How Do I Access the Value of a Tensor in TensorFlow?

Obtaining Tensor Values in TensorFlow

Understanding the values stored in Tensor objects is crucial in TensorFlow. While the code snippet you provided creates and prints a Tensor product, the terminal output displays only a reference to the Tensor object itself.

Easiest Method: Session Evaluation

The straightforward approach to access the actual value of a Tensor is to leverage the Session.run() method. Alternatively, you can employ Tensor.eval() with a default session, as demonstrated below:

import tensorflow as tf

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

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

This approach simplifies the evaluation process, allowing you to determine the value of your Tensor directly.

Deferred Execution and Session Management

TensorFlow 1.x adheres to a paradigm of deferred execution, enabling the efficient construction of complex expressions without immediate evaluation. This allows the back-end to optimize execution, leveraging parallel processing and utilizing GPUs if available.

To streamline the evaluation process further, TensorFlow provides the tf.InteractiveSession class. This class automatically initiates a session at program startup, streamlining Tensor.eval() calls for interactive environments such as shell or IPython notebooks.

Additional Methods

Alternatively, you can employ tf.print() to display a Tensor's value without retrieving it explicitly. However, this method requires explicit execution through the Session.run() method or control dependency specification.

For constant Tensors with efficiently calculable values, tf.get_static_value() can retrieve the constant value.

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