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:


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?

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())

 

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]

Reiterating what others said, its not possible to check the values without running the graph.

A simple snippet for anyone looking for an easy example to print values is as below. The code can be executed without any modification in ipython notebook

import tensorflow as tf

#define a variable to hold normal random values 
normal_rv = tf.Variable( tf.truncated_normal([2,3],stddev = 0.1))

#initialize the variable
init_op = tf.initialize_all_variables()

#run the graph
with tf.Session() as sess:
    sess.run(init_op) #execute init_op
    #print the random values that we sample
    print (sess.run(normal_rv))

 

Output:

[[-0.16702934  0.07173464 -0.04512421]
 [-0.02265321  0.06509651 -0.01419079]]