def test_LearningRateScheduler():
(X_train, y_train), (X_test, y_test) = get_test_data(nb_train=train_samples,
nb_test=test_samples,
input_shape=(input_dim,),
classification=True,
nb_class=nb_class)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
model = Sequential()
model.add(Dense(nb_hidden, input_dim=input_dim, activation='relu'))
model.add(Dense(nb_class, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
cbks = [callbacks.LearningRateScheduler(lambda x: 1. / (1. + x))]
model.fit(X_train, y_train, batch_size=batch_size,
validation_data=(X_test, y_test), callbacks=cbks, nb_epoch=5)
assert (float(K.get_value(model.optimizer.lr)) - 0.2) < K.epsilon()
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