This tutorial will guide you through the basics of TensorFlow, a powerful open-source library for dataflow and differentiable programming across a range of tasks.

Quick Start

Before you dive in, make sure you have Python installed on your system. You can check the version of Python you have by running:

python --version

If you're using a virtual environment, ensure it's activated:

source venv/bin/activate

Installation

To install TensorFlow, you can use pip:

pip install tensorflow

For the latest version, including GPU support, you can use:

pip install tensorflow-gpu

Basic Concepts

Tensors

Tensors are the fundamental data structure in TensorFlow. They are multi-dimensional arrays of primitive data types.

  • 0D Tensor is a scalar.
  • 1D Tensor is a vector.
  • 2D Tensor is a matrix.
  • Higher-dimensional Tensors are arrays with more than two dimensions.

Operations

TensorFlow provides a wide range of operations to manipulate tensors. Some common operations include:

  • Addition: tf.add(a, b)
  • Multiplication: tf.multiply(a, b)
  • Matrices Multiplication: tf.matmul(a, b)

Graphs

TensorFlow uses a dataflow model where computations are represented as a directed graph. Nodes represent operations, and edges represent tensors.

import tensorflow as tf

a = tf.constant([[1, 2], [3, 4]])
b = tf.constant([[5, 6], [7, 8]])
c = tf.matmul(a, b)

In the above code, a, b, and c are nodes, and the edges represent the flow of tensors between these nodes.

Example

Here's a simple example of a TensorFlow program that computes the matrix multiplication of two matrices:

import tensorflow as tf

# Define two matrices
a = tf.constant([[1, 2], [3, 4]])
b = tf.constant([[5, 6], [7, 8]])

# Perform matrix multiplication
c = tf.matmul(a, b)

# Run the computation
with tf.Session() as sess:
    result = sess.run(c)
    print(result)

More Resources

For more in-depth tutorials and guides, check out our Advanced TensorFlow Tutorials.

[center]Matrix Multiplication Example

This tutorial provides a foundation for understanding TensorFlow. To continue your learning journey, explore the TensorFlow official documentation.