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Home > Data > Deep Learning > Python Deep Learning Cookbook
Python Deep Learning Cookbook
Python Deep Learning Cookbook

Python Deep Learning Cookbook: Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

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Python Deep Learning Cookbook

Introduction

The focus of this chapter is to provide solutions to common implementation problems for FNN and other network topologies. The techniques discussed in this chapter also apply to the following chapters.

FNNs are networks where the information only moves in one direction and does not cycle (as we will see in Chapter 4, Recurrent Neural Networks). FNNs are mainly used for supervised learning where the data is not sequential or time-dependent, for example for general classification and regression tasks. We will start by introducing a perceptron and we will show how to implement a perceptron with NumPy. A perceptron demonstrates the mechanics of a single unit. Next, we will increase the complexity by increasing the number of units and introduce single-layer and multi-layer neural networks. The high number of units, in combination with a high number of layers, gives the depth of the architecture and is responsible for the name deep learning. 

Understanding the perceptron

First, we need to understand the basics of neural networks. A neural consists of one or multiple layers of neurons, named after the neurons in human brains. We will demonstrate the mechanics of a single neuron by implementing a perceptron. In a perceptron, a single unit (neuron) performs all the computations. Later, we will scale the number of units to create deep neural networks:

Figure 2.1: Perceptron

A can have multiple inputs. On these inputs, the unit performs some computations and outputs a single value, for example a binary value to classify two classes. The computations performed by the unit are a simple matrix multiplication of the input and the weights. The resulting values are summed up and a bias is added:

These computations can easily be scaled to high dimensional input. An activation function (φ) determines the final output of the in the forward pass:

The weights and bias are initialized. After each epoch (iteration over the training data), the...

Implementing a single-layer neural network

Now we can move on to neural networks. We will start by the simplest form of a neural network: a single-layer neural network. The difference from a perceptron is that the computations are done by multiple units (neurons), hence a network. As you may expect, adding more units will increase the number of problems that can be solved. The units perform their computations separately and are in a layer; we call this layer the hidden layer. Therefore, we call the units in this layer the hidden units. For now, we will only consider a single hidden layer. The output layer performs as a perceptron. This time, as input we have the hidden units in the hidden layer instead of the input variables:

Figure 2.4: Single-layer neural network with two input variables, n hidden units, and a single output unit

In our implementation of the perceptron, we've used a unit step function to determine the class. In the next recipe, we will use a non-linear activation function...

Building a multi-layer neural network

What we've created in the recipe is actually the simplest form of an FNN: a neural network where the information flows only in one direction. For our next recipe, we will extend the number of hidden layers from one to multiple layers. Adding additional layers increases the power of a network to learn complex non-linear patterns. 

Figure 2.7: Two-layer neural network with i input variables, n hidden units, and m hidden units respectively, and a single output unit

As you can see in Figure 2-7, by adding an additional layer the number of connections (weights), also called trainable parameters, increases exponentially. In the next recipe, we will create a network with two hidden layers to predict wine quality. This is a regression task, so we will be using a linear activation for the output layer. For the hidden layers, we use ReLU activation functions. This recipe uses the Keras framework to implement the feed-forward network.

How to do it...

  1. We start by import...

Getting started with activation functions

If we only use linear activation functions, a neural network would represent a large collection of linear combinations. However, the power of neural networks lies in their ability to model complex nonlinear behavior. We briefly introduced the non-linear activation functions sigmoid and ReLU in the previous recipes, and there are many more popular nonlinear functions, such as ELU, Leaky ReLU, TanH, and Maxout.

There is no rule as to activation works best for the units. Deep learning is a new field and most results are obtained by trial and error instead of mathematical proofs. For the output unit, we use a single output unit and a linear activation for regression tasks. For classification tasks with n classes, we use n output nodes and a softmax activation function. The softmax function forces the network to output probabilities between 0 and 1 for mutually exclusive classes and the probabilities sum up to 1. For binary classification, we can...

Experiment with hidden layers and hidden units

The most commonly used layers in neural networks are fully-connected layers. In fully-connected layers, the units in two successive layers are all  connected. However, the units within a layer don't share any connections. As stated before, the connections between the layers are also called trainable parameters. The weights of these connections are trained by the network. The more connections, the more parameters and the more complex patterns can be modeled. Most state-of-the-art models have 100+ million parameters. However, a deep neural network with many layers and units takes more time to train. Also, with extremely deep models the time to infer predictions takes significantly longer (which can be problematic in a real-time environment). In the following chapters, we will introduce other popular layer types that are specific to their network types. 

Picking the correct number of hidden layers and hidden units can be important. When using too...

Implementing an autoencoder

For autoencoders, we use a network architecture, as shown in the following figure. In the first couple of layers, we decrease the number of hidden units. Halfway, we start increasing the number of hidden units again until the number of hidden units is the same as the number of input variables. The middle hidden layer can be seen as an encoded variant of the inputs, where the output determines the quality of the encoded variant:

Figure 2.13: Autoencoder network with three hidden layers, with m < n

In the next recipe, we will implement an in Keras to decode Street View House Numbers (SVHN) from 32 x 32 images to 32 floating numbers. We can determine the quality of the encoder by decoding back to 32 x 32 and comparing the images.

How to do it...

  1. Import the necessary libraries with the following code:
import numpy as np
from matplotlib import pyplot as plt
import scipy.io

from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers...

Tuning the loss function

While training a neural network for a learning problem, the objective of the network is to minimize the loss function. The loss function — also known as error, cost function, or opimization function–compares the prediction with the ground truth during the forward pass. The output of this loss function is used to optimize the weights during the backward pass. Therefore, the loss function is crucial in training the network. By setting the correct loss function, we force the network to optimize towards the desired predictions. For example, for imbalanced datasets we need a different loss function.

In the previous recipes, we've used mean squared error (MSE) and categorical entropy as loss functions. There are also other popular loss functions, and another option is to create a custom loss function. A custom loss function gives the ability to optimize to the desired output. This will be important when we will implement Generative Adversarial Networks (GANs). In the...

Experimenting with different optimizers

The most popular and well optimizer is Stochastic Gradient Descent (SGD). This technique is widely used in other machine learning models as well. SGD is a to find minima or maxima by iteration. There are many popular variants of SGD that try to speed up convergence and less tuning by using an adaptive learning rate. The following table is an overview of the most commonly used optimizers in deep learning:

Optimizer

Hyperparameters

Comments

SGD

Learning rate, decay

+ Learning directly impacts performance (smaller learning rate avoids local minima)

- Requires more manual tuning

- Slow convergence

AdaGrad

Learning rate, epsilon, decay

+ Adaptive learning for all parameters (well suited for sparse data)

- Learning becomes too small and stops learning

AdaDelta

Learning rate, rho, epsilon, decay

+ Faster convergence at start

- Slows near minimum

Adam

Learning rate, beta 1, beta 2, epsilon, decay

+ Adaptive learning rate and momentum for all parameters

RMSprop

Learning rate...

Improving generalization with regularization

Overfitting on the data is one of the biggest of machine learning. There are many machine learning algorithms that are able to train on the training data by remembering all cases. In this scenario, the algorithm might not be able to generalize and make a correct prediction on new data. This is an especially big threat for deep learning, where neural networks have large numbers of trainable parameters. Therefore, it is extremely important to create a representative validation set. 

Note

In deep learning, the general advice when tackling new problems is to overfit as much as you can on the training data first. This ensures that your model is able to train on the training data and is complex enough. Afterwards, you should regularize as much as you can to make sure the model is able to generalize on unseen data (the validation set) as well. 

Most of the techniques used to prevent overfitting can be placed under regularization. Regularization include...

Adding dropout to prevent overfitting

Another popular method for regularization is dropout. A forces a neural network to learn multiple independent representations by randomly removing connections between neurons in the learning phase. For example, when using a dropout of 0.5, the network has to see each example twice before the connection is learned. Therefore, a network with dropout can be seen as an ensemble of networks. 

In the following recipe, we will improve a model that clearly overfits the training data by adding dropouts.

How to do it...

  1. Import the as follows:
import numpy as np 
import pandas as pd
from sklearn.model_selection import train_test_split

from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline


import numpy as np...

Adding dropout to prevent overfitting

Another popular method for regularization is dropout. A dropout forces a neural network to learn multiple independent representations by randomly removing connections between neurons in the learning phase. For example, when using a dropout of 0.5, the network has to see each example twice before the connection is learned. Therefore, a network with dropout can be seen as an ensemble of networks. 

In the following recipe, we will improve a model that clearly overfits the training data by adding dropouts.

How to do it...

  1. Import the libraries as follows:
import numpy as np 
import pandas as pd
from sklearn.model_selection import train_test_split

from keras.models import...
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Key benefits

  • - Practical recipes on training different neural network models and tuning them for optimal performance
  • -Use Python frameworks like TensorFlow, Caffe, Keras, Theano for Natural Language Processing, Computer Vision, and more
  • -A hands-on guide covering the common as well as the not so common problems in deep learning using Python

Description

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.

Who is this book for?

This book is intended for machine learning professionals who are looking to use deep learning algorithms to create real-world applications using Python. Thorough understanding of the machine learning concepts and Python libraries such as NumPy, SciPy and scikit-learn is expected. Additionally, basic knowledge in linear algebra and calculus is desired.

What you will learn

  • • Implement different neural network models in Python
  • • Select the best Python framework for deep learning such as PyTorch, Tensorflow, MXNet and Keras
  • • Apply tips and tricks related to neural networks internals, to boost learning performances
  • • Consolidate machine learning principles and apply them in the deep learning field
  • • Reuse and adapt Python code snippets to everyday problems
  • • Evaluate the cost/benefits and performance implication of each discussed solution
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 27, 2017
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781787125193
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Category :
Data
Languages :
Python
Concepts :
Deep Learning
Tools :
Keras

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Estimated delivery fee Deliver to Philippines

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Product Details

Publication date : Oct 27, 2017
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781787125193
Vendor :
Google
Category :
Data
Languages :
Python
Concepts :
Deep Learning
Tools :
Keras

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Table of Contents

14 Chapters
Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks Chevron down icon Chevron up icon
Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks
Introduction
Setting up a deep learning environment
Launching an instance on Amazon Web Services (AWS)
Launching an instance on Google Cloud Platform (GCP)
Installing CUDA and cuDNN
Installing Anaconda and libraries
Connecting with Jupyter Notebooks on a server
Building state-of-the-art, production-ready models with TensorFlow
Intuitively building networks with Keras
Using PyTorch’s dynamic computation graphs for RNNs
Implementing high-performance models with CNTK
Building efficient models with MXNet
Defining networks using simple and efficient code with Gluon
Feed-Forward Neural Networks Chevron down icon Chevron up icon
Feed-Forward Neural Networks
Introduction
Understanding the perceptron
Implementing a single-layer neural network
Building a multi-layer neural network
Getting started with activation functions
Experiment with hidden layers and hidden units
Implementing an autoencoder
Tuning the loss function
Experimenting with different optimizers
Improving generalization with regularization
Adding dropout to prevent overfitting
Convolutional Neural Networks Chevron down icon Chevron up icon
Convolutional Neural Networks
Introduction
Applying pooling layers
Optimizing with batch normalization
Understanding padding and strides
Experimenting with different types of initialization
Implementing a convolutional autoencoder
Applying a 1D CNN to text
Recurrent Neural Networks Chevron down icon Chevron up icon
Recurrent Neural Networks
Introduction
Implementing a simple RNN
Adding Long Short-Term Memory (LSTM)
Using gated recurrent units (GRUs)
Implementing bidirectional RNNs
Character-level text generation
Reinforcement Learning Chevron down icon Chevron up icon
Reinforcement Learning
Introduction
Implementing policy gradients
Implementing a deep Q-learning algorithm
Generative Adversarial Networks Chevron down icon Chevron up icon
Generative Adversarial Networks
Introduction
Understanding GANs
Implementing Deep Convolutional GANs (DCGANs)
Upscaling the resolution of images with Super-Resolution GANs (SRGANs)
Computer Vision Chevron down icon Chevron up icon
Computer Vision
Introduction
Augmenting images with computer vision techniques
Classifying objects in images
Localizing an object in images
Segmenting classes in images with U-net
Scene understanding (semantic segmentation)
Finding facial key points
Recognizing faces
Transferring styles to images
Natural Language Processing Chevron down icon Chevron up icon
Natural Language Processing
Introduction
Analyzing sentiment
Translating sentences
Summarizing text
Speech Recognition and Video Analysis Chevron down icon Chevron up icon
Speech Recognition and Video Analysis
Introduction
Implementing a speech recognition pipeline from scratch
Identifying speakers with voice recognition
Understanding videos with deep learning
Time Series and Structured Data Chevron down icon Chevron up icon
Time Series and Structured Data
Introduction
Predicting stock prices with neural networks
Predicting bike sharing demand
Using a shallow neural network for binary classification
Game Playing Agents and Robotics Chevron down icon Chevron up icon
Game Playing Agents and Robotics
Introduction
Learning to drive a car with end-to-end learning
Learning to play games with deep reinforcement learning
Genetic Algorithm (GA) to optimize hyperparameters
Hyperparameter Selection, Tuning, and Neural Network Learning Chevron down icon Chevron up icon
Hyperparameter Selection, Tuning, and Neural Network Learning
Introduction
Visualizing training with TensorBoard and Keras
Working with batches and mini-batches
Using grid search for parameter tuning
Learning rates and learning rate schedulers
Comparing optimizers
Determining the depth of the network
Adding dropouts to prevent overfitting
Making a model more robust with data augmentation
Network Internals Chevron down icon Chevron up icon
Network Internals
Introduction
Visualizing training with TensorBoard
Analyzing network weights and more
Freezing layers
Storing the network topology and trained weights
Pretrained Models Chevron down icon Chevron up icon
Pretrained Models
Introduction
Large-scale visual recognition with GoogLeNet/Inception
Extracting bottleneck features with ResNet
Leveraging pretrained VGG models for new classes
Fine-tuning with Xception

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(3 Ratings)
5 star 66.7%
4 star 0%
3 star 0%
2 star 0%
1 star 33.3%
Gert Dec 30, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
positive points:• The book builds on simple recipes toward more complex recipes.• Great book if you want to learn by example!• A lot of useful code is included in the book that you can reuse for different projects.• What I like is that the author focusses on experimentation and doesn’t assume one method is better over another.negative points:• Sometimes a bit more explanation why some of the choices have been made would be good.• It would be better if the images are in colour (especially some charts).
Amazon Verified review Amazon
Some Python Guy Dec 19, 2017
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Found the code files on git but for example the wav/video files from chapter 9 are missing. Likely other source data files are missing. Please provide all relevant input files to run the samples.
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newby19 Nov 19, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I think this book is a great way to get started using deep learning in a hands-on way. Especially for someone who's relatively new to deep learning and wants to experiment and work on different projects.Each recipe in the book is broken down to the different steps to take. Each step is described shortly and to the point. I used the recipes from the Computer Vision chapter right away to create my own image classification project.
Amazon Verified review Amazon

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Generative AI with LangChain
Generative AI with LangChain
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Dec 2023 368 pages
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Causal Inference and Discovery in Python
Causal Inference and Discovery in Python
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May 2023 456 pages
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About the author

Profile icon den Bakker
den Bakker
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Indra den Bakker is an experienced deep learning engineer and mentor. He is the founder of 23insightspart of NVIDIA's Inception programa machine learning start-up building solutions that transform the worlds most important industries. For Udacity, he mentors students pursuing a Nanodegree in deep learning and related fields, and he is also responsible for reviewing student projects. Indra has a background in computational intelligence and worked for several years as a data scientist for IPG Mediabrands and Screen6 before founding 23insights.
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Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela