【AI理论】这个项目火了!各种深度学习架构,模型和技巧的集合

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HWCloudAI 发表于 2019/08/04 15:18:35 2019/08/04
【摘要】 打开GitHub Trending,排行第一的项目成功引起了我的注意——deeplearning-models,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。这份集合的内容到底有多丰富呢?一起来看看

打开GitHub Trending,排行第一的项目成功引起了我的注意——deeplearning-models


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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。


这份集合的内容到底有多丰富呢?一起来看看




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



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


以上列举的都只是冰山一角而已,喜欢的伙伴们可以自己到GitHub上一探究竟,最后附上GitHub地址:https://github.com/rasbt/deeplearning-models



转自:https://mp.weixin.qq.com/s/CibOY2cPDRRtv765TGeuYQ

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