title: Tensorflow input shape explanation
body: <p>So I have multiple images in an array. The shape of one image is (28,28,1). When I now train my model like this :</p>
  <pre><code>model.fit(X,Y, epochs = 100, batch_size = 15, validation_split=0.1, callbacks=[tensorboard])
  </code></pre>
  <p>I get an input shape of (1,28,28,1). Where is the extra dimension coming from? Also the output shape of the final model is (1,1), although my last shape looks like this:</p>
  <pre><code>model.add(layers.Dense(1, activation=&quot;sigmoid&quot;))
  </code></pre>
  <p>As I understand tensorflow the input shape should be (28,28,1) and the output shape should be (1). Can anyone explain this behavior?</p>