Modified comment and fixed batch_size
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@ -12,7 +12,7 @@ from keras.optimizers import RMSprop
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from keras.utils import np_utils
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'''
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This is a reproduction of the IRNN experiment
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This is a reproduction of the IRNN experiment
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with pixel-by-pixel sequential MNIST in
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"A Simple Way to Initialize Recurrent Networks of Rectified Linear Units "
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by Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton
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@ -23,8 +23,8 @@ from keras.utils import np_utils
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Optimizer is replaced with RMSprop which yields more stable and steady
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improvement.
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0.80 train/test accuracy and 0.55 train/test loss after 70 epochs
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(it's still underfitting at that point, though).
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Reaches 0.93 train/test accuracy after 900 epochs (which roughly corresponds
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to 1687500 steps in the original paper.)
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'''
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batch_size = 32
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@ -64,7 +64,7 @@ model.add(Activation('softmax'))
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rmsprop = RMSprop(lr=learning_rate)
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model.compile(loss='categorical_crossentropy', optimizer=rmsprop)
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model.fit(X_train, Y_train, batch_size=16, nb_epoch=nb_epochs,
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model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epochs,
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show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))
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scores = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
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@ -79,9 +79,9 @@ model.add(Activation('softmax'))
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rmsprop = RMSprop(lr=learning_rate)
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model.compile(loss='categorical_crossentropy', optimizer=rmsprop)
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model.fit(X_train, Y_train, batch_size=16, nb_epoch=nb_epochs,
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model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epochs,
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show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))
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scores = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)
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print('LSTM test score:', scores[0])
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print('LSTM test accuracy:', scores[1])
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print('LSTM test accuracy:', scores[1])
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