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
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
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
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)
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
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
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
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)
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
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
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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