Keras ValueError：应在输入列表上调用合并层

Question

当我已经完成我的函数模型时，我不能使用它的predict_classes（），而我尝试使用Sequential（layers = model.layers）让我使用predict_classes（），它告诉我错误，这很奇怪

这是我的功能模型

def resnet_v1(input_shape, depth, num_classes):

    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

model = resnet_v1(input_shape=input_shape, depth=depth)
model = resnet_v1((32,32,1), 26, 26)

当我想将功能模型更改为顺序模型时，它给了我错误 Keras ValueError：应在输入列表上调用合并层