Advanced Deep Learning with TensorFlow 2 and Keras

Advanced Deep Learning with TensorFlow 2 and Keras (Updated for 2nd Edition)

This is the code repository for Advanced Deep Learning with TensoFlow 2 and Keras, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

Please note that the code examples have been updated to support TensorFlow 2.0 Keras API only.

About the Book

Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects.

Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques.

Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance.

Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You’ll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.

Related Products

Installation

It is recommended to run within conda enviroment. Pls download Anacoda from: Anaconda. To install anaconda:

sh <name-of-downloaded-Anaconda3-installer>

A machine with at least 1 NVIDIA GPU (1060 or better) is required. The code examples have been tested on 1060, 1080Ti, RTX 2080Ti, V100, RTX Quadro 8000 on Ubuntu 18.04 LTS. Below is a rough guide to install NVIDIA driver and CuDNN to enable GPU support.

sudo add-apt-repository ppa:graphics-drivers/ppa

sudo apt update

sudo ubuntu-drivers autoinstall

sudo reboot

nvidia-smi

At the time of writing, nvidia-smishows the NVIDIA driver version is 440.64 and CUDA version is 10.2.

We are almost there. The last set of packages must be installed as follows. Some steps might require sudo access.

conda create --name packt

conda activate packt

cd <github-dir>

git clone https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras

cd Advanced-Deep-Learning-with-Keras

pip install -r requirements.txt

sudo apt-get install python-pydot

sudo apt-get install ffmpeg

Test if a simple model can be trained without errors:

cd chapter1-keras-quick-tour

python3 mlp-mnist-1.3.2.py

The final output shows the accuracy of the trained model on MNIST test dataset is about 98.2%.

Alternative TensorFlow Installation

If you are having problems with CUDA libraries (ie tf could not load or find libcudart.so.10.X), TensorFlow and CUDA libraries can be installed together using conda:

pip uninstall tensorflow-gpu
conda install -c anaconda tensorflow-gpu

Advanced Deep Learning with TensorFlow 2 and Keras code examples used in the book.

Chapter 1 - Introduction

  1. MLP on MNIST
  2. CNN on MNIST
  3. RNN on MNIST

Chapter 2 - Deep Networks

  1. Functional API on MNIST
  2. Y-Network on MNIST
  3. ResNet v1 and v2 on CIFAR10
  4. DenseNet on CIFAR10

Chapter 3 - AutoEncoders

  1. Denoising AutoEncoders

Sample outputs for random digits:

Random Digits

  1. Colorization AutoEncoder

Sample outputs for random cifar10 images:

Colorized Images

Chapter 4 - Generative Adversarial Network (GAN)

  1. Deep Convolutional GAN (DCGAN)

Radford, Alec, Luke Metz, and Soumith Chintala. “Unsupervised representation learning with deep convolutional generative adversarial networks.” arXiv preprint arXiv:1511.06434 (2015).

Sample outputs for random digits:

Random Digits

  1. Conditional (GAN)

Mirza, Mehdi, and Simon Osindero. “Conditional generative adversarial nets.” arXiv preprint arXiv:1411.1784 (2014).

Sample outputs for digits 0 to 9:

Zero to Nine

Chapter 5 - Improved GAN

  1. Wasserstein GAN (WGAN)

Arjovsky, Martin, Soumith Chintala, and Léon Bottou. “Wasserstein GAN.” arXiv preprint arXiv:1701.07875 (2017).

Sample outputs for random digits:

Random Digits

  1. Least Squares GAN (LSGAN)

Mao, Xudong, et al. “Least squares generative adversarial networks.” 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017.

Sample outputs for random digits:

Random Digits

  1. Auxiliary Classfier GAN (ACGAN)

Odena, Augustus, Christopher Olah, and Jonathon Shlens. “Conditional image synthesis with auxiliary classifier GANs. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017.”

Sample outputs for digits 0 to 9:

Zero to Nine

Chapter 6 - GAN with Disentangled Latent Representations

  1. Information Maximizing GAN (InfoGAN)

Chen, Xi, et al. “Infogan: Interpretable representation learning by information maximizing generative adversarial nets.” Advances in Neural Information Processing Systems. 2016.

Sample outputs for digits 0 to 9:

Zero to Nine

  1. Stacked GAN

Huang, Xun, et al. “Stacked generative adversarial networks.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Vol. 2. 2017

Sample outputs for digits 0 to 9:

Zero to Nine

Chapter 7 - Cross-Domain GAN

  1. CycleGAN

Zhu, Jun-Yan, et al. “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks.” 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017.

Sample outputs for random cifar10 images:

Colorized Images

Sample outputs for MNIST to SVHN:

MNIST2SVHN

Chapter 8 - Variational Autoencoders (VAE)

  1. VAE MLP MNIST
  2. VAE CNN MNIST
  3. Conditional VAE and Beta VAE

Kingma, Diederik P., and Max Welling. “Auto-encoding Variational Bayes.” arXiv preprint arXiv:1312.6114 (2013).

Sohn, Kihyuk, Honglak Lee, and Xinchen Yan. “Learning structured output representation using deep conditional generative models.” Advances in Neural Information Processing Systems. 2015.

I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner. β-VAE: Learning basic visual concepts with a constrained variational framework. ICLR, 2017.

Generated MNIST by navigating the latent space:

MNIST

Chapter 9 - Deep Reinforcement Learning

  1. Q-Learning
  2. Q-Learning on Frozen Lake Environment
  3. DQN and DDQN on Cartpole Environment

Mnih, Volodymyr, et al. “Human-level control through deep reinforcement learning.” Nature 518.7540 (2015): 529

DQN on Cartpole Environment:

Cartpole

Chapter 10 - Policy Gradient Methods

  1. REINFORCE, REINFORCE with Baseline, Actor-Critic, A2C

Sutton and Barto, Reinforcement Learning: An Introduction

Mnih, Volodymyr, et al. “Asynchronous methods for deep reinforcement learning.” International conference on machine learning. 2016.

Policy Gradient on MountainCar Continuous Environment:

Car

Chapter 11 - Object Detection

  1. Single-Shot Detection

Single-Shot Detection on 3 Objects
SSD

Chapter 12 - Semantic Segmentation

  1. FCN

  2. PSPNet

Semantic Segmentation

Semantic Segmentation

Chapter 13 - Unsupervised Learning using Mutual Information

  1. Invariant Information Clustering

  2. MINE: Mutual Information Estimation

MINE
MINE

Citation

If you find this work useful, please cite:

@book{atienza2020advanced,
  title={Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more},
  author={Atienza, Rowel},
  year={2020},
  publisher={Packt Publishing Ltd}
}

Download Details:

Author: PacktPublishing
The Demo/Documentation: View The Demo/Documentation
Download Link: Download The Source Code
Official Website: https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras
License: MIT

Shirts and Gifts for Your Friends & Loved ☞ https://bit.ly/36PHvXY

#deep-learning #tensorflow #keras #machine-learning

What is GEEK

Buddha Community

Advanced Deep Learning with TensorFlow 2 and Keras
Marget D

Marget D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Advanced Deep Learning with TensorFlow 2 and Keras

Advanced Deep Learning with TensorFlow 2 and Keras (Updated for 2nd Edition)

This is the code repository for Advanced Deep Learning with TensoFlow 2 and Keras, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

Please note that the code examples have been updated to support TensorFlow 2.0 Keras API only.

About the Book

Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects.

Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques.

Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance.

Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You’ll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.

Related Products

Installation

It is recommended to run within conda enviroment. Pls download Anacoda from: Anaconda. To install anaconda:

sh <name-of-downloaded-Anaconda3-installer>

A machine with at least 1 NVIDIA GPU (1060 or better) is required. The code examples have been tested on 1060, 1080Ti, RTX 2080Ti, V100, RTX Quadro 8000 on Ubuntu 18.04 LTS. Below is a rough guide to install NVIDIA driver and CuDNN to enable GPU support.

sudo add-apt-repository ppa:graphics-drivers/ppa

sudo apt update

sudo ubuntu-drivers autoinstall

sudo reboot

nvidia-smi

At the time of writing, nvidia-smishows the NVIDIA driver version is 440.64 and CUDA version is 10.2.

We are almost there. The last set of packages must be installed as follows. Some steps might require sudo access.

conda create --name packt

conda activate packt

cd <github-dir>

git clone https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras

cd Advanced-Deep-Learning-with-Keras

pip install -r requirements.txt

sudo apt-get install python-pydot

sudo apt-get install ffmpeg

Test if a simple model can be trained without errors:

cd chapter1-keras-quick-tour

python3 mlp-mnist-1.3.2.py

The final output shows the accuracy of the trained model on MNIST test dataset is about 98.2%.

Alternative TensorFlow Installation

If you are having problems with CUDA libraries (ie tf could not load or find libcudart.so.10.X), TensorFlow and CUDA libraries can be installed together using conda:

pip uninstall tensorflow-gpu
conda install -c anaconda tensorflow-gpu

Advanced Deep Learning with TensorFlow 2 and Keras code examples used in the book.

Chapter 1 - Introduction

  1. MLP on MNIST
  2. CNN on MNIST
  3. RNN on MNIST

Chapter 2 - Deep Networks

  1. Functional API on MNIST
  2. Y-Network on MNIST
  3. ResNet v1 and v2 on CIFAR10
  4. DenseNet on CIFAR10

Chapter 3 - AutoEncoders

  1. Denoising AutoEncoders

Sample outputs for random digits:

Random Digits

  1. Colorization AutoEncoder

Sample outputs for random cifar10 images:

Colorized Images

Chapter 4 - Generative Adversarial Network (GAN)

  1. Deep Convolutional GAN (DCGAN)

Radford, Alec, Luke Metz, and Soumith Chintala. “Unsupervised representation learning with deep convolutional generative adversarial networks.” arXiv preprint arXiv:1511.06434 (2015).

Sample outputs for random digits:

Random Digits

  1. Conditional (GAN)

Mirza, Mehdi, and Simon Osindero. “Conditional generative adversarial nets.” arXiv preprint arXiv:1411.1784 (2014).

Sample outputs for digits 0 to 9:

Zero to Nine

Chapter 5 - Improved GAN

  1. Wasserstein GAN (WGAN)

Arjovsky, Martin, Soumith Chintala, and Léon Bottou. “Wasserstein GAN.” arXiv preprint arXiv:1701.07875 (2017).

Sample outputs for random digits:

Random Digits

  1. Least Squares GAN (LSGAN)

Mao, Xudong, et al. “Least squares generative adversarial networks.” 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017.

Sample outputs for random digits:

Random Digits

  1. Auxiliary Classfier GAN (ACGAN)

Odena, Augustus, Christopher Olah, and Jonathon Shlens. “Conditional image synthesis with auxiliary classifier GANs. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017.”

Sample outputs for digits 0 to 9:

Zero to Nine

Chapter 6 - GAN with Disentangled Latent Representations

  1. Information Maximizing GAN (InfoGAN)

Chen, Xi, et al. “Infogan: Interpretable representation learning by information maximizing generative adversarial nets.” Advances in Neural Information Processing Systems. 2016.

Sample outputs for digits 0 to 9:

Zero to Nine

  1. Stacked GAN

Huang, Xun, et al. “Stacked generative adversarial networks.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Vol. 2. 2017

Sample outputs for digits 0 to 9:

Zero to Nine

Chapter 7 - Cross-Domain GAN

  1. CycleGAN

Zhu, Jun-Yan, et al. “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks.” 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017.

Sample outputs for random cifar10 images:

Colorized Images

Sample outputs for MNIST to SVHN:

MNIST2SVHN

Chapter 8 - Variational Autoencoders (VAE)

  1. VAE MLP MNIST
  2. VAE CNN MNIST
  3. Conditional VAE and Beta VAE

Kingma, Diederik P., and Max Welling. “Auto-encoding Variational Bayes.” arXiv preprint arXiv:1312.6114 (2013).

Sohn, Kihyuk, Honglak Lee, and Xinchen Yan. “Learning structured output representation using deep conditional generative models.” Advances in Neural Information Processing Systems. 2015.

I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner. β-VAE: Learning basic visual concepts with a constrained variational framework. ICLR, 2017.

Generated MNIST by navigating the latent space:

MNIST

Chapter 9 - Deep Reinforcement Learning

  1. Q-Learning
  2. Q-Learning on Frozen Lake Environment
  3. DQN and DDQN on Cartpole Environment

Mnih, Volodymyr, et al. “Human-level control through deep reinforcement learning.” Nature 518.7540 (2015): 529

DQN on Cartpole Environment:

Cartpole

Chapter 10 - Policy Gradient Methods

  1. REINFORCE, REINFORCE with Baseline, Actor-Critic, A2C

Sutton and Barto, Reinforcement Learning: An Introduction

Mnih, Volodymyr, et al. “Asynchronous methods for deep reinforcement learning.” International conference on machine learning. 2016.

Policy Gradient on MountainCar Continuous Environment:

Car

Chapter 11 - Object Detection

  1. Single-Shot Detection

Single-Shot Detection on 3 Objects
SSD

Chapter 12 - Semantic Segmentation

  1. FCN

  2. PSPNet

Semantic Segmentation

Semantic Segmentation

Chapter 13 - Unsupervised Learning using Mutual Information

  1. Invariant Information Clustering

  2. MINE: Mutual Information Estimation

MINE
MINE

Citation

If you find this work useful, please cite:

@book{atienza2020advanced,
  title={Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more},
  author={Atienza, Rowel},
  year={2020},
  publisher={Packt Publishing Ltd}
}

Download Details:

Author: PacktPublishing
The Demo/Documentation: View The Demo/Documentation
Download Link: Download The Source Code
Official Website: https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras
License: MIT

Shirts and Gifts for Your Friends & Loved ☞ https://bit.ly/36PHvXY

#deep-learning #tensorflow #keras #machine-learning

Mikel  Okuneva

Mikel Okuneva

1603735200

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time


Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.


#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020

Tutorial: A detailed notebook on Keras Sequential API (Tensorflow 2.0)

So, this is the next part of my previous tutorial Tutorial: A quick overview of tensorflow2.0. In the previous tutorial, we learned about all the basics of tensorflow2.0 and different types of Keras API.

Check it out! If you haven’t yet! The link is above.

So let’s get started with a detailed analysis of Sequential API in Keras tensorflow2.0. Here, We are going to predict on Titanic dataset with the help of Sequential API. You can download the dataset from here. and follow along with me in this.

As I have told you before. My preferred way to run tensorflow2.0 is Google Colab. It provides us CPU/ GPU/ TPU support. In Google Colab, directly import the TensorFlow and it will return you the latest version. Keras runs tensorflow2.0 as a backend.

I will show how to download any Kaggle dataset directly into Google Colab in my next tutorial. (Link will be edited/updated here.)

  1. Import Libraries: Let’s import the helper libraries.
## Import Basic Libraries

import numpy as np ## numerical data processing
import pandas as pd ## data processing, CSV file I/O
## This will be some files of titanic dataset downloaded from kaggle
titanic/train.csv
titanic/gender_submission.csv
titanic/test.csv

Let’s import the required TensorFlow libraries.

## Sklearn.preprocessing module for preprocessing of data 
from sklearn import preprocessing

## Import Keras from tensorflow backend
from tensorflow.python import keras
## SimpleImputer for filling missing values
from sklearn.impute import SimpleImputer
## Import tensorflow
import tensorflow as tf
## Import Sequential class (i.e for putting it together)
from tensorflow.python.keras.models import Sequential
## Import different layers from Layer class
from tensorflow.python.keras.layers import Dense, Dropout

#kaggle #keras #tensorflow #machine-learning #deep-learning #deep learning

Gunjan  Khaitan

Gunjan Khaitan

1614952398

TensorFlow And Keras Tutorial | Deep Learning With TensorFlow & Keras | Deep Learning

This video on TensorFlow and Keras tutorial will help you understand Deep Learning frameworks, what is TensorFlow, TensorFlow features and applications, how TensorFlow works, TensorFlow 1.0 vs TensorFlow 2.0, TensorFlow architecture with a demo. Then we will move into understanding what is Keras, models offered in Keras, what are neural networks and they work.

#tensorflow #keras #deep-learning #developer