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

Since TensorFlow 1.12, tf.layers are merely wrappers around tf.keras.layers.

A few examples:

Convolutional tf.layers just inherit from the convolutional tf.keras.layers, see source code here:

@tf_export('layers.Conv2D')
class Conv2D(keras_layers.Conv2D, base.Layer):

The same is true for all core tf.layers, e.g.:

@tf_export('layers.Dense')
class Dense(keras_layers.Dense, base.Layer):

With the integration of Keras into TensorFlow, it would make little sense to maintain several different layer implementations. tf.keras is becoming the de-facto high-level API for TensorFlow, therefore tf.layers are now just wrappers around tf.keras.layers.