title: ValueError: Input 0 is incompatible with layer: expected shape=(None, 48, 187, 621, 64), found shape=(48, 187, 621, 64)
body: <p>I'm training a model in tensorflow, where the input isn't batched but is a single input of shape:</p>
  <p>(48, 187, 621, 64)</p>
  <p>When I pass this input inside the model l_regularization, like so:</p>
  <pre><code>make_regularization(l_cost_volume)
  </code></pre>
  <p>I get the error:</p>
  <pre><code>Traceback (most recent call last):
    File &quot;train.py&quot;, line 300, in &lt;module&gt;
      train(ds, epochs)
    File &quot;train.py&quot;, line 278, in train
      x_train_right, y_train_right_noc)
    File &quot;train.py&quot;, line 242, in train_step
      l_regularization = make_regularization(l_cost_volume)
    File &quot;/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py&quot;, line 977, in __call__
      input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
    File &quot;/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/engine/input_spec.py&quot;, line 274, in assert_input_compatibility
      ', found shape=' + display_shape(x.shape))
  ValueError: Input 0 is incompatible with layer gc-net-part1: expected shape=(None, 48, 187, 621, 64), found shape=(48, 187, 621, 64)
  
  </code></pre>
  <p>How to fix this? Is there a way to extend the dimension of the single image by None or alternatively have the model accept the &quot;found shape&quot; dimension?
  By the way, extending the dimension of the &quot;found shape&quot; doesn't work either. It gives:</p>
  <pre><code>ValueError: Input 0 is incompatible with layer gc-net-part1: expected shape=(None, 48, 187, 621, 64), found shape=(1, 48, 187, 621, 64)
  </code></pre>