inception_resnet_v2.py 文件源码

def block17(input, scale=1.0, activation_fn='relu'):
    if K.image_dim_ordering() == "th":
        channel_axis = 1
    else:
        channel_axis = -1

    shortcut = input

    tower_conv = conv2d_bn(input, 192, 1, 1, activ_fn=activation_fn)

    tower_conv1_0 = conv2d_bn(input, 128, 1, 1, activ_fn=activation_fn)
    tower_conv1_1 = conv2d_bn(tower_conv1_0, 160, 1, 7, activ_fn=activation_fn)
    tower_conv1_2 = conv2d_bn(tower_conv1_1, 192, 7, 1, activ_fn=activation_fn)

    mixed = merge([tower_conv, tower_conv1_2], mode='concat', concat_axis=channel_axis)

    up = conv2d_bn(mixed, 1088, 1, 1, activ_fn=False, normalize=False)

    up = Lambda(do_scale, output_shape=K.int_shape(up)[1:], arguments={'scale':scale})(up)

    net = merge([shortcut, up], mode='sum')

    if activation_fn:
        net = Activation(activation_fn)(net)
    return net