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‎TensorFlow.NET.sln‎

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@@ -15,8 +15,6 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Hub", "src\Te
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Datasets", "src\TensorFlowNET.Datasets\TensorFlowNET.Datasets.csproj", "{494D6CAD-2C0D-4C0B-90E2-B097DB039383}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NumSharp.Core", "..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj", "{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}"
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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Debug|Any CPU = Debug|Any CPU
@@ -99,18 +97,6 @@ Global
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{494D6CAD-2C0D-4C0B-90E2-B097DB039383}.Release|Any CPU.Build.0 = Release|Any CPU
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{494D6CAD-2C0D-4C0B-90E2-B097DB039383}.Release|x64.ActiveCfg = Release|Any CPU
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{494D6CAD-2C0D-4C0B-90E2-B097DB039383}.Release|x64.Build.0 = Release|Any CPU
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{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}.Debug|x64.ActiveCfg = Debug|x64
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{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}.Debug|x64.Build.0 = Debug|x64
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{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}.Publish|Any CPU.ActiveCfg = Publish|Any CPU
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{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}.Publish|Any CPU.Build.0 = Publish|Any CPU
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{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}.Publish|x64.ActiveCfg = Publish|x64
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{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}.Publish|x64.Build.0 = Publish|x64
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{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}.Release|Any CPU.Build.0 = Release|Any CPU
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{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}.Release|x64.ActiveCfg = Release|x64
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{9249BCC4-3FEB-4EF5-8AB9-789FFE4040B4}.Release|x64.Build.0 = Release|x64
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE

‎src/SciSharp.TensorFlow.Redist/README.md‎

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@@ -26,7 +26,7 @@ Related merged [commits](https://github.com/SciSharp/TensorFlow.NET/commit/854a5
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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.
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1. Run `dotnet pack SciSharp.TensorFlow.Redist-CPU.nupkgproj` under `src/SciSharp.TensorFlow.Redist` directory in Linux.
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2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.1.14.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json`
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1. Run `dotnet pack SciSharp.TensorFlow.Redist.nupkgproj` under `src/SciSharp.TensorFlow.Redist` directory in Linux.
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2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.1.15.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json`
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src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist-CPU.nupkgproj renamed to src/SciSharp.TensorFlow.Redist/SciSharp.TensorFlow.Redist.nupkgproj

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@@ -7,7 +7,7 @@
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<TargetArchitecture Condition="'$(TargetArchitecture)' == ''">x64</TargetArchitecture>
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<TargetFramework>netstandard2.0</TargetFramework>
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<TensorFlowVersion>1.14.0</TensorFlowVersion>
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<TensorFlowVersion>1.15.0</TensorFlowVersion>
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<TensorFlowMajorVersion>1</TensorFlowMajorVersion>
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<PackageAssetsPath>$(BinDir)packages\</PackageAssetsPath>
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<TensorFlowConfig Include="windows"
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FileExtension=".zip"
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FilesFromArchive="lib\tensorflow.dll;
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include\tensorflow\c\LICENSE"
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LICENSE"
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Runtime="win-x64"/>
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<TensorFlowConfig Condition="'$(OS)' != 'Windows_NT'"
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Include="linux"
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FileExtension=".tar.gz"
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FilesFromArchive="lib\libtensorflow.so;
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lib\libtensorflow_framework.so.$(TensorFlowMajorVersion);
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include\tensorflow\c\LICENSE"
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LICENSE"
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Runtime="linux-x64" />
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<TensorFlowConfig Condition="'$(OS)' != 'Windows_NT'"
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Include="darwin" FileExtension=".tar.gz"
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FilesFromArchive="lib\libtensorflow.dylib;
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lib\libtensorflow_framework.$(TensorFlowMajorVersion).dylib;
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include\tensorflow\c\LICENSE"
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LICENSE"
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Runtime="osx-x64" />
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<AdditionalDownloadFile Include="https://raw.githubusercontent.com/tensorflow/tensorflow/master/LICENSE"

‎src/TensorFlowNET.Core/APIs/tf.train.cs‎

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@@ -34,6 +34,9 @@ public RefVariable get_global_step(Graph graph)
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public Optimizer GradientDescentOptimizer(float learning_rate)
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=> new GradientDescentOptimizer(learning_rate);
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public Optimizer GradientDescentOptimizer(Tensor learning_rate)
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=> new GradientDescentOptimizer(learning_rate);
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public Optimizer AdamOptimizer(float learning_rate, string name = "Adam")
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=> new AdamOptimizer(learning_rate, name: name);
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‎src/TensorFlowNET.Core/Binding.Util.cs‎

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@@ -129,6 +129,7 @@ public static void tf_with<T>(T py, Action<T> action) where T : IObjectLife
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}
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}
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[DebuggerStepThrough]
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[DebuggerNonUserCode()] // with "Just My Code" enabled this lets the debugger break at the origin of the exception
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public static TOut tf_with<TIn, TOut>(TIn py, Func<TIn, TOut> action) where TIn : IObjectLife
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{

‎src/TensorFlowNET.Core/Framework/meta_graph.cs‎

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}
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break;
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default:
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Console.WriteLine("import_scoped_meta_graph_with_return_elements");
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Console.WriteLine($"import_scoped_meta_graph_with_return_elements {col.Key}");
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continue;
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}
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}

‎src/TensorFlowNET.Core/Framework/smart_module.cs‎

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******************************************************************************/
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using System;
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using static Tensorflow.Binding;
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namespace Tensorflow.Framework
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{

‎src/TensorFlowNET.Core/Graphs/Graph.cs‎

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/// then create a TensorFlow session to run parts of the graph across a set of local and remote devices.
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/// </summary>
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/// <remarks>https://www.tensorflow.org/guide/graphs <br></br>https://www.tensorflow.org/api_docs/python/tf/Graph</remarks>
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public partial class Graph : DisposableObject, IEnumerable<Operation>
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public partial class Graph : DisposableObject//, IEnumerable<Operation>
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{
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private Dictionary<int, ITensorOrOperation> _nodes_by_id;
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public Dictionary<string, ITensorOrOperation> _nodes_by_name;
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if (inputs == null)
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inputs = new Tensor[0];
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foreach ((int idx, Tensor a) in enumerate(inputs))
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{
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}
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if (String.IsNullOrEmpty(name))
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if (string.IsNullOrEmpty(name))
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name = op_type;
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// If a names ends with a '/' it is a "name scope" and we use it as-is,
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// after removing the trailing '/'.
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name = name.EndsWith("/") ? ops.name_from_scope_name(name) : unique_name(name);
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var node_def = ops._NodeDef(op_type, name, device: "", attrs: attrs);
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if (name.Contains("define_loss/bigger_box_loss/mul_13"))
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{
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}
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var input_ops = inputs.Select(x => x.op).ToArray();
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var control_inputs = _control_dependencies_for_inputs(input_ops);
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return debugString;*/
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}
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private IEnumerable<Operation> GetEnumerable()
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/*private IEnumerable<Operation> GetEnumerable()
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=> c_api_util.tf_operations(this);
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IEnumerator<Operation> IEnumerable<Operation>.GetEnumerator()
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=> GetEnumerable().GetEnumerator();
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IEnumerator IEnumerable.GetEnumerator()
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=> throw new NotImplementedException();
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=> throw new NotImplementedException();*/
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public static implicit operator IntPtr(Graph graph)
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{

‎src/TensorFlowNET.Core/Keras/Engine/Sequential.cs‎

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{
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// Instantiate an input layer.
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var x = keras.layers.Input(
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batch_shape: batch_shape,
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dtype: dtype,
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name: layer.name + "_input");
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batch_shape: batch_shape,
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dtype: dtype,
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name: layer.name + "_input");
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// This will build the current layer
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// and create the node connecting the current layer
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if (set_inputs)
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{
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// If an input layer (placeholder) is available.
74-
// outputs = layer._inbound_nodes;
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// outputs = layer.inbound_nodes;
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}
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}

‎src/TensorFlowNET.Core/Keras/Layers/Layer.cs‎

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VariableScope scope = null)
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{
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var input_list = inputs;
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var input = inputs[0];
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Tensor outputs = null;
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// We will attempt to build a TF graph if & only if all inputs are symbolic.
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_maybe_build(inputs[0]);
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outputs = call(inputs[0], training: training);
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(input, outputs) = _set_connectivity_metadata_(input, outputs);
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_handle_activity_regularization(inputs[0], outputs);
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_set_mask_metadata(inputs[0], outputs, null);
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});
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private (Tensor, Tensor) _set_connectivity_metadata_(Tensor inputs, Tensor outputs)
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{
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//_add_inbound_node(input_tensors: inputs, output_tensors: outputs);
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return (inputs, outputs);
156+
}
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private void _handle_activity_regularization(Tensor inputs, Tensor outputs)
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//if(_activity_regularizer != null)
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overwrite: true,
225233
initializer: initializer,
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trainable: trainable.Value);
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backend.track_variable(variable);
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//backend.track_variable(variable);
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_trainable_weights.Add(variable);
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return variable;

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