title: ValueError: input that isn't a symbolic tensor
body: <p>I am new to python and am working on an image processing project and built the below model. I get the error posted below the model.</p>
  
  <p>In my research, I found some answers regarding this error:</p>
  
  <p><a href="https://stackoverflow.com/questions/45979848/merge-2-sequential-models-in-keras">Merge 2 sequential models in Keras</a>
  the issue above is of Concatenate 2 models not Concatenate 2 layers</p>
  
  <p><a href="https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models" rel="nofollow noreferrer" title="Multi-input and multi-output models">https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models</a>
  In the keras example, Concatenate 2 layers but 1 layer is an input layer. I would like to understand how to Concatenate 2 regular layers and use the concatenated layer as a layer in a sequence of layers. similar concept to the Inception Model.</p>
  
  <pre><code>   input
    /  |  \
  a1   b1  c1
  |    |    | 
  a2   b2  c2
  \   |   / 
  concatenate
   /  |   \
  d1   e1  f1
  |    |    | 
  d2   e2  f2
   \   |   /
    output
  </code></pre>
  
  <p>above is my attempt to draw what my final goal :) </p>
  
  <pre><code>def fet_Model():
  
  bnd_input = Input(shape=input_shape)
  
  k31 = Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same')(bnd_input)
  k31 = Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same')(k31)
  k31 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(k31)
  
  in_ly1_cv_1n1 = Conv2D(32, kernel_size=(1, 1), activation='relu', padding='same')(k31)
  in_ly1_cv_3n3 = Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same')(in_ly1_cv_1n1)
  in_ly1_cv_5n5 = Conv2D(64, kernel_size=(5, 5), activation='relu', padding='same')(in_ly1_cv_1n1)
  
  in_ly1_mx_pl = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same')(k31)
  in_ly1_mxcv_1n1 = Conv2D(32, kernel_size=(1, 1), activation='relu', padding='same')(in_ly1_mx_pl)
  
  filt_concat1 = Concatenate([in_ly1_mxcv_1n1, in_ly1_cv_5n5, in_ly1_cv_3n3, k31])
  
  # when running the below line I receive the error
  in_ly2_cv_1n1 = Conv2D(32, kernel_size=(1, 1), activation='relu', padding='same')(filt_concat1)
  in_ly2_cv_3n3 = Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same')(in_ly2_cv_1n1)
  in_ly2_cv_5n5 = Conv2D(64, kernel_size=(5, 5), activation='relu', padding='same')(in_ly2_cv_1n1)
  
  in_ly2_mx_pl = MaxPooling2D(pool_size=(3, 3), strides=(1, 1))(filt_concat1)
  in_ly2_mxcv_1n1 = Conv2D(32, kernel_size=(1, 1), activation='relu', padding='same')(in_ly2_mx_pl)
  
  filt_concat2 = Concatenate([in_ly2_mxcv_1n1, in_ly2_cv_5n5, in_ly2_cv_3n3, k31])
  
  lst_ly_avg_pl = AveragePooling2D(pool_size=(3, 3), strides=(2, 2))(filt_concat2)
  
  lst_ly_cnv2 = Conv2D(32, kernel_size=(1, 1), activation='relu', padding='same')(lst_ly_avg_pl)
  
  lst_ly_den = Dense(1, activation='sigmoid')(lst_ly_cnv2)
  
  model = Model(inputs=bnd_input, outputs=lst_ly_den)
  
  optimizer = Adam(lr= 0.002, beta_1=0.99, beta_2=0.999, epsilon=1e-08, decay=0.01)
  
  model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
  
  return model
  </code></pre>
  
  <p>Error:</p>
  
  <pre><code>Traceback (most recent call last):
     K.is_keras_tensor(x)
     raise ValueError('Unexpectedly found an instance of type `' + 
  str(type(x)) + '`. '
  ValueError: Unexpectedly found an instance of type `&lt;class 'keras.layers.merge.Concatenate'&gt;`. Expected a symbolic tensor instance.
  
  During handling of the above exception, another exception occurred:
  
  Traceback (most recent call last):
     exec(code_obj, self.user_global_ns, self.user_ns)
  File "&lt;ipython-input-194-ebff784b774c&gt;", line 1, in &lt;module&gt;
     in_ly2_cv_1n1 = Conv2D(32, kernel_size=(1, 1), activation='relu', padding='same')(filt_concat1)
     self.assert_input_compatibility(inputs)
     str(inputs) + '. All inputs to the layer '
  ValueError: Layer conv2d_41 was called with an input that isn't a symbolic tensor. Received type: &lt;class 'keras.layers.merge.Concatenate'&gt;. Full input: [&lt;keras.layers.merge.Concatenate object at 0x1cee082b0&gt;]. All inputs to the layer should be tensors.
  </code></pre>