This project seeks to understand - and visualize - Americans' views of climate change as seen through the lens of twitter. As such, this package contains all the resources that were used as we explored different means to classify climate change tweets.
Release notes
*Sourced from [tensorflow's releases](https://github.com/tensorflow/tensorflow/releases).*
> ## TensorFlow 2.0.1
> # Release 2.0.1
>
> ## Bug Fixes and Other Changes
> * Fixes a security vulnerability where converting a Python string to a `tf.float16` value produces a segmentation fault ([CVE-2020-5215](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-5215))
> * Updates `curl` to `7.66.0` to handle [CVE-2019-5482](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5482) and [CVE-2019-5481](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5481)
> * Updates `sqlite3` to `3.30.01` to handle [CVE-2019-19646](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19646), [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645) and [CVE-2019-16168](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-16168)
Changelog
*Sourced from [tensorflow's changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md).*
> # Release 2.0.1
>
> ## Bug Fixes and Other Changes
> * Fixes a security vulnerability where converting a Python string to a `tf.float16` value produces a segmentation fault ([CVE-2020-5215](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-5215))
> * Updates `curl` to `7.66.0` to handle [CVE-2019-5482](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5482) and [CVE-2019-5481](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5481)
> * Updates `sqlite3` to `3.30.01` to handle [CVE-2019-19646](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19646), [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645) and [CVE-2019-16168](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-16168)
>
>
> # Release 1.15.2
>
> ## Bug Fixes and Other Changes
> * Fixes a security vulnerability where converting a Python string to a `tf.float16` value produces a segmentation fault ([CVE-2020-5215](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-5215))
> * Updates `curl` to `7.66.0` to handle [CVE-2019-5482](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5482) and [CVE-2019-5481](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5481)
> * Updates `sqlite3` to `3.30.01` to handle [CVE-2019-19646](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19646), [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645) and [CVE-2019-16168](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-16168)
>
>
> # Release 2.1.0
>
> TensorFlow 2.1 will be the last TF release supporting Python 2. Python 2 support [officially ends an January 1, 2020](https://www.python.org/dev/peps/pep-0373/#update). [As announced earlier](https://groups.google.com/a/tensorflow.org/d/msg/announce/gVwS5RC8mds/dCt1ka2XAAAJ), TensorFlow will also stop supporting Python 2 starting January 1, 2020, and no more releases are expected in 2019.
>
> ## Major Features and Improvements
> * The `tensorflow` pip package now includes GPU support by default (same as `tensorflow-gpu`) for both Linux and Windows. This runs on machines with and without NVIDIA GPUs. `tensorflow-gpu` is still available, and CPU-only packages can be downloaded at `tensorflow-cpu` for users who are concerned about package size.
> * **Windows users:** Officially-released `tensorflow` Pip packages are now built with Visual Studio 2019 version 16.4 in order to take advantage of the new `/d2ReducedOptimizeHugeFunctions` compiler flag. To use these new packages, you must install "Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019", available from Microsoft's website [here](https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads).
> * This does not change the minimum required version for building TensorFlow from source on Windows, but builds enabling `EIGEN_STRONG_INLINE` can take over 48 hours to compile without this flag. Refer to `configure.py` for more information about `EIGEN_STRONG_INLINE` and `/d2ReducedOptimizeHugeFunctions`.
> * If either of the required DLLs, `msvcp140.dll` (old) or `msvcp140_1.dll` (new), are missing on your machine, `import tensorflow` will print a warning message.
> * The `tensorflow` pip package is built with CUDA 10.1 and cuDNN 7.6.
> * `tf.keras`
> * Experimental support for mixed precision is available on GPUs and Cloud TPUs. See [usage guide](https://www.tensorflow.org/guide/keras/mixed_precision).
> * Introduced the `TextVectorization` layer, which takes as input raw strings and takes care of text standardization, tokenization, n-gram generation, and vocabulary indexing. See this [end-to-end text classification example](https://colab.research.google.com/drive/1RvCnR7h0_l4Ekn5vINWToI9TNJdpUZB3).
> * Keras `.compile` `.fit` `.evaluate` and `.predict` are allowed to be outside of the DistributionStrategy scope, as long as the model was constructed inside of a scope.
> * Experimental support for Keras `.compile`, `.fit`, `.evaluate`, and `.predict` is available for Cloud TPUs, Cloud TPU, for all types of Keras models (sequential, functional and subclassing models).
> * Automatic outside compilation is now enabled for Cloud TPUs. This allows `tf.summary` to be used more conveniently with Cloud TPUs.
> * Dynamic batch sizes with DistributionStrategy and Keras are supported on Cloud TPUs.
> * Support for `.fit`, `.evaluate`, `.predict` on TPU using numpy data, in addition to `tf.data.Dataset`.
> * Keras reference implementations for many popular models are available in the TensorFlow [Model Garden](https://github.com/tensorflow/models/tree/master/official).
> * `tf.data`
> * Changes rebatching for `tf.data datasets` + DistributionStrategy for better performance. Note that the dataset also behaves slightly differently, in that the rebatched dataset cardinality will always be a multiple of the number of replicas.
> * `tf.data.Dataset` now supports automatic data distribution and sharding in distributed environments, including on TPU pods.
> * Distribution policies for `tf.data.Dataset` can now be tuned with 1. `tf.data.experimental.AutoShardPolicy(OFF, AUTO, FILE, DATA)` 2. `tf.data.experimental.ExternalStatePolicy(WARN, IGNORE, FAIL)`
> * `tf.debugging`
> * Add `tf.debugging.enable_check_numerics()` and `tf.debugging.disable_check_numerics()` to help debugging the root causes of issues involving infinities and `NaN`s.
> * `tf.distribute`
> * Custom training loop support on TPUs and TPU pods is avaiable through `strategy.experimental_distribute_dataset`, `strategy.experimental_distribute_datasets_from_function`, `strategy.experimental_run_v2`, `strategy.reduce`.
> * Support for a global distribution strategy through `tf.distribute.experimental_set_strategy(),` in addition to `strategy.scope()`.
> * `TensorRT`
> * [TensorRT 6.0](https://developer.nvidia.com/tensorrt#tensorrt-whats-new) is now supported and enabled by default. This adds support for more TensorFlow ops including Conv3D, Conv3DBackpropInputV2, AvgPool3D, MaxPool3D, ResizeBilinear, and ResizeNearestNeighbor. In addition, the TensorFlow-TensorRT python conversion API is exported as `tf.experimental.tensorrt.Converter`.
> * Environment variable `TF_DETERMINISTIC_OPS` has been added. When set to "true" or "1", this environment variable makes `tf.nn.bias_add` operate deterministically (i.e. reproducibly), but currently only when XLA JIT compilation is *not* enabled. Setting `TF_DETERMINISTIC_OPS` to "true" or "1" also makes cuDNN convolution and max-pooling operate deterministically. This makes Keras Conv\*D and MaxPool\*D layers operate deterministically in both the forward and backward directions when running on a CUDA-enabled GPU.
>
> ## Breaking Changes
> * Deletes `Operation.traceback_with_start_lines` for which we know of no usages.
> ... (truncated)
Commits
- [`765ac8d`](https://github.com/tensorflow/tensorflow/commit/765ac8d16eff6d6ff997ee73809b402d8b1194ae) Merge pull request [#35913](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/35913) from tensorflow-jenkins/relnotes-2.0.1-6767
- [`0bcb99b`](https://github.com/tensorflow/tensorflow/commit/0bcb99b37577332ba7ee3f7dd06a4ac4801d3ec2) Add CVE number for main patch
- [`a093c7e`](https://github.com/tensorflow/tensorflow/commit/a093c7ebd4377560d8b544cd30449dcceb572091) Merge pull request [#36085](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/36085) from tensorflow/mm-r2.0-fix-release-builds-pt4
- [`63aedd7`](https://github.com/tensorflow/tensorflow/commit/63aedd7d84370f31d549d8b508a52a7a79db49b8) Disable test that times out on mac non pip builds
- [`619c578`](https://github.com/tensorflow/tensorflow/commit/619c5785813d87e785423a9767218e767e1fd516) Disable the gpu on cpu tests as they were added for 2.1
- [`1a617d6`](https://github.com/tensorflow/tensorflow/commit/1a617d66fe5c55be353a1c99b1458e3ead244efb) Merge pull request [#36047](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/36047) from tensorflow/mm-r2.0-fix-release-builds-pt3
- [`32d9138`](https://github.com/tensorflow/tensorflow/commit/32d9138b2ea3f6e4aca070b3933ec03f677eb1ed) Cleanup the windows builds
- [`dd1ebd7`](https://github.com/tensorflow/tensorflow/commit/dd1ebd75426f3b6f378975e71665892cf68d495b) Cleanup macos builds
- [`3b93059`](https://github.com/tensorflow/tensorflow/commit/3b9305981c825ce456e7ec1154f5f6724280caca) Remove py2 macos scripts
- [`606596f`](https://github.com/tensorflow/tensorflow/commit/606596f080495601a16570012449c6467a91f2aa) Remove builds which are not needed for the release
- Additional commits viewable in [compare view](https://github.com/tensorflow/tensorflow/compare/v2.0.0...v2.0.1)
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commented
4 years ago
Bumps tensorflow from 2.0.0 to 2.0.1.
Release notes
*Sourced from [tensorflow's releases](https://github.com/tensorflow/tensorflow/releases).* > ## TensorFlow 2.0.1 > # Release 2.0.1 > > ## Bug Fixes and Other Changes > * Fixes a security vulnerability where converting a Python string to a `tf.float16` value produces a segmentation fault ([CVE-2020-5215](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-5215)) > * Updates `curl` to `7.66.0` to handle [CVE-2019-5482](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5482) and [CVE-2019-5481](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5481) > * Updates `sqlite3` to `3.30.01` to handle [CVE-2019-19646](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19646), [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645) and [CVE-2019-16168](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-16168)Changelog
*Sourced from [tensorflow's changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md).* > # Release 2.0.1 > > ## Bug Fixes and Other Changes > * Fixes a security vulnerability where converting a Python string to a `tf.float16` value produces a segmentation fault ([CVE-2020-5215](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-5215)) > * Updates `curl` to `7.66.0` to handle [CVE-2019-5482](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5482) and [CVE-2019-5481](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5481) > * Updates `sqlite3` to `3.30.01` to handle [CVE-2019-19646](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19646), [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645) and [CVE-2019-16168](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-16168) > > > # Release 1.15.2 > > ## Bug Fixes and Other Changes > * Fixes a security vulnerability where converting a Python string to a `tf.float16` value produces a segmentation fault ([CVE-2020-5215](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-5215)) > * Updates `curl` to `7.66.0` to handle [CVE-2019-5482](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5482) and [CVE-2019-5481](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5481) > * Updates `sqlite3` to `3.30.01` to handle [CVE-2019-19646](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19646), [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645) and [CVE-2019-16168](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-16168) > > > # Release 2.1.0 > > TensorFlow 2.1 will be the last TF release supporting Python 2. Python 2 support [officially ends an January 1, 2020](https://www.python.org/dev/peps/pep-0373/#update). [As announced earlier](https://groups.google.com/a/tensorflow.org/d/msg/announce/gVwS5RC8mds/dCt1ka2XAAAJ), TensorFlow will also stop supporting Python 2 starting January 1, 2020, and no more releases are expected in 2019. > > ## Major Features and Improvements > * The `tensorflow` pip package now includes GPU support by default (same as `tensorflow-gpu`) for both Linux and Windows. This runs on machines with and without NVIDIA GPUs. `tensorflow-gpu` is still available, and CPU-only packages can be downloaded at `tensorflow-cpu` for users who are concerned about package size. > * **Windows users:** Officially-released `tensorflow` Pip packages are now built with Visual Studio 2019 version 16.4 in order to take advantage of the new `/d2ReducedOptimizeHugeFunctions` compiler flag. To use these new packages, you must install "Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019", available from Microsoft's website [here](https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads). > * This does not change the minimum required version for building TensorFlow from source on Windows, but builds enabling `EIGEN_STRONG_INLINE` can take over 48 hours to compile without this flag. Refer to `configure.py` for more information about `EIGEN_STRONG_INLINE` and `/d2ReducedOptimizeHugeFunctions`. > * If either of the required DLLs, `msvcp140.dll` (old) or `msvcp140_1.dll` (new), are missing on your machine, `import tensorflow` will print a warning message. > * The `tensorflow` pip package is built with CUDA 10.1 and cuDNN 7.6. > * `tf.keras` > * Experimental support for mixed precision is available on GPUs and Cloud TPUs. See [usage guide](https://www.tensorflow.org/guide/keras/mixed_precision). > * Introduced the `TextVectorization` layer, which takes as input raw strings and takes care of text standardization, tokenization, n-gram generation, and vocabulary indexing. See this [end-to-end text classification example](https://colab.research.google.com/drive/1RvCnR7h0_l4Ekn5vINWToI9TNJdpUZB3). > * Keras `.compile` `.fit` `.evaluate` and `.predict` are allowed to be outside of the DistributionStrategy scope, as long as the model was constructed inside of a scope. > * Experimental support for Keras `.compile`, `.fit`, `.evaluate`, and `.predict` is available for Cloud TPUs, Cloud TPU, for all types of Keras models (sequential, functional and subclassing models). > * Automatic outside compilation is now enabled for Cloud TPUs. This allows `tf.summary` to be used more conveniently with Cloud TPUs. > * Dynamic batch sizes with DistributionStrategy and Keras are supported on Cloud TPUs. > * Support for `.fit`, `.evaluate`, `.predict` on TPU using numpy data, in addition to `tf.data.Dataset`. > * Keras reference implementations for many popular models are available in the TensorFlow [Model Garden](https://github.com/tensorflow/models/tree/master/official). > * `tf.data` > * Changes rebatching for `tf.data datasets` + DistributionStrategy for better performance. Note that the dataset also behaves slightly differently, in that the rebatched dataset cardinality will always be a multiple of the number of replicas. > * `tf.data.Dataset` now supports automatic data distribution and sharding in distributed environments, including on TPU pods. > * Distribution policies for `tf.data.Dataset` can now be tuned with 1. `tf.data.experimental.AutoShardPolicy(OFF, AUTO, FILE, DATA)` 2. `tf.data.experimental.ExternalStatePolicy(WARN, IGNORE, FAIL)` > * `tf.debugging` > * Add `tf.debugging.enable_check_numerics()` and `tf.debugging.disable_check_numerics()` to help debugging the root causes of issues involving infinities and `NaN`s. > * `tf.distribute` > * Custom training loop support on TPUs and TPU pods is avaiable through `strategy.experimental_distribute_dataset`, `strategy.experimental_distribute_datasets_from_function`, `strategy.experimental_run_v2`, `strategy.reduce`. > * Support for a global distribution strategy through `tf.distribute.experimental_set_strategy(),` in addition to `strategy.scope()`. > * `TensorRT` > * [TensorRT 6.0](https://developer.nvidia.com/tensorrt#tensorrt-whats-new) is now supported and enabled by default. This adds support for more TensorFlow ops including Conv3D, Conv3DBackpropInputV2, AvgPool3D, MaxPool3D, ResizeBilinear, and ResizeNearestNeighbor. In addition, the TensorFlow-TensorRT python conversion API is exported as `tf.experimental.tensorrt.Converter`. > * Environment variable `TF_DETERMINISTIC_OPS` has been added. When set to "true" or "1", this environment variable makes `tf.nn.bias_add` operate deterministically (i.e. reproducibly), but currently only when XLA JIT compilation is *not* enabled. Setting `TF_DETERMINISTIC_OPS` to "true" or "1" also makes cuDNN convolution and max-pooling operate deterministically. This makes Keras Conv\*D and MaxPool\*D layers operate deterministically in both the forward and backward directions when running on a CUDA-enabled GPU. > > ## Breaking Changes > * Deletes `Operation.traceback_with_start_lines` for which we know of no usages. > ... (truncated)Commits
- [`765ac8d`](https://github.com/tensorflow/tensorflow/commit/765ac8d16eff6d6ff997ee73809b402d8b1194ae) Merge pull request [#35913](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/35913) from tensorflow-jenkins/relnotes-2.0.1-6767 - [`0bcb99b`](https://github.com/tensorflow/tensorflow/commit/0bcb99b37577332ba7ee3f7dd06a4ac4801d3ec2) Add CVE number for main patch - [`a093c7e`](https://github.com/tensorflow/tensorflow/commit/a093c7ebd4377560d8b544cd30449dcceb572091) Merge pull request [#36085](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/36085) from tensorflow/mm-r2.0-fix-release-builds-pt4 - [`63aedd7`](https://github.com/tensorflow/tensorflow/commit/63aedd7d84370f31d549d8b508a52a7a79db49b8) Disable test that times out on mac non pip builds - [`619c578`](https://github.com/tensorflow/tensorflow/commit/619c5785813d87e785423a9767218e767e1fd516) Disable the gpu on cpu tests as they were added for 2.1 - [`1a617d6`](https://github.com/tensorflow/tensorflow/commit/1a617d66fe5c55be353a1c99b1458e3ead244efb) Merge pull request [#36047](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/36047) from tensorflow/mm-r2.0-fix-release-builds-pt3 - [`32d9138`](https://github.com/tensorflow/tensorflow/commit/32d9138b2ea3f6e4aca070b3933ec03f677eb1ed) Cleanup the windows builds - [`dd1ebd7`](https://github.com/tensorflow/tensorflow/commit/dd1ebd75426f3b6f378975e71665892cf68d495b) Cleanup macos builds - [`3b93059`](https://github.com/tensorflow/tensorflow/commit/3b9305981c825ce456e7ec1154f5f6724280caca) Remove py2 macos scripts - [`606596f`](https://github.com/tensorflow/tensorflow/commit/606596f080495601a16570012449c6467a91f2aa) Remove builds which are not needed for the release - Additional commits viewable in [compare view](https://github.com/tensorflow/tensorflow/compare/v2.0.0...v2.0.1)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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