util.py 文件源码

def conv_wrap(params, conv_out, i):
    from keras.layers.normalization import BatchNormalization
    from keras.layers.advanced_activations import PReLU
    from keras.layers.convolutional import Convolution2D
    from keras.layers import Dropout

    # use filter_width_K if it is there, otherwise use 3
    filter_key = "filter_width_%d" % i
    filter_width = params.get(filter_key, 3)
    num_filters = params["num_filters"]
    conv_out = Convolution2D(
        nb_filter=num_filters,
        nb_row=filter_width,
        nb_col=filter_width,
        init='he_normal',
        border_mode='same')(conv_out)
    conv_out = BatchNormalization()(conv_out)
    conv_out = PReLU()(conv_out)
    if params["dropout"] > 0:
        conv_out = Dropout(params["dropout"])(conv_out)
    return conv_out