60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
from __future__ import print_function
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import numpy as np
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np.random.seed(1337)
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from keras.utils.test_utils import get_test_data
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from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam
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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.utils.np_utils import to_categorical
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import unittest
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(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=1000, nb_test=200, input_shape=(10,),
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classification=True, nb_class=2)
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y_train = to_categorical(y_train)
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y_test = to_categorical(y_test)
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def get_model(input_dim, nb_hidden, output_dim):
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model = Sequential()
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model.add(Dense(nb_hidden, input_shape=(input_dim,)))
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model.add(Activation('relu'))
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model.add(Dense(output_dim))
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model.add(Activation('softmax'))
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return model
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def _test_optimizer(optimizer, target=0.9):
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model = get_model(X_train.shape[1], 10, y_train.shape[1])
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model.compile(loss='categorical_crossentropy', optimizer=optimizer)
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history = model.fit(X_train, y_train, nb_epoch=12, batch_size=16, validation_data=(X_test, y_test), show_accuracy=True, verbose=2)
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return history.history['val_acc'][-1] > target
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class TestOptimizers(unittest.TestCase):
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def test_sgd(self):
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print('test SGD')
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sgd = SGD(lr=0.01, momentum=0.9, nesterov=True)
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self.assertTrue(_test_optimizer(sgd))
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def test_rmsprop(self):
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print('test RMSprop')
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self.assertTrue(_test_optimizer(RMSprop()))
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def test_adagrad(self):
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print('test Adagrad')
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self.assertTrue(_test_optimizer(Adagrad()))
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def test_adadelta(self):
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print('test Adadelta')
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self.assertTrue(_test_optimizer(Adadelta()))
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def test_adam(self):
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print('test Adam')
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self.assertTrue(_test_optimizer(Adam()))
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if __name__ == '__main__':
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print('Test optimizers')
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unittest.main()
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