mnist_readme.py 文件源码

def model(x_train, y_train, x_test, y_test):
    """
    Model providing function:

    Create Keras model with double curly brackets dropped-in as needed.
    Return value has to be a valid python dictionary with two customary keys:
        - loss: Specify a numeric evaluation metric to be minimized
        - status: Just use STATUS_OK and see hyperopt documentation if not feasible
    The last one is optional, though recommended, namely:
        - model: specify the model just created so that we can later use it again.
    """
    model = Sequential()
    model.add(Dense(512, input_shape=(784,)))
    model.add(Activation('relu'))
    model.add(Dropout({{uniform(0, 1)}}))
    model.add(Dense({{choice([256, 512, 1024])}}))
    model.add(Activation({{choice(['relu', 'sigmoid'])}}))
    model.add(Dropout({{uniform(0, 1)}}))

    # If we choose 'four', add an additional fourth layer
    if conditional({{choice(['three', 'four'])}}) == 'four':
        model.add(Dense(100))

        # We can also choose between complete sets of layers

        model.add({{choice([Dropout(0.5), Activation('linear')])}})
        model.add(Activation('relu'))

    model.add(Dense(10))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy', metrics=['accuracy'],
                  optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})

    model.fit(x_train, y_train,
              batch_size={{choice([64, 128])}},
              epochs=1,
              verbose=2,
              validation_data=(x_test, y_test))
    score, acc = model.evaluate(x_test, y_test, verbose=0)
    print('Test accuracy:', acc)
    return {'loss': -acc, 'status': STATUS_OK, 'model': model}