Keras for .NET package meta and clean code.

src/TensorFlowNET.Console/MemoryMonitor.cs

@@ -1,7 +1,5 @@
 using System;
-using System.Collections.Generic;
 using System.Diagnostics;
-using System.Text;
 using static Tensorflow.Binding;
 
 namespace Tensorflow

src/TensorFlowNET.Console/MemoryTestingCases.cs

@@ -1,7 +1,5 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using NumSharp;
+using NumSharp;
+using System;
 using static Tensorflow.Binding;
 
 namespace Tensorflow
@@ -59,7 +57,7 @@ namespace Tensorflow
         public Action<int> Gradient
             => (iterate) =>
             {
-                for(int i = 0; i< iterate; i++)
+                for (int i = 0; i < iterate; i++)
                 {
                     var w = tf.constant(3112.0f);
                     using var tape = tf.GradientTape();

src/TensorFlowNET.Core/Keras/ArgsDefinition/NodeArgs.cs

@@ -1,6 +1,4 @@
-using Tensorflow.Keras.Engine;
-
-namespace Tensorflow.Keras.ArgsDefinition
+namespace Tensorflow.Keras.ArgsDefinition
 {
     public class NodeArgs
     {

src/TensorFlowNET.Core/Keras/ArgsDefinition/SequentialArgs.cs

@@ -1,5 +1,4 @@
 using System.Collections.Generic;
-using Tensorflow.Keras.Engine;
 
 namespace Tensorflow.Keras.ArgsDefinition
 {

src/TensorFlowNET.Core/Keras/Engine/IModel.cs

@@ -1,8 +1,4 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-
-namespace Tensorflow.Keras.Engine
+namespace Tensorflow.Keras.Engine
 {
     public interface IModel
     {

src/TensorFlowNET.Core/Keras/Engine/INode.cs

@@ -1,6 +1,4 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+using System.Collections.Generic;
 
 namespace Tensorflow.Keras.Engine
 {

src/TensorFlowNET.Core/Keras/Layers/ILayer.cs

@@ -1,6 +1,4 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+using System.Collections.Generic;
 using Tensorflow.Keras.Engine;
 
 namespace Tensorflow.Keras

src/TensorFlowNET.Core/Operations/Distributions/normal.py.cs

@@ -57,8 +57,8 @@ namespace Tensorflow
                      this._loc = array_ops.identity(loc, name);
                      this._scale = array_ops.identity(scale, name);
                      base._dtype = this._scale.dtype;
-                    // base._reparameterization_type = new ReparameterizationType("FULLY_REPARAMETERIZED");
-                    base._validate_args = validate_args;
+                     // base._reparameterization_type = new ReparameterizationType("FULLY_REPARAMETERIZED");
+                     base._validate_args = validate_args;
                      base._allow_nan_stats = allow_nan_stats;
                      base._parameters = parameters;
                      base._graph_parents = new List<Tensor>(new Tensor[] { this._loc, this._scale });

src/TensorFlowNET.Core/Operations/NnOps/LayerRNNCell.cs

@@ -13,9 +13,9 @@
    See the License for the specific language governing permissions and
    limitations under the License.
 ******************************************************************************/
-using static Tensorflow.Binding;
-using Tensorflow.Keras.Engine;
 using System;
+using Tensorflow.Keras.Engine;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Core/Operations/image_ops_impl.cs

@@ -242,9 +242,9 @@ namespace Tensorflow
                  image = ops.convert_to_tensor(image, name: "image");
                  image = _AssertAtLeast3DImage(image);
 
-                // can't get k to convert to tensor without throwing error about it being an int---
-                // might rework later. for now, k2 == k as Tensor
-                Tensor k2 = ops.convert_to_tensor(k, dtype: dtypes.int32, name: "k");
+                 // can't get k to convert to tensor without throwing error about it being an int---
+                 // might rework later. for now, k2 == k as Tensor
+                 Tensor k2 = ops.convert_to_tensor(k, dtype: dtypes.int32, name: "k");
                  k2.TensorShape.assert_has_rank(0);
                  k2 = gen_ops.mod(k2, tf.constant(4));
 
@@ -465,8 +465,8 @@ or rank = 4. Had rank = {0}", rank));
 
                  var assert_ops = _CheckAtLeast3DImage(image, require_static: false);
 
-                // batch: [0], height: [1], width: [2], depth: [3]
-                int[] bhwd = _ImageDimensions(image, rank: 4);
+                 // batch: [0], height: [1], width: [2], depth: [3]
+                 int[] bhwd = _ImageDimensions(image, rank: 4);
 
                  var after_padding_width = target_width - offset_width - bhwd[2];
 
@@ -544,8 +544,8 @@ or rank = 4. Had rank = {0}", rank));
 
                  var assert_ops = _CheckAtLeast3DImage(image, require_static: false);
 
-                // batch: [0], height: [1], width: [2], depth: [3]
-                int[] bhwd = _ImageDimensions(image, rank: 4);
+                 // batch: [0], height: [1], width: [2], depth: [3]
+                 int[] bhwd = _ImageDimensions(image, rank: 4);
 
                  assert_ops[assert_ops.Length] = _assert(check_ops.assert_greater_equal(tf.constant(offset_height),
                                                          tf.constant(0)), typeof(ValueError),
@@ -976,7 +976,7 @@ new_height, new_width");
 
                  image = image - image_mean;
                  image = tf.div(image, adjusted_stddev, name: scope); // name: scope in python version
-                return convert_image_dtype(image, orig_dtype, saturate: true);
+                 return convert_image_dtype(image, orig_dtype, saturate: true);
              });
         }
 
@@ -1177,9 +1177,9 @@ new_height, new_width");
                  image = ops.convert_to_tensor(image, name: "image");
                  var channels = image.TensorShape.as_list()[image.TensorShape.dims.Length - 1];
                  var orig_dtype = image.dtype;
-                // python code checks to ensure jpeq_quality is a tensor; unnecessary here since
-                // it is passed as a tensor
-                image = gen_ops.encode_jpeg_variable_quality(image, quality: jpeg_quality);
+                 // python code checks to ensure jpeq_quality is a tensor; unnecessary here since
+                 // it is passed as a tensor
+                 image = gen_ops.encode_jpeg_variable_quality(image, quality: jpeg_quality);
 
                  image = gen_ops.decode_jpeg(image, channels: channels);
                  return convert_image_dtype(image, orig_dtype, saturate: true);
@@ -1587,8 +1587,8 @@ new_height, new_width");
                      {
                          if (k > 0)
                          {
-                            // handle flat_imgs
-                            Tensor[] flat_imgs = new Tensor[] { };
+                             // handle flat_imgs
+                             Tensor[] flat_imgs = new Tensor[] { };
                              foreach ((Tensor x, Tensor t) in imgs.Zip(tails, Tuple.Create))
                              {
                                  flat_imgs[flat_imgs.Length] = array_ops.reshape(x, array_ops.concat(new Tensor[] { constant_op.constant(-1), t }, 0));
@@ -1602,42 +1602,42 @@ new_height, new_width");
                                                                  true_fn: () => padded_func_pass(),
                                                                  false_fn: () => flat_imgs);
 
-                            // handle downscaled
-                            Tensor[] downscaled = new Tensor[] { };
+                             // handle downscaled
+                             Tensor[] downscaled = new Tensor[] { };
                              foreach (Tensor x in padded)
                              {
                                  downscaled[downscaled.Length] = gen_ops.avg_pool(x, ksize: divisor, strides: divisor, padding: "VALID");
                              }
 
-                            // handle tails
-                            tails = new Tensor[] { };
+                             // handle tails
+                             tails = new Tensor[] { };
                              foreach (Tensor x in gen_array_ops.shape_n(downscaled))
                              {
                                  tails[tails.Length] = new Tensor(x.dims.Skip(1).Take(tails.Length - 1).ToArray());
                              }
 
                              imgs = new Tensor[] { };
-                            // tuples weren't working; this is hacky, but should work similarly.
-                            // zip loads the values into a tuple (Tensor, Tensor, Tensor) for each
-                            // zip entry; this just gets the length of the longest array, and loops
-                            // that many times, getting values (like zip) and using them similarly.
-                            for (int x = 0; x < Math.Max(Math.Max(downscaled.Length, heads.Length), tails.Length); x++)
+                             // tuples weren't working; this is hacky, but should work similarly.
+                             // zip loads the values into a tuple (Tensor, Tensor, Tensor) for each
+                             // zip entry; this just gets the length of the longest array, and loops
+                             // that many times, getting values (like zip) and using them similarly.
+                             for (int x = 0; x < Math.Max(Math.Max(downscaled.Length, heads.Length), tails.Length); x++)
                              {
                                  imgs[imgs.Length] = array_ops.reshape(downscaled[x], array_ops.concat(new Tensor[] { heads[x], tails[x] }, 0));
                              }
                          }
                      }
 
-                    // python code uses * to unpack imgs; how to replicate that here?
-                    // don't think that this is doing the same thing as the python code.
-                    (ssim_per_channel, cs) = _ssim_per_channel(
-                         img1: imgs[0],
-                         img2: imgs[1],
-                         max_val: max_val,
-                         filter_size: filter_size,
-                         filter_sigma: filter_sigma,
-                         k1: k1,
-                         k2: k2);
+                     // python code uses * to unpack imgs; how to replicate that here?
+                     // don't think that this is doing the same thing as the python code.
+                     (ssim_per_channel, cs) = _ssim_per_channel(
+                          img1: imgs[0],
+                          img2: imgs[1],
+                          max_val: max_val,
+                          filter_size: filter_size,
+                          filter_sigma: filter_sigma,
+                          k1: k1,
+                          k2: k2);
                      mcs.append(gen_nn_ops.relu(cs));
                  }
 

src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -136,8 +136,8 @@ namespace Tensorflow
             return tf_with(ops.name_scope(name, "saturate_cast", new[] { value }), name =>
              {
                  value = ops.convert_to_tensor(value, name: "value");
-                // dtype = dtypes.as_dtype(dtype).as_base_dtype();
-                if (value.dtype.min() < dtype.min())
+                 // dtype = dtypes.as_dtype(dtype).as_base_dtype();
+                 if (value.dtype.min() < dtype.min())
                      value = gen_math_ops.maximum(
                          value,
                          ops.convert_to_tensor(dtype.min(), dtype: value.dtype, name: "min"));
@@ -264,8 +264,8 @@ namespace Tensorflow
         {
             return tf_with(ops.name_scope(name, "Real", new[] { input }), scope =>
              {
-                // name = scope;
-                input = ops.convert_to_tensor(input, name: "input");
+                 // name = scope;
+                 input = ops.convert_to_tensor(input, name: "input");
                  if (input.dtype.is_complex())
                  {
                      var real_dtype = input.dtype.real_dtype();

src/TensorFlowNET.Core/Training/Optimizer.cs

@@ -192,19 +192,19 @@ namespace Tensorflow
                 {
                     tf_with(ops.control_dependencies(new object[] { _finish(update_ops.ToArray(), "update") }), dep =>
                       {
-                        // ops.colocate_with(global_step);
-                        // TODO: port this if branch once ResourceVariable has been ported!
-                        //if (global_step is ResourceVariable)
-                        //{
-                        //        # TODO(apassos): the implicit read in assign_add is slow; consider
-                        //        # making it less so.
-                        //        apply_updates = resource_variable_ops.assign_add_variable_op(
-                        //            global_step.handle,
-                        //            ops.convert_to_tensor(1, dtype = global_step.dtype),
-                        //            name = name)
-                        //}
-                        //else
-                        {
+                          // ops.colocate_with(global_step);
+                          // TODO: port this if branch once ResourceVariable has been ported!
+                          //if (global_step is ResourceVariable)
+                          //{
+                          //        # TODO(apassos): the implicit read in assign_add is slow; consider
+                          //        # making it less so.
+                          //        apply_updates = resource_variable_ops.assign_add_variable_op(
+                          //            global_step.handle,
+                          //            ops.convert_to_tensor(1, dtype = global_step.dtype),
+                          //            name = name)
+                          //}
+                          //else
+                          {
                               apply_updates = state_ops.assign_add(global_step,
                                   ops.convert_to_tensor(1, dtype: global_step.dtype),
                                   name: name);

src/TensorFlowNET.Keras/Engine/MetricsContainer.cs

@@ -2,7 +2,6 @@
 using System.Collections.Generic;
 using System.Linq;
 using Tensorflow.Keras.Metrics;
-using static Tensorflow.Binding;
 using static Tensorflow.KerasExt;
 
 namespace Tensorflow.Keras.Engine

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

@@ -1,6 +1,5 @@
 using System.Collections.Generic;
 using Tensorflow.Keras.Metrics;
-using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Engine
 {
@@ -19,7 +18,7 @@ namespace Tensorflow.Keras.Engine
                         _metrics.add(compiled_metrics.metrics);
                 }
 
-                foreach(var layer in _flatten_layers())
+                foreach (var layer in _flatten_layers())
                 {
                     // _metrics.extend(layer.metrics);
                 }

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

@@ -36,7 +36,7 @@ namespace Tensorflow.Keras.Engine
             // such as loss scaling and gradient clipping.
             _minimize(tape, optimizer, loss, trainable_variables);
             compiled_metrics.update_state(y, y_pred);
-            
+
             return metrics.Select(x => (x.Name, x.result())).ToList();
         }
 

src/TensorFlowNET.Keras/Engine/Sequential.cs

@@ -17,7 +17,6 @@
 using System.Collections.Generic;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Layers;
-using static Tensorflow.Binding;
 using static Tensorflow.KerasExt;
 
 namespace Tensorflow.Keras.Engine

src/TensorFlowNET.Keras/KerasApi.cs

@@ -1,6 +1,4 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+using System.Collections.Generic;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Datasets;
 using Tensorflow.Keras.Engine;

src/TensorFlowNET.Keras/KerasExtension.cs

@@ -1,7 +1,4 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow.Keras;
+using Tensorflow.Keras;
 
 namespace Tensorflow
 {

src/TensorFlowNET.Keras/Metrics/MeanMetricWrapper.cs

@@ -1,6 +1,4 @@
 using System;
-using System.Collections.Generic;
-using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
@@ -8,7 +6,7 @@ namespace Tensorflow.Keras.Metrics
     {
         string name;
         Func<Tensor, Tensor, Tensor> _fn = null;
-        
+
         public MeanMetricWrapper(Func<Tensor, Tensor, Tensor> fn, string name, TF_DataType dtype = TF_DataType.TF_FLOAT)
             : base(name: name, dtype: dtype)
         {

src/TensorFlowNET.Keras/Metrics/MetricsApi.cs

@@ -1,8 +1,4 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-
-namespace Tensorflow.Keras.Metrics
+namespace Tensorflow.Keras.Metrics
 {
     public class MetricsApi
     {
@@ -17,7 +13,7 @@ namespace Tensorflow.Keras.Metrics
             var y_pred_rank = y_pred.TensorShape.ndim;
             var y_true_rank = y_true.TensorShape.ndim;
             // If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
-            if (y_true_rank != -1 && y_pred_rank != -1 
+            if (y_true_rank != -1 && y_pred_rank != -1
                 && y_true.shape.Length == y_pred.shape.Length)
                 y_true = array_ops.squeeze(y_true, axis: new[] { -1 });
             y_pred = math_ops.argmax(y_pred, -1);

src/TensorFlowNET.Keras/Optimizers/OptimizerApi.cs

@@ -54,7 +54,7 @@ namespace Tensorflow.Keras.Optimizers
                 Name = name
             });
 
-        public SGD SGD(float learning_rate) 
+        public SGD SGD(float learning_rate)
             => new SGD(learning_rate);
     }
 }

src/TensorFlowNET.Keras/Preprocessings/Preprocessing.image_dataset_from_directory.cs

@@ -1,5 +1,4 @@
-using static Tensorflow.Binding;
-using static Tensorflow.KerasExt;
+using static Tensorflow.KerasExt;
 
 namespace Tensorflow.Keras
 {

src/TensorFlowNET.Keras/Tensorflow.Keras.csproj

@@ -6,6 +6,25 @@
     <LangVersion>8.0</LangVersion>
     <RootNamespace>Tensorflow.Keras</RootNamespace>
     <Platforms>AnyCPU;x64</Platforms>
+    <Version>0.1.0</Version>
+    <Authors>Haiping Chen</Authors>
+    <Product>Keras for .NET</Product>
+    <Copyright>Apache 2.0, Haiping Chen 2020</Copyright>
+    <PackageId>TensorFlow.Keras</PackageId>
+    <PackageProjectUrl>https://github.com/SciSharp/TensorFlow.NET</PackageProjectUrl>
+    <PackageIconUrl>https://avatars3.githubusercontent.com/u/44989469?s=200&amp;v=4</PackageIconUrl>
+    <RepositoryUrl>https://github.com/SciSharp/TensorFlow.NET</RepositoryUrl>
+    <PackageReleaseNotes>Keras for .NET is a C# version of Keras migrated from the python version.</PackageReleaseNotes>
+    <Description>Keras for .NET developers.
+
+Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent &amp; simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear &amp; actionable error messages.</Description>
+    <Company>SciSharp STACK</Company>
+    <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
+    <PackageTags>tensorflow, keras, deep learning, machine learning</PackageTags>
+  </PropertyGroup>
+
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">
+    <DefineConstants>DEBUG;TRACE</DefineConstants>
   </PropertyGroup>
 
   <ItemGroup>

src/TensorFlowNET.Keras/tf.layers.cs

@@ -64,7 +64,7 @@ namespace Tensorflow.Keras
                     Trainable = trainable,
                     Name = name
                 });
-                
+
                 return layer.Apply(inputs);
             }
 

src/TensorFlowNET.Keras/tf.optimizers.cs

@@ -14,8 +14,6 @@
    limitations under the License.
 ******************************************************************************/
 
-using Tensorflow.Keras.Optimizers;
-
 namespace Tensorflow.Keras
 {
     public class tensorflow_backup

src/TensorFlowNet.Benchmarks/Program.cs

@@ -1,7 +1,7 @@
-using System;
-using System.Reflection;
-using BenchmarkDotNet.Configs;
+using BenchmarkDotNet.Configs;
 using BenchmarkDotNet.Running;
+using System;
+using System.Reflection;
 
 namespace TensorFlowBenchmark
 {

src/TensorFlowNet.Benchmarks/TensorBenchmark.cs

@@ -1,9 +1,6 @@
-using System;
-using BenchmarkDotNet.Attributes;
-using NumSharp;
+using BenchmarkDotNet.Attributes;
 using Tensorflow;
 using Tensorflow.Eager;
-using static Tensorflow.Binding;
 
 namespace TensorFlowBenchmark
 {

src/TensorFlowNet.Benchmarks/Unmanaged/StructCastBenchmark.cs

@@ -1,11 +1,7 @@
-using System;
+using BenchmarkDotNet.Attributes;
+using System;
 using System.Runtime.CompilerServices;
 using System.Runtime.InteropServices;
-using BenchmarkDotNet.Attributes;
-using Google.Protobuf.WellKnownTypes;
-using NumSharp;
-using Tensorflow;
-using static Tensorflow.Binding;
 
 namespace TensorFlowBenchmark.Unmanaged
 {
@@ -57,7 +53,7 @@ namespace TensorFlowBenchmark.Unmanaged
             UnmanagedStruct _;
             for (int i = 0; i < 10000; i++)
             {
-                _ = *(UnmanagedStruct*) dptr;
+                _ = *(UnmanagedStruct*)dptr;
             }
         }
 

test/TensorFlowNET.UnitTest/Basics/QueueTest.cs

@@ -1,8 +1,5 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
-using System.Collections.Generic;
 using System.Linq;
-using System.Text;
 using Tensorflow;
 using Tensorflow.UnitTest;
 using static Tensorflow.Binding;
@@ -20,7 +17,7 @@ namespace TensorFlowNET.UnitTest.Basics
             var enqueue = queue.enqueue(numbers);
             var dequeue_many = queue.dequeue_many(n: 3);
 
-            using(var sess = tf.Session())
+            using (var sess = tf.Session())
             {
                 sess.run(enqueue, (numbers, new[] { 1 }));
                 sess.run(enqueue, (numbers, new[] { 2, 3 }));
@@ -106,7 +103,7 @@ namespace TensorFlowNET.UnitTest.Basics
             {
                 init.run();
 
-                foreach(var i in range(9))
+                foreach (var i in range(9))
                     results += (int)sess.run(x) + ".";
 
                 // output in random order

test/TensorFlowNET.UnitTest/Basics/SessionTest.cs

@@ -1,13 +1,9 @@
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using FluentAssertions;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using NumSharp;
 using System;
 using System.Collections.Generic;
-using System.Reflection;
-using System.Runtime.CompilerServices;
 using System.Text;
-using FluentAssertions;
-using Google.Protobuf;
-using NumSharp.Backends;
 using Tensorflow;
 using Tensorflow.Util;
 using static Tensorflow.Binding;
@@ -46,14 +42,14 @@ namespace TensorFlowNET.UnitTest.NativeAPI
                 inputs.Add(feed, new Tensor(3));
                 csession.SetInputs(inputs);
 
-                var outputs = new TF_Output[] {new TF_Output(add, 0)};
+                var outputs = new TF_Output[] { new TF_Output(add, 0) };
                 csession.SetOutputs(outputs);
 
                 csession.Run(s);
                 Tensor outTensor = csession.output_tensor(0);
                 EXPECT_EQ(TF_DataType.TF_INT32, outTensor.dtype);
                 EXPECT_EQ(0, outTensor.NDims);
-                ASSERT_EQ((ulong) sizeof(uint), outTensor.bytesize);
+                ASSERT_EQ((ulong)sizeof(uint), outTensor.bytesize);
                 var output_contents = outTensor.ToArray<int>();
                 EXPECT_EQ(3 + 2, output_contents[0]);
 
@@ -65,7 +61,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
                 inputs = new Dictionary<Operation, Tensor>();
                 inputs.Add(feed, new Tensor(7));
                 csession.SetInputs(inputs);
-                outputs = new TF_Output[] {new TF_Output(neg, 0)};
+                outputs = new TF_Output[] { new TF_Output(neg, 0) };
                 csession.SetOutputs(outputs);
                 csession.Run(s);
                 ASSERT_EQ(TF_Code.TF_OK, s.Code);
@@ -74,7 +70,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
                 ASSERT_TRUE(outTensor != IntPtr.Zero);
                 EXPECT_EQ(TF_DataType.TF_INT32, outTensor.dtype);
                 EXPECT_EQ(0, outTensor.NDims); // scalar
-                ASSERT_EQ((ulong) sizeof(uint), outTensor.bytesize);
+                ASSERT_EQ((ulong)sizeof(uint), outTensor.bytesize);
                 output_contents = outTensor.ToArray<int>();
                 EXPECT_EQ(-(7 + 2), output_contents[0]);
 
@@ -132,7 +128,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
                 }
             }
         }
-        
+
         [TestMethod]
         public void Autocast_Case0()
         {
@@ -142,14 +138,14 @@ namespace TensorFlowNET.UnitTest.NativeAPI
             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 TensorShape(6));
-            var op = tf.reshape(input, new int[] {2, 3});
+            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)));
 
@@ -163,7 +159,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
         {
             var sess = tf.Session().as_default();
             var input = tf.placeholder(tf.float64, shape: new TensorShape(6));
-            var op = tf.reshape(input, new int[] {2, 3});
+            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(NPTypeCode.Single) + 0.1f));
 
@@ -177,7 +173,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
         {
             var sess = tf.Session().as_default();
             var input = tf.placeholder(tf.int64, shape: new TensorShape(6));
-            var op = tf.reshape(input, new int[] {2, 3});
+            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(NPTypeCode.Single) + 0.1f));
 
@@ -191,7 +187,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
         {
             var sess = tf.Session().as_default();
             var input = tf.placeholder(tf.byte8, shape: new TensorShape(6));
-            var op = tf.reshape(input, new int[] {2, 3});
+            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(NPTypeCode.Single) + 0.1f));
 

test/TensorFlowNET.UnitTest/Basics/TensorShapeTest.cs

@@ -1,6 +1,6 @@
-using System;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using NumSharp;
+using System;
 using Tensorflow;
 using static Tensorflow.Binding;
 
@@ -14,7 +14,7 @@ namespace TensorFlowNET.UnitTest.Basics
         {
             int a = 2;
             int b = 3;
-            var dims = new [] { Unknown, a, b};
+            var dims = new[] { Unknown, a, b };
             new TensorShape(dims).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, 3);
         }
 
@@ -23,8 +23,8 @@ namespace TensorFlowNET.UnitTest.Basics
         {
             int a = 2;
             int b = 3;
-            var dims = new[] { Unknown, a, b};
-            new TensorShape(new [] {dims}).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, 3);
+            var dims = new[] { Unknown, a, b };
+            new TensorShape(new[] { dims }).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, 3);
         }
 
         [TestMethod]
@@ -32,8 +32,8 @@ namespace TensorFlowNET.UnitTest.Basics
         {
             int a = 2;
             int b = Unknown;
-            var dims = new [] { Unknown, a, b};
-            new TensorShape(new [] {dims}).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, -1);
+            var dims = new[] { Unknown, a, b };
+            new TensorShape(new[] { dims }).GetPrivate<Shape>("shape").Should().BeShaped(-1, 2, -1);
         }
 
         [TestMethod]

test/TensorFlowNET.UnitTest/Basics/TensorTest.cs

@@ -1,13 +1,11 @@
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using FluentAssertions;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using NumSharp;
 using System;
 using System.Linq;
 using System.Runtime.InteropServices;
-using System.Threading;
-using FluentAssertions;
 using Tensorflow;
 using static Tensorflow.Binding;
-using Tensorflow.Framework;
 
 namespace TensorFlowNET.UnitTest.NativeAPI
 {
@@ -21,19 +19,19 @@ namespace TensorFlowNET.UnitTest.NativeAPI
             var span = new Span<float>(array, 100, 500);
             fixed (float* ptr = &MemoryMarshal.GetReference(span))
             {
-                using (var t = new Tensor((IntPtr) ptr, new long[] {span.Length}, tf.float32, 4 * span.Length))
+                using (var t = new Tensor((IntPtr)ptr, new long[] { span.Length }, tf.float32, 4 * span.Length))
                 {
                     Assert.IsFalse(t.IsDisposed);
-                    Assert.AreEqual(2000, (int) t.bytesize);
+                    Assert.AreEqual(2000, (int)t.bytesize);
                 }
             }
 
             fixed (float* ptr = &array[0])
             {
-                using (var t = new Tensor((IntPtr) ptr, new long[] {array.Length}, tf.float32, 4 * array.Length))
+                using (var t = new Tensor((IntPtr)ptr, new long[] { array.Length }, tf.float32, 4 * array.Length))
                 {
                     Assert.IsFalse(t.IsDisposed);
-                    Assert.AreEqual(4000, (int) t.bytesize);
+                    Assert.AreEqual(4000, (int)t.bytesize);
                 }
             }
         }
@@ -42,22 +40,22 @@ namespace TensorFlowNET.UnitTest.NativeAPI
         public unsafe void TensorFromArray()
         {
             var array = new float[1000];
-            using (var t = new Tensor(array, new long[] {array.Length}, tf.float32))
+            using (var t = new Tensor(array, new long[] { array.Length }, tf.float32))
             {
                 Assert.IsFalse(t.IsDisposed);
-                Assert.AreEqual(1000 * sizeof(float), (int) t.bytesize);
+                Assert.AreEqual(1000 * sizeof(float), (int)t.bytesize);
             }
 
-            using (var t = new Tensor(new float[] {1}, new long[] {1}, tf.float32))
+            using (var t = new Tensor(new float[] { 1 }, new long[] { 1 }, tf.float32))
             {
                 Assert.IsFalse(t.IsDisposed);
-                Assert.AreEqual(1 * sizeof(float), (int) t.bytesize);
+                Assert.AreEqual(1 * sizeof(float), (int)t.bytesize);
             }
 
-            using (var t = new Tensor(new float[] {1}, null, tf.float32))
+            using (var t = new Tensor(new float[] { 1 }, null, tf.float32))
             {
                 Assert.IsFalse(t.IsDisposed);
-                Assert.AreEqual(1 * sizeof(float), (int) t.bytesize);
+                Assert.AreEqual(1 * sizeof(float), (int)t.bytesize);
                 t.shape.Should().BeEmpty();
             }
         }
@@ -66,11 +64,11 @@ namespace TensorFlowNET.UnitTest.NativeAPI
         public void AllocateTensor()
         {
             ulong num_bytes = 6 * sizeof(float);
-            long[] dims = {2, 3};
+            long[] dims = { 2, 3 };
             Tensor t = c_api.TF_AllocateTensor(TF_DataType.TF_FLOAT, dims, 2, num_bytes);
             EXPECT_EQ(TF_DataType.TF_FLOAT, t.dtype);
             EXPECT_EQ(2, t.NDims);
-            EXPECT_EQ((int) dims[0], t.shape[0]);
+            EXPECT_EQ((int)dims[0], t.shape[0]);
             EXPECT_EQ(num_bytes, t.bytesize);
             t.Dispose();
         }
@@ -104,10 +102,10 @@ namespace TensorFlowNET.UnitTest.NativeAPI
 
             EXPECT_EQ(tensor.dtype, TF_DataType.TF_FLOAT);
             EXPECT_EQ(tensor.rank, nd.ndim);
-            EXPECT_EQ((int) tensor.shape[0], nd.shape[0]);
-            EXPECT_EQ((int) tensor.shape[1], nd.shape[1]);
-            EXPECT_EQ(tensor.bytesize, (ulong) nd.size * sizeof(float));
-            Assert.IsTrue(Enumerable.SequenceEqual(nd.Data<float>(), new float[] {1, 2, 3, 4, 5, 6}));
+            EXPECT_EQ((int)tensor.shape[0], nd.shape[0]);
+            EXPECT_EQ((int)tensor.shape[1], nd.shape[1]);
+            EXPECT_EQ(tensor.bytesize, (ulong)nd.size * sizeof(float));
+            Assert.IsTrue(Enumerable.SequenceEqual(nd.Data<float>(), new float[] { 1, 2, 3, 4, 5, 6 }));
         }
 
         /// <summary>
@@ -136,7 +134,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
             EXPECT_EQ(-1, num_dims);
 
             // Set the shape to be 2 x Unknown
-            long[] dims = {2, -1};
+            long[] dims = { 2, -1 };
             c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, dims.Length, s.Handle);
             Assert.IsTrue(s.Code == TF_Code.TF_OK);
             num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s.Handle);
@@ -165,8 +163,8 @@ namespace TensorFlowNET.UnitTest.NativeAPI
             c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s.Handle);
             Assert.IsTrue(s.Code == TF_Code.TF_OK);
             EXPECT_EQ(2, num_dims);
-            EXPECT_EQ(2, (int) returned_dims[0]);
-            EXPECT_EQ(3, (int) returned_dims[1]);
+            EXPECT_EQ(2, (int)returned_dims[0]);
+            EXPECT_EQ(3, (int)returned_dims[1]);
 
             // Try to set 'unknown' with same rank on the shape and see that
             // it doesn't change.
@@ -177,8 +175,8 @@ namespace TensorFlowNET.UnitTest.NativeAPI
             c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s.Handle);
             Assert.IsTrue(s.Code == TF_Code.TF_OK);
             EXPECT_EQ(2, num_dims);
-            EXPECT_EQ(2, (int) returned_dims[0]);
-            EXPECT_EQ(3, (int) returned_dims[1]);
+            EXPECT_EQ(2, (int)returned_dims[0]);
+            EXPECT_EQ(3, (int)returned_dims[1]);
 
             // Try to fetch a shape with the wrong num_dims
             c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, 5, s.Handle);
@@ -208,7 +206,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
         public void sparse_to_dense()
         {
             var indices = tf.reshape(tf.range(0, 5), new int[] { 5, 1 });
-            var labels = tf.expand_dims(tf.constant(new[] { 0, 1, 2, 3, 4 }),1);
+            var labels = tf.expand_dims(tf.constant(new[] { 0, 1, 2, 3, 4 }), 1);
             var st = tf.concat(values: new[] { indices, labels }, axis: 1);
             var onehot = tf.sparse_to_dense(st, (5, 5), 1);
             using (var sess = tf.Session())

test/TensorFlowNET.UnitTest/Basics/TrainSaverTest.cs

@@ -44,7 +44,7 @@ namespace TensorFlowNET.UnitTest.Basics
         {
             tf_with(Session.LoadFromSavedModel("mobilenet"), sess =>
             {
-                
+
             });
         }
 

test/TensorFlowNET.UnitTest/Basics/VariableTest.cs

@@ -1,8 +1,5 @@
-using FluentAssertions;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
-using NumSharp;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using System.Linq;
-using Tensorflow.UnitTest;
 using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest.Basics

test/TensorFlowNET.UnitTest/Basics/VersionTest.cs

@@ -1,5 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using Tensorflow;
 using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest.Basics

test/TensorFlowNET.UnitTest/Dataset/DatasetTest.cs

@@ -1,11 +1,6 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using System;
-using System.Collections.Generic;
 using System.Linq;
-using System.Text;
-using Tensorflow;
-using Tensorflow.Keras;
-using Tensorflow.UnitTest;
 using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest.Dataset
@@ -20,7 +15,7 @@ namespace TensorFlowNET.UnitTest.Dataset
             long value = 0;
 
             var dataset = tf.data.Dataset.range(3);
-            foreach(var (step, item) in enumerate(dataset))
+            foreach (var (step, item) in enumerate(dataset))
             {
                 Assert.AreEqual(iStep, step);
                 iStep++;
@@ -114,7 +109,7 @@ namespace TensorFlowNET.UnitTest.Dataset
             foreach (var item in dataset)
             {
                 Assert.AreEqual(value, (long)item.Item1);
-                value ++;
+                value++;
             }
         }
 

test/TensorFlowNET.UnitTest/EagerModeTestBase.cs

@@ -1,18 +1,10 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow;
-using Tensorflow.Keras.Layers;
 using static Tensorflow.Binding;
-using static Tensorflow.KerasExt;
 
 namespace TensorFlowNET.UnitTest
 {
     public class EagerModeTestBase : PythonTest
     {
-        protected LayersApi layers = keras.layers;
-
         [TestInitialize]
         public void TestInit()
         {

test/TensorFlowNET.UnitTest/EnforcedSinglethreadingTests.cs

@@ -1,15 +1,9 @@
-using System;
-using System.Collections.Generic;
+using FluentAssertions;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using System;
 using System.Diagnostics;
-using System.IO;
-using System.Linq;
-using System.Runtime.InteropServices;
 using System.Threading;
-using FluentAssertions;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
-using NumSharp;
 using Tensorflow;
-using Tensorflow.Util;
 using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest.NativeAPI
@@ -63,11 +57,13 @@ namespace TensorFlowNET.UnitTest.NativeAPI
                 try
                 {
                     ret = fnc();
-                } catch (Exception ee)
+                }
+                catch (Exception ee)
                 {
                     e = ee;
                     throw;
-                } finally
+                }
+                finally
                 {
                     mrh.Set();
                 }
@@ -90,11 +86,13 @@ namespace TensorFlowNET.UnitTest.NativeAPI
                 try
                 {
                     fnc();
-                } catch (Exception ee)
+                }
+                catch (Exception ee)
                 {
                     e = ee;
                     throw;
-                } finally
+                }
+                finally
                 {
                     mrh.Set();
                 }

test/TensorFlowNET.UnitTest/GradientTest/GradientTest.cs

@@ -1,8 +1,8 @@
-using System;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using NumSharp;
+using System;
 using System.Collections.Generic;
 using System.Linq;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
-using NumSharp;
 using Tensorflow;
 using Tensorflow.UnitTest;
 using static Tensorflow.Binding;

test/TensorFlowNET.UnitTest/GraphModeTestBase.cs

@@ -1,7 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
-using System.Collections.Generic;
-using System.Text;
 using TensorFlowNET.UnitTest;
 using static Tensorflow.Binding;
 

test/TensorFlowNET.UnitTest/ImageTest.cs

@@ -1,12 +1,7 @@
 using FluentAssertions;
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.IO;
 using System.Linq;
-using System.Reflection;
-using System.Text;
 using Tensorflow;
 using Tensorflow.UnitTest;
 using static Tensorflow.Binding;
@@ -51,11 +46,11 @@ namespace TensorFlowNET.UnitTest.Basics
             image = image[tf.newaxis, tf.ellipsis, tf.newaxis];
             image = tf.image.resize(image, (3, 5));
             image = image[0, tf.ellipsis, 0];
-            Assert.IsTrue(Enumerable.SequenceEqual(new float[] { 0.6666667f, 0.3333333f, 0, 0, 0 }, 
+            Assert.IsTrue(Enumerable.SequenceEqual(new float[] { 0.6666667f, 0.3333333f, 0, 0, 0 },
                 image[0].ToArray<float>()));
-            Assert.IsTrue(Enumerable.SequenceEqual(new float[] { 0, 0, 1, 0, 0 }, 
+            Assert.IsTrue(Enumerable.SequenceEqual(new float[] { 0, 0, 1, 0, 0 },
                 image[1].ToArray<float>()));
-            Assert.IsTrue(Enumerable.SequenceEqual(new float[] { 0, 0, 0, 0.3333335f, 0.6666665f }, 
+            Assert.IsTrue(Enumerable.SequenceEqual(new float[] { 0, 0, 0, 0.3333335f, 0.6666665f },
                 image[2].ToArray<float>()));
             tf.compat.v1.disable_eager_execution();
         }

test/TensorFlowNET.UnitTest/Keras/LayersTest.cs

@@ -1,7 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
-using System.Collections.Generic;
-using System.Text;
 using NumSharp;
 using static Tensorflow.KerasExt;
 
@@ -23,6 +20,8 @@ namespace TensorFlowNET.UnitTest.Keras
         [TestMethod]
         public void Functional()
         {
+            var layers = keras.layers;
+
             var inputs = keras.Input(shape: 784);
             Assert.AreEqual((None, 784), inputs.TensorShape);
 

test/TensorFlowNET.UnitTest/Keras/ModelSaveTest.cs

@@ -1,7 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
-using System.Collections.Generic;
-using System.Text;
 using Tensorflow.Keras.Engine;
 using static Tensorflow.KerasExt;
 

test/TensorFlowNET.UnitTest/ManagedAPI/ActivationFunctionTest.cs

@@ -1,8 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
 using Tensorflow;
 using static Tensorflow.Binding;
 
@@ -36,7 +32,7 @@ namespace TensorFlowNET.UnitTest.nn_test
         public void TanH()
         {
             var b = tf.nn.tanh(a, name: "TanH");
-            var expected = new float[] { 0.7615942f, -0.46211717f,  0.9977749f , -0.970452f,  0f, -0.99999547f };
+            var expected = new float[] { 0.7615942f, -0.46211717f, 0.9977749f, -0.970452f, 0f, -0.99999547f };
             var actual = b.ToArray<float>();
             Assert.IsTrue(Equal(expected, actual));
         }

test/TensorFlowNET.UnitTest/ManagedAPI/ConstantTest.cs

@@ -2,8 +2,6 @@
 using NumSharp;
 using System;
 using System.Linq;
-using System.Runtime.InteropServices;
-using System.Text;
 using Tensorflow;
 using static Tensorflow.Binding;
 

test/TensorFlowNET.UnitTest/ManagedAPI/ControlFlowApiTest.cs

@@ -1,10 +1,5 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using System;
-using System.Collections.Generic;
-using System.Linq.Expressions;
-using System.Runtime.CompilerServices;
-using System.Security.Cryptography.X509Certificates;
-using System.Text;
 using Tensorflow;
 using static Tensorflow.Binding;
 

test/TensorFlowNET.UnitTest/ManagedAPI/FunctionApiTest.cs

@@ -1,10 +1,6 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Text;
 using Tensorflow;
-using Tensorflow.Graphs;
 using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest.ManagedAPI

test/TensorFlowNET.UnitTest/ManagedAPI/GradientTest.cs

@@ -1,8 +1,4 @@
-using FluentAssertions;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
-using NumSharp;
-using System.Linq;
-using Tensorflow;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest.ManagedAPI

test/TensorFlowNET.UnitTest/ManagedAPI/LinalgTest.cs

@@ -1,7 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
-using System.Collections.Generic;
-using System.Text;
 using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest.ManagedAPI

test/TensorFlowNET.UnitTest/ManagedAPI/MathApiTest.cs

@@ -1,8 +1,5 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
-using System.Collections.Generic;
 using System.Linq;
-using System.Text;
 using Tensorflow;
 using static Tensorflow.Binding;
 

test/TensorFlowNET.UnitTest/ManagedAPI/StringsApiTest.cs

@@ -1,7 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
-using System.Collections.Generic;
-using System.Text;
 using Tensorflow;
 using static Tensorflow.Binding;
 

test/TensorFlowNET.UnitTest/ManagedAPI/TFNetApiTest.cs

@@ -1,6 +1,4 @@
 using System;
-using System.Collections.Generic;
-using System.Text;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/ManagedAPI/TensorOperate.cs

@@ -1,8 +1,6 @@
-using FluentAssertions;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using NumSharp;
 using System.Linq;
-using Tensorflow;
 using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest.ManagedAPI
@@ -26,17 +24,17 @@ namespace TensorFlowNET.UnitTest.ManagedAPI
         [TestMethod]
         public void InitTensorTest()
         {
-            var a = tf.constant(np.array(new[, ,] 
-            { 
-                { { 1 }, { 2 }, { 3 } }, 
-                { { 4 }, { 5 }, { 6 } } 
+            var a = tf.constant(np.array(new[, ,]
+            {
+                { { 1 }, { 2 }, { 3 } },
+                { { 4 }, { 5 }, { 6 } }
             }));
             Assert.IsTrue(Enumerable.SequenceEqual(new[] { 2, 3, 1 }, a.shape));
 
-            var b = tf.constant(new[, ,] 
-            { 
-                { { 1 }, { 2 }, { 3 } }, 
-                { { 4 }, { 5 }, { 6 } } 
+            var b = tf.constant(new[, ,]
+            {
+                { { 1 }, { 2 }, { 3 } },
+                { { 4 }, { 5 }, { 6 } }
             });
             Assert.IsTrue(Enumerable.SequenceEqual(new[] { 2, 3, 1 }, b.shape));
         }

test/TensorFlowNET.UnitTest/ManagedAPI/ZeroFractionTest.cs

@@ -1,7 +1,7 @@
-using System;
-using System.Linq;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using NumSharp;
+using System;
+using System.Linq;
 using Tensorflow;
 using Tensorflow.UnitTest;
 
@@ -16,7 +16,7 @@ namespace TensorFlowNET.UnitTest.nn_test
             int total_elements = np.prod(x.shape);
 
             var eps = 1e-8;
-            var nonzeros = x.Data<double>().Count(d=>Math.Abs(d)> eps);
+            var nonzeros = x.Data<double>().Count(d => Math.Abs(d) > eps);
             return 1.0 - nonzeros / (double)total_elements;
         }
 
@@ -26,7 +26,7 @@ namespace TensorFlowNET.UnitTest.nn_test
         {
             var x_shape = new Shape(5, 17);
             var x_np = np.random.randint(0, 2, x_shape);
-                //x_np.astype(np.float32);
+            //x_np.astype(np.float32);
             var y_np = this._ZeroFraction(x_np);
 
             var x_tf = constant_op.constant(x_np);
@@ -53,7 +53,7 @@ namespace TensorFlowNET.UnitTest.nn_test
         public void testZeroFraction2_27Zeros()
         {
             var sparsity = nn_impl.zero_fraction(
-                array_ops.zeros(new Shape((int) Math.Pow(2, 27 * 1.01)), dtypes.int8));
+                array_ops.zeros(new Shape((int)Math.Pow(2, 27 * 1.01)), dtypes.int8));
             self.assertAllClose(1.0, self.evaluate<NDArray>(sparsity));
         }
 

test/TensorFlowNET.UnitTest/MultithreadingTests.cs

@@ -1,11 +1,10 @@
-using System;
-using System.Collections.Generic;
+using FluentAssertions;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using NumSharp;
+using System;
 using System.IO;
 using System.Linq;
 using System.Runtime.InteropServices;
-using FluentAssertions;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
-using NumSharp;
 using Tensorflow;
 using Tensorflow.UnitTest;
 using static Tensorflow.Binding;
@@ -147,7 +146,7 @@ namespace TensorFlowNET.UnitTest
                 {
                     for (int i = 0; i < 100; i++)
                     {
-                        var t = new Tensor(new int[] {1, 2, 3});
+                        var t = new Tensor(new int[] { 1, 2, 3 });
                     }
                 }
             }
@@ -171,7 +170,7 @@ namespace TensorFlowNET.UnitTest
                         var v = (int*)Marshal.AllocHGlobal(sizeof(int));
                         c_api.DeallocatorArgs _deallocatorArgs = new c_api.DeallocatorArgs();
                         var handle = c_api.TF_NewTensor(typeof(int).as_dtype(), dims: new long[0], num_dims: 0,
-                            data: (IntPtr) v, len: (UIntPtr) sizeof(int),
+                            data: (IntPtr)v, len: (UIntPtr)sizeof(int),
                             deallocator: (IntPtr data, IntPtr size, ref c_api.DeallocatorArgs args) => Marshal.FreeHGlobal(data),
                             ref _deallocatorArgs);
                         c_api.TF_DeleteTensor(handle);
@@ -190,8 +189,8 @@ namespace TensorFlowNET.UnitTest
             {
                 tf.peak_default_graph().Should().BeNull();
                 //graph is created automatically to perform create these operations
-                var a1 = tf.constant(new[] {2f}, shape: new[] {1});
-                var a2 = tf.constant(new[] {3f}, shape: new[] {1});
+                var a1 = tf.constant(new[] { 2f }, shape: new[] { 1 });
+                var a2 = tf.constant(new[] { 3f }, shape: new[] { 1 });
                 var math = a1 + a2;
                 for (int i = 0; i < 100; i++)
                 {
@@ -215,8 +214,8 @@ namespace TensorFlowNET.UnitTest
                 {
                     tf.peak_default_graph().Should().NotBeNull();
                     //graph is created automatically to perform create these operations
-                    var a1 = tf.constant(new[] {2f}, shape: new[] {1});
-                    var a2 = tf.constant(new[] {3f}, shape: new[] {1});
+                    var a1 = tf.constant(new[] { 2f }, shape: new[] { 1 });
+                    var a2 = tf.constant(new[] { 3f }, shape: new[] { 1 });
                     var math = a1 + a2;
 
                     var result = sess.run(math);
@@ -237,8 +236,8 @@ namespace TensorFlowNET.UnitTest
                 {
                     tf.peak_default_graph().Should().NotBeNull();
                     //graph is created automatically to perform create these operations
-                    var a1 = tf.constant(new[] {2f}, shape: new[] {1});
-                    var a2 = tf.constant(new[] {3f}, shape: new[] {1});
+                    var a1 = tf.constant(new[] { 2f }, shape: new[] { 1 });
+                    var a2 = tf.constant(new[] { 3f }, shape: new[] { 1 });
                     var math = a1 + a2;
                 }
             }
@@ -254,8 +253,8 @@ namespace TensorFlowNET.UnitTest
             {
                 tf.peak_default_graph().Should().BeNull();
                 //graph is created automatically to perform create these operations
-                var a1 = tf.constant(new[] {2f}, shape: new[] {1});
-                var a2 = tf.constant(new[] {3f}, shape: new[] {1});
+                var a1 = tf.constant(new[] { 2f }, shape: new[] { 1 });
+                var a2 = tf.constant(new[] { 3f }, shape: new[] { 1 });
                 var math = a1 + a2;
             }
         }
@@ -270,8 +269,8 @@ namespace TensorFlowNET.UnitTest
             {
                 tf.peak_default_graph().Should().BeNull();
                 //graph is created automatically to perform create these operations
-                var a1 = tf.constant(new[] {2f}, shape: new[] {1});
-                var a2 = tf.constant(new[] {3f}, shape: new[] {1}, name: "ConstantK");
+                var a1 = tf.constant(new[] { 2f }, shape: new[] { 1 });
+                var a2 = tf.constant(new[] { 3f }, shape: new[] { 1 }, name: "ConstantK");
                 var math = a1 + a2;
                 for (int i = 0; i < 100; i++)
                 {
@@ -295,7 +294,7 @@ namespace TensorFlowNET.UnitTest
                 for (int j = 0; j < 100; j++)
                 {
                     var sess = Session.LoadFromSavedModel(modelPath).as_default();
-                    var inputs = new[] {"sp", "fuel"};
+                    var inputs = new[] { "sp", "fuel" };
 
                     var inp = inputs.Select(name => sess.graph.OperationByName(name).output).ToArray();
                     var outp = sess.graph.OperationByName("softmax_tensor").output;

test/TensorFlowNET.UnitTest/NameScopeTest.cs

@@ -1,5 +1,4 @@
-using System;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow;
 using Tensorflow.UnitTest;
 using static Tensorflow.Binding;

test/TensorFlowNET.UnitTest/NativeAPI/CApiFunctionTest.cs

@@ -2,10 +2,7 @@
 using System;
 using System.Collections.Generic;
 using System.Linq;
-using System.Reflection;
-using System.Runtime.InteropServices;
 using Tensorflow;
-using Tensorflow.Functions;
 using static TensorFlowNET.UnitTest.c_test_util;
 
 namespace TensorFlowNET.UnitTest.NativeAPI
@@ -61,7 +58,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
             var func_feed = Placeholder(host_graph_, s_);
             var func_op = Use(new[] { func_feed });
             Run(new[] { new KeyValuePair<Operation, Tensor>(func_feed, Int32Tensor(3)) }, func_op, -3);
-            VerifyFDef(new string[] { "neg_0" }, 
+            VerifyFDef(new string[] { "neg_0" },
                 new List<IOSpec> { new IOSpec("feed", DataType.DtInt32) },
                 new List<IOSpec> { new IOSpec("neg", DataType.DtInt32) },
                 new List<EdgeSpec> { new EdgeSpec("feed", "neg_0:0"), new EdgeSpec("neg_0:y:0", "neg") },
@@ -211,9 +208,9 @@ namespace TensorFlowNET.UnitTest.NativeAPI
             VerifyFDef(new string[] { "add_0" },
                 new List<IOSpec> { new IOSpec("feed1"), new IOSpec("feed2") },
                 new List<IOSpec> { new IOSpec("add") },
-                new List<EdgeSpec> 
-                { 
-                    new EdgeSpec("feed1", "add_0:0"), 
+                new List<EdgeSpec>
+                {
+                    new EdgeSpec("feed1", "add_0:0"),
                     new EdgeSpec("feed2", "add_0:1"),
                     new EdgeSpec("add_0:sum:0", "add")
                 },
@@ -397,7 +394,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
                 outputs.Select(x => new TF_Output(x, 0)).ToArray(),
                 output_names, expect_failure);
 
-        void DefineT(int num_opers, Operation[] opers, 
+        void DefineT(int num_opers, Operation[] opers,
             TF_Output[] inputs, TF_Output[] outputs,
             string[] output_names, bool expect_failure = false)
         {
@@ -405,7 +402,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
                 num_opers, num_opers == -1 ? null : opers.Select(x => (IntPtr)x).ToArray(),
                 inputs.Length, inputs.ToArray(),
                 outputs.Length, outputs.ToArray(),
-                output_names == null || output_names.Length == 0 ? null : output_names, 
+                output_names == null || output_names.Length == 0 ? null : output_names,
                 IntPtr.Zero, null, s_.Handle);
 
             if (expect_failure)
@@ -480,7 +477,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
         void VerifyFDefNodes(FunctionDef fdef, string[] nodes)
         {
             ASSERT_EQ(nodes.Length, fdef.NodeDef.Count);
-            foreach(var node in fdef.NodeDef)
+            foreach (var node in fdef.NodeDef)
             {
                 ASSERT_TRUE(nodes.Contains(node.Name), $"Got unexpected node: {node.Name} in fdef: {fdef}");
             }
@@ -519,7 +516,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
             // Build a set of edges from fdef
             var a_edges = new List<EdgeSpec>(); // actual edges
             // Get edges from inputs to body nodes and between body nodes
-            foreach(var node in fdef.NodeDef)
+            foreach (var node in fdef.NodeDef)
             {
                 for (int i = 0; i < node.Input.Count; ++i)
                 {
@@ -528,10 +525,10 @@ namespace TensorFlowNET.UnitTest.NativeAPI
                 }
             }
             // Get edges from body nodes to outputs and from inputs to outputs
-            foreach(var arg in fdef.Signature.OutputArg)
+            foreach (var arg in fdef.Signature.OutputArg)
             {
                 var iter = fdef.Ret.FirstOrDefault(x => x.Key == arg.Name);
-                if(iter.Key != null)
+                if (iter.Key != null)
                 {
                     a_edges.Add(new EdgeSpec(iter.Value, arg.Name));
                 }
@@ -541,7 +538,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
                 }
             }
             // Verify edges
-            foreach(var edge in e_edges)
+            foreach (var edge in e_edges)
             {
                 ASSERT_TRUE(a_edges.Contains(edge));
             }
@@ -552,14 +549,14 @@ namespace TensorFlowNET.UnitTest.NativeAPI
             // If caller specified all edges, check that we have seen all
             if (is_exact_edges)
             {
-                ASSERT_EQ(e_edges.Count + c_edges.Count, a_edges.Count, 
+                ASSERT_EQ(e_edges.Count + c_edges.Count, a_edges.Count,
                     $"Expected edges: {e_edges}, Expected Control edges: {c_edges}, Actual edges: {a_edges}");
             }
         }
 
         public void Dispose()
         {
-            
+
         }
 
         public struct IOSpec

test/TensorFlowNET.UnitTest/NativeAPI/CApiGradientsTest.cs

@@ -53,7 +53,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
                     c_api.TF_GraphToGraphDef(graph, buffer.Handle, s.Handle);
                     bool ret = TF_GetCode(s) == TF_OK;
                     EXPECT_EQ(TF_OK, TF_GetCode(s));
-                    if (ret) 
+                    if (ret)
                         graph_def = GraphDef.Parser.ParseFrom(buffer.DangerousMemoryBlock.Stream());
                     return ret;
                 }
@@ -114,13 +114,13 @@ namespace TensorFlowNET.UnitTest.NativeAPI
                 IntPtr[] handles = new IntPtr[2] { IntPtr.Zero, IntPtr.Zero };
                 c_api.TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
                                       ninputs, grad_inputs, s_.Handle, handles);
-                
+
                 var op = new Operation(handles[0]);
             }
             else
             {
                 //c_api.TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
-                                        //ninputs, null, s_, grad_outputs);
+                //ninputs, null, s_, grad_outputs);
             }
         }
 
@@ -232,7 +232,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
             return t;
         }
 
-        private Operation MatMul(Graph graph, Status s, Operation l, Operation r, string name, 
+        private Operation MatMul(Graph graph, Status s, Operation l, Operation r, string name,
             bool transpose_a = false, bool transpose_b = false)
         {
             var desc = TF_NewOperation(graph, "MatMul", name);

test/TensorFlowNET.UnitTest/NativeAPI/CApiTest.cs

@@ -4,7 +4,6 @@ using Tensorflow;
 using Tensorflow.Device;
 using Tensorflow.Eager;
 using Tensorflow.UnitTest;
-using Buffer = System.Buffer;
 
 namespace TensorFlowNET.UnitTest
 {

test/TensorFlowNET.UnitTest/NativeAPI/CSession.cs

@@ -25,7 +25,7 @@ namespace TensorFlowNET.UnitTest
         {
             lock (Locks.ProcessWide)
             {
-                var config = new ConfigProto {InterOpParallelismThreads = 4};
+                var config = new ConfigProto { InterOpParallelismThreads = 4 };
                 session_ = new Session(graph, config, s);
             }
         }
@@ -78,7 +78,7 @@ namespace TensorFlowNET.UnitTest
         public unsafe void Run(Status s)
         {
             var inputs_ptr = inputs_.ToArray();
-            var input_values_ptr = input_values_.Select(x => (IntPtr) x).ToArray();
+            var input_values_ptr = input_values_.Select(x => (IntPtr)x).ToArray();
             var outputs_ptr = outputs_.ToArray();
             var output_values_ptr = output_values_.Select(x => IntPtr.Zero).ToArray();
             IntPtr[] targets_ptr = new IntPtr[0];

test/TensorFlowNET.UnitTest/NativeAPI/Eager/CApi.Eager.OpGetInputAndOutputLengths.cs

@@ -1,5 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
 using Tensorflow;
 using Tensorflow.Eager;
 

test/TensorFlowNET.UnitTest/NativeAPI/Eager/CApi.Eager.TensorHandle.cs

@@ -1,5 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
 using Tensorflow;
 using static Tensorflow.Binding;
 

test/TensorFlowNET.UnitTest/NativeAPI/Eager/CApi.Eager.TensorHandleDevices.cs

@@ -1,5 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
 using Tensorflow;
 using Tensorflow.Eager;
 

test/TensorFlowNET.UnitTest/NativeAPI/Eager/CApi.Eager.Variables.cs

@@ -1,5 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
 using Tensorflow;
 using Tensorflow.Eager;
 using static Tensorflow.Binding;

test/TensorFlowNET.UnitTest/NativeAPI/Eager/CApi.Eager.cs

@@ -18,7 +18,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
             var data = new float[] { 1.0f, 2.0f, 3.0f, 4.0f };
             var t = c_api.TF_AllocateTensor(TF_FLOAT, dims, dims.Length, (ulong)data.Length * sizeof(float));
             tf.memcpy(c_api.TF_TensorData(t), data, data.Length * sizeof(float));
-            
+
             using var status = c_api.TF_NewStatus();
             var th = c_api.TFE_NewTensorHandle(t, status);
             CHECK_EQ(TF_OK, TF_GetCode(status), TF_Message(status));
@@ -146,7 +146,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
 
         SafeTensorHandleHandle TestScalarTensorHandle(float value)
         {
-            var data = new [] { value };
+            var data = new[] { value };
             var t = c_api.TF_AllocateTensor(TF_FLOAT, null, 0, sizeof(float));
             tf.memcpy(TF_TensorData(t), data, TF_TensorByteSize(t));
             using var status = TF_NewStatus();

test/TensorFlowNET.UnitTest/NativeAPI/Eager/GradientEagerTest.cs

@@ -1,7 +1,6 @@
-using System;
-using System.Collections.Generic;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using System;
 using System.Linq;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow;
 using static Tensorflow.Binding;
 

test/TensorFlowNET.UnitTest/NativeAPI/GraphBuildTest.cs

@@ -1,8 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using System;
-using System.Collections.Generic;
-using System.Text;
-using System.Linq;
 using Tensorflow;
 using static Tensorflow.Binding;
 
@@ -23,7 +19,7 @@ namespace TensorFlowNET.UnitTest.NativeAPI
 
             Assert.AreEqual(1, one.consumers().Length);
             Assert.AreEqual("add", neg.op.node_def.Input[0]);
-            
+
             // update edge
             neg.op._update_input(0, one);
             // c_api.TF_UpdateEdge(graph, new TF_Output(c1.op, 0), new TF_Input(neg.op, 0), tf.Status.Handle);

test/TensorFlowNET.UnitTest/NativeAPI/GraphTest.cs

@@ -1,9 +1,8 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using System;
-using System.Collections.Generic;
 using Tensorflow;
-using Buffer = Tensorflow.Buffer;
 using static Tensorflow.Binding;
+using Buffer = Tensorflow.Buffer;
 
 namespace TensorFlowNET.UnitTest.NativeAPI
 {

test/TensorFlowNET.UnitTest/NativeAPI/c_test_util.cs

@@ -1,8 +1,6 @@
 using System;
 using System.Diagnostics.CodeAnalysis;
-using System.Runtime.CompilerServices;
 using Tensorflow;
-using Tensorflow.Functions;
 using Tensorflow.Util;
 using Buffer = Tensorflow.Buffer;
 
@@ -90,16 +88,19 @@ namespace TensorFlowNET.UnitTest
                     if (attr.Value.Type == DataType.DtInt32)
                     {
                         found_t = true;
-                    } else
+                    }
+                    else
                     {
                         return false;
                     }
-                } else if (attr.Key == "N")
+                }
+                else if (attr.Key == "N")
                 {
                     if (attr.Value.I == n)
                     {
                         found_n = true;
-                    } else
+                    }
+                    else
                     {
                         return false;
                     }
@@ -131,11 +132,13 @@ namespace TensorFlowNET.UnitTest
                     if (attr.Value.Type == DataType.DtInt32)
                     {
                         found_dtype = true;
-                    } else
+                    }
+                    else
                     {
                         return false;
                     }
-                } else if (attr.Key == "shape")
+                }
+                else if (attr.Key == "shape")
                 {
                     found_shape = true;
                 }
@@ -160,18 +163,21 @@ namespace TensorFlowNET.UnitTest
                     if (attr.Value.Type == DataType.DtInt32)
                     {
                         found_dtype = true;
-                    } else
+                    }
+                    else
                     {
                         return false;
                     }
-                } else if (attr.Key == "value")
+                }
+                else if (attr.Key == "value")
                 {
                     if (attr.Value.Tensor != null &&
                         attr.Value.Tensor.IntVal.Count == 1 &&
                         attr.Value.Tensor.IntVal[0] == v)
                     {
                         found_value = true;
-                    } else
+                    }
+                    else
                     {
                         return false;
                     }

test/TensorFlowNET.UnitTest/OperationsTest.cs

@@ -1,13 +1,13 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
+using NumSharp;
 using System;
 using System.Collections.Generic;
 using System.Linq;
-using NumSharp;
 using Tensorflow;
+using Tensorflow.UnitTest;
 using Tensorflow.Util;
-using Buffer = Tensorflow.Buffer;
 using static Tensorflow.Binding;
-using Tensorflow.UnitTest;
+using Buffer = Tensorflow.Buffer;
 
 namespace TensorFlowNET.UnitTest.Basics
 {
@@ -41,9 +41,9 @@ namespace TensorFlowNET.UnitTest.Basics
             var b = tf.placeholder(tf.float32);
             var c = tf.add(a, b);
 
-            using(var sess = tf.Session())
+            using (var sess = tf.Session())
             {
-                var o = sess.run(c, 
+                var o = sess.run(c,
                     new FeedItem(a, 3.0f),
                     new FeedItem(b, 2.0f));
                 Assert.AreEqual((float)o, 5.0f);
@@ -123,7 +123,7 @@ namespace TensorFlowNET.UnitTest.Basics
                 Assert.IsTrue(o.array_equal(check));
             }
 
-            b = tf.cumsum(a, exclusive:true, reverse: true);
+            b = tf.cumsum(a, exclusive: true, reverse: true);
             check = np.array(15, 14, 12, 9, 5, 0);
 
             using (var sess = tf.Session())
@@ -136,7 +136,7 @@ namespace TensorFlowNET.UnitTest.Basics
         [TestMethod]
         public void logicalOpsTest()
         {
-            var a = tf.constant(new[] {1f, 2f, 3f, 4f, -4f, -3f, -2f, -1f});
+            var a = tf.constant(new[] { 1f, 2f, 3f, 4f, -4f, -3f, -2f, -1f });
             var b = tf.less(a, 0f);
             var c = tf.greater(a, 0f);
             var d = tf.cast(tf.logical_and(b, c), tf.int32);
@@ -516,7 +516,7 @@ namespace TensorFlowNET.UnitTest.Basics
 
         private IEnumerable<int> MultiplyArray(IReadOnlyCollection<int> first, IReadOnlyCollection<int> second)
         {
-            if(first.Count != second.Count)
+            if (first.Count != second.Count)
                 throw new ArgumentException("Arrays should be of equal size!");
 
             var firstEnumerator = first.GetEnumerator();
@@ -535,7 +535,7 @@ namespace TensorFlowNET.UnitTest.Basics
         }
         private IEnumerable<float> MultiplyArray(IReadOnlyCollection<float> first, IReadOnlyCollection<float> second)
         {
-            if(first.Count != second.Count)
+            if (first.Count != second.Count)
                 throw new ArgumentException("Arrays should be of equal size!");
 
             var firstEnumerator = first.GetEnumerator();
@@ -554,7 +554,7 @@ namespace TensorFlowNET.UnitTest.Basics
         }
         private IEnumerable<double> MultiplyArray(IReadOnlyCollection<double> first, IReadOnlyCollection<double> second)
         {
-            if(first.Count != second.Count)
+            if (first.Count != second.Count)
                 throw new ArgumentException("Arrays should be of equal size!");
 
             var firstEnumerator = first.GetEnumerator();
@@ -788,7 +788,7 @@ namespace TensorFlowNET.UnitTest.Basics
 
             var firstFloatFeed = Enumerable.Repeat(firstFloatVal, rows * cols).ToArray();
             var secondFloatFeed = Enumerable.Repeat(secondFloatVal, rows * cols).ToArray();
-            var floatResult = MultiplyArray(firstFloatFeed, secondFloatFeed.Select(x => 1/x).ToArray()).Sum();
+            var floatResult = MultiplyArray(firstFloatFeed, secondFloatFeed.Select(x => 1 / x).ToArray()).Sum();
 
             a = tf.placeholder(tf.float32, shape: new TensorShape(rows, cols));
             b = tf.placeholder(tf.float32, shape: new TensorShape(rows, cols));
@@ -837,7 +837,7 @@ namespace TensorFlowNET.UnitTest.Basics
 
             var firstDoubleFeed = Enumerable.Repeat(firstDoubleVal, rows * cols).ToArray();
             var secondDoubleFeed = Enumerable.Repeat(secondDoubleVal, rows * cols).ToArray();
-            var doubleResult = MultiplyArray(firstDoubleFeed, secondDoubleFeed.Select(x => 1/x).ToArray()).Sum();
+            var doubleResult = MultiplyArray(firstDoubleFeed, secondDoubleFeed.Select(x => 1 / x).ToArray()).Sum();
 
             a = tf.placeholder(tf.float64, shape: new TensorShape(rows, cols));
             b = tf.placeholder(tf.float64, shape: new TensorShape(rows, cols));

test/TensorFlowNET.UnitTest/PythonTest.cs

@@ -1,9 +1,9 @@
-using System;
-using System.Collections;
-using System.Linq;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Newtonsoft.Json.Linq;
 using NumSharp;
+using System;
+using System.Collections;
+using System.Linq;
 using Tensorflow;
 using static Tensorflow.Binding;
 
@@ -37,7 +37,8 @@ namespace TensorFlowNET.UnitTest
             {
                 /*if (g[i] is NDArray && e[i] is NDArray)
                     assertItemsEqual((g[i] as NDArray).GetData<object>(), (e[i] as NDArray).GetData<object>());
-                else*/ if (e[i] is ICollection && g[i] is ICollection)
+                else*/
+                if (e[i] is ICollection && g[i] is ICollection)
                     assertEqual(g[i], e[i]);
                 else
                     Assert.AreEqual(e[i], g[i], $"Items differ at index {i}, expected {e[i]} but got {g[i]}");
@@ -183,11 +184,11 @@ namespace TensorFlowNET.UnitTest
             {
                 using (var sess = tf.Session())
                 {
-                    var ndarray=tensor.eval(sess);
+                    var ndarray = tensor.eval(sess);
                     if (typeof(T) == typeof(double))
                     {
                         double x = ndarray;
-                        result=x;
+                        result = x;
                     }
                     else if (typeof(T) == typeof(int))
                     {

test/TensorFlowNET.UnitTest/Training/BasicLinearModel.cs

@@ -1,8 +1,5 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using NumSharp;
 using System;
-using System.Collections.Generic;
-using System.Text;
 using Tensorflow;
 using static Tensorflow.Binding;
 
@@ -27,7 +24,7 @@ namespace TensorFlowNET.UnitTest.Training
             Func<Tensor, Tensor> model = (x) => W * x + b;
 
             // Define the loss function
-            Func<Tensor, Tensor, Tensor> loss = (target_y, predicted_y) 
+            Func<Tensor, Tensor, Tensor> loss = (target_y, predicted_y)
                 => tf.reduce_mean(tf.square(target_y - predicted_y));
 
             int NUM_EXAMPLES = 1000;
@@ -54,7 +51,7 @@ namespace TensorFlowNET.UnitTest.Training
                 };
 
             var epochs = range(10);
-            foreach(var epoch in epochs)
+            foreach (var epoch in epochs)
             {
                 var current_loss = train(inputs, outputs, 0.1f);
                 print($"Epoch {epoch}: W={(float)W.numpy()} b={(float)b.numpy()}, loss={(float)current_loss.numpy()}");

test/TensorFlowNET.UnitTest/Utilities/MultiThreadedUnitTestExecuter.cs

@@ -1,7 +1,6 @@
 using System;
 using System.Diagnostics;
 using System.Threading;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
 
 namespace TensorFlowNET.UnitTest
 {
@@ -46,7 +45,7 @@ namespace TensorFlowNET.UnitTest
             if (workload == null) throw new ArgumentNullException(nameof(workload));
             if (postRun == null) throw new ArgumentNullException(nameof(postRun));
             if (threadCount <= 0) throw new ArgumentOutOfRangeException(nameof(threadCount));
-            new MultiThreadedUnitTestExecuter(threadCount) {PostRun = postRun}.Run(workload);
+            new MultiThreadedUnitTestExecuter(threadCount) { PostRun = postRun }.Run(workload);
         }
 
         #endregion
@@ -81,12 +80,14 @@ namespace TensorFlowNET.UnitTest
                     try
                     {
                         workloads[0](0);
-                    } catch (Exception e)
+                    }
+                    catch (Exception e)
                     {
                         if (Debugger.IsAttached)
                             throw;
                         ex = e;
-                    } finally
+                    }
+                    finally
                     {
                         done_barrier2.Release(1);
                     }
@@ -111,12 +112,14 @@ namespace TensorFlowNET.UnitTest
                 try
                 {
                     core(threadid);
-                } catch (Exception e)
+                }
+                catch (Exception e)
                 {
                     if (Debugger.IsAttached)
                         throw;
                     return e;
-                } finally
+                }
+                finally
                 {
                     done_barrier2.Release(1);
                 }
@@ -133,7 +136,8 @@ namespace TensorFlowNET.UnitTest
                     var i_local = i;
                     Threads[i] = new Thread(() => Exceptions[i_local] = ThreadCore(workload, i_local));
                 }
-            } else
+            }
+            else
             {
                 for (int i = 0; i < ThreadCount; i++)
                 {

test/TensorFlowNET.UnitTest/Utilities/PrivateObject.cs

@@ -4,7 +4,6 @@
 namespace Microsoft.VisualStudio.TestTools.UnitTesting
 {
     using System;
-    using System.Collections.Generic;
     //using System.Diagnostics;
     //using System.Diagnostics.CodeAnalysis;
     using System.Globalization;
@@ -239,8 +238,9 @@ namespace Microsoft.VisualStudio.TestTools.UnitTesting
                 //Debug.Assert(this.target != null, "target should not be null.");
                 if (typeof(PrivateObject) == obj?.GetType())
                 {
-                    return this.target.Equals(((PrivateObject) obj).target);
-                } else
+                    return this.target.Equals(((PrivateObject)obj).target);
+                }
+                else
                 {
                     return false;
                 }
@@ -571,7 +571,7 @@ namespace Microsoft.VisualStudio.TestTools.UnitTesting
         public void SetFieldOrProperty(string name, BindingFlags bindingFlags, object value)
         {
             Helper.CheckParameterNotNull(name, "name", string.Empty);
-            this.InvokeHelper(name, BindingFlags.SetField | BindingFlags.SetProperty | bindingFlags, new object[] {value}, CultureInfo.InvariantCulture);
+            this.InvokeHelper(name, BindingFlags.SetField | BindingFlags.SetProperty | bindingFlags, new object[] { value }, CultureInfo.InvariantCulture);
         }
 
         ///// <summary>
@@ -745,7 +745,8 @@ namespace Microsoft.VisualStudio.TestTools.UnitTesting
             try
             {
                 return this.originalType.InvokeMember(name, bindingFlags, null, this.target, args, culture);
-            } catch (TargetInvocationException e)
+            }
+            catch (TargetInvocationException e)
             {
                 //Debug.Assert(e.InnerException != null, "Inner exception should not be null.");
                 if (e.InnerException != null)

test/TensorFlowNET.UnitTest/Utilities/PrivateObjectExtensions.cs

@@ -219,7 +219,7 @@ namespace System
                     return true;
                 }
             }
-            catch(MissingMethodException)
+            catch (MissingMethodException)
             {
                 // When getter only property name is given, the property is found but fails to set.
                 return false;

test/TensorFlowNET.UnitTest/Utilities/PrivateType.cs

@@ -375,7 +375,7 @@ namespace Microsoft.VisualStudio.TestTools.UnitTesting
         public void SetStaticFieldOrProperty(string name, BindingFlags bindingFlags, object value)
         {
             Helper.CheckParameterNotNull(name, "name", string.Empty);
-            this.InvokeHelperStatic(name, BindingFlags.SetField | BindingFlags.SetProperty | bindingFlags | BindingFlags.Static, new[] {value}, CultureInfo.InvariantCulture);
+            this.InvokeHelperStatic(name, BindingFlags.SetField | BindingFlags.SetProperty | bindingFlags | BindingFlags.Static, new[] { value }, CultureInfo.InvariantCulture);
         }
 
         ///// <summary>
@@ -509,7 +509,8 @@ namespace Microsoft.VisualStudio.TestTools.UnitTesting
             try
             {
                 return this.type.InvokeMember(name, bindingFlags | BindToEveryThing | BindingFlags.Static, null, null, args, culture);
-            } catch (TargetInvocationException e)
+            }
+            catch (TargetInvocationException e)
             {
                 //Debug.Assert(e.InnerException != null, "Inner Exception should not be null.");
                 if (e.InnerException != null)

test/TensorFlowNET.UnitTest/Utilities/TestHelper.cs

@@ -1,7 +1,4 @@
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Text;
+using System.IO;
 
 namespace TensorFlowNET.UnitTest
 {
@@ -11,6 +8,6 @@ namespace TensorFlowNET.UnitTest
         {
             var dir = Path.Combine(Directory.GetCurrentDirectory(), "..", "..", "..", "..", "..", "data");
             return Path.GetFullPath(Path.Combine(dir, fileName));
-        } 
+        }
     }
 }

test/TensorFlowNET.UnitTest/control_flow_ops_test/ShapeTestCase.cs

@@ -14,10 +14,10 @@ namespace TensorFlowNET.UnitTest.control_flow_ops_test
         [TestMethod]
         public void testShape()
         {
-            var tensor = constant_op.constant(new[]{1.0, 2.0});
-            self.assertEquals(new int[] {2}, tensor.shape);
-            self.assertEquals(new int[] {2},
-                control_flow_ops.with_dependencies(new[] {constant_op.constant(1.0).op}, tensor).shape);
+            var tensor = constant_op.constant(new[] { 1.0, 2.0 });
+            self.assertEquals(new int[] { 2 }, tensor.shape);
+            self.assertEquals(new int[] { 2 },
+                control_flow_ops.with_dependencies(new[] { constant_op.constant(1.0).op }, tensor).shape);
         }
 
     }

test/TensorFlowNET.UnitTest/control_flow_ops_test/SwitchTestCase.cs

@@ -1,5 +1,4 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
-using Tensorflow;
 
 namespace TensorFlowNET.UnitTest.control_flow_ops_test
 {
@@ -16,7 +15,7 @@ namespace TensorFlowNET.UnitTest.control_flow_ops_test
         {
 
             //var embedding_matrix = variable_scope.get_variable(
-                //"embedding_matrix", initializer: new double[,] { { 2.0 }, { 3.0 } }, use_resource: true);
+            //"embedding_matrix", initializer: new double[,] { { 2.0 }, { 3.0 } }, use_resource: true);
 
             /*
             Tensor cond(Tensor it, Tensor _)

test/TensorFlowNET.UnitTest/control_flow_ops_test/WhileContextTestCase.cs

@@ -1,5 +1,5 @@
-using System;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using System;
 using Tensorflow;
 using Tensorflow.UnitTest;
 using static Tensorflow.Binding;

test/TensorFlowNET.UnitTest/functional_ops_test/ScanTestCase.cs

@@ -1,6 +1,6 @@
-using System;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using NumSharp;
+using System;
 using Tensorflow;
 using Tensorflow.UnitTest;
 using static Tensorflow.Binding;
@@ -17,24 +17,24 @@ namespace TensorFlowNET.UnitTest.functional_ops_test
         public void ScanForward()
         {
             var fn = new Func<Tensor, Tensor, Tensor>((a, x) => tf.add(a, x));
-            
+
             var sess = tf.Session().as_default();
 
             var input = tf.placeholder(TF_DataType.TF_INT32, new TensorShape(6));
             var scan = functional_ops.scan(fn, input);
-            sess.run(scan, (input, np.array(1,2,3,4,5,6))).Should().Be(np.array(1,3,6,10,15,21));
+            sess.run(scan, (input, np.array(1, 2, 3, 4, 5, 6))).Should().Be(np.array(1, 3, 6, 10, 15, 21));
         }
 
         [TestMethod, Ignore("need UpdateEdge API")]
         public void ScanReverse()
         {
             var fn = new Func<Tensor, Tensor, Tensor>((a, x) => tf.add(a, x));
-            
+
             var sess = tf.Session().as_default();
 
             var input = tf.placeholder(TF_DataType.TF_INT32, new TensorShape(6));
-            var scan = functional_ops.scan(fn, input, reverse:true);
-            sess.run(scan, (input, np.array(1,2,3,4,5,6))).Should().Be(np.array(21,20,18,15,11,6));
+            var scan = functional_ops.scan(fn, input, reverse: true);
+            sess.run(scan, (input, np.array(1, 2, 3, 4, 5, 6))).Should().Be(np.array(21, 20, 18, 15, 11, 6));
         }
     }
 }

test/TensorFlowNET.UnitTest/nest_test/NestTest.cs

@@ -1,9 +1,9 @@
-using System;
-using System.Collections;
-using System.Collections.Generic;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Newtonsoft.Json.Linq;
 using NumSharp;
+using System;
+using System.Collections;
+using System.Collections.Generic;
 using Tensorflow;
 using Tensorflow.UnitTest;
 using Tensorflow.Util;
@@ -393,11 +393,11 @@ namespace TensorFlowNET.UnitTest.nest_test
             var s = JArray.FromObject(structure1_plus1).ToString();
             Console.WriteLine(s);
             //    nest.assert_same_structure(structure1, structure1_plus1)
-            self.assertAllEqual( nest.flatten(structure1_plus1), new object[] { 2, 3, 4, 5, 6, 7 });
+            self.assertAllEqual(nest.flatten(structure1_plus1), new object[] { 2, 3, 4, 5, 6, 7 });
             self.assertAllEqual(nest.flatten(structure1_strings), new object[] { "1", "2", "3", "4", "5", "6" });
             var structure1_plus_structure2 = nest.map_structure(x => (int)(x[0]) + (int)(x[1]), structure1, structure2);
             self.assertEqual(
-                new object[] { new object[] { new object[] { 1 + 7, 2 + 8}, 3 + 9}, 4 + 10, new object[] { 5 + 11, 6 + 12}},
+                new object[] { new object[] { new object[] { 1 + 7, 2 + 8 }, 3 + 9 }, 4 + 10, new object[] { 5 + 11, 6 + 12 } },
                 structure1_plus_structure2);
 
             //    self.assertEqual(3, nest.map_structure(lambda x: x - 1, 4))

test/TensorFlowNET.UnitTest/ops_test/ControlDependenciesTest.cs

@@ -1,8 +1,6 @@
-using System;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
 using System.Linq;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow;
-using Tensorflow.Eager;
 using Tensorflow.UnitTest;
 using static Tensorflow.Binding;
 
@@ -193,25 +191,25 @@ namespace TensorFlowNET.UnitTest.ops_test
             });
 
             // Note assertItemsEqual(given, expected), expected and given parameters should be swapped below 
-            assertItemsEqual(new[] {a_1.op}, b_1.op.control_inputs);
-            assertItemsEqual(new[] {a_1.op, a_2.op}, b_2.op.control_inputs);
-            assertItemsEqual(new[] { a_1.op, a_2.op}, b_3.op.control_inputs);
-            assertItemsEqual(new[] {a_1.op, a_2.op}, b_4.op.control_inputs);
+            assertItemsEqual(new[] { a_1.op }, b_1.op.control_inputs);
+            assertItemsEqual(new[] { a_1.op, a_2.op }, b_2.op.control_inputs);
+            assertItemsEqual(new[] { a_1.op, a_2.op }, b_3.op.control_inputs);
+            assertItemsEqual(new[] { a_1.op, a_2.op }, b_4.op.control_inputs);
 
             assertItemsEqual(new object[0], c_1.op.control_inputs);
-            assertItemsEqual(new[] {a_2.op}, c_2.op.control_inputs);
-            assertItemsEqual(new[] {a_2.op, a_3.op}, c_3.op.control_inputs);
-            assertItemsEqual(new[] {a_2.op, a_3.op, a_4.op}, c_4.op.control_inputs);
+            assertItemsEqual(new[] { a_2.op }, c_2.op.control_inputs);
+            assertItemsEqual(new[] { a_2.op, a_3.op }, c_3.op.control_inputs);
+            assertItemsEqual(new[] { a_2.op, a_3.op, a_4.op }, c_4.op.control_inputs);
 
             assertItemsEqual(new object[0], d_1.op.control_inputs);
             assertItemsEqual(new object[0], d_2.op.control_inputs);
             assertItemsEqual(new object[0], d_3.op.control_inputs);
             assertItemsEqual(new object[0], d_4.op.control_inputs);
 
-            assertItemsEqual(new[] {a_1.op}, e_1.op.control_inputs);
-            assertItemsEqual(new[] {a_2.op}, e_2.op.control_inputs);
-            assertItemsEqual(new[] {a_3.op}, e_3.op.control_inputs);
-            assertItemsEqual(new[] {a_4.op}, e_4.op.control_inputs);
+            assertItemsEqual(new[] { a_1.op }, e_1.op.control_inputs);
+            assertItemsEqual(new[] { a_2.op }, e_2.op.control_inputs);
+            assertItemsEqual(new[] { a_3.op }, e_3.op.control_inputs);
+            assertItemsEqual(new[] { a_4.op }, e_4.op.control_inputs);
         }
 
         [Ignore("Don't know how to create an operation with two outputs")]

test/TensorFlowNET.UnitTest/ops_test/CreateOpFromTfOperationTest.cs

@@ -1,6 +1,6 @@
-using System;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using System;
 using System.Linq;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
 using Tensorflow;
 using Tensorflow.Operations;
 using Tensorflow.UnitTest;
@@ -28,14 +28,14 @@ namespace TensorFlowNET.UnitTest.ops_test
         {
             using (var g = tf.Graph().as_default())
             {
-                var x = constant_op.constant(new[,] {{1, 2, 3}, {4, 5, 6}});
-                var (c_op, op_desc) = ops._create_c_op(g, ops._NodeDef("Identity", "myop"), new[] {x}, new Operation[0]);
+                var x = constant_op.constant(new[,] { { 1, 2, 3 }, { 4, 5, 6 } });
+                var (c_op, op_desc) = ops._create_c_op(g, ops._NodeDef("Identity", "myop"), new[] { x }, new Operation[0]);
                 var op = g._create_op_from_tf_operation(c_op);
 
                 Assert.AreEqual("myop", op.name);
                 Assert.AreEqual("Identity", op.type);
                 Assert.AreEqual(1, len(op.outputs));
-                assertItemsEqual(new[] {2, 3}, op.outputs[0].shape);
+                assertItemsEqual(new[] { 2, 3 }, op.outputs[0].shape);
             }
         }
 
@@ -101,7 +101,7 @@ namespace TensorFlowNET.UnitTest.ops_test
         public void TestWhileLoop()
         {
             var graph = tf.Graph().as_default();
-            Operation x=null;
+            Operation x = null;
             x = constant_op.constant(42);
             var body = new Func<int, int>(i =>
             {
@@ -120,7 +120,7 @@ namespace TensorFlowNET.UnitTest.ops_test
             self.assertEqual(op.outputs.Length, 0);
             var op_input = op.inputs[0].op;
             self.assertEqual(op_input.type, "Enter");
-            self.assertItemsEqual(op_input.inputs.OfType<Operation>().ToArray(), new[] {x});
+            self.assertItemsEqual(op_input.inputs.OfType<Operation>().ToArray(), new[] { x });
             self.assertEqual(op.graph, graph);
             self.assertIsNotNone(op._get_control_flow_context());
             self.assertEqual(((ControlFlowContext)op._get_control_flow_context()).Name, "myloop/while_context");

test/TensorFlowNET.UnitTest/ops_test/GraphTest.cs

@@ -22,7 +22,8 @@ namespace TensorFlowNET.UnitTest.ops_test
             ops.reset_default_graph();
         }
 
-        private void _AssertDefault(Graph expected) {
+        private void _AssertDefault(Graph expected)
+        {
             Assert.AreSame(ops.get_default_graph(), expected);
         }
 
@@ -31,13 +32,13 @@ namespace TensorFlowNET.UnitTest.ops_test
         [TestMethod]
         public void testResetDefaultGraphNesting()
         {
-/*
-          def testResetDefaultGraphNesting(self):
-            g0 = ops.Graph()
-            with self.assertRaises(AssertionError):
-              with g0.as_default():
-                ops.reset_default_graph()
-*/
+            /*
+                      def testResetDefaultGraphNesting(self):
+                        g0 = ops.Graph()
+                        with self.assertRaises(AssertionError):
+                          with g0.as_default():
+                            ops.reset_default_graph()
+            */
         }
 
         [Ignore("Todo: Port")]