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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