Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Defining networks using simple and efficient code with Gluon

The newest addition to the range of deep learning frameworks is Gluon. Gluon is recently launched by AWS and Microsoft to provide an API simple, easy-to-understand code without the loss of performance. Gluon is already included in the latest release of MXNet and will be available in future releases of CNTK (and other frameworks). Just like Keras, Gluon is a wrapper around other deep learning frameworks. The main difference between Keras and Gluon, is that Gluon will (at first) focus on imperative frameworks. 

How to do it...

  1. At the moment, gluon is included in the latest release of MXNet (follow the steps in Building efficient models with MXNet to install MXNet). 
  2. After installing, we can directly import gluon as follows:
from mxnet import gluon
  1. Next, we create some dummy data. For this we need the data to be in MXNet's NDArray or Symbol:
import mxnet as mx
import numpy as np
x_input = mx.nd.empty((1, 5), mx.gpu())
x_input[:] = np.array([[1,2,3,4,5]], np.float32)

y_input = mx.nd.empty((1, 5), mx.gpu())
y_input[:] = np.array([[10, 15, 20, 22.5, 25]], np.float32)
  1. With Gluon, it's really straightforward to build a neural network by stacking layers:
net = gluon.nn.Sequential()
with net.name_scope():
    net.add(gluon.nn.Dense(16, activation="relu"))
  1. Next, we initialize the parameters and we store these on our GPU as follows:
net.collect_params().initialize(mx.init.Normal(), ctx=mx.gpu())
  1. With the following code we set the loss function and the optimizer:
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': .1})
  1. We're ready to start training or model:
n_epochs = 10

for e in range(n_epochs):
    for i in range(len(x_input)):
        input = x_input[i]
        target = y_input[i]
        with mx.autograd.record():
            output = net(input)
            loss = softmax_cross_entropy(output, target)


We've shortly demonstrated how to implement a neural network architecture with Gluon. Gluon is a powerful extension that can be used to implement deep learning architectures with clean code. At the same time, there is almost no performance loss when using Gluon.