Table of Difference between tf.Session() and tf.InteractiveSession()

tf.Session()

In TensorFlow, the computations are done using graphs. But when a graph is created, the values and computations are not defined. So a session is used to run the graph. The sessions place the graphs on targeted devices and execute them. It finally returns tensors. In tf.Session() we have to define the whole skeleton of the graph before the session is started.  The entire calculations have to be mentioned in the graph before the session is initialized. We use sess.run(tensor_name) to execute each computation....

tf.InteractiveSession()

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Table of Difference between tf.Session() and tf.InteractiveSession()

This session is much more interactive. It allows us to create dynamic graphs. In this, first the session is created and then the computational graph is created. The computations are executed using eval() function. An interactive session installs itself as the default session. Let us illustrate the InteractiveSession with the help of an example:...