fix variable path for transfer learning and word2vec.

test/TensorFlowNET.Examples/ImageProcessing/RetrainImageClassifier.cs

@@ -74,131 +74,23 @@ namespace TensorFlowNET.Examples.ImageProcess
         Tensor bottleneck_input;
         Tensor cross_entropy;
         Tensor ground_truth_input;
+        Tensor bottleneck_tensor;
+        bool wants_quantization;
+        float test_accuracy;
+        NDArray predictions;
 
         public bool Run()
         {
             PrepareData();
 
-            // Set up the pre-trained graph.
-            var (graph, bottleneck_tensor, resized_image_tensor, wants_quantization) =
-                create_module_graph();
+            var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
 
-            // Add the new layer that we'll be training.
-            with(graph.as_default(), delegate
+            with(tf.Session(graph), sess =>
             {
-                (train_step, cross_entropy, bottleneck_input,
-                 ground_truth_input, final_tensor) = add_final_retrain_ops(
-                     class_count, final_tensor_name, bottleneck_tensor,
-                     wants_quantization, is_training: true);
+                Train(sess);
             });
 
-            var sw = new Stopwatch();
-
-            return with(tf.Session(graph), sess =>
-            {
-                // Initialize all weights: for the module to their pretrained values,
-                // and for the newly added retraining layer to random initial values.
-                var init = tf.global_variables_initializer();
-                sess.run(init);
-
-                var (jpeg_data_tensor, decoded_image_tensor) = add_jpeg_decoding();
-
-                // We'll make sure we've calculated the 'bottleneck' image summaries and
-                // cached them on disk.
-                cache_bottlenecks(sess, image_lists, image_dir,
-                        bottleneck_dir, jpeg_data_tensor,
-                        decoded_image_tensor, resized_image_tensor,
-                        bottleneck_tensor, tfhub_module);
-
-                // Create the operations we need to evaluate the accuracy of our new layer.
-                var (evaluation_step, _) = add_evaluation_step(final_tensor, ground_truth_input);
-
-                // Merge all the summaries and write them out to the summaries_dir
-                var merged = tf.summary.merge_all();
-                var train_writer = tf.summary.FileWriter(summaries_dir + "/train", sess.graph);
-                var validation_writer = tf.summary.FileWriter(summaries_dir + "/validation", sess.graph);
-
-                // Create a train saver that is used to restore values into an eval graph
-                // when exporting models.
-                var train_saver = tf.train.Saver();
-                train_saver.save(sess, CHECKPOINT_NAME);
-
-                sw.Restart();
-
-                for (int i = 0; i < how_many_training_steps; i++)
-                {
-                    var (train_bottlenecks, train_ground_truth, _) = get_random_cached_bottlenecks(
-                         sess, image_lists, train_batch_size, "training",
-                         bottleneck_dir, image_dir, jpeg_data_tensor,
-                         decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
-                         tfhub_module);
-
-                    // Feed the bottlenecks and ground truth into the graph, and run a training
-                    // step. Capture training summaries for TensorBoard with the `merged` op.
-                    var results = sess.run(
-                          new ITensorOrOperation[] { merged, train_step },
-                          new FeedItem(bottleneck_input, train_bottlenecks),
-                          new FeedItem(ground_truth_input, train_ground_truth));
-                    var train_summary = results[0];
-
-                    // TODO
-                    train_writer.add_summary(train_summary, i);
-
-                    // Every so often, print out how well the graph is training.
-                    bool is_last_step = (i + 1 == how_many_training_steps);
-                    if ((i % eval_step_interval) == 0 || is_last_step)
-                    {
-                        results = sess.run(
-                            new Tensor[] { evaluation_step, cross_entropy },
-                            new FeedItem(bottleneck_input, train_bottlenecks),
-                            new FeedItem(ground_truth_input, train_ground_truth));
-                        (float train_accuracy, float cross_entropy_value) = (results[0], results[1]);
-                        print($"{DateTime.Now}: Step {i + 1}: Train accuracy = {train_accuracy * 100}%,  Cross entropy = {cross_entropy_value.ToString("G4")}");
-
-                        var (validation_bottlenecks, validation_ground_truth, _) = get_random_cached_bottlenecks(
-                            sess, image_lists, validation_batch_size, "validation",
-                            bottleneck_dir, image_dir, jpeg_data_tensor,
-                            decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
-                            tfhub_module);
-
-                        // Run a validation step and capture training summaries for TensorBoard
-                        // with the `merged` op.
-                        results = sess.run(new Tensor[] { merged, evaluation_step },
-                            new FeedItem(bottleneck_input, validation_bottlenecks),
-                            new FeedItem(ground_truth_input, validation_ground_truth));
-
-                        (string validation_summary, float validation_accuracy) = (results[0], results[1]);
-
-                        validation_writer.add_summary(validation_summary, i);
-                        print($"{DateTime.Now}: Step {i + 1}: Validation accuracy = {validation_accuracy * 100}% (N={len(validation_bottlenecks)}) {sw.ElapsedMilliseconds}ms");
-                        sw.Restart();
-                    }
-
-                    // Store intermediate results
-                    int intermediate_frequency = intermediate_store_frequency;
-                    if (intermediate_frequency > 0 && i % intermediate_frequency == 0 && i > 0)
-                    {
-
-                    }
-                }
-
-                // After training is complete, force one last save of the train checkpoint.
-                train_saver.save(sess, CHECKPOINT_NAME);
-
-                // We've completed all our training, so run a final test evaluation on
-                // some new images we haven't used before.
-                var (test_accuracy, predictions) = run_final_eval(sess, null, class_count, image_lists,
-                               jpeg_data_tensor, decoded_image_tensor, resized_image_tensor,
-                               bottleneck_tensor);
-
-                // Write out the trained graph and labels with the weights stored as
-                // constants.
-                print($"Save final result to : {output_graph}");
-                save_graph_to_file(output_graph, class_count);
-                File.WriteAllText(output_labels, string.Join("\n", image_lists.Keys));
-
-                return test_accuracy > 0.75f;
-            });
+            return test_accuracy > 0.75f;
         }
 
         /// <summary>
@@ -212,7 +104,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         /// <param name="decoded_image_tensor"></param>
         /// <param name="resized_image_tensor"></param>
         /// <param name="bottleneck_tensor"></param>
-        private (float, NDArray) run_final_eval(Session train_session, object module_spec, int class_count, 
+        private (float, NDArray) run_final_eval(Session train_session, object module_spec, int class_count,
             Dictionary<string, Dictionary<string, string[]>> image_lists,
             Tensor jpeg_data_tensor, Tensor decoded_image_tensor,
             Tensor resized_image_tensor, Tensor bottleneck_tensor)
@@ -233,7 +125,7 @@ namespace TensorFlowNET.Examples.ImageProcess
             return (results[0], results[1]);
         }
 
-        private (Session, Tensor, Tensor, Tensor, Tensor, Tensor) 
+        private (Session, Tensor, Tensor, Tensor, Tensor, Tensor)
             build_eval_session(int class_count)
         {
             // If quantized, we need to create the correct eval graph for exporting.
@@ -245,7 +137,7 @@ namespace TensorFlowNET.Examples.ImageProcess
             with(eval_graph.as_default(), graph =>
             {
                 // Add the new layer for exporting.
-                var (_, _, bottleneck_input, ground_truth_input, final_tensor) = 
+                var (_, _, bottleneck_input, ground_truth_input, final_tensor) =
                     add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
                         wants_quantization, is_training: false);
 
@@ -277,7 +169,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         /// <param name="quantize_layer"></param>
         /// <param name="is_training"></param>
         /// <returns></returns>
-        private (Operation, Tensor, Tensor, Tensor, Tensor) add_final_retrain_ops(int class_count, string final_tensor_name, 
+        private (Operation, Tensor, Tensor, Tensor, Tensor) add_final_retrain_ops(int class_count, string final_tensor_name,
             Tensor bottleneck_tensor, bool quantize_layer, bool is_training)
         {
             var (batch_size, bottleneck_tensor_size) = (bottleneck_tensor.TensorShape.Dimensions[0], bottleneck_tensor.TensorShape.Dimensions[1]);
@@ -365,7 +257,8 @@ namespace TensorFlowNET.Examples.ImageProcess
                 var mean = tf.reduce_mean(var);
                 tf.summary.scalar("mean", mean);
                 Tensor stddev = null;
-                with(tf.name_scope("stddev"), delegate {
+                with(tf.name_scope("stddev"), delegate
+                {
                     stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)));
                 });
                 tf.summary.scalar("stddev", stddev);
@@ -378,7 +271,7 @@ namespace TensorFlowNET.Examples.ImageProcess
         private (Graph, Tensor, Tensor, bool) create_module_graph()
         {
             var (height, width) = (299, 299);
-            
+
             return with(tf.Graph().as_default(), graph =>
             {
                 tf.train.import_meta_graph("graph/InceptionV3.meta");
@@ -390,8 +283,8 @@ namespace TensorFlowNET.Examples.ImageProcess
             });
         }
 
-        private (NDArray, long[], string[]) get_random_cached_bottlenecks(Session sess, Dictionary<string, Dictionary<string, string[]>> image_lists, 
-            int how_many, string category, string bottleneck_dir, string image_dir, 
+        private (NDArray, long[], string[]) get_random_cached_bottlenecks(Session sess, Dictionary<string, Dictionary<string, string[]>> image_lists,
+            int how_many, string category, string bottleneck_dir, string image_dir,
             Tensor jpeg_data_tensor, Tensor decoded_image_tensor, Tensor resized_input_tensor,
             Tensor bottleneck_tensor, string module_name)
         {
@@ -423,7 +316,7 @@ namespace TensorFlowNET.Examples.ImageProcess
                 // Retrieve all bottlenecks.
                 foreach (var (label_index, label_name) in enumerate(image_lists.Keys.ToArray()))
                 {
-                    foreach(var (image_index, image_name) in enumerate(image_lists[label_name][category]))
+                    foreach (var (image_index, image_name) in enumerate(image_lists[label_name][category]))
                     {
                         var bottleneck = get_or_create_bottleneck(
                             sess, image_lists, label_name, image_index, image_dir, category,
@@ -480,17 +373,17 @@ namespace TensorFlowNET.Examples.ImageProcess
         /// <param name="resized_image_tensor"></param>
         /// <param name="bottleneck_tensor"></param>
         /// <param name="tfhub_module"></param>
-        private void cache_bottlenecks(Session sess, Dictionary<string, Dictionary<string, string[]>> image_lists, 
+        private void cache_bottlenecks(Session sess, Dictionary<string, Dictionary<string, string[]>> image_lists,
             string image_dir, string bottleneck_dir, Tensor jpeg_data_tensor, Tensor decoded_image_tensor,
             Tensor resized_input_tensor, Tensor bottleneck_tensor, string module_name)
         {
             int how_many_bottlenecks = 0;
-            foreach(var (label_name, label_lists) in image_lists)
+            foreach (var (label_name, label_lists) in image_lists)
             {
-                foreach(var category in new string[] { "training", "testing", "validation" })
+                foreach (var category in new string[] { "training", "testing", "validation" })
                 {
                     var category_list = label_lists[category];
-                    foreach(var (index, unused_base_name) in enumerate(category_list))
+                    foreach (var (index, unused_base_name) in enumerate(category_list))
                     {
                         get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir, category,
                             bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
@@ -503,8 +396,8 @@ namespace TensorFlowNET.Examples.ImageProcess
             }
         }
 
-        private float[] get_or_create_bottleneck(Session sess, Dictionary<string, Dictionary<string, string[]>> image_lists, 
-            string label_name, int index, string image_dir, string category, string bottleneck_dir, 
+        private float[] get_or_create_bottleneck(Session sess, Dictionary<string, Dictionary<string, string[]>> image_lists,
+            string label_name, int index, string image_dir, string category, string bottleneck_dir,
             Tensor jpeg_data_tensor, Tensor decoded_image_tensor, Tensor resized_input_tensor,
             Tensor bottleneck_tensor, string module_name)
         {
@@ -524,8 +417,8 @@ namespace TensorFlowNET.Examples.ImageProcess
             return bottleneck_values;
         }
 
-        private void create_bottleneck_file(string bottleneck_path, Dictionary<string, Dictionary<string, string[]>> image_lists, 
-            string label_name, int index, string image_dir, string category, Session sess, 
+        private void create_bottleneck_file(string bottleneck_path, Dictionary<string, Dictionary<string, string[]>> image_lists,
+            string label_name, int index, string image_dir, string category, Session sess,
             Tensor jpeg_data_tensor, Tensor decoded_image_tensor, Tensor resized_input_tensor, Tensor bottleneck_tensor)
         {
             // Create a single bottleneck file.
@@ -557,14 +450,14 @@ namespace TensorFlowNET.Examples.ImageProcess
                             Tensor decoded_image_tensor, Tensor resized_input_tensor, Tensor bottleneck_tensor)
         {
             // First decode the JPEG image, resize it, and rescale the pixel values.
-            var resized_input_values = sess.run(decoded_image_tensor, new FeedItem(image_data_tensor, new Tensor( image_data, TF_DataType.TF_STRING)));
+            var resized_input_values = sess.run(decoded_image_tensor, new FeedItem(image_data_tensor, new Tensor(image_data, TF_DataType.TF_STRING)));
             // Then run it through the recognition network.
             var bottleneck_values = sess.run(bottleneck_tensor, new FeedItem(resized_input_tensor, resized_input_values));
             bottleneck_values = np.squeeze(bottleneck_values);
             return bottleneck_values;
         }
 
-        private string get_bottleneck_path(Dictionary<string, Dictionary<string, string[]>> image_lists, string label_name, int index, 
+        private string get_bottleneck_path(Dictionary<string, Dictionary<string, string[]>> image_lists, string label_name, int index,
             string bottleneck_dir, string category, string module_name)
         {
             module_name = (module_name.Replace("://", "~")  // URL scheme.
@@ -667,7 +560,7 @@ namespace TensorFlowNET.Examples.ImageProcess
 
             var result = new Dictionary<string, Dictionary<string, string[]>>();
 
-            foreach(var sub_dir in sub_dirs)
+            foreach (var sub_dir in sub_dirs)
             {
                 var dir_name = sub_dir.Split(Path.DirectorySeparatorChar).Last();
                 print($"Looking for images in '{dir_name}'");
@@ -689,7 +582,22 @@ namespace TensorFlowNET.Examples.ImageProcess
 
         public Graph ImportGraph()
         {
-            throw new NotImplementedException();
+            Graph graph;
+
+            // Set up the pre-trained graph.
+            (graph, bottleneck_tensor, resized_image_tensor, wants_quantization) =
+                create_module_graph();
+
+            // Add the new layer that we'll be training.
+            with(graph.as_default(), delegate
+            {
+                (train_step, cross_entropy, bottleneck_input,
+                 ground_truth_input, final_tensor) = add_final_retrain_ops(
+                     class_count, final_tensor_name, bottleneck_tensor,
+                     wants_quantization, is_training: true);
+            });
+
+            return graph;
         }
 
         public Graph BuildGraph()
@@ -699,7 +607,108 @@ namespace TensorFlowNET.Examples.ImageProcess
 
         public void Train(Session sess)
         {
-            throw new NotImplementedException();
+            var sw = new Stopwatch();
+
+            // Initialize all weights: for the module to their pretrained values,
+            // and for the newly added retraining layer to random initial values.
+            var init = tf.global_variables_initializer();
+            sess.run(init);
+
+            var (jpeg_data_tensor, decoded_image_tensor) = add_jpeg_decoding();
+
+            // We'll make sure we've calculated the 'bottleneck' image summaries and
+            // cached them on disk.
+            cache_bottlenecks(sess, image_lists, image_dir,
+                    bottleneck_dir, jpeg_data_tensor,
+                    decoded_image_tensor, resized_image_tensor,
+                    bottleneck_tensor, tfhub_module);
+
+            // Create the operations we need to evaluate the accuracy of our new layer.
+            var (evaluation_step, _) = add_evaluation_step(final_tensor, ground_truth_input);
+
+            // Merge all the summaries and write them out to the summaries_dir
+            var merged = tf.summary.merge_all();
+            var train_writer = tf.summary.FileWriter(summaries_dir + "/train", sess.graph);
+            var validation_writer = tf.summary.FileWriter(summaries_dir + "/validation", sess.graph);
+
+            // Create a train saver that is used to restore values into an eval graph
+            // when exporting models.
+            var train_saver = tf.train.Saver();
+            train_saver.save(sess, CHECKPOINT_NAME);
+
+            sw.Restart();
+
+            for (int i = 0; i < how_many_training_steps; i++)
+            {
+                var (train_bottlenecks, train_ground_truth, _) = get_random_cached_bottlenecks(
+                     sess, image_lists, train_batch_size, "training",
+                     bottleneck_dir, image_dir, jpeg_data_tensor,
+                     decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
+                     tfhub_module);
+
+                // Feed the bottlenecks and ground truth into the graph, and run a training
+                // step. Capture training summaries for TensorBoard with the `merged` op.
+                var results = sess.run(
+                      new ITensorOrOperation[] { merged, train_step },
+                      new FeedItem(bottleneck_input, train_bottlenecks),
+                      new FeedItem(ground_truth_input, train_ground_truth));
+                var train_summary = results[0];
+
+                // TODO
+                train_writer.add_summary(train_summary, i);
+
+                // Every so often, print out how well the graph is training.
+                bool is_last_step = (i + 1 == how_many_training_steps);
+                if ((i % eval_step_interval) == 0 || is_last_step)
+                {
+                    results = sess.run(
+                        new Tensor[] { evaluation_step, cross_entropy },
+                        new FeedItem(bottleneck_input, train_bottlenecks),
+                        new FeedItem(ground_truth_input, train_ground_truth));
+                    (float train_accuracy, float cross_entropy_value) = (results[0], results[1]);
+                    print($"{DateTime.Now}: Step {i + 1}: Train accuracy = {train_accuracy * 100}%,  Cross entropy = {cross_entropy_value.ToString("G4")}");
+
+                    var (validation_bottlenecks, validation_ground_truth, _) = get_random_cached_bottlenecks(
+                        sess, image_lists, validation_batch_size, "validation",
+                        bottleneck_dir, image_dir, jpeg_data_tensor,
+                        decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
+                        tfhub_module);
+
+                    // Run a validation step and capture training summaries for TensorBoard
+                    // with the `merged` op.
+                    results = sess.run(new Tensor[] { merged, evaluation_step },
+                        new FeedItem(bottleneck_input, validation_bottlenecks),
+                        new FeedItem(ground_truth_input, validation_ground_truth));
+
+                    (string validation_summary, float validation_accuracy) = (results[0], results[1]);
+
+                    validation_writer.add_summary(validation_summary, i);
+                    print($"{DateTime.Now}: Step {i + 1}: Validation accuracy = {validation_accuracy * 100}% (N={len(validation_bottlenecks)}) {sw.ElapsedMilliseconds}ms");
+                    sw.Restart();
+                }
+
+                // Store intermediate results
+                int intermediate_frequency = intermediate_store_frequency;
+                if (intermediate_frequency > 0 && i % intermediate_frequency == 0 && i > 0)
+                {
+
+                }
+            }
+
+            // After training is complete, force one last save of the train checkpoint.
+            train_saver.save(sess, CHECKPOINT_NAME);
+
+            // We've completed all our training, so run a final test evaluation on
+            // some new images we haven't used before.
+            (test_accuracy, predictions) = run_final_eval(sess, null, class_count, image_lists,
+                           jpeg_data_tensor, decoded_image_tensor, resized_image_tensor,
+                           bottleneck_tensor);
+
+            // Write out the trained graph and labels with the weights stored as
+            // constants.
+            print($"Save final result to : {output_graph}");
+            save_graph_to_file(output_graph, class_count);
+            File.WriteAllText(output_labels, string.Join("\n", image_lists.Keys));
         }
 
         public void Predict(Session sess)

test/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs

@@ -51,7 +51,7 @@ namespace TensorFlowNET.Examples
 
             var graph = tf.Graph().as_default();
 
-            tf.train.import_meta_graph("graph/word2vec.meta");
+            tf.train.import_meta_graph($"graph{Path.DirectorySeparatorChar}word2vec.meta");
 
             // Input data
             Tensor X = graph.OperationByName("Placeholder");
@@ -169,10 +169,10 @@ namespace TensorFlowNET.Examples
             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");
+            Compress.UnZip($"word2vec{Path.DirectorySeparatorChar}text8.zip", "word2vec");
 
             int wordId = 0;
-            text_words = File.ReadAllText(@"word2vec\text8").Trim().ToLower().Split();
+            text_words = File.ReadAllText($"word2vec{Path.DirectorySeparatorChar}text8").Trim().ToLower().Split();
             // Build the dictionary and replace rare words with UNK token
             word2id = text_words.GroupBy(x => x)
                 .Select(x => new WordId