop(lr=learning_rate)
@ -74,7 +72,7 @@ print('IRNN test accuracy:', scores[1])
print('Compare to LSTM...')
model = Sequential()
model.add(LSTM(hidden_units, input_shape=(None, 1)))
model.add(LSTM(hidden_units, input_shape=X_train.shape[1:]))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
rmsprop = RMSprop(lr=learning_rate)