這個項目火了!各種深度學習架構,模型和技巧的集合

大數據技術2019-06-19 11:04:41

來自:開源最前線(ID:OpenSourceTop)


打開GitHub Trending,排行第一的項目成功引起了我的注意——deeplearning-models



該項目是Jupyter Notebook中TensorFlow和PyTorch的各種深度學習架構,模型和技巧的集合。


這份集合的內容到底有多豐富呢?一起來看看



傳統機器學習


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



邏輯迴歸


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



多層感知器


多層感知器


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多層感知器


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



具有批量歸一化的多層感知器


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



具有反向傳播的多層感知器


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


基礎


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


用等效卷積層代替完全連接


PyTorch:

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


全卷積:全卷積神經網絡


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 



度量學習:具有多層感知器的孿生網絡


TensorFlow 1:

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



自動編碼機


全連接自動編碼機:自動編碼機


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



具有解卷積/轉置卷積的卷積自動編碼機


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



具有解卷積的卷積自動編碼機(無池化操作)


PyTorch:

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



具有最近鄰插值的卷積自動編碼機


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



具有最近鄰插值的卷積自動編碼機 - 在CelebA上進行訓練


PyTorch:

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


具有最近鄰插值的卷積自動編碼機 - 在Quickdraw上訓練


PyTorch:

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



變分自動編碼機


變分自動編碼機


PyTorch:

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


卷積變分自動編碼機


PyTorch:

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



條件變分自動編碼機


條件變分自動編碼機(重建丟失中帶標籤)


PyTorch:

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


條件變分自動編碼機(重建損失中沒有標籤)


PyTorch:

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


卷積條件變分自動編碼機(重建丟失中帶標籤)


PyTorch:

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


卷積條件變分自動編碼機(重建損失中沒有標籤)


PyTorch:

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



GAN


MNIST上完全連接的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上的卷積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



具有標籤平滑的MNIST上的卷積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


以上列舉的都只是冰山一角而已,喜歡的夥伴們可以自己到GitHub上一探究竟,最後附上GitHub地址:https://github.com/rasbt/deeplearning-models



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