Welcome to the TensorFlow Basics tutorial! TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. This tutorial will help you get started with TensorFlow and understand its fundamental concepts.

Install TensorFlow

Before you begin, make sure you have TensorFlow installed. You can install it using pip:

pip install tensorflow

For more detailed installation instructions, please visit our TensorFlow Installation Guide.

Hello World

Let's start with a simple "Hello World" example to get a feel for TensorFlow.

import tensorflow as tf

# Create a graph.
a = tf.constant(5)
b = tf.constant(6)

# Add two constants.
c = a + b

# Launch the default session and evaluate the constant addition.
print(c.eval())

This code will output 11, which is the sum of 5 and 6.

Sessions

In TensorFlow, operations are represented as nodes in a computation graph. To execute these operations, you need to create a session.

# Create a session.
with tf.Session() as sess:
  print(sess.run(c))

This will also output 11.

Variables

Variables in TensorFlow are a type of container that can hold any type of data. They are essential for storing data that needs to persist across multiple operations.

# Create a variable.
w = tf.Variable(10)

# Initialize the variable.
w.initializer.run()

# Add two variables.
c = w + 1

# Print the result.
print(c.eval())

This will output 11, just like before.

More Resources

To learn more about TensorFlow, we recommend the following resources:

Images

Here is a Golden Retriever image for inspiration!

Golden Retriever

By following this tutorial, you should now have a basic understanding of TensorFlow. Happy coding!