diff --git a/src/TensorFlowNET.Core/APIs/tf.math.cs b/src/TensorFlowNET.Core/APIs/tf.math.cs
index c7aa46704..83653c8bb 100644
--- a/src/TensorFlowNET.Core/APIs/tf.math.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.math.cs
@@ -1,5 +1,5 @@
/*****************************************************************************
- Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
+ 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.
@@ -57,7 +57,7 @@ public Tensor softplus(Tensor features, string name = null)
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.
///
@@ -93,6 +93,16 @@ public Tensor in_top_k(Tensor predictions, Tensor targets, int k, string name =
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)
@@ -537,7 +547,7 @@ public Tensor reduce_prod(Tensor input_tensor, Axis? axis = null, bool keepdims
public Tensor reduce_sum(Tensor input, Axis? axis = null, Axis? reduction_indices = null,
bool keepdims = false, string name = null)
{
- if(keepdims)
+ 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));
@@ -585,5 +595,7 @@ 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);
}
}
diff --git a/src/TensorFlowNET.Core/Operations/gen_ops.cs b/src/TensorFlowNET.Core/Operations/gen_ops.cs
index 26a9b5be8..c9693f055 100644
--- a/src/TensorFlowNET.Core/Operations/gen_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/gen_ops.cs
@@ -730,12 +730,7 @@ public static (Tensor sampled_candidates, Tensor true_expected_count, Tensor sam
///
public static Tensor angle(Tensor input, TF_DataType? Tout = null, string name = "Angle")
{
- var dict = new Dictionary();
- dict["input"] = input;
- if (Tout.HasValue)
- dict["Tout"] = Tout.Value;
- var op = tf.OpDefLib._apply_op_helper("Angle", name: name, keywords: dict);
- return op.output;
+ return tf.Context.ExecuteOp("Angle", name, new ExecuteOpArgs(input).SetAttributes(new { Tout = Tout }));
}
///
@@ -4976,15 +4971,14 @@ public static Tensor compare_and_bitpack(Tensor input, Tensor threshold, string
/// tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]]
///
///
- public static Tensor complex(Tensor real, Tensor imag, TF_DataType? Tout = null, string name = "Complex")
+ public static Tensor complex(Tensor real, Tensor imag, TF_DataType? a_Tout = null, string name = "Complex")
{
- var dict = new Dictionary();
- dict["real"] = real;
- dict["imag"] = imag;
- if (Tout.HasValue)
- dict["Tout"] = Tout.Value;
- var op = tf.OpDefLib._apply_op_helper("Complex", name: name, keywords: dict);
- return op.output;
+ TF_DataType Tin = real.GetDataType();
+ if (a_Tout is null)
+ {
+ a_Tout = (Tin == TF_DataType.TF_DOUBLE)? TF_DataType.TF_COMPLEX128: TF_DataType.TF_COMPLEX64;
+ }
+ return tf.Context.ExecuteOp("Complex", name, new ExecuteOpArgs(real, imag).SetAttributes(new { T=Tin, Tout=a_Tout }));
}
///
@@ -5008,12 +5002,7 @@ public static Tensor complex(Tensor real, Tensor imag, TF_DataType? Tout = null,
///
public static Tensor complex_abs(Tensor x, TF_DataType? Tout = null, string name = "ComplexAbs")
{
- var dict = new Dictionary();
- dict["x"] = x;
- if (Tout.HasValue)
- dict["Tout"] = Tout.Value;
- var op = tf.OpDefLib._apply_op_helper("ComplexAbs", name: name, keywords: dict);
- return op.output;
+ return tf.Context.ExecuteOp("ComplexAbs", name, new ExecuteOpArgs(x).SetAttributes(new { Tout = Tout }));
}
///
@@ -5313,10 +5302,7 @@ public static Tensor configure_distributed_t_p_u(string embedding_config = null,
///
public static Tensor conj(Tensor input, string name = "Conj")
{
- var dict = new Dictionary();
- dict["input"] = input;
- var op = tf.OpDefLib._apply_op_helper("Conj", name: name, keywords: dict);
- return op.output;
+ return tf.Context.ExecuteOp("Conj", name, new ExecuteOpArgs(new object[] { input }));
}
///
@@ -13325,14 +13311,12 @@ public static Tensor igammac(Tensor a, Tensor x, string name = "Igammac")
/// tf.imag(input) ==> [4.75, 5.75]
///
///
- public static Tensor imag(Tensor input, TF_DataType? Tout = null, string name = "Imag")
+ public static Tensor imag(Tensor input, TF_DataType? a_Tout = null, string name = "Imag")
{
- var dict = new Dictionary();
- dict["input"] = input;
- if (Tout.HasValue)
- dict["Tout"] = Tout.Value;
- var op = tf.OpDefLib._apply_op_helper("Imag", name: name, keywords: dict);
- return op.output;
+ TF_DataType Tin = input.GetDataType();
+ return tf.Context.ExecuteOp("Imag", name, new ExecuteOpArgs(input).SetAttributes(new { T = Tin, Tout = a_Tout }));
+
+ // return tf.Context.ExecuteOp("Imag", name, new ExecuteOpArgs(new object[] { input }));
}
///
@@ -23863,14 +23847,12 @@ public static Tensor reader_serialize_state_v2(Tensor reader_handle, string name
/// tf.real(input) ==> [-2.25, 3.25]
///
///
- public static Tensor real(Tensor input, TF_DataType? Tout = null, string name = "Real")
+ public static Tensor real(Tensor input, TF_DataType? a_Tout = null, string name = "Real")
{
- var dict = new Dictionary();
- dict["input"] = input;
- if (Tout.HasValue)
- dict["Tout"] = Tout.Value;
- var op = tf.OpDefLib._apply_op_helper("Real", name: name, keywords: dict);
- return op.output;
+ TF_DataType Tin = input.GetDataType();
+ return tf.Context.ExecuteOp("Real", name, new ExecuteOpArgs(input).SetAttributes(new { T = Tin, Tout = a_Tout }));
+
+// return tf.Context.ExecuteOp("Real", name, new ExecuteOpArgs(new object[] {input}));
}
///
diff --git a/src/TensorFlowNET.Core/Operations/math_ops.cs b/src/TensorFlowNET.Core/Operations/math_ops.cs
index 36f7db794..a89e7a22c 100644
--- a/src/TensorFlowNET.Core/Operations/math_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/math_ops.cs
@@ -20,6 +20,7 @@
using System.Linq;
using Tensorflow.Framework;
using static Tensorflow.Binding;
+using Tensorflow.Operations;
namespace Tensorflow
{
@@ -35,8 +36,9 @@ public static Tensor abs(Tensor x, string name = null)
name = scope;
x = ops.convert_to_tensor(x, name: "x");
if (x.dtype.is_complex())
- throw new NotImplementedException("math_ops.abs for dtype.is_complex");
- //return gen_math_ops.complex_abs(x, Tout: x.dtype.real_dtype, name: name);
+ {
+ return gen_ops.complex_abs(x, Tout: x.dtype.real_dtype(), name: name);
+ }
return gen_math_ops._abs(x, name: name);
});
}
diff --git a/test/TensorFlowNET.Graph.UnitTest/ComplexTest.cs b/test/TensorFlowNET.Graph.UnitTest/ComplexTest.cs
new file mode 100644
index 000000000..a57ec9291
--- /dev/null
+++ b/test/TensorFlowNET.Graph.UnitTest/ComplexTest.cs
@@ -0,0 +1,202 @@
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Tensorflow.NumPy;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using Tensorflow;
+using static Tensorflow.Binding;
+using Buffer = Tensorflow.Buffer;
+using TensorFlowNET.Keras.UnitTest;
+
+namespace TensorFlowNET.UnitTest.Basics
+{
+ [TestClass]
+ public class ComplexTest : EagerModeTestBase
+ {
+ // Tests for Complex128
+
+ [TestMethod]
+ public void complex128_basic()
+ {
+ double[] d_real = new double[] { 1.0, 2.0, 3.0, 4.0 };
+ double[] d_imag = new double[] { -1.0, -3.0, 5.0, 7.0 };
+
+ Tensor t_real = tf.constant(d_real, dtype:TF_DataType.TF_DOUBLE);
+ Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_DOUBLE);
+
+ Tensor t_complex = tf.complex(t_real, t_imag);
+
+ Tensor t_real_result = tf.math.real(t_complex);
+ Tensor t_imag_result = tf.math.imag(t_complex);
+
+ NDArray n_real_result = t_real_result.numpy();
+ NDArray n_imag_result = t_imag_result.numpy();
+
+ double[] d_real_result =n_real_result.ToArray();
+ double[] d_imag_result = n_imag_result.ToArray();
+
+ Assert.IsTrue(base.Equal(d_real_result, d_real));
+ Assert.IsTrue(base.Equal(d_imag_result, d_imag));
+ }
+ [TestMethod]
+ public void complex128_abs()
+ {
+ tf.enable_eager_execution();
+
+ double[] d_real = new double[] { -3.0, -5.0, 8.0, 7.0 };
+ double[] d_imag = new double[] { -4.0, 12.0, -15.0, 24.0 };
+
+ double[] d_abs = new double[] { 5.0, 13.0, 17.0, 25.0 };
+
+ Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_DOUBLE);
+ Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_DOUBLE);
+
+ Tensor t_complex = tf.complex(t_real, t_imag);
+
+ Tensor t_abs_result = tf.abs(t_complex);
+
+ double[] d_abs_result = t_abs_result.numpy().ToArray();
+ Assert.IsTrue(base.Equal(d_abs_result, d_abs));
+ }
+ [TestMethod]
+ public void complex128_conj()
+ {
+ double[] d_real = new double[] { -3.0, -5.0, 8.0, 7.0 };
+ double[] d_imag = new double[] { -4.0, 12.0, -15.0, 24.0 };
+
+ double[] d_real_expected = new double[] { -3.0, -5.0, 8.0, 7.0 };
+ double[] d_imag_expected = new double[] { 4.0, -12.0, 15.0, -24.0 };
+
+ Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_DOUBLE);
+ Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_DOUBLE);
+
+ Tensor t_complex = tf.complex(t_real, t_imag, TF_DataType.TF_COMPLEX128);
+
+ Tensor t_result = tf.math.conj(t_complex);
+
+ NDArray n_real_result = tf.math.real(t_result).numpy();
+ NDArray n_imag_result = tf.math.imag(t_result).numpy();
+
+ double[] d_real_result = n_real_result.ToArray();
+ double[] d_imag_result = n_imag_result.ToArray();
+
+ Assert.IsTrue(base.Equal(d_real_result, d_real_expected));
+ Assert.IsTrue(base.Equal(d_imag_result, d_imag_expected));
+ }
+ [TestMethod]
+ public void complex128_angle()
+ {
+ double[] d_real = new double[] { 0.0, 1.0, -1.0, 0.0 };
+ double[] d_imag = new double[] { 1.0, 0.0, -2.0, -3.0 };
+
+ double[] d_expected = new double[] { 1.5707963267948966, 0, -2.0344439357957027, -1.5707963267948966 };
+
+ Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_DOUBLE);
+ Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_DOUBLE);
+
+ Tensor t_complex = tf.complex(t_real, t_imag, TF_DataType.TF_COMPLEX128);
+
+ Tensor t_result = tf.math.angle(t_complex);
+
+ NDArray n_result = t_result.numpy();
+
+ double[] d_result = n_result.ToArray();
+
+ Assert.IsTrue(base.Equal(d_result, d_expected));
+ }
+
+ // Tests for Complex64
+ [TestMethod]
+ public void complex64_basic()
+ {
+ tf.init_scope();
+ float[] d_real = new float[] { 1.0f, 2.0f, 3.0f, 4.0f };
+ float[] d_imag = new float[] { -1.0f, -3.0f, 5.0f, 7.0f };
+
+ Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_FLOAT);
+ Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_FLOAT);
+
+ Tensor t_complex = tf.complex(t_real, t_imag);
+
+ Tensor t_real_result = tf.math.real(t_complex);
+ Tensor t_imag_result = tf.math.imag(t_complex);
+
+ // Convert the EagerTensors to NumPy arrays directly
+ float[] d_real_result = t_real_result.numpy().ToArray();
+ float[] d_imag_result = t_imag_result.numpy().ToArray();
+
+ Assert.IsTrue(base.Equal(d_real_result, d_real));
+ Assert.IsTrue(base.Equal(d_imag_result, d_imag));
+ }
+ [TestMethod]
+ public void complex64_abs()
+ {
+ tf.enable_eager_execution();
+
+ float[] d_real = new float[] { -3.0f, -5.0f, 8.0f, 7.0f };
+ float[] d_imag = new float[] { -4.0f, 12.0f, -15.0f, 24.0f };
+
+ float[] d_abs = new float[] { 5.0f, 13.0f, 17.0f, 25.0f };
+
+ Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_FLOAT);
+ Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_FLOAT);
+
+ Tensor t_complex = tf.complex(t_real, t_imag, TF_DataType.TF_COMPLEX64);
+
+ Tensor t_abs_result = tf.abs(t_complex);
+
+ NDArray n_abs_result = t_abs_result.numpy();
+
+ float[] d_abs_result = n_abs_result.ToArray();
+ Assert.IsTrue(base.Equal(d_abs_result, d_abs));
+
+ }
+ [TestMethod]
+ public void complex64_conj()
+ {
+ float[] d_real = new float[] { -3.0f, -5.0f, 8.0f, 7.0f };
+ float[] d_imag = new float[] { -4.0f, 12.0f, -15.0f, 24.0f };
+
+ float[] d_real_expected = new float[] { -3.0f, -5.0f, 8.0f, 7.0f };
+ float[] d_imag_expected = new float[] { 4.0f, -12.0f, 15.0f, -24.0f };
+
+ Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_FLOAT);
+ Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_FLOAT);
+
+ Tensor t_complex = tf.complex(t_real, t_imag, TF_DataType.TF_COMPLEX64);
+
+ Tensor t_result = tf.math.conj(t_complex);
+
+ NDArray n_real_result = tf.math.real(t_result).numpy();
+ NDArray n_imag_result = tf.math.imag(t_result).numpy();
+
+ float[] d_real_result = n_real_result.ToArray();
+ float[] d_imag_result = n_imag_result.ToArray();
+
+ Assert.IsTrue(base.Equal(d_real_result, d_real_expected));
+ Assert.IsTrue(base.Equal(d_imag_result, d_imag_expected));
+
+ }
+ [TestMethod]
+ public void complex64_angle()
+ {
+ float[] d_real = new float[] { 0.0f, 1.0f, -1.0f, 0.0f };
+ float[] d_imag = new float[] { 1.0f, 0.0f, -2.0f, -3.0f };
+
+ float[] d_expected = new float[] { 1.5707964f, 0f, -2.0344439f, -1.5707964f };
+
+ Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_FLOAT);
+ Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_FLOAT);
+
+ Tensor t_complex = tf.complex(t_real, t_imag, TF_DataType.TF_COMPLEX64);
+
+ Tensor t_result = tf.math.angle(t_complex);
+
+ NDArray n_result = t_result.numpy();
+
+ float[] d_result = n_result.ToArray();
+
+ Assert.IsTrue(base.Equal(d_result, d_expected));
+ }
+ }
+}
\ No newline at end of file
diff --git a/test/TensorFlowNET.Graph.UnitTest/TensorFlowNET.Graph.UnitTest.csproj b/test/TensorFlowNET.Graph.UnitTest/TensorFlowNET.Graph.UnitTest.csproj
index 7f6f3c670..6762e6035 100644
--- a/test/TensorFlowNET.Graph.UnitTest/TensorFlowNET.Graph.UnitTest.csproj
+++ b/test/TensorFlowNET.Graph.UnitTest/TensorFlowNET.Graph.UnitTest.csproj
@@ -36,6 +36,7 @@
+