88 lines
3.0 KiB
Python
88 lines
3.0 KiB
Python
from __future__ import absolute_import
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from __future__ import print_function
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import numpy as np
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np.random.seed(1337) # for reproducibility
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from keras.datasets import mnist
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from keras.models import Sequential
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from keras.layers.core import Dense, Activation
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from keras.initializations import normal, identity
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from keras.layers.recurrent import SimpleRNN, LSTM
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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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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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arXiv:1504.00941v2 [cs.NE] 7 Apr 201
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http://arxiv.org/pdf/1504.00941v2.pdf
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Optimizer is replaced with RMSprop which yields more stable and steady
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improvement.
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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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nb_classes = 10
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nb_epochs = 200
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hidden_units = 100
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learning_rate = 1e-6
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clip_norm = 1.0
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BPTT_truncate = 28*28
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# the data, shuffled and split between train and test sets
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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X_train = X_train.reshape(X_train.shape[0], -1, 1)
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X_test = X_test.reshape(X_test.shape[0], -1, 1)
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X_train = X_train.astype("float32")
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X_test = X_test.astype("float32")
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X_train /= 255
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X_test /= 255
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print('X_train shape:', X_train.shape)
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print(X_train.shape[0], 'train samples')
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print(X_test.shape[0], 'test samples')
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# convert class vectors to binary class matrices
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Y_train = np_utils.to_categorical(y_train, nb_classes)
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Y_test = np_utils.to_categorical(y_test, nb_classes)
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print('Evaluate IRNN...')
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model = Sequential()
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model.add(SimpleRNN(input_dim=1, output_dim=hidden_units,
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init=lambda shape: normal(shape, scale=0.001),
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inner_init=lambda shape: identity(shape, scale=1.0),
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activation='relu', truncate_gradient=BPTT_truncate))
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model.add(Dense(hidden_units, nb_classes))
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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=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('IRNN test score:', scores[0])
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print('IRNN test accuracy:', scores[1])
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print('Compare to LSTM...')
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model = Sequential()
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model.add(LSTM(1, hidden_units))
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model.add(Dense(hidden_units, nb_classes))
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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=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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