u_model.py 文件源码

def inception_block(inputs, depth, batch_mode=0, splitted=False, activation='relu'):
    assert depth % 16 == 0
    actv = activation == 'relu' and (lambda: LeakyReLU(0.0)) or activation == 'elu' and (lambda: ELU(1.0)) or None

    c1_1 = Convolution2D(depth/4, 1, 1, init='he_normal', border_mode='same')(inputs)

    c2_1 = Convolution2D(depth/8*3, 1, 1, init='he_normal', border_mode='same')(inputs)
    c2_1 = actv()(c2_1)
    if splitted:
        c2_2 = Convolution2D(depth/2, 1, 3, init='he_normal', border_mode='same')(c2_1)
        c2_2 = BatchNormalization(mode=batch_mode, axis=1)(c2_2)
        c2_2 = actv()(c2_2)
        c2_3 = Convolution2D(depth/2, 3, 1, init='he_normal', border_mode='same')(c2_2)
    else:
        c2_3 = Convolution2D(depth/2, 3, 3, init='he_normal', border_mode='same')(c2_1)

    c3_1 = Convolution2D(depth/16, 1, 1, init='he_normal', border_mode='same')(inputs)
    #missed batch norm
    c3_1 = actv()(c3_1)
    if splitted:
        c3_2 = Convolution2D(depth/8, 1, 5, init='he_normal', border_mode='same')(c3_1)
        c3_2 = BatchNormalization(mode=batch_mode, axis=1)(c3_2)
        c3_2 = actv()(c3_2)
        c3_3 = Convolution2D(depth/8, 5, 1, init='he_normal', border_mode='same')(c3_2)
    else:
        c3_3 = Convolution2D(depth/8, 5, 5, init='he_normal', border_mode='same')(c3_1)

    p4_1 = MaxPooling2D(pool_size=(3,3), strides=(1,1), border_mode='same')(inputs)
    c4_2 = Convolution2D(depth/8, 1, 1, init='he_normal', border_mode='same')(p4_1)

    res = merge([c1_1, c2_3, c3_3, c4_2], mode='concat', concat_axis=1)
    res = BatchNormalization(mode=batch_mode, axis=1)(res)
    res = actv()(res)
    return res