if (depth - 2) % 6 != 0:
    raise ValueError('depth should be 6n+2 (eg: 20, 32, 44 in [a])')
# Start model definition.
num_filters = 16
num_res_blocks = int((depth - 2) / 6)

inputs = Input(shape=input_shape)
x = resnet_layer(inputs=inputs)
# Instantiate the stack of residual units
for stack in range(3):
    for res_block in range(num_res_blocks):
        strides = 1
        if stack > 0 and res_block == 0:  # first layer but not first stack
            strides = 2  # downsample
        y = resnet_layer(inputs=x,
                         num_filters=num_filters,
                         strides=strides)
        y = resnet_layer(inputs=y,
                         num_filters=num_filters,
                         activation=None)
        if stack > 0 and res_block == 0:  # first layer but not first stack
            # linear projection residual shortcut connection to match
            # changed dims
            x = resnet_layer(inputs=x,
                             num_filters=num_filters,
                             kernel_size=1,
                             strides=strides,
                             activation=None,
                             batch_normalization=False)
        x = keras.layers.add([x, y])
        x = Activation('relu')(x)
        #x = LeakyReLU()(x) #改relu -> LeakyReLU
    num_filters *= 2

# Add classifier on top.
# v1 does not use BN after last shortcut connection-ReLU
x = AveragePooling2D(pool_size=8)(x)
y = Flatten()(x)
outputs = Dense(num_classes,
                activation='softmax',
                kernel_initializer='he_normal')(y)

# Instantiate model.
model = Model(inputs=inputs, outputs=outputs)
return model