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abstract PrepareData interface for example
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‎README.md

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@@ -72,6 +72,7 @@ Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflow
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* [Basic Operations](test/TensorFlowNET.Examples/BasicOperations.cs)
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* [Image Recognition](test/TensorFlowNET.Examples/ImageRecognition.cs)
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* [Linear Regression](test/TensorFlowNET.Examples/LinearRegression.cs)
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* [Logistic Regression](test/TensorFlowNET.Examples/LogisticRegression.cs)
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* [Text Classification](test/TensorFlowNET.Examples/TextClassificationWithMovieReviews.cs)
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* [CNN Text Classification](test/TensorFlowNET.Examples/CnnTextClassification.cs)
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* [Naive Bayes Classification](test/TensorFlowNET.Examples/NaiveBayesClassifier.cs)

‎src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

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@@ -17,7 +17,8 @@
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<Description>Google's TensorFlow binding in .NET Standard.
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Docs: https://tensorflownet.readthedocs.io</Description>
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<AssemblyVersion>0.5.0.0</AssemblyVersion>
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<PackageReleaseNotes>Add a lot of APIs to build neural networks model</PackageReleaseNotes>
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<PackageReleaseNotes>Add Logistic Regression to do MNIST.
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Add a lot of APIs to build neural networks model</PackageReleaseNotes>
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<LangVersion>7.2</LangVersion>
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<FileVersion>0.5.0.0</FileVersion>
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</PropertyGroup>

‎test/TensorFlowNET.Examples/BasicEagerApi.cs

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@@ -12,6 +12,7 @@ namespace TensorFlowNET.Examples
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public class BasicEagerApi : IExample
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{
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private Tensor a, b, c, d;
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public void Run()
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{
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// Set Eager API
@@ -34,5 +35,9 @@ public void Run()
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// Full compatibility with Numpy
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}
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public void PrepareData()
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{
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}
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}
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}

‎test/TensorFlowNET.Examples/BasicOperations.cs

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@@ -96,5 +96,9 @@ public void Run()
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}
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}
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}
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public void PrepareData()
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{
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}
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}
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}

‎test/TensorFlowNET.Examples/HelloWorld.cs

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@@ -33,5 +33,9 @@ of the Constant op. */
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}
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}
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}
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public void PrepareData()
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{
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}
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}
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}

‎test/TensorFlowNET.Examples/IExample.cs

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@@ -11,5 +11,6 @@ namespace TensorFlowNET.Examples
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public interface IExample
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{
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void Run();
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void PrepareData();
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}
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}

‎test/TensorFlowNET.Examples/ImageRecognition.cs

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@@ -78,7 +78,7 @@ private NDArray ReadTensorFromImageFile(string file_name,
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});
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}
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private void PrepareData()
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public void PrepareData()
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{
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Directory.CreateDirectory(dir);
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‎test/TensorFlowNET.Examples/InceptionArchGoogLeNet.cs

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@@ -83,7 +83,7 @@ private NDArray ReadTensorFromImageFile(string file_name,
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});
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}
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private void PrepareData()
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public void PrepareData()
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{
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Directory.CreateDirectory(dir);
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‎test/TensorFlowNET.Examples/LinearRegression.cs

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@@ -19,14 +19,13 @@ public class LinearRegression : Python, IExample
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int training_epochs = 1000;
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int display_step = 50;
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NDArray train_X, train_Y;
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int n_samples;
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public void Run()
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{
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// Training Data
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var train_X = np.array(3.3f, 4.4f, 5.5f, 6.71f, 6.93f, 4.168f, 9.779f, 6.182f, 7.59f, 2.167f,
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7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f);
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var train_Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f,
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2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f);
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var n_samples = train_X.shape[0];
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PrepareData();
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// tf Graph Input
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var X = tf.placeholder(tf.float32);
@@ -95,5 +94,14 @@ public void Run()
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Console.WriteLine($"Absolute mean square loss difference: {Math.Abs((float)training_cost - (float)testing_cost)}");
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});
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}
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public void PrepareData()
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{
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train_X = np.array(3.3f, 4.4f, 5.5f, 6.71f, 6.93f, 4.168f, 9.779f, 6.182f, 7.59f, 2.167f,
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7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f);
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train_Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f,
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2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f);
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n_samples = train_X.shape[0];
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}
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}
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}

‎test/TensorFlowNET.Examples/LogisticRegression.cs

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@@ -21,9 +21,11 @@ public class LogisticRegression : Python, IExample
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private int batch_size = 100;
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private int display_step = 1;
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Datasets mnist;
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public void Run()
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{
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var mnist = PrepareData();
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PrepareData();
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// tf Graph Input
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var x = tf.placeholder(tf.float32, new TensorShape(-1, 784)); // mnist data image of shape 28*28=784
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});
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}
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private Datasets PrepareData()
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public void PrepareData()
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{
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var mnist = MnistDataSet.read_data_sets("logistic_regression", one_hot: true);
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return mnist;
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mnist = MnistDataSet.read_data_sets("logistic_regression", one_hot: true);
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}
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}
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}

‎test/TensorFlowNET.Examples/MetaGraph.cs

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@@ -27,5 +27,9 @@ private void ImportMetaGraph(string dir)
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logits: logits);
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});
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}
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public void PrepareData()
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{
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}
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}
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}

‎test/TensorFlowNET.Examples/NaiveBayesClassifier.cs

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@@ -15,9 +15,9 @@ public class NaiveBayesClassifier : Python, IExample
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public Normal dist { get; set; }
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public void Run()
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{
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np.array<float>(1.0f, 1.0f);
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var X = np.array<float>(new float[][] { new float[] { 1.0f, 1.0f }, new float[] { 2.0f, 2.0f }, new float[] { -1.0f, -1.0f }, new float[] { -2.0f, -2.0f }, new float[] { 1.0f, -1.0f }, new float[] { 2.0f, -2.0f }, });
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var y = np.array<int>(0,0,1,1,2,2);
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np.array(1.0f, 1.0f);
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var X = np.array(new float[][] { new float[] { 1.0f, 1.0f }, new float[] { 2.0f, 2.0f }, new float[] { -1.0f, -1.0f }, new float[] { -2.0f, -2.0f }, new float[] { 1.0f, -1.0f }, new float[] { 2.0f, -2.0f }, });
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var y = np.array(0,0,1,1,2,2);
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fit(X, y);
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// Create a regular grid and classify each point
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}
@@ -102,5 +102,10 @@ public Tensor predict (NDArray X)
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// exp to get the actual probabilities
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return tf.exp(log_prob);
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}
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public void PrepareData()
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{
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}
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}
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}

‎test/TensorFlowNET.Examples/NamedEntityRecognition.cs

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{
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throw new NotImplementedException();
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}
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public void PrepareData()
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{
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throw new NotImplementedException();
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}
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}
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}

‎test/TensorFlowNET.Examples/TextClassification/TextClassificationTrain.cs

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@@ -23,7 +23,7 @@ public class TextClassificationTrain : Python, IExample
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public void Run()
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{
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download_dbpedia();
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PrepareData();
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Console.WriteLine("Building dataset...");
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var (x, y, alphabet_size) = DataHelpers.build_char_dataset("train", "vdcnn", CHAR_MAX_LEN);
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with(tf.Session(), sess =>
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{
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new VdCnn(alphabet_size, CHAR_MAX_LEN, NUM_CLASS);
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});
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}
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public void download_dbpedia()
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{
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string url = "https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz";
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Web.Download(url, dataDir, dataFileName);
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Compress.ExtractTGZ(Path.Join(dataDir, dataFileName), dataDir);
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}
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private (int[][], int[][], int[], int[]) train_test_split(int[][] x, int[] y, float test_size = 0.3f)
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{
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int len = x.Length;
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return (train_x.ToArray(), valid_x.ToArray(), train_y.ToArray(), valid_y.ToArray());
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}
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public void PrepareData()
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{
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string url = "https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz";
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Web.Download(url, dataDir, dataFileName);
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Compress.ExtractTGZ(Path.Join(dataDir, dataFileName), dataDir);
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}
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}
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}

‎test/TensorFlowNET.Examples/TextClassification/TextClassificationWithMovieReviews.cs

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@@ -13,10 +13,11 @@ public class TextClassificationWithMovieReviews : Python, IExample
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{
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string dir = "text_classification_with_movie_reviews";
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string dataFile = "imdb.zip";
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NDArray train_data, train_labels, test_data, test_labels;
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public void Run()
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{
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var((train_data, train_labels), (test_data, test_labels)) = PrepareData();
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PrepareData();
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Console.WriteLine($"Training entries: {train_data.size}, labels: {train_labels.size}");
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model.add(keras.layers.Embedding(vocab_size, 16));
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}
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private ((NDArray, NDArray), (NDArray, NDArray)) PrepareData()
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public void PrepareData()
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{
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Directory.CreateDirectory(dir);
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var y_train = labels_train;
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var y_test = labels_test;
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return ((x_train, y_train), (x_test, y_test));
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x_train = train_data;
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train_labels = y_train;
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test_data = x_test;
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test_labels = y_test;
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}
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private NDArray ReadData(string file)

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