【AI前沿动态】GitHub趋势榜第一：TensorFlow+PyTorch深度学习资源大汇总

【新智元导读】该项目是Jupyter Notebook中TensorFlow和PyTorch的各种深度学习架构，模型和技巧的集合。内容非常丰富，适用于Python 3.7，适合当做工具书。

本文搜集整理了Jupyter Notebook中TensorFlow和PyTorch的各种深度学习架构，模型和技巧，内容非常丰富，适用于Python 3.7，适合当做工具书。

大家可以将内容按照需要进行分割，打印出来，或者做成电子书等，随时查阅。

传统机器学习

感知器

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/perceptron.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/perceptron.ipynb

逻辑回归

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/logistic-regression.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/logistic-regression.ipynb

Softmax Regression (Multinomial Logistic Regression)

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/softmax-regression.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/softmax-regression.ipynb

多层感知器

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-basic.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-basic.ipynb

具有Dropout多层感知器

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-dropout.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-dropout.ipynb

具有批量归一化的多层感知器

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-batchnorm.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-batchnorm.ipynb

具有反向传播的多层感知器

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-lowlevel.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-fromscratch__sigmoid-mse.ipynb

CNN

基础

CNN

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/cnn/convnet.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-basic.ipynb

具有He初始化的CNN

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-he-init.ipynb

概念

用等效卷积层代替完全连接

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/fc-to-conv.ipynb

全卷积

全卷积神经网络

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-allconv.ipynb

AlexNet

AlexNet on CIFAR-10

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-alexnet-cifar10.ipynb

VGG

CNN VGG-16

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/cnn/cnn-vgg16.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16.ipynb

VGG-16 Gender Classifier Trained on CelebA

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16-celeba.ipynb

CNN VGG-19

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg19.ipynb

ResNet

ResNet and Residual Blocks

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/resnet-ex-1.ipynb

ResNet-18 Digit Classifier Trained on MNIST

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet18-mnist.ipynb

ResNet-18 Gender Classifier Trained on CelebA

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet18-celeba-dataparallel.ipynb

ResNet-34 Digit Classifier Trained on MNIST

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet34-mnist.ipynb

ResNet-34 Gender Classifier Trained on CelebA

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet34-celeba-dataparallel.ipynb

ResNet-50 Digit Classifier Trained on MNIST

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet50-mnist.ipynb

ResNet-50 Gender Classifier Trained on CelebA

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet50-celeba-dataparallel.ipynb

ResNet-101 Gender Classifier Trained on CelebA

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet101-celeba.ipynb

ResNet-152 Gender Classifier Trained on CelebA

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet152-celeba.ipynb

Network in Network

Network in Network CIFAR-10 Classifier

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/nin-cifar10.ipynb

度量学习

具有多层感知器的孪生网络

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/metric/siamese-1.ipynb

自动编码机

全连接自动编码机

自动编码机

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/autoencoder.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-basic.ipynb

具有解卷积/转置卷积的卷积自动编码机

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/ae-deconv.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-deconv.ipynb

具有解卷积的卷积自动编码机（无池化操作）

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/aer-deconv-nopool.ipynb

具有最近邻插值的卷积自动编码机

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/autoencoder-conv-nneighbor.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor.ipynb

具有最近邻插值的卷积自动编码机 - 在CelebA上进行训练

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor-celeba.ipynb

具有最近邻插值的卷积自动编码机 - 在Quickdraw上训练

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor-quickdraw-1.ipynb

变分自动编码机

变分自动编码机

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-var.ipynb

卷积变分自动编码机

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-var.ipynb

条件变分自动编码机

条件变分自动编码机（重建丢失中带标签）

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cvae.ipynb

条件变分自动编码机（重建损失中没有标签）

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cvae_no-out-concat.ipynb

卷积条件变分自动编码机（重建丢失中带标签）

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cnn-cvae.ipynb

卷积条件变分自动编码机（重建损失中没有标签）

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cnn-cvae_no-out-concat.ipynb

GAN

MNIST上完全连接的GAN

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/gan/gan.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/gan/gan.ipynb

MNIST上的卷积GAN

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/gan/gan-conv.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/gan/gan-conv.ipynb

具有标签平滑的MNIST上的卷积GAN

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/gan/gan-conv-smoothing.ipynb

RNN

Many-to-one: Sentiment Analysis / Classification

A simple single-layer RNN (IMDB)

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_simple_imdb.ipynb

A simple single-layer RNN with packed sequences to ignore padding characters (IMDB)

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_simple_packed_imdb.ipynb

RNN with LSTM cells (IMDB)

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_imdb.ipynb

RNN with LSTM cells and Own Dataset in CSV Format (IMDB)

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_own_csv_imdb.ipynb

RNN with GRU cells (IMDB)

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_gru_packed_imdb.ipynb

Multilayer bi-directional RNN (IMDB)

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_gru_packed_imdb.ipynb

Many-to-Many / Sequence-to-Sequence

A simple character RNN to generate new text (Charles Dickens)

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb

序数回归

Ordinal Regression CNN -CORAL w. ResNet34 on AFAD-Lite

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-coral-afadlite.ipynb

Ordinal Regression CNN -Niu et al. 2016 w. ResNet34 on AFAD-Lite

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-niu-afadlite.ipynb

Ordinal Regression CNN -Beckham and Pal 2016 w. ResNet34 on AFAD-Lite

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-niu-afadlite.ipynb

技巧和窍门

Cyclical Learning Rate

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/tricks/cyclical-learning-rate.ipynb

PyTorch工作流程和机制

自定义数据集

使用PyTorch数据集加载实用程序用于自定义数据集-CSV文件转换为HDF5

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-csv.ipynb

使用PyTorch数据集加载自定义数据集的实用程序 - 来自CelebA的图像

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-celeba.ipynb

使用PyTorch数据集加载自定义数据集的实用程序 - 从Quickdraw中提取

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-quickdraw.ipynb

使用PyTorch数据集加载实用程序用于自定义数据集 - 从街景房号（SVHN）数据集中绘制

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/custom-data-loader-svhn.ipynb

训练和预处理

带固定内存的数据加载

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet34-cifar10-pinmem.ipynb

标准化图像

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-standardized.ipynb

图像转换示例

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/torchvision-transform-examples.ipynb

Char-RNN with Own Text File

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb

Sentiment Classification RNN with Own CSV File

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_own_csv_imdb.ipynb

并行计算

在CelebA上使用具有DataParallel -VGG-16性别分类器的多个GPU

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16-celeba-data-parallel.ipynb

其它

Sequential API and hooks

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-sequential.ipynb

图层内的权重共享

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/cnn-weight-sharing.ipynb

仅使用Matplotlib在Jupyter Notebook中绘制实时训练性能

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/plot-jupyter-matplotlib.ipynb

Autograd

在PyTorch中获取中间变量的渐变

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/manual-gradients.ipynb

TensorFlow工作流及机制

自定义数据集

使用NumPy NPZ Archives为Minibatch训练添加图像数据集

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/image-data-chunking-npz.ipynb

使用HDF5存储用于Minibatch培训的图像数据集

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/image-data-chunking-hdf5.ipynb

使用输入Pipeline从TFRecords文件中读取数据

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/tfrecords.ipynb

使用队列运行器直接从磁盘提供图像

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/file-queues.ipynb

使用TensorFlow的Dataset API

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/dataset-api.ipynb

训练和预处理

保存和加载训练模型 - 来自TensorFlow Checkpoint文件和NumPy NPZ Archives

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/saving-and-reloading-models.ipynb

参考链接：

https://github.com/rasbt/deeplearning-models

转自：新智元

EI 人工智能 AI

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