Introduction to the Python Deep Learning Library TensorFlow for Beginners

In this tutorial, we are going to be covering some basics on what TensorFlow is, and how to begin using it.

TensorFlow is a Python library for fast numerical computing created and released by Google.

It is a foundation library that can be used to create Deep Learning models directly or by using wrapper libraries that simplify the process built on top of TensorFlow.

In this post you will discover the TensorFlow library for Deep Learning.

Discover how to develop deep learning models for a range of predictive modeling problems with just a few lines of code in my new book, with 18 step-by-step tutorials and 9 projects.

What is TensorFlow?

TensorFlow is an open source library for fast numerical computing.

It was created and is maintained by Google and released under the Apache 2.0 open source license. The API is nominally for the Python programming language, although there is access to the underlying C++ API.

Unlike other numerical libraries intended for use in Deep Learning like Theano, TensorFlow was designed for use both in research and development and in production systems, not least RankBrain in Google search and the fun DeepDream project.

It can run on single CPU systems, GPUs as well as mobile devices and large scale distributed systems of hundreds of machines.

You may also like: Deep Learning Using TensorFlow

How to Install TensorFlow

Installation of TensorFlow is straightforward if you already have a Python SciPy environment.

TensorFlow works with Python 2.7 and Python 3.3+. You can follow the Download and Setup instructions on the TensorFlow website. Installation is probably simplest via PyPI and specific instructions of the pip command to use for your Linux or Mac OS X platform are on the Download and Setup webpage.

There are also virtualenv and docker images that you can use if you prefer.

To make use of the GPU, only Linux is supported and it requires the Cuda Toolkit.

Your First Examples in TensorFlow

Computation is described in terms of data flow and operations in the structure of a directed graph.

• Nodes: Nodes perform computation and have zero or more inputs and outputs. Data that moves between nodes are known as tensors, which are multi-dimensional arrays of real values.
• Edges: The graph defines the flow of data, branching, looping and updates to state. Special edges can be used to synchronize behavior within the graph, for example waiting for computation on a number of inputs to complete.
• Operation: An operation is a named abstract computation which can take input attributes and produce output attributes. For example, you could define an add or multiply operation.

Computation with TensorFlow

This first example is a modified version of the example on the TensorFlow website. It shows how you can create a session, define constants and perform computation with those constants using the session.

import tensorflow as tf
sess = tf.Session()
a = tf.constant(10)
b = tf.constant(32)
print(sess.run(a+b))

Running this example displays:

42

Linear Regression with TensorFlow

This next example comes from the introduction on the TensorFlow tutorial.

This examples shows how you can define variables (e.g. W and b) as well as variables that are the result of computation (y).

We get some sense of TensorFlow separates the definition and declaration of the computation from the execution in the session and the calls to run.

import tensorflow as tf
import numpy as np

# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3

# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but Tensorflow will
# figure that out for us.)
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b

# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

# Before starting, initialize the variables.  We will 'run' this first.
init = tf.initialize_all_variables()

# Launch the graph.
sess = tf.Session()
sess.run(init)

# Fit the line.
for step in xrange(201):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(W), sess.run(b))

# Learns best fit is W: [0.1], b: [0.3]

Running this example prints the following output:

(0, array([ 0.2629351], dtype=float32), array([ 0.28697217], dtype=float32))
(20, array([ 0.13929555], dtype=float32), array([ 0.27992988], dtype=float32))
(40, array([ 0.11148042], dtype=float32), array([ 0.2941364], dtype=float32))
(60, array([ 0.10335406], dtype=float32), array([ 0.29828694], dtype=float32))
(80, array([ 0.1009799], dtype=float32), array([ 0.29949954], dtype=float32))
(100, array([ 0.10028629], dtype=float32), array([ 0.2998538], dtype=float32))
(120, array([ 0.10008363], dtype=float32), array([ 0.29995731], dtype=float32))
(140, array([ 0.10002445], dtype=float32), array([ 0.29998752], dtype=float32))
(160, array([ 0.10000713], dtype=float32), array([ 0.29999638], dtype=float32))
(180, array([ 0.10000207], dtype=float32), array([ 0.29999897], dtype=float32))
(200, array([ 0.1000006], dtype=float32), array([ 0.29999971], dtype=float32))

You can learn more about the mechanics of TensorFlow in the Basic Usage guide.

More Deep Learning Models

Your TensorFlow installation comes with a number of Deep Learning models that you can use and experiment with directly.

Firstly, you need to find out where TensorFlow was installed on your system. For example, you can use the following Python script:

python -c 'import os; import inspect; import tensorflow; print(os.path.dirname(inspect.getfile(tensorflow)))'

For example, this could be:

/usr/lib/python2.7/site-packages/tensorflow

Change to this directory and take note of the models subdirectory. Included are a number of deep learning models with tutorial-like comments, such as:

• Multi-threaded word2vec mini-batched skip-gram model.
• Multi-threaded word2vec unbatched skip-gram model.
• CNN for the CIFAR-10 network.
• Simple, end-to-end, LeNet-5-like convolutional MNIST model example.
• Sequence-to-sequence model with an attention mechanism.

Also check the examples directory as it contains an example using the MNIST dataset.

There is also an excellent list of tutorials on the main TensorFlow website. They show how to use different network types, different datasets and how to use the framework in various different ways. Finally, there is the TensorFlow playground where you can experiment with small networks right in your web browser.

Summary

In this post you discovered the TensorFlow Python library for deep learning.

You learned that it is a library for fast numerical computation, specifically designed for the types of operations that are required in the development and evaluation of large deep learning models.

Learn More

Deploying a Keras Deep Learning Model as a Web Application in Python

Deep Learning With TensorFlow 2.0

deep-learning tensorflow python

In this article, we will learn how deep learning works and get familiar with its terminology — such as backpropagation and batch size

Deep Learning Using TensorFlow. In this TensorFlow tutorial for professionals and enthusiasts who are interested in applying Deep Learning Algorithm using TensorFlow to solve various problems.

Deep Learning with TensorFlow 2.0. A quick introduction to the new TensorFlow 2.0 way of doing Deep Learning using Keras. You’ll walk away with a better understanding of how you can get started building machine learning models in Python with TensorFlow 2.0 as well as the other exciting available features!

This full course introduces the concept of client-side artificial neural networks. We will learn how to deploy and run models along with full deep learning applications in the browser! To implement this cool capability, we’ll be using TensorFlow.js (TFJS), TensorFlow’s JavaScript library.

Learn how to go from reading a paper on deep deterministic policy gradients to implementing the concepts in Tensorflow. Learn how to code a deep deterministic policy gradient (DDPG) agent using Python and PyTorch, to beat the continuous lunar lander environment (a classic machine learning problem).