Problem Formulation
Given a TensorFlow variable created with tf.Variable()
. As this variable may have changed during the training process (e.g., using assign()), you want to get the current value of it. How to accomplish this in TensorFlow?
x = tf.Variable(...) # What's the current value?
Sessions Are Gone in TensorFlow 2
In TensorFlow 1, computations were performed within Sessions. That’s why many people proposed to solve this problem in TensorFlow 1 via the Session().run(x)
call. For example, look at this code from here:
# OLD: WORKS ONLY IN TENSORFLOW 1!!! import tensorflow as tf x = tf.Variable([42.0, 21.0]) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) your_var = sess.run(x) print(your_var)
However, the new API of the TensorFlow 2 framework has largely removed the need to explicitly run computations in sessions:
“Sessions are gone in TensorFlow 2. There is one global runtime in the background that executes all computation, whether run eagerly or as a compiled tf.function
.” — source
Get Current Value of Variable in TensorFlow 2
To get the current value of a variable x
in TensorFlow 2, you can simply print it with print(x)
. This prints a representation of the tf.Variable
object that also shows you its current value. If you want a clean representation of a tf.Variable
stored in variable x
, try x.numpy()
.
Here’s an example that showcases both variants:
import tensorflow as tf x = tf.Variable(42) print(x) print(x.numpy())
The output of this code snippet is:
<tf.Variable 'Variable:0' shape=() dtype=int32, numpy=42> 42
You can try it yourself in the interactive Jupyter Notebook here:
Where to Go From Here
TensorFlow is an exciting framework! ? We’ve compiled a number of TensorFlow cheat sheets in our article here. Check them out!
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