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CNN Text classification.
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‎README.md‎

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@@ -73,6 +73,7 @@ Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflow
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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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* [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)
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* [Named Entity Recognition](test/TensorFlowNET.Examples/NamedEntityRecognition.cs)
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‎test/TensorFlowNET.Examples/CnnTextClassification/CnnTextTrain.cs‎

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using NumSharp.Core;
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using System;
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using System.Collections.Generic;
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using System.Text;
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using Tensorflow;
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namespace TensorFlowNET.Examples.CnnTextClassification
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{
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public class CnnTextTrain : Python, IExample
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{
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// Percentage of the training data to use for validation
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private float dev_sample_percentage = 0.1f;
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// Data source for the positive data.
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private string positive_data_file = "https://raw.githubusercontent.com/dennybritz/cnn-text-classification-tf/master/data/rt-polaritydata/rt-polarity.pos";
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// Data source for the negative data.
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private string negative_data_file = "https://raw.githubusercontent.com/dennybritz/cnn-text-classification-tf/master/data/rt-polaritydata/rt-polarity.neg";
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// Dimensionality of character embedding (default: 128)
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private int embedding_dim = 128;
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// Comma-separated filter sizes (default: '3,4,5')
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private string filter_sizes = "3,4,5";
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// Number of filters per filter size (default: 128)
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private int num_filters = 128;
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// Dropout keep probability (default: 0.5)
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private float dropout_keep_prob = 0.5f;
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// L2 regularization lambda (default: 0.0)
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private float l2_reg_lambda = 0.0f;
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// Batch Size (default: 64)
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private int batch_size = 64;
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// Number of training epochs (default: 200)
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private int num_epochs = 200;
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// Evaluate model on dev set after this many steps (default: 100)
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private int evaluate_every = 100;
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// Save model after this many steps (default: 100)
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private int checkpoint_every = 100;
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// Number of checkpoints to store (default: 5)
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private int num_checkpoints = 5;
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// Allow device soft device placement
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private bool allow_soft_placement = true;
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// Log placement of ops on devices
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private bool log_device_placement = false;
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public void Run()
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{
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var (x_train, y_train, vocab_processor, x_dev, y_dev) = preprocess();
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}
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public (NDArray, NDArray, NDArray, NDArray, NDArray) preprocess()
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{
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DataHelpers.load_data_and_labels(positive_data_file, negative_data_file);
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throw new NotImplementedException("");
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}
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}
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}

‎test/TensorFlowNET.Examples/CnnTextClassification/DataHelpers.cs‎

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using NumSharp.Core;
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using System;
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using System.Collections.Generic;
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using System.IO;
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using System.Linq;
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using System.Text;
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using System.Text.RegularExpressions;
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namespace TensorFlowNET.Examples.CnnTextClassification
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{
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public class DataHelpers
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{
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/// <summary>
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/// Loads MR polarity data from files, splits the data into words and generates labels.
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/// Returns split sentences and labels.
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/// </summary>
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/// <param name="positive_data_file"></param>
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/// <param name="negative_data_file"></param>
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/// <returns></returns>
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public static (NDArray, NDArray) load_data_and_labels(string positive_data_file, string negative_data_file)
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{
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Directory.CreateDirectory("CnnTextClassification");
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Utility.Web.Download(positive_data_file, "CnnTextClassification/rt-polarity.pos");
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Utility.Web.Download(negative_data_file, "CnnTextClassification/rt-polarity.neg");
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// Load data from files
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var positive_examples = File.ReadAllLines("CnnTextClassification/rt-polarity.pos")
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.Select(x => x.Trim())
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.ToArray();
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var negative_examples = File.ReadAllLines("CnnTextClassification/rt-polarity.neg")
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.Select(x => x.Trim())
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.ToArray();
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var x_text = new List<string>();
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x_text.AddRange(positive_examples);
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x_text.AddRange(negative_examples);
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x_text = x_text.Select(x => clean_str(x)).ToList();
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var positive_labels = positive_examples.Select(x => new int[2] { 0, 1 }).ToArray();
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var negative_labels = negative_examples.Select(x => new int[2] { 1, 0 }).ToArray();
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// var y = np.
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// return (x_text, y);
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throw new NotImplementedException("load_data_and_labels");
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}
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private static string clean_str(string str)
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{
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str = Regex.Replace(str, @"[^A-Za-z0-9(),!?\'\`]", " ");
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str = Regex.Replace(str, @"\'s", " \'s");
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return str;
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}
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}
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}

‎test/TensorFlowNET.Examples/CnnTextClassification/TextCNN.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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using Tensorflow;
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namespace TensorFlowNET.Examples.CnnTextClassification
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{
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/// <summary>
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/// Convolutional Neural Network for Text Classification
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/// https://github.com/dennybritz/cnn-text-classification-tf
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/// </summary>
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public class TextCNN : Python
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{
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}
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}

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