The easiest[A] way to evaluate the actual value of a Tensor
object is to pass it to the Session.run()
method, or call Tensor.eval()
when you have a default session (i.e. in a with tf.Session():
block, or see below). In general[B], you cannot print the value of a tensor without running some code in a session.
If you are experimenting with the programming model, and want an easy way to evaluate tensors, the tf.InteractiveSession
lets you open a session at the start of your program, and then use that session for all Tensor.eval()
(and Operation.run()
) calls. This can be easier in an interactive setting, such as the shell or an IPython notebook, when it’s tedious to pass around a Session
object everywhere. For example, the following works in a Jupyter notebook:
with tf.Session() as sess: print(product.eval())
This might seem silly for such a small expression, but one of the key ideas in Tensorflow 1.x is deferred execution: it’s very cheap to build a large and complex expression, and when you want to evaluate it, the back-end (to which you connect with a Session
) is able to schedule its execution more efficiently (e.g. executing independent parts in parallel and using GPUs).
[A]: To print the value of a tensor without returning it to your Python program, you can use the tf.print()
operator, as Andrzej suggests in another answer. According to the official documentation:
To make sure the operator runs, users need to pass the produced op to
tf.compat.v1.Session
‘s run method, or to use the op as a control dependency for executed ops by specifying withtf.compat.v1.control_dependencies([print_op]
), which is printed to standard output.
Also note that:
In Jupyter notebooks and colabs,
tf.print
prints to the notebook cell outputs. It will not write to the notebook kernel’s console logs.
[B]: You might be able to use the tf.get_static_value()
function to get the constant value of the given tensor if its value is efficiently calculable.