How to print the value of a Tensor object in TensorFlow?

Question

matrix1 = tf.constant([[3., 3.]]) matrix2 = tf.constant([[2.],[2.]]) product = tf.matmul(matrix1, matrix2) When I print the product, it is displaying it as a Tensor object: <tensorflow.python.framework.ops.Tensor object at 0x10470fcd0> But how do I know the value of product? The following doesn't help: print product Tensor("MatMul:0", shape=TensorShape([Dimension(1), Dimension(1)]), dtype=float32) I know that graphs run on Sessions, but isn't there any way I can check the output of a Tensorobject without running the graph in a session?

Gufran Mirza · Accepted Answer

While other answers are correct that you cannot print the value until you evaluate the graph, they do not talk about one easy way of actually printing a value inside the graph, once you evaluate it. The easiest way to see a value of a tensor whenever the graph is evaluated (using run or eval) is to use the Print operation as in this example: # Initialize session import tensorflow as tf sess = tf.InteractiveSession() # Some tensor we want to print the value of a = tf.constant([1.0, 3.0]) # Add print operation a = tf.Print(a, [a], message="This is a: ") # Add more elements of the graph using a b = tf.add(a, a) Now, whenever we evaluate the whole graph, e.g. using b.eval(), we get: I tensorflow/core/kernels/logging_ops.cc:79] This is a: [1 3]

Lex Hoffman · Answer

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. This might seem silly for such a small expression, but one of the key ideas in Tensorflow 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).

Tensorflow

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