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cesarsouza
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migueldeicazaGH-180: Missing ReduceProd. (migueldeicaza#181)
1 parent 8d9fb12 commit 42c2e76

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‎TensorFlowSharp/OperationsExtras.cs‎

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@@ -63,6 +63,28 @@ public TFOutput ReduceSum (TFOutput input, TFOutput? axis = null, bool? keep_dim
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return Sum (input, this.ReduceDims (input, axis), keep_dims, operName);
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}
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/// <summary>
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/// Computes the product of elements across dimensions of a tensor.
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/// </summary>
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/// <returns>The reduced tensor.</returns>
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/// <param name="input">The tensor to reduce. Should have numeric type.</param>
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/// <param name="axis">The dimensions to reduce. If not se (the default), reduces all dimensions.</param>
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/// <param name="keep_dims">If set to <c>true</c> retains reduced dimensions with length 1.</param>
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/// <param name="operName">A name for the operation, optional.</param>
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/// <remarks>
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/// Reduces input_tensor along the dimensions given in axis.
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/// Unless keep_dims is true, the rank of the tensor is reduced by 1 for each
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/// entry in axis. If keep_dims is true, the reduced dimensions
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/// are retained with length 1.
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///
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/// If axis has no entries, all dimensions are reduced, and a
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/// tensor with a single element is returned.
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/// </remarks>
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public TFOutput ReduceProd (TFOutput input, TFOutput? axis = null, bool? keep_dims = false, string operName = null)
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{
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return Prod (input, this.ReduceDims (input, axis), keep_dims, operName);
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}
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/// <summary>
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/// Computes the mean of elements across dimensions of a tensor.
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/// </summary>
@@ -89,6 +111,7 @@ public TFOutput ReduceMean (TFOutput input, TFOutput? axis = null, bool? keep_di
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return this.Mean (input, this.ReduceDims (input, axis), keep_dims, operName);
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}
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// Helper method to create a variable and track it.
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Variable MakeVariable (TFOutput initialValue, bool trainable, string operName)
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{

‎TensorFlowSharp/Tensorflow.cs‎

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@@ -3454,6 +3454,16 @@ public TFTensor AsTensor ()
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Array.Copy (right.dims, 0, full, left.dims.Length, right.dims.Length);
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return new TFShape (full);
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}
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/// <summary>
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/// Performs an implicit conversion from <see cref="TFShape"/> to <see cref="TFTensor"/>.
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/// </summary>
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/// <param name="shape">The shape.</param>
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/// <returns>The result of the conversion.</returns>
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public static implicit operator TFTensor (TFShape shape)
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{
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return shape.AsTensor ();
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}
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}
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‎tests/TensorFlowSharp.Tests.CSharp/MathTests.cs‎

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@@ -101,5 +101,84 @@ public void Should_SigmoidCrossEntropyWithLogits (double [] labels, double [] lo
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}
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}
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private static IEnumerable<object []> reduceProdData ()
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{
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// Example from https://www.tensorflow.org/api_docs/python/tf/reduce_mean but adapted to return prod
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var x = new double [,] { { 1, 1 },
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{ 2, 2 } };
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yield return new object [] { x, null, 4.0 };
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yield return new object [] { x, 0, new double [] { 2, 2 } };
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yield return new object [] { x, 1, new double [] { 1, 4 } };
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}
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[Theory]
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[MemberData (nameof (reduceProdData))]
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public void Should_ReduceProd (double [,] input, int? axis, object expected)
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{
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using (var graph = new TFGraph ())
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using (var session = new TFSession (graph)) {
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var tinput = graph.Placeholder (TFDataType.Double, new TFShape (2, 2));
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TFTensor [] result;
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if (axis != null) {
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var taxis = graph.Const (axis.Value);
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TFOutput y = graph.ReduceProd (tinput, taxis);
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result = session.Run (new [] { tinput, taxis }, new TFTensor [] { input, axis }, new [] { y });
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double [] actual = (double [])result [0].GetValue ();
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TestUtils.MatrixEqual (expected, actual, precision: 8);
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} else {
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TFOutput y = graph.ReduceProd (tinput, axis: null);
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result = session.Run (new [] { tinput }, new TFTensor [] { input }, new [] { y });
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double actual = (double)result [0].GetValue ();
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TestUtils.MatrixEqual (expected, actual, precision: 8);
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}
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}
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}
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private static IEnumerable<object []> reduceProdData2 ()
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{
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yield return new object [] { null, 170170.0 };
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yield return new object [] { -3, new [] { 1.0 } };
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yield return new object [] { -2, new [] { 22.0, 65.0, 119.0 } };
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yield return new object [] { -1, new [] { 70.0, 2431.0 } };
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yield return new object [] { 0, new [] { 22.0, 65.0, 119.0 } };
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yield return new object [] { 1, new [] { 70.0, 2431.0 } };
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yield return new object [] { 2, new [] { 1.0 } };
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yield return new object [] { 3, new [] { 1.0 } };
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}
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[Theory]
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[MemberData (nameof (reduceProdData2))]
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public void Should_ReduceProd2 (int? axis, object expected)
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{
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using (var graph = new TFGraph ())
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using (var session = new TFSession (graph)) {
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double [,] test = {
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{ 2, 5, 7 },
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{ 11, 13, 17 },
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};
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var x = graph.Const (test);
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if (axis == null || axis >= -2 && axis < 2) {
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TFOutput y = graph.ReduceProd (x, axis: axis == null ? (TFOutput?)null : graph.Const(axis));
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TFTensor [] result = session.Run (new TFOutput [] { }, new TFTensor [] { }, new [] { y });
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object actual = result [0].GetValue ();
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TestUtils.MatrixEqual (expected, actual, precision: 8);
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} else {
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Assert.Throws<TFException> (() => {
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TFOutput y = graph.ReduceProd (x, axis: axis == null ? (TFOutput?)null : graph.Const (axis));
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session.Run (new TFOutput [] { }, new TFTensor [] { }, new [] { y });
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});
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}
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}
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}
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}
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}

‎tests/TensorFlowSharp.Tests.CSharp/ShapeTests.cs‎

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@@ -0,0 +1,26 @@
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using System;
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using System.Collections.Generic;
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using TensorFlow;
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using Xunit;
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namespace TensorFlowSharp.Tests.CSharp
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{
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public class ShapeTests
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{
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[Fact]
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public void Should_ShapeAutomaticallyConvertToTensor ()
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{
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using (var graph = new TFGraph ())
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using (var session = new TFSession (graph)) {
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var x = graph.Const (new TFShape(2, 3));
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TFTensor [] result = session.Run (new TFOutput [] { }, new TFTensor [] { }, new TFOutput [] { x });
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int[] actual = (int[])result [0].GetValue ();
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Assert.Equal (new [] { 2, 3 }, actual);
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}
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}
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}
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}

‎tests/TensorFlowSharp.Tests.CSharp/TensorFlowSharp.Tests.CSharp.csproj‎

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@@ -66,6 +66,7 @@
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<Compile Include="CondTests.cs" />
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<Compile Include="GradientTests.cs" />
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<Compile Include="ArrayTests.cs" />
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<Compile Include="ShapeTests.cs" />
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<Compile Include="TensorTests.cs" />
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<Compile Include="ClipTests.cs" />
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<Compile Include="BitwiseOperationTests.cs" />

‎tests/TensorFlowSharp.Tests.CSharp/TestUtils.cs‎

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@@ -56,6 +56,8 @@ public static void MatrixEqual (object expected, object actual, int precision)
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Assert.Equal ((double)expected, (double)actual, precision: precision);
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} else if (expectedType == typeof (float)) {
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Assert.Equal ((float)expected, (float)actual, precision: precision);
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} else if (expectedType == typeof (int)) {
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Assert.Equal ((int)expected, (int)actual);
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} else {
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Assert.True (Object.Equals (expected, actual));
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}

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