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Initial Word2Vec example.
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‎data/text8.zip‎

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‎graph/word2vec.meta‎

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test/TensorFlowNET.Examples/TextClassification/DataHelpers.cs renamed to test/TensorFlowNET.Examples/Text/DataHelpers.cs

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test/TensorFlowNET.Examples/TextClassification/TextClassificationTrain.cs renamed to test/TensorFlowNET.Examples/Text/TextClassificationTrain.cs

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test/TensorFlowNET.Examples/TextClassification/TextClassificationWithMovieReviews.cs renamed to test/TensorFlowNET.Examples/Text/TextClassificationWithMovieReviews.cs

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‎test/TensorFlowNET.Examples/Text/Word2Vec.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
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{
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/// <summary>
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/// Implement Word2Vec algorithm to compute vector representations of words.
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/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/word2vec.py
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/// </summary>
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public class Word2Vec : Python, IExample
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{
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public int Priority => 12;
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public bool Enabled { get; set; } = true;
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public string Name => "Word2Vec";
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// Training Parameters
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float learning_rate = 0.1f;
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int batch_size = 128;
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int num_steps = 3000000;
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int display_step = 10000;
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int eval_step = 200000;
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// Evaluation Parameters
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string[] eval_words = new string[] { "five", "of", "going", "hardware", "american", "britain" };
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public bool Run()
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{
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PrepareData();
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var graph = tf.Graph().as_default();
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tf.train.import_meta_graph("graph/word2vec.meta");
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return false;
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}
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public void PrepareData()
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{
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var url = "";
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}
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}
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}

test/TensorFlowNET.Examples/TextClassification/cnn_models/VdCnn.cs renamed to test/TensorFlowNET.Examples/Text/cnn_models/VdCnn.cs

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