Hands-On Reinforcement Learning with Python

Sessions

Computation graphs will only be defined; in order to execute the computation graph, we use TensorFlow sessions:

sess = tf.Session()

We can create the session for our computation graph using the tf.Session() method, which will allocate the memory for storing the current value of the variable. After creating the session, we can execute our graph with the sess.run() method.

In order to run anything in TensorFlow, we need to start the TensorFlow session for an instance; please refer to the code:

import tensorflow as tfa = tf.multiply(2,3)print(a)

It will print a TensorFlow object instead of 6. As already said, whenever we import TensorFlow a default computation graph will automatically be created and all nodes a that we created will ...