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-`tl.alphas` and `tl.alphas_like` added following the tf.ones/zeros and tf.zeros_like/ones_like (by @DEKHTIARJonathan in #580)
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- CI Tool:
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-[Stale Probot](https://github.com/probot/stale) added to clean stale issues (by @DEKHTIARJonathan in #573)
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-[Changelog Probot](https://github.com/mikz/probot-changelog) Configuration added (by @DEKHTIARJonathan in #637)
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- Travis Builds now handling a matrix of TF Version from TF==1.6.0 to TF==1.8.0 (by @DEKHTIARJonathan in #644)
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- CircleCI added to build and upload Docker Containers for each PR merged and tag release (by @DEKHTIARJonathan in #648)
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- Decorator:
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-`tl.decorators` API created including `deprecated_alias` and `private_method` (by @DEKHTIARJonathan in #660)
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- Docker:
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- Containers for each release and for each PR merged on master built (by @DEKHTIARJonathan in #648)
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- Containers built in the following configurations (by @DEKHTIARJonathan in #648):
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- py2 + cpu
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- py2 + gpu
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- py3 + cpu
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- py3 + gpu
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- Documentation:
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- Release semantic version added on index page (by @DEKHTIARJonathan in #633)
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- Optimizers page added (by @DEKHTIARJonathan in #636)
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-`AMSGrad` added on Optimizers page added (by @DEKHTIARJonathan in #636)
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- Layer:
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- ElementwiseLambdaLayer added to use custom function to connect multiple layer inputs (by @One-sixth in #579)
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- Optimizer:
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- AMSGrad Optimizer added based on `On the Convergence of Adam and Beyond (ICLR 2018)` (by @DEKHTIARJonathan in #636)
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- Setup:
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- Creation of installation flaggs `all`, `all_cpu`, and `all_gpu` (by @DEKHTIARJonathan in #660)
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- Test:
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-`test_utils_predict.py` added to reproduce and fix issue #288 (by @2wins in #566)
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-`Layer_DeformableConvolution_Test` added to reproduce issue #572 with deformable convolution (by @DEKHTIARJonathan in #573)
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-`Array_Op_Alphas_Test` and `Array_Op_Alphas_Like_Test` added to test `tensorlayer/array_ops.py` file (by @DEKHTIARJonathan in #580)
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-`test_optimizer_amsgrad.py` added to test `AMSGrad` optimizer (by @DEKHTIARJonathan in #636)
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-`test_logging.py` added to insure robustness of the logging API (by @DEKHTIARJonathan in #645)
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-`test_decorators.py` added (by @DEKHTIARJonathan in #660)
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- Tutorials:
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-`tutorial_tfslim` has been introduced to show how to use `SlimNetsLayer` (by @2wins in #560).
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### Changed
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- Tensorflow CPU & GPU dependencies moved to separated requirement files in order to allow PyUP.io to parse them (by @DEKHTIARJonathan in #573)
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- The document of LambdaLayer for linking it with ElementwiseLambdaLayer (by @zsdonghao in #587)
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- RTD links point to stable documentation instead of latest used for development (by @DEKHTIARJonathan in #633)
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- TF Version older than 1.6.0 are officially unsupported and raises an exception (by @DEKHTIARJonathan in #644)
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- Readme Badges Updated with Support Python and Tensorflow Versions (by @DEKHTIARJonathan in #644)
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- TL logging API has been consistent with TF logging API and thread-safe (by @DEKHTIARJonathan in #645)
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- Relative Imports changed for absolute imports (by @DEKHTIARJonathan in #657)
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-`tl.files` refactored into a directory with numerous files (by @DEKHTIARJonathan in #657)
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-`tl.files.voc_dataset` fixed because of original Pascal VOC website was down (by @DEKHTIARJonathan in #657)
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- extra requirements hidden inside the library added in the project requirements (by @DEKHTIARJonathan in #657)
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- requirements files refactored in `requirements/` directory (by @DEKHTIARJonathan in #657)
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- README.md and other markdown files have been refactored and cleaned. (by @zsdonghao@DEKHTIARJonathan@luomai in #639)
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- Ternary Convolution Layer added in unittest (by @DEKHTIARJonathan in #658)
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- Convolution Layers unittests have been cleaned & refactored (by @DEKHTIARJonathan in #658)
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- All the tests are now using a DEBUG level verbosity when run individualy (by @DEKHTIARJonathan in #660)
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### Fixed
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- Issue #498 - Deprecation Warning Fix in `tl.layers.RNNLayer` with `inspect` (by @DEKHTIARJonathan in #574)
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- Issue #498 - Deprecation Warning Fix in `tl.files` with truth value of an empty array is ambiguous (by @DEKHTIARJonathan in #575)
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- Issue #565 related to `tl.utils.predict` fixed - `np.hstack` problem in which the results for multiple batches are stacked along `axis=1` (by @2wins in #566)
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- Issue #572 with `tl.layers.DeformableConv2d` fixed (by @DEKHTIARJonathan in #573)
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- Typo of the document of ElementwiseLambdaLayer (by @zsdonghao in #588)
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- Error in `tl.layers.TernaryConv2d` fixed - self.inputs not defined (by @DEKHTIARJonathan in #658)
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- Deprecation warning fixed in `tl.layers.binary._compute_threshold()` (by @DEKHTIARJonathan in #658)
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- All references to `tf.logging` replaced by `tl.logging` (by @DEKHTIARJonathan in #661)
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- Tutorial:
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-`tutorial_word2vec_basic.py` saving issue #476 fixed (by @DEKHTIARJonathan in #635)
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- All tutorials tested and errors have been fixed (by @DEKHTIARJonathan in #635)
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### Dependencies Update
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- Update pytest from 3.5.1 to 3.6.0 (by @DEKHTIARJonathan and @pyup-bot in #647)
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- Update progressbar2 from 3.37.1 to 3.38.0 (by @DEKHTIARJonathan and @pyup-bot in #651)
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- Update scikit-image from 0.13.1 to 0.14.0 (by @DEKHTIARJonathan and @pyup-bot in #656)
TensorLayer is a deep learning and reinforcement learning library on top of [TensorFlow](https://www.tensorflow.org). It provides rich neural layers and utility functions to help researchers and engineers build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the prestigious [ACM Multimedia Society](http://www.acmmm.org/2017/mm-2017-awardees/).
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@@ -42,7 +43,7 @@ As TensorFlow users, we have been looking for a library that can serve for vario
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phases. This library is easy for beginners by providing rich neural network implementations,
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examples and tutorials. Later, its APIs shall naturally allow users to leverage the powerful
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features of TensorFlow, exhibiting best performance in addressing real-world problems. In the
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end, the extra abstraction shall not compromise TensorFlow performance, and thus suit for
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end, the extra abstraction shall not compromise TensorFlow performance, and thus suit for
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production deployment. TensorLayer is a novel library that aims to satisfy these requirements.
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It has three key features:
@@ -66,15 +67,15 @@ on a Titan X Pascal GPU.
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Similar to TensorLayer, Keras and TFLearn are also popular TensorFlow wrapper libraries.
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These libraries are comfortable to start with. They provide high-level abstractions;
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but mask the underlying engine from users. It is thus hard to customize model behaviors
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and touch the essential features of TensorFlow.
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but mask the underlying engine from users. It is thus hard to customize model behaviors
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and touch the essential features of TensorFlow.
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Without compromise in simplicity, TensorLayer APIs are generally more flexible and transparent.
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Users often find it easy to start with the examples and tutorials of TensorLayer, and then dive
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into the TensorFlow low-level APIs only if need. TensorLayer does not create library lock-in. Users can easily import models from Keras, TFSlim and TFLearn into a TensorLayer environment.
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TensorLayer has a fast growing usage in academic and industry organizations. It is used by researchers from
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Imperial College London, Carnegie Mellon University, Stanford University,
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Imperial College London, Carnegie Mellon University, Stanford University,
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University of Technology of Compiegne (UTC), Tsinghua University, UCLA,
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and etc., as well as engineers from Google, Microsoft, Alibaba, Tencent, Xiaomi, Penguins Innovate, Bloomberg and many others.
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