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

Implementing high-performance models with CNTK

Microsoft also introduced its open source deep framework not too long ago: Microsoft Cognitive Toolkit. This framework is better known as CNTK. is written in C++ for performance reasons and has a Python API. CNTK supports GPUs and multi-GPU usage. 

How to do it...

  1. First, we install CNTK with pip as follows:
pip install

Adjust the wheel file if necessary (see 

  1. After installing CNTK, we can import it into our Python environment:
import cntk
  1. Let's create some simple dummy data that we can use for training:
import numpy as np
x_input = np.array([[1,2,3,4,5]], np.float32)
y_input = np.array([[10]], np.float32)
  1. Next, we need to define the placeholders for the input data:
X = cntk.input_variable(5, np.float32)
y = cntk.input_variable(1, np.float32)
  1. With CNTK, it's straightforward to stack multiple layers. We stack a dense layer with 32 inputs on top of an output layer with 1 output:
from cntk.layers import Dense, Sequential
model = Sequential([Dense(32),
  1. Next, we define the loss function:
loss = cntk.squared_error(model, y)
  1. Now, we are ready to finalize our model with an optimizer:
learning_rate = 0.001
trainer = cntk.Trainer(model, (loss), cntk.adagrad(model.parameters, learning_rate))
  1. Finally, we can train our model as follows:
for epoch in range(10):
        trainer.train_minibatch({X: x_input, y: y_input})


As we have demonstrated in this introduction, it is straightforward to build models in CNTK with the appropriate high-level wrappers. However, just like TensorFlow and PyTorch, you can choose to implement your model on a more granular level, which gives you a lot of freedom.