title: Merge layer keras with tensorflow backend
body: <p>I wanted to merge two sequential models into one using a <code>Merge</code> layer but it is showing me an error. I am working with images, with size 128x128 (RGB image) and batch size is 32.</p>
  
  <p>The error is: </p>
  
  <pre><code>ValueError: The model expects 3 input arrays, but only received one array. Found: array with shape (32, 3, 128, 128)
  </code></pre>
  
  <p>The model is defined as:</p>
  
  <pre><code>model = Sequential() leftBranch = Sequential() 
  
  leftBranch.add(Reshape((3,128,128), input_shape=(3, img_width, img_height))) 
  leftBranch.add(Convolution2D(14, 3, 1, activation='relu')) 
  leftBranch.add(ZeroPadding2D((1, 1))) 
  leftBranch.add(Flatten()) 
  
  rightBranch = Sequential() 
  rightBranch.add(Reshape((3,128,128), input_shape=(3, img_width, img_height))) 
  rightBranch.add(Convolution2D(14, 1, 3, activation='relu')) 
  rightBranch.add(MaxPooling2D((2, 2), strides=(2, 2))) 
  rightBranch.add(Flatten()) 
  
  centralBranch = Sequential() 
  centralBranch.add(Reshape((3,128,128), input_shape=(3, img_width, img_height))) 
  centralBranch.add(Convolution2D(14, 5, 5, activation='relu'))
  centralBranch.add(MaxPooling2D((2, 2), strides=(2, 2)))
  centralBranch.add(Flatten()) 
  merged = Merge([leftBranch, centralBranch, rightBranch], mode='concat') 
  
  model = Sequential() 
  model.add(merged) model.add(Dense(64)) 
  model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(1)) 
  model.add(Activation('sigmoid'))
  </code></pre>
  
  <hr>
  
  <p>Error coming is : </p>
  
  <pre><code>ValueError: The model expects 3 input arrays, but only received one array. Found: array with shape (32, 3, 128, 128)
  </code></pre>
  
  <p>So, whats is the proper way to concatenate two sequential model with convolutional layers. I just want to Merge convolutional layers output like I did it here.</p>