Advanced-Deep-Learning-with-Keras | Advanced Deep Learning with Keras , published by Packt | Machine Learning library

by PacktPublishing Python Version: Current License: MIT

X-Ray Key Features Code Snippets(2)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Advanced-Deep-Learning-with-Keras Summary

Advanced-Deep-Learning-with-Keras is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras applications. Advanced-Deep-Learning-with-Keras has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

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.

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Advanced-Deep-Learning-with-Keras has a medium active ecosystem.

It has 1564 star(s) with 872 fork(s). There are 63 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 17 have been closed. On average issues are closed in 17 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Advanced-Deep-Learning-with-Keras is current.

Quality

Advanced-Deep-Learning-with-Keras has no bugs reported.

Security

Advanced-Deep-Learning-with-Keras has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Advanced-Deep-Learning-with-Keras is licensed under the MIT License. This license is Permissive.

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Advanced-Deep-Learning-with-Keras releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed Advanced-Deep-Learning-with-Keras and discovered the below as its top functions. This is intended to give you an instant insight into Advanced-Deep-Learning-with-Keras implemented functionality, and help decide if they suit your requirements.

Build and train and train MNIST model
Train the network
Construct a discriminator layer
Plot generated images
Argument parser
Resnet V2D convolution layer
Resnet layer
Performs a features pyramid
2D convolutional convolution layer
Loop through the detector
Generate a dataset
Plots the results of the prediction
Plot anchors
Runs the test generator
Evaluate the test results
Evaluate the test image
Train the model
Load and display test images
Cross color cifar10
Run test generator
Load image data
Cross validation for MNIST
Build autoencoder model
Train the objective function
Sample the policy prediction
Setup log files
Set up the agent
Build the model

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Advanced-Deep-Learning-with-Keras Key Features

No Key Features are available at this moment for Advanced-Deep-Learning-with-Keras.

Advanced-Deep-Learning-with-Keras Examples and Code Snippets

Advanced Deep Learning with Keras,Instructions and Navigation

Python

Lines of Code : 13

License : Permissive (MIT)

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def encoder_layer(inputs,
                  filters=16,
                  kernel_size=3,
                  strides=2,
                  activation='relu',
                  instance_norm=True):
"""Builds a generic encoder layer made of Conv2D-IN-Leak

Advanced Deep Learning with Keras,Citation

Python

Lines of Code : 6

License : Permissive (MIT)

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@book{atienza2018advanced,
  title={Advanced Deep Learning with Keras},
  author={Atienza, Rowel},
  year={2018},
  publisher={Packt Publishing Ltd}
}

Community Discussions

Trending Discussions on Advanced-Deep-Learning-with-Keras

Can't instantiate a Keras model when batch_normalization is used

QUESTION

Can't instantiate a Keras model when batch_normalization is used

Asked 2019-Jun-08 at 17:15

I am not sure what I am doing wrong but I am following the code from a book to create a GAN model, and during instantiation the Python shell is just freezing. The code is actually a subset of some code from a book, but the book code also fails to create a model.

If I comment out the batch_norm however I can instantiate a model.

Here:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter4-gan/dcgan-mnist-4.2.1.py

Docs: https://keras.io/layers/normalization/

...

ANSWER

Answered 2019-Jun-08 at 17:15

I tried your code in google colab. The following is generated. I think it's not a problem of the code. You may check other problem, e.g. setting.

Source https://stackoverflow.com/questions/56508349

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Advanced-Deep-Learning-with-Keras

It is recommended to run within conda enviroment. Pls download Anacoda from: Anaconda. To install anaconda:. 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. 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.
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:.
MLP on MNIST
CNN on MNIST
RNN on MNIST
Functional API on MNIST
Y-Network on MNIST
ResNet v1 and v2 on CIFAR10
DenseNet on CIFAR10
Denoising AutoEncoders
Colorization AutoEncoder
Deep Convolutional GAN (DCGAN)
Conditional (GAN)
Wasserstein GAN (WGAN)
Least Squares GAN (LSGAN)
Auxiliary Classfier GAN (ACGAN)
Information Maximizing GAN (InfoGAN)
Stacked GAN
CycleGAN
VAE MLP MNIST
VAE CNN MNIST
Conditional VAE and Beta VAE
Q-Learning
Q-Learning on Frozen Lake Environment
DQN and DDQN on Cartpole Environment
REINFORCE, REINFORCE with Baseline, Actor-Critic, A2C
Single-Shot Detection
FCN
PSPNet
Invariant Information Clustering
MINE: Mutual Information Estimation

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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