What is the difference between tf.keras.layers versus tf.layers?

Question

What is the difference between tf.keras.layers versus tf.layers?
E.g. both of them have Conv2d, do they provide different outputs?
Is there any benefits if you mix

Answer 1

tf.layers module is Tensorflow attempt at creating a Keras like API whereas tf.keras.layers is a compatibility wrapper. In fact, most of the implementation refers back to tf.layers, for example the tf.keras.layers.Dense inherits the core implementation:

@tf_export('keras.layers.Dense')
class Dense(tf_core_layers.Dense, Layer):
  # ...

Because the tf.keras compatibility module is checked into the Tensorflow repo separately, it might lack behind what Keras actually offers. I would use Keras directly or tf.layers but not necessarily mix them.