diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 000000000..176a458f9
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1 @@
+* text=auto
diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
new file mode 100644
index 000000000..1f011157e
--- /dev/null
+++ b/.github/FUNDING.yml
@@ -0,0 +1,12 @@
+# These are supported funding model platforms
+
+github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: # Replace with a single Ko-fi username
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+custom: []# Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
diff --git a/.github/ISSUE_TEMPLATE/blank_issue.yml b/.github/ISSUE_TEMPLATE/blank_issue.yml
new file mode 100644
index 000000000..bbd855958
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/blank_issue.yml
@@ -0,0 +1,12 @@
+name: Blank Issue
+description: Submit an issue about Tensorflow.NET.
+labels: [Blank Issue]
+body:
+ - type: textarea
+ id: description
+ attributes:
+ label: Description
+ description: Please describe the issue here.
+ placeholder: Description
+ validations:
+ required: false
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
new file mode 100644
index 000000000..14e237951
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -0,0 +1,48 @@
+name: BUG Report
+description: Report a BUG of Tensorflow.NET.
+title: "[BUG Report]: "
+labels: [bug-report]
+body:
+ - type: markdown
+ attributes:
+ value: |
+ We welcome bug reports! Any unexpected behavior could be a BUG and this template help us gather the information to fix it.
+ - type: textarea
+ id: background
+ attributes:
+ label: Description
+ description: Please share a clear and concise description of the problem.
+ placeholder: Description
+ validations:
+ required: true
+ - type: textarea
+ id: repro-steps
+ attributes:
+ label: Reproduction Steps
+ description: |
+ Please include minimal steps to reproduce the problem if possible. E.g.: the smallest possible code snippet; or a small project, with steps to run it. It will greatly help us to locate the reason of the problem.
+ placeholder: Minimal Reproduction
+ validations:
+ required: false
+ - type: textarea
+ id: known-workarounds
+ attributes:
+ label: Known Workarounds
+ description: |
+ Please provide a description of any known workarounds.
+ placeholder: Known Workarounds
+ validations:
+ required: false
+ - type: textarea
+ id: configuration
+ attributes:
+ label: Configuration and Other Information
+ description: |
+ Please provide more information on your configuration:
+ * Which version of Tensorflow.NET is the code depending on?
+ * Which version of .NET runtime is the code running on?
+ * What is the OS?
+ * Any other information about this problem?
+ placeholder: Configuration
+ validations:
+ required: false
\ No newline at end of file
diff --git a/.github/ISSUE_TEMPLATE/documention_issue.yml b/.github/ISSUE_TEMPLATE/documention_issue.yml
new file mode 100644
index 000000000..f8a04e40f
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/documention_issue.yml
@@ -0,0 +1,30 @@
+name: Documentation Issue
+description: Report an issue about Tensorflow.NET ducumention or require a documention.
+title: "[Documention Issue]: "
+labels: [Documention Issue]
+body:
+ - type: markdown
+ attributes:
+ value: |
+ Welcome to suggest to Tensorflow.NET documention! This template will help us gather the information we need to improve it.
+ - type: textarea
+ id: brief-description
+ attributes:
+ label: Brief Description
+ description: Please describe the problem or the requst for new documention here.
+ placeholder: Description
+ validations:
+ required: true
+ - type: textarea
+ id: alternatives
+ attributes:
+ label: Alternatives
+ description: |
+ Please provide some alternative information here, if any.
+ placeholder: Alternatives
+ validations:
+ required: false
+ - type: markdown
+ attributes:
+ value: |
+ Thanks for your contributing!
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
new file mode 100644
index 000000000..9ce3f1663
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -0,0 +1,50 @@
+name: Feature Request
+description: Request/Propose a new feature of Tensorflow.NET.
+title: "[Feature Request]: "
+labels: [feature-request]
+body:
+ - type: markdown
+ attributes:
+ value: |
+ We welcome feature proposal/request! This template will help us gather the information we need to implement the new feature.
+ - type: textarea
+ id: background
+ attributes:
+ label: Background and Feature Description
+ description: Please describe the purpose and value of the new feature here. If the feature is linked to a specific problem, please describe it or put the link here.
+ placeholder: Purpose
+ validations:
+ required: true
+ - type: textarea
+ id: api-proposal
+ attributes:
+ label: API Definition and Usage
+ description: |
+ Please tell us the new API related to the requested feature, if any.
+ placeholder: API declaration (no method bodies)
+ value: |
+ ```cs
+ public Tensor NewFunc(Tensor x, int y);
+
+ var result = NewFunc(input, index);
+ ```
+ validations:
+ required: false
+ - type: textarea
+ id: alternatives
+ attributes:
+ label: Alternatives
+ description: |
+ Please provide some alternative information of the feature, if any. For example, if you request a feature which depends on a specific device, please provide the device information.
+ placeholder: Alternatives
+ validations:
+ required: false
+ - type: textarea
+ id: risks
+ attributes:
+ label: Risks
+ description: |
+ Please mention any risks that to your knowledge the API proposal might entail, such as breaking changes, performance regressions, etc.
+ placeholder: Risks
+ validations:
+ required: false
\ No newline at end of file
diff --git a/.github/ISSUE_TEMPLATE/performance_issue.yml b/.github/ISSUE_TEMPLATE/performance_issue.yml
new file mode 100644
index 000000000..cbe86d329
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/performance_issue.yml
@@ -0,0 +1,48 @@
+name: Performance Issue
+description: Submit an issue about performance problem or regression of Tensorflow.NET.
+title: "[Performance Issue]: "
+labels: [Performance Issue]
+body:
+ - type: markdown
+ attributes:
+ value: |
+ We welcome issues about Tensorflow.NET performance! This template will help us gather the information we need to locate the problem improve the performance.
+ - type: textarea
+ id: brief-description
+ attributes:
+ label: Brief Description
+ description: Please give a brief description about the performance issue here.
+ placeholder: Description
+ validations:
+ required: true
+ - type: textarea
+ id: device-and-context
+ attributes:
+ label: Device and Context
+ description: |
+ Please describe the device and context you used when you encounter the performance problem/regression.
+ placeholder: Device and Context
+ validations:
+ required: true
+ - type: textarea
+ id: benchmark
+ attributes:
+ label: Benchmark
+ description: |
+ We will appreciate it if you'd like to provide benchmark comparison of the performance issue.
+ placeholder: Benchmark
+ validations:
+ required: false
+ - type: textarea
+ id: alternatives
+ attributes:
+ label: Alternatives
+ description: |
+ Please provide some alternative information of the performance issue here, if any. For example, we'll appreciate it if you'd like to provide the the code to reproduce the performance problem.
+ placeholder: Alternatives
+ validations:
+ required: false
+ - type: markdown
+ attributes:
+ value: |
+ Thanks for your contributing!
diff --git a/.github/ISSUE_TEMPLATE/question.yml b/.github/ISSUE_TEMPLATE/question.yml
new file mode 100644
index 000000000..ca38be340
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/question.yml
@@ -0,0 +1,30 @@
+name: Question
+description: Ask any question about Tensorflow.NET and discuss with community members.
+title: "[Question]: "
+labels: [Question]
+body:
+ - type: markdown
+ attributes:
+ value: |
+ Any question about Tensorflow.NET is welcomed! This template will help us get your point.
+ - type: textarea
+ id: description
+ attributes:
+ label: Description
+ description: Please describe your question here.
+ placeholder: Description
+ validations:
+ required: true
+ - type: textarea
+ id: alternatives
+ attributes:
+ label: Alternatives
+ description: |
+ Please provide some alternative information here, if any.
+ placeholder: Alternatives
+ validations:
+ required: false
+ - type: markdown
+ attributes:
+ value: |
+ We are always willing to answer your questions!
diff --git a/.github/workflows/build_and_test.yml b/.github/workflows/build_and_test.yml
new file mode 100644
index 000000000..9fd34fc49
--- /dev/null
+++ b/.github/workflows/build_and_test.yml
@@ -0,0 +1,66 @@
+# This workflow will build a .NET project
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-net
+
+name: build_and_test
+
+on:
+ push:
+ branches: [ "master" ]
+ pull_request:
+ branches: [ "master" ]
+ types: ["opened", "reopened", "synchronize", "ready_for_review", "auto_merge_enabled"]
+
+jobs:
+ windows:
+
+ runs-on: windows-latest
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Setup .NET 6
+ uses: actions/setup-dotnet@v3
+ with:
+ dotnet-version: 6.0.x
+ - name: Restore dependencies
+ run: dotnet restore
+ - name: Build CPU version
+ run: dotnet build --no-restore
+ - name: Test CPU version
+ run: dotnet test --no-build --verbosity normal
+ - name: uninstall redist cpu for unit tests
+ run: dotnet remove tools/Tensorflow.UnitTest.RedistHolder package SciSharp.TensorFlow.Redist
+ - name: install redist gpu for unit tests
+ run: dotnet add tools/Tensorflow.UnitTest.RedistHolder package SciSharp.TensorFlow.Redist-Windows-GPU
+ - name: Restore dependencies
+ run: dotnet restore
+ - name: Build GPU version
+ run: dotnet build --no-restore
+# - name: Test GPU version
+# run: dotnet test --no-build --verbosity normal
+
+ linux:
+
+ runs-on: ubuntu-latest
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Setup .NET
+ uses: actions/setup-dotnet@v3
+ with:
+ dotnet-version: 6.0.x
+ - name: Restore dependencies
+ run: dotnet restore
+ - name: Build CPU version
+ run: dotnet build --no-restore
+ - name: Test CPU version
+ run: dotnet test --no-build --verbosity normal
+ - name: uninstall redist cpu for unit tests
+ run: dotnet remove tools/Tensorflow.UnitTest.RedistHolder package SciSharp.TensorFlow.Redist
+ - name: install redist gpu for unit tests
+ run: dotnet add tools/Tensorflow.UnitTest.RedistHolder package SciSharp.TensorFlow.Redist-Linux-GPU
+ - name: Restore dependencies
+ run: dotnet restore
+ - name: Build GPU version
+ run: dotnet build --no-restore
+# - name: Test GPU version
+# run: dotnet test --no-build --verbosity normal
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
new file mode 100644
index 000000000..02601764c
--- /dev/null
+++ b/.github/workflows/release.yml
@@ -0,0 +1,62 @@
+name: auto-release
+
+on:
+ workflow_run:
+ workflows: ["release-prepare"]
+ types:
+ - completed
+
+env:
+ MYGET_API_TOKEN: ${{ SECRETS.MYGET_API_KEY }}
+ GITHUB_TOKEN: ${{ SECRETS.RINNE_GITHUB_TOKEN }}
+
+jobs:
+ release_to_myget:
+ runs-on: windows-latest
+# needs: run-semantic-release
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Setup .NET 6.0.x SDK
+ uses: actions/setup-dotnet@v3
+ with:
+ dotnet-version: 6.0.x
+
+ - name: Check .NET info
+ run: dotnet --info
+
+ - name: Install dependencies
+ run: dotnet restore
+
+ - name: Build solution
+ run: dotnet build -c Release --no-restore
+
+ - name: Pack packages
+ run: |
+ git fetch --unshallow;
+ git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*";
+ git fetch origin;
+ $LastTag = git describe --tags;
+ $DroppedTag = ($LastTag).TrimStart('v');
+ echo "Last tag is: $DroppedTag";
+ $Suffix = "-nightly"
+ $Version = "${DroppedTag}${Suffix}";
+ echo "Publishing version: $Version";
+ dotnet pack ./src/TensorFlowNET.Core/Tensorflow.Binding.csproj -c Release -o packages /p:PackageVersion=$Version /p:Version=$Version;
+ dotnet pack ./src/TensorFlowNET.Keras/Tensorflow.Keras.csproj -c Release -o packages /p:PackageVersion=$Version /p:Version=$Version;
+ dotnet pack ./src/TensorflowNET.Hub/Tensorflow.Hub.csproj -c Release -o packages /p:PackageVersion=$Version /p:Version=$Version;
+
+ if($LastExitCode -ne 0)
+ {
+ Write-Warning -Message "Pack packages warming, last exit code is ${LastExitCode}."
+ $LastExitCode = 0;
+ }
+
+ - name: Upload packages artifacts
+ uses: actions/upload-artifact@v4.0.0
+ with:
+ name: "drop-ci-packages"
+ path: './packages'
+
+ - name: Push TensorFlow.NET to myget.org
+ run: dotnet nuget push .\packages\TensorFlow*.nupkg --source https://www.myget.org/F/scisharp/api/v3/index.json -k ${{ secrets.MYGET_API_KEY }} --skip-duplicate
diff --git a/.github/workflows/release_prepare.yml b/.github/workflows/release_prepare.yml
new file mode 100644
index 000000000..b21c6665c
--- /dev/null
+++ b/.github/workflows/release_prepare.yml
@@ -0,0 +1,46 @@
+name: release-prepare
+
+on:
+ pull_request:
+ branches:
+ - master
+ types: [ closed ]
+
+env:
+ MYGET_API_TOKEN: ${{ SECRETS.MYGET_API_KEY }}
+ GITHUB_TOKEN: ${{ SECRETS.RINNE_GITHUB_TOKEN }}
+
+jobs:
+ build:
+ if: contains(github.event.pull_request.labels.*.name, 'auto-release')
+ runs-on: windows-latest
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Setup .NET 6.0.x SDK
+ uses: actions/setup-dotnet@v3
+ with:
+ dotnet-version: 6.0.x
+
+ - name: Check .NET info
+ run: dotnet --info
+
+ - name: Install dependencies
+ run: dotnet restore
+
+ - name: Build solution
+ run: dotnet build -c Release --no-restore
+
+# run-semantic-release:
+# runs-on: ubuntu-latest
+# needs: build
+
+# steps:
+# - name: Checkout
+# uses: actions/checkout@v2
+
+# - name: Run semantic-release
+# run: |
+# export PATH=$PATH:$(yarn global bin)
+# yarn global add semantic-release@17.4.3
+# semantic-release
\ No newline at end of file
diff --git a/.github/workflows/semantic.yml b/.github/workflows/semantic.yml
new file mode 100644
index 000000000..db8c06a3e
--- /dev/null
+++ b/.github/workflows/semantic.yml
@@ -0,0 +1,17 @@
+name: Semantic
+
+on:
+ pull_request:
+ branches: [ "master" ]
+
+jobs:
+ semantic-pull-request:
+ name: Semantic check
+ runs-on: windows-latest
+ steps:
+ - name: semantic-pull-request
+ uses: amannn/action-semantic-pull-request@v4
+ env:
+ GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+ with:
+ validateSingleCommit: true
diff --git a/.gitignore b/.gitignore
index eee1dc7bb..231d8379a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -332,3 +332,10 @@ src/TensorFlowNET.Native/bazel-*
src/TensorFlowNET.Native/c_api.h
/.vscode
test/TensorFlowNET.Examples/mnist
+
+
+# training model resources
+.resources
+/redist
+*.xml
+*.xsd
diff --git a/CODEOWNERS b/CODEOWNERS
new file mode 100644
index 000000000..ee3236a46
--- /dev/null
+++ b/CODEOWNERS
@@ -0,0 +1,3 @@
+# You can find more information about CODEOWNERS here: https://help.github.com/en/articles/about-code-owners
+# These owners will be the default owners for everything in the repo.
+* @Oceania2018
\ No newline at end of file
diff --git a/Directory.Build.props b/Directory.Build.props
new file mode 100644
index 000000000..065690ec9
--- /dev/null
+++ b/Directory.Build.props
@@ -0,0 +1,17 @@
+
+
+
+
+
+ true
+ $(NoWarn),1573,1591,1712
+
+
+
diff --git a/Directory.Build.targets b/Directory.Build.targets
new file mode 100644
index 000000000..341027f3c
--- /dev/null
+++ b/Directory.Build.targets
@@ -0,0 +1,3 @@
+
+
+
diff --git a/README.md b/README.md
index 545cea13b..75cad0aa7 100644
--- a/README.md
+++ b/README.md
@@ -1,172 +1,236 @@
-# TensorFlow.NET
-TensorFlow.NET (TF.NET) provides a .NET Standard binding for [TensorFlow](https://www.tensorflow.org/). It aims to implement the complete Tensorflow API in CSharp which allows .NET developers to develop, train and deploy Machine Learning models with the cross-platform .NET Standard framework.
+
+**TensorFlow.NET** (TF.NET) provides a .NET Standard binding for [TensorFlow](https://www.tensorflow.org/). It aims to implement the complete Tensorflow API in C# which allows .NET developers to develop, train and deploy Machine Learning models with the cross-platform .NET Standard framework. TensorFlow.NET has built-in Keras high-level interface and is released as an independent package [TensorFlow.Keras](https://www.nuget.org/packages/TensorFlow.Keras/).
+
+[](https://discord.gg/qRVm82fKTS)
+[](http://qm.qq.com/cgi-bin/qm/qr?_wv=1027&k=sN9VVMwbWjs5L0ATpizKKxOcZdEPMrp8&authKey=RLDw41bLTrEyEgZZi%2FzT4pYk%2BwmEFgFcrhs8ZbkiVY7a4JFckzJefaYNW6Lk4yPX&noverify=0&group_code=985366726)
[](https://gitter.im/sci-sharp/community)
-[](https://ci.appveyor.com/project/Haiping-Chen/tensorflow-net)
-[](https://codecov.io/gh/SciSharp/NumSharp)
-[](https://www.nuget.org/packages/TensorFlow.NET)
+[](https://github.com/SciSharp/TensorFlow.NET/actions/workflows/build_and_test.yml)
[](https://tensorflownet.readthedocs.io/en/latest/?badge=latest)
+[](https://www.nuget.org/packages/TensorFlow.NET)
+[](https://www.nuget.org/packages/TensorFlow.Keras)
+[](https://www.myget.org/feed/scisharp/package/nuget/Tensorflow.NET)
[](https://996.icu/#/en_US)
+[](https://mybinder.org/v2/gh/javiercp/BinderTF.NET/master?urlpath=lab)
+
+English | [中文](docs/README-CN.md)
+
+> [!IMPORTANT]
+> We're happy that our work on tensorflow.net has attracted many users. However, at this time, none of the main maintainers of this repo is available for new features and bug fix. We won't refuse PRs and will help to review them.
+>
+> If you would like to be a contributor or maintainer of tensorflow.net, we'd like to help you to start up.
+>
+> We feel sorry for that and we'll resume the maintaining for this project once one of us has bandwidth for it.
+>
+
+*master branch and v0.100.x is corresponding to tensorflow v2.10, v0.6x branch is from tensorflow v2.6, v0.15-tensorflow1.15 is from tensorflow1.15. Please add `https://www.myget.org/F/scisharp/api/v3/index.json` to nuget source to use nightly release.*
-TF.NET is a member project of [SciSharp STACK](https://github.com/SciSharp).
-### Why TensorFlow.NET ?
+## Why Tensorflow.NET ?
-`SciSharp STASK`'s mission is to bring popular data science technology into the .NET world and to provide .NET developers with a powerful Machine Learning tool set without reinventing the wheel. Scince the APIs are kept as similar as possible you can immediately adapt any existing Tensorflow code in C# with a zero learning curve. Take a look at a comparison picture and see how comfortably a Tensorflow/Python script translates into a C# program with TensorFlow.NET.
+`SciSharp STACK`'s mission is to bring popular data science technology into the .NET world and to provide .NET developers with a powerful Machine Learning tool set without reinventing the wheel. Since the APIs are kept as similar as possible you can immediately adapt any existing TensorFlow code in C# or F# with a zero learning curve. Take a look at a comparison picture and see how comfortably a TensorFlow/Python script translates into a C# program with TensorFlow.NET.
-
+
-SciSharp's philosophy allows a large number of machine learning code written in Python to be quickly migrated to .NET, enabling .NET developers to use cutting edge machine learning models and access a vast number of Tensorflow resources which would not be possible without this project.
+SciSharp's philosophy allows a large number of machine learning code written in Python to be quickly migrated to .NET, enabling .NET developers to use cutting edge machine learning models and access a vast number of TensorFlow resources which would not be possible without this project.
-In comparison to other projects, like for instance TensorFlowSharp which only provide Tensorflow's low-level C++ API and can only run models that were built using Python, Tensorflow.NET also implements Tensorflow's high level API where all the magic happens. This computation graph building layer is still under active development. Once it is completely implemented you can build new Machine Learning models in C#.
+In comparison to other projects, like for instance [TensorFlowSharp](https://www.nuget.org/packages/TensorFlowSharp/) which only provide TensorFlow's low-level C++ API and can only run models that were built using Python, Tensorflow.NET makes it possible to build the pipeline of training and inference with pure C# and F#. Besides, Tensorflow.NET provides binding of Tensorflow.Keras to make it easy to transfer your code from python to .NET.
-### How to use
+[ML.NET](https://github.com/dotnet/machinelearning) also take Tensorflow.NET as one of the backends to train and infer your model, which provides better integration with .NET.
-Install TF.NET through NuGet.
-```sh
-PM> Install-Package TensorFlow.NET
-```
+## Documention
-If you are using Linux or Mac OS, please download the pre-compiled dll [here](tensorflowlib) and place it in the working folder. This is only need for Linux and Mac OS, and already packed into NuGet for Windows.
+Introduction and simple examples：[Tensorflow.NET Documents](https://scisharp.github.io/tensorflow-net-docs)
-Import TF.NET.
+Detailed documention：[The Definitive Guide to Tensorflow.NET](https://tensorflownet.readthedocs.io/en/latest/FrontCover.html)
-```cs
-using Tensorflow;
+Examples：[TensorFlow.NET Examples](https://github.com/SciSharp/TensorFlow.NET-Examples)
+
+Troubleshooting of running example or installation：[Tensorflow.NET FAQ](tensorflowlib/README.md)
+
+## Usage
+
+### Installation
+
+You can search the package name in NuGet Manager, or use the commands below in package manager console.
+
+The installation contains two parts, the first is the main body:
+
+```sh
+### Install Tensorflow.NET
+PM> Install-Package TensorFlow.NET
+
+### Install Tensorflow.Keras
+PM> Install-Package TensorFlow.Keras
```
-Add two constants:
-```cs
-// Create a Constant op
-var a = tf.constant(4.0f);
-var b = tf.constant(5.0f);
-var c = tf.add(a, b);
+The second part is the computing support part. Only one of the following packages is needed, depending on your device and system.
-using (var sess = tf.Session())
-{
- var o = sess.run(c);
-}
```
+### CPU version for Windows and Linux
+PM> Install-Package SciSharp.TensorFlow.Redist
-Feed placeholder:
-```cs
-// Create a placeholder op
-var a = tf.placeholder(tf.float32);
-var b = tf.placeholder(tf.float32);
-var c = tf.add(a, b);
+### CPU version for MacOS
+PM> Install-Package SciSharp.TensorFlow.Redist-OSX
-using(var sess = tf.Session())
-{
- var o = sess.run(c, new FeedItem(a, 3.0f), new FeedItem(b, 2.0f));
-}
+### GPU version for Windows (CUDA and cuDNN are required)
+PM> Install-Package SciSharp.TensorFlow.Redist-Windows-GPU
+
+### GPU version for Linux (CUDA and cuDNN are required)
+PM> Install-Package SciSharp.TensorFlow.Redist-Linux-GPU
```
-Linear Regression:
-```c#
-// We can set a fixed init value in order to debug
-var W = tf.Variable(-0.06f, name: "weight");
-var b = tf.Variable(-0.73f, name: "bias");
+Two simple examples are given here to introduce the basic usage of Tensorflow.NET. As you can see, it's easy to write C# code just like that in Python.
+
+### Example - Linear Regression in `Eager` mode
-// Construct a linear model
-var pred = tf.add(tf.multiply(X, W), b);
+```csharp
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
+using Tensorflow;
+using Tensorflow.NumPy;
-// Mean squared error
-var cost = tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * n_samples);
+// Parameters
+var training_steps = 1000;
+var learning_rate = 0.01f;
+var display_step = 100;
-// Gradient descent
-// Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
-var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);
+// Sample data
+var X = np.array(3.3f, 4.4f, 5.5f, 6.71f, 6.93f, 4.168f, 9.779f, 6.182f, 7.59f, 2.167f,
+ 7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f);
+var Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f,
+ 2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f);
+var n_samples = X.shape[0];
-// Initialize the variables (i.e. assign their default value)
-var init = tf.global_variables_initializer();
+// We can set a fixed init value in order to demo
+var W = tf.Variable(-0.06f, name: "weight");
+var b = tf.Variable(-0.73f, name: "bias");
+var optimizer = keras.optimizers.SGD(learning_rate);
-// Start training
-with(tf.Session(), sess =>
+// Run training for the given number of steps.
+foreach (var step in range(1, training_steps + 1))
{
- // Run the initializer
- sess.run(init);
-
- // Fit all training data
- for (int epoch = 0; epoch < training_epochs; epoch++)
+ // Run the optimization to update W and b values.
+ // Wrap computation inside a GradientTape for automatic differentiation.
+ using var g = tf.GradientTape();
+ // Linear regression (Wx + b).
+ var pred = W * X + b;
+ // Mean square error.
+ var loss = tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * n_samples);
+ // should stop recording
+ // Compute gradients.
+ var gradients = g.gradient(loss, (W, b));
+
+ // Update W and b following gradients.
+ optimizer.apply_gradients(zip(gradients, (W, b)));
+
+ if (step % display_step == 0)
{
- foreach (var (x, y) in zip(train_X, train_Y))
- sess.run(optimizer, new FeedItem(X, x), new FeedItem(Y, y));
-
- // Display logs per epoch step
- if ((epoch + 1) % display_step == 0)
- {
- var c = sess.run(cost, new FeedItem(X, train_X), new FeedItem(Y, train_Y));
- Console.WriteLine($"Epoch: {epoch + 1} cost={c} " + $"W={sess.run(W)} b={sess.run(b)}");
- }
-
- Console.WriteLine("Optimization Finished!");
- var training_cost = sess.run(cost, new FeedItem(X, train_X), new FeedItem(Y, train_Y));
- Console.WriteLine($"Training cost={training_cost} W={sess.run(W)} b={sess.run(b)}");
-
- // Testing example
- var test_X = np.array(6.83f, 4.668f, 8.9f, 7.91f, 5.7f, 8.7f, 3.1f, 2.1f);
- var test_Y = np.array(1.84f, 2.273f, 3.2f, 2.831f, 2.92f, 3.24f, 1.35f, 1.03f);
- Console.WriteLine("Testing... (Mean square loss Comparison)");
-
- var testing_cost = sess.run(tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * test_X.shape[0]), new FeedItem(X, test_X), new FeedItem(Y, test_Y));
- Console.WriteLine($"Testing cost={testing_cost}");
-
- var diff = Math.Abs((float)training_cost - (float)testing_cost);
- Console.WriteLine($"Absolute mean square loss difference: {diff}");
+ pred = W * X + b;
+ loss = tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * n_samples);
+ print($"step: {step}, loss: {loss.numpy()}, W: {W.numpy()}, b: {b.numpy()}");
}
-});
+}
```
+Run this example in [Jupyter Notebook](https://github.com/SciSharp/SciSharpCube).
+### Example - Toy version of `ResNet` in `Keras` functional API
-Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflownet.readthedocs.io/en/latest/FrontCover.html).
+```csharp
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
+using Tensorflow;
+using Tensorflow.NumPy;
+
+var layers = keras.layers;
+// input layer
+var inputs = keras.Input(shape: (32, 32, 3), name: "img");
+// convolutional layer
+var x = layers.Conv2D(32, 3, activation: "relu").Apply(inputs);
+x = layers.Conv2D(64, 3, activation: "relu").Apply(x);
+var block_1_output = layers.MaxPooling2D(3).Apply(x);
+x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(block_1_output);
+x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(x);
+var block_2_output = layers.Add().Apply(new Tensors(x, block_1_output));
+x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(block_2_output);
+x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(x);
+var block_3_output = layers.Add().Apply(new Tensors(x, block_2_output));
+x = layers.Conv2D(64, 3, activation: "relu").Apply(block_3_output);
+x = layers.GlobalAveragePooling2D().Apply(x);
+x = layers.Dense(256, activation: "relu").Apply(x);
+x = layers.Dropout(0.5f).Apply(x);
+// output layer
+var outputs = layers.Dense(10).Apply(x);
+// build keras model
+var model = keras.Model(inputs, outputs, name: "toy_resnet");
+model.summary();
+// compile keras model in tensorflow static graph
+model.compile(optimizer: keras.optimizers.RMSprop(1e-3f),
+ loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true),
+ metrics: new[] { "acc" });
+// prepare dataset
+var ((x_train, y_train), (x_test, y_test)) = keras.datasets.cifar10.load_data();
+// normalize the input
+x_train = x_train / 255.0f;
+// training
+model.fit(x_train[new Slice(0, 2000)], y_train[new Slice(0, 2000)],
+ batch_size: 64,
+ epochs: 10,
+ validation_split: 0.2f);
+// save the model
+model.save("./toy_resnet_model");
+```
-### More examples:
+The F# example for linear regression is available [here](docs/Example-fsharp.md).
-Run specific example in shell:
+More adcanced examples could be found in [TensorFlow.NET Examples](https://github.com/SciSharp/TensorFlow.NET-Examples).
-```cs
-dotnet TensorFlowNET.Examples.dll "Retrain Image Classifier"
-```
+## Version Relationships
+
+| TensorFlow.NET Versions | tensorflow 1.14, cuda 10.0 | tensorflow 1.15, cuda 10.0 | tensorflow 2.3, cuda 10.1 | tensorflow 2.4, cuda 11 | tensorflow 2.7, cuda 11 |tensorflow 2.10, cuda 11 |
+| -------------------------- | ------------- | -------------- | ------------- | ------------- | ------------ | ------------ |
+| tf.net 0.10x, tf.keras 0.10 | | | | | | x |
+| tf.net 0.7x, tf.keras 0.7 | | | | | x | |
+| tf.net 0.4x, tf.keras 0.5 | | | | x | | |
+| tf.net 0.3x, tf.keras 0.4 | | | x | | | |
+| tf.net 0.2x | | x | x | | | |
+| tf.net 0.15 | x | x | | | | |
+| tf.net 0.14 | x | | | | | |
-Example runner will download all the required files like training data and model pb files.
-* [Hello World](test/TensorFlowNET.Examples/HelloWorld.cs)
-* [Basic Operations](test/TensorFlowNET.Examples/BasicOperations.cs)
-* [Linear Regression](test/TensorFlowNET.Examples/BasicModels/LinearRegression.cs)
-* [Logistic Regression](test/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs)
-* [Nearest Neighbor](test/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs)
-* [Naive Bayes Classification](test/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs)
-* [Image Recognition](test/TensorFlowNET.Examples/ImageProcess)
-* [K-means Clustering](test/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs)
-* [NN XOR](test/TensorFlowNET.Examples/BasicModels/NeuralNetXor.cs)
-* [Object Detection](test/TensorFlowNET.Examples/ImageProcess/ObjectDetection.cs)
-* [Text Classification](test/TensorFlowNET.Examples/TextProcess/BinaryTextClassification.cs)
-* [CNN Text Classification](test/TensorFlowNET.Examples/TextProcess/cnn_models/VdCnn.cs)
+```
+tf.net 0.4x -> tf native 2.4
+tf.net 0.6x -> tf native 2.6
+tf.net 0.7x -> tf native 2.7
+tf.net 0.10x -> tf native 2.10
+...
+```
-* [Named Entity Recognition](test/TensorFlowNET.Examples/TextProcess/NER)
-* [Transfer Learning for Image Classification in InceptionV3](test/TensorFlowNET.Examples/ImageProcess/RetrainImageClassifier.cs)
+## Contribution:
-### Contribute:
+Feel like contributing to one of the hottest projects in the Machine Learning field? Want to know how Tensorflow magically creates the computational graph?
-Feel like contributing to one of the hottest projects in the Machine Learning field? Want to know how Tensorflow magically creates the computational graph? We appreciate every contribution however small. There are tasks for novices to experts alike, if everyone tackles only a small task the sum of contributions will be huge.
+We appreciate every contribution however small! There are tasks for novices to experts alike, if everyone tackles only a small task the sum of contributions will be huge.
You can:
-* Let everyone know about this project (trivial)
-* Port Tensorflow unit tests from Python to C# (easy)
-* Port missing Tensorflow code from Python to C# (easy)
-* Port Tensorflow examples to C# and raise issues if you come accross missing parts of the API (easy)
-* Debug one of the unit tests that is marked as Ignored to get it to work (can be challenging)
-* Debug one of the not yet working examples and get it to work (hard)
+- Star Tensorflow.NET or share it with others
+- Tell us about the missing APIs compared to Tensorflow
+- Port Tensorflow unit tests from Python to C# or F#
+- Port Tensorflow examples to C# or F# and raise issues if you come accross missing parts of the API or BUG
+- Debug one of the unit tests that is marked as Ignored to get it to work
+- Debug one of the not yet working examples and get it to work
+- Help us to complete the documentions.
-### How to debug unit tests:
-The best way to find out why a unit test is failing is to single step it in C# and its pendant Python at the same time to see where the flow of execution digresses or where variables exhibit different values. Good Python IDEs like PyCharm let you single step into the tensorflow library code.
+#### How to debug unit tests:
-### Git Knowhow for Contributors
+The best way to find out why a unit test is failing is to single step it in C# or F# and its corresponding Python at the same time to see where the flow of execution digresses or where variables exhibit different values. Good Python IDEs like PyCharm let you single step into the tensorflow library code.
+
+#### Git Knowhow for Contributors
Add SciSharp/TensorFlow.NET as upstream to your local repo ...
```git
@@ -178,13 +242,20 @@ Please make sure you keep your fork up to date by regularly pulling from upstrea
git pull upstream master
```
-### Contact
-
-Feel free to star or raise issue on [Github](https://github.com/SciSharp/TensorFlow.NET).
+### Support
+Buy our book to make open source project be sustainable [TensorFlow.NET实战](https://item.jd.com/13441549.html)
+
+
+
+
+
-Join our chat on [Gitter](https://gitter.im/sci-sharp/community) or
+### Contact
-Scan QR code to join Tencent TIM group:
+Join our chat on [Discord](https://discord.gg/qRVm82fKTS) or [Gitter](https://gitter.im/sci-sharp/community).
-
+Follow us on [Twitter](https://twitter.com/ScisharpStack), [Facebook](https://www.facebook.com/scisharp.stack.9), [Medium](https://medium.com/scisharp), [LinkedIn](https://www.linkedin.com/company/scisharp-stack/).
+TensorFlow.NET is a part of [SciSharp STACK](https://scisharp.github.io/SciSharp/)
+
+
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diff --git a/TensorFlow.NET.sln.DotSettings b/TensorFlow.NET.sln.DotSettings
new file mode 100644
index 000000000..aba8725cc
--- /dev/null
+++ b/TensorFlow.NET.sln.DotSettings
@@ -0,0 +1,2 @@
+
+ True
\ No newline at end of file
diff --git a/data/img001.bmp b/data/img001.bmp
new file mode 100644
index 000000000..d149d76f1
Binary files /dev/null and b/data/img001.bmp differ
diff --git a/data/nb_example.npy b/data/nb_example.npy
new file mode 100644
index 000000000..4547812ca
Binary files /dev/null and b/data/nb_example.npy differ
diff --git a/docs/Example-fsharp.md b/docs/Example-fsharp.md
new file mode 100644
index 000000000..578543454
--- /dev/null
+++ b/docs/Example-fsharp.md
@@ -0,0 +1,55 @@
+Linear Regression in `Eager` mode:
+
+```fsharp
+#r "nuget: TensorFlow.Net"
+#r "nuget: TensorFlow.Keras"
+#r "nuget: SciSharp.TensorFlow.Redist"
+
+open Tensorflow
+open Tensorflow.NumPy
+open type Tensorflow.Binding
+open type Tensorflow.KerasApi
+
+let tf = New()
+tf.enable_eager_execution()
+
+// Parameters
+let training_steps = 1000
+let learning_rate = 0.01f
+let display_step = 100
+
+// Sample data
+let train_X =
+ np.array(3.3f, 4.4f, 5.5f, 6.71f, 6.93f, 4.168f, 9.779f, 6.182f, 7.59f, 2.167f,
+ 7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f)
+let train_Y =
+ np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f,
+ 2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f)
+let n_samples = train_X.shape.[0]
+
+// We can set a fixed init value in order to demo
+let W = tf.Variable(-0.06f,name = "weight")
+let b = tf.Variable(-0.73f, name = "bias")
+let optimizer = keras.optimizers.SGD(learning_rate)
+
+// Run training for the given number of steps.
+for step = 1 to (training_steps + 1) do
+ // Run the optimization to update W and b values.
+ // Wrap computation inside a GradientTape for automatic differentiation.
+ use g = tf.GradientTape()
+ // Linear regression (Wx + b).
+ let pred = W * train_X + b
+ // Mean square error.
+ let loss = tf.reduce_sum(tf.pow(pred - train_Y,2)) / (2 * n_samples)
+ // should stop recording
+ // compute gradients
+ let gradients = g.gradient(loss,struct (W,b))
+
+ // Update W and b following gradients.
+ optimizer.apply_gradients(zip(gradients, struct (W,b)))
+
+ if (step % display_step) = 0 then
+ let pred = W * train_X + b
+ let loss = tf.reduce_sum(tf.pow(pred-train_Y,2)) / (2 * n_samples)
+ printfn $"step: {step}, loss: {loss.numpy()}, W: {W.numpy()}, b: {b.numpy()}"
+```
\ No newline at end of file
diff --git a/docs/README-CN.md b/docs/README-CN.md
new file mode 100644
index 000000000..9776b0fb8
--- /dev/null
+++ b/docs/README-CN.md
@@ -0,0 +1,228 @@
+
+
+**Tensorflow.NET**是AI框架[TensorFlow](https://www.tensorflow.org/)在.NET平台上的实现，支持C#和F#，可以用来搭建深度学习模型并进行训练和推理，并内置了Numpy API，可以用来进行其它科学计算。
+
+Tensorflow.NET并非对于Python的简单封装，而是基于C API的pure C#实现，因此使用时无需额外的环境，可以很方便地用NuGet直接安装使用。并且dotnet团队提供的[ML.NET](https://github.com/dotnet/machinelearning)也依赖于Tensorflow.NET，支持调用Tensorflow.NET进行训练和推理，可以很方便地融入.NET生态。
+
+与tensorflow相同，Tensorflow.NET也内置了Keras这一高级API，只要在安装Tensorflow.NET的同时安装Tensorflow.Keras就可以使用，Keras支持以模块化的方式调用模型，给模型的搭建提供了极大的便利。
+
+[](https://gitter.im/sci-sharp/community)
+[](https://ci.appveyor.com/project/Haiping-Chen/tensorflow-net)
+[](https://www.nuget.org/packages/TensorFlow.NET)
+[](https://tensorflownet.readthedocs.io/en/latest/?badge=latest)
+[](https://996.icu/#/en_US)
+[](https://mybinder.org/v2/gh/javiercp/BinderTF.NET/master?urlpath=lab)
+
+中文 | [English](https://github.com/SciSharp/TensorFlow.NET#readme)
+
+*当前主分支与Tensorflow2.10版本相对应，支持Eager Mode，同时也支持v1的静态图。*
+
+
+
+
+## Why Tensorflow.NET?
+
+`SciSharp STACK`开源社区的目标是构建.NET平台下易用的科学计算库，而Tensorflow.NET就是其中最具代表性的仓库之一。在深度学习领域Python是主流，无论是初学者还是资深开发者，模型的搭建和训练都常常使用Python写就的AI框架，比如tensorflow。但在实际应用深度学习模型的时候，又可能希望用到.NET生态，亦或只是因为.NET是自己最熟悉的领域，这时候Tensorflow.NET就有显著的优点，因为它不仅可以和.NET生态很好地贴合，其API还使得开发者很容易将Python代码迁移过来。下面的对比就是很好的例子，Python代码和C#代码有着高度相似的API，这会使得迁移的时候无需做过多修改。
+
+
+
+除了高度相似的API外，Tensorflow.NET与tensorflow也已经打通数据通道，tensorflow训练并保存的模型可以在Tensorflow.NET中直接读取并继续训练或推理，反之Tensorflow.NET保存的模型也可以在tensorflow中读取，这大大方便了模型的训练和部署。
+
+与其它类似的库比如[TensorFlowSharp](https://www.nuget.org/packages/TensorFlowSharp/)相比，Tensorflow.NET的实现更加完全，提供了更多的高级API，使用起来更为方便，更新也更加迅速。
+
+
+## 文档
+
+基本介绍与简单用例：[Tensorflow.NET Documents](https://scisharp.github.io/tensorflow-net-docs)
+
+详细文档：[The Definitive Guide to Tensorflow.NET](https://tensorflownet.readthedocs.io/en/latest/FrontCover.html)
+
+例程：[TensorFlow.NET Examples](https://github.com/SciSharp/TensorFlow.NET-Examples)
+
+运行例程常见问题：[Tensorflow.NET FAQ](tensorflowlib/README.md)
+
+## 安装与使用
+
+安装可以在NuGet包管理器中搜索包名安装，也可以用下面命令行的方式。
+
+安装分为两个部分，第一部分是Tensorflow.NET的主体：
+
+```sh
+### 安装Tensorflow.NET
+PM> Install-Package TensorFlow.NET
+
+### 安装Tensorflow.Keras
+PM> Install-Package TensorFlow.Keras
+```
+
+第二部分是计算支持部分，只需要根据自己的设备和系统选择下面之一即可：
+
+```
+### CPU版本，支持Windows、Linux和Mac
+PM> Install-Package SciSharp.TensorFlow.Redist
+
+### Windows下的GPU版本（需要安装CUDA和cuDNN）
+PM> Install-Package SciSharp.TensorFlow.Redist-Windows-GPU
+
+### Linux下的GPU版本（需要安装CUDA和cuDNN）
+PM> Install-Package SciSharp.TensorFlow.Redist-Linux-GPU
+```
+
+下面给出两个简单的例子，更多例子可以在[TensorFlow.NET Examples]中查看。
+
+### 简单例子（使用Eager Mode进行线性回归）
+
+```csharp
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
+using Tensorflow;
+using Tensorflow.NumPy;
+
+// Parameters
+var training_steps = 1000;
+var learning_rate = 0.01f;
+var display_step = 100;
+
+// Sample data
+var X = np.array(3.3f, 4.4f, 5.5f, 6.71f, 6.93f, 4.168f, 9.779f, 6.182f, 7.59f, 2.167f,
+ 7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f);
+var Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f,
+ 2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f);
+var n_samples = X.shape[0];
+
+// We can set a fixed init value in order to demo
+var W = tf.Variable(-0.06f, name: "weight");
+var b = tf.Variable(-0.73f, name: "bias");
+var optimizer = keras.optimizers.SGD(learning_rate);
+
+// Run training for the given number of steps.
+foreach (var step in range(1, training_steps + 1))
+{
+ // Run the optimization to update W and b values.
+ // Wrap computation inside a GradientTape for automatic differentiation.
+ using var g = tf.GradientTape();
+ // Linear regression (Wx + b).
+ var pred = W * X + b;
+ // Mean square error.
+ var loss = tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * n_samples);
+ // should stop recording
+ // Compute gradients.
+ var gradients = g.gradient(loss, (W, b));
+
+ // Update W and b following gradients.
+ optimizer.apply_gradients(zip(gradients, (W, b)));
+
+ if (step % display_step == 0)
+ {
+ pred = W * X + b;
+ loss = tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * n_samples);
+ print($"step: {step}, loss: {loss.numpy()}, W: {W.numpy()}, b: {b.numpy()}");
+ }
+}
+```
+
+这一用例也可以在[Jupyter Notebook Example](https://github.com/SciSharp/SciSharpCube)进行运行.
+
+### 简单例子（使用Keras搭建Resnet）
+
+```csharp
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
+using Tensorflow;
+using Tensorflow.NumPy;
+
+var layers = keras.layers;
+// input layer
+var inputs = keras.Input(shape: (32, 32, 3), name: "img");
+// convolutional layer
+var x = layers.Conv2D(32, 3, activation: "relu").Apply(inputs);
+x = layers.Conv2D(64, 3, activation: "relu").Apply(x);
+var block_1_output = layers.MaxPooling2D(3).Apply(x);
+x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(block_1_output);
+x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(x);
+var block_2_output = layers.Add().Apply(new Tensors(x, block_1_output));
+x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(block_2_output);
+x = layers.Conv2D(64, 3, activation: "relu", padding: "same").Apply(x);
+var block_3_output = layers.Add().Apply(new Tensors(x, block_2_output));
+x = layers.Conv2D(64, 3, activation: "relu").Apply(block_3_output);
+x = layers.GlobalAveragePooling2D().Apply(x);
+x = layers.Dense(256, activation: "relu").Apply(x);
+x = layers.Dropout(0.5f).Apply(x);
+// output layer
+var outputs = layers.Dense(10).Apply(x);
+// build keras model
+var model = keras.Model(inputs, outputs, name: "toy_resnet");
+model.summary();
+// compile keras model in tensorflow static graph
+model.compile(optimizer: keras.optimizers.RMSprop(1e-3f),
+ loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true),
+ metrics: new[] { "acc" });
+// prepare dataset
+var ((x_train, y_train), (x_test, y_test)) = keras.datasets.cifar10.load_data();
+// normalize the input
+x_train = x_train / 255.0f;
+// training
+model.fit(x_train[new Slice(0, 2000)], y_train[new Slice(0, 2000)],
+ batch_size: 64,
+ epochs: 10,
+ validation_split: 0.2f);
+// save the model
+model.save("./toy_resnet_model");
+```
+
+此外，Tensorflow.NET也支持用F#搭建上述模型进行训练和推理。
+
+## Tensorflow.NET版本对应关系
+
+| TensorFlow.NET Versions | tensorflow 1.14, cuda 10.0 | tensorflow 1.15, cuda 10.0 | tensorflow 2.3, cuda 10.1 | tensorflow 2.4, cuda 11 | tensorflow 2.7, cuda 11 |tensorflow 2.10, cuda 11 |
+| -------------------------- | ------------- | -------------- | ------------- | ------------- | ------------ | ------------ |
+| tf.net 0.10x, tf.keras 0.10 | | | | | | x |
+| tf.net 0.7x, tf.keras 0.7 | | | | | x | |
+| tf.net 0.4x, tf.keras 0.5 | | | | x | | |
+| tf.net 0.3x, tf.keras 0.4 | | | x | | | |
+| tf.net 0.2x | | x | x | | | |
+| tf.net 0.15 | x | x | | | | |
+| tf.net 0.14 | x | | | | | |
+
+
+```
+tf.net 0.4x -> tf native 2.4
+tf.net 0.6x -> tf native 2.6
+tf.net 0.7x -> tf native 2.7
+tf.net 0.10x -> tf native 2.10
+...
+```
+
+如果使用过程中发现有缺失的版本，请告知我们，谢谢！
+
+请注意Tensorflow.NET与Tensorflow.Keras版本存在一一对应关系，请安装与Tensorflow.NET对应的Tensorflow.Keras版本。
+
+## 参与我们的开发:
+
+我们欢迎任何人的任何形式的贡献！无论是文档中的错误纠正，新特性提议，还是BUG修复等等，都会使得Tensorflow.NET项目越来越好，Tensorflow.NET的全体开发者也会积极帮助解决您提出的问题。
+
+下面任何一种形式都可以帮助Tensorflow.NET越来越好：
+
+* Star和分享Tensorflow.NET项目
+* 为Tensorflow.NET添加更多的用例
+* 在issue中告知我们Tensorflow.NET目前相比tensorflow缺少的API或者没有对齐的特性
+* 在issue中提出Tensorflow.NET存在的BUG或者可以改进的地方
+* 在待办事项清单中选择一个进行或者解决某个issue
+* 帮助我们完善文档，这也十分重要
+
+
+## 支持我们
+我们推出了[TensorFlow.NET实战](https://item.jd.com/13441549.html)这本书，包含了Tensorflow.NET主要开发者编写的讲解与实战例程，欢迎您的购买，希望这本书可以给您带来帮助。
+
+
+
+
+
+
+## 联系我们
+
+可以在 [Twitter](https://twitter.com/ScisharpStack), [Facebook](https://www.facebook.com/scisharp.stack.9), [Medium](https://medium.com/scisharp), [LinkedIn](https://www.linkedin.com/company/scisharp-stack/)中关注我们，也可以在[Gitter](https://gitter.im/sci-sharp/community)中与项目开发者以及其它使用者进行沟通交流，也欢迎在仓库中提起issue。
+
+TensorFlow.NET is a part of [SciSharp STACK](https://scisharp.github.io/SciSharp/)
+
+
diff --git a/docs/README.md b/docs/README.md
index 0d3fa1edd..0e3c00484 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -13,4 +13,9 @@ sphinx-quickstarts
### Build the docs
```cmd
make html
-```
\ No newline at end of file
+```
+
+
+
+Access the compiled docs: [https://tensorflownet.readthedocs.io](https://tensorflownet.readthedocs.io/)
+
diff --git a/docs/RELEASE.md b/docs/RELEASE.md
new file mode 100644
index 000000000..62a1be238
--- /dev/null
+++ b/docs/RELEASE.md
@@ -0,0 +1,44 @@
+# Release Notes
+
+**Thanks to our Contributors!**
+
+This release contains contributions from many people at SciSharp as well as the external contributors.
+
+**Release Date 02/06/2021**
+
+### TensorFlow.Binding v0.33.0
+
+* Improve memory usage
+* Fix minor bugs
+
+### TensorFlow.Keras v0.4.0
+
+* Add Subtract layer
+
+* Add model.load_weights and model.save_weights
+
+* Fix memory leak issue
+
+* Support to build YOLOv3 object detection model
+
+
+
+**Release Date 01/09/2021**
+
+### TensorFlow.Binding v0.32.0
+
+* Fix input `dtype` for `MapDataset`.
+* Fix `image_dataset_from_directory` function.
+* Fix `tf.transpose`.
+* Add `array_ops.where_v2`, `array_ops.select_v2`, `array_ops.softplus`.
+* Add `dataset.dataset_cardinality`.
+
+### TensorFlow.Keras v0.3.0
+
+* Fix `weight` init value for `double` type in `compute_weighted_loss`.
+* Add `MeanSquaredError `, `MeanAbsolutePercentageError `, `MeanAbsoluteError` and `MeanSquaredLogarithmicError` loss functions.
+* `Sequential` model API works.
+* Add `ShellProgressBar` to show training progress better.
+
+
+
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+++ b/docs/_config.yml
@@ -0,0 +1 @@
+theme: jekyll-theme-cayman
\ No newline at end of file
diff --git a/docs/assets/Logo.md b/docs/assets/Logo.md
new file mode 100644
index 000000000..21e7858ae
--- /dev/null
+++ b/docs/assets/Logo.md
@@ -0,0 +1,3 @@
+TensorFlow.NET logo (c) 2019 by Meinrad Recheis.
+
+The logo is based on the original Tensorflow logo which is copyrighted by the respective creator.
\ No newline at end of file
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+++ b/docs/assets/tf.net.architecture.svg
@@ -0,0 +1,370 @@
+
+
+
+
diff --git a/docs/assets/tf.net.icon-purple.svg b/docs/assets/tf.net.icon-purple.svg
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+++ b/docs/assets/tf.net.icon-purple.svg
@@ -0,0 +1,141 @@
+
+
+
+
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@@ -0,0 +1,141 @@
+
+
+
+
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+++ b/docs/assets/tf.net.logo.svg
@@ -0,0 +1,210 @@
+
+
+
+
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diff --git a/docs/source/Constant.md b/docs/source/Constant.md
index 655a4b084..dd6aa3bf0 100644
--- a/docs/source/Constant.md
+++ b/docs/source/Constant.md
@@ -1,26 +1,17 @@
-# Chapter. Constant
+# Chapter 2. Constant
-In TensorFlow, a constant is a special Tensor that cannot be modified while the graph is running. Like in a linear model $\tilde{y_i}=\boldsymbol{w}x_i+b$, constant $b$ can be represented as a Constant Tensor. Since the constant is a Tensor, it also has all the data characteristics of Tensor, including:
+In TensorFlow, a constant is a special Tensor that cannot be modified while the graph is running. Like in a linear model `y = ax + b`, constant `b` can be represented as a `Constant` Tensor. Since the constant is a Tensor, it also has all the data characteristics of Tensor, including:
* value: scalar value or constant list matching the data type defined in TensorFlow;
* dtype: data type;
* shape: dimensions;
* name: constant's name;
-在TensorFlow中，常量是一种特殊的Tensor，它在计算图运行的时候，不能被修改。比如在线性模型里$\tilde{y_i}=\boldsymbol{w}x_i+b$, 常数$b$就可以用一个常量来表示。既然常量是一种Tensor，那么它也就具有Tensor的所有数据特性，它包括：
-* value: 符合TensorFlow中定义的数据类型的常数值或者常数列表;
-* dtype：数据类型;
-* shape：常量的形状;
-* name：常量的名字;
+### How to create a Constant
-
-##### How to create a Constant
-
-TensorFlow provides a handy function to create a Constant. In TF.NET, you can use the same function name `tf.constant` to create it. TF.NET takes the same name as python binding to the API. Naming, although this will make developers who are used to C# naming habits feel uncomfortable, but after careful consideration, I decided to give up the C# convention naming method.
-
-TensorFlow提供了一个很方便的函数用来创建一个Constant, 在TF.NET，可以使用同样的函数名`tf.constant`来创建，TF.NET采取尽可能使用和python binding一样的命名方式来对API命名，虽然这样会让习惯C#命名习惯的开发者感到不舒服，但我经过深思熟虑之后还是决定放弃C#的约定命名方式。
+TensorFlow provides a handy function to create a Constant. In TF.NET, you can use the same function name `tf.constant` to create it. TF.NET takes the same name as python binding for the API. Naming, although this will make developers who are used to C# naming convention feel uncomfortable, but after careful consideration, I decided to give up the C# convention naming method. One of reason is for model developer, they don't have to learn a totally new different APIs.
Initialize a scalar constant:
@@ -33,23 +24,57 @@ var c4 = tf.constant("Big Tree"); // string
Initialize a constant through ndarray:
+TF.NET works very well with `NumSharp`'s `NDArray`. You can create a tensor from .NET primitive data type and NDArray as well. An `ndarray` is a (usually fixed-size) multidimensional container of items of the same type and size. The number of dimensions and items in an array is defined by its `shape`, which is a tuple of N non-negative integers that specify the sizes of each dimension.
+
```csharp
// dtype=int, shape=(2, 3)
-var nd = np.array(new int[][]
+var nd = np.array(new int[,]
{
- new int[]{3, 1, 1},
- new int[]{2, 3, 1}
+ {1, 2, 3},
+ {4, 5, 6}
});
var tensor = tf.constant(nd);
```
-##### Dive in Constant
+### Dive in Constant
+
+Now let's explore how `constant` works in `eager` mode inside the black box.
+
+Let's continue using the last examples, we're going to initialize a tensor in an ndarray of `[shape(2, 3), int32]`.
+
+##### NDArray
+
+The first thing we need to know is about `ndarray`'s memory model. The ndarray memory model is a very important data structure, and almost all underlying computation are inseparable from this datb a structure. One fundamental aspect of the ndarray is that an array is seen as a "chunk" of memory starting at some location. The interpretation of this memory depends on the stride information. A segment of memory is inherently 1-dimensional, and there are many different schemes for arranging the items of an N-dimensional array in a 1-dimensional block. `ndarray` objects can accommodate any strided indexing scheme. In a strided scheme, the N-dimensional index corresponds to the offset (in bytes) : .
+
+
+
+If we take a look at the real memory allocation in Visual Studio, below diagram helps us understand the data structure more intuitively. The strides keep track the size of every single dimension, help identify the actual offset in heap memory. The formula to calculate offset is: `offset = i * strides[0] + j * strides[1]`.
-Now let's explore how constant works.
+For example: if you want to seek the value in `[1, 1]`, you just need to calculate `1 * 3 + 1 * 1 = 4`, converted to pointer is `0x000002556B194260 + 4 = 0x000002556B194264` where has a value `05`.
-现在让我探究一下`tf.constant`是怎么工作的。
+
+Through the above diagram, we know how the data is stored in memory, and then we will look at how the data is transferred to `TensorFlow`.
+
+##### Tensor
+
+If you don't understand very well what `Tensor` is, you can go back to the chapter `Tensor` there is pretty much explanation if you skipped that chapter. Tensor is actually an NDArray that is with more than 2 dimensions.
+
+TensorFlow will decide whether to copy the data or use the same pointer. Normally speaking, it's more safe whenever you copy data for the following process, especially in interoperating between .NET runtime and C++ runtime that they all have their own garbage collection (GC) mechanism, application will crash if someone access a block of destroyed memory. `TF_STRING` and `TF_RESOURCE` tensors have a different representation in `TF_Tensor` than they do in `tensorflow::Tensor`. Other types have the same representation, so copy only if it is safe to do so.
+
+
+
+Before tensorflow is creating the `TF_Tensor`, it checks the shape and data size. If the size doesn't match, it will return `nullptr` pointer.
+
+##### Get the data of Tensor
+
+For `eager` mode, it's pretty simple to view the actual value in a `tensor`.
+
+```csharp
+var data = tensor.numpy()
+```
+The `data` will be a `ndarray` variable.
##### Other functions to create a Constant
diff --git a/docs/source/ConvolutionNeuralNetwork.md b/docs/source/ConvolutionNeuralNetwork.md
new file mode 100644
index 000000000..6b47c9d8d
--- /dev/null
+++ b/docs/source/ConvolutionNeuralNetwork.md
@@ -0,0 +1,350 @@
+# Chapter. Convolution Neural Network
+
+In this chapter, we'll implement a simple Convolutional Neural Network model. We'll implement this model to classify MNIST dataset.
+
+
+
+The structure of the neural network we're going to build is as follows. The hand-written digits images of the MNIST data which has 10 classes (from 0 to 9). The network is with 2 convolutional layers followed by 2 full-connected layers at the end.
+
+
+
+Get started with the implementation:
+
+1. **Prepare data**
+
+ MNIST is dataset of handwritten digits which contains 55,000 examples for training, 5,000 examples for validation and 10,000 example for testing. The digits have been size-normalized and centered in a fixed-size image (28 x 28 pixels) with values from 0 and 1.Each image has been flattened and converted to a 1-D array of 784 features. It's also kind of benchmark of datasets for deep learning.
+
+ 
+
+ We define some variables makes it easier to modify them later.
+
+ ```csharp
+ using System;
+ using NumSharp;
+ using Tensorflow;
+ using TensorFlowNET.Examples.Utility;
+ using static Tensorflow.Python;
+ ```
+
+ ```csharp
+ const int img_h = 28;
+ const int img_w = 28;
+ int n_classes = 10; // Number of classes, one class per digit
+ int n_channels = 1;
+ ```
+
+ We'll write the function which automatically loads the MNIST data and returns it in our desired shape and format. There is an MNIST data helper to make life easier.
+
+ ```csharp
+ Datasets mnist;
+ public void PrepareData()
+ {
+ mnist = MnistDataSet.read_data_sets("mnist", one_hot: true);
+ }
+ ```
+
+ Other than a function for loading the images and corresponding labels, we still need three more functions:
+
+ **reformat:** reformats the data to the format acceptable for convolutional layer.
+
+ ```csharp
+ private (NDArray, NDArray) Reformat(NDArray x, NDArray y)
+ {
+ var (img_size, num_ch, num_class) = (np.sqrt(x.shape[1]), 1, len(np.unique(np.argmax(y, 1))));
+ var dataset = x.reshape(x.shape[0], img_size, img_size, num_ch).astype(np.float32);
+ //y[0] = np.arange(num_class) == y[0];
+ //var labels = (np.arange(num_class) == y.reshape(y.shape[0], 1, y.shape[1])).astype(np.float32);
+ return (dataset, y);
+ }
+ ```
+
+
+
+ **randomize**: which randomizes the order of images and their labels. At the beginning of each epoch, we will re-randomize the order of data samples to make sure that the trained model is not sensitive to the order of data.
+
+ ```csharp
+ private (NDArray, NDArray) randomize(NDArray x, NDArray y)
+ {
+ var perm = np.random.permutation(y.shape[0]);
+
+ np.random.shuffle(perm);
+ return (mnist.train.images[perm], mnist.train.labels[perm]);
+ }
+ ```
+
+ **get_next_batch**: which only selects a few number of images determined by the batch_size variable (as per Stochastic Gradient Descent method).
+
+ ```csharp
+ private (NDArray, NDArray) get_next_batch(NDArray x, NDArray y, int start, int end)
+ {
+ var x_batch = x[$"{start}:{end}"];
+ var y_batch = y[$"{start}:{end}"];
+ return (x_batch, y_batch);
+ }
+ ```
+
+2. **Set Hyperparameters**
+
+ There're about 55,000 images in training set, it takes a long time to calculate the gradient of the model using all there images. Therefore we use a small batch of images in each iteration of the optimizer by Stochastic Gradient Descent.
+
+ * epoch: one forward pass and one backward pass of all the training examples.
+ * batch size: the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you'll need.
+ * iteration: one forward pass and one backward pass of one batch of images the training examples.
+
+ ```csharp
+ int epochs = 10;
+ int batch_size = 100;
+ float learning_rate = 0.001f;
+ int display_freq = 200; // Frequency of displaying the training results
+ ```
+
+3. **Network configuration**
+
+ 1st convolutional layer:
+
+ ```csharp
+ int filter_size1 = 5; // Convolution filters are 5 x 5 pixels.
+ int num_filters1 = 16; // There are 16 of these filters.
+ int stride1 = 1; // The stride of the sliding window
+ ```
+
+ 2nd convolutional layer:
+
+ ```csharp
+ int filter_size2 = 5; // Convolution filters are 5 x 5 pixels.
+ int num_filters2 = 32;// There are 32 of these filters.
+ int stride2 = 1; // The stride of the sliding window
+ ```
+
+ Fully-connected layer:
+
+ ```csharp
+ h1 = 128 # Number of neurons in fully-connected layer.
+ ```
+
+
+
+4. **Building the neural network**
+
+ Let's make some functions to help build computation graph.
+
+ **variables**: We need to define two variables `W` and `b` to construct our linear model. We use `Tensorflow Variables` of proper size and initialization to define them.
+
+ ```csharp
+ // Create a weight variable with appropriate initialization
+ private RefVariable weight_variable(string name, int[] shape)
+ {
+ var initer = tf.truncated_normal_initializer(stddev: 0.01f);
+ return tf.get_variable(name,
+ dtype: tf.float32,
+ shape: shape,
+ initializer: initer);
+ }
+
+ // Create a bias variable with appropriate initialization
+ private RefVariable bias_variable(string name, int[] shape)
+ {
+ var initial = tf.constant(0f, shape: shape, dtype: tf.float32);
+ return tf.get_variable(name,
+ dtype: tf.float32,
+ initializer: initial);
+ }
+ ```
+
+ **2D convolution layer**: This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.
+
+ ```csharp
+ private Tensor conv_layer(Tensor x, int filter_size, int num_filters, int stride, string name)
+ {
+ return with(tf.variable_scope(name), delegate {
+
+ var num_in_channel = x.shape[x.NDims - 1];
+ var shape = new[] { filter_size, filter_size, num_in_channel, num_filters };
+ var W = weight_variable("W", shape);
+ // var tf.summary.histogram("weight", W);
+ var b = bias_variable("b", new[] { num_filters });
+ // tf.summary.histogram("bias", b);
+ var layer = tf.nn.conv2d(x, W,
+ strides: new[] { 1, stride, stride, 1 },
+ padding: "SAME");
+ layer += b;
+ return tf.nn.relu(layer);
+ });
+ }
+ ```
+
+ **max-pooling layer**: Max pooling operation for temporal data.
+
+ ```csharp
+ private Tensor max_pool(Tensor x, int ksize, int stride, string name)
+ {
+ return tf.nn.max_pool(x,
+ ksize: new[] { 1, ksize, ksize, 1 },
+ strides: new[] { 1, stride, stride, 1 },
+ padding: "SAME",
+ name: name);
+ }
+ ```
+
+ **flatten_layer**: Flattens the output of the convolutional layer to be fed into fully-connected layer.
+
+ ```csharp
+ private Tensor flatten_layer(Tensor layer)
+ {
+ return with(tf.variable_scope("Flatten_layer"), delegate
+ {
+ var layer_shape = layer.TensorShape;
+ var num_features = layer_shape[new Slice(1, 4)].Size;
+ var layer_flat = tf.reshape(layer, new[] { -1, num_features });
+
+ return layer_flat;
+ });
+ }
+ ```
+
+
+
+ **fully-connected layer**: Neural network consists of stacks of fully-connected (dense) layers. Having the weight (W) and bias (b) variables, a fully-connected layer is defined as `activation(W x X + b)`. The complete `fc_layer` function is as below:
+
+ ```csharp
+ private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true)
+ {
+ return with(tf.variable_scope(name), delegate
+ {
+ var in_dim = x.shape[1];
+
+ var W = weight_variable("W_" + name, shape: new[] { in_dim, num_units });
+ var b = bias_variable("b_" + name, new[] { num_units });
+
+ var layer = tf.matmul(x, W) + b;
+ if (use_relu)
+ layer = tf.nn.relu(layer);
+
+ return layer;
+ });
+ }
+ ```
+
+ **inputs**: Now we need to define the proper tensors to feed in the input to our model. Placeholder variable is the suitable choice for the input images and corresponding labels. This allow us to change the inputs (images and labels) to the TensorFlow graph.
+
+ ```csharp
+ with(tf.name_scope("Input"), delegate
+ {
+ // Placeholders for inputs (x) and outputs(y)
+ x = tf.placeholder(tf.float32, shape: (-1, img_h, img_w, n_channels), name: "X");
+ y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y");
+ });
+ ```
+
+ Placeholder `y` is the variable for the true labels associated with the images that were input in the placeholder variable `x`. It holds an arbitrary number of labels and each label is a vector of length `num_classes` which is 10.
+
+ **network layers**: After creating the proper input, we have to pass it to our model. Since we have a neural network, we can stack multiple fully-connected layers using `fc_layer` method. Note that we will not use any activation function (use_relu = false) in the last layer. The reason is that we can use `tf.nn.softmax_cross_entropy_with_logits` to calculate the loss.
+
+ ```csharp
+ var conv1 = conv_layer(x, filter_size1, num_filters1, stride1, name: "conv1");
+ var pool1 = max_pool(conv1, ksize: 2, stride: 2, name: "pool1");
+ var conv2 = conv_layer(pool1, filter_size2, num_filters2, stride2, name: "conv2");
+ var pool2 = max_pool(conv2, ksize: 2, stride: 2, name: "pool2");
+ var layer_flat = flatten_layer(pool2);
+ var fc1 = fc_layer(layer_flat, h1, "FC1", use_relu: true);
+ var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false);
+ ```
+
+ **loss function, optimizer, accuracy, prediction**: After creating the network, we have to calculate the loss and optimize it, we have to calculate the `prediction` and `accuracy`.
+
+ ```csharp
+ with(tf.variable_scope("Train"), delegate
+ {
+
+
+ with(tf.variable_scope("Optimizer"), delegate
+ {
+ optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss);
+ });
+
+ with(tf.variable_scope("Accuracy"), delegate
+ {
+ var correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name: "correct_pred");
+ accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy");
+ });
+
+ with(tf.variable_scope("Prediction"), delegate
+ {
+ cls_prediction = tf.argmax(output_logits, axis: 1, name: "predictions");
+ });
+ });
+ ```
+
+ **initialize variables**: We have to invoke a variable initializer operation to initialize all variables.
+
+```csharp
+ var init = tf.global_variables_initializer();
+```
+
+5. **Train**
+
+ After creating the graph, we can train our model. To train the model, we have to create a session and run the graph in the session.
+
+ ```csharp
+ // Number of training iterations in each epoch
+ var num_tr_iter = y_train.len / batch_size;
+
+ var init = tf.global_variables_initializer();
+ sess.run(init);
+
+ float loss_val = 100.0f;
+ float accuracy_val = 0f;
+
+ foreach (var epoch in range(epochs))
+ {
+ print($"Training epoch: {epoch + 1}");
+ // Randomly shuffle the training data at the beginning of each epoch
+ (x_train, y_train) = mnist.Randomize(x_train, y_train);
+
+ foreach (var iteration in range(num_tr_iter))
+ {
+ var start = iteration * batch_size;
+ var end = (iteration + 1) * batch_size;
+ var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
+
+ // Run optimization op (backprop)
+ sess.run(optimizer, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
+
+ if (iteration % display_freq == 0)
+ {
+ // Calculate and display the batch loss and accuracy
+ var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
+ loss_val = result[0];
+ accuracy_val = result[1];
+ print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}");
+ }
+ }
+
+ // Run validation after every epoch
+ var results1 = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_valid), new FeedItem(y, y_valid));
+ loss_val = results1[0];
+ accuracy_val = results1[1];
+ print("---------------------------------------------------------");
+ print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
+ print("---------------------------------------------------------");
+ }
+ ```
+
+6. **Test**
+
+ After the training is done, we have to test our model to see how good it performs on a new dataset.
+
+ ```csharp
+ public void Test(Session sess)
+ {
+ var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_test), new FeedItem(y, y_test));
+ loss_test = result[0];
+ accuracy_test = result[1];
+ print("---------------------------------------------------------");
+ print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
+ print("---------------------------------------------------------");
+ }
+ ```
+
+
+
diff --git a/docs/source/EagerMode.md b/docs/source/EagerMode.md
index cbb0ea026..ded56d41f 100644
--- a/docs/source/EagerMode.md
+++ b/docs/source/EagerMode.md
@@ -1,2 +1,3 @@
-# Chapter. Eager Mode
+# Chapter 4. Eager Mode
+TensorFlow's eager execution is an imperative programming environment that evaluates operations immediately, without building graphs: operations return concrete values instead of constructing a computational graph to run later. This makes it easy to get started with TensorFlow and debug models, and it reduces boilerplate as well.
\ No newline at end of file
diff --git a/docs/source/Foreword.md b/docs/source/Foreword.md
index af8ff8d36..256094f57 100644
--- a/docs/source/Foreword.md
+++ b/docs/source/Foreword.md
@@ -2,16 +2,10 @@
One of the most nerve-wracking periods when releasing the first version of an open source project occurs when the [gitter](https://gitter.im/sci-sharp/community) community is created. You are all alone, eagerly hoping and wishing for the first user to come along. I still vividly remember those days.
-最让人紧张的时刻是当我为自己的开源项目发布第一个版本并在gitter里开放一个聊天社区，而里面只有你一个人，饥渴地等待第一个进入聊天室的用户，我仍然清楚地记得那个时期。
-
TensorFlow.NET is my third open source project. BotSharp and NumSharp are the first two. The response is pretty good. I also got a lot of stars on github. Although the first two projects are very difficult, I can't admit that TensorFlow.NET is much more difficult than the previous two, and it is an area I have never been involved with. Mainly related to GPU parallel computing, distributed computing and neural network model. When I started writing this project, I was also sorting out the idea of the coding process. TensorFlow is a huge and complicated project, and it is easy to go beyond the scope of personal ability. Therefore, I want to record the thoughts at the time as much as possible. The process of recording and sorting clears the way of thinking.
-TensorFlow.NET是我写的第3个开源项目，BotSharp和NumSharp是前两个，反应都还不错，在github上也收获了不少星。虽然前两个项目的难度很大，但是我不得承认TensorFlow.NET的难度要比之前两个要大的多，是我从未涉入过的领域。主要涉及GPU并行计算，分布式计算和神经网络模型。当我开始写这个项目的时候，我同时也在整理编码过程时候的想法，TensorFlow是个巨大最复杂的工程，很容易超出个人能力范围，所以想尽可能地把当时的思路记录下来，也想趁着记录整理的过程把思路理清。
-
All the examples in this book can be found in the github repository of TensorFlow.NET. When the source code and the code in the book are inconsistent, please refer to the source code. The sample code is typically located in the Example or UnitTest project.
-
-本书中的所有例子都可以在TensorFlow.NET的github仓库中找到，当源代码和书中的代码不一致时，请以源代码为准。示例代码一般都位于Example或者是UnitTest项目里。
\ No newline at end of file
diff --git a/docs/source/Graph.md b/docs/source/Graph.md
index 7bc473f25..874cd9a42 100644
--- a/docs/source/Graph.md
+++ b/docs/source/Graph.md
@@ -1,4 +1,4 @@
-# Chapter. Graph
+# Chapter 3. Graph
TensorFlow uses a **dataflow graph** to represent your computation in terms of the dependencies between individual operations. A graph defines the computation. It doesn't compute anything, it doesn't hold any values, it just defines the operations that you specified in your code.
diff --git a/docs/source/HelloWorld.md b/docs/source/HelloWorld.md
index 5375ec159..8b7fbf733 100644
--- a/docs/source/HelloWorld.md
+++ b/docs/source/HelloWorld.md
@@ -2,19 +2,17 @@
I would describe TensorFlow as an open source machine learning framework developed by Google which can be used to build neural networks and perform a variety of machine learning tasks. it works on data flow graph where nodes are the mathematical operations and the edges are the data in the form of tensor, hence the name Tensor-Flow.
-按照我的理解，TensorFlow是Google公司开发的一个开源机器学习框架，可以用来搭建神经网络模型和其它传统机器学习模型，它采用了图计算模型，图的节点和边分别代表了操作和数据输入或输出，数据在图的单个方向传递，因此这个过程形象地取名叫做TensorFlow。
-Let's run a classic HelloWorld program first and see if TensorFlow is running on .NET. I can't think of a simpler way to be a HelloWorld.
-让我们先运行一个经典的HelloWorld程序，看看TensorFlow在.NET上面运行的效果，我想不出有比做个HelloWorld更简单的方式了。
+Let's run a classic HelloWorld program first and see if TensorFlow is running on .NET. I can't think of a simpler way to be a HelloWorld.
### Install the TensorFlow.NET SDK
-TensorFlow.NET uses the .NET Standard 2.0 standard, so your new project Target Framework can be .NET Framework or .NET Core. All the examples in this book are using .NET Core 2.2 and Microsoft Visual Studio Community 2017. To start building TensorFlow program you just need to download and install the .NET SDK (Software Development Kit). You have to download the latest .NET Core SDK from offical website: https://dotnet.microsoft.com/download.
+TensorFlow.NET uses the .NET Standard 2.0 standard, so your new project Target Framework can be .NET Framework or .NET Core/ .NET 5. All the examples in this book are using .NET Core 3.1 and Microsoft Visual Studio Community 2019. To start building TensorFlow program you just need to download and install the .NET SDK (Software Development Kit). You have to download the latest .NET Core SDK from offical website: https://dotnet.microsoft.com/download.
+
-TensorFlow.NET采用.NET标准库2.0版本，因此你的新建工程可以是.NET Framework或者是基于.NET Core的。本文中的所有例子都是用的.NET Core 2.2的，IDE用的是Microsoft Visual Studio Community 2017。为了能编译和运行TensorFlow工程，你需要从这里下载最新的.NET Core SDK: https://dotnet.microsoft.com/download。
1. New a project
@@ -27,54 +25,53 @@ TensorFlow.NET采用.NET标准库2.0版本，因此你的新建工程可以是.N
```cmd
+### install tensorflow C# binding
PM> Install-Package TensorFlow.NET
+
+### Install tensorflow binary
+### For CPU version
+PM> Install-Package SciSharp.TensorFlow.Redist
+
+### For GPU version (CUDA and cuDNN are required)
+PM> Install-Package SciSharp.TensorFlow.Redist-Windows-GPU
```
### Start coding Hello World
-After installing the TensorFlow.NET package, you can use the `using Tensorflow` to introduce the TensorFlow library.
+After installing the TensorFlow.NET package, you can use the `using static Tensorflow.Binding` to introduce the TensorFlow .NET library.
-安装完TensorFlow.NET包后，你就可以使用`using Tensorflow`来引入TensorFlow库了。
+TensorFlow 2.x enabled `Eager Mode` by default. About what eager mode is, I will introduce it in detail in the following chapters.
```csharp
using System;
-using Tensorflow;
+using static Tensorflow.Binding;
namespace TensorFlowNET.Examples
{
///
/// Simple hello world using TensorFlow
///
- public class HelloWorld : IExample
+ class Program
{
- public void Run()
+ static void Main(string[] args)
{
- /* Create a Constant op
- The op is added as a node to the default graph.
-
- The value returned by the constructor represents the output
- of the Constant op. */
var hello = tf.constant("Hello, TensorFlow!");
-
- // Start tf session
- using (var sess = tf.Session())
- {
- // Run the op
- var result = sess.run(hello);
- Console.WriteLine(result);
- }
+ Console.WriteLine(hello);
}
}
}
```
After CTRL + F5 run, you will get the output.
```cmd
-2019-01-05 10:53:42.145931: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
-Hello, TensorFlow!
-Press any key to continue . . .
+9/20/2020 2:15:09 AM Starting Hello World
+tf.Tensor: shape=(), dtype=string, numpy=Hello, TensorFlow.NET!
+9/20/2020 2:15:09 AM Completed Hello World
+Example: Hello World in 0.1273463s is OK!
+TensorFlow.NET v0.20.1.0
+TensorFlow Binary v2.3.0
+1 of 21 example(s) are completed.
+Press [Enter] to continue...
```
-This sample code can be found at [here](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/HelloWorld.cs).
-
-此示例代码可以在[这里](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/HelloWorld.cs)找到。
+This sample code can be found at [here](https://github.com/SciSharp/SciSharp-Stack-Examples/blob/master/src/TensorFlowNET.Examples/HelloWorld.cs).
diff --git a/docs/source/ImageRecognition.md b/docs/source/ImageRecognition.md
index 14d1c32b4..74d3ee5bf 100644
--- a/docs/source/ImageRecognition.md
+++ b/docs/source/ImageRecognition.md
@@ -133,4 +133,5 @@ grace_hopper.jpg: 466 bulletproof vest, 0.005350832
2/18/2019 3:56:25 AM Completed InceptionArchGoogLeNet
```
-You can find the full source code from [github](https://github.com/SciSharp/TensorFlow.NET/tree/master/test/TensorFlowNET.Examples/ImageProcess).
\ No newline at end of file
+You can find the full source code from [github](https://github.com/SciSharp/TensorFlow.NET-Examples/tree/master/src/TensorFlowNET.Examples/ImageProcessing).
+
diff --git a/docs/source/LinearRegression.md b/docs/source/LinearRegression.md
index d9921270a..8033625c3 100644
--- a/docs/source/LinearRegression.md
+++ b/docs/source/LinearRegression.md
@@ -1,5 +1,7 @@
# Chapter. Linear Regression
+
+
### What is linear regression?
Linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).
@@ -8,9 +10,7 @@ Consider the case of a single variable of interest y and a single predictor vari
We have some data $D=\{x{\tiny i},y{\tiny i}\}$ and we assume a simple linear model of this dataset with Gaussian noise:
-线性回归是一种线性建模方法，这种方法用来描述自变量与一个或多个因变量的之间的关系。在只有一个因变量y和一个自变量的情况下。自变量还有以下几种叫法：
-协变量，输入，特征；因变量通常被叫做响应变量，输出，输出结果。
-假如我们有数据$D=\{x{\tiny i},y{\tiny i}\}$，并且假设这个数据集是满足高斯分布的线性模型：
+
```csharp
// Prepare training Data
var train_X = np.array(3.3f, 4.4f, 5.5f, 6.71f, 6.93f, 4.168f, 9.779f, 6.182f, 7.59f, 2.167f, 7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f);
@@ -21,15 +21,13 @@ var n_samples = train_X.shape[0];
Based on the given data points, we try to plot a line that models the points the best. The red line can be modelled based on the linear equation: $y = wx + b$. The motive of the linear regression algorithm is to find the best values for $w$ and $b$. Before moving on to the algorithm, le's have a look at two important concepts you must know to better understand linear regression.
-按照上图根据数据描述的数据点，在这些数据点之间画出一条线，这条线能达到最好模拟点的分布的效果。红色的线能够通过下面呢线性等式来描述：$y = wx + b$。
-线性回归算法的目标就是找到这条线对应的最好的参数$w$和$b$。在介绍线性回归算法之前，我们先看两个重要的概念，这两个概念有助于你理解线性回归算法。
+
### Cost Function
The cost function helps us to figure out the best possible values for $w$ and $b$ which would provide the best fit line for the data points. Since we want the best values for $w$ and $b$, we convert this search problem into a minimization problem where we would like to minimize the error between the predicted value and the actual value.
-损失函数帮助我们估算出最优的参数$w$和$b$，这个最优的参数能够最好的拟合数据点的分布。由于我们想找到最优的参数$w$和$b$，因此我们把这个问题转化成求
-预测参数与实际参数之差的最小值问题。
+
@@ -37,8 +35,7 @@ We choose the above function to minimize. The difference between the predicted v
value by the total number of data points. This provides the average squared error over all the data points. Therefore, this cost function is also known as the Mean Squared Error(MSE) function. Now, using this MSE
function we are going to change the values of $w$ and $b$ such that the MSE value settles at the minima.
-我们选择最小化上面的函数。预测值和真实值之间的差异的大小衡量了预测结果的偏差。我们用所有点的偏差的平方和除以所有点所有点的数量大小来表示说有点的平均
-的误差大小。因此，损失函数又叫均方误差（简称MSE）。到此，我们可以通过调整参数$w$和$b$来使MSE达到最小值。
+
```csharp
// tf Graph Input
@@ -56,13 +53,13 @@ var pred = tf.add(tf.multiply(X, W), b);
var cost = tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * n_samples);
```
+
+
### Gradient Descent
-### 梯度下降法
The another important concept needed to understand is gradient descent. Gradient descent is a method of updating $w$ and $b$ to minimize the cost function. The idea is that we start with some random values for $w$ and $b$ and then we change these values iteratively to reduce the cost. Gradient descent helps us on how to update the values or which direction we would go next. Gradient descent is also know as **steepest descent**.
-另一个需要理解的重要概念是梯度下降法。梯度下降法是通过更新参数$w$和$b$来最小化损失函数。梯度下降法的思想就是首先以任意的参数$w$和$b$开始计算损失
-函数，然后通过递归的方式不断地变化参数来减小损失。梯度下降法帮助我们如何更新参数，或者说告诉我们下一个参数该如何设置。梯度下降法也称为“最快下降法”。
+
@@ -72,9 +69,7 @@ of steps to reach the bottom. If you decide to take one step at a time you would
reach sooner but, there is a chance that you could overshoot the bottom of the pit and not exactly at the bottom. In the gradient descent algorithm, the number of steps you take is the learning rate. This
decides on how fast the algorithm converges to the minima.
-这里做一个类比，想象着你站在一个U形坑的最上面，你的目标是达到坑的最低端。有一个条件是，你不确定你走多少步能到达底端。如果你选择一步一步的走到坑的
-底端，这样可能需要的时间很长。如果你每次大步的往前走，你可能很快到达坑的底端，但是你有可能错过坑的最底端。在梯度下降算法中，你所采用的步数就是训练
-速率。训练速率决定了算法以多块的速度使得损失函数达到最小值。
+
```csharp
@@ -87,4 +82,4 @@ When we visualize the graph in TensorBoard:
-The full example is [here](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/BasicModels/LinearRegression.cs).
+The full example is [here](https://github.com/SciSharp/TensorFlow.NET-Examples/blob/master/src/TensorFlowNET.Examples/BasicModels/LinearRegression.cs).
diff --git a/docs/source/LogisticRegression.md b/docs/source/LogisticRegression.md
index ba0b8f572..ddf75f846 100644
--- a/docs/source/LogisticRegression.md
+++ b/docs/source/LogisticRegression.md
@@ -4,13 +4,13 @@
Logistic regression is a statistical analysis method used to predict a data value based on prior observations of a data set. A logistic regression model predicts a dependent data variable by analyzing the relationship between one or more existing independent variables.
-逻辑回归是一种统计分析方法，用于根据已有得观察数据来预测未知数据。逻辑回归模型通过分析一个或多个现有自变量之间的关系来预测从属数据变量。
+
The dependent variable of logistics regression can be two-category or multi-category, but the two-category is more common and easier to explain. So the most common use in practice is the logistics of the two classifications. An example used by TensorFlow.NET is a hand-written digit recognition, which is a multi-category.
-逻辑回归的因变量可以是二分类的，也可以是多分类的，但是二分类的更为常用，也更加容易解释。 TensorFlow.NET用的例子是一个手写数字识别，它是一个多分类的问题。
+
Softmax regression allows us to handle  where K is the number of classes.
-The full example is [here](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs).
+The full example is [here](https://github.com/SciSharp/TensorFlow.NET-Examples/blob/master/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs).
diff --git a/docs/source/MnistInRnn.md b/docs/source/MnistInRnn.md
new file mode 100644
index 000000000..ce8a13909
--- /dev/null
+++ b/docs/source/MnistInRnn.md
@@ -0,0 +1,5 @@
+# Chapter. MNIST In RNN
+
+### Recurrent Neural Networks
+
+Recurrent Neural Networks (RNNs) are popular models that have shown great promise in sequential data classification task. The traditional neural network model cannot make the next prediction input based on the knowledge that has been learned before.
\ No newline at end of file
diff --git a/docs/source/NearestNeighbor.md b/docs/source/NearestNeighbor.md
index 861181aae..94e300df6 100644
--- a/docs/source/NearestNeighbor.md
+++ b/docs/source/NearestNeighbor.md
@@ -2,4 +2,4 @@
The nearest neighbour algorithm was one of the first algorithms used to solve the travelling salesman problem. In it, the salesman starts at a random city and repeatedly visits the nearest city until all have been visited. It quickly yields a short tour, but usually not the optimal one.
-The full example is [here](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs).
\ No newline at end of file
+The full example is [here](https://github.com/SciSharp/TensorFlow.NET-Examples/blob/master/src/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs).
\ No newline at end of file
diff --git a/docs/source/NeuralNetwork.md b/docs/source/NeuralNetwork.md
new file mode 100644
index 000000000..1d46111b7
--- /dev/null
+++ b/docs/source/NeuralNetwork.md
@@ -0,0 +1,244 @@
+# Chapter. Neural Network
+
+In this chapter, we'll learn how to build a graph of neural network model. The key advantage of neural network compared to Linear Classifier is that it can separate data which it not linearly separable. We'll implement this model to classify hand-written digits images from the MNIST dataset.
+
+
+
+The structure of the neural network we're going to build is as follows. The hand-written digits images of the MNIST data which has 10 classes (from 0 to 9). The network is with 2 hidden layers: the first layer with 200 hidden units (neurons) and the second one (known as classifier layer) with 10 neurons.
+
+
+
+Get started with the implementation step by step:
+
+1. **Prepare data**
+
+ MNIST is dataset of handwritten digits which contains 55,000 examples for training, 5,000 examples for validation and 10,000 example for testing. The digits have been size-normalized and centered in a fixed-size image (28 x 28 pixels) with values from 0 and 1.Each image has been flattened and converted to a 1-D array of 784 features. It's also kind of benchmark of datasets for deep learning.
+
+ 
+
+ We define some variables makes it easier to modify them later. It's important to note that in a linear model, we have to flatten the input images to a vector.
+
+ ```csharp
+ using System;
+ using NumSharp;
+ using Tensorflow;
+ using TensorFlowNET.Examples.Utility;
+ using static Tensorflow.Python;
+ ```
+
+ ```csharp
+ const int img_h = 28;
+ const int img_w = 28;
+ int img_size_flat = img_h * img_w; // 784, the total number of pixels
+ int n_classes = 10; // Number of classes, one class per digit
+ ```
+
+ We'll write the function which automatically loads the MNIST data and returns it in our desired shape and format. There is an MNIST data helper to make life easier.
+
+ ```csharp
+ Datasets mnist;
+ public void PrepareData()
+ {
+ mnist = MnistDataSet.read_data_sets("mnist", one_hot: true);
+ }
+ ```
+
+ Other than a function for loading the images and corresponding labels, we still need two more functions:
+
+ **randomize**: which randomizes the order of images and their labels. At the beginning of each epoch, we will re-randomize the order of data samples to make sure that the trained model is not sensitive to the order of data.
+
+ ```csharp
+ private (NDArray, NDArray) randomize(NDArray x, NDArray y)
+ {
+ var perm = np.random.permutation(y.shape[0]);
+
+ np.random.shuffle(perm);
+ return (mnist.train.images[perm], mnist.train.labels[perm]);
+ }
+ ```
+
+ **get_next_batch**: which only selects a few number of images determined by the batch_size variable (as per Stochastic Gradient Descent method).
+
+ ```csharp
+ private (NDArray, NDArray) get_next_batch(NDArray x, NDArray y, int start, int end)
+ {
+ var x_batch = x[$"{start}:{end}"];
+ var y_batch = y[$"{start}:{end}"];
+ return (x_batch, y_batch);
+ }
+ ```
+
+2. **Set Hyperparameters**
+
+ There're about 55,000 images in training set, it takes a long time to calculate the gradient of the model using all there images. Therefore we use a small batch of images in each iteration of the optimizer by Stochastic Gradient Descent.
+
+ * epoch: one forward pass and one backward pass of all the training examples.
+ * batch size: the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you'll need.
+ * iteration: one forward pass and one backward pass of one batch of images the training examples.
+
+ ```csharp
+ int epochs = 10;
+ int batch_size = 100;
+ float learning_rate = 0.001f;
+ int h1 = 200; // number of nodes in the 1st hidden layer
+ ```
+
+3. **Building the neural network**
+
+ Let's make some functions to help build computation graph.
+
+ **variables**: We need to define two variables `W` and `b` to construct our linear model. We use `Tensorflow Variables` of proper size and initialization to define them.
+
+ ```csharp
+ // weight_variable
+ var in_dim = x.shape[1];
+
+ var initer = tf.truncated_normal_initializer(stddev: 0.01f);
+ var W = tf.get_variable("W_" + name,
+ dtype: tf.float32,
+ shape: (in_dim, num_units),
+ initializer: initer);
+
+ // bias_variable
+ var initial = tf.constant(0f, num_units);
+ var b = tf.get_variable("b_" + name,
+ dtype: tf.float32,
+ initializer: initial);
+ ```
+
+ **fully-connected layer**: Neural network consists of stacks of fully-connected (dense) layers. Having the weight (W) and bias (b) variables, a fully-connected layer is defined as `activation(W x X + b)`. The complete `fc_layer` function is as below:
+
+ ```csharp
+ private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true)
+ {
+ var in_dim = x.shape[1];
+
+ var initer = tf.truncated_normal_initializer(stddev: 0.01f);
+ var W = tf.get_variable("W_" + name,
+ dtype: tf.float32,
+ shape: (in_dim, num_units),
+ initializer: initer);
+
+ var initial = tf.constant(0f, num_units);
+ var b = tf.get_variable("b_" + name,
+ dtype: tf.float32,
+ initializer: initial);
+
+ var layer = tf.matmul(x, W) + b;
+ if (use_relu)
+ layer = tf.nn.relu(layer);
+
+ return layer;
+ }
+ ```
+
+ **inputs**: Now we need to define the proper tensors to feed in the input to our model. Placeholder variable is the suitable choice for the input images and corresponding labels. This allow us to change the inputs (images and labels) to the TensorFlow graph.
+
+ ```csharp
+ // Placeholders for inputs (x) and outputs(y)
+ x = tf.placeholder(tf.float32, shape: (-1, img_size_flat), name: "X");
+ y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y");
+ ```
+
+ Placeholder `x` is defined for the images, the shape is set to `[None, img_size_flat]`, where `None` means that the tensor may hold an arbitrary number of images with each image being a vector of length `img_size_flat`.
+
+ Placeholder `y` is the variable for the true labels associated with the images that were input in the placeholder variable `x`. It holds an arbitrary number of labels and each label is a vector of length `num_classes` which is 10.
+
+ **network layers**: After creating the proper input, we have to pass it to our model. Since we have a neural network, we can stack multiple fully-connected layers using `fc_layer` method. Note that we will not use any activation function (use_relu = false) in the last layer. The reason is that we can use `tf.nn.softmax_cross_entropy_with_logits` to calculate the loss.
+
+ ```csharp
+ // Create a fully-connected layer with h1 nodes as hidden layer
+ var fc1 = fc_layer(x, h1, "FC1", use_relu: true);
+ // Create a fully-connected layer with n_classes nodes as output layer
+ var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false);
+ ```
+
+ **loss function**: After creating the network, we have to calculate the loss and optimize it, we have to calculate the `correct_prediction` and `accuracy`.
+
+ ```csharp
+ // Define the loss function, optimizer, and accuracy
+ var logits = tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits);
+ loss = tf.reduce_mean(logits, name: "loss");
+ optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss);
+ var correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name: "correct_pred");
+ accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy");
+ ```
+
+ **initialize variables**: We have to invoke a variable initializer operation to initialize all variables.
+
+ ```csharp
+ var init = tf.global_variables_initializer();
+ ```
+
+ The complete computation graph is looks like below:
+
+ 
+
+4. **Train**
+
+ After creating the graph, we can train our model. To train the model, we have to create a session and run the graph in the session.
+
+ ```csharp
+ // Number of training iterations in each epoch
+ var num_tr_iter = mnist.train.labels.len / batch_size;
+ with(tf.Session(), sess =>
+ {
+ sess.run(init);
+
+ float loss_val = 100.0f;
+ float accuracy_val = 0f;
+
+ foreach (var epoch in range(epochs))
+ {
+ print($"Training epoch: {epoch + 1}");
+ // Randomly shuffle the training data at the beginning of each epoch
+ var (x_train, y_train) = randomize(mnist.train.images, mnist.train.labels);
+
+ foreach (var iteration in range(num_tr_iter))
+ {
+ var start = iteration * batch_size;
+ var end = (iteration + 1) * batch_size;
+ var (x_batch, y_batch) = get_next_batch(x_train, y_train, start, end);
+
+ // Run optimization op (backprop)
+ sess.run(optimizer, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
+
+ if (iteration % display_freq == 0)
+ {
+ // Calculate and display the batch loss and accuracy
+ var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
+ loss_val = result[0];
+ accuracy_val = result[1];
+ print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}");
+ }
+ }
+
+ // Run validation after every epoch
+ var results1 = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.validation.images), new FeedItem(y, mnist.validation.labels));
+ loss_val = results1[0];
+ accuracy_val = results1[1];
+ print("---------------------------------------------------------");
+ print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
+ print("---------------------------------------------------------");
+ }
+ });
+ ```
+
+5. **Test**
+
+ After the training is done, we have to test our model to see how good it performs on a new dataset.
+
+ ```csharp
+ var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.test.images), new FeedItem(y, mnist.test.labels));
+ loss_test = result[0];
+ accuracy_test = result[1];
+ print("---------------------------------------------------------");
+ print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
+ print("---------------------------------------------------------");
+ ```
+
+ 
+
+
+
+
diff --git a/docs/source/Placeholder.md b/docs/source/Placeholder.md
index 2407bfca6..2cf345bd0 100644
--- a/docs/source/Placeholder.md
+++ b/docs/source/Placeholder.md
@@ -2,19 +2,19 @@
In this chapter we will talk about another common data type in TensorFlow: Placeholder. It is a simplified variable that can be passed to the required value by the session when the graph is run, that is, when you build the graph, you don't need to specify the value of that variable, but delay the session to the beginning. In TensorFlow terminology, we then feed data into the graph through these placeholders. The difference between placeholders and constants is that placeholders can specify coefficient values more flexibly without modifying the code that builds the graph. For example, mathematical constants are suitable for Constant, and some model smoothing values can be specified with Placeholder.
-这章我们讲一下TensorFlow里的另一种常用数据类型：占位符。它是一种简化的变量，可以在图运行的时候由会话传入所需要的值，就是说你在构建图的时候，不需要具体指定那个变量的值，而是延迟到会话开始的时候以参数的方式从外部传入初始值。占位符和常量的区别是占位符可以更灵活的指定系数值，而不需要修改构建图的代码。比如数学常量就适合用Constant, 有些模型平滑值可以用Placeholder来指定。
+
```csharp
var x = tf.placeholder(tf.int32);
var y = x * 3;
-Python.with(tf.Session(), sess =>
+using (var sess = tf.Session())
{
var result = sess.run(y, feed_dict: new FeedItem[]
{
new FeedItem(x, 2)
});
// (int)result should be 6;
-});
+}
```
diff --git a/docs/source/Preface.md b/docs/source/Preface.md
index c924c2800..65db8c1d2 100644
--- a/docs/source/Preface.md
+++ b/docs/source/Preface.md
@@ -6,36 +6,10 @@ Why do I start the TensorFlow.NET project?
In a few days, it was Christmas in 2018. I watched my children grow up and be sensible every day, and I felt that time passed too fast. IT technology updates are faster than ever, and a variety of front-end technologies are emerging. Big data, Artificial Intelligence and Blockchain, Container technology and Microservices, Distributed Computing and Serverless technology are dazzling. The Amazon AI service interface claims that engineers who don't need any machine learning experience can use it, so that the idea of just calming down for two years and planning to switch to an AI architecture in the future is a splash of cold water.
-再过几天就是2018年圣诞节，看着孩子一天天长大并懂事，感慨时间过得太快。IT技术更新换代比以往任何时候都更快，各种前后端技术纷纷涌现。大数据，人工智能和区块链，容器技术和微服务，分布式计算和无服务器技术，让人眼花缭乱。Amazon AI服务接口宣称不需要具有任何机器学习经验的工程师就能使用，让像我这样刚静下心来学习了两年并打算将来转行做AI架构的想法泼了一桶凉水。
-
TensorFlow is an open source project for machine learning especially for deep learning. It's used for both research and production at Google company. It's designed according to dataflow programming pattern across a range of tasks. TensorFlow is not just a deep learning library. As long as you can represent your calculation process as a data flow diagram, you can use TensorFlow for distributed computing. TensorFlow uses a computational graph to build a computing network while operating on the graph. Users can write their own upper-level models in Python based on TensorFlow, or extend the underlying C++ custom action code to TensorFlow.
-TensorFlow是一个用于机器学习的开源项目，尤其适用于深度学习。 它最初是谷歌公司的用于内部研究和生产的工具，后来开源出来给社区使用。TensorFlow并不仅仅是一个深度学习库，只要可以把你的计算过程表示称一个数据流图的过程，就可以使用TensorFlow来进行分布式计算。TensorFlow用计算图的方式建立计算网络，同时对图进行操作。用户可以基于TensorFlow的基础上用python编写自己的上层模型，也可以扩展底层的C++自定义操作代码添加到TensorFlow中。
-
In order to avoid confusion, the unique classes defined in TensorFlow are not translated in this book. For example, Tensor, Graph, Shape will retain the English name.
-
-为了避免混淆，本书中对TensorFlow中定义的特有类不进行翻译，比如Tensor, Graph, Session, Shape这些词都会保留英文名称。
-
-
-
-Terminology 术语:
-
-TF: Google TensorFlow
-
-TF.NET: TensorFlow.NET
-
-Graph: 计算图
-
-Session: 会话
-
-Variable: 变量
-
-Tensor: 张量
-
-Operation: 操作
-
-Node: 节点
\ No newline at end of file
diff --git a/docs/source/Queue.md b/docs/source/Queue.md
new file mode 100644
index 000000000..7f137fb32
--- /dev/null
+++ b/docs/source/Queue.md
@@ -0,0 +1,157 @@
+# Chapter. Queue
+
+ThensorFlow is capable to handle multiple threads, and queues are powerful mechanism for asynchronous computation. If we have large datasets this can significantly speed up the training process of our models. This functionality is especially handy when reading, pre-processing and extracting in mini-batches our training data. The secret to being able to do professional and high performance training of our model is understanding TensorFlow queuing operations. TensorFlow has implemented 4 types of Queue: **FIFOQueue**, **PaddingFIFOQueue**, **PriorityQueue** and **RandomShuffleQueue**.
+
+
+
+Like everything in TensorFlow, a queue is a node in a computation graph. It's a stateful node, like a variable: other nodes can modify its content, In particular, nodes can enqueue new items into the queue, or dequeue existing items from the queue.
+
+To get started with queue, let's consider a simple example. We will create a "first in, first out" queue (FIFOQueue) and fill it with numbers. Then we'll construct a graph that takes an item off the queue, adds one to that item, and puts it back on the end of the queue.
+
+```csharp
+[TestMethod]
+public void FIFOQueue()
+{
+ // create a first in first out queue with capacity up to 2
+ // and data type set as int32
+ var queue = tf.FIFOQueue(2, tf.int32);
+ // init queue, push 2 elements into queue.
+ var init = queue.enqueue_many(new[] { 10, 20 });
+ // pop out the first element
+ var x = queue.dequeue();
+ // add 1
+ var y = x + 1;
+ // push back into queue
+ var inc = queue.enqueue(y);
+
+ using (var sess = tf.Session())
+ {
+ // init queue
+ init.run();
+
+ // pop out first element and push back calculated y
+ (int dequeued, _) = sess.run((x, inc));
+ Assert.AreEqual(10, dequeued);
+
+ (dequeued, _) = sess.run((x, inc));
+ Assert.AreEqual(20, dequeued);
+
+ (dequeued, _) = sess.run((x, inc));
+ Assert.AreEqual(11, dequeued);
+
+ (dequeued, _) = sess.run((x, inc));
+ Assert.AreEqual(21, dequeued);
+
+ // thread will hang or block if you run sess.run(x) again
+ // until queue has more element.
+ }
+}
+```
+
+`Enqueue`, `EnqueueMany` and `Dequeue` are special nodes. They take a pointer to the queue instead of a normal value, allowing them to change it. I first create a FIFOQueue *queue* of size up to 3, I enqueue two values into the *queue*. Then I immediately attempt to *dequeue* a value from it and assign it to *y* where I simply add 1 to the dequeued variable. Next, we start up a *session* and run. After we've run this operation a few times the queue will be empty - if we try and run the operation again, the main thread of the program will hang or block - this is because it will be waiting for another operation to be run to put more values in the queue.
+
+#### FIFOQueue
+
+Creates a queue that dequeues elements in a first-in first-out order. A `FIFOQueue` has bounded capacity; supports multiple concurrent producers and consumers; and provides exactly-once delivery. A `FIFOQueue` holds a list of up to `capacity` elements. Each element is a fixed-length tuple of tensors whose dtypes are described by `dtypes`, and whose shapes are optionally described by the `shapes` argument.
+
+#### PaddingFIFOQueue
+
+A FIFOQueue that supports batching variable-sized tensors by padding. A `PaddingFIFOQueue` may contain components with dynamic shape, while also supporting `dequeue_many`. A `PaddingFIFOQueue` holds a list of up to `capacity` elements. Each element is a fixed-length tuple of tensors whose dtypes are described by `dtypes`, and whose shapes are described by the `shapes` argument.
+
+```chsarp
+[TestMethod]
+public void PaddingFIFOQueue()
+{
+ var numbers = tf.placeholder(tf.int32);
+ var queue = tf.PaddingFIFOQueue(10, tf.int32, new TensorShape(-1));
+ var enqueue = queue.enqueue(numbers);
+ var dequeue_many = queue.dequeue_many(n: 3);
+
+ using(var sess = tf.Session())
+ {
+ sess.run(enqueue, (numbers, new[] { 1 }));
+ sess.run(enqueue, (numbers, new[] { 2, 3 }));
+ sess.run(enqueue, (numbers, new[] { 3, 4, 5 }));
+
+ var result = sess.run(dequeue_many[0]);
+
+ Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 1, 0, 0 }, result[0].ToArray()));
+ Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 2, 3, 0 }, result[1].ToArray()));
+ Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 3, 4, 5 }, result[2].ToArray()));
+ }
+}
+```
+
+
+
+#### PriorityQueue
+
+A queue implementation that dequeues elements in prioritized order. A `PriorityQueue` has bounded capacity; supports multiple concurrent producers and consumers; and provides exactly-once delivery. A `PriorityQueue` holds a list of up to `capacity` elements. Each element is a fixed-length tuple of tensors whose dtypes are described by `types`, and whose shapes are optionally described by the `shapes` argument.
+
+```csharp
+[TestMethod]
+public void PriorityQueue()
+{
+ var queue = tf.PriorityQueue(3, tf.@string);
+ var init = queue.enqueue_many(new[] { 2L, 4L, 3L }, new[] { "p1", "p2", "p3" });
+ var x = queue.dequeue();
+
+ using (var sess = tf.Session())
+ {
+ init.run();
+
+ // output will 2, 3, 4
+ var result = sess.run(x);
+ Assert.AreEqual(result[0].GetInt64(), 2L);
+
+ result = sess.run(x);
+ Assert.AreEqual(result[0].GetInt64(), 3L);
+
+ result = sess.run(x);
+ Assert.AreEqual(result[0].GetInt64(), 4L);
+ }
+}
+```
+
+
+
+#### RandomShuffleQueue
+
+A queue implementation that dequeues elements in a random order. A `RandomShuffleQueue` has bounded capacity; supports multiple concurrent producers and consumers; and provides exactly-once delivery. A `RandomShuffleQueue` holds a list of up to `capacity` elements. Each element is a fixed-length tuple of tensors whose dtypes are described by `dtypes`, and whose shapes are optionally described by the `shapes` argument.
+
+```csharp
+[TestMethod]
+public void RandomShuffleQueue()
+{
+ var queue = tf.RandomShuffleQueue(10, min_after_dequeue: 1, dtype: tf.int32);
+ var init = queue.enqueue_many(new[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 });
+ var x = queue.dequeue();
+
+ string results = "";
+ using (var sess = tf.Session())
+ {
+ init.run();
+
+ foreach(var i in range(9))
+ results += (int)sess.run(x) + ".";
+
+ // output in random order
+ // 1.2.3.4.5.6.7.8.9.
+ }
+}
+```
+
+
+
+Queue methods must run on the same device as the queue. `FIFOQueue` and `RandomShuffleQueue` are important TensorFlow objects for computing tensor asynchronously in a graph. For example, a typical input architecture is to use a `RandomShuffleQueue` to prepare inputs for training a model:
+
+* Multiple threads prepare training examples and push them in the queue.
+* A training thread executes a training op that dequeues mini-batches from the queue.
+
+This architecture simplifies the construction of input pipelines.
+
+
+
+From the above example, once the output gets to the point above you’ll actually have to terminate the program as it is blocked. Now, this isn’t very useful. What we really want to happen is for our little program to reload or enqueue more values whenever our queue is empty or is about to become empty. We could fix this by explicitly running our *enqueue_op* again in the code above to reload our queue with values. However, for large, more realistic programs, this will become unwieldy. Thankfully, TensorFlow has a solution.
+
+TensorFlow provides two classes to help multi-threading task: `tf.Coordinator` and `tf.QueueRunner`. There two classes are designed to be used together. The `Coordinator` class helps multiple threads stop together and report exceptions to a main thread. The `QueueRunner` class is used to create a number of threads cooperating to enqueue tensors in the same queue.
diff --git a/docs/source/Session.md b/docs/source/Session.md
index ffa5882e9..d6f249048 100644
--- a/docs/source/Session.md
+++ b/docs/source/Session.md
@@ -2,13 +2,13 @@
TensorFlow **session** runs parts of the graph across a set of local and remote devices. A session allows to execute graphs or part of graphs. It allocates resources (on one or more machines) for that and holds the actual values of intermediate results and variables.
-TensorFlow **Session** 运行预定义的计算图，并且支持跨设备运行和分配GPU。Session可以运行整个计算图或者图的一部分，这样做的好处是对开发模型来话很方便，不需要每次都执行整个图。会话还负责当前计算图的内存分配，保留和传递中间结果。
+
### Running Computations in a Session
Let's complete the example in last chapter. To run any of the operations, we need to create a session for that graph. The session will also allocate memory to store the current value of the variable.
-让我们完成上一章的例子，在那个例子里我们只是定义了一个图的结构。为了运行这个图，我们需要创建一个Session来根据图定义来分配资源运行它。
+
```csharp
with(tf.Graph(), graph =>
@@ -27,5 +27,3 @@ with(tf.Graph(), graph =>
```
The value of our variables is only valid within one session. If we try to get the value in another session. TensorFlow will raise an error of `Attempting to use uninitialized value foo`. Of course, we can use the graph in more than one session, because session copies graph definition to new memory area. We just have to initialize the variables again. The values in the new session will be completely independent from the previous one.
-
-变量值只会在一个Session里有效。如果我们试图从本Session来访问另一个Session创建的变量和值，就会得到一个`变量未初始化`的错误提示。当然，我们能从多个Session运行同一个计算图，因为计算图只是一个定义，Session初始化的时候会复制整图的定义到新的内存空间里。所以每个Session里的变量值是互相隔离的。
\ No newline at end of file
diff --git a/docs/source/Tensor.md b/docs/source/Tensor.md
index b460bede3..aefb884f7 100644
--- a/docs/source/Tensor.md
+++ b/docs/source/Tensor.md
@@ -1,4 +1,4 @@
-# Chapter. Tensor
+# Chapter 1. Tensor
### Represents one of the outputs of an Operation
@@ -6,17 +6,13 @@
##### What is Tensor?
-Tensor holds a multi-dimensional array of elements of a single data type which is very similar with numpy's ndarray. When the dimension is zero, it can be called a scalar. When the dimension is 2, it can be called a matrix. When the dimension is greater than 2, it is usually called a tensor. If you are very familiar with numpy, then understanding Tensor will be quite easy.
-
-Tensor是一个具有单一数据类型的多维数组容器，当维度为零时，可以称之为标量，当维度为2时，可以称之为矩阵，当维度大于2时，通常称之为张量。Tensor的数据结构非常类似于numpy里的ndarray。如果你对numpy非常熟悉的话，那么对Tensor的理解会相当容易。
-
+Tensor holds a multi-dimensional array of elements of a single data type which is very similar with `NumPy`'s `ndarray`. When the dimension is zero, it can be called a scalar. When the dimension is 2, it can be called a matrix. When the dimension is greater than 2, it is usually called a tensor. If you are very familiar with `NumPy`, then understanding Tensor will be quite easy.
+
##### How to create a Tensor?
-There are many ways to initialize a Tensor object in TF.NET. It can be initialized from a scalar, string, matrix or tensor.
-
-在TF.NET中有很多种方式可以初始化一个Tensor对象。它可以从一个标量，字符串，矩阵或张量来初始化。
+There are many ways to initialize a Tensor object in TF.NET. It can be initialized from a scalar, string, matrix or tensor. But the best way to create a Tensor is using high level APIs like `tf.constant`, `tf.zeros` and `tf.ones`. We'll talk about constant more detail in next chapter.
```csharp
// Create a tensor holds a scalar value
@@ -36,15 +32,9 @@ Console.WriteLine($"t1: {t1}, t2: {t2}, t3: {t3}");
##### Data Structure of Tensor
-
-
-
-
TF uses column major order. If we use NumSharp to generate a 2 x 3 matrix, if we access the data from 0 to 5 in order, we won't get a number of 1-6, but we get the order of 1, 4, 2, 5, 3, 6. a set of numbers.
-TF 采用的是按列存储模式，如果我们用NumSharp产生一个2 X 3的矩阵，如果按顺序从0到5访问数据的话，是不会得到1-6的数字的，而是得到1，4， 2， 5， 3， 6这个顺序的一组数字。
-
-```cs
+```csharp
// Generate a matrix:[[1, 2, 3], [4, 5, 6]]
var nd = np.array(1f, 2f, 3f, 4f, 5f, 6f).reshape(2, 3);
// The index will be 0 2 4 1 3 5, it's column-major order.
@@ -55,3 +45,8 @@ var nd = np.array(1f, 2f, 3f, 4f, 5f, 6f).reshape(2, 3);
+
+##### Index/ Slice of Tensor
+
+Tensor element can be accessed by `index` and `slice` related operations. Through some high level APIs, we can easily access specific dimension's data.
+
diff --git a/docs/source/Variable.md b/docs/source/Variable.md
index 48eaa9aef..c4f6a6af6 100644
--- a/docs/source/Variable.md
+++ b/docs/source/Variable.md
@@ -2,7 +2,7 @@
The variables in TensorFlow are mainly used to represent variable parameter values in the machine learning model. Variables can be initialized by the `tf.Variable` function. During the graph computation the variables are modified by other operations. Variables exist in the session, as long as they are in the same session, other computing nodes on the network can access the same variable value. Variables use lazy loading and will only request memory space when they are used.
-TensorFlow中变量主要用来表示机器学习模型中的可变参数值，变量通过可以通过`tf.Variable` 类进行初始化。在图运行过程中，通过各种操作对变量进行修改。变量存在于会话当中，只要是在同一个会话里，网络上的其它计算结节都可以访问到相同的变量值。变量采用延迟加载的方式，只有使用的时候才会申请内存空间。
+
```csharp
var x = tf.Variable(10, name: "x");
@@ -16,4 +16,3 @@ using (var session = tf.Session())
The above code first creates a variable operation, initializes the variable, then runs the session, and finally gets the result. This code is very simple, but it shows the complete process how TensorFlow operates on variables. When creating a variable, you pass a `tensor` as the initial value to the function `Variable()`. TensorFlow provides a series of operators to initialize the tensor, the initial value is a constant or a random value.
-以上代码先创建变量操作，初始化变量，再运行会话，最后得到结果。这段代码非常简单，但是它体现了整个TensorFlow对变量操作的完整流程。当创建一个变量时，你将一个`张量`作为初始值传入函数`Variable()`。TensorFlow提供了一系列操作符来初始化张量，初始值是常量或是随机值。
\ No newline at end of file
diff --git a/docs/source/_static/FIFOQueue-example.jpg b/docs/source/_static/FIFOQueue-example.jpg
new file mode 100644
index 000000000..ac2749346
Binary files /dev/null and b/docs/source/_static/FIFOQueue-example.jpg differ
diff --git a/docs/source/_static/constant/n-index-formula-offset.svg b/docs/source/_static/constant/n-index-formula-offset.svg
new file mode 100644
index 000000000..6c5a3219c
--- /dev/null
+++ b/docs/source/_static/constant/n-index-formula-offset.svg
@@ -0,0 +1,41 @@
+
+
+
\ No newline at end of file
diff --git a/docs/source/_static/constant/n-index-formula.svg b/docs/source/_static/constant/n-index-formula.svg
new file mode 100644
index 000000000..5d05c06f0
--- /dev/null
+++ b/docs/source/_static/constant/n-index-formula.svg
@@ -0,0 +1,33 @@
+
+
+
\ No newline at end of file
diff --git a/docs/source/_static/contiguous-block-of-memory-ndarray-example-1.png b/docs/source/_static/contiguous-block-of-memory-ndarray-example-1.png
new file mode 100644
index 000000000..140e37716
Binary files /dev/null and b/docs/source/_static/contiguous-block-of-memory-ndarray-example-1.png differ
diff --git a/docs/source/_static/contiguous-block-of-memory.png b/docs/source/_static/contiguous-block-of-memory.png
new file mode 100644
index 000000000..44d3ab62f
Binary files /dev/null and b/docs/source/_static/contiguous-block-of-memory.png differ
diff --git a/docs/source/_static/tensor-constant-ndarray.png b/docs/source/_static/tensor-constant-ndarray.png
new file mode 100644
index 000000000..3610ee0cd
Binary files /dev/null and b/docs/source/_static/tensor-constant-ndarray.png differ
diff --git a/docs/source/_static/tensor-naming.png b/docs/source/_static/tensor-naming.png
new file mode 100644
index 000000000..7b1d408b9
Binary files /dev/null and b/docs/source/_static/tensor-naming.png differ
diff --git a/docs/source/index.rst b/docs/source/index.rst
index e26f8378e..61f0d752e 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -22,10 +22,13 @@ Welcome to TensorFlow.NET's documentation!
Graph
Session
Operation
+ Queue
Gradient
Train
EagerMode
LinearRegression
LogisticRegression
NearestNeighbor
- ImageRecognition
\ No newline at end of file
+ ImageRecognition
+ NeuralNetwork
+ ConvolutionNeuralNetwork
\ No newline at end of file
diff --git a/graph/InceptionV3.meta b/graph/InceptionV3.meta
index 2a11b0829..fe220cce1 100644
Binary files a/graph/InceptionV3.meta and b/graph/InceptionV3.meta differ
diff --git a/redist/SciSharp.TensorFlow-Cpu.Redist/SciSharp.TensorFlow-Cpu.Redist.csproj b/redist/SciSharp.TensorFlow-Cpu.Redist/SciSharp.TensorFlow-Cpu.Redist.csproj
new file mode 100644
index 000000000..4d0fa1f0e
--- /dev/null
+++ b/redist/SciSharp.TensorFlow-Cpu.Redist/SciSharp.TensorFlow-Cpu.Redist.csproj
@@ -0,0 +1,63 @@
+
+
+
+ netstandard2.0
+ win-x64;linux-x64
+ SciSharp.Tensorflow-Cpu.Redist
+
+ SciSharp.Tensorflow-Cpu.Redist
+ 1.0.0
+ SciSharp team
+ SciSharp STACK
+ https://github.com/SciSharp/TensorFlow.NET
+ git
+
+ Meta-package for GPU Tensoflow library runtime distribution.
+ Libraries can be directly downloaded from https://storage.googleapis.com/tensorflow/libtensorflow/
+
+ Apache-2.0
+
+ https://github.com/SciSharp/TensorFlow.NET
+ native;tensorflow;machine-learning;ML
+ ../../packages
+ false
+
+ false
+ false
+ false
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ../../packages;$(RestoreSources);https://api.nuget.org/v3/index.json
+
+
+
+
+
+
+
+
+
+
+
+
+ runtime.json
+ true
+ PreserveNewest
+
+
+
+
diff --git a/redist/SciSharp.TensorFlow-Cpu.Redist/runtime.json b/redist/SciSharp.TensorFlow-Cpu.Redist/runtime.json
new file mode 100644
index 000000000..a7a39cb52
--- /dev/null
+++ b/redist/SciSharp.TensorFlow-Cpu.Redist/runtime.json
@@ -0,0 +1,14 @@
+{
+ "runtimes": {
+ "linux-x64": {
+ "SciSharp.TensorFlow-Gpu.Redist": {
+ "runtime.linux-x64.SciSharp.Tensorflow-Cpu.Redist": "1.0.0"
+ }
+ },
+ "win-x64": {
+ "SciSharp.TensorFlow-Gpu.Redist": {
+ "runtime.win-x64.SciSharp.Tensorflow-Cpu.Redist": "1.0.0"
+ }
+ }
+ }
+}
diff --git a/redist/SciSharp.TensorFlow-Gpu.Redist/SciSharp.TensorFlow-Gpu.Redist.csproj b/redist/SciSharp.TensorFlow-Gpu.Redist/SciSharp.TensorFlow-Gpu.Redist.csproj
new file mode 100644
index 000000000..61ea992ed
--- /dev/null
+++ b/redist/SciSharp.TensorFlow-Gpu.Redist/SciSharp.TensorFlow-Gpu.Redist.csproj
@@ -0,0 +1,81 @@
+
+
+
+ Library
+ netstandard2.0
+
+ win-x64;linux-x64
+ SciSharp.Tensorflow-Gpu.Redist
+
+ SciSharp.Tensorflow-Gpu.Redist
+ 1.0.0
+ SciSharp team
+ SciSharp STACK
+ https://github.com/SciSharp/TensorFlow.NET
+ git
+
+ Meta-package for GPU Tensoflow library runtime distribution.
+ Libraries can be directly downloaded from https://storage.googleapis.com/tensorflow/libtensorflow/
+
+ Apache-2.0
+
+ https://github.com/SciSharp/TensorFlow.NET
+ native;tensorflow;machine-learning;ML
+ ../../packages
+ false
+
+ false
+ false
+ false
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ../../packages;$(RestoreSources);https://api.nuget.org/v3/index.json
+
+
+
+
+
+
+
+
+
+
+
+
+ runtime.json
+ true
+ PreserveNewest
+
+
+
+
diff --git a/redist/SciSharp.TensorFlow-Gpu.Redist/runtime.json b/redist/SciSharp.TensorFlow-Gpu.Redist/runtime.json
new file mode 100644
index 000000000..392dc3ccf
--- /dev/null
+++ b/redist/SciSharp.TensorFlow-Gpu.Redist/runtime.json
@@ -0,0 +1,14 @@
+{
+ "runtimes": {
+ "linux-x64": {
+ "SciSharp.TensorFlow-Gpu.Redist": {
+ "runtime.linux-x64.SciSharp.Tensorflow-Gpu.Redist": "1.0.0"
+ }
+ },
+ "win-x64": {
+ "SciSharp.TensorFlow-Gpu.Redist": {
+ "runtime.win-x64.SciSharp.Tensorflow-Gpu.Redist": "1.0.0"
+ }
+ }
+ }
+}
diff --git a/redist/TensorFlow.NET.Redist.sln b/redist/TensorFlow.NET.Redist.sln
new file mode 100644
index 000000000..a21dc9dc9
--- /dev/null
+++ b/redist/TensorFlow.NET.Redist.sln
@@ -0,0 +1,60 @@
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diff --git a/redist/runtime.linux-x64.SciSharp.TensorFlow-Cpu.Redist/runtime.linux-x64.SciSharp.TensorFlow-Cpu.Redist.csproj b/redist/runtime.linux-x64.SciSharp.TensorFlow-Cpu.Redist/runtime.linux-x64.SciSharp.TensorFlow-Cpu.Redist.csproj
new file mode 100644
index 000000000..ea6d4186d
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+++ b/redist/runtime.linux-x64.SciSharp.TensorFlow-Cpu.Redist/runtime.linux-x64.SciSharp.TensorFlow-Cpu.Redist.csproj
@@ -0,0 +1,39 @@
+
+
+
+ netstandard2.0
+ linux-x64
+ SciSharp.Tensorflow-Cpu.Redist
+
+ runtime.linux-x64.SciSharp.Tensorflow-Cpu.Redist
+ 1.0.0
+ SciSharp team
+ SciSharp STACK
+ https://github.com/SciSharp/TensorFlow.NET
+ git
+
+ Distribution of the Linux CPU Tensoflow library.
+ The libraries can be directly downloaded from https://storage.googleapis.com/tensorflow/libtensorflow/
+
+ Apache-2.0
+
+ https://github.com/SciSharp/TensorFlow.NET
+ native;tensorflow;machine-learning;ML
+ ../../packages
+ false
+
+ false
+ false
+ false
+
+
+
+
+
+ runtimes/$(RuntimeIdentifier)/native/%(Filename)%(Extension)
+ true
+ PreserveNewest
+
+
+
+
diff --git a/redist/runtime.linux-x64.SciSharp.TensorFlow-Gpu.Redist/runtime.linux-x64.SciSharp.TensorFlow-Gpu.Redist.csproj b/redist/runtime.linux-x64.SciSharp.TensorFlow-Gpu.Redist/runtime.linux-x64.SciSharp.TensorFlow-Gpu.Redist.csproj
new file mode 100644
index 000000000..d680f38a6
--- /dev/null
+++ b/redist/runtime.linux-x64.SciSharp.TensorFlow-Gpu.Redist/runtime.linux-x64.SciSharp.TensorFlow-Gpu.Redist.csproj
@@ -0,0 +1,40 @@
+
+
+
+ Library
+ netstandard2.0
+ linux-x64
+ SciSharp.Tensorflow-Gpu.Redist
+
+ runtime.linux-x64.SciSharp.Tensorflow-Gpu.Redist
+ 1.0.0
+ SciSharp team
+ SciSharp STACK
+ https://github.com/SciSharp/TensorFlow.NET
+ git
+
+ Distribution of the Linux GPU Tensoflow library.
+ Dll can be directly downloaded from https://storage.googleapis.com/tensorflow/libtensorflow/
+
+ Apache-2.0
+
+ https://github.com/SciSharp/TensorFlow.NET
+ native;tensorflow;machine-learning;ML
+ ../../packages
+ false
+
+ false
+ false
+ false
+
+
+
+
+
+ runtimes/$(RuntimeIdentifier)/native/%(Filename)%(Extension)
+ true
+ PreserveNewest
+
+
+
+
\ No newline at end of file
diff --git a/redist/runtime.win-x64.SciSharp.TensorFlow-Cpu.Redist/runtime.win-x64.SciSharp.TensorFlow-Cpu.Redist.csproj b/redist/runtime.win-x64.SciSharp.TensorFlow-Cpu.Redist/runtime.win-x64.SciSharp.TensorFlow-Cpu.Redist.csproj
new file mode 100644
index 000000000..19e7854cb
--- /dev/null
+++ b/redist/runtime.win-x64.SciSharp.TensorFlow-Cpu.Redist/runtime.win-x64.SciSharp.TensorFlow-Cpu.Redist.csproj
@@ -0,0 +1,39 @@
+
+
+
+ netstandard2.0
+ win-x64
+ SciSharp.Tensorflow-Cpu.Redist
+
+ runtime.win-x64.SciSharp.Tensorflow-Cpu.Redist
+ 1.0.0
+ SciSharp team
+ SciSharp STACK
+ https://github.com/SciSharp/TensorFlow.NET
+ git
+
+ Distribution of the windows GPU Tensoflow library.
+ The libraries can be directly downloaded from https://storage.googleapis.com/tensorflow/libtensorflow/
+
+ Apache-2.0
+
+ https://github.com/SciSharp/TensorFlow.NET
+ native;tensorflow;machine-learning;ML
+ ../../packages
+ false
+
+ false
+ false
+ false
+
+
+
+
+
+ runtimes/$(RuntimeIdentifier)/native/%(Filename)%(Extension)
+ true
+ PreserveNewest
+
+
+
+
diff --git a/redist/runtime.win-x64.SciSharp.TensorFlow-Gpu.Redist/.gitignore b/redist/runtime.win-x64.SciSharp.TensorFlow-Gpu.Redist/.gitignore
new file mode 100644
index 000000000..fca132d9b
--- /dev/null
+++ b/redist/runtime.win-x64.SciSharp.TensorFlow-Gpu.Redist/.gitignore
@@ -0,0 +1 @@
+tensorflow.dll
diff --git a/redist/runtime.win-x64.SciSharp.TensorFlow-Gpu.Redist/runtime.win-x64.SciSharp.TensorFlow-Gpu.Redist.csproj b/redist/runtime.win-x64.SciSharp.TensorFlow-Gpu.Redist/runtime.win-x64.SciSharp.TensorFlow-Gpu.Redist.csproj
new file mode 100644
index 000000000..915e0e2a5
--- /dev/null
+++ b/redist/runtime.win-x64.SciSharp.TensorFlow-Gpu.Redist/runtime.win-x64.SciSharp.TensorFlow-Gpu.Redist.csproj
@@ -0,0 +1,40 @@
+
+
+
+ Library
+ netstandard2.0
+ win-x64
+ SciSharp.Tensorflow-Gpu.Redist
+
+ runtime.win-x64.SciSharp.Tensorflow-Gpu.Redist
+ 1.0.0
+ SciSharp team
+ SciSharp STACK
+ https://github.com/SciSharp/TensorFlow.NET
+ git
+
+ Distribution of the windows GPU Tensoflow library.
+ Dll can be directly downloaded from https://storage.googleapis.com/tensorflow/libtensorflow/
+
+ Apache-2.0
+
+ https://github.com/SciSharp/TensorFlow.NET
+ native;tensorflow;machine-learning;ML
+ ../../packages
+ false
+
+ false
+ false
+ false
+
+
+
+
+
+ runtimes/$(RuntimeIdentifier)/native/%(Filename)%(Extension)
+ true
+ PreserveNewest
+
+
+
+
\ No newline at end of file
diff --git a/src/KerasNET.Core/Core.cs b/src/KerasNET.Core/Core.cs
deleted file mode 100644
index 8adae9388..000000000
--- a/src/KerasNET.Core/Core.cs
+++ /dev/null
@@ -1,15 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow;
-
-namespace Keras
-{
- public static class Keras
- {
- public static Tensor create_tensor(int[] shape, float mean = 0, float stddev = 1, TF_DataType dtype = TF_DataType.TF_FLOAT, int? seed = null, string name = null)
- {
- return tf.truncated_normal(shape: shape, mean: mean, stddev: stddev, dtype: dtype, seed: seed, name: name);
- }
- }
-}
diff --git a/src/KerasNET.Core/IInitializer.cs b/src/KerasNET.Core/IInitializer.cs
deleted file mode 100644
index 53cb9112a..000000000
--- a/src/KerasNET.Core/IInitializer.cs
+++ /dev/null
@@ -1,10 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-
-namespace Keras
-{
- interface IInitializer
- {
- }
-}
diff --git a/src/KerasNET.Core/Initializer/BaseInitializer.cs b/src/KerasNET.Core/Initializer/BaseInitializer.cs
deleted file mode 100644
index 84a420a7e..000000000
--- a/src/KerasNET.Core/Initializer/BaseInitializer.cs
+++ /dev/null
@@ -1,13 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow;
-using Tensorflow.Layers;
-
-namespace Keras.Initializer
-{
- class BaseInitializer : IInitializer
- {
- public int seed;
- }
-}
diff --git a/src/KerasNET.Core/Keras.Core.csproj b/src/KerasNET.Core/Keras.Core.csproj
deleted file mode 100644
index 3a7cfc6be..000000000
--- a/src/KerasNET.Core/Keras.Core.csproj
+++ /dev/null
@@ -1,13 +0,0 @@
-
-
-
- netstandard2.0
- Keras
- Keras
-
-
-
-
-
-
-
diff --git a/src/KerasNET.Core/Layers/Dense.cs b/src/KerasNET.Core/Layers/Dense.cs
deleted file mode 100644
index c6c086a44..000000000
--- a/src/KerasNET.Core/Layers/Dense.cs
+++ /dev/null
@@ -1,57 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using System.Linq;
-using Tensorflow;
-using static Keras.Keras;
-using Keras;
-using NumSharp;
-using Tensorflow.Operations.Activation;
-
-namespace Keras.Layers
-{
- public class Dense : ILayer
- {
- RefVariable W;
- int units;
- TensorShape WShape;
- string name;
- IActivation activation;
-
- public Dense(int units, string name = null, IActivation activation = null)
- {
- this.activation = activation;
- this.units = units;
- this.name = (string.IsNullOrEmpty(name) || string.IsNullOrWhiteSpace(name))?this.GetType().Name + "_" + this.GetType().GUID:name;
- }
- public ILayer __build__(TensorShape input_shape, int seed = 1, float stddev = -1f)
- {
- Console.WriteLine("Building Layer \"" + name + "\" ...");
- if (stddev == -1)
- stddev = (float)(1 / Math.Sqrt(2));
- var dim = input_shape.Dimensions;
- var input_dim = dim[dim.Length - 1];
- W = tf.Variable(create_tensor(new int[] { input_dim, units }, seed: seed, stddev: (float)stddev));
- WShape = new TensorShape(W.shape);
- return this;
- }
- public Tensor __call__(Tensor x)
- {
- var dot = tf.matmul(x, W);
- if (this.activation != null)
- dot = activation.Activate(dot);
- Console.WriteLine("Calling Layer \"" + name + "(" + np.array(dot.GetShape().Dimensions).ToString() + ")\" ...");
- return dot;
- }
- public TensorShape __shape__()
- {
- return WShape;
- }
- public TensorShape output_shape(TensorShape input_shape)
- {
- var output_shape = input_shape.Dimensions;
- output_shape[output_shape.Length - 1] = units;
- return new TensorShape(output_shape);
- }
- }
-}
diff --git a/src/KerasNET.Core/Layers/ILayer.cs b/src/KerasNET.Core/Layers/ILayer.cs
deleted file mode 100644
index 2c033eefa..000000000
--- a/src/KerasNET.Core/Layers/ILayer.cs
+++ /dev/null
@@ -1,16 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow;
-using NumSharp;
-
-namespace Keras.Layers
-{
- public interface ILayer
- {
- TensorShape __shape__();
- ILayer __build__(TensorShape input_shape, int seed = 1, float stddev = -1f);
- Tensor __call__(Tensor x);
- TensorShape output_shape(TensorShape input_shape);
- }
-}
diff --git a/src/KerasNET.Core/Model.cs b/src/KerasNET.Core/Model.cs
deleted file mode 100644
index cb9601694..000000000
--- a/src/KerasNET.Core/Model.cs
+++ /dev/null
@@ -1,127 +0,0 @@
-using Keras.Layers;
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow;
-using static Keras.Keras;
-using static Tensorflow.Python;
-
-namespace Keras
-{
- public class Model
- {
- public Tensor Flow;
- List layer_stack;
-
- public TensorShape InputShape;
-
- public Model()
- {
- layer_stack = new List();
- }
- public Model Add(ILayer layer)
- {
- layer_stack.Add(layer);
- return this;
- }
- public Model Add(IEnumerable layers)
- {
- layer_stack.AddRange(layers);
- return this;
- }
- public Tensor getFlow()
- {
- try
- {
- return Flow;
- }
- catch (Exception ex)
- {
- return null;
- }
- }
- public (Operation, Tensor, Tensor) make_graph(Tensor features, Tensor labels)
- {
-
- // TODO : Creating Loss Functions And Optimizers.....
-
- #region Model Layers Graph
- /*
- var stddev = 1 / Math.Sqrt(2);
-
- var d1 = new Dense(num_hidden);
- d1.__build__(features.getShape());
- var hidden_activations = tf.nn.relu(d1.__call__(features));
-
- var d1_output = d1.output_shape(features.getShape());
-
-
- var d2 = new Dense(1);
- d2.__build__(d1.output_shape(features.getShape()), seed: 17, stddev: (float)(1/ Math.Sqrt(num_hidden)));
- var logits = d2.__call__(hidden_activations);
- var predictions = tf.sigmoid(tf.squeeze(logits));
- */
- #endregion
-
- #region Model Graph Form Layer Stack
- var flow_shape = features.GetShape();
- Flow = features;
- for (int i = 0; i < layer_stack.Count; i++)
- {
- layer_stack[i].__build__(flow_shape);
- flow_shape = layer_stack[i].output_shape(flow_shape);
- Flow = layer_stack[i].__call__(Flow);
- }
- var predictions = tf.sigmoid(tf.squeeze(Flow));
-
- #endregion
-
- #region loss and optimizer
- var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name: "loss");
-
- var gs = tf.Variable(0, trainable: false, name: "global_step");
- var train_op = tf.train.GradientDescentOptimizer(0.2f).minimize(loss, global_step: gs);
- #endregion
-
- return (train_op, loss, gs);
- }
- public float train(int num_steps, (NDArray, NDArray) training_dataset)
- {
- var (X, Y) = training_dataset;
- var x_shape = X.shape;
- var batch_size = x_shape[0];
- var graph = tf.Graph().as_default();
-
- var features = tf.placeholder(tf.float32, new TensorShape(batch_size, 2));
- var labels = tf.placeholder(tf.float32, new TensorShape(batch_size));
-
- var (train_op, loss, gs) = this.make_graph(features, labels);
-
- var init = tf.global_variables_initializer();
-
- float loss_value = 0;
- with(tf.Session(graph), sess =>
- {
- sess.run(init);
- var step = 0;
-
-
- while (step < num_steps)
- {
- var result = sess.run(
- new ITensorOrOperation[] { train_op, gs, loss },
- new FeedItem(features, X),
- new FeedItem(labels, Y));
- loss_value = result[2];
- step = result[1];
- if (step % 1000 == 0)
- Console.WriteLine($"Step {step} loss: {loss_value}");
- }
- Console.WriteLine($"Final loss: {loss_value}");
- });
-
- return loss_value;
- }
- }
-}
diff --git a/src/SciSharp.TensorFlow.Redist/CommonPackage.props b/src/SciSharp.TensorFlow.Redist/CommonPackage.props
new file mode 100644
index 000000000..08fbb153a
--- /dev/null
+++ b/src/SciSharp.TensorFlow.Redist/CommonPackage.props
@@ -0,0 +1,24 @@
+
+
+
+
+
+ PreserveNewest
+ false
+ %(Filename)%(Extension)
+
+
+ PreserveNewest
+ false
+ %(Filename)%(Extension)
+
+
+
+
\ No newline at end of file
diff --git a/src/SciSharp.TensorFlow.Redist/README.md b/src/SciSharp.TensorFlow.Redist/README.md
new file mode 100644
index 000000000..4002aa21d
--- /dev/null
+++ b/src/SciSharp.TensorFlow.Redist/README.md
@@ -0,0 +1,40 @@
+## SciSharp.TensorFlow.Redist ##
+
+
+`SciSharp.TensorFlow.Redist` is a migration from [Microsoft.ML.TensorFlow.Redist](https://github.com/dotnet/machinelearning/tree/release/1.2/src/Redist/Microsoft.ML.TensorFlow.Redist). [ML.NET](https://github.com/dotnet/machinelearning) team will not maintain the package since [ML.NET](https://www.nuget.org/packages/Microsoft.ML) v1.3.0 going forward.
+
+* CPU version for all platforms (Windows, Linux, OSX)
+```powershell
+PM> Install-Package SciSharp.TensorFlow.Redist
+```
+
+* GPU version for Windows
+```powershell
+PM> Install-Package SciSharp.TensorFlow.Redist-Windows-GPU
+```
+
+* GPU version for Linux
+```powershell
+PM> Install-Package SciSharp.TensorFlow.Redist-Linux-GPU
+```
+
+https://www.nuget.org/packages/SciSharp.TensorFlow.Redist
+
+Related merged [commits](https://github.com/SciSharp/TensorFlow.NET/commit/854a5ba61ad0e400623821236bd117cc24c6cb77).
+
+
+
+#### Download pre-build package
+
+[Mac OSX CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.10.0.tar.gz), [Linux CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.10.0.tar.gz), [Linux GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.10.0.tar.gz), [Windows CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.10.0.zip), [Windows GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.10.0.zip)
+
+
+
+#### Pack and Deploy ####
+
+On Windows, the tar command does not support extracting archives with symlinks. So when `dotnet pack` runs on Windows it will only package the Windows binaries.
+
+1. Run `dotnet pack SciSharp.TensorFlow.Redist.nupkgproj` under `src/SciSharp.TensorFlow.Redist` directory in Linux.
+2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.2.10.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json -t 600`
+
+
diff --git a/src/SciSharp.TensorFlow.Redist/Redist-CPU.nuspec b/src/SciSharp.TensorFlow.Redist/Redist-CPU.nuspec
new file mode 100644
index 000000000..1524a0f86
--- /dev/null
+++ b/src/SciSharp.TensorFlow.Redist/Redist-CPU.nuspec
@@ -0,0 +1,27 @@
+
+
+
+ $packageId$
+ $version$
+ The TensorFlow Authors
+ The TensorFlow Authors
+ true
+ LICENSE.txt
+ https://aka.ms/deprecateLicenseUrl
+ https://www.tensorflow.org/
+ https://avatars3.githubusercontent.com/u/44989469?s=200&v=4
+ $packageId$ contains the TensorFlow C library CPU version $version$ redistributed as a NuGet package.
+ https://github.com/tensorflow/tensorflow/releases/tag/v$version$
+ Copyright 2019 The TensorFlow Authors. All rights reserved.
+ TensorFlow
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/src/SciSharp.TensorFlow.Redist/Redist-Linux-GPU.nuspec b/src/SciSharp.TensorFlow.Redist/Redist-Linux-GPU.nuspec
new file mode 100644
index 000000000..3a08b37d6
--- /dev/null
+++ b/src/SciSharp.TensorFlow.Redist/Redist-Linux-GPU.nuspec
@@ -0,0 +1,27 @@
+
+
+
+ $packageId$
+ $version$
+ The TensorFlow Authors
+ The TensorFlow Authors
+ true
+ LICENSE.txt
+ https://aka.ms/deprecateLicenseUrl
+ https://www.tensorflow.org/
+ https://avatars3.githubusercontent.com/u/44989469?s=200&v=4
+ $packageId$ contains the TensorFlow C library GPU for Linux version $version$ redistributed as a NuGet package.
+ https://github.com/tensorflow/tensorflow/releases/tag/v$version$
+ Copyright 2019 The TensorFlow Authors. All rights reserved.
+ TensorFlow
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/src/SciSharp.TensorFlow.Redist/Redist-Windows-GPU.nuspec b/src/SciSharp.TensorFlow.Redist/Redist-Windows-GPU.nuspec
new file mode 100644
index 000000000..769838368
--- /dev/null
+++ b/src/SciSharp.TensorFlow.Redist/Redist-Windows-GPU.nuspec
@@ -0,0 +1,27 @@
+
+
+
+ $packageId$
+ $version$
+ The TensorFlow Authors
+ The TensorFlow Authors
+ true
+ LICENSE.txt
+ https://aka.ms/deprecateLicenseUrl
+ https://www.tensorflow.org/
+ https://avatars3.githubusercontent.com/u/44989469?s=200&v=4
+ $packageId$ contains the TensorFlow C library GPU for Windows version $version$ redistributed as a NuGet package.
+ https://github.com/tensorflow/tensorflow/releases/tag/v$version$
+ Copyright 2019 The TensorFlow Authors. All rights reserved.
+ TensorFlow
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist-Linux-GPU.nupkgproj b/src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist-Linux-GPU.nupkgproj
new file mode 100644
index 000000000..63e11ed4e
--- /dev/null
+++ b/src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist-Linux-GPU.nupkgproj
@@ -0,0 +1,174 @@
+
+
+
+ $(MSBuildThisFileDirectory)
+ $(ProjDir)bin\
+ $(ProjDir)obj\
+
+ x64
+ netstandard2.0
+ 1.14.0
+ 1
+
+ $(BinDir)packages\
+ $(MSBuildProjectName)
+ $(TensorFlowVersion)
+
+ true
+ false
+
+ Redist-Linux-GPU.nuspec
+ packageId=$(PackageId);version=$(PackageVersion)
+ $(ProjDir)
+
+ CopyFilesFromArchive
+
+ win
+ linux
+ osx
+ $(PackageRid)-$(TargetArchitecture)
+
+
+
+
+ false
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ <_downloadFiles Include="@(TensorFlowArchive);@(AdditionalDownloadFile)" Url="%(Identity)" DestinationFile="%(DownloadFile)" />
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ @(FilesWithHashes->'%(FileHash)')
+ $([System.IO.File]::ReadAllText('%(LocalShaFile)').Replace("%0A", "").Replace("%0D", ""))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ <_fileFromArchive Include="%(TensorFlowArchive.FilesFromArchive)" ExtractDirectory="%(TensorFlowArchive.ExtractDirectory)" Runtime="%(TensorFlowArchive.Runtime)" />
+ <_fileFromArchive DestinationFile="%(FileName)%(Extension)"/>
+ <_fileFromArchive PackagePath="runtimes\%(_fileFromArchive.Runtime)\native\%(_fileFromArchive.DestinationFile)" />
+
+
+ <_fileFromArchive Condition="'%(DestinationFile)' == 'LICENSE'" PackagePath="THIRD_PARTY_NOTICES.txt" Runtime="" />
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist-Windows-GPU.nupkgproj b/src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist-Windows-GPU.nupkgproj
new file mode 100644
index 000000000..e2b101fac
--- /dev/null
+++ b/src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist-Windows-GPU.nupkgproj
@@ -0,0 +1,172 @@
+
+
+
+ $(MSBuildThisFileDirectory)
+ $(ProjDir)bin\
+ $(ProjDir)obj\
+
+ x64
+ netstandard2.0
+ 1.14.0
+ 1
+
+ $(BinDir)packages\
+ $(MSBuildProjectName)
+ $(TensorFlowVersion)
+
+ true
+ false
+
+ Redist-Windows-GPU.nuspec
+ packageId=$(PackageId);version=$(PackageVersion)
+ $(ProjDir)
+
+ CopyFilesFromArchive
+
+ win
+ linux
+ osx
+ $(PackageRid)-$(TargetArchitecture)
+
+
+
+
+ false
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ <_downloadFiles Include="@(TensorFlowArchive);@(AdditionalDownloadFile)" Url="%(Identity)" DestinationFile="%(DownloadFile)" />
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ @(FilesWithHashes->'%(FileHash)')
+ $([System.IO.File]::ReadAllText('%(LocalShaFile)').Replace("%0A", "").Replace("%0D", ""))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ <_fileFromArchive Include="%(TensorFlowArchive.FilesFromArchive)" ExtractDirectory="%(TensorFlowArchive.ExtractDirectory)" Runtime="%(TensorFlowArchive.Runtime)" />
+ <_fileFromArchive DestinationFile="%(FileName)%(Extension)"/>
+ <_fileFromArchive PackagePath="runtimes\%(_fileFromArchive.Runtime)\native\%(_fileFromArchive.DestinationFile)" />
+
+
+ <_fileFromArchive Condition="'%(DestinationFile)' == 'LICENSE'" PackagePath="THIRD_PARTY_NOTICES.txt" Runtime="" />
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist.nupkgproj b/src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist.nupkgproj
new file mode 100644
index 000000000..85ca28984
--- /dev/null
+++ b/src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist.nupkgproj
@@ -0,0 +1,187 @@
+
+
+
+ $(MSBuildThisFileDirectory)
+ $(ProjDir)bin\
+ $(ProjDir)obj\
+
+ x64
+ netstandard2.0
+ 1.15.0
+ 1
+
+ $(BinDir)packages\
+ $(MSBuildProjectName)
+ $(TensorFlowVersion)
+
+ true
+ false
+
+ Redist-CPU.nuspec
+ packageId=$(PackageId);version=$(PackageVersion)
+ $(ProjDir)
+
+ CopyFilesFromArchive
+
+ win
+ linux
+ osx
+ $(PackageRid)-$(TargetArchitecture)
+
+
+
+
+ false
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ <_downloadFiles Include="@(TensorFlowArchive);@(AdditionalDownloadFile)" Url="%(Identity)" DestinationFile="%(DownloadFile)" />
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ @(FilesWithHashes->'%(FileHash)')
+ $([System.IO.File]::ReadAllText('%(LocalShaFile)').Replace("%0A", "").Replace("%0D", ""))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ <_fileFromArchive Include="%(TensorFlowArchive.FilesFromArchive)" ExtractDirectory="%(TensorFlowArchive.ExtractDirectory)" Runtime="%(TensorFlowArchive.Runtime)" />
+ <_fileFromArchive DestinationFile="%(FileName)%(Extension)"/>
+ <_fileFromArchive PackagePath="runtimes\%(_fileFromArchive.Runtime)\native\%(_fileFromArchive.DestinationFile)" />
+
+
+ <_fileFromArchive Condition="'%(DestinationFile)' == 'LICENSE'" PackagePath="THIRD_PARTY_NOTICES.txt" Runtime="" />
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/src/TensorFlowNET.Core/APIs/c_api.cs b/src/TensorFlowNET.Core/APIs/c_api.cs
index 93d939598..a91b86827 100644
--- a/src/TensorFlowNET.Core/APIs/c_api.cs
+++ b/src/TensorFlowNET.Core/APIs/c_api.cs
@@ -1,7 +1,22 @@
-using System;
-using System.Collections.Generic;
+/*****************************************************************************
+ Copyright 2020 Haiping Chen. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using System;
using System.Runtime.InteropServices;
-using System.Text;
+using static Tensorflow.CppShapeInferenceResult.Types;
namespace Tensorflow
{
@@ -36,9 +51,57 @@ public static string StringPiece(IntPtr handle)
return handle == IntPtr.Zero ? String.Empty : Marshal.PtrToStringAnsi(handle);
}
- public delegate void Deallocator(IntPtr data, IntPtr size, ref bool deallocator);
+ public unsafe static byte[] ByteStringPiece(Buffer? handle)
+ {
+ if (handle is null)
+ {
+ return new byte[0];
+ }
+ var data = handle.ToArray();
+ return data;
+ }
+
+ public unsafe static byte[] ByteStringPieceFromNativeString(IntPtr handle)
+ {
+ if (handle == IntPtr.Zero)
+ {
+ return new byte[0];
+ }
+
+ byte* str_data = (byte*)handle.ToPointer();
+ List bytes = new List();
+ byte current = 255;
+ while (current != ((byte)'\0'))
+ {
+ current = *(str_data++);
+ bytes.Add(current);
+ }
+ var data = bytes.ToArray();
+ return data;
+ }
+
+ [UnmanagedFunctionPointer(CallingConvention.Winapi)]
+ public delegate void Deallocator(IntPtr data, IntPtr size, ref DeallocatorArgs args);
+
+ [UnmanagedFunctionPointer(CallingConvention.Winapi)]
+ public delegate void DeallocatorV2(IntPtr data, long size, IntPtr args);
+
+ public struct DeallocatorArgs
+ {
+ internal static unsafe c_api.DeallocatorArgs* EmptyPtr;
+ internal static unsafe IntPtr Empty;
+
+ static unsafe DeallocatorArgs()
+ {
+ Empty = new IntPtr(EmptyPtr = (DeallocatorArgs*)Marshal.AllocHGlobal(Marshal.SizeOf()));
+ *EmptyPtr = new DeallocatorArgs() { gc_handle = IntPtr.Zero, deallocator_called = false };
+ }
+
+ public bool deallocator_called;
+ public IntPtr gc_handle;
+ }
[DllImport(TensorFlowLibName)]
- public static unsafe extern IntPtr TF_Version();
+ internal static extern IntPtr TF_Version();
}
}
diff --git a/src/TensorFlowNET.Core/APIs/c_api.customize.cs b/src/TensorFlowNET.Core/APIs/c_api.customize.cs
new file mode 100644
index 000000000..bee4897ee
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/c_api.customize.cs
@@ -0,0 +1,17 @@
+using System;
+using System.Collections.Generic;
+using System.Runtime.InteropServices;
+using System.Text;
+
+namespace Tensorflow
+{
+ public partial class c_api
+ {
+ [DllImport(TensorFlowLibName)]
+ public static extern void TF_SetAttr(SafeGraphHandle graph, IntPtr op, string attr_name, SafeBufferHandle attr_value_proto, SafeStatusHandle status);
+ [DllImport(TensorFlowLibName)]
+ public static extern SafeBufferHandle TF_GetHandleShapeAndType(SafeGraphHandle c_graph, TF_Output output);
+ [DllImport(TensorFlowLibName)]
+ public static extern void TF_SetHandleShapeAndType(SafeGraphHandle c_graph, TF_Output output, byte[] data, long proto_len, SafeStatusHandle status);
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/c_api_lite.cs b/src/TensorFlowNET.Core/APIs/c_api_lite.cs
new file mode 100644
index 000000000..5a437d261
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/c_api_lite.cs
@@ -0,0 +1,91 @@
+using System;
+using System.Collections.Generic;
+using System.Runtime.InteropServices;
+using System.Text;
+using Tensorflow.Lite;
+
+namespace Tensorflow
+{
+ public class c_api_lite
+ {
+ public const string TensorFlowLibName = "tensorflowlite_c";
+
+ public static string StringPiece(IntPtr handle)
+ {
+ return handle == IntPtr.Zero ? String.Empty : Marshal.PtrToStringAnsi(handle);
+ }
+
+ [DllImport(TensorFlowLibName)]
+ public static extern IntPtr TfLiteVersion();
+
+ [DllImport(TensorFlowLibName)]
+ public static extern SafeTfLiteModelHandle TfLiteModelCreateFromFile(string model_path);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern void TfLiteModelDelete(IntPtr model);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern SafeTfLiteInterpreterOptionsHandle TfLiteInterpreterOptionsCreate();
+
+ [DllImport(TensorFlowLibName)]
+ public static extern void TfLiteInterpreterOptionsDelete(IntPtr options);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern void TfLiteInterpreterOptionsSetNumThreads(SafeTfLiteInterpreterOptionsHandle options, int num_threads);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern SafeTfLiteInterpreterHandle TfLiteInterpreterCreate(SafeTfLiteModelHandle model, SafeTfLiteInterpreterOptionsHandle optional_options);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern void TfLiteInterpreterDelete(IntPtr interpreter);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern TfLiteStatus TfLiteInterpreterAllocateTensors(SafeTfLiteInterpreterHandle interpreter);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern int TfLiteInterpreterGetInputTensorCount(SafeTfLiteInterpreterHandle interpreter);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern int TfLiteInterpreterGetOutputTensorCount(SafeTfLiteInterpreterHandle interpreter);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern TfLiteStatus TfLiteInterpreterResizeInputTensor(SafeTfLiteInterpreterHandle interpreter,
+ int input_index, int[] input_dims, int input_dims_size);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern TfLiteTensor TfLiteInterpreterGetInputTensor(SafeTfLiteInterpreterHandle interpreter, int input_index);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern TfLiteDataType TfLiteTensorType(TfLiteTensor tensor);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern int TfLiteTensorNumDims(TfLiteTensor tensor);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern int TfLiteTensorDim(TfLiteTensor tensor, int dim_index);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern int TfLiteTensorByteSize(TfLiteTensor tensor);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern IntPtr TfLiteTensorData(TfLiteTensor tensor);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern IntPtr TfLiteTensorName(TfLiteTensor tensor);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern TfLiteQuantizationParams TfLiteTensorQuantizationParams(TfLiteTensor tensor);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern TfLiteStatus TfLiteTensorCopyFromBuffer(TfLiteTensor tensor, IntPtr input_data, int input_data_size);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern TfLiteStatus TfLiteInterpreterInvoke(SafeTfLiteInterpreterHandle interpreter);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern IntPtr TfLiteInterpreterGetOutputTensor(SafeTfLiteInterpreterHandle interpreter, int output_index);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern TfLiteStatus TfLiteTensorCopyToBuffer(TfLiteTensor output_tensor, IntPtr output_data, int output_data_size);
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/keras.layers.cs b/src/TensorFlowNET.Core/APIs/keras.layers.cs
deleted file mode 100644
index 00fe7ee1a..000000000
--- a/src/TensorFlowNET.Core/APIs/keras.layers.cs
+++ /dev/null
@@ -1,44 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
-using Tensorflow.Keras;
-using Tensorflow.Keras.Engine;
-using Tensorflow.Keras.Layers;
-
-namespace Tensorflow
-{
- public static partial class keras
- {
- public static class layers
- {
- public static Embedding Embedding(int input_dim, int output_dim,
- IInitializer embeddings_initializer = null,
- bool mask_zero = false) => new Embedding(input_dim, output_dim,
- embeddings_initializer,
- mask_zero);
-
- public static Tensor[] Input(int[] batch_shape = null,
- TF_DataType dtype = TF_DataType.DtInvalid,
- string name = null,
- bool sparse = false,
- Tensor tensor = null)
- {
- var batch_size = batch_shape[0];
- var shape = batch_shape.Skip(1).ToArray();
-
- var input_layer = new InputLayer(
- input_shape: shape,
- batch_size: batch_size,
- name: name,
- dtype: dtype,
- sparse: sparse,
- input_tensor: tensor);
-
- var outputs = input_layer.inbound_nodes[0].output_tensors;
-
- return outputs;
- }
- }
- }
-}
diff --git a/src/TensorFlowNET.Core/APIs/keras.preprocessing.cs b/src/TensorFlowNET.Core/APIs/keras.preprocessing.cs
deleted file mode 100644
index 97363bfbc..000000000
--- a/src/TensorFlowNET.Core/APIs/keras.preprocessing.cs
+++ /dev/null
@@ -1,15 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow.Keras;
-using Tensorflow.Keras.Engine;
-
-namespace Tensorflow
-{
- public static partial class keras
- {
- public static Preprocessing preprocessing => new Preprocessing();
- public static Sequence sequence = new Sequence();
- public static Sequential Sequential() => new Sequential();
- }
-}
diff --git a/src/TensorFlowNET.Core/APIs/tf.array.cs b/src/TensorFlowNET.Core/APIs/tf.array.cs
index 4e733f180..b529cd319 100644
--- a/src/TensorFlowNET.Core/APIs/tf.array.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.array.cs
@@ -1,12 +1,78 @@
-using System;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using Tensorflow.NumPy;
using System.Collections.Generic;
+using System.Diagnostics;
using System.Linq;
-using System.Text;
+using static Tensorflow.Binding;
+using Tensorflow.Operations;
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
+ ///
+ /// A convenient alias for None, useful for indexing arrays.
+ ///
+ public Slice newaxis = Slice.NewAxis;
+ ///
+ /// A convenient alias for ...
+ ///
+ public Slice ellipsis = Slice.Ellipsis;
+
+ ///
+ /// BatchToSpace for N-D tensors of type T.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor batch_to_space_nd(T input, int[] block_shape, int[,] crops, string name = null)
+ => gen_array_ops.batch_to_space_nd(ops.convert_to_tensor(input), ops.convert_to_tensor(block_shape),
+ ops.convert_to_tensor(crops), name: name);
+
+ ///
+ /// Apply boolean mask to tensor.
+ ///
+ ///
+ ///
+ /// N-D tensor.
+ /// K-D boolean tensor, K <= N and K must be known statically.
+ ///
+ /// A 0-D int Tensor representing the axis in tensor to mask from.
+ /// (N-K+1)-dimensional tensor populated by entries in tensor corresponding to True values in mask.
+ public Tensor boolean_mask(T1 tensor, T2 mask, string name = "boolean_mask", int axis = 0)
+ => array_ops.boolean_mask(tensor, mask, name: name, axis: axis);
+
+ ///
+ /// Broadcast an array for a compatible shape.
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor broadcast_to(Tensor input, Shape shape, string name = null)
+ => gen_array_ops.broadcast_to(input, shape, name: name);
+
+ public Tensor check_numerics(Tensor tensor, string message, string name = null)
+ => gen_array_ops.check_numerics(tensor, message, name: name);
+
///
/// Concatenates tensors along one dimension.
///
@@ -14,12 +80,18 @@ public static partial class tf
///
///
/// A `Tensor` resulting from concatenation of the input tensors.
- public static Tensor concat(IList values, int axis, string name = "concat")
+ public Tensor concat(IEnumerable values, int axis, string name = "concat")
{
- if (values.Count == 1)
- throw new NotImplementedException("tf.concat length is 1");
-
- return gen_array_ops.concat_v2(values.ToArray(), axis, name: name);
+ if (values.Count() == 1)
+ {
+ return tf_with(ops.name_scope(name), scope =>
+ {
+ var tensor = ops.convert_to_tensor(axis, name: "concat_dim", dtype: dtypes.int32);
+ Debug.Assert(tensor.shape.ndim == 0);
+ return identity(values.First(), name: scope);
+ });
+ }
+ return array_ops.concat(values.ToArray(), axis, name: name);
}
///
@@ -28,13 +100,12 @@ public static Tensor concat(IList values, int axis, string name = "conca
///
///
///
- ///
///
/// A `Tensor` with the same data as `input`, but its shape has an additional
/// dimension of size 1 added.
///
- public static Tensor expand_dims(Tensor input, int axis = -1, string name = null, int dim = -1)
- => array_ops.expand_dims(input, axis, name, dim);
+ public Tensor expand_dims(Tensor input, int axis = -1, string name = null)
+ => array_ops.expand_dims(input, axis, name);
///
/// Creates a tensor filled with a scalar value.
@@ -43,14 +114,47 @@ public static Tensor expand_dims(Tensor input, int axis = -1, string name = null
///
///
///
- public static Tensor fill(Tensor dims, T value, string name = null)
- => gen_array_ops.fill(dims, value, name: name);
+ public Tensor fill(Tensor dims, T value, string name = null)
+ => gen_array_ops.fill(dims, ops.convert_to_tensor(value), name: name);
+
+ public Tensor fill(Shape dims, T value, string name = null)
+ => array_ops.fill(dims, value, name: name);
+
+ ///
+ /// Return a tensor with the same shape and contents as input.
+ ///
+ ///
+ ///
+ ///
+ public Tensor identity(Tensor input, string name = null)
+ => array_ops.identity(input, name: name);
+
+ ///
+ /// Gather slices from params axis axis according to indices.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor gather(Tensor @params, Tensor indices, string name = null, int axis = 0)
+ => array_ops.gather(@params, indices, name: name, axis: ops.convert_to_tensor(axis));
+
+ ///
+ /// Gather slices from `params` into a Tensor with shape specified by `indices`.
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor gather_nd(Tensor @params, Tensor indices, string name = null)
+ => gen_array_ops.gather_nd(@params, indices, name: name);
///
/// Return the elements, either from `x` or `y`, depending on the `condition`.
///
///
- public static Tensor where(Tensor condition, Tx x, Ty y, string name = null)
+ public Tensor where(Tensor condition, Tx x, Ty y, string name = null)
=> array_ops.where(condition, x, y, name);
///
@@ -61,11 +165,51 @@ public static Tensor where(Tensor condition, Tx x, Ty y, string name = n
///
///
///
- public static Tensor transpose(T1 a, int[] perm = null, string name = "transpose", bool conjugate = false)
+ public Tensor transpose(T1 a, Axis perm = null, string name = "transpose", bool conjugate = false)
=> array_ops.transpose(a, perm, name, conjugate);
- public static Tensor squeeze(Tensor input, int[] axis = null, string name = null, int squeeze_dims = -1)
- => gen_array_ops.squeeze(input, axis, name);
+ ///
+ /// Reverses specific dimensions of a tensor.
+ ///
+ ///
+ /// The indices of the dimensions to reverse. Must be in the range [-rank(tensor), rank(tensor)).
+ ///
+ ///
+ public Tensor reverse(Tensor tensor, Axis axis, string name = null)
+ {
+ if (axis.IsScalar)
+ {
+ axis = new Axis(axis.axis);
+ }
+ return array_ops.reverse(tensor, axis, name: name);
+ }
+
+ ///
+ /// Returns the rank of a tensor.
+ ///
+ ///
+ ///
+ /// Returns a 0-D `int32` `Tensor` representing the rank of `input`.
+ public Tensor rank(Tensor input, string name = null)
+ => array_ops.rank(input, name: name);
+
+ ///
+ /// Extracts a slice from a tensor.
+ ///
+ /// A `Tensor`.
+ /// An `int32` or `int64` `Tensor`.
+ /// An `int32` or `int64` `Tensor`.
+ /// A name for the operation (optional).
+ /// A `Tensor` the same type as `input`.
+ public Tensor slice(Tensor input, Tb[] begin, Ts[] size, string name = null)
+ => array_ops.slice(input, begin.Select(x => ops.convert_to_tensor(x)).ToArray(),
+ size.Select(x => ops.convert_to_tensor(x)).ToArray(), name: name);
+
+ public Tensor squeeze(Tensor input, int axis, string name = null, int squeeze_dims = -1)
+ => array_ops.squeeze(input, new[] { axis }, name);
+
+ public Tensor squeeze(Tensor input, int[] axis = null, string name = null, int squeeze_dims = -1)
+ => array_ops.squeeze(input, axis, name);
///
/// Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.
@@ -74,15 +218,44 @@ public static Tensor squeeze(Tensor input, int[] axis = null, string name = null
///
///
///
- public static Tensor stack(object values, int axis = 0, string name = "stack")
+ public Tensor stack(object values, int axis = 0, string name = "stack")
=> array_ops.stack(values, axis, name: name);
- public static Tensor one_hot(Tensor indices, int depth,
+ ///
+ /// Creates a tensor with all elements set to 1.
+ ///
+ ///
+ ///
+ /// A name for the operation (optional).
+ ///
+ /// if true, attempt to statically determine the shape of 'tensor' and
+ /// encode it as a constant.
+ ///
+ /// A `Tensor` with all elements set to 1.
+ public Tensor ones_like(Tensor tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
+ => array_ops.ones_like(tensor, dtype: dtype, name: name, optimize: optimize);
+
+ public Tensor ones_like(NDArray nd, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
+ => array_ops.ones_like(nd, dtype: dtype, name: name, optimize: optimize);
+
+ public Tensor one_hot(Tensor indices, int depth,
Tensor on_value = null,
Tensor off_value = null,
TF_DataType dtype = TF_DataType.DtInvalid,
int axis = -1,
- string name = null) => array_ops.one_hot(indices, depth, dtype: dtype, axis: axis, name: name);
+ string name = null) => array_ops.one_hot(indices, ops.convert_to_tensor(depth), dtype: dtype, axis: axis, name: name);
+
+ ///
+ /// Pads a tensor
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor pad(Tensor tensor, Tensor paddings, string mode = "CONSTANT", string name = null, int constant_values = 0)
+ => array_ops.pad(tensor, paddings, mode: mode, name: name, constant_values: constant_values);
///
/// A placeholder op that passes through `input` when its output is not fed.
@@ -90,12 +263,88 @@ public static Tensor one_hot(Tensor indices, int depth,
///
/// A `Tensor`. The default value to produce when output is not fed.
///
- /// A `tf.TensorShape` or list of `int`s. The (possibly partial) shape of
+ /// A `tf.Shape` or list of `int`s. The (possibly partial) shape of
/// the tensor.
///
/// A name for the operation (optional).
/// A `Tensor`. Has the same type as `input`.
- public static Tensor placeholder_with_default(T input, int[] shape, string name = null)
- => gen_array_ops.placeholder_with_default(input, shape, name: name);
+ public Tensor placeholder_with_default(T input, int[] shape, string name = null)
+ => gen_array_ops.placeholder_with_default(ops.convert_to_tensor(input), shape, name: name);
+
+ ///
+ /// Returns the shape of a tensor.
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor shape(Tensor input, string name = null, TF_DataType out_type = TF_DataType.TF_INT32)
+ => array_ops.shape_internal(input, name, optimize: true, out_type: out_type);
+
+ ///
+ /// Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.
+ ///
+ ///
+ ///
+ ///
+ /// A stacked `Tensor` with the same type as `values`.
+ public Tensor stack(Tensor[] values, int axis = 0, string name = "stack")
+ => array_ops.stack(values, axis: axis, name: name);
+
+ ///
+ /// Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor[] unstack(Tensor value, int? num = null, int axis = 0, string name = "unstack")
+ => array_ops.unstack(value, num: num, axis: axis, name: name);
+
+ ///
+ /// Creates a tensor with all elements set to zero.
+ ///
+ ///
+ ///
+ ///
+ ///
+ /// A `Tensor` with all elements set to zero.
+ public Tensor zeros_like(Tensor tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
+ => array_ops.zeros_like(tensor, dtype: dtype, name: name, optimize: optimize);
+
+ public Tensor zeros_like(NDArray nd, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
+ => array_ops.zeros_like(nd, dtype: dtype, name: name, optimize: optimize);
+
+ ///
+ /// Stops gradient computation.
+ ///
+ ///
+ ///
+ ///
+ public Tensor stop_gradient(Tensor x, string name = null)
+ => gen_array_ops.stop_gradient(x, name: name);
+
+ public TensorArray TensorArray(TF_DataType dtype, int size = 0, bool dynamic_size = false,
+ bool clear_after_read = true, Shape? element_shape = null, bool colocate_with_first_write_call = true,
+ bool infer_shape = true)
+ => tf.executing_eagerly() ?
+ new _EagerTensorArray(dtype, size: constant_op.constant(size), dynamic_size: dynamic_size,
+ clear_after_read: clear_after_read, element_shape: element_shape, infer_shape: infer_shape,
+ colocate_with_first_write_call: colocate_with_first_write_call) :
+ new _GraphTensorArray(dtype, size: constant_op.constant(size), dynamic_size: dynamic_size,
+ clear_after_read: clear_after_read, element_shape: element_shape, infer_shape: infer_shape,
+ colocate_with_first_write_call: colocate_with_first_write_call);
+
+ public TensorArray TensorArray(TF_DataType dtype, Tensor size, bool dynamic_size = false,
+ bool clear_after_read = true, Shape? element_shape = null, bool colocate_with_first_write_call = true,
+ bool infer_shape = true)
+ => tf.executing_eagerly() ?
+ new _EagerTensorArray(dtype, size: size, dynamic_size: dynamic_size,
+ clear_after_read: clear_after_read, element_shape: element_shape, infer_shape: infer_shape,
+ colocate_with_first_write_call: colocate_with_first_write_call) :
+ new _GraphTensorArray(dtype, size: size, dynamic_size: dynamic_size,
+ clear_after_read: clear_after_read, element_shape: element_shape, infer_shape: infer_shape,
+ colocate_with_first_write_call: colocate_with_first_write_call);
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.audio.cs b/src/TensorFlowNET.Core/APIs/tf.audio.cs
new file mode 100644
index 000000000..573b11ec3
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.audio.cs
@@ -0,0 +1,37 @@
+/*****************************************************************************
+ Copyright 2021 Haiping Chen. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using System.Collections.Generic;
+using Tensorflow.IO;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public AudioAPI audio { get; } = new AudioAPI();
+
+ public class AudioAPI
+ {
+ audio_ops audio_ops = new audio_ops();
+
+ public Tensors decode_wav(Tensor contents, int desired_channels = -1, int desired_samples = -1, string name = null)
+ => audio_ops.decode_wav(contents,
+ desired_channels: desired_channels,
+ desired_samples: desired_samples,
+ name: name);
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.autograph.cs b/src/TensorFlowNET.Core/APIs/tf.autograph.cs
new file mode 100644
index 000000000..55acac621
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.autograph.cs
@@ -0,0 +1,25 @@
+/*****************************************************************************
+ Copyright 2020 Haiping Chen. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using Tensorflow.Graphs;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public AutoGraph autograph = new AutoGraph();
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.bitwise.cs b/src/TensorFlowNET.Core/APIs/tf.bitwise.cs
new file mode 100644
index 000000000..b05182447
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.bitwise.cs
@@ -0,0 +1,25 @@
+/*****************************************************************************
+ Copyright 2020 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using Tensorflow.Operations;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public bitwise_ops bitwise = new bitwise_ops();
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.compat.cs b/src/TensorFlowNET.Core/APIs/tf.compat.cs
new file mode 100644
index 000000000..8a30badd9
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.compat.cs
@@ -0,0 +1,71 @@
+/*****************************************************************************
+ Copyright 2020 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using Google.Protobuf;
+using System.Text;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public CompatApi compat { get; } = new CompatApi();
+
+ public class CompatApi
+ {
+ public CompatV1Api v1 { get; } = new CompatV1Api();
+
+ internal string as_text(string bytes_or_text, Encoding? encoding = null)
+ {
+ if(encoding is null) encoding = Encoding.UTF8;
+ return bytes_or_text;
+ }
+ internal string as_text(byte[] bytes_or_text, Encoding? encoding = null)
+ {
+ if(encoding is null) encoding = Encoding.UTF8;
+ return encoding.GetString(bytes_or_text);
+ }
+
+ internal string as_str(string bytes_or_text, Encoding? encoding = null)
+ {
+ return as_text(bytes_or_text, encoding);
+ }
+ internal string as_str(byte[] bytes_or_text, Encoding? encoding = null)
+ {
+ return as_text(bytes_or_text, encoding);
+ }
+
+ public ByteString as_bytes(ByteString bytes, Encoding encoding = null)
+ {
+ return bytes;
+ }
+ public ByteString as_bytes(byte[] bytes, Encoding encoding = null)
+ {
+ return ByteString.CopyFrom(bytes);
+ }
+ public ByteString as_bytes(string text, Encoding encoding = null)
+ {
+ if(encoding is null)
+ {
+ encoding = Encoding.UTF8;
+ }
+ return ByteString.CopyFrom(encoding.GetBytes(text));
+ }
+ }
+
+ public bool executing_eagerly()
+ => Context.executing_eagerly();
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.compat.v1.cs b/src/TensorFlowNET.Core/APIs/tf.compat.v1.cs
new file mode 100644
index 000000000..982e7ccce
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.compat.v1.cs
@@ -0,0 +1,60 @@
+/*****************************************************************************
+ Copyright 2020 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using System.Collections.Generic;
+using static Tensorflow.Binding;
+
+namespace Tensorflow
+{
+ public class CompatV1Api
+ {
+ public void disable_eager_execution()
+ => tf.Context.graph_mode();
+
+ public IVariableV1 get_variable(string name,
+ Shape shape = null,
+ TF_DataType dtype = TF_DataType.DtInvalid,
+ object initializer = null, // IInitializer or Tensor
+ bool? trainable = null,
+ List collections = null,
+ bool? use_resource = null,
+ bool validate_shape = true,
+ VariableSynchronization synchronization = VariableSynchronization.Auto,
+ VariableAggregation aggregation = VariableAggregation.None)
+ {
+ var scope = Tensorflow.variable_scope.get_variable_scope();
+ var store = Tensorflow.variable_scope._get_default_variable_store();
+ return scope.get_variable(store,
+ name,
+ shape: shape,
+ dtype: dtype,
+ use_resource: use_resource,
+ validate_shape: validate_shape,
+ initializer: initializer,
+ trainable: trainable,
+ collections: collections);
+ }
+
+ public Operation global_variables_initializer()
+ {
+ var g = variables.global_variables();
+ return variables.variables_initializer(g.ToArray());
+ }
+
+ public Session Session()
+ => new Session().as_default();
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.config.cs b/src/TensorFlowNET.Core/APIs/tf.config.cs
new file mode 100644
index 000000000..3c30ffb48
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.config.cs
@@ -0,0 +1,32 @@
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using Tensorflow.Contexts;
+using Tensorflow.Framework;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ ///
+ /// Public API for tf.debugging namespace
+ /// https://www.tensorflow.org/api_docs/python/tf/debugging
+ /// More debugging instructions
+ /// https://developer.ibm.com/technologies/artificial-intelligence/tutorials/debug-tensorflow/
+ ///
+ public ConfigImpl config => new ConfigImpl();
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.control.cs b/src/TensorFlowNET.Core/APIs/tf.control.cs
deleted file mode 100644
index 1b6bc3b88..000000000
--- a/src/TensorFlowNET.Core/APIs/tf.control.cs
+++ /dev/null
@@ -1,12 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-
-namespace Tensorflow
-{
- public static partial class tf
- {
- public static _ControlDependenciesController control_dependencies(Operation[] control_inputs)
- => ops.control_dependencies(control_inputs);
- }
-}
diff --git a/src/TensorFlowNET.Core/APIs/tf.control_flow.cs b/src/TensorFlowNET.Core/APIs/tf.control_flow.cs
new file mode 100644
index 000000000..cd5a71e50
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.control_flow.cs
@@ -0,0 +1,73 @@
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using System;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public Tensor cond(Tensor pred,
+ Tensor true_value,
+ Tensor false_false)
+ => control_flow_ops.cond(pred, () => true_value, () => false_false);
+
+ public Tensor cond(Tensor pred,
+ Func true_fn = null,
+ Func false_fn = null,
+ string name = null)
+ => control_flow_ops.cond(pred, true_fn, false_fn, name: name);
+
+ ///
+ /// Create an op that groups multiple operations.
+ ///
+ ///
+ ///
+ ///
+ /// An Operation that executes all its inputs.
+ public Operation group(T[] inputs, string name = null) where T : ITensorOrOperation
+ => control_flow_ops.group(inputs, name: name);
+
+ public Tensor while_loop(Func cond,
+ Func body,
+ Tensor loop_vars,
+ int parallel_iterations = 10)
+ {
+ Func cond1 = x
+ => cond(x[0]);
+
+ Func body1 = x
+ => new[] { body(x[0]) };
+
+ var results = control_flow_ops.while_loop(cond1,
+ body1,
+ new[] { loop_vars });
+ return results[0];
+ }
+
+ public Tensor[] while_loop(Func cond,
+ Func body,
+ Tensors loop_vars,
+ int parallel_iterations = 10,
+ string name = null)
+ => control_flow_ops.while_loop(cond, body, loop_vars,
+ parallel_iterations: parallel_iterations,
+ name: name);
+
+ public _ControlDependenciesController control_dependencies(ITensorOrOperation[] control_inputs)
+ => ops.control_dependencies(control_inputs);
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.data.cs b/src/TensorFlowNET.Core/APIs/tf.data.cs
new file mode 100644
index 000000000..6c41a8393
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.data.cs
@@ -0,0 +1,31 @@
+/*****************************************************************************
+ Copyright 2020 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public DataOps data { get; } = new DataOps();
+
+ public class DataOps
+ {
+ public int AUTOTUNE = -1;
+ public int INFINITE_CARDINALITY = -1;
+ public int UNKNOWN_CARDINALITY = -2;
+ public DatasetManager Dataset { get; } = new DatasetManager();
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.data_flow.cs b/src/TensorFlowNET.Core/APIs/tf.data_flow.cs
new file mode 100644
index 000000000..e4c0a83cc
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.data_flow.cs
@@ -0,0 +1,43 @@
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ ///
+ /// Interleave the values from the data tensors into a single tensor.
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor dynamic_stitch(Tensor[] indices, Tensor[] data, string name = null)
+ => gen_data_flow_ops.dynamic_stitch(indices, data, name: name);
+
+ ///
+ /// Partitions `data` into `num_partitions` tensors using indices from `partitions`.
+ ///
+ ///
+ ///
+ /// The number of partitions to output.
+ ///
+ ///
+ public Tensor[] dynamic_partition(Tensor data, Tensor partitions, int num_partitions,
+ string name = null)
+ => gen_data_flow_ops.dynamic_partition(data, partitions, num_partitions, name: name);
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.debugging.cs b/src/TensorFlowNET.Core/APIs/tf.debugging.cs
new file mode 100644
index 000000000..b3b3529e4
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.debugging.cs
@@ -0,0 +1,35 @@
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using Tensorflow.Debugging;
+using static Tensorflow.Binding;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ ///
+ /// Public API for tf.debugging namespace
+ /// https://www.tensorflow.org/api_docs/python/tf/debugging
+ /// More debugging instructions
+ /// https://developer.ibm.com/technologies/artificial-intelligence/tutorials/debug-tensorflow/
+ ///
+ public DebugImpl debugging => new DebugImpl();
+
+ public void print(Tensor input)
+ => tf.logging.print_v2(input);
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.distributions.cs b/src/TensorFlowNET.Core/APIs/tf.distributions.cs
index fec6626a4..c9ccad917 100644
--- a/src/TensorFlowNET.Core/APIs/tf.distributions.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.distributions.cs
@@ -1,14 +1,28 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static class distributions
+ public distributions_internal distributions { get; } = new distributions_internal();
+
+ public class distributions_internal
{
- public static Normal Normal(Tensor loc,
+ public Normal Normal(Tensor loc,
Tensor scale,
bool validate_args = false,
bool allow_nan_stats = true,
diff --git a/src/TensorFlowNET.Core/APIs/tf.exp.cs b/src/TensorFlowNET.Core/APIs/tf.exp.cs
deleted file mode 100644
index 885cd96d2..000000000
--- a/src/TensorFlowNET.Core/APIs/tf.exp.cs
+++ /dev/null
@@ -1,13 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-
-namespace Tensorflow
-{
- public static partial class tf
- {
- public static Tensor exp(Tensor x,
- string name = null) => gen_math_ops.exp(x, name);
-
- }
-}
diff --git a/src/TensorFlowNET.Core/APIs/tf.gradients.cs b/src/TensorFlowNET.Core/APIs/tf.gradients.cs
index 0961288ca..d722cb143 100644
--- a/src/TensorFlowNET.Core/APIs/tf.gradients.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.gradients.cs
@@ -1,12 +1,47 @@
-using System;
+/*****************************************************************************
+ Copyright 2020 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
using System.Collections.Generic;
-using System.Text;
+using Tensorflow.Gradients;
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static Tensor[] gradients(Tensor[] ys,
+ GradientTape _tapeSet;
+
+ ///
+ /// Record operations for automatic differentiation.
+ ///
+ ///
+ ///
+ /// Tape set
+ public GradientTape GradientTape(bool persistent = false,
+ bool watch_accessed_variables = true)
+ {
+ var tape = _tapeSet.PushTape(persistent: persistent,
+ watch_accessed_variables: watch_accessed_variables);
+ tape.StartRecord();
+ return _tapeSet;
+ }
+
+ public Stack GetTapeSet()
+ => _tapeSet.GetTapeSet();
+
+ public Tensor[] gradients(Tensor[] ys,
Tensor[] xs,
Tensor[] grad_ys = null,
string name = "gradients",
@@ -15,16 +50,16 @@ public static Tensor[] gradients(Tensor[] ys,
int? aggregation_method = null,
Tensor[] stop_gradients = null)
{
- return gradients_util._GradientsHelper(ys,
- xs,
- grad_ys,
- name,
- colocate_gradients_with_ops,
+ return gradients_util._GradientsHelper(ys,
+ xs,
+ grad_ys,
+ name,
+ colocate_gradients_with_ops,
gate_gradients,
stop_gradients: stop_gradients);
}
- public static Tensor[] gradients(Tensor ys,
+ public Tensor[] gradients(Tensor ys,
Tensor[] xs,
Tensor[] grad_ys = null,
string name = "gradients",
@@ -42,7 +77,7 @@ public static Tensor[] gradients(Tensor ys,
stop_gradients: stop_gradients);
}
- public static Tensor[] gradients(Tensor ys,
+ public Tensor[] gradients(Tensor ys,
Tensor xs,
Tensor[] grad_ys = null,
string name = "gradients",
diff --git a/src/TensorFlowNET.Core/APIs/tf.graph.cs b/src/TensorFlowNET.Core/APIs/tf.graph.cs
index c343050a4..c1b033aee 100644
--- a/src/TensorFlowNET.Core/APIs/tf.graph.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.graph.cs
@@ -1,18 +1,43 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using static Tensorflow.ops;
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static graph_util_impl graph_util => new graph_util_impl();
- public static Graph get_default_graph()
- {
- return ops.get_default_graph();
- }
+ public graph_util_impl graph_util { get; } = new graph_util_impl();
+ public GraphTransformer graph_transforms { get; } = new GraphTransformer();
+ public GraphKeys GraphKeys { get; } = new GraphKeys();
+
+ public void reset_default_graph()
+ => ops.reset_default_graph();
+
+ public Graph get_default_graph()
+ => ops.get_default_graph();
- public static Graph Graph() => new Graph();
+ public Graph peak_default_graph()
+ => ops.peak_default_graph();
+ ///
+ /// Creates a new graph.
+ ///
+ ///Has no interaction with graph defaulting. Equivalent to new Graph();
+ public Graph Graph()
+ => new Graph();
}
-}
+}
\ No newline at end of file
diff --git a/src/TensorFlowNET.Core/APIs/tf.image.cs b/src/TensorFlowNET.Core/APIs/tf.image.cs
new file mode 100644
index 000000000..41ef52967
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.image.cs
@@ -0,0 +1,376 @@
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using OneOf.Types;
+using System;
+using System.Buffers.Text;
+using Tensorflow.Contexts;
+using static Tensorflow.Binding;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public image_internal image = new image_internal();
+
+ public class image_internal
+ {
+ public Tensor random_flip_up_down(Tensor image, int seed = 0)
+ => image_ops_impl.random_flip_up_down(image, seed);
+
+ public Tensor random_flip_left_right(Tensor image, int seed = 0)
+ => image_ops_impl.random_flip_left_right(image, seed);
+
+ public Tensor flip_left_right(Tensor image)
+ => image_ops_impl.flip_left_right(image);
+
+ public Tensor flip_up_down(Tensor image)
+ => image_ops_impl.flip_up_down(image);
+
+ public Tensor rot90(Tensor image, int k = 1, string name = null)
+ => image_ops_impl.rot90(image, k, name);
+
+ public Tensor transpose(Tensor image, string name = null)
+ => image_ops_impl.transpose(image, name);
+
+ public Tensor central_crop(Tensor image, float central_fraction)
+ => image_ops_impl.central_crop(image, central_fraction);
+
+ public Tensor pad_to_bounding_box(Tensor image, int offset_height, int offset_width, int target_height, int target_width)
+ => image_ops_impl.pad_to_bounding_box(image, offset_height, offset_width, target_height, target_width);
+
+ public Tensor crop_to_bounding_box(Tensor image, int offset_height, int offset_width, int target_height, int target_width)
+ => image_ops_impl.crop_to_bounding_box(image, offset_height, offset_width, target_height, target_width);
+
+ public Tensor resize_image_with_crop_or_pad(Tensor image, object target_height, object target_width)
+ => image_ops_impl.resize_image_with_crop_or_pad(image, target_height, target_width);
+
+ public Tensor resize_images(Tensor images, Tensor size, string method = ResizeMethod.BILINEAR, bool preserve_aspect_ratio = false, bool antialias = false,
+ string name = null)
+ => image_ops_impl.resize_images(images, size, method, preserve_aspect_ratio, antialias, name);
+
+ public Tensor resize_images_v2(Tensor images, Shape size, string method = ResizeMethod.BILINEAR, bool preserve_aspect_ratio = false, bool antialias = false,
+ string name = null)
+ => image_ops_impl.resize_images_v2(images, size, method, preserve_aspect_ratio, antialias, name);
+
+ public Tensor resize_images_v2(Tensor images, Tensor size, string method = ResizeMethod.BILINEAR, bool preserve_aspect_ratio = false, bool antialias = false,
+ string name = null)
+ => image_ops_impl.resize_images_v2(images, size, method, preserve_aspect_ratio, antialias, name);
+
+ public Tensor resize_images_with_pad(Tensor image, int target_height, int target_width, string method, bool antialias)
+ => image_ops_impl.resize_images_with_pad(image, target_height, target_width, method, antialias);
+
+ public Tensor per_image_standardization(Tensor image)
+ => image_ops_impl.per_image_standardization(image);
+
+ public Tensor random_brightness(Tensor image, float max_delta, int seed = 0)
+ => image_ops_impl.random_brightness(image, max_delta, seed);
+
+ public Tensor random_contrast(Tensor image, float lower, float upper, int seed = 0)
+ => image_ops_impl.random_contrast(image, lower, upper, seed);
+
+ public Tensor adjust_brightness(Tensor image, Tensor delta)
+ => image_ops_impl.adjust_brightness(image, delta);
+
+ public Tensor adjust_contrast(Tensor images, Tensor contrast_factor)
+ => image_ops_impl.adjust_contrast(images, contrast_factor);
+
+ public Tensor adjust_gamma(Tensor image, int gamma = 1, int gain = 1)
+ => image_ops_impl.adjust_gamma(image, gamma, gain);
+
+ public Tensor rgb_to_grayscale(Tensor images, string name = null)
+ => image_ops_impl.rgb_to_grayscale(images, name);
+
+ public Tensor grayscale_to_rgb(Tensor images, string name = null)
+ => image_ops_impl.grayscale_to_rgb(images, name);
+
+ public Tensor random_hue(Tensor image, float max_delta, int seed = 0)
+ => image_ops_impl.random_hue(image, max_delta, seed);
+
+ public Tensor adjust_hue(Tensor image, Tensor delta, string name = null)
+ => image_ops_impl.adjust_hue(image, delta, name);
+
+ public Tensor random_jpeg_quality(Tensor image, float min_jpeg_quality, float max_jpeg_quality, int seed = 0)
+ => image_ops_impl.random_jpeg_quality(image, min_jpeg_quality, max_jpeg_quality, seed);
+
+ public Tensor adjust_jpeg_quality(Tensor image, Tensor jpeg_quality, string name = null)
+ => image_ops_impl.adjust_jpeg_quality(image, jpeg_quality, name);
+
+ public Tensor random_saturation(Tensor image, float lower, float upper, int seed = 0)
+ => image_ops_impl.random_saturation(image, lower, upper, seed);
+
+ public Tensor adjust_saturation(Tensor image, Tensor saturation_factor, string name = null)
+ => image_ops_impl.adjust_saturation(image, saturation_factor, name);
+
+ public Tensor total_variation(Tensor images, string name = null)
+ => image_ops_impl.total_variation(images, name);
+
+ public (Tensor, Tensor, Tensor) sample_distorted_bounding_box(Tensor image_size, Tensor bounding_boxes,
+ int seed = 0,
+ Tensor min_object_covered = null,
+ float[] aspect_ratio_range = null,
+ float[] area_range = null,
+ int max_attempts = 100,
+ bool use_image_if_no_bounding_boxes = false,
+ string name = null)
+ => image_ops_impl.sample_distorted_bounding_box_v2(image_size, bounding_boxes, seed, min_object_covered, aspect_ratio_range,
+ area_range, max_attempts, use_image_if_no_bounding_boxes, name);
+
+ public Tensor non_max_suppression(Tensor boxes, Tensor scores, Tensor max_output_size, float iou_threshold = 0.5f,
+ float score_threshold = -1f / 0f, /*float soft_nms_sigma = 0.0f,*/ string name = null)
+ => image_ops_impl.non_max_suppression(boxes, scores, max_output_size, iou_threshold, score_threshold, name);
+
+ public Tensor non_max_suppression_with_overlaps(Tensor overlaps, Tensor scores, Tensor max_output_size,
+ float overlap_threshold = 0.5f, float score_threshold = -1 / 0f, string name = null)
+ => image_ops_impl.non_max_suppression_with_overlaps(overlaps, scores, max_output_size, overlap_threshold, score_threshold, name);
+
+ public Tensor rgb_to_yiq(Tensor images)
+ => image_ops_impl.rgb_to_yiq(images);
+
+ public Tensor yiq_to_rgb(Tensor images)
+ => image_ops_impl.yiq_to_rgb(images);
+
+ public Tensor rgb_to_yuv(Tensor images)
+ => image_ops_impl.rgb_to_yuv(images);
+
+ public Tensor yuv_to_rgb(Tensor images)
+ => image_ops_impl.yuv_to_rgb(images);
+
+ public Tensor psnr(Tensor a, Tensor b, Tensor max_val, string name = null)
+ => image_ops_impl.psnr(a, b, max_val, name);
+
+ public Tensor ssim(Tensor img1, Tensor img2, float max_val = 1f, float filter_size = 11f, float filter_sigma = 1.5f,
+ float k1 = 0.01f, float k2 = 0.03f)
+ => image_ops_impl.ssim(img1, img2, max_val, filter_size, filter_sigma, k1, k2);
+
+ public Tensor ssim_multiscale(Tensor img1, Tensor img2, float max_val, float[] power_factors = null, float filter_size = 11f,
+ float filter_sigma = 1.5f, float k1 = 0.01f, float k2 = 0.03f)
+ => image_ops_impl.ssim_multiscale(img1, img2, max_val, power_factors, filter_size, filter_sigma, k1, k2);
+
+ public (Tensor, Tensor) image_gradients(Tensor image)
+ => image_ops_impl.image_gradients(image);
+
+ public Tensor sobel_edges(Tensor image)
+ => image_ops_impl.sobel_edges(image);
+
+ ///
+ /// Adjust contrast of RGB or grayscale images.
+ ///
+ /// Images to adjust. At least 3-D.
+ ///
+ /// A float multiplier for adjusting contrast.
+ /// The contrast-adjusted image or images.
+ public Tensor adjust_contrast(Tensor images, float contrast_factor, string name = null)
+ => gen_image_ops.adjust_contrastv2(images, contrast_factor, name);
+
+ ///
+ /// Adjust hue of RGB images.
+ ///
+ /// RGB image or images. The size of the last dimension must be 3.
+ /// float. How much to add to the hue channel.
+ /// A name for this operation (optional).
+ /// Adjusted image(s), same shape and DType as `image`.
+ /// if `delta` is not in the interval of `[-1, 1]`.
+ public Tensor adjust_hue(Tensor images, float delta, string name = null)
+ {
+ if (tf.Context.executing_eagerly())
+ {
+ if (delta < -1f || delta > 1f)
+ throw new ValueError("delta must be in the interval [-1, 1]");
+ }
+ return gen_image_ops.adjust_hue(images, delta, name: name);
+ }
+
+ ///
+ /// Adjust saturation of RGB images.
+ ///
+ /// RGB image or images. The size of the last dimension must be 3.
+ /// float. Factor to multiply the saturation by.
+ /// A name for this operation (optional).
+ /// Adjusted image(s), same shape and DType as `image`.
+ public Tensor adjust_saturation(Tensor image, float saturation_factor, string name = null)
+ => gen_image_ops.adjust_saturation(image, saturation_factor, name);
+
+ ///
+ /// Greedily selects a subset of bounding boxes in descending order of score.
+ ///
+ ///
+ /// A 4-D float `Tensor` of shape `[batch_size, num_boxes, q, 4]`. If `q`
+ /// is 1 then same boxes are used for all classes otherwise, if `q` is equal
+ /// to number of classes, class-specific boxes are used.
+ ///
+ ///
+ /// A 3-D float `Tensor` of shape `[batch_size, num_boxes, num_classes]`
+ /// representing a single score corresponding to each box(each row of boxes).
+ ///
+ ///
+ /// A scalar integer `Tensor` representing the
+ /// maximum number of boxes to be selected by non-max suppression per class
+ ///
+ ///
+ /// A int32 scalar representing maximum number of boxes retained
+ /// over all classes.Note that setting this value to a large number may
+ /// result in OOM error depending on the system workload.
+ ///
+ ///
+ /// A float representing the threshold for deciding whether boxes
+ /// overlap too much with respect to IOU.
+ ///
+ ///
+ /// A float representing the threshold for deciding when to
+ /// remove boxes based on score.
+ ///
+ ///
+ /// If false, the output nmsed boxes, scores and classes are
+ /// padded/clipped to `max_total_size`. If true, the output nmsed boxes, scores and classes are padded to be of length `max_size_per_class`*`num_classes`,
+ /// unless it exceeds `max_total_size` in which case it is clipped to `max_total_size`. Defaults to false.
+ ///
+ ///
+ /// If true, the coordinates of output nmsed boxes will be clipped
+ /// to[0, 1]. If false, output the box coordinates as it is. Defaults to true.
+ ///
+ ///
+ /// 'nmsed_boxes': A [batch_size, max_detections, 4] float32 tensor containing the non-max suppressed boxes.
+ /// 'nmsed_scores': A [batch_size, max_detections] float32 tensor containing the scores for the boxes.
+ /// 'nmsed_classes': A [batch_size, max_detections] float32 tensor containing the class for boxes.
+ /// 'valid_detections': A [batch_size] int32 tensor indicating the number of
+ /// valid detections per batch item. Only the top valid_detections[i] entries
+ /// in nms_boxes[i], nms_scores[i] and nms_class[i] are valid. The rest of the
+ /// entries are zero paddings.
+ ///
+ public (Tensor, Tensor, Tensor, Tensor) combined_non_max_suppression(
+ Tensor boxes,
+ Tensor scores,
+ int max_output_size_per_class,
+ int max_total_size,
+ float iou_threshold,
+ float score_threshold,
+ bool pad_per_class = false,
+ bool clip_boxes = true)
+ {
+ var iou_threshold_t = ops.convert_to_tensor(iou_threshold, TF_DataType.TF_FLOAT, name: "iou_threshold");
+ var score_threshold_t = ops.convert_to_tensor(score_threshold, TF_DataType.TF_FLOAT, name: "score_threshold");
+ var max_total_size_t = ops.convert_to_tensor(max_total_size);
+ var max_output_size_per_class_t = ops.convert_to_tensor(max_output_size_per_class);
+ return gen_image_ops.combined_non_max_suppression(boxes, scores, max_output_size_per_class_t, max_total_size_t,
+ iou_threshold_t, score_threshold_t, pad_per_class, clip_boxes);
+ }
+
+ ///
+ /// Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling (possibly with aspect ratio change) to a common output size specified by crop_size. This is more general than the crop_to_bounding_box op which extracts a fixed size slice from the input image and does not allow resizing or aspect ratio change.
+ /// Returns a tensor with crops from the input image at positions defined at the bounding box locations in boxes.The cropped boxes are all resized(with bilinear or nearest neighbor interpolation) to a fixed size = [crop_height, crop_width].The result is a 4 - D tensor[num_boxes, crop_height, crop_width, depth].The resizing is corner aligned. In particular, if boxes = [[0, 0, 1, 1]], the method will give identical results to using tf.image.resize_bilinear() or tf.image.resize_nearest_neighbor() (depends on the method argument) with align_corners = True.
+ ///
+ /// A Tensor. Must be one of the following types: uint8, uint16, int8, int16, int32, int64, half, float32, float64. A 4-D tensor of shape [batch, image_height, image_width, depth]. Both image_height and image_width need to be positive.
+ /// A Tensor of type float32. A 2-D tensor of shape [num_boxes, 4]. The i-th row of the tensor specifies the coordinates of a box in the box_ind[i] image and is specified in normalized coordinates [y1, x1, y2, x2]. A normalized coordinate value of y is mapped to the image coordinate at y * (image_height - 1), so as the [0, 1] interval of normalized image height is mapped to [0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the [0, 1] range are allowed, in which case we use extrapolation_value to extrapolate the input image values.
+ /// A Tensor of type int32. A 1-D tensor of shape [num_boxes] with int32 values in [0, batch). The value of box_ind[i] specifies the image that the i-th box refers to.
+ /// A Tensor of type int32. A 1-D tensor of 2 elements, size = [crop_height, crop_width]. All cropped image patches are resized to this size. The aspect ratio of the image content is not preserved. Both crop_height and crop_width need to be positive.
+ /// An optional string from: "bilinear", "nearest". Defaults to "bilinear". A string specifying the sampling method for resizing. It can be either "bilinear" or "nearest" and default to "bilinear". Currently two sampling methods are supported: Bilinear and Nearest Neighbor.
+ /// An optional float. Defaults to 0. Value used for extrapolation, when applicable.
+ /// A name for the operation (optional).
+ /// A 4-D tensor of shape [num_boxes, crop_height, crop_width, depth].
+ public Tensor crop_and_resize(Tensor image, Tensor boxes, Tensor box_ind, Tensor crop_size, string method = "bilinear", float extrapolation_value = 0f, string name = null) =>
+ gen_image_ops.crop_and_resize(image, boxes, box_ind, crop_size, method, extrapolation_value, name);
+
+ public Tensor decode_jpeg(Tensor contents,
+ int channels = 0,
+ int ratio = 1,
+ bool fancy_upscaling = true,
+ bool try_recover_truncated = false,
+ int acceptable_fraction = 1,
+ string dct_method = "",
+ string name = null)
+ => gen_image_ops.decode_jpeg(contents, channels: channels, ratio: ratio,
+ fancy_upscaling: fancy_upscaling, try_recover_truncated: try_recover_truncated,
+ acceptable_fraction: acceptable_fraction, dct_method: dct_method);
+
+ public Tensor extract_glimpse(Tensor input, Tensor size, Tensor offsets, bool centered = true, bool normalized = true,
+ bool uniform_noise = true, string name = null)
+ => image_ops_impl.extract_glimpse(input, size, offsets, centered, normalized, uniform_noise, name);
+
+ public (Tensor, Tensor, Tensor, Tensor) combined_non_max_suppression(Tensor boxes, Tensor scores, Tensor max_output_size_per_class,
+ Tensor max_total_size, float iou_threshold = 0.5f, float score_threshold = -1f / 0f, bool pad_per_class = false, bool clip_boxes = true,
+ string name = null)
+ => image_ops_impl.combined_non_max_suppression(boxes, scores, max_output_size_per_class, max_total_size, iou_threshold, score_threshold,
+ pad_per_class, clip_boxes, name);
+
+ public (Tensor, Tensor) non_max_suppression_padded(Tensor boxes, Tensor scores, Tensor max_output_size,
+ float iou_threshold = 0.5f,
+ float score_threshold = -1f / 0f,
+ bool pad_to_max_output_size = false,
+ string name = null,
+ bool sorted_input = false,
+ bool canonicalized_coordinates = false,
+ int tile_size = 512)
+ => image_ops_impl.non_max_suppression_padded(boxes, scores, max_output_size, iou_threshold, score_threshold, pad_to_max_output_size,
+ name, sorted_input, canonicalized_coordinates, tile_size);
+
+ public Tensor resize(Tensor image, Shape size, string method = ResizeMethod.BILINEAR)
+ => image_ops_impl.resize_images_v2(image, size, method: method);
+
+ public Tensor resize(Tensor image, Tensor size, string method = ResizeMethod.BILINEAR)
+ => image_ops_impl.resize_images_v2(image, size, method: method);
+
+ public Tensor resize_bilinear(Tensor images, Tensor size, bool align_corners = false, bool half_pixel_centers = false, string name = null)
+ => gen_image_ops.resize_bilinear(images, size, align_corners: align_corners, half_pixel_centers: half_pixel_centers, name: name);
+
+ public Tensor resize_images(Tensor images, Tensor size, string method = ResizeMethod.BILINEAR,
+ bool preserve_aspect_ratio = false, string name = null)
+ => image_ops_impl.resize_images(images, size, method: method,
+ preserve_aspect_ratio: preserve_aspect_ratio, name: name);
+
+ public Tensor convert_image_dtype(Tensor image, TF_DataType dtype, bool saturate = false, string name = null)
+ => gen_image_ops.convert_image_dtype(image, dtype, saturate: saturate, name: name);
+
+ public Tensor decode_image(Tensor contents, int channels = 0, TF_DataType dtype = TF_DataType.TF_UINT8,
+ string name = null, bool expand_animations = true)
+ => image_ops_impl.decode_image(contents, channels: channels, dtype: dtype,
+ name: name, expand_animations: expand_animations);
+
+ public Tensor encode_png(Tensor contents, string name = null)
+ => image_ops_impl.encode_png(contents, name: name);
+
+ public Tensor encode_jpeg(Tensor contents, string name = null)
+ => image_ops_impl.encode_jpeg(contents, name: name);
+
+
+ ///
+ /// Convenience function to check if the 'contents' encodes a JPEG image.
+ ///
+ ///
+ ///
+ ///
+ public Tensor is_jpeg(Tensor contents, string name = null)
+ => image_ops_impl.is_jpeg(contents, name: name);
+
+ ///
+ /// Resize `images` to `size` using nearest neighbor interpolation.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor resize_nearest_neighbor(Tensor images, Tsize size, bool align_corners = false,
+ string name = null, bool half_pixel_centers = false)
+ => image_ops_impl.resize_nearest_neighbor(images, size, align_corners: align_corners,
+ name: name, half_pixel_centers: half_pixel_centers);
+
+ public Tensor draw_bounding_boxes(Tensor images, Tensor boxes, Tensor colors = null, string name = null)
+ => image_ops_impl.draw_bounding_boxes(images, boxes, colors, name);
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.init.cs b/src/TensorFlowNET.Core/APIs/tf.init.cs
index c4cc415ec..8635f6620 100644
--- a/src/TensorFlowNET.Core/APIs/tf.init.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.init.cs
@@ -1,18 +1,36 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
using Tensorflow.Operations.Initializers;
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static IInitializer zeros_initializer => new Zeros();
- public static IInitializer ones_initializer => new Ones();
- public static IInitializer glorot_uniform_initializer => new GlorotUniform();
- public static IInitializer uniform_initializer => new RandomUniform();
+ public InitializersImpl initializers { get; } = new InitializersImpl();
+
+ public IInitializer constant_initializer(T value, TF_DataType dtype = TF_DataType.TF_FLOAT, bool verify_shape = false)
+ => new Constant(value, dtype: dtype, verify_shape: verify_shape);
+ public IInitializer zeros_initializer => new Zeros();
+ public IInitializer ones_initializer => new Ones();
+ public IInitializer glorot_uniform_initializer => new GlorotUniform();
+ public IInitializer random_uniform_initializer => new RandomUniform();
+ public IInitializer orthogonal_initializer => new Orthogonal();
- public static variable_scope variable_scope(string name,
+ public variable_scope variable_scope(string name,
string default_name = null,
Tensor[] values = null,
bool? reuse = null,
@@ -22,7 +40,7 @@ public static variable_scope variable_scope(string name,
reuse: reuse,
auxiliary_name_scope: auxiliary_name_scope);
- public static variable_scope variable_scope(VariableScope scope,
+ public variable_scope variable_scope(VariableScope scope,
string default_name = null,
Tensor[] values = null,
bool? reuse = null,
@@ -32,12 +50,55 @@ public static variable_scope variable_scope(VariableScope scope,
reuse: reuse,
auxiliary_name_scope: auxiliary_name_scope);
- public static IInitializer truncated_normal_initializer(float mean = 0.0f,
+ public IInitializer truncated_normal_initializer(float mean = 0.0f,
float stddev = 1.0f,
int? seed = null,
TF_DataType dtype = TF_DataType.DtInvalid) => new TruncatedNormal(mean: mean,
stddev: stddev,
seed: seed,
dtype: dtype);
+
+ public IInitializer random_normal_initializer(float mean = 0.0f,
+ float stddev = 1.0f,
+ int? seed = null,
+ TF_DataType dtype = TF_DataType.DtInvalid) => new RandomNormal(mean: mean,
+ stddev: stddev,
+ seed: seed,
+ dtype: dtype);
+
+ ///
+ /// Initializer capable of adapting its scale to the shape of weights tensors.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public IInitializer variance_scaling_initializer(float factor = 1.0f,
+ string mode = "fan_in",
+ string distribution = "truncated_normal",
+ int? seed = null,
+ TF_DataType dtype = TF_DataType.TF_FLOAT) => new VarianceScaling(
+ scale: factor,
+ mode: mode,
+ distribution: distribution,
+ seed: seed,
+ dtype: dtype);
+
+ public class InitializersImpl
+ {
+ public IInitializer random_normal_initializer(float mean = 0.0f,
+ float stddev = 0.05f,
+ int? seed = null,
+ TF_DataType dtype = TF_DataType.TF_FLOAT) => new RandomNormal(mean: mean,
+ stddev: stddev,
+ seed: seed,
+ dtype: dtype);
+
+ public IInitializer zeros_initializer(Shape shape = null,
+ TF_DataType dtype = TF_DataType.TF_FLOAT) => new Zeros(shape: shape,
+ dtype: dtype);
+ }
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.io.cs b/src/TensorFlowNET.Core/APIs/tf.io.cs
index f504bdc64..ea1e44b28 100644
--- a/src/TensorFlowNET.Core/APIs/tf.io.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.io.cs
@@ -1,21 +1,66 @@
-using System;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
using System.Collections.Generic;
-using System.Text;
using Tensorflow.IO;
+using Tensorflow.Operations;
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static GFile gfile = new GFile();
- public static Tensor read_file(string filename, string name = null) => gen_io_ops.read_file(filename, name);
+ public IoApi io { get; } = new IoApi();
+
+ public class IoApi
+ {
+ io_ops ops;
+ public GFile gfile;
+ public IoApi()
+ {
+ ops = new io_ops();
+ gfile = new GFile();
+ }
+
+ public Tensor read_file(string filename, string name = null)
+ => ops.read_file(filename, name);
+
+ public Tensor read_file(Tensor filename, string name = null)
+ => ops.read_file(filename, name);
+
+ public Operation save_v2(Tensor prefix, string[] tensor_names,
+ string[] shape_and_slices, Tensor[] tensors, string name = null)
+ => ops.save_v2(prefix, tensor_names, shape_and_slices, tensors, name: name);
+
+ public Tensor[] restore_v2(Tensor prefix, string[] tensor_names,
+ string[] shape_and_slices, TF_DataType[] dtypes, string name = null)
+ => ops.restore_v2(prefix, tensor_names, shape_and_slices, dtypes, name: name);
+
+ public Operation write_file(string filename, Tensor conentes, string name = null)
+ => write_file(Tensorflow.ops.convert_to_tensor(filename, TF_DataType.TF_STRING), conentes, name);
+
+ public Operation write_file(Tensor filename, Tensor conentes, string name = null)
+ => gen_ops.write_file(filename, conentes, name);
+ }
- public static gen_image_ops image => new gen_image_ops();
+ public GFile gfile = new GFile();
- public static void import_graph_def(GraphDef graph_def,
+ public ITensorOrOperation[] import_graph_def(GraphDef graph_def,
Dictionary input_map = null,
string[] return_elements = null,
string name = null,
- OpList producer_op_list = null) => importer.import_graph_def(graph_def, input_map, return_elements, name, producer_op_list);
+ OpList producer_op_list = null) => importer.import_graph_def(graph_def, input_map, return_elements, name: name, producer_op_list: producer_op_list);
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.layers.cs b/src/TensorFlowNET.Core/APIs/tf.layers.cs
deleted file mode 100644
index ea35e869c..000000000
--- a/src/TensorFlowNET.Core/APIs/tf.layers.cs
+++ /dev/null
@@ -1,152 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow.Keras.Layers;
-using Tensorflow.Operations.Activation;
-
-namespace Tensorflow
-{
- public static partial class tf
- {
- public static class layers
- {
- public static Tensor conv2d(Tensor inputs,
- int filters,
- int[] kernel_size,
- int[] strides = null,
- string padding = "valid",
- string data_format= "channels_last",
- int[] dilation_rate = null,
- bool use_bias = true,
- IActivation activation = null,
- IInitializer kernel_initializer = null,
- IInitializer bias_initializer = null,
- bool trainable = true,
- string name = null)
- {
- if (strides == null)
- strides = new int[] { 1, 1 };
- if (dilation_rate == null)
- dilation_rate = new int[] { 1, 1 };
- if (bias_initializer == null)
- bias_initializer = tf.zeros_initializer;
-
- var layer = new Conv2D(filters,
- kernel_size: kernel_size,
- strides: strides,
- padding: padding,
- data_format: data_format,
- dilation_rate: dilation_rate,
- activation: activation,
- use_bias: use_bias,
- kernel_initializer: kernel_initializer,
- bias_initializer: bias_initializer,
- trainable: trainable,
- name: name);
-
- return layer.apply(inputs);
- }
-
- ///
- /// Functional interface for the batch normalization layer.
- /// http://arxiv.org/abs/1502.03167
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- public static Tensor batch_normalization(Tensor inputs,
- int axis = -1,
- float momentum = 0.99f,
- float epsilon = 0.001f,
- bool center = true,
- bool scale = true,
- IInitializer beta_initializer = null,
- IInitializer gamma_initializer = null,
- IInitializer moving_mean_initializer = null,
- IInitializer moving_variance_initializer = null,
- Tensor training = null,
- bool trainable = true,
- string name = null,
- bool renorm = false,
- float renorm_momentum = 0.99f)
- {
- var layer = new BatchNormalization(
- axis: axis,
- momentum: momentum,
- epsilon: epsilon,
- center: center,
- scale: scale,
- beta_initializer: beta_initializer,
- gamma_initializer: gamma_initializer,
- moving_mean_initializer: moving_mean_initializer,
- moving_variance_initializer: moving_variance_initializer,
- renorm: renorm,
- renorm_momentum: renorm_momentum,
- trainable: trainable,
- name: name);
-
- return layer.apply(inputs, training: training);
- }
-
- ///
- /// Max pooling layer for 2D inputs (e.g. images).
- ///
- /// The tensor over which to pool. Must have rank 4.
- ///
- ///
- ///
- ///
- ///
- ///
- public static Tensor max_pooling2d(Tensor inputs,
- int[] pool_size,
- int[] strides,
- string padding = "valid",
- string data_format = "channels_last",
- string name = null)
- {
- var layer = new MaxPooling2D(pool_size: pool_size,
- strides: strides,
- padding: padding,
- data_format: data_format,
- name: name);
-
- return layer.apply(inputs);
- }
-
- public static Tensor dense(Tensor inputs,
- int units,
- IActivation activation = null,
- bool use_bias = true,
- IInitializer kernel_initializer = null,
- IInitializer bias_initializer = null,
- bool trainable = true,
- string name = null,
- bool? reuse = null)
- {
- if (bias_initializer == null)
- bias_initializer = tf.zeros_initializer;
-
- var layer = new Dense(units, activation,
- use_bias: use_bias,
- bias_initializer: bias_initializer,
- kernel_initializer: kernel_initializer);
-
- return layer.apply(inputs);
- }
- }
- }
-}
diff --git a/src/TensorFlowNET.Core/APIs/tf.linalg.cs b/src/TensorFlowNET.Core/APIs/tf.linalg.cs
index fd751322c..32f64ec35 100644
--- a/src/TensorFlowNET.Core/APIs/tf.linalg.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.linalg.cs
@@ -1,15 +1,111 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+using Tensorflow.NumPy;
+using static Tensorflow.Binding;
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static Tensor diag(Tensor diagonal, string name = null)
+ public LinalgApi linalg { get; } = new LinalgApi();
+
+ public class LinalgApi
+ {
+ linalg_ops ops = new linalg_ops();
+
+ public Tensor einsum(string equation, Tensors inputs, string name = null)
+ => math_ops.einsum(equation, inputs, name: name);
+
+ public Tensor eye(int num_rows,
+ int num_columns = -1,
+ Shape batch_shape = null,
+ TF_DataType dtype = TF_DataType.TF_DOUBLE,
+ string name = null)
+ => ops.eye(num_rows, num_columns: num_columns, batch_shape: batch_shape, dtype: dtype, name: name);
+
+ public Tensor diag(Tensor diagonal, string name = null)
+ => gen_array_ops.diag(diagonal, name: name);
+
+ public Tensor matmul(Tensor a, Tensor b)
+ => math_ops.matmul(a, b);
+
+ public Tensor norm(Tensor a, string ord = "euclidean", Axis axis = null, string name = null)
+ => ops.norm(a, ord: ord, axis: axis, name: name);
+
+ public Tensor batch_matmul(Tensor x, Tensor y, bool adj_x = false, bool adj_y = false, string name = null)
+ => math_ops.batch_matmul(x, y, adj_x: adj_x, adj_y: adj_y, name: name);
+
+ public Tensor inv(Tensor input, bool adjoint = false, string name = null)
+ => ops.matrix_inverse(input, adjoint: adjoint, name: name);
+
+ public Tensor global_norm(Tensor[] t_list, string name = null)
+ => clip_ops.global_norm(t_list, name: name);
+
+ public Tensor l2_normalize(Tensor x,
+ int axis = 0,
+ float epsilon = 1e-12f,
+ string name = null)
+ => nn_impl.l2_normalize(x, axis: axis, epsilon: constant_op.constant(epsilon), name: name);
+
+ public Tensor lstsq(Tensor matrix, Tensor rhs,
+ NDArray l2_regularizer = null, bool fast = true, string name = null)
+ => ops.matrix_solve_ls(matrix, rhs, l2_regularizer: l2_regularizer, fast: fast, name: name);
+
+ public Tensors qr(Tensor input, bool full_matrices = true, string name = null)
+ => ops.qr(input, full_matrices: full_matrices, name: name);
+
+ public Tensor tensor_diag_part(Tensor input, string name = null)
+ => gen_array_ops.diag_part(input, name: name);
+
+ public Tensor tensordot(Tensor x, Tensor y, NDArray axes, string name = null)
+ => math_ops.tensordot(x, y, axes, name: name);
+ }
+
+ public Tensor diag(Tensor diagonal, string name = null)
=> gen_array_ops.diag(diagonal, name: name);
- public static Tensor matmul(Tensor a, Tensor b)
- => gen_math_ops.mat_mul(a, b);
+ public Tensor matmul(Tensor a, Tensor b, bool transpose_a = false, bool transpose_b = false)
+ => math_ops.matmul(a, b, transpose_a: transpose_a, transpose_b: transpose_b);
+
+ ///
+ /// Multiply slices of the two matrices "x" and "y".
+ ///
+ ///
+ /// The `BatchMatMul` operation is embedded into the
+ /// `MatMul` operation on the DLL side. However the expected
+ /// attributes are not the same, hence we need to expose this
+ /// method to have the right args list on the `_apply_op_helper`
+ /// function.
+ ///
+ /// For each rank > 2 the first rank - 2 dimensions are considered
+ /// as fixed, and have to be consistent across the two matrices. A
+ /// common matrix multiplication is then applied over the residual
+ /// 2 dimensions.
+ ///
+ /// e.g.
+ /// x is (3, 6, 12); y is (3, 12, 6)
+ /// batch_matmul(x, y) ==> (3, 6, 6)
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor batch_matmul(Tensor x, Tensor y, bool adj_x = false, bool adj_y = false, string name = null)
+ => math_ops.batch_matmul(x, y, adj_x: adj_x, adj_y: adj_y, name: name);
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.logging.cs b/src/TensorFlowNET.Core/APIs/tf.logging.cs
new file mode 100644
index 000000000..0e10c1610
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.logging.cs
@@ -0,0 +1,23 @@
+/*****************************************************************************
+ Copyright 2021 Haiping Chen. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public logging_ops logging => new logging_ops();
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.loss.cs b/src/TensorFlowNET.Core/APIs/tf.loss.cs
index 90162603d..48ed01500 100644
--- a/src/TensorFlowNET.Core/APIs/tf.loss.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.loss.cs
@@ -1,11 +1,23 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static LossesImpl losses => new LossesImpl();
+ public LossesImpl losses => new LossesImpl();
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.math.cs b/src/TensorFlowNET.Core/APIs/tf.math.cs
index a8ec223a8..da54a9dd7 100644
--- a/src/TensorFlowNET.Core/APIs/tf.math.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.math.cs
@@ -1,12 +1,122 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+/*****************************************************************************
+ Copyright 2023 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using Tensorflow.NumPy;
+using Tensorflow.Operations;
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static Tensor abs(Tensor x, string name = null)
+ public MathApi math { get; } = new MathApi();
+ public class MathApi
+ {
+ public Tensor argmax(Tensor input, Axis axis = null, string name = null, int? dimension = null, TF_DataType output_type = TF_DataType.TF_INT64)
+ => gen_math_ops.arg_max(input, axis, name: name, output_type: output_type);
+
+ public Tensor count_nonzero(Tensor input, Axis? axis = null, bool? keepdims = null, TF_DataType dtype = TF_DataType.TF_INT64, string name = null)
+ => math_ops.count_nonzero_v2(input, axis: axis, keepdims: keepdims ?? false, dtype: dtype);
+ public Tensor log(Tensor x, string name = null)
+ => gen_math_ops.log(x, name);
+
+ ///
+ /// Computes the Gauss error function of `x` element-wise.
+ ///
+ ///
+ ///
+ ///
+ public Tensor erf(Tensor x, string name = null)
+ => math_ops.erf(x, name);
+
+ public Tensor multiply(Tensor x, Tensor y, string name = null)
+ => math_ops.multiply(x, y, name: name);
+ public Tensor divide_no_nan(Tensor a, Tensor b, string name = null)
+ => math_ops.div_no_nan(a, b);
+
+ ///
+ /// Computes the Euclidean norm of elements across dimensions of a tensor.
+ ///
+ /// The tensor to reduce. Should have numeric type.
+ /// The dimensions to reduce. If `None` (the default), reduces all dimensions.Must be in the range `[-rank(input_tensor), rank(input_tensor))`
+ /// If true, retains reduced dimensions with length 1.
+ /// A name for the operation (optional).
+ /// The reduced tensor, of the same dtype as the input_tensor.
+ public Tensor reduce_euclidean_norm(Tensor input_tensor, Axis? axis = null, bool keepdims = false, string name = null)
+ => math_ops.reduce_euclidean_norm(input_tensor, axis: axis, keepdims: keepdims, name);
+
+ public Tensor square(Tensor x, string name = null)
+ => math_ops.square(x, name: name);
+
+ public Tensor sum(Tensor x, Axis? axis = null, string name = null)
+ => math_ops.reduce_sum(x, axis: axis, name: name);
+
+ public Tensor softplus(Tensor features, string name = null)
+ => nn_ops.softplus(features, name: name);
+
+ public Tensor tanh(Tensor x, string name = null)
+ => math_ops.tanh(x, name: name);
+
+ ///
+ /// Finds values and indices of the `k` largest entries for the last dimension.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensors top_k(Tensor input, int k, bool sorted = true, string name = null)
+ => nn_ops.top_kv2(input, k, sorted: sorted, name: name);
+
+ public Tensor in_top_k(Tensor predictions, Tensor targets, int k, string name = "InTopK")
+ => nn_ops.in_top_k(predictions, targets, k, name);
+
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor bincount(Tensor arr, Tensor weights = null,
+ Tensor minlength = null,
+ Tensor maxlength = null,
+ TF_DataType dtype = TF_DataType.TF_INT32,
+ string name = null,
+ Shape axis = null,
+ bool binary_output = false)
+ => math_ops.bincount(arr, weights: weights, minlength: minlength, maxlength: maxlength,
+ dtype: dtype, name: name, axis: axis, binary_output: binary_output);
+
+ public Tensor real(Tensor x, string name = null)
+ => gen_ops.real(x, x.dtype.real_dtype(), name);
+ public Tensor imag(Tensor x, string name = null)
+ => gen_ops.imag(x, x.dtype.real_dtype(), name);
+
+ public Tensor conj(Tensor x, string name = null)
+ => gen_ops.conj(x, name);
+ public Tensor angle(Tensor x, string name = null)
+ => gen_ops.angle(x, x.dtype.real_dtype(), name);
+ }
+
+ public Tensor abs(Tensor x, string name = null)
=> math_ops.abs(x, name);
///
@@ -15,7 +125,7 @@ public static Tensor abs(Tensor x, string name = null)
///
///
///
- public static Tensor acos(Tensor x, string name = null)
+ public Tensor acos(Tensor x, string name = null)
=> gen_math_ops.acos(x, name);
///
@@ -24,11 +134,23 @@ public static Tensor acos(Tensor x, string name = null)
///
///
///
- public static Tensor asin(Tensor x, string name = null)
+ public Tensor asin(Tensor x, string name = null)
=> gen_math_ops.asin(x, name);
- public static Tensor add(Tx a, Ty b)
- => gen_math_ops.add(a, b);
+ public Tensor add(Tensor a, Tensor b, string name = null)
+ => gen_math_ops.add(a, b, name: name);
+
+ public Tensor add(Tx a, Ty b, string name = null)
+ => gen_math_ops.add(ops.convert_to_tensor(a), ops.convert_to_tensor(b), name: name);
+
+ ///
+ /// Adds all input tensors element-wise.
+ ///
+ ///
+ ///
+ /// A `Tensor` of same shape and type as the elements of `inputs`.
+ public Tensor add_n(Tensor[] inputs, string name = null)
+ => math_ops.add_n(inputs, name: name);
///
/// Computes atan of x element-wise.
@@ -36,14 +158,20 @@ public static Tensor add(Tx a, Ty b)
///
///
///
- public static Tensor atan(Tensor x, string name = null)
+ public Tensor atan(Tensor x, string name = null)
=> gen_math_ops.atan(x, name);
- public static Tensor arg_max(Tensor input, int dimension, TF_DataType output_type = TF_DataType.TF_INT64, string name = null)
- => gen_math_ops.arg_max(input, dimension, output_type: output_type, name: name);
+ public Tensor arg_max(Tensor input, int dimension, TF_DataType output_type = TF_DataType.TF_INT64, string name = null)
+ => gen_math_ops.arg_max(input, ops.convert_to_tensor(dimension), output_type: output_type, name: name);
- public static Tensor arg_min(Tensor input, int dimension, TF_DataType output_type = TF_DataType.TF_INT64, string name = null)
- => gen_math_ops.arg_min(input, dimension, output_type: output_type, name: name);
+ public Tensor arg_min(Tensor input, int dimension, TF_DataType output_type = TF_DataType.TF_INT64, string name = null)
+ => gen_math_ops.arg_min(input, ops.convert_to_tensor(dimension), output_type: output_type, name: name);
+
+ public Tensor is_finite(Tensor input, string name = null)
+ => gen_math_ops.is_finite(input, name);
+
+ public Tensor is_nan(Tensor input, string name = null)
+ => gen_math_ops.is_nan(input, name);
///
/// Returns element-wise smallest integer not less than x.
@@ -51,7 +179,7 @@ public static Tensor arg_min(Tensor input, int dimension, TF_DataType output_typ
///
///
///
- public static Tensor ceil(Tensor x, string name = null)
+ public Tensor ceil(Tensor x, string name = null)
=> gen_math_ops.ceil(x, name);
///
@@ -60,7 +188,7 @@ public static Tensor ceil(Tensor x, string name = null)
///
///
///
- public static Tensor sin(Tensor x, string name = null)
+ public Tensor sin(Tensor x, string name = null)
=> gen_math_ops.sin(x, name);
///
@@ -69,7 +197,7 @@ public static Tensor sin(Tensor x, string name = null)
///
///
///
- public static Tensor sinh(Tensor x, string name = null)
+ public Tensor sinh(Tensor x, string name = null)
=> gen_math_ops.sinh(x, name);
///
@@ -78,22 +206,25 @@ public static Tensor sinh(Tensor x, string name = null)
///
///
///
- public static Tensor cos(Tensor x, string name = null)
+ public Tensor cos(Tensor x, string name = null)
=> gen_math_ops.cos(x, name);
+ public Tensor cos(float x, string name = null)
+ => gen_math_ops.cos(ops.convert_to_tensor(x), name);
+
///
/// Computes hyperbolic cosine of x element-wise.
///
///
///
///
- public static Tensor cosh(Tensor x, string name = null)
+ public Tensor cosh(Tensor x, string name = null)
=> gen_math_ops.cosh(x, name);
- public static Tensor tan(Tensor x, string name = null)
+ public Tensor tan(Tensor x, string name = null)
=> gen_math_ops.tan(x, name);
- public static Tensor tanh(Tensor x, string name = null)
+ public Tensor tanh(Tensor x, string name = null)
=> gen_math_ops.tanh(x, name);
///
@@ -102,7 +233,7 @@ public static Tensor tanh(Tensor x, string name = null)
///
///
///
- public static Tensor floor(Tensor x, string name = null)
+ public Tensor floor(Tensor x, string name = null)
=> gen_math_ops.floor(x, name);
///
@@ -114,8 +245,8 @@ public static Tensor floor(Tensor x, string name = null)
///
///
///
- public static Tensor greater(Tx x, Ty y, string name = null)
- => gen_math_ops.greater(x, y, name);
+ public Tensor greater(Tx x, Ty y, string name = null)
+ => gen_math_ops.greater(ops.convert_to_tensor(x), ops.convert_to_tensor(y), name);
///
/// Returns the truth value of (x >= y) element-wise.
@@ -126,11 +257,11 @@ public static Tensor greater(Tx x, Ty y, string name = null)
///
///
///
- public static Tensor greater_equal(Tx x, Ty y, string name = null)
- => gen_math_ops.greater_equal(x, y, name);
+ public Tensor greater_equal(Tx x, Ty y, string name = null)
+ => gen_math_ops.greater_equal(ops.convert_to_tensor(x), ops.convert_to_tensor(y), name);
///
- /// Returns the truth value of (x < y) element-wise.
+ /// Returns the truth value of (x < y) element-wise.
///
///
///
@@ -138,11 +269,20 @@ public static Tensor greater_equal(Tx x, Ty y, string name = null)
///
///
///
- public static Tensor less(Tx x, Ty y, string name = null)
- => gen_math_ops.less(x, y, name);
+ public Tensor less(Tx x, Ty y, string name = null)
+ => gen_math_ops.less(ops.convert_to_tensor(x), ops.convert_to_tensor(y), name);
+
+ ///
+ /// Computes the log of the absolute value of `Gamma(x)` element-wise.
+ ///
+ /// A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
+ /// A name for the operation (optional).
+ /// A `Tensor`. Has the same type as `x`.
+ public Tensor lgamma(Tensor x, string name = null)
+ => gen_math_ops.lgamma(x, name: name);
///
- /// Returns the truth value of (x <= y) element-wise.
+ /// Returns the truth value of (x <= y) element-wise.
///
///
///
@@ -150,8 +290,8 @@ public static Tensor less(Tx x, Ty y, string name = null)
///
///
///
- public static Tensor less_equal(Tx x, Ty y, string name = null)
- => gen_math_ops.less_equal(x, y, name);
+ public Tensor less_equal(Tx x, Ty y, string name = null)
+ => gen_math_ops.less_equal(ops.convert_to_tensor(x), ops.convert_to_tensor(y), name);
///
/// Computes natural logarithm of (1 + x) element-wise.
@@ -159,9 +299,24 @@ public static Tensor less_equal(Tx x, Ty y, string name = null)
///
///
///
- public static Tensor log1p(Tensor x, string name = null)
+ public Tensor log1p(Tensor x, string name = null)
=> gen_math_ops.log1p(x, name);
+ public Tensor logical_and(T x, T y, string name = null)
+ => gen_math_ops.logical_and(ops.convert_to_tensor(x), ops.convert_to_tensor(y), name);
+
+ public Tensor logical_not(Tensor x, string name = null)
+ => gen_math_ops.logical_not(x, name);
+
+ public Tensor logical_or(Tensor x, Tensor y, string name = null)
+ => gen_math_ops.logical_or(x, y, name);
+
+ public Tensor logical_xor(Tensor x, Tensor y, string name = "LogicalXor")
+ {
+ return gen_math_ops.logical_and(gen_math_ops.logical_or(x, y),
+ gen_math_ops.logical_not(gen_math_ops.logical_and(x, y)), name);
+ }
+
///
/// Clips tensor values to a specified min and max.
///
@@ -170,26 +325,69 @@ public static Tensor log1p(Tensor x, string name = null)
///
///
///
- public static Tensor _clip_by_value(Tensor t, Tensor clip_value_min, Tensor clip_value_max, string name = null)
- => gen_math_ops._clip_by_value(t, clip_value_min, clip_value_max);
+ public Tensor _clip_by_value(Tensor t, Tensor clip_value_min, Tensor clip_value_max, string name = null)
+ => gen_math_ops.clip_by_value(t, clip_value_min, clip_value_max);
- public static Tensor sub(Tensor a, Tensor b)
- => gen_math_ops.sub(a, b);
-
- public static Tensor divide(Tensor a, Tensor b)
- => gen_math_ops.real_div(a, b);
-
- public static Tensor sqrt(Tensor a, string name = null)
- => gen_math_ops.sqrt(a, name);
-
- public static Tensor subtract(Tensor x, T[] y, string name = null) where T : struct
+ ///
+ /// Clips tensor values to a specified min and max.
+ ///
+ ///
+ /// A Tensor.
+ ///
+ ///
+ /// A 0-D (scalar) Tensor, or a Tensor with the same shape
+ /// as t. The minimum value to clip by.
+ ///
+ ///
+ /// A 0-D (scalar) Tensor, or a Tensor with the same shape
+ /// as t. The maximum value to clip by.
+ ///
+ ///
+ /// If specified, the created operation in the graph will be this one, otherwise it will be named 'ClipByValue'.
+ ///
+ ///
+ /// A clipped Tensor with the same shape as input 't'.
+ /// The Operation can be fetched from the resulting Tensor, by fetching the Operation property from the result.
+ ///
+ ///
+ /// Given a tensor t, this operation returns a tensor of the same type and
+ /// shape as t with its values clipped to clip_value_min and clip_value_max.
+ /// Any values less than clip_value_min are set to clip_value_min. Any values
+ /// greater than clip_value_max are set to clip_value_max.
+ ///
+ public Tensor clip_by_value(Tensor t, T1 clip_value_min, T2 clip_value_max, string name = "ClipByValue")
+ => clip_ops.clip_by_value(t, clip_value_min, clip_value_max, name);
+
+ public Tensor sub(Tx a, Ty b, string name = null)
+ => gen_math_ops.sub(ops.convert_to_tensor(a), ops.convert_to_tensor(b), name: name);
+
+ public Tensor divide(Tensor a, Tensor b)
+ => a / b;
+
+ public Tensor sqrt(Tensor a, string name = null)
+ => math_ops.sqrt(a, name);
+
+ public Tensor sign(Tensor a, string name = null)
+ => gen_math_ops.sign(a, name);
+
+ public Tensor subtract(Tensor x, T[] y, string name = null) where T : struct
=> gen_math_ops.sub(x, ops.convert_to_tensor(y, dtype: x.dtype.as_base_dtype(), name: "y"), name);
- public static Tensor log(Tensor x, string name = null)
+ ///
+ /// return x - y
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor subtract(Tensor x, Tensor y, string name = null)
+ => gen_math_ops.sub(x, y, name);
+
+ public Tensor log(Tensor x, string name = null)
=> gen_math_ops.log(x, name);
- public static Tensor equal(Tensor x, Tensor y, string name = null)
- => gen_math_ops.equal(x, y, name);
+ public Tensor equal(Tensor x, Tensor y, string name = null)
+ => gen_math_ops.equal(x, y, name: name);
///
/// Computes arctangent of `y/x` element-wise, respecting signs of the arguments.
@@ -198,7 +396,7 @@ public static Tensor equal(Tensor x, Tensor y, string name = null)
///
///
///
- public static Tensor atan2(Tensor y, Tensor x, string name = null)
+ public Tensor atan2(Tensor y, Tensor x, string name = null)
=> gen_math_ops.atan2(y, x, name);
///
@@ -211,8 +409,8 @@ public static Tensor atan2(Tensor y, Tensor x, string name = null)
///
///
///
- public static Tensor max(Tx input, Ty axis, bool keep_dims = false, string name = null)
- => gen_math_ops._max(input, axis, keep_dims: keep_dims, name: name);
+ public Tensor max(Tx input, Ty axis, bool keep_dims = false, string name = null)
+ => gen_math_ops.max(ops.convert_to_tensor(input), ops.convert_to_tensor(axis), keep_dims: keep_dims, name: name);
///
/// Computes the minimum of elements across dimensions of a tensor.
@@ -224,8 +422,8 @@ public static Tensor max(Tx input, Ty axis, bool keep_dims = false, stri
///
///
///
- public static Tensor min(Tx input, Ty axis, bool keep_dims = false, string name = null)
- => gen_math_ops._min(input, axis, keep_dims: keep_dims, name: name);
+ public Tensor min(Tx input, Ty axis, bool keep_dims = false, string name = null)
+ => gen_math_ops.min(ops.convert_to_tensor(input), ops.convert_to_tensor(axis), keep_dims: keep_dims, name: name);
///
/// Returns the max of x and y (i.e. x > y ? x : y) element-wise.
@@ -236,11 +434,11 @@ public static Tensor min(Tx input, Ty axis, bool keep_dims = false, stri
///
///
///
- public static Tensor maximum(T1 x, T2 y, string name = null)
- => gen_math_ops.maximum(x, y, name: name);
+ public Tensor maximum(T1 x, T2 y, string name = null)
+ => gen_math_ops.maximum(ops.convert_to_tensor(x), ops.convert_to_tensor(y), name: name);
///
- /// Returns the min of x and y (i.e. x < y ? x : y) element-wise.
+ /// Returns the min of x and y (i.e. x < y ? x : y) element-wise.
///
///
///
@@ -248,15 +446,48 @@ public static Tensor maximum(T1 x, T2 y, string name = null)
///
///
///
- public static Tensor minimum(T1 x, T2 y, string name = null)
- => gen_math_ops.minimum(x, y, name: name);
+ public Tensor minimum(T1 x, T2 y, string name = null)
+ => gen_math_ops.minimum(ops.convert_to_tensor(x), ops.convert_to_tensor(y), name: name);
- public static Tensor multiply(Tx x, Ty y)
- => gen_math_ops.mul(x, y);
+ public Tensor multiply(Tensor x, Tensor y, string name = null)
+ => gen_math_ops.mul(x, y, name: name);
- public static Tensor negative(Tensor x, string name = null)
+ ///
+ /// return x * y
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor multiply(Tx x, Ty y, string name = null)
+ => gen_math_ops.mul(ops.convert_to_tensor(x), ops.convert_to_tensor(y), name: name);
+ ///
+ /// return scalar product
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor dot_prod(Tx x, Ty y, NDArray axes, string name = null)
+ => math_ops.tensordot(convert_to_tensor(x), convert_to_tensor(y), axes, name: name);
+ public Tensor negative(Tensor x, string name = null)
=> gen_math_ops.neg(x, name);
+ ///
+ /// Returns the truth value of (x != y) element-wise.
+ ///
+ ///
+ ///
+ ///
+ /// A `Tensor` of type bool with the same size as that of x or y.
+ public Tensor not_equal(Tx x, Ty y, string name = null)
+ => math_ops.not_equal(x, y, name: name);
+
///
/// Divides x / y elementwise (using Python 2 division operator semantics).
///
@@ -264,14 +495,73 @@ public static Tensor negative(Tensor x, string name = null)
///
///
///
- public static Tensor div(Tensor x, Tensor y, string name = null)
+ public Tensor div(Tensor x, Tensor y, string name = null)
=> math_ops.div(x, y, name: name);
- public static Tensor divide(Tensor x, T[] y, string name = null) where T : struct
+ public Tensor divide(Tensor x, T[] y, string name = null) where T : struct
=> x / ops.convert_to_tensor(y, dtype: x.dtype.as_base_dtype(), name: "y");
- public static Tensor pow(T1 x, T2 y)
- => gen_math_ops.pow(x, y);
+ public Tensor pow(T1 x, T2 y, string name = "pow")
+ => math_ops.pow(x, y, name: name);
+
+ ///
+ /// Divides `x / y` elementwise, rounding toward the most negative integer.
+ ///
+ ///
+ ///
+ ///
+ /// `x / y` rounded down.
+ public Tensor floordiv(Tensor x, Tensor y, string name = null)
+ => math_ops.floordiv(x, y, name: name);
+
+ ///
+ /// Divides x / y elementwise (using Python 3 division operator semantics).
+ ///
+ ///
+ ///
+ ///
+ /// `x / y` evaluated in floating point.
+ public static Tensor truediv(Tensor x, Tensor y, string name = null)
+ => math_ops.truediv(x, y, name: name);
+
+ public Tensor range(object start, object limit = null, object delta = null, TF_DataType? dtype = null, string name = "range")
+ => math_ops.range(start, limit: limit, delta: delta, dtype: dtype, name: name);
+
+ public Tensor real(Tensor input, string name = null)
+ => math_ops.real(input, name);
+
+ ///
+ /// Computes the "logical or" of elements across dimensions of a tensor.
+ ///
+ /// The boolean tensor to reduce.
+ /// The dimensions to reduce.
+ /// If true, retains reduced dimensions with length 1.
+ ///
+ /// The reduced tensor.
+ public Tensor reduce_any(Tensor input_tensor, Axis? axis = null, bool keepdims = false, string name = null)
+ => math_ops.reduce_any(input_tensor, axis: axis, keepdims: keepdims, name: name);
+
+ ///
+ /// Computes the "logical and" of elements across dimensions of a tensor.
+ ///
+ ///
+ ///
+ ///
+ ///
+ /// The reduced tensor.
+ public Tensor reduce_all(Tensor input_tensor, Axis? axis = null, bool keepdims = false, string name = null)
+ => math_ops.reduce_all(input_tensor, axis: axis, keepdims: keepdims, name: name);
+
+ ///
+ /// Computes the product of elements across dimensions of a tensor.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor reduce_prod(Tensor input_tensor, Axis? axis = null, bool keepdims = false, string name = null)
+ => math_ops.reduce_prod(input_tensor, axis: axis, keepdims: keepdims, name: name);
///
/// Computes the sum of elements across dimensions of a tensor.
@@ -279,16 +569,15 @@ public static Tensor pow(T1 x, T2 y)
///
///
///
- public static Tensor reduce_sum(Tensor input, int? axis = null, int? reduction_indices = null)
+ public Tensor reduce_sum(Tensor input, Axis? axis = null, Axis? reduction_indices = null,
+ bool keepdims = false, string name = null)
{
- if(!axis.HasValue && reduction_indices.HasValue)
- return math_ops.reduce_sum(input, reduction_indices.Value);
- return math_ops.reduce_sum(input);
+ if (keepdims)
+ return math_ops.reduce_sum(input, axis: constant_op.constant(axis ?? reduction_indices), keepdims: keepdims, name: name);
+ else
+ return math_ops.reduce_sum(input, axis: constant_op.constant(axis ?? reduction_indices));
}
- public static Tensor reduce_sum(Tensor input, int axis, int? reduction_indices = null)
- => math_ops.reduce_sum(input, axis);
-
///
/// Computes the maximum of elements across dimensions of a tensor.
///
@@ -297,31 +586,43 @@ public static Tensor reduce_sum(Tensor input, int axis, int? reduction_indices =
///
///
///
- public static Tensor reduce_max(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
+ public Tensor reduce_max(Tensor input_tensor, Axis? axis = null, bool keepdims = false, string name = null)
=> math_ops.reduce_max(input_tensor, axis, keepdims, name);
- public static Tensor reduce_min(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null)
+ public Tensor reduce_min(Tensor input_tensor, Axis? axis = null, bool keepdims = false, string name = null)
=> math_ops.reduce_min(input_tensor, axis, keepdims, name);
- public static Tensor sigmoid(T x, string name = null)
+ public Tensor reduce_std(Tensor input_tensor, Axis? axis = null, bool keepdims = false, string name = null)
+ => math_ops.reduce_std(input_tensor, axis, keepdims, name);
+
+ public Tensor reduce_variance(Tensor input_tensor, Axis? axis = null, bool keepdims = false, string name = null)
+ => math_ops.reduce_variance(input_tensor, axis, keepdims, name);
+
+ public Tensor sigmoid(T x, string name = null)
=> math_ops.sigmoid(x, name: name);
- public static Tensor sum(Tensor input, int axis, bool keep_dims = false, string name = null)
- => gen_math_ops._sum(input, axis, keep_dims: keep_dims, name: name);
+ public Tensor sum(Tensor input, int axis, bool keep_dims = false, string name = null)
+ => gen_math_ops.sum(input, ops.convert_to_tensor(axis), keep_dims: keep_dims, name: name);
- public static Tensor reduce_mean(Tensor input_tensor, int[] axis = null, bool keepdims = false, string name = null, int? reduction_indices = null)
+ public Tensor reduce_mean(Tensor input_tensor, Axis? axis = null, bool keepdims = false, string name = null, int? reduction_indices = null)
=> math_ops.reduce_mean(input_tensor, axis: axis, keepdims: keepdims, name: name, reduction_indices: reduction_indices);
- public static Tensor round(Tensor x, string name = null)
+ public Tensor round(Tensor x, string name = null)
=> gen_math_ops.round(x, name: name);
- public static Tensor cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, string name = null)
+ public Tensor cast(Tensor x, TF_DataType dtype, string name = null)
=> math_ops.cast(x, dtype, name);
- public static Tensor argmax(Tensor input, int axis = -1, string name = null, int? dimension = null, TF_DataType output_type = TF_DataType.TF_INT64)
- => gen_math_ops.arg_max(input, axis, name: name, output_type: output_type);
+ public Tensor cumsum(Tensor x, int axis = 0, bool exclusive = false, bool reverse = false, string name = null)
+ => math_ops.cumsum(x, axis: axis, exclusive: exclusive, reverse: reverse, name: name);
- public static Tensor square(Tensor x, string name = null)
+ public Tensor square(Tensor x, string name = null)
=> gen_math_ops.square(x, name: name);
+ public Tensor squared_difference(Tensor x, Tensor y, string name = null)
+ => gen_math_ops.squared_difference(x: x, y: y, name: name);
+ public Tensor complex(Tensor real, Tensor imag, Tensorflow.TF_DataType? dtype = null,
+ string name = null) => gen_ops.complex(real, imag, dtype, name);
+ public Tensor exp(Tensor x,
+ string name = null) => gen_math_ops.exp(x, name);
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.nn.cs b/src/TensorFlowNET.Core/APIs/tf.nn.cs
index bfa71f315..112c48628 100644
--- a/src/TensorFlowNET.Core/APIs/tf.nn.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.nn.cs
@@ -1,15 +1,42 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using System.Xml.Linq;
using Tensorflow.Operations;
using Tensorflow.Operations.Activation;
+using static Tensorflow.Binding;
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static class nn
+ public nn_internal nn { get; } = new nn_internal();
+
+ public class nn_internal
{
+ public Tensor conv2d(Tensor input, Tensor filter, int[] strides, string padding, bool use_cudnn_on_gpu = true,
+ string data_format = "NHWC", int[] dilations = null, string name = null)
+ {
+ return gen_nn_ops.conv2d(input, filter, strides, padding, use_cudnn_on_gpu,
+ data_format: data_format, dilations: dilations, name: name);
+ }
+
+ public Tensor[] ctc_greedy_decoder(Tensor inputs, Tensor sequence_length, bool merge_repeated = true, string name = null)
+ => gen_ctc_ops.ctc_greedy_decoder(inputs, sequence_length, merge_repeated: merge_repeated, name: name);
+
///
/// Computes dropout.
///
@@ -20,26 +47,44 @@ public static class nn
///
/// A scalar `Tensor` with the same type as `x`.
/// A Tensor of the same shape of `x`.
- public static Tensor dropout(Tensor x, Tensor keep_prob = null, Tensor noise_shape = null, int? seed = null, string name = null,
+ public Tensor dropout(Tensor x, Tensor keep_prob = null, Tensor noise_shape = null, int? seed = null, string name = null,
float? rate = null)
{
Tensor keep = null;
if (keep_prob != null)
keep = 1.0f - keep_prob;
- var rate_tensor = keep;
-
+ var rate_tensor = rate.HasValue ? tf.constant(rate.Value) : keep;
return nn_ops.dropout_v2(x, rate: rate_tensor, noise_shape: noise_shape, seed: seed, name: name);
}
- public static (Tensor, Tensor) moments(Tensor x,
- int[] axes,
+ ///
+ /// Creates a recurrent neural network specified by RNNCell `cell`.
+ ///
+ /// An instance of RNNCell.
+ /// The RNN inputs.
+ ///
+ ///
+ ///
+ /// A pair (outputs, state)
+ public (Tensor, Tensor) dynamic_rnn(RnnCell cell, Tensor inputs,
+ Tensor sequence_length = null, TF_DataType dtype = TF_DataType.DtInvalid,
+ int? parallel_iterations = null, bool swap_memory = false, bool time_major = false)
+ => rnn.dynamic_rnn(cell, inputs, sequence_length: sequence_length, dtype: dtype,
+ parallel_iterations: parallel_iterations, swap_memory: swap_memory,
+ time_major: time_major);
+
+ public Tensor elu(Tensor features, string name = null)
+ => gen_nn_ops.elu(features, name: name);
+
+ public (Tensor, Tensor) moments(Tensor x,
+ Axis axes,
string name = null,
- bool keep_dims = false) => nn_impl.moments(x,
- axes,
- name: name,
+ bool keep_dims = false) => nn_impl.moments(x,
+ axes,
+ name: name,
keep_dims: keep_dims);
- public static Tensor embedding_lookup(RefVariable @params,
+ public Tensor embedding_lookup(IVariableV1 @params,
Tensor ids,
string partition_strategy = "mod",
string name = null) => embedding_ops._embedding_lookup_and_transform(@params,
@@ -47,7 +92,7 @@ public static Tensor embedding_lookup(RefVariable @params,
partition_strategy: partition_strategy,
name: name);
- public static Tensor embedding_lookup(Tensor @params,
+ public Tensor embedding_lookup(Tensor @params,
Tensor ids,
string partition_strategy = "mod",
string name = null) => embedding_ops._embedding_lookup_and_transform(new Tensor[] { @params },
@@ -55,41 +100,106 @@ public static Tensor embedding_lookup(Tensor @params,
partition_strategy: partition_strategy,
name: name);
- public static IActivation relu() => new relu();
+ public IActivation relu() => new relu();
+
+
+ public IActivation swish() => new swish();
+ public IActivation tanh() => new tanh();
+
+ public IActivation softmax() => new softmax();
+ public Tensor tanh(Tensor x, string name = null)
+ => gen_math_ops.tanh(x, name);
- public static Tensor relu(Tensor features, string name = null) => gen_nn_ops.relu(features, name);
+ public Tensor relu(Tensor features, string name = null)
+ => gen_nn_ops.relu(features, name);
- public static Tensor[] fused_batch_norm(Tensor x,
- RefVariable scale,
- RefVariable offset,
+ public Tensor relu6(Tensor features, string name = null)
+ => gen_nn_ops.relu6(features, name);
+
+ public Tensor[] fused_batch_norm(Tensor x,
+ Tensor scale,
+ Tensor offset,
Tensor mean = null,
Tensor variance = null,
float epsilon = 0.001f,
string data_format = "NHWC",
bool is_training = true,
- string name = null) => nn_impl.fused_batch_norm(x, scale, offset, mean, variance,
+ string name = null,
+ float exponential_avg_factor = 1.0f) => nn_impl.fused_batch_norm(x, scale, offset, mean, variance,
epsilon: epsilon,
data_format: data_format,
is_training: is_training,
- name: name);
+ name: name,
+ exponential_avg_factor: exponential_avg_factor);
+
+ ///
+ /// Normalizes a tensor by `mean` and `variance`, and applies (optionally) a`scale` \\(\gamma\\) to it, as well as an `offset` \\(\beta\\).
+ ///
+ /// A floating point tensor.
+ /// A mean `Tensor`.
+ /// A variance `Tensor`.
+ /// An offset `Tensor`, often denoted \\(\beta\\) in equations, or NULL. If present, will be added to the normalized tensor.
+ /// A scale `Tensor`, often denoted \\(\gamma\\) in equations, or NULL. If present, the scale is applied to the normalized tensor.
+ /// A small float number to avoid dividing by 0.
+ /// A name for this operation.
+ /// the normalized, scaled, offset tensor.
+ public Tensor batch_normalization(Tensor x,
+ Tensor mean,
+ Tensor variance,
+ Tensor offset,
+ Tensor scale,
+ float variance_epsilon,
+ string name = null) => nn_impl.batch_normalization(x, mean, variance, offset, scale, variance_epsilon, name);
+
+
+ public Tensor max_pool(Tensor value, int[] ksize, int[] strides, string padding, string data_format = "NHWC", string name = null)
+ => nn_ops.max_pool(value, ksize, strides, padding, data_format: data_format, name: name);
- public static IPoolFunction max_pool => new MaxPoolFunction();
+ public Tensor in_top_k(Tensor predictions, Tensor targets, int k, string name = "InTopK")
+ => nn_ops.in_top_k(predictions, targets, k, name);
- public static Tensor[] top_k(Tensor input, int k = 1, bool sorted = true, string name = null)
- => gen_nn_ops.top_kv2(input, k: k, sorted: sorted, name: name);
+ public Tensor[] top_k(Tensor input, int k = 1, bool sorted = true, string name = null)
+ => gen_nn_ops.top_kv2(input, k: ops.convert_to_tensor(k), sorted: sorted, name: name);
- public static Tensor bias_add(Tensor value, RefVariable bias, string data_format = null, string name = null)
+ public Tensor bias_add(Tensor value, IVariableV1 bias, string data_format = null, string name = null)
{
- return Python.with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
+ return tf_with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
{
name = scope;
- return gen_nn_ops.bias_add(value, bias, data_format: data_format, name: name);
+ return gen_nn_ops.bias_add(value, ops.convert_to_tensor(bias), data_format: data_format, name: name);
});
}
- public static Tensor softmax(Tensor logits, int axis = -1, string name = null)
+ public Tensor l2_loss(Tensor t, string name = null)
+ => nn_ops.l2_loss(t, name: name);
+
+ ///
+ /// Local Response Normalization.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor lrn(Tensor input, int depth_radius = 5, int bias = 1,
+ int alpha = 1, float beta = 0.5f, string name = null)
+ => gen_nn_ops.lrn(input, depth_radius: depth_radius, bias: bias,
+ alpha: alpha, beta: beta, name: name);
+
+ public Tensor leaky_relu(Tensor features, float alpha = 0.2f, string name = null)
+ => nn_ops.leaky_relu(features, alpha: alpha, name: name);
+
+ public rnn_cell_impl rnn_cell => new rnn_cell_impl();
+
+ public Tensor sigmoid_cross_entropy_with_logits(Tensor labels, Tensor logits, string name = null)
+ => nn_impl.sigmoid_cross_entropy_with_logits(labels: labels, logits: logits, name: name);
+
+ public Tensor softmax(Tensor logits, int axis = -1, string name = null)
=> gen_nn_ops.softmax(logits, name);
+
///
/// Computes sparse softmax cross entropy between `logits` and `labels`.
///
@@ -97,12 +207,42 @@ public static Tensor softmax(Tensor logits, int axis = -1, string name = null)
///
///
///
- public static Tensor sparse_softmax_cross_entropy_with_logits(Tensor labels = null,
+ public Tensor sparse_softmax_cross_entropy_with_logits(Tensor labels = null,
Tensor logits = null, string name = null)
=> nn_ops.sparse_softmax_cross_entropy_with_logits(labels: labels, logits: logits, name: name);
- public static Tensor softmax_cross_entropy_with_logits_v2(Tensor labels, Tensor logits, int axis = -1, string name = null)
+ ///
+ /// Computes softmax cross entropy between `logits` and `labels`.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor softmax_cross_entropy_with_logits(Tensor labels, Tensor logits, int dim = -1, string name = null)
+ {
+ tf_with(ops.name_scope(name, "softmax_cross_entropy_with_logits_sg", new { logits, labels }), scope =>
+ {
+ name = scope;
+ labels = array_ops.stop_gradient(labels, name: "labels_stop_gradient");
+ });
+
+ return softmax_cross_entropy_with_logits_v2(labels, logits, axis: dim, name: name);
+ }
+
+ public Tensor softmax_cross_entropy_with_logits_v2(Tensor labels, Tensor logits, int axis = -1, string name = null)
=> nn_ops.softmax_cross_entropy_with_logits_v2_helper(labels, logits, axis: axis, name: name);
+
+ ///
+ /// Computes sigmoid of `x` element-wise.
+ /// Specifically, `y = 1 / (1 + exp(-x))`.
+ ///
+ ///
+ ///
+ /// A name for the operation (optional).
+ /// A Tensor with the same type as `x`.
+ public Tensor sigmoid(T x, string name = null)
+ => math_ops.sigmoid(x, name: name);
}
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.numpy.cs b/src/TensorFlowNET.Core/APIs/tf.numpy.cs
new file mode 100644
index 000000000..392ba915f
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.numpy.cs
@@ -0,0 +1,29 @@
+/*****************************************************************************
+ Copyright 2021 Haiping Chen. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using Tensorflow.NumPy;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ ///
+ /// NumPy API on TensorFlow
+ /// https://www.tensorflow.org/api_docs/python/tf/experimental/numpy
+ ///
+ public NumPyImpl numpy => new NumPyImpl();
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.ops.cs b/src/TensorFlowNET.Core/APIs/tf.ops.cs
index f789d4731..ebf35e3f9 100644
--- a/src/TensorFlowNET.Core/APIs/tf.ops.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.ops.cs
@@ -1,13 +1,51 @@
-using System;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using System;
using System.Collections.Generic;
-using System.Text;
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static object get_collection(string key, string scope = "") => get_default_graph()
- .get_collection(key, scope: scope);
+ public void add_to_collection(string name, T value)
+ => get_default_graph().add_to_collection(name, value);
+
+ public void add_to_collections(List names, T value)
+ => get_default_graph().add_to_collections(names, value);
+
+ public (Tensors, Tensor) clip_by_global_norm(Tensor[] t_list, float clip_norm, Tensor use_norm = null, string name = null)
+ => clip_ops.clip_by_global_norm(t_list, clip_norm, use_norm: use_norm, name: name);
+
+ public Tensor assign(IVariableV1 @ref, object value, bool validate_shape = true, bool use_locking = true, string name = null)
+ => state_ops.assign(@ref, value, validate_shape, use_locking, name);
+
+ public void device(string device_name)
+ => get_default_graph().device(device_name);
+
+ public List get_collection(string key, string scope = "")
+ => get_default_graph().get_collection(key, scope: scope);
+
+ ///
+ /// A context manager that lifts ops out of control-flow scopes and function-building graphs.
+ /// When eager execution is enabled, code inside an init_scope block runs with
+ /// eager execution enabled even when tracing a `tf.function`.
+ ///
+ public ops.NameScope init_scope()
+ => ops.init_scope();
///
/// Returns a context manager that creates hierarchical names for operations.
@@ -16,8 +54,44 @@ public static object get_collection(string key, string scope = "") => get_defaul
/// The default name to use if the name argument is None.
/// The list of Tensor arguments that are passed to the op function.
/// The scope name.
- public static ops.NameScope name_scope(string name,
- string default_name = "",
- object values = null) => new ops.NameScope(name, default_name, values);
+ public ops.NameScope name_scope(string name, string default_name = "", object values = null)
+ => new ops.NameScope(name, default_name, values);
+
+ ///
+ /// Does nothing. Only useful as a placeholder for control edges.
+ ///
+ ///
+ ///
+ public Operation no_op(string name = null)
+ => gen_control_flow_ops.no_op(name: name);
+
+ ///
+ /// map on the list of tensors unpacked from `elems` on dimension 0.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ /// A tensor or (possibly nested) sequence of tensors.
+ public Tensor map_fn(Func fn,
+ Tensor elems,
+ TF_DataType dtype = TF_DataType.DtInvalid,
+ int parallel_iterations = -1,
+ bool back_prop = true,
+ bool swap_memory = false,
+ bool infer_shape = true,
+ string name = null)
+ => Operation.map_fn(fn,
+ elems,
+ dtype,
+ parallel_iterations: parallel_iterations,
+ back_prop: back_prop,
+ swap_memory: swap_memory,
+ infer_shape: infer_shape,
+ name: name);
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.queue.cs b/src/TensorFlowNET.Core/APIs/tf.queue.cs
new file mode 100644
index 000000000..a4757890e
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.queue.cs
@@ -0,0 +1,126 @@
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using Tensorflow.Queues;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ ///
+ /// A FIFOQueue that supports batching variable-sized tensors by padding.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public PaddingFIFOQueue PaddingFIFOQueue(int capacity,
+ TF_DataType[] dtypes,
+ Shape[] shapes,
+ string[] names = null,
+ string shared_name = null,
+ string name = "padding_fifo_queue")
+ => new PaddingFIFOQueue(capacity,
+ dtypes,
+ shapes,
+ names,
+ shared_name: shared_name,
+ name: name);
+
+ public PaddingFIFOQueue PaddingFIFOQueue(int capacity,
+ TF_DataType dtype,
+ Shape shape,
+ string shared_name = null,
+ string name = "padding_fifo_queue")
+ => new PaddingFIFOQueue(capacity,
+ new[] { dtype },
+ new[] { shape },
+ shared_name: shared_name,
+ name: name);
+
+ ///
+ /// A queue implementation that dequeues elements in first-in first-out order.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public FIFOQueue FIFOQueue(int capacity,
+ TF_DataType[] dtypes,
+ Shape[] shapes = null,
+ string[] names = null,
+ string shared_name = null,
+ string name = "fifo_queue")
+ => new FIFOQueue(capacity,
+ dtypes,
+ shapes,
+ names,
+ shared_name: shared_name,
+ name: name);
+
+ public FIFOQueue FIFOQueue(int capacity,
+ TF_DataType dtype,
+ Shape shape = null,
+ string shared_name = null,
+ string name = "fifo_queue")
+ => new FIFOQueue(capacity,
+ new[] { dtype },
+ new[] { shape ?? Shape.Null },
+ shared_name: shared_name,
+ name: name);
+
+ ///
+ /// Creates a queue that dequeues elements in a first-in first-out order.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public PriorityQueue PriorityQueue(int capacity,
+ TF_DataType dtype,
+ Shape shape = null,
+ string shared_name = null,
+ string name = "priority_queue")
+ => new PriorityQueue(capacity,
+ new[] { dtype },
+ new[] { shape ?? Shape.Null },
+ shared_name: shared_name,
+ name: name);
+
+ public RandomShuffleQueue RandomShuffleQueue(int capacity,
+ int min_after_dequeue,
+ TF_DataType dtype,
+ Shape shape = null,
+ int? seed = null,
+ string shared_name = null,
+ string name = "random_shuffle_queue")
+ => new RandomShuffleQueue(capacity,
+ min_after_dequeue: min_after_dequeue,
+ new[] { dtype },
+ new[] { shape ?? Shape.Null },
+ seed: seed,
+ shared_name: shared_name,
+ name: name);
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.random.cs b/src/TensorFlowNET.Core/APIs/tf.random.cs
index b56df1447..4f4962840 100644
--- a/src/TensorFlowNET.Core/APIs/tf.random.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.random.cs
@@ -1,41 +1,128 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- ///
- /// Outputs random values from a normal distribution.
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- ///
- public static Tensor random_normal(int[] shape,
- float mean = 0.0f,
- float stddev = 1.0f,
- TF_DataType dtype = TF_DataType.TF_FLOAT,
- int? seed = null,
- string name = null) => random_ops.random_normal(shape, mean, stddev, dtype, seed, name);
+ public Random random => new Random();
+
+ public class Random
+ {
+ ///
+ /// Outputs random values from a normal distribution.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor normal(Shape shape,
+ float mean = 0.0f,
+ float stddev = 1.0f,
+ TF_DataType dtype = TF_DataType.TF_FLOAT,
+ int? seed = null,
+ string name = null) => random_ops.random_normal(shape, mean, stddev, dtype, seed, name);
- public static Tensor random_uniform(int[] shape,
+ public Tensor stateless_normal(Shape shape,
+ float mean = 0.0f,
+ float stddev = 1.0f,
+ TF_DataType dtype = TF_DataType.TF_FLOAT,
+ string name = null) => stateless_random_ops.stateless_random_normal(shape, mean, stddev, dtype, name: name);
+
+ ///
+ /// Outputs random values from a truncated normal distribution.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor truncated_normal(Shape shape,
+ float mean = 0.0f,
+ float stddev = 1.0f,
+ TF_DataType dtype = TF_DataType.TF_FLOAT,
+ int? seed = null,
+ string name = null) => random_ops.truncated_normal(shape, mean, stddev, dtype, seed, name);
+
+ public Tensor categorical(
+ Tensor logits,
+ int num_samples,
+ int? seed = null,
+ string name = null,
+ TF_DataType output_dtype = TF_DataType.DtInvalid) => random_ops.multinomial(logits, num_samples, seed: seed, name: name, output_dtype: output_dtype);
+
+ public Tensor uniform(Shape shape,
+ float minval = 0,
+ float maxval = 1,
+ TF_DataType dtype = TF_DataType.TF_FLOAT,
+ int? seed = null,
+ string name = null)
+ {
+ if (dtype.is_integer())
+ return random_ops.random_uniform_int(shape, (int)minval, (int)maxval, seed, name);
+ else
+ return random_ops.random_uniform(shape, minval, maxval, dtype, seed, name);
+ }
+ }
+
+ public Tensor random_uniform(Shape shape,
float minval = 0,
float maxval = 1,
TF_DataType dtype = TF_DataType.TF_FLOAT,
int? seed = null,
- string name = null) => random_ops.random_uniform(shape, minval, maxval, dtype, seed, name);
+ string name = null)
+ => random.uniform(shape, minval: minval, maxval: maxval, dtype: dtype, seed: seed, name: name);
- public static Tensor truncated_normal(int[] shape,
+ public Tensor truncated_normal(Shape shape,
float mean = 0.0f,
float stddev = 1.0f,
TF_DataType dtype = TF_DataType.TF_FLOAT,
int? seed = null,
string name = null)
=> random_ops.truncated_normal(shape, mean, stddev, dtype, seed, name);
+
+ ///
+ /// Randomly shuffles a tensor along its first dimension.
+ ///
+ ///
+ ///
+ ///
+ ///
+ /// A tensor of same shape and type as value, shuffled along its
+ /// first dimension.
+ ///
+ public Tensor random_shuffle(Tensor value, int? seed = null, string name = null)
+ => random_ops.random_shuffle(value, seed: seed, name: name);
+
+ public void set_random_seed(int seed)
+ {
+ if (executing_eagerly())
+ Context.set_global_seed(seed);
+ else
+ ops.get_default_graph().seed = seed;
+ }
+
+ public Tensor multinomial(Tensor logits, int num_samples, int? seed = null,
+ string name = null, TF_DataType output_dtype = TF_DataType.DtInvalid)
+ => random_ops.multinomial(logits, num_samples, seed: seed,
+ name: name, output_dtype: output_dtype);
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.reduce_logsumexp.cs b/src/TensorFlowNET.Core/APIs/tf.reduce_logsumexp.cs
index 32140389b..41f0ec45d 100644
--- a/src/TensorFlowNET.Core/APIs/tf.reduce_logsumexp.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.reduce_logsumexp.cs
@@ -1,13 +1,25 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static Tensor reduce_logsumexp(Tensor input_tensor,
- int[] axis = null,
+ public Tensor reduce_logsumexp(Tensor input_tensor,
+ Axis? axis = null,
bool keepdims = false,
string name = null) => math_ops.reduce_logsumexp(input_tensor, axis, keepdims, name);
diff --git a/src/TensorFlowNET.Core/APIs/tf.reshape.cs b/src/TensorFlowNET.Core/APIs/tf.reshape.cs
index 300985811..102a81323 100644
--- a/src/TensorFlowNET.Core/APIs/tf.reshape.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.reshape.cs
@@ -1,17 +1,36 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static Tensor reshape(Tensor tensor,
+ public Tensor reshape(Tensor tensor,
+ Shape shape,
+ string name = null)
+ => gen_array_ops.reshape(tensor, shape, name);
+
+ public Tensor reshape(Tensor tensor,
Tensor shape,
- string name = null) => gen_array_ops.reshape(tensor, shape, name);
+ string name = null)
+ => gen_array_ops.reshape(tensor, shape, name);
- public static Tensor reshape(Tensor tensor,
- int[] shape,
- string name = null) => gen_array_ops.reshape(tensor, shape, name);
+ public Tensor reshape(Tensor tensor,
+ object[] shape,
+ string name = null)
+ => array_ops.reshape(tensor, shape, name);
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.saved_model.cs b/src/TensorFlowNET.Core/APIs/tf.saved_model.cs
new file mode 100644
index 000000000..ef6251ca8
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.saved_model.cs
@@ -0,0 +1,20 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Train;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public SavedModelAPI saved_model { get; } = new SavedModelAPI();
+ }
+
+ public class SavedModelAPI
+ {
+ public Trackable load(string export_dir, LoadOptions? options = null)
+ {
+ return Loader.load(export_dir, options);
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.scan.cs b/src/TensorFlowNET.Core/APIs/tf.scan.cs
new file mode 100644
index 000000000..5642eaaf1
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.scan.cs
@@ -0,0 +1,35 @@
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using System;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public Tensor scan(
+ Func fn,
+ Tensor elems,
+ Tensor initializer = null,
+ int parallel_iterations = 10,
+ bool back_prop = true,
+ bool swap_memory = false,
+ bool infer_shape = true,
+ bool reverse = false,
+ string name = null) => functional_ops.scan(fn, elems, initializer, parallel_iterations, back_prop,
+ swap_memory, infer_shape, reverse, name);
+ }
+}
\ No newline at end of file
diff --git a/src/TensorFlowNET.Core/APIs/tf.signal.cs b/src/TensorFlowNET.Core/APIs/tf.signal.cs
new file mode 100644
index 000000000..2471124c5
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.signal.cs
@@ -0,0 +1,40 @@
+/*****************************************************************************
+ Copyright 2023 Konstantin Balashov All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using Tensorflow.Operations;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public SignalApi signal { get; } = new SignalApi();
+ public class SignalApi
+ {
+ public Tensor fft(Tensor input, string name = null)
+ => gen_ops.f_f_t(input, name: name);
+ public Tensor ifft(Tensor input, string name = null)
+ => gen_ops.i_f_f_t(input, name: name);
+ public Tensor fft2d(Tensor input, string name = null)
+ => gen_ops.f_f_t2d(input, name: name);
+ public Tensor ifft2d(Tensor input, string name = null)
+ => gen_ops.i_f_f_t2d(input, name: name);
+ public Tensor fft3d(Tensor input, string name = null)
+ => gen_ops.f_f_t3d(input, name: name);
+ public Tensor ifft3d(Tensor input, string name = null)
+ => gen_ops.i_f_f_t3d(input, name: name);
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.sparse.cs b/src/TensorFlowNET.Core/APIs/tf.sparse.cs
new file mode 100644
index 000000000..f124f6105
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.sparse.cs
@@ -0,0 +1,62 @@
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using System;
+using Tensorflow.Framework;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public SparseTensor SparseTensor(long[,] indices, Array values, long[] dense_shape)
+ => new SparseTensor(indices, values, dense_shape);
+
+ public Tensor sparse_tensor_to_dense(SparseTensor sp_input,
+ Array default_value = default,
+ bool validate_indices = true,
+ string name = null)
+ => gen_sparse_ops.sparse_to_dense(sp_input.indices,
+ sp_input.dense_shape,
+ sp_input.values,
+ default_value: default_value,
+ validate_indices: validate_indices,
+ name: name);
+
+ ///
+ /// Converts a sparse representation into a dense tensor.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ /// Dense `Tensor` of shape `output_shape`. Has the same type as `sparse_values`.
+ public Tensor sparse_to_dense(Tensor sparse_indices,
+ Shape output_shape,
+ T sparse_values,
+ T default_value = default,
+ bool validate_indices = true,
+ string name = null)
+ => gen_sparse_ops.sparse_to_dense(sparse_indices,
+ output_shape,
+ sparse_values,
+ default_value: default_value,
+ validate_indices: validate_indices,
+ name: name);
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.state.cs b/src/TensorFlowNET.Core/APIs/tf.state.cs
new file mode 100644
index 000000000..d86f88b17
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.state.cs
@@ -0,0 +1,25 @@
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public ITensorOrOperation assign_add(IVariableV1 @ref, T value,
+ bool use_locking = false, string name = null)
+ => state_ops.assign_add(@ref, value, use_locking: use_locking, name: name);
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.strings.cs b/src/TensorFlowNET.Core/APIs/tf.strings.cs
new file mode 100644
index 000000000..ecaf775d0
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.strings.cs
@@ -0,0 +1,95 @@
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using static Tensorflow.Binding;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public StringsApi strings { get; } = new StringsApi();
+
+ public class StringsApi
+ {
+ string_ops ops = new string_ops();
+
+ ///
+ /// Converts all uppercase characters into their respective lowercase replacements.
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor lower(Tensor input, string encoding = "", string name = null)
+ => ops.lower(input: input, encoding: encoding, name: name);
+
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor regex_replace(Tensor input, string pattern, string rewrite,
+ bool replace_global = true, string name = null)
+ => ops.regex_replace(input, pattern, rewrite,
+ replace_global: replace_global, name: name);
+
+ ///
+ /// Return substrings from `Tensor` of strings.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor substr(Tensor input, int pos, int len,
+ string name = null, string @uint = "BYTE")
+ => ops.substr(input, pos, len, @uint: @uint, name: name);
+
+ public Tensor substr(string input, int pos, int len,
+ string name = null, string @uint = "BYTE")
+ => ops.substr(input, pos, len, @uint: @uint, name: name);
+
+ ///
+ /// String lengths of `input`.
+ ///
+ ///
+ ///
+ ///
+ ///
+ public Tensor string_length(Tensor input, string name = null, string unit = "BYTE")
+ => ops.string_length(input, name: name, unit: unit);
+
+ public Tensor format(string template, Tensor[] inputs, string placeholder = "{}", int summarize = 3, string name = null)
+ => ops.string_format(inputs, template: template, placeholder: placeholder, summarize: summarize, name: name);
+
+ public RaggedTensor split(Tensor input, char sep = ' ', int maxsplit = -1, string name = null)
+ => ops.string_split_v2(input, sep: sep.ToString(), maxsplit : maxsplit, name : name);
+
+ public (RaggedTensor, RaggedTensor) unicode_decode_with_offsets(Tensor input, string input_encoding,
+ string errors = "replace", int replacement_char = 0xFFFD,
+ bool replace_control_characters = false, string name = null)
+ => ops.unicode_decode_with_offsets(input, input_encoding, errors,
+ replacement_char: replacement_char,
+ replace_control_characters: replace_control_characters,
+ name: name);
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.summary.cs b/src/TensorFlowNET.Core/APIs/tf.summary.cs
index 618806ddf..4d0492b60 100644
--- a/src/TensorFlowNET.Core/APIs/tf.summary.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.summary.cs
@@ -1,15 +1,26 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow.IO;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static Summaries.Summary summary = new Summaries.Summary();
+ public Summaries.Summary summary = new Summaries.Summary();
- public static Tensor scalar(string name, Tensor tensor)
+ public Tensor scalar(string name, Tensor tensor)
=> summary.scalar(name, tensor);
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.tensor.cs b/src/TensorFlowNET.Core/APIs/tf.tensor.cs
index 33d86e149..b03168ab3 100644
--- a/src/TensorFlowNET.Core/APIs/tf.tensor.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.tensor.cs
@@ -1,13 +1,97 @@
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.Text;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using Tensorflow.Operations;
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static Tensor convert_to_tensor(object value,
- string name = null) => ops.convert_to_tensor(value, name: name);
+ public Tensor convert_to_tensor(object value, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, TF_DataType preferred_dtype = TF_DataType.DtInvalid)
+ => ops.convert_to_tensor(value, dtype, name, preferred_dtype: preferred_dtype);
+
+ public Tensor strided_slice(Tensor input, Tensor begin, Tensor end, Tensor strides = null,
+ int begin_mask = 0,
+ int end_mask = 0,
+ int ellipsis_mask = 0,
+ int new_axis_mask = 0,
+ int shrink_axis_mask = 0,
+ string name = null) => gen_array_ops.strided_slice(input: input,
+ begin: begin,
+ end: end,
+ strides: strides,
+ begin_mask: begin_mask,
+ end_mask: end_mask,
+ ellipsis_mask: ellipsis_mask,
+ new_axis_mask: new_axis_mask,
+ shrink_axis_mask: shrink_axis_mask,
+ name: name);
+
+ public Tensor strided_slice(Tensor input, T[] begin, T[] end, T[] strides = null,
+ int begin_mask = 0,
+ int end_mask = 0,
+ int ellipsis_mask = 0,
+ int new_axis_mask = 0,
+ int shrink_axis_mask = 0,
+ string name = null) => array_ops.strided_slice(input,
+ begin: ops.convert_to_tensor(begin),
+ end: ops.convert_to_tensor(end),
+ strides: ops.convert_to_tensor(strides),
+ begin_mask: begin_mask,
+ end_mask: end_mask,
+ ellipsis_mask: ellipsis_mask,
+ new_axis_mask: new_axis_mask,
+ shrink_axis_mask: shrink_axis_mask,
+ name: name);
+
+ ///
+ /// Splits a tensor into sub tensors.
+ ///
+ /// The Tensor to split.
+ /// Either an integer indicating the number of splits along split_dim or a 1-D integer
+ /// Tensor or Python list containing the sizes of each output tensor along split_dim.
+ /// If a scalar then it must evenly divide value.shape[axis]; otherwise the sum of sizes along the split dimension must match that of the value.
+ /// An integer or scalar int32 Tensor. The dimension along which to split. Must be in the range [-rank(value), rank(value)). Defaults to 0.
+ /// A name for the operation (optional)
+ /// if num_or_size_splits is a scalar returns num_or_size_splits Tensor objects;
+ /// if num_or_size_splits is a 1-D Tensor returns num_or_size_splits.get_shape[0] Tensor objects resulting from splitting value.
+ public Tensor[] split(Tensor value, int num_split, Axis axis, string name = null)
+ => array_ops.split(
+ value: value,
+ num_or_size_splits: num_split,
+ axis: axis,
+ name: name);
+
+ public Tensor[] split(Tensor value, int[] num_split, Axis axis, string name = null)
+ => array_ops.split(
+ value: value,
+ num_or_size_splits: num_split,
+ axis: axis,
+ name: name);
+
+ //public Tensor[] split(Tensor value, int num_split, Axis axis, string name = null)
+ // => array_ops.split(
+ // value: value,
+ // num_or_size_splits: num_split,
+ // axis: axis,
+ // name: name);
+
+ public Tensor ensure_shape(Tensor x, Shape shape, string name = null)
+ {
+ return gen_ops.ensure_shape(x, shape, name);
+ }
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.tile.cs b/src/TensorFlowNET.Core/APIs/tf.tile.cs
index 21a32a988..a3b497e8a 100644
--- a/src/TensorFlowNET.Core/APIs/tf.tile.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.tile.cs
@@ -1,18 +1,34 @@
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.Text;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+using static Tensorflow.Binding;
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static Tensor tile(Tensor input,
- Tensor multiples,
- string name = null) => gen_array_ops.tile(input, multiples, name);
- public static Tensor tile(NDArray input,
- int[] multiples,
- string name = null) => gen_array_ops.tile(input, multiples, name);
+ public Tensor tile(Tensor input, Tensor multiples, string name = null)
+ => gen_array_ops.tile(input, multiples, name);
+
+ public Tensor tile(Tensor input, object[] multiples, string name = null)
+ => array_ops.tile(input, constant_op.constant(shape_utils.from_object_array(multiples).dims), name);
+ public Tensor tile(Tensor input, Shape multiples, string name = null)
+ {
+ var multiples_tensor = constant_op.constant(multiples);
+ return gen_array_ops.tile(input, multiples_tensor, name);
+ }
}
}
diff --git a/src/TensorFlowNET.Core/APIs/tf.train.cs b/src/TensorFlowNET.Core/APIs/tf.train.cs
new file mode 100644
index 000000000..cf02ed599
--- /dev/null
+++ b/src/TensorFlowNET.Core/APIs/tf.train.cs
@@ -0,0 +1,110 @@
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using System.Collections.Generic;
+using Tensorflow.Train;
+
+namespace Tensorflow
+{
+ public partial class tensorflow
+ {
+ public train_internal train { get; } = new train_internal();
+
+ public class train_internal
+ {
+ public IVariableV1 create_global_step(Graph graph)
+ => TrainingUtil.create_global_step(graph);
+
+ public IVariableV1 get_global_step(Graph graph)
+ => TrainingUtil.get_global_step(graph);
+
+ public Optimizer GradientDescentOptimizer(float learning_rate)
+ => new GradientDescentOptimizer(learning_rate);
+
+ public Optimizer GradientDescentOptimizer(Tensor learning_rate)
+ => new GradientDescentOptimizer(learning_rate);
+
+ public Optimizer AdamOptimizer(float learning_rate, float epsilon = 1e-8f, string name = "Adam")
+ => new AdamOptimizer(learning_rate, epsilon: epsilon, name: name);
+
+ public Optimizer AdamOptimizer(float learning_rate, TF_DataType dtype, string name = "Adam")
+ => new AdamOptimizer(learning_rate, name: name, dtype: dtype);
+
+ public Optimizer AdamOptimizer(IVariableV1 learning_rate, string name = "Adam")
+ => new AdamOptimizer(learning_rate.AsTensor(), name: name);
+
+ public Optimizer AdamOptimizer(Tensor learning_rate, string name = "Adam")
+ => new AdamOptimizer(learning_rate, name: name);
+
+ public ExponentialMovingAverage ExponentialMovingAverage(float decay)
+ => new ExponentialMovingAverage(decay);
+
+ public Saver Saver(IVariableV1[] var_list = null, int max_to_keep = 5)
+ => new Saver(var_list: var_list, max_to_keep: max_to_keep);
+
+ public string write_graph(Graph graph, string logdir, string name, bool as_text = true)
+ => graph_io.write_graph(graph, logdir, name, as_text);
+
+ public Graph load_graph(string freeze_graph_pb)
+ => saver.load_graph(freeze_graph_pb);
+
+ public string freeze_graph(string checkpoint_dir, string output_pb_name, string[] output_node_names)
+ => saver.freeze_graph(checkpoint_dir, output_pb_name, output_node_names);
+
+ public Saver import_meta_graph(string meta_graph_or_file,
+ bool clear_devices = false,
+ string import_scope = "") => saver._import_meta_graph_with_return_elements(meta_graph_or_file,
+ clear_devices,
+ import_scope).Item1;
+
+ public (MetaGraphDef, Dictionary) export_meta_graph(string filename = "",
+ bool as_text = false,
+ bool clear_devices = false,
+ bool clear_extraneous_savers = false,
+ bool strip_default_attrs = false) => meta_graph.export_scoped_meta_graph(filename: filename,
+ as_text: as_text,
+ clear_devices: clear_devices,
+ clear_extraneous_savers: clear_extraneous_savers,
+ strip_default_attrs: strip_default_attrs);
+
+ public string latest_checkpoint(string checkpoint_dir, string latest_filename = null)
+ => checkpoint_management.latest_checkpoint(checkpoint_dir, latest_filename: latest_filename);
+
+ public CheckpointState get_checkpoint_state(string checkpoint_dir, string latest_filename = null)
+ => checkpoint_management.get_checkpoint_state(checkpoint_dir, latest_filename: latest_filename);
+
+ /*public Tensor polynomial_decay(float learning_rate,
+ RefVariable global_step,
+ float decay_steps,
+ float end_learning_rate = 0.0001f,
+ float power = 1.0f,
+ bool cycle = false,
+ string name = null)
+ {
+ var decayed = new PolynomialDecay(learning_rate,
+ decay_steps,
+ end_learning_rate: end_learning_rate,
+ power: power,
+ cycle: cycle,
+ name: name);
+
+ var decayed_lr = decayed.__call__(global_step);
+
+ return decayed_lr;
+ }*/
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/APIs/tf.variable.cs b/src/TensorFlowNET.Core/APIs/tf.variable.cs
index 266d5799f..9ce864bd8 100644
--- a/src/TensorFlowNET.Core/APIs/tf.variable.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.variable.cs
@@ -1,39 +1,53 @@
-using System;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
using System.Collections.Generic;
-using System.Text;
+using static Tensorflow.Binding;
namespace Tensorflow
{
- public static partial class tf
+ public partial class tensorflow
{
- public static VariableV1[] global_variables(string scope = null)
+ public IVariableV1[] global_variables(string scope = null)
{
- return (ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, scope) as List)
+ return (ops.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope) as List)
.ToArray();
}
- public static Operation global_variables_initializer()
- {
- var g = variables.global_variables();
- return variables.variables_initializer(g.ToArray());
- }
+ ///
+ /// Returns an Op that initializes a list of variables.
+ ///
+ /// List of `Variable` objects to initialize.
+ /// Optional name for the returned operation.
+ /// An Op that run the initializers of all the specified variables.
+ public Operation variables_initializer(IVariableV1[] var_list, string name = "init")
+ => variables.variables_initializer(var_list, name: name);
- public static RefVariable get_variable(string name,
- TensorShape shape = null,
- TF_DataType dtype = TF_DataType.DtInvalid,
- object initializer = null, // IInitializer or Tensor
- bool? trainable = null,
- VariableSynchronization synchronization = VariableSynchronization.Auto,
- VariableAggregation aggregation = VariableAggregation.None)
- {
- var scope = Tensorflow.variable_scope.get_variable_scope();
- var store = Tensorflow.variable_scope._get_default_variable_store();
- return scope.get_variable(store,
- name,
- shape: shape,
- dtype: dtype,
- initializer: initializer,
- trainable: trainable);
- }
+ public Operation global_variables_initializer()
+ => tf.compat.v1.global_variables_initializer();
+
+ ///
+ /// Returns all variables created with `trainable=True`.
+ ///
+ ///
+ ///
+ public IVariableV1[] trainable_variables(string scope = null)
+ => (variables.trainable_variables() as List).ToArray();
+
+ public VariableScope get_variable_scope()
+ => Tensorflow.variable_scope.get_variable_scope();
}
}
diff --git a/src/TensorFlowNET.Core/Assembly/Properties.cs b/src/TensorFlowNET.Core/Assembly/Properties.cs
new file mode 100644
index 000000000..290a72df0
--- /dev/null
+++ b/src/TensorFlowNET.Core/Assembly/Properties.cs
@@ -0,0 +1,4 @@
+using System.Runtime.CompilerServices;
+#if DEBUG
+[assembly: InternalsVisibleTo("Tensorflow.UnitTest, PublicKey=00240000048000009400000006020000002400005253413100040000010001004b86c4cb78549b34bab61a3b1800e23bfeb5b3ec390074041536a7e3cbd97f5f04cf0f857155a8928eaa29ebfd11cfbbad3ba70efea7bda3226c6a8d370a4cd303f714486b6ebc225985a638471e6ef571cc92a4613c00b8fa65d61ccee0cbe5f36330c9a01f4183559f1bef24cc2917c6d913e3a541333a1d05d9bed22b38cb")]
+#endif
diff --git a/src/TensorFlowNET.Core/Attributes/c_api.ops.cs b/src/TensorFlowNET.Core/Attributes/c_api.ops.cs
index 8f067d3ef..ba6f653a1 100644
--- a/src/TensorFlowNET.Core/Attributes/c_api.ops.cs
+++ b/src/TensorFlowNET.Core/Attributes/c_api.ops.cs
@@ -1,7 +1,21 @@
-using System;
-using System.Collections.Generic;
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using System;
using System.Runtime.InteropServices;
-using System.Text;
namespace Tensorflow
{
@@ -18,7 +32,7 @@ public partial class c_api
/// TF_Status*
///
[DllImport(TensorFlowLibName)]
- public static extern TF_AttrMetadata TF_OperationGetAttrMetadata(IntPtr oper, string attr_name, IntPtr status);
+ public static extern TF_AttrMetadata TF_OperationGetAttrMetadata(IntPtr oper, string attr_name, SafeStatusHandle status);
///
/// Fills in `value` with the value of the attribute `attr_name`. `value` must
@@ -32,8 +46,8 @@ public partial class c_api
/// size_t
/// TF_Status*
[DllImport(TensorFlowLibName)]
- public static extern void TF_OperationGetAttrString(IntPtr oper, string attr_name, IntPtr value, uint max_length, IntPtr status);
-
+ public static extern void TF_OperationGetAttrString(IntPtr oper, string attr_name, IntPtr value, uint max_length, SafeStatusHandle status);
+
///
/// Sets `output_attr_value` to the binary-serialized AttrValue proto
/// representation of the value of the `attr_name` attr of `oper`.
@@ -41,13 +55,28 @@ public partial class c_api
///
///
[DllImport(TensorFlowLibName)]
- public static extern int TF_OperationGetAttrValueProto(IntPtr oper, string attr_name, IntPtr output_attr_value, IntPtr status);
+ public static extern int TF_OperationGetAttrValueProto(IntPtr oper, string attr_name, SafeBufferHandle output_attr_value, SafeStatusHandle status);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern void TF_OperationGetAttrType(IntPtr oper, string attr_name, IntPtr value, SafeStatusHandle status);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern void TF_OperationGetAttrInt(IntPtr oper, string attr_name, IntPtr value, SafeStatusHandle status);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern void TF_OperationGetAttrFloat(IntPtr oper, string attr_name, IntPtr value, SafeStatusHandle status);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern void TF_OperationGetAttrBool(IntPtr oper, string attr_name, IntPtr value, SafeStatusHandle status);
+
+ [DllImport(TensorFlowLibName)]
+ public static extern void TF_OperationGetAttrShape(IntPtr oper, string attr_name, long[] value, int num_dims, SafeStatusHandle status);
[DllImport(TensorFlowLibName)]
public static extern void TF_SetAttrBool(IntPtr desc, string attr_name, bool value);
[DllImport(TensorFlowLibName)]
- public static extern void TF_SetAttrValueProto(IntPtr desc, string attr_name, IntPtr proto, uint proto_len, IntPtr status);
+ public static extern void TF_SetAttrValueProto(IntPtr desc, string attr_name, byte[] proto, ulong proto_len, SafeStatusHandle status);
///
/// Set `num_dims` to -1 to represent "unknown rank".
@@ -85,7 +114,7 @@ public partial class c_api
public static extern void TF_SetAttrStringList(IntPtr desc, string attr_name, IntPtr[] values, uint[] lengths, int num_values);
[DllImport(TensorFlowLibName)]
- public static extern void TF_SetAttrTensor(IntPtr desc, string attr_name, IntPtr value, IntPtr status);
+ public static extern void TF_SetAttrTensor(IntPtr desc, string attr_name, SafeTensorHandle value, SafeStatusHandle status);
[DllImport(TensorFlowLibName)]
public static extern void TF_SetAttrType(IntPtr desc, string attr_name, TF_DataType value);
diff --git a/src/TensorFlowNET.Core/Binding.FuncTools.cs b/src/TensorFlowNET.Core/Binding.FuncTools.cs
new file mode 100644
index 000000000..42a7b4ef9
--- /dev/null
+++ b/src/TensorFlowNET.Core/Binding.FuncTools.cs
@@ -0,0 +1,25 @@
+using System;
+
+namespace Tensorflow
+{
+ public static partial class Binding
+ {
+ public static class functools
+ {
+ public static PartialFunc partial(Func func, Tin arg)
+ => new PartialFunc
+ {
+ args = arg,
+ invoke = func
+ };
+ }
+
+ public class PartialFunc
+ {
+ public Tin args { get; set; }
+ public object[] keywords { get; set; }
+
+ public Func invoke { get; set; }
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/Binding.Util.cs b/src/TensorFlowNET.Core/Binding.Util.cs
new file mode 100644
index 000000000..99ed5c1f3
--- /dev/null
+++ b/src/TensorFlowNET.Core/Binding.Util.cs
@@ -0,0 +1,537 @@
+/*****************************************************************************
+ Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+******************************************************************************/
+
+using Tensorflow.NumPy;
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.ComponentModel;
+using System.Diagnostics;
+using System.IO;
+using System.Linq;
+using Tensorflow.Operations;
+
+namespace Tensorflow
+{
+ ///
+ /// Binding utilities to mimic python functions.
+ ///
+ public static partial class Binding
+ {
+ public static T2 get(this Dictionary dict, T1 key)
+ => key == null ?
+ default :
+ (dict.ContainsKey(key) ? dict[key] : default);
+
+ public static void Update(this IList list, T element)
+ {
+ var index = list.IndexOf(element);
+ if (index < 0)
+ list.Add(element);
+ else
+ {
+ list[index] = element;
+ }
+ }
+
+ public static void difference_update(this IList list, IList list2)
+ {
+ foreach(var el in list2)
+ {
+ if (list.Contains(el))
+ list.Remove(el);
+ }
+ }
+
+ public static void add(this IList list, T element)
+ => list.Add(element);
+
+ public static void add(this IList list, IEnumerable elements)
+ {
+ foreach (var ele in elements)
+ list.Add(ele);
+ }
+
+ public static void append(this IList list, T element)
+ => list.Insert(list.Count, element);
+
+ public static void append(this IList list, IList elements)
+ {
+ for (int i = 0; i < elements.Count(); i++)
+ list.Insert(list.Count, elements[i]);
+ }
+
+ public static T[] concat(this IList list1, IList list2)
+ {
+ var list = new List();
+ list.AddRange(list1);
+ list.AddRange(list2);
+ return list.ToArray();
+ }
+
+ public static void extend(this List list, IEnumerable elements)
+ => list.AddRange(elements);
+
+ private static string _tostring(object obj)
+ {
+ switch (obj)
+ {
+ case NDArray nd:
+ return nd.ToString();
+ /*case Array arr:
+ if (arr.Rank != 1 || arr.GetType().GetElementType()?.IsArray == true)
+ arr = Arrays.Flatten(arr);
+ var objs = toObjectArray(arr);
+ return $"[{string.Join(", ", objs.Select(_tostring))}]";*/
+ default:
+ return obj?.ToString() ?? "null";
+ }
+ }
+
+ private static TextWriter _writer = Console.Out;
+
+ public static TextWriter tf_output_redirect {
+ set
+ {
+ if(_writer != null)
+ {
+ _writer.Flush();
+ if (_writer is StringWriter sw)
+ sw.GetStringBuilder().Clear();
+ }
+
+ _writer = value;
+ }
+ get => _writer ?? Console.Out;
+ }
+
+ public static void print(object obj)
+ {
+ tf_output_redirect.WriteLine(_tostring(obj));
+ }
+
+ public static void print(string format, params object[] objects)
+ {
+ if (!format.Contains("{}"))
+ {
+ tf_output_redirect.WriteLine(format + " " + string.Join(" ", objects.Select(x => x.ToString())));
+ return;
+ }
+
+ foreach (var obj in objects)
+ {
+
+ }
+
+ tf_output_redirect.WriteLine(format);
+ }
+
+ public static int len(object a)
+ {
+ switch (a)
+ {
+ case Tensor tensor:
+ return (int)tensor.shape[0];
+ case Tensors arr:
+ return arr.Length;
+ case Array arr:
+ return arr.Length;
+ case IList arr:
+ return arr.Count;
+ case ICollection arr:
+ return arr.Count;
+ case IEnumerable enumerable:
+ return enumerable.OfType
///
[DllImport(TensorFlowLibName)]
- public static extern IntPtr TF_NewBuffer();
+ public static extern SafeBufferHandle TF_NewBuffer();
[DllImport(TensorFlowLibName)]
- public static extern IntPtr TF_GetBuffer(TF_Buffer buffer);
+ public static extern TF_Buffer TF_GetBuffer(SafeBufferHandle buffer);
///
/// Makes a copy of the input and sets an appropriate deallocator. Useful for
@@ -28,6 +42,6 @@ public partial class c_api
/// size_t
///
[DllImport(TensorFlowLibName)]
- public static extern IntPtr TF_NewBufferFromString(IntPtr proto, ulong proto_len);
+ public static extern SafeBufferHandle TF_NewBufferFromString(IntPtr proto, ulong proto_len);
}
}
diff --git a/src/TensorFlowNET.Core/Checkpoint/CheckPointUtils.cs b/src/TensorFlowNET.Core/Checkpoint/CheckPointUtils.cs
new file mode 100644
index 000000000..071b41875
--- /dev/null
+++ b/src/TensorFlowNET.Core/Checkpoint/CheckPointUtils.cs
@@ -0,0 +1,171 @@
+using System;
+using System.Collections.Generic;
+using System.Diagnostics;
+using System.IO;
+using System.Linq;
+using Tensorflow.Functions;
+using Tensorflow.Train;
+using Tensorflow.Training;
+using pbc = global::Google.Protobuf.Collections;
+
+namespace Tensorflow.Checkpoint;
+
+public static class CheckPointUtils
+{
+ private static string _ESCAPE_CHAR = ".";
+ public static (IList, IDictionary>, IDictionary,
+ IDictionary>,
+ IDictionary) objects_ids_and_slot_variables_and_paths(ObjectGraphView graph_view)
+ {
+ var (trackable_objects, node_paths) = graph_view.breadth_first_traversal();
+ Dictionary object_names = new();
+ foreach (var pair in node_paths)
+ {
+ object_names[pair.Key] = TrackableUtils.object_path_to_string(pair.Value);
+ }
+
+ Dictionary node_ids = new();
+ for (int i = 0; i < trackable_objects.Count; i++)
+ {
+ node_ids[trackable_objects[i]] = i;
+ }
+
+ var slot_variables = serialize_slot_variables(trackable_objects, node_ids, object_names);
+ return (trackable_objects, node_paths, node_ids, slot_variables, object_names);
+ }
+
+ public static
+ IDictionary>
+ serialize_slot_variables(IEnumerable trackable_objects,
+ IDictionary node_ids, IDictionary object_names)
+ {
+ var non_slot_objects = trackable_objects.ToList();
+ Dictionary>
+ slot_variables = new();
+ foreach (var trackable in non_slot_objects)
+ {
+ if (trackable is not Optimizer)
+ {
+ continue;
+ }
+
+ var optim = (Optimizer)trackable;
+ var slot_names = optim.get_slot_names();
+ foreach (var slot_name in slot_names)
+ {
+ for (int original_variable_node_id = 0;
+ original_variable_node_id < non_slot_objects.Count;
+ original_variable_node_id++)
+ {
+ var original_variable = non_slot_objects[original_variable_node_id];
+ IVariableV1 slot_variable;
+ if (original_variable is not IVariableV1)
+ {
+ slot_variable = null;
+ }
+ slot_variable = optim.get_slot((IVariableV1)original_variable, slot_name);
+ if(slot_variable is null) continue;
+
+ // There're some problems about the inherits of `Variable` and `Trackable`.
+ throw new NotImplementedException();
+ }
+ }
+ }
+
+ return slot_variables;
+ }
+
+ public static Trackable get_mapped_trackable(Trackable trackable, IDictionary? object_map)
+ {
+ if (object_map is null || !object_map.TryGetValue(trackable, out var possible_res))
+ {
+ return trackable;
+ }
+ else
+ {
+ return possible_res;
+ }
+ }
+
+ public static string get_full_name(Trackable variable)
+ {
+ // TODO: This state is not correct, the whole framework need to be updated in the future.
+ if (!(variable is IVariableV1 || resource_variable_ops.is_resource_variable(variable)))
+ {
+ return "";
+ }
+ // skip the check of attribute `_save_slice_info` .
+
+ // TODO: Need to be revised!!!
+ Debug.Assert(variable is BaseResourceVariable);
+ return ((BaseResourceVariable)variable).Name;
+ }
+
+ public static void add_checkpoint_values_check(TrackableObjectGraph object_graph_proto)
+ {
+ HashSet checkpointed_trackables = new();
+ Dictionary> parents = new();
+ for (int i = 0; i < object_graph_proto.Nodes.Count; i++)
+ {
+ var object_proto = object_graph_proto.Nodes[i];
+ // skip the process of registered saver.
+ if (object_proto.Attributes is not null && object_proto.Attributes.Count > 0 ||
+ object_proto.SlotVariables is not null && object_proto.SlotVariables.Count > 0)
+ {
+ checkpointed_trackables.Add(i);
+ }
+
+ foreach (var child_proto in object_proto.Children)
+ {
+ var child = child_proto.NodeId;
+ if (!parents.ContainsKey(child))
+ {
+ parents[child] = new HashSet();
+ }
+
+ parents[child].Add(i);
+ }
+ }
+
+ Queue to_visit = new(checkpointed_trackables.AsEnumerable());
+ while (to_visit.Count > 0)
+ {
+ var trackable = to_visit.Dequeue();
+ if (!parents.ContainsKey(trackable)) continue;
+ var current_parents = parents[trackable];
+ foreach (var parent in current_parents)
+ {
+ checkpointed_trackables.Add(parent);
+ if (parents.ContainsKey(parent))
+ {
+ to_visit.Enqueue(parent);
+ }
+ }
+ parents.Remove(trackable);
+ }
+
+ // TODO: Complete it after supporting checkpoint.
+ // for (int i = 0; i < object_graph_proto.Nodes.Count; i++)
+ // {
+ // object_graph_proto.Nodes[i].has_checkpoint_values.value = checkpointed_trackables.Contains(i);
+ // }
+ }
+
+ ///
+ /// Traverse the object graph and list all accessible objects.
+ ///
+ ///
+ public static IList list_objects(ObjectGraphView graph_view)
+ {
+ return objects_ids_and_slot_variables_and_paths(graph_view).Item1;
+ }
+
+ internal static IEnumerable _objects_with_attributes(IEnumerable full_list)
+ {
+ return full_list.Where(x =>
+ {
+ var saveables = x.gather_saveables_for_checkpoint();
+ return saveables is not null && saveables.Count > 0;
+ });
+ }
+}
diff --git a/src/TensorFlowNET.Core/Checkpoint/CheckpointOptions.cs b/src/TensorFlowNET.Core/Checkpoint/CheckpointOptions.cs
new file mode 100644
index 000000000..75b392af8
--- /dev/null
+++ b/src/TensorFlowNET.Core/Checkpoint/CheckpointOptions.cs
@@ -0,0 +1,5 @@
+namespace Tensorflow.Checkpoint;
+
+public record class CheckpointOptions(
+ string? experimental_io_device = null,
+ bool experimental_enable_async_checkpoint = false);
diff --git a/src/TensorFlowNET.Core/Checkpoint/CheckpointReader.cs b/src/TensorFlowNET.Core/Checkpoint/CheckpointReader.cs
new file mode 100644
index 000000000..a1dba371c
--- /dev/null
+++ b/src/TensorFlowNET.Core/Checkpoint/CheckpointReader.cs
@@ -0,0 +1,69 @@
+namespace Tensorflow.Checkpoint;
+
+public class CheckpointReader
+{
+ private SafeCheckpointReaderHandle _handle;
+ public Dictionary VariableToDataTypeMap { get; set; }
+ public Dictionary VariableToShapeMap { get; set; }
+
+ public CheckpointReader(string filename)
+ {
+ Status status = new Status();
+ VariableToDataTypeMap = new Dictionary();
+ VariableToShapeMap = new Dictionary();
+ _handle = c_api.TF_NewCheckpointReader(filename, status);
+ status.Check(true);
+ ReadAllShapeAndType();
+ }
+
+ public int HasTensor(string name)
+ => c_api.TF_CheckpointReaderHasTensor(_handle, name);
+
+ ///
+ /// Get the variable name.
+ ///
+ ///
+ ///
+ public string GetVariable(int index)
+ => c_api.StringPiece(c_api.TF_CheckpointReaderGetVariable(_handle, index));
+
+ public int Size()
+ => c_api.TF_CheckpointReaderSize(_handle);
+
+ public TF_DataType GetVariableDataType(string name)
+ => c_api.TF_CheckpointReaderGetVariableDataType(_handle, name);
+
+ public Shape GetVariableShape(string name)
+ {
+ int num_dims = GetVariableNumDims(name);
+ long[] dims = new long[num_dims];
+ Status status = new Status();
+ c_api.TF_CheckpointReaderGetVariableShape(_handle, name, dims, num_dims, status);
+ status.Check(true);
+ return new Shape(dims);
+ }
+
+ public int GetVariableNumDims(string name)
+ => c_api.TF_CheckpointReaderGetVariableNumDims(_handle, name);
+
+ public unsafe Tensor GetTensor(string name, TF_DataType dtype = TF_DataType.DtInvalid)
+ {
+ Status status = new Status();
+ var tensor = c_api.TF_CheckpointReaderGetTensor(_handle, name, status);
+ status.Check(true);
+ return new Tensor(tensor);
+ }
+
+ private void ReadAllShapeAndType()
+ {
+ int size = Size();
+ for(int i = 0; i < size; i++)
+ {
+ var name = GetVariable(i);
+ var shape = GetVariableShape(name);
+ var dtype = GetVariableDataType(name);
+ VariableToDataTypeMap[name] = dtype;
+ VariableToShapeMap[name] = shape;
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/Checkpoint/ObjectGraphView.cs b/src/TensorFlowNET.Core/Checkpoint/ObjectGraphView.cs
new file mode 100644
index 000000000..f435dd88b
--- /dev/null
+++ b/src/TensorFlowNET.Core/Checkpoint/ObjectGraphView.cs
@@ -0,0 +1,64 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Serilog.Debugging;
+using Tensorflow.Keras.Saving.SavedModel;
+using Tensorflow.Train;
+
+namespace Tensorflow.Checkpoint;
+
+public class ObjectGraphView: TrackableView, ICloneable
+{
+ protected IEnumerable? _attached_dependencies;
+ // TODO: attached_dependencies
+ public ObjectGraphView(Trackable root, IEnumerable? attached_dependencies = null): base(root)
+ {
+ _attached_dependencies = attached_dependencies;
+ }
+
+ public object Clone()
+ {
+ // TODO: Implement real deep copy corresponding to tensorflow/python/checkpoint/graph_view.ObjectGraphView.__deepcopy__
+ return new ObjectGraphView(Root, _attached_dependencies);
+ }
+
+ public virtual List list_children(Trackable obj, SaveType save_type = SaveType.CHECKPOINT, IDictionary>? serialization_cache = null)
+ {
+ List res = base.children(obj, save_type, serialization_cache)
+ .Select(x => new TrackableReference(x.Key, x.Value)).ToList();
+ // Check the reference, not value.
+ if (obj == Root && _attached_dependencies is not null)
+ {
+ res.AddRange(_attached_dependencies);
+ }
+
+ return res;
+ }
+
+ public override IDictionary children(Trackable obj, SaveType save_type = SaveType.CHECKPOINT, IDictionary>? serialization_cache = null)
+ {
+ return list_children(obj, save_type, serialization_cache).ToDictionary(x => x.Name, x => x.Refer);
+ }
+
+ public IEnumerable? AttachedDependencies
+ {
+ get => _attached_dependencies;
+ }
+
+ public virtual (IList, IDictionary>) breadth_first_traversal()
+ {
+ return base._descendants_with_paths();
+ }
+
+ // TODO: complete the implementation
+ public void serialize_object_graph(object? saveables_cache = null)
+ {
+ throw new NotImplementedException();
+ }
+
+ // TODO: complete the implementation
+ public void frozen_saveable_objects(object? object_map = null, object? to_graph = null, object call_with_mapped_captures = null)
+ {
+ throw new NotImplementedException();
+ }
+}
diff --git a/src/TensorFlowNET.Core/Checkpoint/SafeCheckpointReaderHandle.cs b/src/TensorFlowNET.Core/Checkpoint/SafeCheckpointReaderHandle.cs
new file mode 100644
index 000000000..674e83512
--- /dev/null
+++ b/src/TensorFlowNET.Core/Checkpoint/SafeCheckpointReaderHandle.cs
@@ -0,0 +1,21 @@
+using Tensorflow.Util;
+
+namespace Tensorflow.Checkpoint;
+
+public sealed class SafeCheckpointReaderHandle : SafeTensorflowHandle
+{
+ private SafeCheckpointReaderHandle() : base ()
+ {
+ }
+
+ public SafeCheckpointReaderHandle(IntPtr handle) : base(handle)
+ {
+ }
+
+ protected override bool ReleaseHandle()
+ {
+ c_api.TF_DeleteCheckpointReader(handle);
+ SetHandle(IntPtr.Zero);
+ return true;
+ }
+}
diff --git a/src/TensorFlowNET.Core/Checkpoint/SaveUtil.cs b/src/TensorFlowNET.Core/Checkpoint/SaveUtil.cs
new file mode 100644
index 000000000..7a5da7e3a
--- /dev/null
+++ b/src/TensorFlowNET.Core/Checkpoint/SaveUtil.cs
@@ -0,0 +1,261 @@
+using OneOf;
+using System;
+using System.Collections.Generic;
+using System.Diagnostics;
+using System.Linq;
+using System.Text;
+using Tensorflow.Train;
+using Tensorflow.Training;
+using Tensorflow.Common.Extensions;
+using pbc = global::Google.Protobuf.Collections;
+
+namespace Tensorflow.Checkpoint
+{
+ internal record class TrackableData(
+ // A trackable in the root Trackable object graph.
+ Trackable trackable,
+ // The index at which the Trackable appears in TrackableObjectGraph.nodes.
+ int node_id,
+ // The BFS-generated path from the root object / used to generate readable checkpoint keys.
+ string object_name,
+ // A list of ObjectReference for each child connected to this Trackable.
+ pbc::RepeatedField children_proto,
+ // A list of SlotVariableReference to save to the object (only valid for Optimizer objects).
+ pbc::RepeatedField slot_variable_proto,
+ // The object to save to checkpoint. Usually this is the same as `trackable`,
+ // but can differ when the the caller wants to specify a different object to
+ // save. For example, when saving checkpoints asynchronously, variables are
+ // copied to the CPU. `object_to_save` is set as the copied variable.
+ Trackable object_to_save
+ );
+ public static class SaveUtil
+ {
+ public static (IDictionary>>>, IDictionary, IDictionary>, TrackableObjectGraph)
+ serialize_graph_view(ObjectGraphView graph_view, IDictionary? object_map = null, bool call_with_mapped_captures = false, object? cache = null)
+ {
+ var (trackable_data, node_ids) = gather_trackable_data(graph_view, object_map);
+ var (tensor_trackables, pystate_trackables, registered_trackables) = split_trackables(trackable_data);
+
+ var object_graph_proto = fill_object_graph_proto(trackable_data);
+
+ var serialized_tensors = get_and_write_tensors_to_serialize(tensor_trackables, node_ids, call_with_mapped_captures, cache, object_graph_proto);
+ var registered_savers = get_and_write_registered_savers(registered_trackables, object_graph_proto);
+
+ Dictionary feed_additions;
+ if(cache is null)
+ {
+ feed_additions = null;
+ serialized_tensors = serialized_tensors.Concat(get_and_write_tensors_to_serialize(pystate_trackables, node_ids, call_with_mapped_captures,
+ cache, object_graph_proto)).ToDictionary(x => x.Key, x => x.Value);
+ }
+ else
+ {
+ feed_additions = null;
+ // TODO: deal with cache.
+ throw new NotFiniteNumberException();
+ }
+
+ CheckPointUtils.add_checkpoint_values_check(object_graph_proto);
+
+ return (serialized_tensors, feed_additions, registered_savers, object_graph_proto);
+ }
+
+ private static (IList, IDictionary) gather_trackable_data(ObjectGraphView graph_view, IDictionary? object_map)
+ {
+ var (trackable_objects, node_paths) = graph_view.breadth_first_traversal();
+ Dictionary object_names = new();
+ foreach(var pair in node_paths)
+ {
+ object_names[pair.Key] = TrackableUtils.object_path_to_string(pair.Value);
+ }
+ Dictionary node_ids = new();
+ for(int i = 0; i < trackable_objects.Count; i++)
+ {
+ node_ids[trackable_objects[i]] = i;
+ }
+ var slot_variables = CheckPointUtils.serialize_slot_variables(trackable_objects, node_ids, object_names);
+ List trackable_data = new();
+ foreach(var trackable in trackable_objects)
+ {
+ pbc::RepeatedField children_proto = new();
+ foreach(var child in graph_view.list_children(trackable))
+ {
+ children_proto.Add(new TrackableObjectGraph.Types.TrackableObject.Types.ObjectReference()
+ {
+ NodeId = node_ids[child.Refer],
+ LocalName = child.Name
+ });
+ }
+ slot_variables.TryGetValue(trackable, out var slot_variable);
+ trackable_data.Add(new TrackableData(
+ trackable: trackable,
+ node_id: node_ids[trackable],
+ object_name: object_names[trackable],
+ children_proto: children_proto,
+ slot_variable_proto: slot_variable??new pbc.RepeatedField(),
+ object_to_save: CheckPointUtils.get_mapped_trackable(trackable, object_map)
+ ));
+ }
+ return (trackable_data, node_ids);
+ }
+
+ private static TrackableObjectGraph fill_object_graph_proto(IList trackable_data)
+ {
+ TrackableObjectGraph object_graph_proto = new();
+ for(int i = 0; i < trackable_data.Count; i++)
+ {
+ var td = trackable_data[i];
+ Debug.Assert(td.node_id == i);
+ TrackableObjectGraph.Types.TrackableObject trackable_object = new();
+ trackable_object.SlotVariables.AddRange(td.slot_variable_proto);
+ trackable_object.Children.AddRange(td.children_proto);
+ object_graph_proto.Nodes.Add(trackable_object);
+ }
+ return object_graph_proto;
+ }
+
+ ///
+ /// Creates dictionary of tensors to checkpoint, and updates the proto.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ private static IDictionary>>> get_and_write_tensors_to_serialize(IList tensor_trackables, IDictionary node_ids,
+ bool call_with_mapped_captures, object? cache, TrackableObjectGraph object_graph_proto)
+ {
+ Dictionary>>> serialized_tensors = new();
+ foreach(var td in tensor_trackables)
+ {
+ // TODO: deal with cache.
+ var legacy_name = SaveableCompat.get_saveable_name(td.object_to_save) ?? "";
+ Trackable trackable = null;
+ IDictionary>> tensor_dict;
+ if(!saveable_object_util.trackable_has_serialize_to_tensor(td.object_to_save) || legacy_name.Length > 0)
+ {
+ (trackable, tensor_dict) = get_tensors_from_legacy_saveable(td, node_ids, call_with_mapped_captures, object_graph_proto);
+ }
+ else
+ {
+ tensor_dict = get_tensors_from_trackable(td, call_with_mapped_captures, object_graph_proto);
+ trackable = td.object_to_save;
+ }
+ if(trackable is not null)
+ {
+ serialized_tensors[trackable] = tensor_dict;
+ }
+ else
+ {
+ serialized_tensors[Trackable.None] = tensor_dict;
+ }
+ }
+ return serialized_tensors;
+ }
+
+ private static IDictionary>> get_tensors_from_trackable(TrackableData trackable_data, bool call_with_mapped_captures, TrackableObjectGraph object_graph_proto)
+ {
+ var trackable = trackable_data.object_to_save;
+
+ // TODO: complete it. Note that actually `call_with_mapped_captures` is of function type.
+ IDictionary>> ret_tensor_dict;
+ if (call_with_mapped_captures)
+ {
+ throw new NotImplementedException();
+ }
+ else
+ {
+ ret_tensor_dict = trackable.serialize_to_tensors();
+ }
+
+ Dictionary>> tensor_dict = new();
+ foreach(var pair in ret_tensor_dict)
+ {
+ var local_name = TrackableUtils.escape_local_name(pair.Key);
+ var maybe_tensor = pair.Value;
+ var checkpoint_key = TrackableUtils.checkpoint_key(trackable_data.object_name, local_name);
+
+ tensor_dict[checkpoint_key] = maybe_tensor;
+
+ foreach(var key in maybe_tensor.Keys)
+ {
+ if (maybe_tensor[key].IsTypeOrDeriveFrom())
+ {
+ maybe_tensor[key].AsT1.name = local_name + maybe_tensor[key].AsT1.name;
+ }
+ }
+
+ if(object_graph_proto is not null)
+ {
+ object_graph_proto.Nodes[trackable_data.node_id].Attributes.Add(new TrackableObjectGraph.Types.TrackableObject.Types.SerializedTensor()
+ {
+ Name = local_name,
+ CheckpointKey = checkpoint_key,
+ FullName = CheckPointUtils.get_full_name(trackable)
+ });
+ }
+ }
+ return tensor_dict;
+ }
+
+ ///
+ /// Gets tensors to serialize from a Trackable with legacy SaveableObjects.
+ ///
+ ///
+ ///
+ ///
+ ///
+ ///
+ private static (Trackable, IDictionary>>) get_tensors_from_legacy_saveable(TrackableData trackable_data, IDictionary node_ids,
+ bool call_with_mapped_captures, TrackableObjectGraph object_graph_proto)
+ {
+ Dictionary object_names = new();
+ object_names[trackable_data.trackable] = trackable_data.object_name;
+ Dictionary object_map = new();
+ object_map[trackable_data.trackable] = trackable_data.object_to_save;
+
+ var (checkpoint_factory_map, _) = SaveUtilV1.get_checkpoint_factories_and_keys(object_names, object_map);
+ var (named_saveable_objects, _) = SaveUtilV1.generate_saveable_objects(checkpoint_factory_map, object_graph_proto, node_ids, object_map,
+ call_with_mapped_captures, saveables_cache: null);
+ var trackable = new SaveableCompatibilityConverter(trackable_data.object_to_save, named_saveable_objects);
+ return (trackable, trackable.serialize_to_tensors());
+ }
+
+ private static IDictionary> get_and_write_registered_savers(IDictionary> registered_trackables, TrackableObjectGraph object_graph_proto)
+ {
+ Dictionary> registered_savers = new();
+ foreach(var pair in registered_trackables)
+ {
+ foreach(var td in pair.Value)
+ {
+ if (registered_savers.ContainsKey(pair.Key))
+ {
+ registered_savers[pair.Key] = new Dictionary();
+ }
+ else
+ {
+ registered_savers[pair.Key][td.object_name] = td.object_to_save;
+ }
+
+ var object_proto = object_graph_proto.Nodes[td.node_id];
+ // TODO: add APIs and complete it. Now the `TrackableObjectGraph.Types.TrackableObject` lacks `registered_savers`.
+ }
+ }
+ return registered_savers;
+ }
+
+ private static (IList, IList, IDictionary>) split_trackables(IEnumerable trackable_data)
+ {
+ List tensor_trackables = new();
+ List py_state_trackables = new(); // skip the process of `PyState` for the lack of API. This is only a pleceholder.
+ Dictionary> registered_trackables = new();
+
+ foreach(var td in trackable_data)
+ {
+ // TODO: deal with registration.
+ tensor_trackables.Add(td);
+ }
+ return (tensor_trackables, py_state_trackables, registered_trackables);
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/Checkpoint/SaveUtilV1.cs b/src/TensorFlowNET.Core/Checkpoint/SaveUtilV1.cs
new file mode 100644
index 000000000..9280179c0
--- /dev/null
+++ b/src/TensorFlowNET.Core/Checkpoint/SaveUtilV1.cs
@@ -0,0 +1,225 @@
+using System;
+using System.Collections.Generic;
+using System.Diagnostics;
+using System.Linq;
+using Tensorflow.Exceptions;
+using Tensorflow.Train;
+using Tensorflow.Training;
+using pbc = global::Google.Protobuf.Collections;
+using static Tensorflow.Binding;
+using Google.Protobuf;
+using OneOf;
+
+namespace Tensorflow.Checkpoint;
+
+public static class SaveUtilV1
+{
+ public static (IDictionary>, object?) get_checkpoint_factories_and_keys(IDictionary object_names,
+ IDictionary? object_map = null)
+ {
+ // According to https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/registration/README.md,
+ // till now only internal registrations are allowed. So, we won't return a saver in this function.
+ // The implementation of this function should be updated if tensorflow update it.
+ Dictionary> checkpoint_factory_map = new();
+ foreach (var pair in object_names)
+ {
+ var trackable = pair.Key;
+ var object_name = pair.Value;
+ var object_to_save = CheckPointUtils.get_mapped_trackable(trackable, object_map);
+
+ // skip the registration process.
+
+ List current_list = new();
+ foreach (var name_and_factory in saveable_object_util.saveable_objects_from_trackable(object_to_save))
+ {
+ // treat name as key_suffix.
+ var name = name_and_factory.Key;
+ var checkpoint_key = TrackableUtils.checkpoint_key(object_name, name);
+
+ current_list.Add(new CheckpointFactoryData(name_and_factory.Value, name, checkpoint_key));
+ }
+
+ checkpoint_factory_map[trackable] = current_list;
+ }
+
+ return (checkpoint_factory_map, null);
+ }
+
+ public static (IList, IDictionary>?) frozen_saveables_and_savers(ObjectGraphView graph_view,
+ IDictionary object_map, Graph? to_graph, bool call_with_mapped_captures,
+ object? saveables_cache = null)
+ {
+ if (to_graph is not null)
+ {
+ var g = to_graph.as_default();
+ var (named_saveable_objects, graph_proto, _, registered_savers) = serialize_gathered_objects(graph_view,
+ object_map, call_with_mapped_captures, saveables_cache);
+ var object_graph_tensor = tf_with(ops.device("/cpu:0"), _ =>
+ {
+ // TODO(Rinne): locate the error that causes transferring TF_STRING to this function throws an exception.
+ return constant_op.constant(graph_proto.ToByteArray());
+ });
+ named_saveable_objects.Add(new NoRestoreSaveable(object_graph_tensor, Trackable.Constants.OBJECT_GRAPH_PROTO_KEY));
+ g.Exit();
+ return (named_saveable_objects, registered_savers);
+ }
+ else
+ {
+ using (new ops.NullContextManager())
+ {
+ var (named_saveable_objects, graph_proto, _, registered_savers) = serialize_gathered_objects(graph_view,
+ object_map, call_with_mapped_captures, saveables_cache);
+ var object_graph_tensor = tf_with(ops.device("/cpu:0"), _ =>
+ {
+ return constant_op.constant(graph_proto.ToString());
+ });
+ named_saveable_objects.Add(new NoRestoreSaveable(object_graph_tensor, Trackable.Constants.OBJECT_GRAPH_PROTO_KEY));
+ return (named_saveable_objects, registered_savers);
+ }
+ }
+ }
+
+ public static (IList, TrackableObjectGraph, object?, IDictionary>?) serialize_gathered_objects(ObjectGraphView graph_view,
+ IDictionary object_map, bool call_with_mapped_captures, object? saveables_cache = null)
+ {
+ var (trackable_objects, node_paths) = graph_view.breadth_first_traversal();
+ Dictionary object_names = new();
+ foreach (var pair in node_paths)
+ {
+ object_names[pair.Key] = TrackableUtils.object_path_to_string(pair.Value);
+ }
+
+ Dictionary node_ids = new();
+ for (int i = 0; i < trackable_objects.Count; i++)
+ {
+ node_ids[trackable_objects[i]] = i;
+ }
+
+ var slot_variables = CheckPointUtils.serialize_slot_variables(trackable_objects, node_ids, object_names);
+ var object_graph_proto = fill_object_graph_proto(graph_view, trackable_objects, node_ids, slot_variables);
+ var (named_saveable_objects, feed_additions, registered_savers) = add_attributes_to_object_graph(
+ trackable_objects, object_graph_proto, node_ids, object_names, object_map, call_with_mapped_captures,
+ saveables_cache);
+
+ CheckPointUtils.add_checkpoint_values_check(object_graph_proto);
+ return (named_saveable_objects, object_graph_proto, feed_additions, registered_savers);
+ }
+
+ private static TrackableObjectGraph fill_object_graph_proto(ObjectGraphView graph_view, IList trackable_objects,
+ IDictionary node_ids,
+ IDictionary>
+ slot_variables)
+ {
+ TrackableObjectGraph object_graph_proto = new();
+ for (int i = 0; i < trackable_objects.Count; i++)
+ {
+ var trackable = trackable_objects[i];
+ Debug.Assert(node_ids[trackable] == i);
+ var object_proto = new TrackableObjectGraph.Types.TrackableObject();
+ if (slot_variables.TryGetValue(trackable, out var slots))
+ {
+ object_proto.SlotVariables.AddRange(slots);
+ }
+ object_graph_proto.Nodes.Add(object_proto);
+ foreach (var child in graph_view.list_children(trackable))
+ {
+ object_proto.Children.Add(new TrackableObjectGraph.Types.TrackableObject.Types.ObjectReference()
+ { NodeId = node_ids[child.Refer], LocalName = child.Name });
+ }
+ }
+
+ return object_graph_proto;
+ }
+
+ private static (IList, object?, IDictionary>?) add_attributes_to_object_graph(
+ IList trackable_objects,
+ TrackableObjectGraph object_graph_proto, IDictionary node_ids,
+ IDictionary object_names, IDictionary object_map,
+ bool call_with_mapped_captures, object? saveables_cache = null)
+ {
+ int cnt = Math.Min(trackable_objects.Count, object_graph_proto.Nodes.Count);
+ for (int i = 0; i < cnt; i++)
+ {
+ Debug.Assert(node_ids[trackable_objects[i]] == i);
+ }
+
+ var (checkpoint_factory_map, unmmaped_registered_savers) =
+ get_checkpoint_factories_and_keys(object_names, object_map);
+
+ // skip the process of registered savers
+
+ var (named_saveable_objects, feed_additions) = generate_saveable_objects(checkpoint_factory_map,
+ object_graph_proto, node_ids, object_map, call_with_mapped_captures, saveables_cache);
+ return (named_saveable_objects, feed_additions, null);
+ }
+
+ public static (IList, object?) generate_saveable_objects(
+ IDictionary> checkpoint_factory_map,
+ TrackableObjectGraph? object_graph_proto, IDictionary? node_ids,
+ IDictionary object_map, bool call_with_mapped_captures, object? saveables_cache = null)
+ {
+ List named_saveable_objects = new();
+ foreach (var pair in checkpoint_factory_map)
+ {
+ var trackable = pair.Key;
+ var factory_data_list = pair.Value;
+ bool fill_object_proto = object_graph_proto is not null && node_ids is not null;
+ TrackableObjectGraph.Types.TrackableObject object_proto = null!;
+ if (fill_object_proto)
+ {
+ object_proto = object_graph_proto.Nodes[node_ids[trackable]];
+ }
+
+ var object_to_save = CheckPointUtils.get_mapped_trackable(trackable, object_map);
+ // skip cache
+
+ foreach (var factory_data in factory_data_list)
+ {
+ var name = factory_data.name;
+ var key = factory_data.checkpoint_key;
+ var maybe_saveable = saveable_object_util.create_saveable_object(name, key, factory_data.factory);
+
+ // TODO: tensorflow python has a process with callable `saveable_factory`.
+ List saveables = new();
+ if (maybe_saveable.TryPickT1(out var s, out var variable))
+ {
+ saveables.Add(s);
+ }
+ else
+ {
+ saveables.AddRange(saveable_object_util.saveable_objects_for_op(variable as Trackable, key));
+ }
+
+ foreach (var saveable in saveables)
+ {
+ if (!saveable.name.Contains(key))
+ {
+ throw new AssertionError($"The object {trackable} produced a SaveableObject with name " +
+ $"'{saveable.name}' for attribute '{name}'. Expected a name" +
+ $" containing '{key}'.");
+ }
+ }
+
+ // skip the process of PythonState
+
+ named_saveable_objects.AddRange(saveables);
+
+ if(!fill_object_proto) continue;
+
+ // skip the process of `TrackableSaveable` because of lack of APIs.
+
+ object_proto!.Attributes.Add(new TrackableObjectGraph.Types.TrackableObject.Types.SerializedTensor()
+ { Name = name, CheckpointKey = key, FullName = CheckPointUtils.get_full_name(object_to_save) });
+ }
+ }
+
+ return (named_saveable_objects, null);
+ }
+}
+
+public record class CheckpointFactoryData
+(
+ Func> factory,
+ string name,
+ string checkpoint_key
+);
diff --git a/src/TensorFlowNET.Core/Checkpoint/SaveableCompat.cs b/src/TensorFlowNET.Core/Checkpoint/SaveableCompat.cs
new file mode 100644
index 000000000..fa441d799
--- /dev/null
+++ b/src/TensorFlowNET.Core/Checkpoint/SaveableCompat.cs
@@ -0,0 +1,16 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Train;
+
+namespace Tensorflow.Checkpoint
+{
+ internal static class SaveableCompat
+ {
+ public static string? get_saveable_name(Trackable cls_or_obj)
+ {
+ // TODO: implement it with Attribute.
+ return null;
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/Checkpoint/TrackableView.cs b/src/TensorFlowNET.Core/Checkpoint/TrackableView.cs
new file mode 100644
index 000000000..dab6d5d97
--- /dev/null
+++ b/src/TensorFlowNET.Core/Checkpoint/TrackableView.cs
@@ -0,0 +1,82 @@
+using System;
+using Tensorflow.Train;
+using System.Collections.Generic;
+using System.IO;
+using Tensorflow.Keras.Saving.SavedModel;
+
+namespace Tensorflow.Checkpoint;
+
+public class TrackableView
+{
+ protected WeakReference _root_ref;
+ public TrackableView(Trackable obj)
+ {
+ _root_ref = new WeakReference(obj);
+ }
+
+ public TrackableView(WeakReference obj)
+ {
+ _root_ref = obj;
+ }
+
+ public virtual IDictionary children(Trackable obj, SaveType save_type = SaveType.CHECKPOINT, IDictionary>? cache = null)
+ {
+ obj._maybe_initialize_trackable();
+ Dictionary children = new();
+ // Note: in python the return type of `Trackable._trackable_children` is not fixed.
+ // Therefore it uses `convert_to_trackable` to have an extra process.
+ foreach (var pair in obj._trackable_children(save_type, cache))
+ {
+ children[pair.Key] = pair.Value;
+ }
+ return children;
+ }
+
+ public Trackable Root
+ {
+ get
+ {
+ if (_root_ref.TryGetTarget(out Trackable res))
+ {
+ return res;
+ }
+ else
+ {
+ throw new InvalidDataException(
+ "Cannot get the object from the weak reference. Please consider if a null reference is passed to the constructor.");
+ }
+ }
+ }
+
+ ///
+ /// Returns a list of all nodes and its paths from self.root using a breadth first traversal.
+ /// Corresponding to tensorflow/python/checkpoint/trackable_view.Trackable._descendants_with_paths
+ ///
+ protected (IList, IDictionary>) _descendants_with_paths()
+ {
+ List bfs_sorted = new();
+ Queue to_visit = new();
+ to_visit.Enqueue(Root);
+ Dictionary> node_paths = new();
+ node_paths[this.Root] = new List();
+ while (!to_visit.empty())
+ {
+ var current_trackable = to_visit.Dequeue();
+ bfs_sorted.Add(current_trackable);
+ var children_dict = this.children(current_trackable);
+ foreach (var name in children_dict.Keys)
+ {
+ var dependency = children_dict[name];
+ if (!node_paths.ContainsKey(dependency))
+ {
+ var list = new List(node_paths[current_trackable]);
+ list.Add(new TrackableReference(name, dependency));
+ node_paths[dependency] = list;
+ to_visit.Enqueue(dependency);
+ }
+ }
+ }
+
+ return (bfs_sorted, node_paths);
+ }
+}
\ No newline at end of file
diff --git a/src/TensorFlowNET.Core/Checkpoint/c_api.checkpoint.cs b/src/TensorFlowNET.Core/Checkpoint/c_api.checkpoint.cs
new file mode 100644
index 000000000..f956e3337
--- /dev/null
+++ b/src/TensorFlowNET.Core/Checkpoint/c_api.checkpoint.cs
@@ -0,0 +1,27 @@
+using System.Runtime.InteropServices;
+using Tensorflow.Checkpoint;
+
+namespace Tensorflow
+{
+ public unsafe partial class c_api
+ {
+ [DllImport(TensorFlowLibName)]
+ internal static extern SafeCheckpointReaderHandle TF_NewCheckpointReader(string filename, SafeStatusHandle status);
+ [DllImport(TensorFlowLibName)]
+ internal static extern void TF_DeleteCheckpointReader(IntPtr reader);
+ [DllImport(TensorFlowLibName)]
+ internal static extern int TF_CheckpointReaderHasTensor(SafeCheckpointReaderHandle reader, string name);
+ [DllImport(TensorFlowLibName)]
+ internal static extern IntPtr TF_CheckpointReaderGetVariable(SafeCheckpointReaderHandle reader, int index);
+ [DllImport(TensorFlowLibName)]
+ internal static extern int TF_CheckpointReaderSize(SafeCheckpointReaderHandle reader);
+ [DllImport(TensorFlowLibName)]
+ internal static extern TF_DataType TF_CheckpointReaderGetVariableDataType(SafeCheckpointReaderHandle reader, string name);
+ [DllImport(TensorFlowLibName)]
+ internal static extern void TF_CheckpointReaderGetVariableShape(SafeCheckpointReaderHandle reader, string name, long[] dims, int num_dims, SafeStatusHandle status);
+ [DllImport(TensorFlowLibName)]
+ internal static extern int TF_CheckpointReaderGetVariableNumDims(SafeCheckpointReaderHandle reader, string name);
+ [DllImport(TensorFlowLibName)]
+ internal static extern SafeTensorHandle TF_CheckpointReaderGetTensor(SafeCheckpointReaderHandle reader, string name, SafeStatusHandle status);
+ }
+}
diff --git a/src/TensorFlowNET.Core/Checkpoint/checkpoint.cs b/src/TensorFlowNET.Core/Checkpoint/checkpoint.cs
new file mode 100644
index 000000000..30d45e82c
--- /dev/null
+++ b/src/TensorFlowNET.Core/Checkpoint/checkpoint.cs
@@ -0,0 +1,582 @@
+using Google.Protobuf;
+using System;
+using System.Collections.Generic;
+using System.Diagnostics;
+using System.Linq;
+using Tensorflow.Contexts;
+using Tensorflow.Eager;
+using Tensorflow.Train;
+using Tensorflow.Exceptions;
+using static Tensorflow.TrackableObjectGraph.Types.TrackableObject.Types;
+using static Tensorflow.Binding;
+using Tensorflow.Operations;
+using Newtonsoft.Json;
+using Tensorflow.Training;
+using OneOf;
+
+namespace Tensorflow.Checkpoint;
+
+///
+/// Saves and restores a `Trackable` object and its dependencies.
+///
+public class TrackableSaver
+{
+ private ObjectGraphView _graph_view;
+ private Tensor _cached_save_operation;
+ private TrackableObjectGraph _last_save_object_graph;
+ private Tensor? _object_graph_feed_tensor = null;
+ private Tensor? _file_prefix_feed_tensor = null;
+ private Tensor? _file_prefix_placeholder = null;
+ private Dictionary? _object_map = null;
+ private object? _cache = null;
+ public Tensor? FilePrefixPlaceHolder
+ {
+ get
+ {
+ return _file_prefix_placeholder;
+ }
+ set
+ {
+ _file_prefix_placeholder = value;
+ }
+ }
+ public TrackableSaver(ObjectGraphView graph_view)
+ {
+ _graph_view = graph_view;
+
+ // TODO: cache when not executing eagerly.
+ // including `_cache`, `_file_prefix_feed_tensor`, `_file_prefix_placeholder`
+ // `_object_graph_feed_tensor`, `_object_map`, `_restore_op_cache`, `_saveables_cache`
+
+ }
+
+ private (IDictionary>>>, IDictionary, IDictionary>, TrackableObjectGraph)
+ gather_serialized_tensors(Tensor? object_graph_tensor = null)
+ {
+ var (serialized_tensors, feed_additions, registered_savers, graph_proto) = SaveUtil.serialize_graph_view(_graph_view, _object_map, cache:_cache);
+
+ // TODO: cache.
+
+ if(object_graph_tensor is null)
+ {
+ tf_with(ops.device("/cpu:0"), _ =>
+ {
+ object_graph_tensor = constant_op.constant(graph_proto.ToByteArray());
+ });
+ }
+ else
+ {
+ feed_additions[object_graph_tensor] = graph_proto.ToByteArray();
+ }
+ Debug.Assert(!serialized_tensors.ContainsKey(Trackable.None) || !serialized_tensors[Trackable.None].ContainsKey(Trackable.Constants.OBJECT_GRAPH_PROTO_KEY));
+ if (!serialized_tensors.ContainsKey(Trackable.None))
+ {
+ serialized_tensors[Trackable.None] = new Dictionary>>();
+ }
+ serialized_tensors[Trackable.None][Trackable.Constants.OBJECT_GRAPH_PROTO_KEY] = new Dictionary>();
+ serialized_tensors[Trackable.None][Trackable.Constants.OBJECT_GRAPH_PROTO_KEY].Add(saveable_object_util.NO_SLICE_SPEC_KEY, object_graph_tensor);
+ return (serialized_tensors, feed_additions, registered_savers, graph_proto);
+ }
+
+ private (Tensor, IDictionary) save_cached_when_graph_building(Tensor file_prefix, Tensor object_graph_tensor, CheckpointOptions options)
+ {
+ var (serialized_tensors, feed_additions, registered_savers, graph_proto) = gather_serialized_tensors(object_graph_tensor);
+
+ Func<(Tensor, IDictionary)> run_save = () =>
+ {
+ if (_last_save_object_graph != graph_proto || tf.Context.executing_eagerly() || ops.inside_function())
+ {
+ var saver = new MultiDeviceSaver(serialized_tensors, registered_savers);
+ var save_op = saver.save(file_prefix, options);
+
+ // tensorflow python: `with ops.device("/cpu:0"):`
+ using (ops.control_dependencies(new object[] { save_op }))
+ {
+ _cached_save_operation = array_ops.identity(file_prefix);
+ }
+ _last_save_object_graph = graph_proto;
+ }
+ return (_cached_save_operation, feed_additions);
+ };
+
+ if (options.experimental_enable_async_checkpoint)
+ {
+ throw new NotImplementedException();
+ }
+
+ return run_save();
+ }
+
+ private (Tensor, IDictionary) save_cached_when_graph_building(string file_prefix, Tensor object_graph_tensor, CheckpointOptions options)
+ {
+ var (serialized_tensors, feed_additions, registered_savers, graph_proto) = gather_serialized_tensors(object_graph_tensor);
+
+ Func<(Tensor, IDictionary
+
+
+
diff --git a/TensorFlow.NET.sln b/TensorFlow.NET.sln
index 51125309e..e0c273568 100644
--- a/TensorFlow.NET.sln
+++ b/TensorFlow.NET.sln
@@ -1,61 +1,391 @@
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+ {9738D16A-CFA0-405C-A7DF-D3D203B0CB18} = {01A1787F-A9BE-4221-84E8-6360DD010AB6}
+ {7DEA8760-E401-4872-81F3-405F185A13A0} = {1B0918B9-65AD-4F34-A287-AF4597B27DBD}
+ {3D92142F-EEDB-469B-B03C-4E38728BFE4C} = {E1A5D2B7-10AF-4876-85C0-7714EF274214}
+ {AB131FA7-B7C3-4ABF-ABDE-E059C72A613C} = {E1A5D2B7-10AF-4876-85C0-7714EF274214}
+ {D24FCAA5-548C-4251-B226-A1B6535D0845} = {E1A5D2B7-10AF-4876-85C0-7714EF274214}
+ {C23563DB-FE21-48E7-A411-87A109E4A899} = {E1A5D2B7-10AF-4876-85C0-7714EF274214}
+ {1DC32255-BA1F-4D6D-A9C9-5BD5ED71CAA0} = {E1A5D2B7-10AF-4876-85C0-7714EF274214}
+ {654A027D-1364-4729-880B-144DFE1FF5BB} = {1B0918B9-65AD-4F34-A287-AF4597B27DBD}
+ {A73DF5A6-866E-4AED-9017-AA2EE86368C4} = {1B0918B9-65AD-4F34-A287-AF4597B27DBD}
+ EndGlobalSection
+ GlobalSection(ExtensibilityGlobals) = postSolution
+ SolutionGuid = {2DEAD3CC-486B-4918-A607-50B0DE7B114A}
+ EndGlobalSection
+EndGlobal
diff --git a/TensorFlow.NET.sln.DotSettings b/TensorFlow.NET.sln.DotSettings
new file mode 100644
index 000000000..aba8725cc
--- /dev/null
+++ b/TensorFlow.NET.sln.DotSettings
@@ -0,0 +1,2 @@
+
corresponds to the offset (in bytes) : 
.
+
+
+
+If we take a look at the real memory allocation in Visual Studio, below diagram helps us understand the data structure more intuitively. The strides keep track the size of every single dimension, help identify the actual offset in heap memory. The formula to calculate offset is: `offset = i * strides[0] + j * strides[1]`.
-Now let's explore how constant works.
+For example: if you want to seek the value in `[1, 1]`, you just need to calculate `1 * 3 + 1 * 1 = 4`, converted to pointer is `0x000002556B194260 + 4 = 0x000002556B194264` where has a value `05`.
-现在让我探究一下`tf.constant`是怎么工作的。
+
+Through the above diagram, we know how the data is stored in memory, and then we will look at how the data is transferred to `TensorFlow`.
+
+##### Tensor
+
+If you don't understand very well what `Tensor` is, you can go back to the chapter `Tensor` there is pretty much explanation if you skipped that chapter. Tensor is actually an NDArray that is with more than 2 dimensions.
+
+TensorFlow will decide whether to copy the data or use the same pointer. Normally speaking, it's more safe whenever you copy data for the following process, especially in interoperating between .NET runtime and C++ runtime that they all have their own garbage collection (GC) mechanism, application will crash if someone access a block of destroyed memory. `TF_STRING` and `TF_RESOURCE` tensors have a different representation in `TF_Tensor` than they do in `tensorflow::Tensor`. Other types have the same representation, so copy only if it is safe to do so.
+
+
+
+Before tensorflow is creating the `TF_Tensor`, it checks the shape and data size. If the size doesn't match, it will return `nullptr` pointer.
+
+##### Get the data of Tensor
+
+For `eager` mode, it's pretty simple to view the actual value in a `tensor`.
+
+```csharp
+var data = tensor.numpy()
+```
+The `data` will be a `ndarray` variable.
##### Other functions to create a Constant
diff --git a/docs/source/ConvolutionNeuralNetwork.md b/docs/source/ConvolutionNeuralNetwork.md
new file mode 100644
index 000000000..6b47c9d8d
--- /dev/null
+++ b/docs/source/ConvolutionNeuralNetwork.md
@@ -0,0 +1,350 @@
+# Chapter. Convolution Neural Network
+
+In this chapter, we'll implement a simple Convolutional Neural Network model. We'll implement this model to classify MNIST dataset.
+
+
+
+The structure of the neural network we're going to build is as follows. The hand-written digits images of the MNIST data which has 10 classes (from 0 to 9). The network is with 2 convolutional layers followed by 2 full-connected layers at the end.
+
+
+
+Get started with the implementation:
+
+1. **Prepare data**
+
+ MNIST is dataset of handwritten digits which contains 55,000 examples for training, 5,000 examples for validation and 10,000 example for testing. The digits have been size-normalized and centered in a fixed-size image (28 x 28 pixels) with values from 0 and 1.Each image has been flattened and converted to a 1-D array of 784 features. It's also kind of benchmark of datasets for deep learning.
+
+ 
+
+ We define some variables makes it easier to modify them later.
+
+ ```csharp
+ using System;
+ using NumSharp;
+ using Tensorflow;
+ using TensorFlowNET.Examples.Utility;
+ using static Tensorflow.Python;
+ ```
+
+ ```csharp
+ const int img_h = 28;
+ const int img_w = 28;
+ int n_classes = 10; // Number of classes, one class per digit
+ int n_channels = 1;
+ ```
+
+ We'll write the function which automatically loads the MNIST data and returns it in our desired shape and format. There is an MNIST data helper to make life easier.
+
+ ```csharp
+ Datasets mnist;
+ public void PrepareData()
+ {
+ mnist = MnistDataSet.read_data_sets("mnist", one_hot: true);
+ }
+ ```
+
+ Other than a function for loading the images and corresponding labels, we still need three more functions:
+
+ **reformat:** reformats the data to the format acceptable for convolutional layer.
+
+ ```csharp
+ private (NDArray, NDArray) Reformat(NDArray x, NDArray y)
+ {
+ var (img_size, num_ch, num_class) = (np.sqrt(x.shape[1]), 1, len(np.unique
##### How to create a Tensor?
-There are many ways to initialize a Tensor object in TF.NET. It can be initialized from a scalar, string, matrix or tensor.
-
-在TF.NET中有很多种方式可以初始化一个Tensor对象。它可以从一个标量，字符串，矩阵或张量来初始化。
+There are many ways to initialize a Tensor object in TF.NET. It can be initialized from a scalar, string, matrix or tensor. But the best way to create a Tensor is using high level APIs like `tf.constant`, `tf.zeros` and `tf.ones`. We'll talk about constant more detail in next chapter.
```csharp
// Create a tensor holds a scalar value
@@ -36,15 +32,9 @@ Console.WriteLine($"t1: {t1}, t2: {t2}, t3: {t3}");
##### Data Structure of Tensor
-
-
-
-
TF uses column major order. If we use NumSharp to generate a 2 x 3 matrix, if we access the data from 0 to 5 in order, we won't get a number of 1-6, but we get the order of 1, 4, 2, 5, 3, 6. a set of numbers.
-TF 采用的是按列存储模式，如果我们用NumSharp产生一个2 X 3的矩阵，如果按顺序从0到5访问数据的话，是不会得到1-6的数字的，而是得到1，4， 2， 5， 3， 6这个顺序的一组数字。
-
-```cs
+```csharp
// Generate a matrix:[[1, 2, 3], [4, 5, 6]]
var nd = np.array(1f, 2f, 3f, 4f, 5f, 6f).reshape(2, 3);
// The index will be 0 2 4 1 3 5, it's column-major order.
@@ -55,3 +45,8 @@ var nd = np.array(1f, 2f, 3f, 4f, 5f, 6f).reshape(2, 3);
+
+##### Index/ Slice of Tensor
+
+Tensor element can be accessed by `index` and `slice` related operations. Through some high level APIs, we can easily access specific dimension's data.
+
diff --git a/docs/source/Variable.md b/docs/source/Variable.md
index 48eaa9aef..c4f6a6af6 100644
--- a/docs/source/Variable.md
+++ b/docs/source/Variable.md
@@ -2,7 +2,7 @@
The variables in TensorFlow are mainly used to represent variable parameter values in the machine learning model. Variables can be initialized by the `tf.Variable` function. During the graph computation the variables are modified by other operations. Variables exist in the session, as long as they are in the same session, other computing nodes on the network can access the same variable value. Variables use lazy loading and will only request memory space when they are used.
-TensorFlow中变量主要用来表示机器学习模型中的可变参数值，变量通过可以通过`tf.Variable` 类进行初始化。在图运行过程中，通过各种操作对变量进行修改。变量存在于会话当中，只要是在同一个会话里，网络上的其它计算结节都可以访问到相同的变量值。变量采用延迟加载的方式，只有使用的时候才会申请内存空间。
+
```csharp
var x = tf.Variable(10, name: "x");
@@ -16,4 +16,3 @@ using (var session = tf.Session())
The above code first creates a variable operation, initializes the variable, then runs the session, and finally gets the result. This code is very simple, but it shows the complete process how TensorFlow operates on variables. When creating a variable, you pass a `tensor` as the initial value to the function `Variable()`. TensorFlow provides a series of operators to initialize the tensor, the initial value is a constant or a random value.
-以上代码先创建变量操作，初始化变量，再运行会话，最后得到结果。这段代码非常简单，但是它体现了整个TensorFlow对变量操作的完整流程。当创建一个变量时，你将一个`张量`作为初始值传入函数`Variable()`。TensorFlow提供了一系列操作符来初始化张量，初始值是常量或是随机值。
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diff --git a/docs/source/_static/FIFOQueue-example.jpg b/docs/source/_static/FIFOQueue-example.jpg
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diff --git a/docs/source/_static/constant/n-index-formula-offset.svg b/docs/source/_static/constant/n-index-formula-offset.svg
new file mode 100644
index 000000000..6c5a3219c
--- /dev/null
+++ b/docs/source/_static/constant/n-index-formula-offset.svg
@@ -0,0 +1,41 @@
+
+
+
\ No newline at end of file
diff --git a/docs/source/_static/constant/n-index-formula.svg b/docs/source/_static/constant/n-index-formula.svg
new file mode 100644
index 000000000..5d05c06f0
--- /dev/null
+++ b/docs/source/_static/constant/n-index-formula.svg
@@ -0,0 +1,33 @@
+
+
+
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diff --git a/docs/source/_static/contiguous-block-of-memory-ndarray-example-1.png b/docs/source/_static/contiguous-block-of-memory-ndarray-example-1.png
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index 000000000..140e37716
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diff --git a/docs/source/_static/tensor-constant-ndarray.png b/docs/source/_static/tensor-constant-ndarray.png
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diff --git a/docs/source/_static/tensor-naming.png b/docs/source/_static/tensor-naming.png
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diff --git a/docs/source/index.rst b/docs/source/index.rst
index e26f8378e..61f0d752e 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -22,10 +22,13 @@ Welcome to TensorFlow.NET's documentation!
Graph
Session
Operation
+ Queue
Gradient
Train
EagerMode
LinearRegression
LogisticRegression
NearestNeighbor
- ImageRecognition
\ No newline at end of file
+ ImageRecognition
+ NeuralNetwork
+ ConvolutionNeuralNetwork
\ No newline at end of file
diff --git a/graph/InceptionV3.meta b/graph/InceptionV3.meta
index 2a11b0829..fe220cce1 100644
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diff --git a/redist/SciSharp.TensorFlow-Cpu.Redist/SciSharp.TensorFlow-Cpu.Redist.csproj b/redist/SciSharp.TensorFlow-Cpu.Redist/SciSharp.TensorFlow-Cpu.Redist.csproj
new file mode 100644
index 000000000..4d0fa1f0e
--- /dev/null
+++ b/redist/SciSharp.TensorFlow-Cpu.Redist/SciSharp.TensorFlow-Cpu.Redist.csproj
@@ -0,0 +1,63 @@
+