I have been using the introductory example of matrix multiplication in TensorFlow.

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 Tensor object without running the graph in a session?

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]

This post was edited by Nitara Bobal at October 14, 2020 11:54 AM IST

I think you need to get some fundamentals right. With the examples above you have created tensors (multi dimensional array). But for tensor flow to really work you have to initiate a "session" and run your "operation" in the session. Notice the word "session" and "operation". You need to know 4 things to work with tensorflow:

1. tensors
2. Operations
3. Sessions
4. Graphs

Now from what you wrote out you have given the tensor, and the operation but you have no session running nor a graph. Tensor (edges of the graph) flow through graphs and are manipulated by operations (nodes of the graph). There is default graph but you can initiate yours in a session.

When you say print , you only access the shape of the variable or constant you defined.

So you can see what you are missing :

with tf.Session() as sess:     
           print(sess.run(product))
           print (product.eval())

Hope it helps!

In Tensorflow 1.x

import tensorflow as tf
tf.enable_eager_execution()
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.],[2.]])
product = tf.matmul(matrix1, matrix2)

#print the product
print(product)         # tf.Tensor([[12.]], shape=(1, 1), dtype=float32)
print(product.numpy()) # [[12.]]

With Tensorflow 2.x, eager mode is enabled by default. so the following code works with TF2.0.

import tensorflow as tf
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.],[2.]])
product = tf.matmul(matrix1, matrix2)

#print the product
print(product)         # tf.Tensor([[12.]], shape=(1, 1), dtype=float32)
print(product.numpy()) # [[12.]]