title: Sequential Keras model output not matching label input
body: <p>I am trying to build a sequential model in Keras that will take an input feature of <code>shape(1, n, 5)</code> and an input label of <code>shape(1, 1)</code> and return an output of <code>shape(1, 1)</code> to match the input label.</p>
  <p>I built a model from a tutorial that works well with features <code>shape(n, 5)</code> and labels <code>shape(n, 1)</code> and returns an output of <code>shape(n, 1)</code>, which matches the input label.</p>
  <p>Here is the model</p>
  <pre><code>class Model(keras.Model):
  
      def __init__(self, units):
          super().__init__()
          self.dense1 = keras.layers.Dense(
              units=units,
              activation=tf.nn.relu,
              kernel_initializer=tf.random.normal,
              bias_initializer=tf.random.normal,
          )
          self.dense2 = keras.layers.Dense(1)
  
      def call(self, x2, training=True, mask=None):
          x1 = self.dense1(x2)
          x1 = self.dense2(x1)
          tf.squeeze(x1, axis=-1)
          return x1
  </code></pre>
  <p>Here is how I call it</p>
  <pre><code>            model.fit(
                  features, labels,
                  epochs=100,
                  verbose=1,
              )
  </code></pre>
  <p>When I give the model features of <code>shape(1, n, 5)</code> and labels of <code>shape(1, 1)</code>, it returns <code>shape(1, n, 5)</code>. Does this mean all n feature sets were checked against the one label? I want the model to treat the <code>shape(n, 5)</code> as a single input.</p>
  <p>To do that, I tried this</p>
  <pre><code>self.dense1 = keras.layers.Dense(
              input_shape=[None, 5],
              units=units,
              activation=tf.nn.relu,
              kernel_initializer=tf.random.normal,
              bias_initializer=tf.random.normal,
          )
  </code></pre>
  <p>but that did not do anything to the output shape.</p>
  <p>How do I get the model to treat the <code>shape(n, 5)</code> as a single input?</p>