Tensorlayer Versions Save

Deep Learning and Reinforcement Learning Library for Scientists and Engineers

3.0.0-alpha

2 years ago

Dear all,

It is our great honour to pre-released TensorLayer 3.0.0-alpha. It supports TensorFlow and MindSpore backends, and supports some PaddlePaddle operator backends, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend.

In the next step, we support TensorFlow, MindSpore, PaddlePaddle, and PyTorch backends in the future. Feel free to use it and make suggestions.

TensorLayer 3.0.0-alpha is a maintenance release.

v2.2.4

3 years ago

TensorLayer 2.2.4 is a maintenance release.

Added

Changed

Dependencies Update

Deprecated

Fixed

  • Fix batchnorm(#1104)
  • Fix recurrent(#1106)

Removed

Security

Contributors

  • @zsdonghao
  • @Laicheng0830(#1104)
  • @Thinkre(#1106)

2.2.3

3 years ago

TensorLayer 2.2.3 is a maintenance release. It contains numerous bug fixes.

Added

Changed

Dependencies Update

Deprecated

Fixed

  • Fix VGG. (#1078, 1079, 1089)
  • Fix norm layer. (#1080)
  • Fix DeCov2d layer. (#1081)
  • Fix ModelLayer and LayerList doc. (#1083)
  • Fix bug in SAC. (#1085)
  • Fix refactoring: Deduplication. (#1086)
  • Fix maxpool, batchnorm Data format fixed, vgg forward. (#1089)
  • Fix package info. (#1090)

Removed

Security

Contributors

  • @zsdonghao
  • @tiancheng2000 (#1078 #1079 #1080 #1081)
  • @ChrisWu1997 (#1083)
  • @quantumiracle (#1085)
  • @marload (#1086)
  • @Gyx-One (#1089)
  • @Laicheng0830 (#1090)

2.2.2

4 years ago

TensorLayer 2.2.2 is a maintenance release.

Added

  • Reinforcement learning(#1065)
  • Mish activation(#1068)

Fixed

  • Fix README.
  • Fix package info.

Contributors

  • @zsdonghao
  • @quantumiracle(#1065)
  • @Laicheng0830(#1068)

2.2.1

4 years ago

TensorLayer 2.2.1 is a maintenance release. It contains numerous bug fixes.

Fixed

  • Fix README. (#1044)
  • Fix package info. (#1046)
  • Fix build test (Using YAPF 0.29) (#1057)

Contributors

  • @luomai (#1044, #1046, #1057)

v2.2.0

4 years ago

TensorLayer 2.2.0 is a maintenance release. It contains numerous API improvement and bug fixes. This release is compatible with TensorFlow 2 RC1.

Added

  • Support nested layer customization (#PR 1015)
  • Support string dtype in InputLayer (#PR 1017)
  • Support Dynamic RNN in RNN (#PR 1023)
  • Add ResNet50 static model (#PR 1030)
  • Add performance test code for static models (#PR 1041)

Changed

  • SpatialTransform2dAffine auto in_channels
  • support TensorFlow 2.0.0-rc1
  • Update model weights property, now returns its copy (#PR 1010)

Fixed

  • RNN updates: remove warnings, fix if seq_len=0, unitest (#PR 1033)
  • BN updates: fix BatchNorm1d for 2D data, refactored (#PR 1040)

Dependencies Update

Deprecated

Fixed

  • Fix tf.models.Model._construct_graph for list of outputs, e.g. STN case (PR #1010)
  • Enable better in_channels exception raise. (PR #1015)
  • Set allow_pickle=True in np.load() (#PR 1021)
  • Remove private_method decorator (#PR 1025)
  • Copy original model's trainable_weights and nontrainable_weights when initializing ModelLayer (#PR 1026)
  • Copy original model's trainable_weights and nontrainable_weights when initializing LayerList (#PR 1029)
  • Remove redundant parts in model.all_layers (#PR 1029)
  • Replace tf.image.resize_image_with_crop_or_pad with tf.image.resize_with_crop_or_pad (#PR 1032)
  • Fix a bug in ResNet50 static model (#PR 1041)

Removed

Security

Contributors

  • @zsdonghao
  • @luomai
  • @ChrisWu1997: #1010 #1015 #1025 #1030 #1040
  • @warshallrho: #1017 #1021 #1026 #1029 #1032 #1041
  • @ArnoldLIULJ: #1023
  • @JingqingZ: #1023

2.1.0

4 years ago

Dear All,

Three things need to be mentioned for this release.

  • Deep Reinforcement Learning Model Zoo Release!!!
  • We are going to support more Attention models for NLP officially.
  • The model.conf is almost stable, the AIoT team from Sipeed is now working hard to support TL model on the AI Chips.

Enjoy!

TensorLayer Team

Changed

  • Add version_info in model.config. (PR #992)
  • Replace tf.nn.func with tf.nn.func.__name__ in model config.
  • Add Reinforcement learning tutorials. (PR #995)
  • Add RNN layers with simple rnn cell, GRU cell, LSTM cell. (PR #998)
  • Update Seq2seq (#998)
  • Add Seq2seqLuongAttention model (#998)

Contributors

  • @warshallrho:
  • @quantumiracle: #995
  • @Tokarev-TT-33: #995
  • @initial-h: #995
  • @Officium: #995
  • @ArnoldLIULJ: #998
  • @JingqingZ: #998

2.0.2

4 years ago

Hello, we want to tell you some GOOD NEWS. Today, AI chip is anywhere, from our phone to our car, however, it still hard for us to have our own AI chip. To end this, TensorLayer team starts to work on AIoT and will soon support to run the TensorLayer models on the low-cost AI chip (e.g., K210) and microcontrollers (e.g., STM32). Details in the following:

  • NNoM is a higher-level layer-based Neural Network library specifically for microcontrollers (MCU), our team and the author of NNoM is working hard to make TensorLayer models to run on different MCUs. Yes! Something like BinaryNet.
  • K210 is a low-cost AI chip, we are collaborating with the designers of K210 and the Sipeed team to make TensorLayer models to run on the K210 AI chip.

If you are interested in AIoT, feel free to discuss in Slack.

TensorLayer, Sipeed, NNoM teams

=======

Maintain release, recommended to update.

Changed

  • change the format of network config, change related code and files; change layer act (PR #980)
  • update Seq2seq (#989)

Fixed

  • Fix dynamic model cannot track PRelu weights gradients problem (PR #982)
  • Raise .weights warning (commit)

Contributors

  • @warshallrho: #980
  • @ArnoldLIULJ: #989
  • @1FengL: #982

2.0.1

4 years ago

Maintain release, recommended to update.

Changed

  • remove tl.layers.initialize_global_variables(sess) (PR #931)
  • support trainable_weights (PR #966)

Added

  • Layer
    • InstanceNorm, InstanceNorm1d, InstanceNorm2d, InstanceNorm3d (PR #963)

Changed

  • remove tl.layers.initialize_global_variables(sess) (PR #931)
  • change tl.layers.core, tl.models.core (PR #966)
  • change weights into all_weights, trainable_weights, nontrainable_weights

Dependencies Update

  • nltk>=3.3,<3.4 => nltk>=3.3,<3.5 (PR #892)
  • pytest>=3.6,<3.11 => pytest>=3.6,<4.1 (PR #889)
  • yapf>=0.22,<0.25 => yapf==0.25.0 (PR #896)
  • imageio==2.5.0 progressbar2==3.39.3 scikit-learn==0.21.0 scikit-image==0.15.0 scipy==1.2.1 wrapt==1.11.1 pymongo==3.8.0 sphinx==2.0.1 wrapt==1.11.1 opencv-python==4.1.0.25 requests==2.21.0 tqdm==4.31.1 lxml==4.3.3 pycodestyle==2.5.0 sphinx==2.0.1 yapf==0.27.0(PR #967)

Fixed

  • fix docs of models @zsdonghao #957
  • In BatchNorm, keep dimensions of mean and variance to suit channels first (PR #963)

Contributors

  • @warshallrho: #966
  • @zsdonghao: #931
  • @yd-yin: #963
  • @dvklopfenstein: #971

2.0.0

5 years ago

Dear all,

It is our great honour to release TensorLayer 2.0.0. In the past few months, we have refactored all layers to support TensorFlow 2.0.0-alpha0 and the dynamic mode! The new API designs allow you to customize layers easily, compared with other libraries.

We would like to thanks all contributors especially our core members from Peking University and Imperial College London, they are @zsdonghao @JingqingZ @ChrisWu1997 @warshallrho. All contributions are listed in the following.

In the next step, we are interested in supporting more advanced features for 3D Vision, such as PointCNN and GraphCNN. Also, we still have some remaining examples that need to be updated, such as A3C and distributed training. If you are interested in joining the development team, feel free to contact us: [email protected]

Enjoy coding!

TensorLayer Team

References

Contribution List

All contribution can be found as follows:

Layers

  • core.py:
    • Layer:
      • refactored @JingqingZ 2019/01/28
      • tested @JingqingZ 2019/01/31 2019/03/06
      • documentation @JingqingZ 2019/03/06
    • ModelLayer:
      • created @JingqingZ 2019/01/28
      • tested @JingqingZ 2019/03/06
      • documentation @JingqingZ 2019/03/06
    • LayerList:
      • created @JingqingZ 2019/01/28 @ChrisWu1997
      • tested @JingqingZ 2019/03/06
      • documentation @JingqingZ 2019/03/06
    • LayerNode:
      • created @ChrisWu1997
      • tested @ChrisWu1997 2019/03/22
      • documentation @ChrisWu1997 2019/03/22
  • activation.py:
    • PRelu:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/03/20
      • tested @JingqingZ 2019/03/20
      • documentation @JingqingZ 2019/03/20
    • PRelu6:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/03/20
      • tested @JingqingZ 2019/03/20
      • documentation @JingqingZ 2019/03/20
    • PTRelu6:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/03/20
      • tested @JingqingZ 2019/03/20
      • documentation @JingqingZ 2019/03/20
  • convolution/
    • AtrousConv1dLayer, AtrousConv2dLayer and AtrousDeConv2d are removed, use Conv1d/2d and DeConv2d with dilation_rate instead. (🀄️remember to change CN docs)
    • BinaryConv2d:
      • refactored @zsdonghao 2018/12/05
      • tested @warshallrho 2019/03/16
      • documentation @warshallrho 2019/03/20
    • Conv1d:
      • refactored @zsdonghao 2019/01/16
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/17
    • Conv2d:
      • refactored @zsdonghao 2019/01/16
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/17
    • Conv3d:
      • add @zsdonghao 2019/01/16 : (🀄️remember to change CN docs)
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/17
    • Conv1dLayer:
      • refactored @zsdonghao 2018/12/05
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/17
    • Conv2dLayer:
      • refactored @zsdonghao 2018/12/05
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/17
    • Conv3dLayer:
      • refactored @zsdonghao 2018/12/05
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/17
    • DeConv1dLayer:
      • refactored @warshallrho 2019/03/16
      • tested @warshallrho 2019/03/16
      • documentation @warshallrho 2019/03/17
    • DeConv2dLayer:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/17
    • DeConv3dLayer:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/17
    • DeConv2d:
      • refactored @zsdonghao 2019/01/16
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/17
    • DeConv3d:
      • refactored @zsdonghao 2019/01/16
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/17
    • DeformableConv2d:
      • refactored @warshallrho 2019/03/18
      • tested @warshallrho 2019/03/18
      • documentation @warshallrho 2019/03/18
    • DepthwiseConv2d:
      • refactored @zsdonghao 2018/12/05
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/18
    • DorefaConv2d:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/17
      • documentation @warshallrho 2019/03/20
    • GroupConv2d:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/17
      • documentation @warshallrho 2019/03/20
    • QuanConv2d:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/17
      • documentation @warshallrho 2019/03/20
    • QuanConv2dWithBN:
      • refactored
      • tested
      • documentation
    • SeparableConv1d:
      • refactored @zsdonghao 2019/01/16
      • tested @warshallrho 2019/03/17
      • documentation @warshallrho 2019/03/18
    • SeparableConv2d:
      • refactored @zsdonghao 2019/01/16
      • tested @warshallrho 2019/03/17
      • documentation @warshallrho 2019/03/18
    • SubpixelConv1d:
      • refactored @zsdonghao 2018/12/05 @warshallrho 2019/03/18
      • tested @warshallrho 2019/03/18
      • documentation @warshallrho 2019/03/18
    • SubpixelConv2d:
      • refactored @zsdonghao 2018/12/05 @warshallrho 2019/03/18
      • tested @warshallrho 2019/03/18
      • documentation @warshallrho 2019/03/18
    • TernaryConv2d:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/17
      • documentation @warshallrho 2019/03/20
  • dense/ [WIP] @ChrisWu1997
    • BinaryDense:
      • refactored @zsdonghao 2018/12/06
      • tested @ChrisWu1997 2019/04/23 need further test by example
      • documentation @ChrisWu1997 2019/04/23
    • Dense:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/01/28
      • tested @JingqingZ 2019/01/31 2019/03/06 2019/03/15
      • documentation @JingqingZ 2019/03/15
    • DorefaDense:
      • refactored @zsdonghao 2018/12/04
      • tested @ChrisWu1997 2019/04/23 need further test by example
      • documentation @ChrisWu1997 2019/04/23
    • DropconnectDense:
      • refactored @zsdonghao 2018/12/05
      • tested @ChrisWu1997 2019/04/23 need further test by example
      • documentation @ChrisWu1997 2019/04/23
    • QuanDense:
      • refactored @zsdonghao 2018/12/06
      • tested @ChrisWu1997 2019/04/23 need further test by example
      • documentation @ChrisWu1997 2019/04/23
    • QuanDenseWithBN:
      • refactored
      • tested
      • documentation
    • TernaryDense:
      • refactored @zsdonghao 2018/12/06
      • tested @ChrisWu1997 2019/04/23 need further test by example
      • documentation @ChrisWu1997 2019/04/23
  • dropout.py
    • Dropout:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/01/28
      • tested @JingqingZ 2019/01/31 2019/03/06 2019/03/15
      • documentation @JingqingZ 2019/03/15
  • extend.py
    • ExpandDims:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/03/22
      • tested @JingqingZ 2019/03/22
      • documentation @JingqingZ 2019/03/22
    • Tile:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/03/22
      • tested @JingqingZ 2019/03/22
      • documentation @JingqingZ 2019/03/22
  • image_resampling.py
    • UpSampling2d:
      • refactored @zsdonghao 2018/12/04 @ChrisWu1997 2019/04/03
      • tested @ChrisWu1997 2019/04/03
      • documentation @ChrisWu1997 2019/04/03
    • DownSampling2d:
      • refactored @zsdonghao 2018/12/04 @ChrisWu1997 2019/04/03
      • tested @ChrisWu1997 2019/04/03
      • documentation @ChrisWu1997 2019/04/03
  • importer.py
    • SlimNets:
      • refactored
      • tested
      • documentation
    • Keras:
      • refactored
      • tested
      • documentation
  • inputs.py
    • Input:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/01/28
      • tested @JingqingZ 2019/03/06
      • documentation @JingqingZ 2019/03/06
  • embedding.py
    • OneHotInput: --> OneHot (🀄️remember to change CN docs)
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/02/23
      • tested @JingqingZ 2019/03/19
      • documentation @JingqingZ 2019/03/19
    • Word2vecEmbeddingInput: --> Word2vecEmbedding (🀄️remember to change CN docs)
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/02/21
      • tested @JingqingZ 2019/03/19
      • documentation @JingqingZ 2019/03/19
    • EmbeddingInput: --> Embedding
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/02/22
      • tested @JingqingZ 2019/03/19
      • documentation @JingqingZ 2019/03/19
    • AverageEmbeddingInput: --> AverageEmbedding (🀄️remember to change CN docs)
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/02/20
      • tested @JingqingZ 2019/03/19
      • documentation @JingqingZ 2019/03/19
  • lambda_layers.py
    • ElementwiseLambda:
      • refactored @JingqingZ 2019/03/24
      • tested @JingqingZ 2019/03/24
      • documentation @JingqingZ 2019/03/24
    • Lambda:
      • refactored @JingqingZ 2019/03/24
      • tested @JingqingZ 2019/03/24
      • documentation @JingqingZ 2019/03/24
  • merge.py
    • Concat:
      • refactored @zsdonghao 2018/12/04
      • tested @JingqingZ 2019/03/15
      • documentation @JingqingZ 2019/03/15
    • Elementwise:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/03/15
      • tested @JingqingZ 2019/03/15
      • documentation @JingqingZ 2019/03/15
  • noise.py
    • GaussianNoise:
      • refactored @zsdonghao 2018/12/04
      • tested @warshallrho 2019/03/20
      • documentation @warshallrho 2019/03/20
  • normalization.py
    • BatchNorm:
      • refactored @ChrisWu1997 2019/01/22 @ChrisWu1997 2019/03/05
      • tested @ChrisWu1997 2019/03/22
      • documentation @ChrisWu1997 2019/03/22
    • BatchNorm1d:
      • refactored @ChrisWu1997 2019/03/05
      • tested @ChrisWu1997 2019/03/22
      • documentation @ChrisWu1997 2019/03/22
    • BatchNorm2d:
      • refactored @ChrisWu1997 2019/03/05
      • tested @ChrisWu1997 2019/03/22
      • documentation @ChrisWu1997 2019/03/22
    • BatchNorm3d:
      • refactored @ChrisWu1997 2019/03/05
      • tested @ChrisWu1997 2019/03/22
      • documentation @ChrisWu1997 2019/03/22
    • GroupNorm:
      • refactored @zsdonghao 2018/12/05
      • tested
      • documentation
    • InstanceNorm:
      • refactored @zsdonghao 2018/12/05
      • tested
      • documentation
    • LayerNorm:
      • refactored @ChrisWu1997 2019/01/23
      • tested
      • documentation
    • LocalResponseNorm:
      • refactored @zsdonghao 2018/12/05
      • tested
      • documentation
    • SwitchNorm:
      • refactored @zsdonghao 2018/12/05
      • tested
      • documentation
  • padding.py
    • PadLayer:
      • refactored @zsdonghao 2018/12/04
      • tested @warshallrho 2019/03/21
      • documentation @warshallrho 2019/03/21
    • ZeroPad1d:
      • refactored @zsdonghao 2018/12/04
      • tested @warshallrho 2019/03/21
      • documentation @warshallrho 2019/03/21
    • ZeroPad2d:
      • refactored @zsdonghao 2018/12/04
      • tested @warshallrho 2019/03/21
      • documentation @warshallrho 2019/03/21
    • ZeroPad3d:
      • refactored @zsdonghao 2018/12/04
      • tested @warshallrho 2019/03/21
      • documentation @warshallrho 2019/03/21
  • pooling/
    • MaxPool1d:
      • refactored @zsdonghao 2019/01/08
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/19
    • MaxPool2d:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/19
    • MaxPool3d:
      • refactored @zsdonghao 2019/01/08
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/19
    • MeanPool1d:
      • refactored @zsdonghao 2019/01/08
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/19
    • MeanPool2d:
      • refactored @zsdonghao 2019/01/08
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/19
    • MeanPool3d:
      • refactored @zsdonghao 2019/01/08
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/19
    • GlobalMaxPool1d:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/15
    • GlobalMaxPool2d:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/15
    • GlobalMaxPool3d:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/15
    • GlobalMeanPool1d:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/15
    • GlobalMeanPool2d:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/15
    • GlobalMeanPool3d:
      • refactored @zsdonghao 2018/12/06
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/15
    • PoolLayer:
      • refactored @zsdonghao 2018/12/04
      • tested @warshallrho 2019/03/15
      • documentation @warshallrho 2019/03/18
  • quantize_layers.py
    • Sign:
      • refactored
      • tested
      • documentation
  • recurrent/
    • BiRNN:
      • refactored @JingqingZ 2019/04/08
      • tested @JingqingZ 2019/04/08
      • documentation @JingqingZ 2019/04/08
    • ConvLSTM:
      • refactored
      • tested
      • documentation
    • RNN:
      • refactored @JingqingZ 2019/03/31
      • tested @JingqingZ 2019/03/31
      • documentation @JingqingZ 2019/03/31
    • Seq2Seq:
      • refactored
      • tested
      • documentation
  • shape.py
    • Flatten:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/03/22
      • tested @JingqingZ 2019/03/22
      • documentation @JingqingZ 2019/03/22
    • Reshape:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/03/22
      • tested @JingqingZ 2019/03/22
      • documentation @JingqingZ 2019/03/22
    • Transpose:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/03/22
      • tested @JingqingZ 2019/03/22
      • documentation @JingqingZ 2019/03/22
  • scale.py
    • Scale:
      • refactored @zsdonghao 2018/12/04 @JingqingZ 2019/03/22
      • tested @JingqingZ 2019/03/22
      • documentation @JingqingZ 2019/03/22
  • contrib
    • ROIPooling:
      • refactored
      • tested
      • documentation
  • spatial_transformer.py
    • SpatialTransformer2dAffine: see test_layers_spatial_transformer.py
      • refactored
      • tested
      • documentation
  • stack.py [WIP] @ChrisWu1997
    • Stack:
      • refactored @zsdonghao 2018/12/04
      • tested @ChrisWu1997 2019/04/23
      • documentation @ChrisWu1997 2019/04/23
    • UnStack:
      • refactored @zsdonghao 2018/12/04
      • tested @ChrisWu1997 2019/04/23
      • documentation @ChrisWu1997 2019/04/23

tl.models

  • core.py
    • Model:
      • refactored @JingqingZ 2019/01/28 @ChrisWu1997 2019/02/16 2019/02/22
      • tested @ChrisWu1997 2019/03/21
      • documentation @ChrisWu1997 2019/03/21
  • vgg.py
    • vgg:
      • refactored @warshallrho 2019/02/19
      • tested
      • documentation @warshallrho 2019/03/21 @ChrisWu1997 2019/03/21
    • vgg16:
      • refactored @warshallrho 2019/02/19
      • tested
      • documentation @warshallrho 2019/03/21 @ChrisWu1997 2019/03/21
    • vgg19:
      • refactored @warshallrho 2019/03/09
      • tested
      • documentation @warshallrho 2019/03/21 @ChrisWu1997 2019/03/21
  • mobilenetv1.py
    • MobileNet:
      • refactored @ChrisWu1997 2019/04/23
      • tested @ChrisWu1997 2019/04/23
      • documentation @ChrisWu1997 2019/04/23
    • SqueezeNet:
      • refactored @ChrisWu1997 2019/04/23
      • tested @ChrisWu1997 2019/04/23
      • documentation @ChrisWu1997 2019/04/23

Examples

  • basic_tutorials Too many basic tutorials, some codes can be removed.
    • Static model example MNIST @JingqingZ 2019/01/28 2019/03/24
    • Dynamic model example MNIST @JingqingZ 2019/01/28 2019/03/24
    • Static model example CIFAR10 (with dataset API) @ChrisWu1997 2019/03/24
    • Siamese example MNIST @ChrisWu1997 2019/03/26
    • tutorial_mnist_float16.py removed by @ChrisWu1997
    • tutorial_mnist_simple.py removed by @ChrisWu1997
  • data_process
    • tutorial_fast_affine_transform.py
      • refactored @ChrisWu1997 2019/04/11
      • tested @ChrisWu1997 2019/04/11
    • tutorial_image_preprocess.py removed by @zsdonghao
    • tutorial_tf_dataset_voc.py
      • refactored @ChrisWu1997 2019/04/11
      • tested @ChrisWu1997 2019/04/11
    • tutorial_tfrecord.py
      • refactored @ChrisWu1997 2019/04/11
      • tested @ChrisWu1997 2019/04/11
    • tutorial_tfrecord2.py
      • refactored @ChrisWu1997 2019/04/11
      • tested @ChrisWu1997 2019/04/11
    • tutorial_tfrecord3.py
      • refactored
      • tested
  • database
    • refactored
    • tested
  • distributed_training
    • tutorial_cifar10_distributed_trainer.py
      • refactored
      • tested
    • tutorial_mnist_distributed_trainer.py
      • refactored
      • tested
  • keras_tfslim
    • tutorial_keras.py
      • refactored @ChrisWu1997 2019/04/11
      • tested @ChrisWu1997 2019/04/11
    • tutorial_tfslim.py removed by @ChrisWu1997
  • pretrained_cnn
    • tutorial_inceptionV3_tfslim.py
    • tutorial_mobilenet.py removed by @ChrisWu1997 2019/04/23
    • tutorial_models_mobilenetv1.py
      • refactored @ChrisWu1997 2019/04/23
      • tested @ChrisWu1997 2019/04/23
    • tutorial_models_squeezenetv1.py
      • refactored @ChrisWu1997 2019/04/23
      • tested @ChrisWu1997 2019/04/23
    • tutorial_models_vgg.py
      • refactored @warshallrho 2019/04/30
      • tested
    • tutorial_models_vgg_static.py
      • refactored @warshallrho 2019/04/30
      • tested
    • tutorial_models_vgg16.py
      • refactored @warshallrho 2019/02/19
      • tested
    • tutorial_models_vgg19.py
      • refactored @warshallrho 2019/03/09
      • tested
    • tutorial_squeezenet.py removed by @ChrisWu1997 2019/04/23
    • tutorial_vgg16.py removed by @warshallrho 2019/04/30
    • tutorial_vgg19.py removed by @warshallrho 2019/04/30
  • quantized_net
    • tutorial_binarynet_cifar10_tfrecord.py
      • refactored
      • tested
    • tutorial_binarynet_mnist_cnn.py
      • refactored
      • tested
    • tutorial_dorefanet_cifar10_tfrecord.py
      • refactored
      • tested
    • tutorial_dorefanet_mnist_cnn.py
      • refactored
      • tested
    • tutorial_quanconv_cifar10.py
      • refactored
      • tested
    • tutorial_quanconv_mnist.py
      • refactored
      • tested
    • tutorial_ternaryweight_cifar10_tfrecord.py
      • refactored
      • tested
    • tutorial_ternaryweight_mnist_cnn.py
      • refactored
      • tested
  • reinforcement_learning
    • tutorial_atari_pong.py @zsdonghao 2019/01/21
      • refactored
      • tested
    • tutorial_bipedalwalker_a3c_continuous_action.py
      • refactored
      • tested
    • tutorial_cartpole_ac.py @zsdonghao 2019/02/17
      • refactored
      • tested
    • tutorial_frozenlake_dqn.py @zsdonghao 2019/02/16
      • refactored
      • tested
    • tutorial_frozenlake_q_table.py @zsdonghao 2019/02/16
      • refactored
      • tested
  • text_classification
    • tutorial_imdb_fasttext.py @JingqingZ 2019/03/14
      • refactored
      • tested
  • text_generation
    • tutorial_generate_text.py
      • refactored
      • tested
  • text_ptb Are they duplicated?
    • tutorial_ptb_lstm_state_is_tuple.py
      • refactored
      • tested
    • tutorial_ptb_lstm.py
      • refactored
      • tested
  • text_word_embedding
    • tutorial_word2vec_basic.py @JingqingZ 2019/02/21 2019/03/19
      • refactored
      • tested

Others

  • tl.activation.py
    • refactored @JingqingZ 2019/03/06
    • tested @JingqingZ 2019/03/06
    • documentation @JingqingZ 2019/03/06
  • tl.cli
    • refactored no update needed @ChrisWu1997 2019/04/12
  • tl.decorators
    • refactored no update needed @ChrisWu1997 2019/04/12
  • tl.logging
    • refactored no update needed @ChrisWu1997 2019/04/12
  • tl.optimizers
    • refactored
  • tl.third_party
    • refactored
  • tl.array_ops
    • refactored no update needed @ChrisWu1997 2019/04/12
  • tl.cost
    • refactored @ChrisWu1997 2019/04/12
    • documentation @ChrisWu1997 2019/04/12
  • tl.db [WIP] @ChrisWu1997
    • refactored
  • tl.distributed
    • refactored
  • tl.initializers
    • refactored @ChrisWu1997 2019/04/12
    • tested @ChrisWu1997 2019/04/12
    • documentation @ChrisWu1997 2019/04/12
  • tl.iterate
    • refactored no update needed @ChrisWu1997 2019/04/12
  • tl.lazy_imports
    • refactored no update needed @ChrisWu1997 2019/04/12
  • tl.nlp @OliverZijia @JingqingZ
    • refactored
  • tl.package_info
    • refactored
  • tl.prepro
    • refactored @ChrisWu1997 2019/04/11
  • tl.rein
    • refactored
  • tl.utils
    • refactored @ChrisWu1997 2019/04/17
    • tested by tutorial_mnist_simple.py @ChrisWu1997 2019/04/17
    • documentation @ChrisWu1997 2019/04/17
  • tl.visualize
    • refactored no update needed @ChrisWu1997 2019/04/12

Unittests Status:

  • performance_test
    • VGG @JingqingZ @ChrisWu1997 @warshallrho 2019/03/20
  • layers
    • test_layernode.py @ChrisWu1997 2019/03/22
    • test_layers_activation.py @JingqingZ 2019/03/20
    • test_layers_convolution.py (1d, 2d, 3d) @warshallrho 2019/03/20
    • test_layers_core_basedense_dropout.py @JingqingZ 2019/03/06
    • test_layers_convolution_deformable.py @warshallrho 2019/03/18
    • test_layers_embedding.py @JingqingZ 2019/03/19
    • test_layers_extend.py @JingqingZ 2019/03/22
    • test_layers_lambda.py @JingqingZ 2019/03/24
    • test_layers_merge.py @JingqingZ 2019/03/15
    • test_layers_noise.py @warshallrho 2019/03/21
    • test_layers_padding.py @warshallrho 2019/03/21
    • test_layers_pooling.py @warshallrho 2019/03/18
    • test_layers_recurrent.py @JingqingZ 2019/03/06
    • test_layers_scale.py @JingqingZ 2019/03/22
    • test_layers_shape.py @JingqingZ 2019/03/22
  • test_activations.py @JingqingZ 2019/03/06
  • models
    • test_model_save_graph.py @warshallrho 2019/04/30

Unittests Status (Pending):

Some testing codes can be removed.

  • test_array_ops.py
  • test_decorators.py
  • test_documentation.py
  • test_layers_basic.py
  • test_layers_flow_control.py removed in favour of eager mode @zsdonghao 2018/12/04 (🀄️remember to change CN docs)
  • test_layers_importer.py
  • test_layers_normalization.py
  • test_layers_padding.py
  • test_layers_spatial_transformer.py
  • test_layers_stack.py
  • test_layers_super_resolution.py
  • test_layers_time_distributed.py
  • test_logging.py
  • test_logging_hyperdash.py
  • test_mnist_simple.py
  • test_model_compilednetwork.py
  • test_models.py
  • test_network_custom_2d.py
  • test_network_custom_input_layers.py
  • test_network_custom_multiple_inputs.py
  • test_network_custom_multiple_outputs.py
  • test_network_sequential_1d.py
  • test_network_sequential_2d.py
  • test_network_sequential_3d.py
  • test_network_sequential_rnn.py
  • test_optimizer_amsgrad.py
  • test_pydocstyle.py
  • test_reuse_mlp.py
  • test_tf_layers.py
  • test_timeout.py
  • test_utils_predict.py
  • test_yapf_format.py

tl.files

All save/load methods are also wrapped as class method in model core.

  • save_hdf5_graph
    • created @warshallrho 2019/04/27
    • tested @warshallrho 2019/04/27
    • documentation @warshallrho 2019/04/27
  • load_hdf5_graph
    • created @warshallrho 2019/04/27
    • tested @warshallrho 2019/04/27
    • documentation @warshallrho 2019/04/27
  • save_weights_to_hdf5
    • created
    • tested @ChrisWu1997 2019/03/26
    • documentation @ChrisWu1997 2019/03/26
  • load_hdf5_to_weights_in_order
    • created
    • tested @ChrisWu1997 2019/03/26
    • documentation @ChrisWu1997 2019/03/26
  • load_hdf5_to_weights
    • created
    • tested @ChrisWu1997 2019/03/26
    • documentation @ChrisWu1997 2019/03/26
  • save_npz([save_list, name, sess]) @ChrisWu1997 2019/02/21 --> save_npz([save_list, name]) @ChrisWu1997 2019/03/21
    • refactored
    • tested @ChrisWu1997 2019/03/26
    • documentation @ChrisWu1997 2019/03/26
  • load_npz([path, name]) @ChrisWu1997 2019/02/21
    • refactored
    • tested @ChrisWu1997 2019/03/26
    • documentation @ChrisWu1997 2019/03/26
  • assign_params(sess, params, network) --> assign_weights (🀄️remember to change CN docs) @ChrisWu1997 2019/02/22
    • refactored
    • tested
  • load_and_assign_npz([sess, name, network]) @ChrisWu1997 2019/02/21 --> load_and_assign_npz([name, network]) @ChrisWu1997 2019/03/21
    • refactored
    • tested @ChrisWu1997 2019/03/26
    • documentation @ChrisWu1997 2019/03/26
  • save_npz_dict([save_list, name, sess]) @ChrisWu1997 2019/02/22 --> save_npz_dict([save_list, name]) @ChrisWu1997 2019/03/21
    • refactored
    • tested @ChrisWu1997 2019/03/26
    • documentation @ChrisWu1997 2019/03/26
  • load_and_assign_npz_dict([name, sess]) --> ([name, network]) @ChrisWu1997 2019/03/21
    • refactored
    • tested @ChrisWu1997 2019/03/26
    • documentation @ChrisWu1997 2019/03/26