title: Tensorflow model.fit() returns NaN with all float input
body: <p>I'm trying to run a regression model very similar to the Tensorflow tutorial (with my own dataset). My data and labels are <code>float</code> values in numpy arrays, e.g.:</p>
  
  <p>Data, varname <code>input_array</code>:</p>
  
  <pre><code>[[0.63473794 0.16736857 0.19028714 1.38746357 1.00389089 1.78771353]
   [0.58772032 0.19225393 0.22888241 1.41612279 1.09661261 1.52805896]
   [0.37530531 0.19501039 0.24683006 1.31084661 1.14897087 0.86286696]
   [0.29121152 0.19009779 0.255342   1.16257489 1.16012728 0.1838582 ]]
  </code></pre>
  
  <p>Labels, varname <code>label_array</code>:</p>
  
  <pre><code>[[-0.00362812]
   [-0.00136054]
   [-0.00181653]
   [ 0.00136302]]
  </code></pre>
  
  <p>My model is built with:</p>
  
  <pre><code>model = keras.Sequential([
  layers.Dense(64, activation=tf.nn.relu, input_shape=[len(input_array.keys())]),
  
  layers.Dense(64, activation=tf.nn.relu),
  layers.Dense(1)
  ])
  optimizer = tf.keras.optimizers.RMSprop(0.001)
  
  model.compile(loss='mean_squared_error',
                optimizer=optimizer,
                metrics=['mean_absolute_error', 'mean_squared_error'])
  </code></pre>
  
  <p>Training function is:</p>
  
  <pre><code>history = model.fit(
    input_array, label_array,
    epochs=EPOCHS, validation_split = 0.2, verbose=0,
    callbacks=[cp_callback])
  </code></pre>
  
  <p>For any number of epochs, the model returns:</p>
  
  <pre><code>   loss  mean_absolute_error  ...  val_mean_squared_error  epoch
  0   NaN                  NaN  ...                     NaN      0
  </code></pre>
  
  <p>I'm not sure what non-number could be causing the problem, since all the values I gave the model are numbers.</p>