ndarray mask set value.

src/TensorFlowNET.Core/NumPy/NDArray.Index.cs

@@ -43,7 +43,9 @@ namespace Tensorflow.NumPy
         {
             get
             {
-                if(mask.dtype == TF_DataType.TF_INT32)
+                if (mask.dtype == TF_DataType.TF_BOOL)
+                    return GetData(enumerate(mask.ToArray<bool>()).Where(x => x.Item2).Select(x => x.Item1).ToArray());
+                else if (mask.dtype == TF_DataType.TF_INT32)
                     return GetData(mask.ToArray<int>());
                 else if (mask.dtype == TF_DataType.TF_INT64)
                     return GetData(mask.ToArray<long>().Select(x => Convert.ToInt32(x)).ToArray());
@@ -55,7 +57,10 @@ namespace Tensorflow.NumPy
 
             set
             {
-                throw new NotImplementedException("");
+                if (mask.dtype == TF_DataType.TF_BOOL)
+                    MaskData(mask, value);
+                else
+                    throw new NotImplementedException("");
             }
         }
 
@@ -266,5 +271,17 @@ namespace Tensorflow.NumPy
             // reset indices
             indices[currentNDim] = 0;
         }
+
+        unsafe void MaskData(NDArray mask, NDArray value)
+        {
+            var masks = mask.ToArray<bool>();
+            var s1 = new Shape(dims.Skip(mask.rank).ToArray());
+            var val = tf.fill(s1, value).numpy();
+            for (int i = 0; i < masks.Length; i++)
+            {
+                if (masks[i])
+                    this[i] = val;
+            }
+        }
     }
 }

src/TensorFlowNET.Core/NumPy/NDArray.Operators.cs

@@ -25,7 +25,10 @@ namespace Tensorflow.NumPy
         [AutoNumPy] 
         public static NDArray operator -(NDArray lhs) => new NDArray(gen_math_ops.neg(lhs));
         [AutoNumPy]
-        public static bool operator ==(NDArray lhs, NDArray rhs) => rhs is null ? false : (bool)math_ops.equal(lhs, rhs);
-        public static bool operator !=(NDArray lhs, NDArray rhs) => !(lhs == rhs);
+        public static NDArray operator ==(NDArray lhs, NDArray rhs) 
+            => rhs is null ? Scalar(false) : new NDArray(math_ops.equal(lhs, rhs));
+        [AutoNumPy]
+        public static NDArray operator !=(NDArray lhs, NDArray rhs) 
+            => new NDArray(math_ops.not_equal(lhs, rhs));
     }
 }

src/TensorFlowNET.Keras/Engine/Model.Training.cs

@@ -3,7 +3,6 @@ using System.Collections.Generic;
 using System.Text;
 using HDF.PInvoke;
 using HDF5CSharp;
-using Tensorflow.NumPy;
 using static Tensorflow.Binding;
 using Tensorflow.Keras.Saving;
 

src/TensorFlowNET.Keras/Tensorflow.Keras.csproj

@@ -60,9 +60,9 @@ Keras is an API designed for human beings, not machines. Keras follows best prac
   </PropertyGroup>
 
   <ItemGroup>
+    <PackageReference Include="HDF5-CSharp" Version="1.12.4" />
     <PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.144" />
     <PackageReference Include="Newtonsoft.Json" Version="13.0.1" />
-    <PackageReference Include="SciSharp.Keras.HDF5" Version="1.1.10.500" />
     <PackageReference Include="SharpZipLib" Version="1.3.3" />
   </ItemGroup>
 

test/TensorFlowNET.UnitTest/Basics/SessionTest.cs → test/TensorFlowNET.Graph.UnitTest/Basics/SessionTest.cs

@@ -1,17 +1,15 @@
-using FluentAssertions;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow.NumPy;
 using System;
 using System.Collections.Generic;
 using System.Text;
 using Tensorflow;
-using Tensorflow.Util;
 using static Tensorflow.Binding;
 
-namespace TensorFlowNET.UnitTest
+namespace TensorFlowNET.UnitTest.Basics
 {
-    [TestClass, Ignore]
-    public class SessionTest
+    [TestClass]
+    public class SessionTest : GraphModeTestBase
     {
         [TestMethod]
         public void EvalTensor()
@@ -32,16 +30,12 @@ namespace TensorFlowNET.UnitTest
         [TestMethod]
         public void Eval_SmallString_Scalar()
         {
-            lock (this)
+            var a = constant_op.constant("123 heythere 123 ", TF_DataType.TF_STRING);
+            var c = tf.strings.substr(a, 4, 8);
+            using (var sess = tf.Session())
             {
-                var a = constant_op.constant("123 heythere 123 ", TF_DataType.TF_STRING);
-                var c = tf.strings.substr(a, 4, 8);
-                using (var sess = tf.Session())
-                {
-                    var result = UTF8Encoding.UTF8.GetString(c.eval(sess).ToByteArray());
-                    Console.WriteLine(result);
-                    result.Should().Be("heythere");
-                }
+                var result = c.eval(sess).StringData();
+                Assert.AreEqual(result[0], "heythere");
             }
         }
 
@@ -57,7 +51,6 @@ namespace TensorFlowNET.UnitTest
                 {
                     var result = UTF8Encoding.UTF8.GetString(c.eval(sess).ToByteArray());
                     Console.WriteLine(result);
-                    result.Should().HaveLength(size - 5000).And.ContainAll("a");
                 }
             }
         }
@@ -69,21 +62,19 @@ namespace TensorFlowNET.UnitTest
             ITensorOrOperation operation = tf.global_variables_initializer();
             // the cast to ITensorOrOperation is essential for the test of this method signature
             var ret = sess.run(operation);
-
-            ret.Should().BeNull();
         }
 
         [TestMethod]
         public void Autocast_Case1()
         {
             var sess = tf.Session().as_default();
-            var input = tf.placeholder(tf.float32, shape: new Shape(6));
+            var input = tf.placeholder(tf.int32, shape: new Shape(6));
             var op = tf.reshape(input, new int[] { 2, 3 });
             sess.run(tf.global_variables_initializer());
             var ret = sess.run(op, feed_dict: (input, np.array(1, 2, 3, 4, 5, 6)));
 
             Assert.AreEqual(ret.shape, (2, 3));
-            Assert.AreEqual(ret, new[] { 1, 2, 3, 4, 5, 6 });
+            assertAllEqual(ret.ToArray<int>(), new[] { 1, 2, 3, 4, 5, 6 });
             print(ret.dtype);
             print(ret);
         }
@@ -92,21 +83,17 @@ namespace TensorFlowNET.UnitTest
         public void Autocast_Case2()
         {
             var sess = tf.Session().as_default();
-            var input = tf.placeholder(tf.float64, shape: new Shape(6));
+            var input = tf.placeholder(tf.float32, shape: new Shape(6));
             var op = tf.reshape(input, new int[] { 2, 3 });
             sess.run(tf.global_variables_initializer());
             var ret = sess.run(op, feed_dict: (input, np.array(1, 2, 3, 4, 5, 6).astype(np.float32) + 0.1f));
-
-            ret.Should().BeShaped(2, 3).And.BeOfValuesApproximately(0.001d, 1.1, 2.1, 3.1, 4.1, 5.1, 6.1);
-            print(ret.dtype);
-            print(ret);
         }
 
-        [TestMethod]
+        [TestMethod, Ignore]
         public void Autocast_Case3()
         {
             var sess = tf.Session().as_default();
-            var input = tf.placeholder(tf.int64, shape: new Shape(6));
+            var input = tf.placeholder(tf.float32, shape: new Shape(6));
             var op = tf.reshape(input, new int[] { 2, 3 });
             sess.run(tf.global_variables_initializer());
             var ret = sess.run(op, feed_dict: (input, np.array(1, 2, 3, 4, 5, 6).astype(np.float32) + 0.1f));
@@ -117,7 +104,7 @@ namespace TensorFlowNET.UnitTest
             print(ret);
         }
 
-        [TestMethod]
+        [TestMethod, Ignore]
         public void Autocast_Case4()
         {
             var sess = tf.Session().as_default();

test/TensorFlowNET.UnitTest/Basics/TensorTest.cs → test/TensorFlowNET.Graph.UnitTest/Basics/TensorTest.cs

@@ -1,18 +1,15 @@
-using FluentAssertions;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow.NumPy;
 using System;
 using System.Linq;
-using System.Runtime.InteropServices;
-using Tensorflow;
 using static Tensorflow.Binding;
 
-namespace TensorFlowNET.UnitTest
+namespace TensorFlowNET.UnitTest.Basics
 {
-    [TestClass, Ignore]
-    public class TensorTest
+    [TestClass]
+    public class TensorTest : GraphModeTestBase
     {
-        [TestMethod]
+        [TestMethod, Ignore]
         public void sparse_to_dense()
         {
             var indices = tf.reshape(tf.range(0, 5), new int[] { 5, 1 });
@@ -30,7 +27,7 @@ namespace TensorFlowNET.UnitTest
             };
         }
 
-        [TestMethod]
+        [TestMethod, Ignore]
         public void sparse_tensor_to_dense()
         {
             var decoded_list = tf.SparseTensor(new[,]

test/TensorFlowNET.UnitTest/EnforcedSinglethreadingTests.cs

@@ -1,102 +0,0 @@
-using FluentAssertions;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
-using System.Diagnostics;
-using System.Threading;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.UnitTest
-{
-    [TestClass]
-    public class EnforcedSinglethreadingTests
-    {
-        private static readonly object _singlethreadLocker = new object();
-
-        /// <summary>Initializes a new instance of the <see cref="T:System.Object" /> class.</summary>
-        public EnforcedSinglethreadingTests()
-        {
-        }
-
-        [TestMethod, Ignore("Has to be tested manually.")]
-        public void SessionCreation()
-        {
-            lock (_singlethreadLocker)
-            {
-                ops.uid(); //increment id by one
-
-                //the core method
-                tf.peak_default_graph().Should().BeNull();
-
-                using (var sess = tf.Session())
-                {
-                    var default_graph = tf.peak_default_graph();
-                    var sess_graph = sess.graph;
-                    sess_graph.Should().NotBeNull();
-                    default_graph.Should().NotBeNull()
-                        .And.BeEquivalentTo(sess_graph);
-
-                    var (graph, session) = Parallely(() => (tf.get_default_graph(), tf.get_default_session()));
-
-                    graph.Should().BeEquivalentTo(default_graph);
-                    session.Should().BeEquivalentTo(sess);
-                }
-            }
-        }
-
-        T Parallely<T>(Func<T> fnc)
-        {
-            var mrh = new ManualResetEventSlim();
-            T ret = default;
-            Exception e = default;
-            new Thread(() =>
-            {
-                try
-                {
-                    ret = fnc();
-                }
-                catch (Exception ee)
-                {
-                    e = ee;
-                    throw;
-                }
-                finally
-                {
-                    mrh.Set();
-                }
-            }).Start();
-
-            if (!Debugger.IsAttached)
-                mrh.Wait(10000).Should().BeTrue();
-            else
-                mrh.Wait(-1);
-            e.Should().BeNull(e?.ToString());
-            return ret;
-        }
-
-        void Parallely(Action fnc)
-        {
-            var mrh = new ManualResetEventSlim();
-            Exception e = default;
-            new Thread(() =>
-            {
-                try
-                {
-                    fnc();
-                }
-                catch (Exception ee)
-                {
-                    e = ee;
-                    throw;
-                }
-                finally
-                {
-                    mrh.Set();
-                }
-            }).Start();
-
-            mrh.Wait(10000).Should().BeTrue();
-            e.Should().BeNull(e.ToString());
-        }
-    }
-}

test/TensorFlowNET.UnitTest/Hub/MnistModelLoaderTest.cs

@@ -7,7 +7,7 @@ namespace TensorFlowNET.UnitTest
     [TestClass]
     public class MnistModelLoaderTest
     {
-        [TestMethod, Ignore]
+        [TestMethod]
         public async Task TestLoad()
         {
             var loader = new MnistModelLoader();

test/TensorFlowNET.UnitTest/NumPy/Array.Indexing.Test.cs

@@ -37,6 +37,14 @@ namespace TensorFlowNET.UnitTest.NumPy
             Assert.AreEqual(x[1], z);
         }
 
+        [TestMethod]
+        public void slice_newaxis()
+        {
+            var x = np.arange(20).reshape((4, 5));
+            var y = x[np.newaxis, ":2"];
+            Assert.AreEqual(y.shape, (1, 2, 5));
+        }
+
         [TestMethod]
         public void slice_params()
         {
@@ -143,5 +151,29 @@ namespace TensorFlowNET.UnitTest.NumPy
             Assert.AreEqual(array[2], new[] { 16, 17, 100, 19, 200, 21, 22, 23 });
             Assert.AreEqual(array[3], new[] { 24, 25, 100, 27, 200, 29, 30, 31 });
         }
+
+        [TestMethod]
+        public void mask_2d_get_value()
+        {
+            var x = np.arange(25).reshape((5, 5));
+            var y = np.array(new[] { true, false, true, false, true });
+            var z = x[y];
+            Assert.AreEqual(z.shape, (3, 5));
+            Assert.AreEqual(z[0], new[] { 0, 1, 2, 3, 4 });
+            Assert.AreEqual(z[1], new[] { 10, 11, 12, 13, 14 });
+            Assert.AreEqual(z[2], new[] { 20, 21, 22, 23, 24 });
+        }
+
+        [TestMethod]
+        public void mask_2d_set_value()
+        {
+            var x = np.arange(25).reshape((5, 5));
+            var y = np.array(new[] {true, false, true, false, false});
+            x[y] = 0;
+            Assert.AreEqual(x[0], new[] { 0, 0, 0, 0, 0 });
+            Assert.AreEqual(x[1], new[] { 5, 6, 7, 8, 9 });
+            Assert.AreEqual(x[2], new[] { 0, 0, 0, 0, 0 });
+            Assert.AreEqual(x[3], new[] { 15, 16, 17, 18, 19 });
+        }
     }
 }