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

Building efficient models with MXNet

The MXNet deep learning framework you to build efficient deep learning models in Python. Next to Python, it also let you build models in popular languages as R, Scala, and Julia. Apache MXNet is supported by Amazon and Baidu, amongst others. MXNet has proven to be fast in benchmarks and it supports GPU and multi-GPU usages. By using lazy evaluation, MXNet is able to automatically execute operations in parallel. Furthermore, the MXNet frameworks uses a symbolic interface, called Symbol. This simplifies building neural network architectures.

How to do it...

  1. To install MXNet on Ubuntu with GPU support, we can use the command in the terminal:
pip install mxnet-cu80==0.11.0

For other platforms and non-GPU support, have a look at

  1. Next, we are ready to import mxnet in our Python environment:
  1. We create some simple dummy data that we assign to the GPU and CPU:
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.cpu())
y_input[:] = np.array([[10, 15, 20, 22.5, 25]], np.float32)
  1. We can easily copy and adjust the data. Where possible MXNet will automatically execute operations in parallel:
w_input = x_input
z_input = x_input.copyto(mx.cpu())
x_input += 1
w_input /= 2
z_input *= 2
  1. We can print the output as follows:
  1. If we want to feed our data to a model, we should create an iterator first:
batch_size = 1
train_iter =, y_input, batch_size, shuffle=True, data_name='input', label_name='target')
  1. Next, we can create the symbols for our model:
X = mx.sym.Variable('input')
Y = mx.symbol.Variable('target')
fc1 = mx.sym.FullyConnected(data=X, name='fc1', num_hidden = 5)
lin_reg = mx.sym.LinearRegressionOutput(data=fc1, label=Y, name="lin_reg")
  1. Before we can start training, we need to define our model:
model = mx.mod.Module(
    symbol = lin_reg,
    label_names = ['target']
  1. Let's start training:,
            optimizer_params={'learning_rate':0.01, 'momentum': 0.9},
            batch_end_callback = mx.callback.Speedometer(batch_size, 2))
  1. To use the trained model for prediction we:


We've shortly introduced the MXNet framework. In this introduction, we've demonstrated how easily one can assign variables and computations to a CPU or GPU and how to use the Symbol interface. However, there is much more to explore and the MXNet is a powerful framework for building flexible and efficient deep learning models.