title: ValueError: A `Concatenate` layer requires inputs with matching shapes
body: <pre><code>    from fr_utils import *
      from inception_blocks_v2 import *
  
      def triplet_loss(y_true, y_pred, alpha=0.3):
          """
          Implementation of the triplet loss as defined by formula (3)
  
          Arguments:
          y_pred -- python list containing three objects:
                  anchor -- the encodings for the anchor images, of shape (None, 128)
                  positive -- the encodings for the positive images, of shape (None, 128)
                  negative -- the encodings for the negative images, of shape (None, 128)
  
          Returns:
          loss -- real number, value of the loss
          """
  
          anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
  
          # Step 1: Compute the (encoding) distance between the anchor and the positive, you will need to sum over axis=-1
          pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis=-1)
          # Step 2: Compute the (encoding) distance between the anchor and the negative, you will need to sum over axis=-1
          neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis=-1)
          # Step 3: subtract the two previous distances and add alpha.
          basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)
          # Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.
          loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))
  
          return loss
  
      def main():
          FRmodel = faceRecoModel(input_shape=(3, 96, 96))
          FRmodel.compile(optimizer='adam', loss=triplet_loss, metrics=['accuracy'])
          FRmodel.save('face-rec_Google.h5')
          print_summary(model)
  
      main()
  </code></pre>
  
  <p>The error shown in this code is as follows  </p>
  
  <p>Got inputs shapes: %s' % (input_shape))</p>
  
  <p>ValueError: A <code>Concatenate</code> layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 128, 12, 192), (None, 32, 12, 192), (None, 32, 12, 102), (None, 64, 12, 192)]</p>
  
  <p>I try looking on the internet for he error but did not find a solution</p>