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AlexLewandowski/Awesome-CV 1

Modified Awesome CV LaTeX template with two column styles

AlexLewandowski/abseil-py 0

Abseil Common Libraries (Python)

AlexLewandowski/ArcadeLearningEnvironment.jl 0

ArcadeLearningEnvironment Julia interface

AlexLewandowski/batch-dkl 0

Paper and code for "Batch Normalized Deep Kernel Learning for Weight Uncertainty" at the Bayesian deep learning workshop at NIPS2017

AlexLewandowski/completing-tensors-ibp 0

Paper and code for Introduction to Machine Learning (CMPUT 551) at the University of Alberta

AlexLewandowski/CycleGAN-tensorflow 0

Tensorflow implementation for learning an image-to-image translation without input-output pairs. https://arxiv.org/pdf/1703.10593.pdf

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An implementation of DiscoGAN in tensorflow

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An Emacs framework for the stubborn martian hacker

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A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference. Runs on TensorFlow.

release ray-project/ray

ray-1.4.0

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delete branch dchui1/659-project

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PR closed dchui1/659-project

Bump tensorflow from 1.12.0 to 2.3.1 dependencies

Bumps tensorflow from 1.12.0 to 2.3.1. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/tensorflow/tensorflow/releases">tensorflow's releases</a>.</em></p> <blockquote> <h2>TensorFlow 2.3.1</h2> <h1>Release 2.3.1</h1> <h2>Bug Fixes and Other Changes</h2> <ul> <li>Fixes an undefined behavior causing a segfault in <code>tf.raw_ops.Switch</code> (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15190">CVE-2020-15190</a>)</li> <li>Fixes three vulnerabilities in conversion to DLPack format (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15191">CVE-2020-15191</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15192">CVE-2020-15192</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15193">CVE-2020-15193</a>)</li> <li>Fixes two vulnerabilities in <code>SparseFillEmptyRowsGrad</code> (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15194">CVE-2020-15194</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15195">CVE-2020-15195</a>)</li> <li>Fixes several vulnerabilities in <code>RaggedCountSparseOutput</code> and <code>SparseCountSparseOutput</code> operations (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15196">CVE-2020-15196</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15197">CVE-2020-15197</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15198">CVE-2020-15198</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15199">CVE-2020-15199</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15200">CVE-2020-15200</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15201">CVE-2020-15201</a>)</li> <li>Fixes an integer truncation vulnerability in code using the work sharder API (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15202">CVE-2020-15202</a>)</li> <li>Fixes a format string vulnerability in <code>tf.strings.as_string</code> (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15203">CVE-2020-15203</a>)</li> <li>Fixes segfault raised by calling session-only ops in eager mode (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15204">CVE-2020-15204</a>)</li> <li>Fixes data leak and potential ASLR violation from <code>tf.raw_ops.StringNGrams</code> (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15205">CVE-2020-15205</a>)</li> <li>Fixes segfaults caused by incomplete <code>SavedModel</code> validation (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15206">CVE-2020-15206</a>)</li> <li>Fixes a data corruption due to a bug in negative indexing support in TFLite (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15207">CVE-2020-15207</a>)</li> <li>Fixes a data corruption due to dimension mismatch in TFLite (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15208">CVE-2020-15208</a>)</li> <li>Fixes several vulnerabilities in TFLite saved model format (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15209">CVE-2020-15209</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15210">CVE-2020-15210</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15211">CVE-2020-15211</a>)</li> <li>Fixes several vulnerabilities in TFLite implementation of segment sum (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15212">CVE-2020-15212</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15213">CVE-2020-15213</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15214">CVE-2020-15214</a>)</li> <li>Updates <code>sqlite3</code> to <code>3.33.00</code> to handle <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15358">CVE-2020-15358</a>.</li> <li>Fixes deprecated usage of <code>collections</code> API</li> <li>Removes <code>scipy</code> dependency from <code>setup.py</code> since TensorFlow does not need it to install the pip package</li> </ul> <h2>TensorFlow 2.3.0</h2> <h1>Release 2.3.0</h1> <h2>Major Features and Improvements</h2> <ul> <li><code>tf.data</code> adds two new mechanisms to solve input pipeline bottlenecks and save resources: <ul> <li><a href="https://www.tensorflow.org/api_docs/python/tf/data/experimental/snapshot">snapshot</a></li> <li><a href="https://www.tensorflow.org/api_docs/python/tf/data/experimental/service">tf.data service</a>.</li> </ul> </li> </ul> <p>In addition checkout the detailed <a href="https://www.tensorflow.org/guide/data_performance_analysis">guide</a> for analyzing input pipeline performance with TF Profiler.</p> <ul> <li> <p><a href="https://www.tensorflow.org/api_docs/python/tf/distribute/TPUStrategy"><code>tf.distribute.TPUStrategy</code></a> is now a stable API and no longer considered experimental for TensorFlow. (earlier <code>tf.distribute.experimental.TPUStrategy</code>).</p> </li> <li> <p><a href="https://www.tensorflow.org/guide/profiler">TF Profiler</a> introduces two new tools: a memory profiler to visualize your model’s memory usage over time and a <a href="https://www.tensorflow.org/guide/profiler#events">python tracer</a> which allows you to trace python function calls in your model. Usability improvements include better diagnostic messages and <a href="https://tensorflow.org/guide/profiler#collect_performance_data">profile options</a> to customize the host and device trace verbosity level.</p> </li> <li> <p>Introduces experimental support for Keras Preprocessing Layers API (<a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing?version=nightly"><code>tf.keras.layers.experimental.preprocessing.*</code></a>) to handle data preprocessing operations, with support for composite tensor inputs. Please see below for additional details on these layers.</p> </li> <li> <p>TFLite now properly supports dynamic shapes during conversion and inference. We’ve also added opt-in support on Android and iOS for <a href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/delegates/xnnpack">XNNPACK</a>, a highly optimized set of CPU kernels, as well as opt-in support for <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/performance/gpu_advanced.md#running-quantized-models-experimental">executing quantized models on the GPU</a>.</p> </li> <li> <p>Libtensorflow packages are available in GCS starting this release. We have also started to <a href="https://github.com/tensorflow/tensorflow#official-builds">release a nightly version of these packages</a>.</p> </li> <li> <p>The experimental Python API <a href="https://www.tensorflow.org/api_docs/python/tf/debugging/experimental/enable_dump_debug_info"><code>tf.debugging.experimental.enable_dump_debug_info()</code></a> now allows you to instrument a TensorFlow program and dump debugging information to a directory on the file system. The directory can be read and visualized by a new interactive dashboard in TensorBoard 2.3 called <a href="https://www.tensorflow.org/tensorboard/debugger_v2">Debugger V2</a>, which reveals the details of the TensorFlow program including graph structures, history of op executions at the Python (eager) and intra-graph levels, the runtime dtype, shape, and numerical composistion of tensors, as well as their code locations.</p> </li> </ul> <h2>Breaking Changes</h2> <ul> <li>Increases the <strong>minimum bazel version</strong> required to build TF to <strong>3.1.0</strong>.</li> <li><code>tf.data</code> <ul> <li>Makes the following (breaking) changes to the <code>tf.data</code>.</li> <li>C++ API: - <code>IteratorBase::RestoreInternal</code>, <code>IteratorBase::SaveInternal</code>, and <code>DatasetBase::CheckExternalState</code> become pure-virtual and subclasses are now expected to provide an implementation.</li> <li>The deprecated <code>DatasetBase::IsStateful</code> method is removed in favor of <code>DatasetBase::CheckExternalState</code>.</li> <li>Deprecated overrides of <code>DatasetBase::MakeIterator</code> and <code>MakeIteratorFromInputElement</code> are removed.</li> </ul> </li> </ul> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Changelog</summary> <p><em>Sourced from <a href="https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md">tensorflow's changelog</a>.</em></p> <blockquote> <h1>Release 2.3.1</h1> <h2>Bug Fixes and Other Changes</h2> <ul> <li>Fixes an undefined behavior causing a segfault in <code>tf.raw_ops.Switch</code> (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15190">CVE-2020-15190</a>)</li> <li>Fixes three vulnerabilities in conversion to DLPack format (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15191">CVE-2020-15191</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15192">CVE-2020-15192</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15193">CVE-2020-15193</a>)</li> <li>Fixes two vulnerabilities in <code>SparseFillEmptyRowsGrad</code> (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15194">CVE-2020-15194</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15195">CVE-2020-15195</a>)</li> <li>Fixes several vulnerabilities in <code>RaggedCountSparseOutput</code> and <code>SparseCountSparseOutput</code> operations (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15196">CVE-2020-15196</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15197">CVE-2020-15197</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15198">CVE-2020-15198</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15199">CVE-2020-15199</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15200">CVE-2020-15200</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15201">CVE-2020-15201</a>)</li> <li>Fixes an integer truncation vulnerability in code using the work sharder API (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15202">CVE-2020-15202</a>)</li> <li>Fixes a format string vulnerability in <code>tf.strings.as_string</code> (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15203">CVE-2020-15203</a>)</li> <li>Fixes segfault raised by calling session-only ops in eager mode (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15204">CVE-2020-15204</a>)</li> <li>Fixes data leak and potential ASLR violation from <code>tf.raw_ops.StringNGrams</code> (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15205">CVE-2020-15205</a>)</li> <li>Fixes segfaults caused by incomplete <code>SavedModel</code> validation (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15206">CVE-2020-15206</a>)</li> <li>Fixes a data corruption due to a bug in negative indexing support in TFLite (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15207">CVE-2020-15207</a>)</li> <li>Fixes a data corruption due to dimension mismatch in TFLite (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15208">CVE-2020-15208</a>)</li> <li>Fixes several vulnerabilities in TFLite saved model format (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15209">CVE-2020-15209</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15210">CVE-2020-15210</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15211">CVE-2020-15211</a>)</li> <li>Fixes several vulnerabilities in TFLite implementation of segment sum (<a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15212">CVE-2020-15212</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15213">CVE-2020-15213</a>, <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15214">CVE-2020-15214</a>)</li> <li>Updates <code>sqlite3</code> to <code>3.33.00</code> to handle <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15358">CVE-2020-15358</a>.</li> <li>Fixes deprecated usage of <code>collections</code> API</li> <li>Removes <code>scipy</code> dependency from <code>setup.py</code> since TensorFlow does not need it to install the pip package</li> </ul> <h1>Release 2.2.1</h1> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/tensorflow/tensorflow/commit/fcc4b966f1265f466e82617020af93670141b009"><code>fcc4b96</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/43446">#43446</a> from tensorflow-jenkins/version-numbers-2.3.1-16251</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/4cf223069a94c78b208e6c829d5f938a0fae7d07"><code>4cf2230</code></a> Update version numbers to 2.3.1</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/eee82247288e52e9b8a5c2badeb65f871b4da4c4"><code>eee8224</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/43441">#43441</a> from tensorflow-jenkins/relnotes-2.3.1-24672</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/0d41b1dfc97500e1177cb718a0b14b04914df661"><code>0d41b1d</code></a> Update RELEASE.md</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/d99bd631ea9b67ffc39c22b35fbf7deca77ad1f7"><code>d99bd63</code></a> Insert release notes place-fill</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/d71d3ce2520587b752e5d27b2d4a4ba8720e4bd5"><code>d71d3ce</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/43414">#43414</a> from tensorflow/mihaimaruseac-patch-1-1</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/9c91596d4d24bc07b6d36ae48581a2e7b2584edf"><code>9c91596</code></a> Fix missing import</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/f9f12f61867159120ce6eb08fdbd225d454232b5"><code>f9f12f6</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/43391">#43391</a> from tensorflow/mihaimaruseac-patch-4</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/3ed271b0b05b4f1dfd5660944c54b5fe8cc3d8dc"><code>3ed271b</code></a> Solve leftover from merge conflict</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/9cf3773b717dfd46b37be2ba8cad4f038a8ff6f7"><code>9cf3773</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/43358">#43358</a> from tensorflow/mm-patch-r2.3</li> <li>Additional commits viewable in <a href="https://github.com/tensorflow/tensorflow/compare/v1.12.0...v2.3.1">compare view</a></li> </ul> </details> <br />

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pull request commentdchui1/659-project

Bump tensorflow from 1.12.0 to 2.3.1

Superseded by #36.

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PR opened dchui1/659-project

Bump tensorflow from 1.12.0 to 2.5.0

Bumps tensorflow from 1.12.0 to 2.5.0. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/tensorflow/tensorflow/releases">tensorflow's releases</a>.</em></p> <blockquote> <h2>TensorFlow 2.5.0</h2> <h1>Release 2.5.0</h1> <h2>Major Features and Improvements</h2> <ul> <li>Support for Python3.9 has been added.</li> <li><code>tf.data</code>: <ul> <li><code>tf.data</code> service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.</li> <li>tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing <code>compression=None</code> to <code>tf.data.experimental.service.distribute(...)</code>.</li> <li><code>tf.data.Dataset.batch()</code> now supports <code>num_parallel_calls</code> and <code>deterministic</code> arguments. <code>num_parallel_calls</code> is used to indicate that multiple input batches should be computed in parallel. With <code>num_parallel_calls</code> set, <code>deterministic</code> is used to indicate that outputs can be obtained in the non-deterministic order.</li> <li>Options returned by <code>tf.data.Dataset.options()</code> are no longer mutable.</li> <li>tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as <code>map</code>. The debug mode can be enabled through <code>tf.data.experimental.enable_debug_mode()</code>.</li> </ul> </li> <li><code>tf.lite</code> <ul> <li>Enabled the new MLIR-based quantization backend by default <ul> <li>The new backend is used for 8 bits full integer post-training quantization</li> <li>The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)</li> <li>Set <code>experimental_new_quantizer</code> in tf.lite.TFLiteConverter to False to disable this change</li> </ul> </li> </ul> </li> <li><code>tf.keras</code> <ul> <li><code>tf.keras.metrics.AUC</code> now support logit predictions.</li> <li>Enabled a new supported input type in <code>Model.fit</code>, <code>tf.keras.utils.experimental.DatasetCreator</code>, which takes a callable, <code>dataset_fn</code>. <code>DatasetCreator</code> is intended to work across all <code>tf.distribute</code> strategies, and is the only input type supported for Parameter Server strategy.</li> </ul> </li> <li><code>tf.distribute</code> <ul> <li><code>tf.distribute.experimental.ParameterServerStrategy</code> now supports training with Keras <code>Model.fit</code> when used with <code>DatasetCreator</code>.</li> <li>Creating <code>tf.random.Generator</code> under <code>tf.distribute.Strategy</code> scopes is now allowed (except for <code>tf.distribute.experimental.CentralStorageStrategy</code> and <code>tf.distribute.experimental.ParameterServerStrategy</code>). Different replicas will get different random-number streams.</li> </ul> </li> <li>TPU embedding support <ul> <li>Added <code>profile_data_directory</code> to <code>EmbeddingConfigSpec</code> in <code>_tpu_estimator_embedding.py</code>. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.</li> </ul> </li> <li>PluggableDevice <ul> <li>Third-party devices can now connect to TensorFlow as plug-ins through <a href="https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api.md">StreamExecutor C API</a>. and <a href="https://github.com/tensorflow/community/blob/master/rfcs/20200624-pluggable-device-for-tensorflow.md">PluggableDevice</a> interface. <ul> <li>Add custom ops and kernels through <a href="https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md">kernel and op registration C API</a>.</li> <li>Register custom graph optimization passes with <a href="https://github.com/tensorflow/community/blob/master/rfcs/20201027-modular-tensorflow-graph-c-api.md">graph optimization C API</a>.</li> </ul> </li> </ul> </li> <li><a href="https://github.com/oneapi-src/oneDNN">oneAPI Deep Neural Network Library (oneDNN)</a> CPU performance optimizations from <a href="https://software.intel.com/content/www/us/en/develop/articles/intel-optimization-for-tensorflow-installation-guide.html">Intel-optimized TensorFlow</a> are now available in the official x86-64 Linux and Windows builds. <ul> <li>They are off by default. Enable them by setting the environment variable <code>TF_ENABLE_ONEDNN_OPTS=1</code>.</li> <li>We do not recommend using them in GPU systems, as they have not been sufficiently tested with GPUs yet.</li> </ul> </li> <li>TensorFlow pip packages are now built with CUDA11.2 and cuDNN 8.1.0</li> </ul> <h2>Breaking Changes</h2> <ul> <li>The <code>TF_CPP_MIN_VLOG_LEVEL</code> environment variable has been renamed to to <code>TF_CPP_MAX_VLOG_LEVEL</code> which correctly describes its effect.</li> </ul> <h2>Bug Fixes and Other Changes</h2> <ul> <li><code>tf.keras</code>: <ul> <li>Preprocessing layers API consistency changes: <ul> <li><code>StringLookup</code> added <code>output_mode</code>, <code>sparse</code>, and <code>pad_to_max_tokens</code> arguments with same semantics as <code>TextVectorization</code>.</li> <li><code>IntegerLookup</code> added <code>output_mode</code>, <code>sparse</code>, and <code>pad_to_max_tokens</code> arguments with same semantics as <code>TextVectorization</code>. Renamed <code>max_values</code>, <code>oov_value</code> and <code>mask_value</code> to <code>max_tokens</code>, <code>oov_token</code> and <code>mask_token</code> to align with <code>StringLookup</code> and <code>TextVectorization</code>.</li> <li><code>TextVectorization</code> default for <code>pad_to_max_tokens</code> switched to False.</li> <li><code>CategoryEncoding</code> no longer supports <code>adapt</code>, <code>IntegerLookup</code> now supports equivalent functionality. <code>max_tokens</code> argument renamed to <code>num_tokens</code>.</li> <li><code>Discretization</code> added <code>num_bins</code> argument for learning bins boundaries through calling <code>adapt</code> on a dataset. Renamed <code>bins</code> argument to <code>bin_boundaries</code> for specifying bins without <code>adapt</code>.</li> </ul> </li> <li>Improvements to model saving/loading: <ul> <li><code>model.load_weights</code> now accepts paths to saved models.</li> </ul> </li> </ul> </li> </ul> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Changelog</summary> <p><em>Sourced from <a href="https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md">tensorflow's changelog</a>.</em></p> <blockquote> <h1>Release 2.5.0</h1> <!-- raw HTML omitted --> <h2>Breaking Changes</h2> <ul> <li> <!-- raw HTML omitted --> </li> <li>The <code>TF_CPP_MIN_VLOG_LEVEL</code> environment variable has been renamed to to <code>TF_CPP_MAX_VLOG_LEVEL</code> which correctly describes its effect.</li> </ul> <h2>Known Caveats</h2> <ul> <li><!-- raw HTML omitted --></li> <li><!-- raw HTML omitted --></li> <li><!-- raw HTML omitted --></li> </ul> <h2>Major Features and Improvements</h2> <ul> <li> <p><!-- raw HTML omitted --></p> </li> <li> <p><!-- raw HTML omitted --></p> </li> <li> <p>TPU embedding support</p> <ul> <li>Added <code>profile_data_directory</code> to <code>EmbeddingConfigSpec</code> in <code>_tpu_estimator_embedding.py</code>. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.</li> </ul> </li> <li> <p><code>tf.keras.metrics.AUC</code> now support logit predictions.</p> </li> <li> <p>Creating <code>tf.random.Generator</code> under <code>tf.distribute.Strategy</code> scopes is now allowed (except for <code>tf.distribute.experimental.CentralStorageStrategy</code> and <code>tf.distribute.experimental.ParameterServerStrategy</code>). Different replicas will get different random-number streams.</p> </li> <li> <p><code>tf.data</code>:</p> <ul> <li>tf.data service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.</li> <li>tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing <code>compression=None</code> to <code>tf.data.experimental.service.distribute(...)</code>.</li> <li><code>tf.data.Dataset.batch()</code> now supports <code>num_parallel_calls</code> and <code>deterministic</code> arguments. <code>num_parallel_calls</code> is used to indicate that multiple input batches should be computed in parallel. With <code>num_parallel_calls</code> set, <code>deterministic</code> is used to indicate that outputs can be obtained in the non-deterministic order.</li> <li>Options returned by <code>tf.data.Dataset.options()</code> are no longer mutable.</li> <li>tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as <code>map</code>. The debug mode can be enabled through <code>tf.data.experimental.enable_debug_mode()</code>.</li> </ul> </li> <li> <p><code>tf.lite</code></p> <ul> <li>Enabled the new MLIR-based quantization backend by default <ul> <li>The new backend is used for 8 bits full integer post-training quantization</li> <li>The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)</li> </ul> </li> </ul> </li> </ul> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/tensorflow/tensorflow/commit/a4dfb8d1a71385bd6d122e4f27f86dcebb96712d"><code>a4dfb8d</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/49124">#49124</a> from tensorflow/mm-cherrypick-tf-data-segfault-fix-...</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/2107b1dc414edb3fc78e632bca4f4936171093b2"><code>2107b1d</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/49116">#49116</a> from tensorflow-jenkins/version-numbers-2.5.0-17609</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/16b813906fcb46306aef29a04ddd0cbdb4e77918"><code>16b8139</code></a> Update snapshot_dataset_op.cc</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/86a0d86cb5da6a28b78ea7f886ec2831d23f6d6b"><code>86a0d86</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/49126">#49126</a> from geetachavan1/cherrypicks_X9ZNY</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/9436ae693ef66a9efb7e7e7888134173d9a0821d"><code>9436ae6</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/49128">#49128</a> from geetachavan1/cherrypicks_D73J5</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/6b2bf99cd9336026689579b683a709c5efcb4ae9"><code>6b2bf99</code></a> Validate that a and b are proper sparse tensors</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/c03ad1a46d5b3f23df67dad03185a0ee16020c96"><code>c03ad1a</code></a> Ensure validation sticks in banded_triangular_solve_op</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/12a6ead7ac968c402feb85ce0a8069ccbc6bf735"><code>12a6ead</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/49120">#49120</a> from geetachavan1/cherrypicks_KJ5M9</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/b67f5b8a0a098c34c71c679aa46480035c46886e"><code>b67f5b8</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/49118">#49118</a> from geetachavan1/cherrypicks_BIDTR</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/a13c0ade86295bd3a8356b4b8cc980cf0c5e70e0"><code>a13c0ad</code></a> [tf.data][cherrypick] Fix snapshot segfault when using repeat and prefecth</li> <li>Additional commits viewable in <a href="https://github.com/tensorflow/tensorflow/compare/v1.12.0...v2.5.0">compare view</a></li> </ul> </details> <br />

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