title: Keras ValueError: A merge layer should be called on a list of inputs
body: <p>When I have already finish my function model , I can't use it the predict_classes(), and I try Sequential(layers=model.layers) to let me use predict_classes() it tell me the wrong, It's very strange     </p>
  
  <h3>This is my function model</h3>
  
  <pre><code>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 &gt; 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 &gt; 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 -&gt; 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)
  </code></pre>
  
  <p>It give me the Error, when I want to change function model to Sequential model
  Keras ValueError: A merge layer should be called on a list of inputs</p>