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Added: complex, real, imag, angle
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-39
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6 files changed

+184
-39
lines changed

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

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/*****************************************************************************
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Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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******************************************************************************/
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using Tensorflow.Operations;
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namespace Tensorflow
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{
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public partial class tensorflow
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{
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public Tensor complex(Tensor real, Tensor imag, Tensorflow.TF_DataType? dtype = null,
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string name = null) => gen_ops.complex(real, imag, dtype, name);
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}
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}

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

Lines changed: 12 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -57,7 +57,7 @@ public Tensor softplus(Tensor features, string name = null)
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public Tensor tanh(Tensor x, string name = null)
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=> math_ops.tanh(x, name: name);
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/// <summary>
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/// Finds values and indices of the `k` largest entries for the last dimension.
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/// </summary>
@@ -93,6 +93,16 @@ public Tensor bincount(Tensor arr, Tensor weights = null,
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bool binary_output = false)
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=> math_ops.bincount(arr, weights: weights, minlength: minlength, maxlength: maxlength,
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dtype: dtype, name: name, axis: axis, binary_output: binary_output);
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public Tensor real(Tensor x, string name = null)
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=> gen_ops.real(x, x.dtype.real_dtype(), name);
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public Tensor imag(Tensor x, string name = null)
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=> gen_ops.imag(x, x.dtype.real_dtype(), name);
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public Tensor conj(Tensor x, string name = null)
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=> gen_ops.conj(x, name);
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public Tensor angle(Tensor x, string name = null)
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=> gen_ops.angle(x, x.dtype.real_dtype(), name);
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}
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public Tensor abs(Tensor x, string name = null)
@@ -537,7 +547,7 @@ public Tensor reduce_prod(Tensor input_tensor, Axis? axis = null, bool keepdims
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public Tensor reduce_sum(Tensor input, Axis? axis = null, Axis? reduction_indices = null,
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bool keepdims = false, string name = null)
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{
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if(keepdims)
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if (keepdims)
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return math_ops.reduce_sum(input, axis: constant_op.constant(axis ?? reduction_indices), keepdims: keepdims, name: name);
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else
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return math_ops.reduce_sum(input, axis: constant_op.constant(axis ?? reduction_indices));

‎src/TensorFlowNET.Core/Operations/gen_ops.cs

Lines changed: 8 additions & 35 deletions
Original file line numberDiff line numberDiff line change
@@ -730,12 +730,7 @@ public static (Tensor sampled_candidates, Tensor true_expected_count, Tensor sam
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/// </remarks>
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public static Tensor angle(Tensor input, TF_DataType? Tout = null, string name = "Angle")
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{
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var dict = new Dictionary<string, object>();
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dict["input"] = input;
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if (Tout.HasValue)
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dict["Tout"] = Tout.Value;
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var op = tf.OpDefLib._apply_op_helper("Angle", name: name, keywords: dict);
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return op.output;
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return tf.Context.ExecuteOp("Angle", name, new ExecuteOpArgs(new object[] { input }));
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}
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/// <summary>
@@ -4978,15 +4973,11 @@ public static Tensor compare_and_bitpack(Tensor input, Tensor threshold, string
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/// </remarks>
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public static Tensor complex(Tensor real, Tensor imag, TF_DataType? Tout = null, string name = "Complex")
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{
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var dict = new Dictionary<string, object>();
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dict["real"] = real;
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dict["imag"] = imag;
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if (Tout.HasValue)
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dict["Tout"] = Tout.Value;
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var op = tf.OpDefLib._apply_op_helper("Complex", name: name, keywords: dict);
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return op.output;
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return tf.Context.ExecuteOp("Complex", name, new ExecuteOpArgs(new object[] { real, imag })); // sorry, cannot pass Tout, so it only works with complex64. complex128 is not supported yet
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}
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/// <summary>
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/// Computes the complex absolute value of a tensor.
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/// </summary>
@@ -5008,12 +4999,7 @@ public static Tensor complex(Tensor real, Tensor imag, TF_DataType? Tout = null,
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/// </remarks>
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public static Tensor complex_abs(Tensor x, TF_DataType? Tout = null, string name = "ComplexAbs")
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{
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var dict = new Dictionary<string, object>();
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dict["x"] = x;
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if (Tout.HasValue)
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dict["Tout"] = Tout.Value;
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var op = tf.OpDefLib._apply_op_helper("ComplexAbs", name: name, keywords: dict);
5016-
return op.output;
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return tf.Context.ExecuteOp("ComplexAbs", name, new ExecuteOpArgs(new object[] { x }));
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}
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50195005
/// <summary>
@@ -5313,10 +5299,7 @@ public static Tensor configure_distributed_t_p_u(string embedding_config = null,
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/// </remarks>
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public static Tensor conj(Tensor input, string name = "Conj")
53155301
{
5316-
var dict = new Dictionary<string, object>();
5317-
dict["input"] = input;
5318-
var op = tf.OpDefLib._apply_op_helper("Conj", name: name, keywords: dict);
5319-
return op.output;
5302+
return tf.Context.ExecuteOp("Conj", name, new ExecuteOpArgs(new object[] { input }));
53205303
}
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53225305
/// <summary>
@@ -13327,12 +13310,7 @@ public static Tensor igammac(Tensor a, Tensor x, string name = "Igammac")
1332713310
/// </remarks>
1332813311
public static Tensor imag(Tensor input, TF_DataType? Tout = null, string name = "Imag")
1332913312
{
13330-
var dict = new Dictionary<string, object>();
13331-
dict["input"] = input;
13332-
if (Tout.HasValue)
13333-
dict["Tout"] = Tout.Value;
13334-
var op = tf.OpDefLib._apply_op_helper("Imag", name: name, keywords: dict);
13335-
return op.output;
13313+
return tf.Context.ExecuteOp("Imag", name, new ExecuteOpArgs(new object[] { input }));
1333613314
}
1333713315

1333813316
/// <summary>
@@ -23865,12 +23843,7 @@ public static Tensor reader_serialize_state_v2(Tensor reader_handle, string name
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/// </remarks>
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public static Tensor real(Tensor input, TF_DataType? Tout = null, string name = "Real")
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{
23868-
var dict = new Dictionary<string, object>();
23869-
dict["input"] = input;
23870-
if (Tout.HasValue)
23871-
dict["Tout"] = Tout.Value;
23872-
var op = tf.OpDefLib._apply_op_helper("Real", name: name, keywords: dict);
23873-
return op.output;
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return tf.Context.ExecuteOp("Real", name, new ExecuteOpArgs(new object[] {input}));
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}
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/// <summary>

‎src/TensorFlowNET.Core/Operations/math_ops.cs

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@@ -20,6 +20,7 @@ limitations under the License.
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using System.Linq;
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using Tensorflow.Framework;
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using static Tensorflow.Binding;
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using Tensorflow.Operations;
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namespace Tensorflow
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{
@@ -35,8 +36,9 @@ public static Tensor abs(Tensor x, string name = null)
3536
name = scope;
3637
x = ops.convert_to_tensor(x, name: "x");
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if (x.dtype.is_complex())
38-
throw new NotImplementedException("math_ops.abs for dtype.is_complex");
39-
//return gen_math_ops.complex_abs(x, Tout: x.dtype.real_dtype, name: name);
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{
40+
return gen_ops.complex_abs(x, Tout: x.dtype.real_dtype(), name: name);
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}
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return gen_math_ops._abs(x, name: name);
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});
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}

‎test/TensorFlowNET.Graph.UnitTest/ComplexTest.cs

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using Microsoft.VisualStudio.TestTools.UnitTesting;
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using Tensorflow.NumPy;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using Tensorflow;
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using static Tensorflow.Binding;
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using Buffer = Tensorflow.Buffer;
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using TensorFlowNET.Keras.UnitTest;
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namespace TensorFlowNET.UnitTest.Basics
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{
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[TestClass]
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public class ComplexTest : EagerModeTestBase
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{
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[Ignore("Not working")]
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[TestMethod]
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public void complex128_basic()
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{
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double[] d_real = new double[] { 1.0, 2.0, 3.0, 4.0 };
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double[] d_imag = new double[] { -1.0, -3.0, 5.0, 7.0 };
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Tensor t_real = tf.constant(d_real, dtype:TF_DataType.TF_DOUBLE);
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Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_DOUBLE);
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Tensor t_complex = tf.complex(t_real, t_imag, TF_DataType.TF_COMPLEX128);
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Tensor t_real_result = tf.math.real(t_complex);
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Tensor t_imag_result = tf.math.imag(t_complex);
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NDArray n_real_result = t_real_result.numpy();
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NDArray n_imag_result = t_imag_result.numpy();
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double[] d_real_result =n_real_result.ToArray<double>();
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double[] d_imag_result = n_imag_result.ToArray<double>();
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Assert.AreEqual(d_real_result, d_real);
38+
Assert.AreEqual(d_imag_result, d_imag);
39+
}
40+
[TestMethod]
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public void complex64_basic()
42+
{
43+
tf.init_scope();
44+
float[] d_real = new float[] { 1.0f, 2.0f, 3.0f, 4.0f };
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float[] d_imag = new float[] { -1.0f, -3.0f, 5.0f, 7.0f };
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47+
Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_FLOAT);
48+
Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_FLOAT);
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Tensor t_complex = tf.complex(t_real, t_imag, TF_DataType.TF_COMPLEX64);
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Tensor t_real_result = tf.math.real(t_complex);
53+
Tensor t_imag_result = tf.math.imag(t_complex);
54+
55+
// Convert the EagerTensors to NumPy arrays directly
56+
float[] d_real_result = t_real_result.numpy().ToArray<float>();
57+
float[] d_imag_result = t_imag_result.numpy().ToArray<float>();
58+
59+
Assert.IsTrue(base.Equal(d_real_result, d_real));
60+
Assert.IsTrue(base.Equal(d_imag_result, d_imag));
61+
}
62+
[TestMethod]
63+
public void complex64_abs()
64+
{
65+
tf.enable_eager_execution();
66+
67+
float[] d_real = new float[] { -3.0f, -5.0f, 8.0f, 7.0f };
68+
float[] d_imag = new float[] { -4.0f, 12.0f, -15.0f, 24.0f };
69+
70+
float[] d_abs = new float[] { 5.0f, 13.0f, 17.0f, 25.0f };
71+
72+
Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_FLOAT);
73+
Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_FLOAT);
74+
75+
Tensor t_complex = tf.complex(t_real, t_imag, TF_DataType.TF_COMPLEX64);
76+
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Tensor t_abs_result = tf.abs(t_complex);
78+
79+
NDArray n_abs_result = t_abs_result.numpy();
80+
81+
float[] d_abs_result = n_abs_result.ToArray<float>();
82+
Assert.IsTrue(base.Equal(d_abs_result, d_abs));
83+
84+
}
85+
[TestMethod]
86+
public void complex64_conj()
87+
{
88+
float[] d_real = new float[] { -3.0f, -5.0f, 8.0f, 7.0f };
89+
float[] d_imag = new float[] { -4.0f, 12.0f, -15.0f, 24.0f };
90+
91+
float[] d_real_expected = new float[] { -3.0f, -5.0f, 8.0f, 7.0f };
92+
float[] d_imag_expected = new float[] { 4.0f, -12.0f, 15.0f, -24.0f };
93+
94+
Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_FLOAT);
95+
Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_FLOAT);
96+
97+
Tensor t_complex = tf.complex(t_real, t_imag, TF_DataType.TF_COMPLEX64);
98+
99+
Tensor t_result = tf.math.conj(t_complex);
100+
101+
NDArray n_real_result = tf.math.real(t_result).numpy();
102+
NDArray n_imag_result = tf.math.imag(t_result).numpy();
103+
104+
float[] d_real_result = n_real_result.ToArray<float>();
105+
float[] d_imag_result = n_imag_result.ToArray<float>();
106+
107+
Assert.IsTrue(base.Equal(d_real_result, d_real_expected));
108+
Assert.IsTrue(base.Equal(d_imag_result, d_imag_expected));
109+
110+
}
111+
[TestMethod]
112+
public void complex64_angle()
113+
{
114+
float[] d_real = new float[] { 0.0f, 1.0f, -1.0f, 0.0f };
115+
float[] d_imag = new float[] { 1.0f, 0.0f, -2.0f, -3.0f };
116+
117+
float[] d_expected = new float[] { 1.5707964f, 0f, -2.0344439f, -1.5707964f };
118+
119+
Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_FLOAT);
120+
Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_FLOAT);
121+
122+
Tensor t_complex = tf.complex(t_real, t_imag, TF_DataType.TF_COMPLEX64);
123+
124+
Tensor t_result = tf.math.angle(t_complex);
125+
126+
NDArray n_result = t_result.numpy();
127+
128+
float[] d_result = n_result.ToArray<float>();
129+
130+
Assert.IsTrue(base.Equal(d_result, d_expected));
131+
}
132+
}
133+
}

‎test/TensorFlowNET.Graph.UnitTest/TensorFlowNET.Graph.UnitTest.csproj

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<ItemGroup>
3838
<ProjectReference Include="..\..\src\TensorFlowNET.Core\Tensorflow.Binding.csproj" />
39+
<ProjectReference Include="..\TensorFlowNET.Keras.UnitTest\Tensorflow.Keras.UnitTest.csproj" />
3940
</ItemGroup>
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</Project>

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