tf.nn.relu vs tf.keras.activations.relu [closed]

Question

I see both tf.nn.relu and tf.keras.activations.relu computes only the ReLU function (no additional fully connected layer or something, as described here), so what's the difference between them? Does one just wraps the other?

Answer 1

• tf.nn.relu : It comes from TensorFlow library. It is located in the nn module. Hence, it is used as an operation in neural networks. If x is a tensor then,

  y = tf.nn.relu( x )
  

  It is used in creating custom layers and NN. If you use it with Keras, you may face some problems while loading or saving the models or converting the model to TF Lite.

• tf.keras.activations.relu : It comes from the Keras library included in TensorFlow. It is located in the activations module which also provides another activation functions. It is mostly used in Keras Layers ( tf.keras.layers ) for the activation= argument :

  model.add( keras.layers.Dense( 25 , activation=tf.keras.activations.relu  ) )
  

  But, it can also be used as the example in the above section. It is more specific to Keras ( Sequential or Model ) rather than raw TensorFlow computations.

tf.nn.relu is a TensorFlow specific whereas tf.keras.activations.relu has more uses in Keras own library. If I create a NN with only TF, I will most probably use tf.nn.relu and if I am creating a Keras Sequential model then I will use tf.keras.activations.relu.