word2vec in progress.

test/TensorFlowNET.Examples/Text/Word2Vec.cs

@@ -1,7 +1,10 @@
 using System;
 using System.Collections.Generic;
+using System.IO;
+using System.Linq;
 using System.Text;
 using Tensorflow;
+using TensorFlowNET.Examples.Utility;
 
 namespace TensorFlowNET.Examples
 {
@@ -24,6 +27,17 @@ namespace TensorFlowNET.Examples
 
         // Evaluation Parameters
         string[] eval_words = new string[] { "five", "of", "going", "hardware", "american", "britain" };
+        string[] text_words;
+
+        // Word2Vec Parameters
+        int embedding_size = 200; // Dimension of the embedding vector
+        int max_vocabulary_size = 50000; // Total number of different words in the vocabulary
+        int min_occurrence = 10; // Remove all words that does not appears at least n times
+        int skip_window = 3; // How many words to consider left and right
+        int num_skips = 2; // How many times to reuse an input to generate a label
+        int num_sampled = 64; // Number of negative examples to sample
+
+        int data_index;
 
         public bool Run()
         {
@@ -33,12 +47,78 @@ namespace TensorFlowNET.Examples
 
             tf.train.import_meta_graph("graph/word2vec.meta");
 
+            // Initialize the variables (i.e. assign their default value)
+            var init = tf.global_variables_initializer();
+
+            with(tf.Session(graph), sess =>
+            {
+                sess.run(init);
+            });
+
             return false;
         }
 
+        // Generate training batch for the skip-gram model
+        private void next_batch()
+        {
+
+        }
+
         public void PrepareData()
         {
-            var url = "";
+            // Download graph meta
+            var url = "https://github.com/SciSharp/TensorFlow.NET/raw/master/graph/word2vec.meta";
+            Web.Download(url, "graph", "word2vec.meta");
+
+            // Download a small chunk of Wikipedia articles collection
+            url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/text8.zip";
+            Web.Download(url, "word2vec", "text8.zip");
+            // Unzip the dataset file. Text has already been processed
+            Compress.UnZip(@"word2vec\text8.zip", "word2vec");
+
+            int wordId = 0;
+            text_words = File.ReadAllText(@"word2vec\text8").Trim().ToLower().Split();
+            // Build the dictionary and replace rare words with UNK token
+            var word2id = text_words.GroupBy(x => x)
+                .Select(x => new WordId
+                {
+                    Word = x.Key,
+                    Occurrence = x.Count()
+                })
+                .Where(x => x.Occurrence >= min_occurrence) // Remove samples with less than 'min_occurrence' occurrences
+                .OrderByDescending(x => x.Occurrence) // Retrieve the most common words
+                .Select(x => new WordId
+                {
+                    Word = x.Word,
+                    Id = ++wordId, // Assign an id to each word
+                    Occurrence = x.Occurrence
+                })
+                .ToList();
+
+            // Retrieve a word id, or assign it index 0 ('UNK') if not in dictionary
+            var data = (from word in text_words
+                        join id in word2id on word equals id.Word into wi
+                        from wi2 in wi.DefaultIfEmpty()
+                        select wi2 == null ? 0 : wi2.Id).ToList();
+
+            word2id.Insert(0, new WordId { Word = "UNK", Id = 0, Occurrence = data.Count(x => x == 0) });
+
+            print($"Words count: {text_words.Length}");
+            print($"Unique words: {text_words.Distinct().Count()}");
+            print($"Vocabulary size: {word2id.Count}");
+            print($"Most common words: {string.Join(", ", word2id.Take(10))}");
+        }
+
+        private class WordId
+        {
+            public string Word { get; set; }
+            public int Id { get; set; }
+            public int Occurrence { get; set; }
+
+            public override string ToString()
+            {
+                return Word + " " + Id + " " + Occurrence;
+            }
         }
     }
 }