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.
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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.
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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.
To make use of the GPU, only Linux is supported and it requires the Cuda Toolkit.
Computation is described in terms of data flow and operations in the structure of a directed graph.
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:
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(, -1.0, 1.0)) b = tf.Variable(tf.zeros()) 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.
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:
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:
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.
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.
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!
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