diff --git a/src/TensorFlowNET.Core/TensorFlowNET.Core.csproj b/src/TensorFlowNET.Core/TensorFlowNET.Core.csproj
index f1ee0cdd..51b9fd8e 100644
--- a/src/TensorFlowNET.Core/TensorFlowNET.Core.csproj
+++ b/src/TensorFlowNET.Core/TensorFlowNET.Core.csproj
@@ -54,7 +54,7 @@ Docs: https://tensorflownet.readthedocs.io
-
+
diff --git a/test/KerasNET.Test/Keras.UnitTest.csproj b/test/KerasNET.Test/Keras.UnitTest.csproj
index 1e9a2253..a775ad25 100644
--- a/test/KerasNET.Test/Keras.UnitTest.csproj
+++ b/test/KerasNET.Test/Keras.UnitTest.csproj
@@ -26,7 +26,7 @@
-
+
diff --git a/test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionNN.cs b/test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionNN.cs
new file mode 100644
index 00000000..c46453dd
--- /dev/null
+++ b/test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionNN.cs
@@ -0,0 +1,63 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow;
+using TensorFlowNET.Examples.Utility;
+
+namespace TensorFlowNET.Examples.ImageProcess
+{
+ ///
+ /// Neural Network classifier for Hand Written Digits
+ /// Sample Neural Network architecture with two layers implemented for classifying MNIST digits
+ /// http://www.easy-tensorflow.com/tf-tutorials/neural-networks
+ ///
+ public class DigitRecognitionNN : IExample
+ {
+ public bool Enabled { get; set; } = true;
+ public bool IsImportingGraph { get; set; } = false;
+
+ public string Name => "Digits Recognition Neural Network";
+
+ const int img_h = 28;
+ const int img_w = 28;
+ int img_size_flat = img_h * img_w; // 784, the total number of pixels
+ int n_classes = 10; // Number of classes, one class per digit
+ int training_epochs = 10;
+ int? train_size = null;
+ int validation_size = 5000;
+ int? test_size = null;
+ int batch_size = 100;
+ Datasets mnist;
+
+ public bool Run()
+ {
+ PrepareData();
+ return true;
+ }
+
+ public Graph BuildGraph()
+ {
+ throw new NotImplementedException();
+ }
+
+ public Graph ImportGraph()
+ {
+ throw new NotImplementedException();
+ }
+
+ public bool Predict()
+ {
+ throw new NotImplementedException();
+ }
+
+ public void PrepareData()
+ {
+ mnist = MnistDataSet.read_data_sets("mnist", one_hot: true, train_size: train_size, validation_size: validation_size, test_size: test_size);
+ }
+
+ public bool Train()
+ {
+ throw new NotImplementedException();
+ }
+ }
+}
diff --git a/test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj b/test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj
index 470399ff..176a4d58 100644
--- a/test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj
+++ b/test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj
@@ -17,7 +17,7 @@
-
+
diff --git a/test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs b/test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs
index 883783b2..3339840f 100644
--- a/test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs
+++ b/test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs
@@ -61,11 +61,6 @@ namespace TensorFlowNET.Examples
valid_y = y[new Slice(start: train_size)];
Console.WriteLine("\tDONE");
- train_x = np.Load(Path.Join("word_cnn", "train_x.npy"));
- valid_x = np.Load(Path.Join("word_cnn", "valid_x.npy"));
- train_y = np.Load(Path.Join("word_cnn", "train_y.npy"));
- valid_y = np.Load(Path.Join("word_cnn", "valid_y.npy"));
-
return (train_x, valid_x, train_y, valid_y);
}
diff --git a/test/TensorFlowNET.UnitTest/TensorFlowNET.UnitTest.csproj b/test/TensorFlowNET.UnitTest/TensorFlowNET.UnitTest.csproj
index f76ca132..b8917b91 100644
--- a/test/TensorFlowNET.UnitTest/TensorFlowNET.UnitTest.csproj
+++ b/test/TensorFlowNET.UnitTest/TensorFlowNET.UnitTest.csproj
@@ -16,7 +16,7 @@
-
+