train.py 文件源码

def nn_mlp(input_shape, params):
    model = Sequential()

    for i, layer_size in enumerate(params['layers']):
        reg = regularizer(params)

        if i == 0:
            model.add(Dense(layer_size, init='he_normal', W_regularizer=reg, input_shape=input_shape))
        else:
            model.add(Dense(layer_size, init='he_normal', W_regularizer=reg))

        if params.get('batch_norm', False):
            model.add(BatchNormalization())

        if 'dropouts' in params:
            model.add(Dropout(params['dropouts'][i]))

        model.add(PReLU())

    model.add(Dense(1, init='he_normal'))

    return model