AI/ML

Dive into TensorFlow (1) – Tensor and Session

Tensor

In TensorFlow, data isn’t stored as integers, floats, or strings. These values are encapsulated in an object called a tensor. Tensors come in a variety of sizes as shown below:

# A is a 0-dimensional int32 tensor
A = tf.constant(1234) 
# B is a 1-dimensional int32 tensor
B = tf.constant([123,456,789]) 
# C is a 2-dimensional int32 tensor
C = tf.constant([ [123,456,789], [222,333,444] ])

tf.constant() is one of many TensorFlow operations. The tensor returned by tf.constant() is called a constant tensor, because the value of the tensor never changes.

Session

TensorFlow’s API is built around the idea of a computational graph, a way of visualizing a mathematical process. A “TensorFlow Session” is an environment for running a graph. The session is in charge of allocating the operations to GPU(s) and/or CPU(s), including remote machines.

Hello World

import tensorflow as tf

# Create TensorFlow object called hello_constant
hello_constant = tf.constant('Hello World!')

with tf.Session() as sess:
    # Run the tf.constant operation in the session
    output = sess.run(hello_constant)
    print(output)

The code first creates the tensor, hello_constant, from the previous lines. The next step is to evaluate the tensor in a session.

The code creates a session instance, sess, using tf.Session. The sess.run() function then evaluates the tensor and returns the results.

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