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extend gradient function capability.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,2 +1,15 @@ | ||
| # Chapter. Gradient | ||
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| ### Register custom gradient function | ||
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| TF.NET is extensible which can be added custom gradient function. | ||
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| ```csharp | ||
| // define gradient function | ||
| ops.RegisterGradientFunction("ConcatV2", (oper, out_grads) => | ||
| { | ||
| var grad = grads[0]; | ||
| return new Tensor[]{ }; | ||
| }); | ||
| ``` | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,16 @@ | ||
| using System; | ||
| using System.Collections.Generic; | ||
| using System.Text; | ||
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| namespace Tensorflow.Gradients | ||
| { | ||
| public class RegisterGradient : Attribute | ||
| { | ||
| public string Name { get; set; } | ||
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| public RegisterGradient(string name) | ||
| { | ||
| Name = name; | ||
| } | ||
| } | ||
| } |
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105 changes: 45 additions & 60 deletions
105
src/TensorFlowNET.Core/Gradients/ops.gradient_function_mapping.cs
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,80 +1,65 @@ | ||
| using System; | ||
| using System.Collections.Generic; | ||
| using System.Linq; | ||
| using System.Reflection; | ||
| using System.Text; | ||
| using Tensorflow.Gradients; | ||
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| namespace Tensorflow | ||
| { | ||
| public partial class ops | ||
| { | ||
| static Dictionary<string, Func<Operation, Tensor[], Tensor[]>> gradientFunctions = null; | ||
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| /// <summary> | ||
| /// Regiter new gradient function | ||
| /// </summary> | ||
| /// <param name="name">operation type</param> | ||
| /// <param name="func">function delegate</param> | ||
| public static void RegisterGradientFunction(string name, Func<Operation, Tensor[], Tensor[]> func) | ||
| { | ||
| if(gradientFunctions == null) | ||
| gradientFunctions = new Dictionary<string, Func<Operation, Tensor[], Tensor[]>>(); | ||
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| gradientFunctions[name] = func; | ||
| } | ||
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| public static Func<Operation, Tensor[], Tensor[]> get_gradient_function(Operation op) | ||
| { | ||
| if (op.inputs == null) return null; | ||
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| // map tensorflow\python\ops\math_grad.py | ||
| return (oper, out_grads) => | ||
| if (gradientFunctions == null) | ||
| { | ||
| // Console.WriteLine($"get_gradient_function: {oper.type} '{oper.name}'"); | ||
| gradientFunctions = new Dictionary<string, Func<Operation, Tensor[], Tensor[]>>(); | ||
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| switch (oper.type) | ||
| var gradGroups = Assembly.GetExecutingAssembly() | ||
| .GetTypes() | ||
| .Where(x => x.GetCustomAttribute<RegisterGradient>() != null) | ||
| .ToArray(); | ||
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| foreach (var g in gradGroups) | ||
| { | ||
| case "Add": | ||
| return math_grad._AddGrad(oper, out_grads); | ||
| case "BiasAdd": | ||
| return nn_grad._BiasAddGrad(oper, out_grads); | ||
| case "ConcatV2": | ||
| return array_grad._ConcatGradV2(oper, out_grads); | ||
| case "DivNoNan": | ||
| return math_grad._DivNoNanGrad(oper, out_grads); | ||
| case "Exp": | ||
| return math_grad._ExpGrad(oper, out_grads); | ||
| case "Identity": | ||
| return math_grad._IdGrad(oper, out_grads); | ||
| case "Log": | ||
| return math_grad._LogGrad(oper, out_grads); | ||
| case "MatMul": | ||
| return math_grad._MatMulGrad(oper, out_grads); | ||
| case "Merge": | ||
| return control_flow_grad._MergeGrad(oper, out_grads); | ||
| case "Mul": | ||
| return math_grad._MulGrad(oper, out_grads); | ||
| case "Mean": | ||
| return math_grad._MeanGrad(oper, out_grads); | ||
| case "Neg": | ||
| return math_grad._NegGrad(oper, out_grads); | ||
| case "Sum": | ||
| return math_grad._SumGrad(oper, out_grads); | ||
| case "Sub": | ||
| return math_grad._SubGrad(oper, out_grads); | ||
| case "Pow": | ||
| return math_grad._PowGrad(oper, out_grads); | ||
| case "RealDiv": | ||
| return math_grad._RealDivGrad(oper, out_grads); | ||
| case "Reshape": | ||
| return array_grad._ReshapeGrad(oper, out_grads); | ||
| case "Relu": | ||
| return nn_grad._ReluGrad(oper, out_grads); | ||
| case "Sigmoid": | ||
| return math_grad._SigmoidGrad(oper, out_grads); | ||
| case "Square": | ||
| return math_grad._SquareGrad(oper, out_grads); | ||
| case "Squeeze": | ||
| return array_grad._SqueezeGrad(oper, out_grads); | ||
| case "Softmax": | ||
| return nn_grad._SoftmaxGrad(oper, out_grads); | ||
| case "SoftmaxCrossEntropyWithLogits": | ||
| return nn_grad._SoftmaxCrossEntropyWithLogitsGrad(oper, out_grads); | ||
| case "SparseSoftmaxCrossEntropyWithLogits": | ||
| return nn_grad._SparseSoftmaxCrossEntropyWithLogitsGrad(oper, out_grads); | ||
| case "Transpose": | ||
| return array_grad._TransposeGrad(oper, out_grads); | ||
| case "TopK": | ||
| case "TopKV2": | ||
| return nn_grad._TopKGrad(oper, out_grads); | ||
| default: | ||
| throw new NotImplementedException($"get_gradient_function {oper.type}"); | ||
| var methods = g.GetMethods().Where(x => x.GetCustomAttribute<RegisterGradient>() != null) | ||
| .ToArray(); | ||
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| foreach (var m in methods) | ||
| { | ||
| RegisterGradientFunction(m.GetCustomAttribute<RegisterGradient>().Name, | ||
| (oper, out_grads) => | ||
| g.InvokeMember(m.Name, | ||
| BindingFlags.InvokeMethod, | ||
| null, | ||
| null, | ||
| args: new object[] { oper, out_grads }) as Tensor[] | ||
| ); | ||
| } | ||
| } | ||
| }; | ||
| } | ||
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| if (!gradientFunctions.ContainsKey(op.type)) | ||
| throw new NotImplementedException($"can't get graident function through get_gradient_function {op.type}"); | ||
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| return gradientFunctions[op.type]; | ||
| } | ||
| } | ||
| } |
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