keras_example.py 文件源码

def multilayer_perceptron():
    train, test, valid = load_data('mnist.pkl.gz')

    num_labels = 10
    train_y = make_one_hot(train[1], num_labels)
    valid_y = make_one_hot(valid[1], num_labels)
    test_y = make_one_hot(test[1], num_labels)

    mlp_model = Sequential()
    mlp_model.add(Dense(300, activation='relu', input_dim=784))
    mlp_model.add(Dense(10, activation='softmax'))
    mlp_model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
    mlp_model.fit(train[0], train_y, validation_data=(valid[0],valid_y), batch_size=32, epochs=10)
    print('Test set loss and accuracy:', mlp_model.evaluate(test[0], test_y))