I got the following deprecation warning in my tensorflow code:
The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
tf.session
tf.compat.v1.Session
tensorflow - The name tf. Session is deprecated. Please use tf. compat.
tf. compat allows you to write code that works both in TensorFlow 1.
tf.Session() initiates a TensorFlow Graph object in which tensors are processed through operations (or ops). The with block terminates the session as soon as the operations are completed. Hence, there is no need for calling Session.close . Also, a session contains variables, global variables, placeholders, and ops.
To make TensorFlow be more "Pythonic" in version 2.0, by design TF 2.0 does not have tf.Session.
TensorFlow 1.X requires users to manually stitch together an abstract syntax tree (the graph) by making tf.* API calls. It then requires users to manually compile the abstract syntax tree by passing a set of output tensors and input tensors to a session.run() call.
TensorFlow 2.0 executes eagerly (like Python normally does) and in 2.0, graphs and sessions should feel like implementation details.
You could use:
import tensorflow.compat.v1 as tf tf.disable_v2_behavior()
However, this does not let you take advantage of many of the improvements made in TensorFlow 2.0.
The better solution is:
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