深度学习工程化神器Keras教程：《Keras深度学习进阶》随书代码

【导读】Keras是目前最好用的深度学习框架之一，具有非常完备和友好的工程化API接口。目前TensorFlow直接将Keras（tf.keras）作为面向使用者的主要接口。本文介绍Github上的《Keras深度学习进阶》随书代码项目。

Keras是目前最好用的深度学习框架之一，在模型构建、模型训练、模型评价、模型保存、预测、日志等功能上都具有非常完备和友好的工程化API接口。另外，由于Keras与TensorFlow无缝兼容（无论是Keras还是tf.keras），使得Keras可以依附于TensorFlow强大的生态圈。因此，Keras被工业界广泛应用在模型研发和线上业务中。

本文介绍图书《Advanced Deep Learning with Keras》（《Keras深度学习进阶》）在Github上的随书代码项目。该图书由浅入深地介绍了MLP（多层感知机）、CNN（卷积神经网络）、Autoencoder（自编码器）、GAN（生成式对抗网络）等模型的原理及Keras实现。该Github项目地址为：

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

包含内容大致如下：

Chapter 1 - Introduction

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MLP on MNIST:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter1-keras-quick-tour/mlp-mnist-1.3.2.py

CNN on MNIST:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter1-keras-quick-tour/cnn-mnist-1.4.1.py

RNN on MNIST:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter1-keras-quick-tour/rnn-mnist-1.5.1.py

Chapter 2 - Deep Networks

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Functional API on MNIST:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter2-deep-networks/cnn-functional-2.1.1.py

Y-Network on MNIST:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter2-deep-networks/cnn-y-network-2.1.2.py

ResNet v1 and v2 on CIFAR10:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter2-deep-networks/resnet-cifar10-2.2.1.py

DenseNet on CIFAR10:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter2-deep-networks/densenet-cifar10-2.4.1.py

Chapter 3 - AutoEncoders

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Denoising AutoEncoders:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter3-autoencoders/denoising-autoencoder-mnist-3.3.1.py

Colorization AutoEncoder:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter3-autoencoders/colorization-autoencoder-cifar10-3.4.1.py

Chapter 4 - Generative Adversarial Network (GAN)

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Deep Convolutional GAN (DCGAN):

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

Conditional (GAN):

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter4-gan/cgan-mnist-4.3.1.py

Chapter 5 - Improved GAN

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Wasserstein GAN (WGAN):

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter5-improved-gan/wgan-mnist-5.1.2.py

Least Squares GAN (LSGAN):

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter5-improved-gan/lsgan-mnist-5.2.1.py

Auxiliary Classfier GAN (ACGAN):

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter5-improved-gan/acgan-mnist-5.3.1.py

Chapter 6 - GAN with Disentangled Latent Representations

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Information Maximizing GAN (InfoGAN):

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter6-disentangled-gan/infogan-mnist-6.1.1.py

Stacked GAN:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter6-disentangled-gan/stackedgan-mnist-6.2.1.py

Chapter 7 - Cross-Domain GAN

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CycleGAN:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter7-cross-domain-gan/cyclegan-7.1.1.py

Chapter 8 - Variational Autoencoders (VAE)

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VAE MLP MNIST:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter8-vae/vae-mlp-mnist-8.1.1.py

VAE CNN MNIST:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter8-vae/cvae-cnn-mnist-8.2.1.py

Conditional VAE and Beta VAE:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter8-vae/cvae-cnn-mnist-8.2.1.py

Chapter 9 - Deep Reinforcement Learning

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Q-Learning:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter9-drl/q-learning-9.3.1.py

Q-Learning on Frozen Lake Environment:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter9-drl/q-frozenlake-9.5.1.py

DQN and DDQN on Cartpole Environment:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter9-drl/dqn-cartpole-9.6.1.py

Chapter 10 - Policy Gradient Methods

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REINFORCE, REINFORCE with Baseline, Actor-Critic, A2C:

https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter10-policy/policygradient-car-10.1.1.py

参考链接：

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

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