Understanding Tensors and Their Evaluation in TensorFlow

In this tutorial, we will explore the concept of tensors within TensorFlow and how to evaluate them. Understanding when and how tensor values are computed is crucial for effectively using TensorFlow’s computational graph model.

What Are Tensors?

Tensors are a fundamental data structure used throughout TensorFlow. They represent multi-dimensional arrays with uniform data types (like floats or integers). In TensorFlow, operations on tensors build a computation graph. A Tensor object in this context does not hold the actual values; instead, it represents an operation that will produce these values when executed.

Evaluating Tensors

A tensor’s value is computed within what is known as a session in TensorFlow 1.x or through eager execution in TensorFlow 2.x. In older versions, tensors are symbolic and require a session to execute the graph and obtain their concrete values. This evaluation step is essential because TensorFlow separates computation definition from execution for efficient processing.

Using Sessions (TensorFlow 1.x)

In TensorFlow 1.x, you need to establish a session to compute tensor values:

import tensorflow as tf

# Define tensors using constants or variables
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.],[2.]])
product = tf.matmul(matrix1, matrix2)

# Create and run the session to evaluate 'product'
with tf.Session() as sess:
    result = sess.run(product)
    print(result)  # Output: [[12.]]

The sess.run() method triggers the computation of all operations leading up to the tensor you wish to evaluate.

Eager Execution (TensorFlow 2.x)

With TensorFlow 2.x, eager execution is enabled by default, allowing immediate evaluation of tensors:

import tensorflow as tf

# Define tensors using constants or variables
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.],[2.]])
product = tf.matmul(matrix1, matrix2)

# Directly print the tensor's value
print(product)          # Output: [[12.]]
print(product.numpy())  # Output: array([[12.]], dtype=float32)

Here, tensors can be evaluated on-the-fly, and their values accessed using methods like .numpy().

Additional Methods for Evaluation

Interactive Sessions: In interactive environments like Jupyter notebooks, you might use tf.InteractiveSession() to simplify the process of evaluating tensors.
Print Operation: For debugging or visualization during graph execution, TensorFlow provides a tf.Print operation that can be integrated into your computation graph.

import tensorflow as tf

# Initialize an Interactive Session for easier evaluation in interactive environments
sess = tf.InteractiveSession()

a = tf.constant([1.0, 3.0])
a = tf.Print(a, [a], message="This is a: ")
b = tf.add(a, a)

print(b.eval())  # Output includes: I tensorflow/core/kernels/logging_ops.cc:79] This is a: [1 3]

Eager Execution Configuration: In TensorFlow 1.x, eager execution can be manually enabled for similar on-the-fly tensor evaluations.

import tensorflow as tf

tf.enable_eager_execution()

# Define and evaluate tensors directly
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.],[2.]])
product = tf.matmul(matrix1, matrix2)

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

Key Considerations

Deferred Execution: TensorFlow’s model is based on deferred execution; the actual computation only happens when you explicitly run a session or in eager mode.
Performance Implications: Delaying the evaluation until necessary allows for optimizations like parallel processing and efficient memory use, which can be crucial for large-scale machine learning tasks.

Understanding these concepts will enable you to work effectively with TensorFlow’s computational model and harness its full potential in building complex neural network architectures.