test_temporal_data_tasks.py 文件源码

def test_temporal_classification():
    '''
    Classify temporal sequences of float numbers
    of length 3 into 2 classes using
    single layer of GRU units and softmax applied
    to the last activations of the units
    '''
    (X_train, y_train), (X_test, y_test) = get_test_data(nb_train=500,
                                                         nb_test=500,
                                                         input_shape=(3, 5),
                                                         classification=True,
                                                         nb_class=2)
    y_train = to_categorical(y_train)
    y_test = to_categorical(y_test)

    model = Sequential()
    model.add(GRU(y_train.shape[-1],
                  input_shape=(X_train.shape[1], X_train.shape[2]),
                  activation='softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='adagrad',
                  metrics=['accuracy'])
    history = model.fit(X_train, y_train, nb_epoch=20, batch_size=32,
                        validation_data=(X_test, y_test),
                        verbose=0)
    assert(history.history['val_acc'][-1] >= 0.8)