message: Add doctests for Dropout
diff: @@ -127,9 +127,37 @@ class Masking(Layer):
  class Dropout(Layer):
  """Applies Dropout to the input.
  - Dropout consists in randomly setting
  - a fraction `rate` of input units to 0 at each update during training time,
  - which helps prevent overfitting.
  + The Dropout layer randomly sets input units to 0 with a frequency of `rate`
  + at each step during training time, which helps prevent overfitting.
  + Inputs not set to 0 are scaled up by 1/(1 - rate) such that the sum over
  + all inputs is unchanged.
  +
  + Note that the Dropout layer only applies when `training` is set to True
  + such that no values are dropped during inference. When using `model.fit`,
  + `training` will be appropriately set to True automatically, and in other
  + contexts, you can set the kwarg explicitly to True when calling the layer.
  +
  + (This is in contrast to setting `trainable=False` for a Dropout layer.
  + `trainable` does not affect the layer's behavior, as Dropout does
  + not have any variables/weights that can be frozen during training.)
  +
  + >>> tf.random.set_seed(0)
  + >>> layer = tf.keras.layers.Dropout(.2, input_shape=(2,))
  + >>> data = np.arange(10).reshape(5, 2).astype(np.float32)
  + >>> print(data)
  + [[0. 1.]
  + [2. 3.]
  + [4. 5.]
  + [6. 7.]
  + [8. 9.]]
  + >>> outputs = layer(data, training=True)
  + >>> print(outputs)
  + tf.Tensor(
  + [[ 0. 1.25]
  + [ 2.5 3.75]
  + [ 5. 6.25]
  + [ 7.5 8.75]
  + [10. 0. ]], shape=(5, 2), dtype=float32)
  Arguments:
  rate: Float between 0 and 1. Fraction of the input units to drop.
  