rename Text to TextProcess.

test/TensorFlowNET.Examples/AudioProcess/README.md

@@ -0,0 +1 @@
+

test/TensorFlowNET.Examples/MetaGraph.cs

@@ -1,43 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Runtime.InteropServices;
-using System.Text;
-using Tensorflow;
-using static Tensorflow.Python;
-
-namespace TensorFlowNET.Examples
-{
-    public class MetaGraph : IExample
-    {
-        public int Priority => 100;
-        public bool Enabled { get; set; } = false;
-        public string Name => "Meta Graph";
-        public bool ImportGraph { get; set; } = true;
-
-
-        public bool Run()
-        {
-            ImportMetaGraph("my-save-dir/");
-            return false;
-        }
-
-        private void ImportMetaGraph(string dir)
-        {
-            with(tf.Session(), sess =>
-            {
-                var new_saver = tf.train.import_meta_graph(dir + "my-model-10000.meta");
-                new_saver.restore(sess, dir + "my-model-10000");
-                var labels = tf.constant(0, dtype: tf.int32, shape: new int[] { 100 }, name: "labels");
-                var batch_size = tf.size(labels);
-                var logits = (tf.get_collection("logits") as List<ITensorOrOperation>)[0] as Tensor;
-                var loss = tf.losses.sparse_softmax_cross_entropy(labels: labels,
-                                                logits: logits);
-            });
-        }
-
-        public void PrepareData()
-        {
-        }
-    }
-}

test/TensorFlowNET.Examples/NeuralNetXor.cs → test/TensorFlowNET.Examples/Models/NeuralNetXor.cs

@@ -1,156 +1,156 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using NumSharp;
-using Tensorflow;
-using TensorFlowNET.Examples.Utility;
-using static Tensorflow.Python;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Simple vanilla neural net solving the famous XOR problem
-    /// https://github.com/amygdala/tensorflow-workshop/blob/master/workshop_sections/getting_started/xor/README.md
-    /// </summary>
-    public class NeuralNetXor : IExample
-    {
-        public int Priority => 10;
-        public bool Enabled { get; set; } = true;
-        public string Name => "NN XOR";
-        public bool ImportGraph { get; set; } = false;
-
-        public int num_steps = 10000;
-
-        private NDArray data;
-
-        private (Operation, Tensor, Tensor) make_graph(Tensor features,Tensor labels, int num_hidden = 8)
-        {
-            var stddev = 1 / Math.Sqrt(2);
-            var hidden_weights = tf.Variable(tf.truncated_normal(new int []{2, num_hidden}, seed:1, stddev: (float) stddev ));
-
-            // Shape [4, num_hidden]
-            var hidden_activations = tf.nn.relu(tf.matmul(features, hidden_weights));
-
-            var output_weights = tf.Variable(tf.truncated_normal(
-                new[] {num_hidden, 1},
-                seed: 17,
-                stddev: (float) (1 / Math.Sqrt(num_hidden))
-            ));
-
-            // Shape [4, 1]
-            var logits = tf.matmul(hidden_activations, output_weights);
-
-            // Shape [4]
-            var predictions = tf.sigmoid(tf.squeeze(logits));
-            var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name:"loss");
-
-            var gs = tf.Variable(0, trainable: false, name: "global_step");
-            var train_op = tf.train.GradientDescentOptimizer(0.2f).minimize(loss, global_step: gs);
-
-            return (train_op, loss, gs);
-        }
-
-        public bool Run()
-        {
-            PrepareData();
-            float loss_value = 0;
-            if (ImportGraph)
-                loss_value = RunWithImportedGraph();
-            else
-                loss_value = RunWithBuiltGraph();
-
-            return loss_value < 0.0628;
-        }
-
-        private float RunWithImportedGraph()
-        {
-            var graph = tf.Graph().as_default();
-
-            tf.train.import_meta_graph("graph/xor.meta");
-
-            Tensor features = graph.get_operation_by_name("Placeholder");
-            Tensor labels = graph.get_operation_by_name("Placeholder_1");
-            Tensor loss = graph.get_operation_by_name("loss");
-            Tensor train_op = graph.get_operation_by_name("train_op");
-            Tensor global_step = graph.get_operation_by_name("global_step");
-
-            var init = tf.global_variables_initializer();
-            float loss_value = 0;
-            // Start tf session
-            with(tf.Session(graph), sess =>
-            {
-                sess.run(init);
-                var step = 0;
-
-                var y_ = np.array(new int[] { 1, 0, 0, 1 }, dtype: np.int32);
-                while (step < num_steps)
-                {
-                    // original python:
-                    //_, step, loss_value = sess.run(
-                    //          [train_op, gs, loss],
-                    //          feed_dict={features: xy, labels: y_}
-                    //      )
-                    var result = sess.run(new ITensorOrOperation[] { train_op, global_step, loss }, new FeedItem(features, data), new FeedItem(labels, y_));
-                    loss_value = result[2];
-                    step = result[1];
-                    if (step % 1000 == 0)
-                        Console.WriteLine($"Step {step} loss: {loss_value}");
-                }
-                Console.WriteLine($"Final loss: {loss_value}");
-            });
-
-            return loss_value;
-        }
-
-        private float RunWithBuiltGraph()
-        {
-            var graph = tf.Graph().as_default();
-
-            var features = tf.placeholder(tf.float32, new TensorShape(4, 2));
-            var labels = tf.placeholder(tf.int32, new TensorShape(4));
-
-            var (train_op, loss, gs) = make_graph(features, labels);
-
-            var init = tf.global_variables_initializer();
-
-            float loss_value = 0;
-            // Start tf session
-            with(tf.Session(graph), sess =>
-            {
-                sess.run(init);
-                var step = 0;
-
-                var y_ = np.array(new int[] { 1, 0, 0, 1 }, dtype: np.int32);
-                while (step < num_steps)
-                {
-                    var result = sess.run(new ITensorOrOperation[] { train_op, gs, loss }, new FeedItem(features, data), new FeedItem(labels, y_));
-                    loss_value = result[2];
-                    step = result[1];
-                    if (step % 1000 == 0)
-                        Console.WriteLine($"Step {step} loss: {loss_value}");
-                }
-                Console.WriteLine($"Final loss: {loss_value}");
-            });
-
-            return loss_value;
-        }
-
-        public void PrepareData()
-        {
-            data = new float[,]
-            {
-                {1, 0 },
-                {1, 1 },
-                {0, 0 },
-                {0, 1 }
-            };
-
-            if (ImportGraph)
-            {
-                // download graph meta data
-                string url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/xor.meta";
-                Web.Download(url, "graph", "xor.meta");
-            }
-        }
-    }
-}
+using System;
+using System.Collections.Generic;
+using System.Text;
+using NumSharp;
+using Tensorflow;
+using TensorFlowNET.Examples.Utility;
+using static Tensorflow.Python;
+
+namespace TensorFlowNET.Examples
+{
+    /// <summary>
+    /// Simple vanilla neural net solving the famous XOR problem
+    /// https://github.com/amygdala/tensorflow-workshop/blob/master/workshop_sections/getting_started/xor/README.md
+    /// </summary>
+    public class NeuralNetXor : IExample
+    {
+        public int Priority => 10;
+        public bool Enabled { get; set; } = true;
+        public string Name => "NN XOR";
+        public bool ImportGraph { get; set; } = false;
+
+        public int num_steps = 10000;
+
+        private NDArray data;
+
+        private (Operation, Tensor, Tensor) make_graph(Tensor features,Tensor labels, int num_hidden = 8)
+        {
+            var stddev = 1 / Math.Sqrt(2);
+            var hidden_weights = tf.Variable(tf.truncated_normal(new int []{2, num_hidden}, seed:1, stddev: (float) stddev ));
+
+            // Shape [4, num_hidden]
+            var hidden_activations = tf.nn.relu(tf.matmul(features, hidden_weights));
+
+            var output_weights = tf.Variable(tf.truncated_normal(
+                new[] {num_hidden, 1},
+                seed: 17,
+                stddev: (float) (1 / Math.Sqrt(num_hidden))
+            ));
+
+            // Shape [4, 1]
+            var logits = tf.matmul(hidden_activations, output_weights);
+
+            // Shape [4]
+            var predictions = tf.sigmoid(tf.squeeze(logits));
+            var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name:"loss");
+
+            var gs = tf.Variable(0, trainable: false, name: "global_step");
+            var train_op = tf.train.GradientDescentOptimizer(0.2f).minimize(loss, global_step: gs);
+
+            return (train_op, loss, gs);
+        }
+
+        public bool Run()
+        {
+            PrepareData();
+            float loss_value = 0;
+            if (ImportGraph)
+                loss_value = RunWithImportedGraph();
+            else
+                loss_value = RunWithBuiltGraph();
+
+            return loss_value < 0.0628;
+        }
+
+        private float RunWithImportedGraph()
+        {
+            var graph = tf.Graph().as_default();
+
+            tf.train.import_meta_graph("graph/xor.meta");
+
+            Tensor features = graph.get_operation_by_name("Placeholder");
+            Tensor labels = graph.get_operation_by_name("Placeholder_1");
+            Tensor loss = graph.get_operation_by_name("loss");
+            Tensor train_op = graph.get_operation_by_name("train_op");
+            Tensor global_step = graph.get_operation_by_name("global_step");
+
+            var init = tf.global_variables_initializer();
+            float loss_value = 0;
+            // Start tf session
+            with(tf.Session(graph), sess =>
+            {
+                sess.run(init);
+                var step = 0;
+
+                var y_ = np.array(new int[] { 1, 0, 0, 1 }, dtype: np.int32);
+                while (step < num_steps)
+                {
+                    // original python:
+                    //_, step, loss_value = sess.run(
+                    //          [train_op, gs, loss],
+                    //          feed_dict={features: xy, labels: y_}
+                    //      )
+                    var result = sess.run(new ITensorOrOperation[] { train_op, global_step, loss }, new FeedItem(features, data), new FeedItem(labels, y_));
+                    loss_value = result[2];
+                    step = result[1];
+                    if (step % 1000 == 0)
+                        Console.WriteLine($"Step {step} loss: {loss_value}");
+                }
+                Console.WriteLine($"Final loss: {loss_value}");
+            });
+
+            return loss_value;
+        }
+
+        private float RunWithBuiltGraph()
+        {
+            var graph = tf.Graph().as_default();
+
+            var features = tf.placeholder(tf.float32, new TensorShape(4, 2));
+            var labels = tf.placeholder(tf.int32, new TensorShape(4));
+
+            var (train_op, loss, gs) = make_graph(features, labels);
+
+            var init = tf.global_variables_initializer();
+
+            float loss_value = 0;
+            // Start tf session
+            with(tf.Session(graph), sess =>
+            {
+                sess.run(init);
+                var step = 0;
+
+                var y_ = np.array(new int[] { 1, 0, 0, 1 }, dtype: np.int32);
+                while (step < num_steps)
+                {
+                    var result = sess.run(new ITensorOrOperation[] { train_op, gs, loss }, new FeedItem(features, data), new FeedItem(labels, y_));
+                    loss_value = result[2];
+                    step = result[1];
+                    if (step % 1000 == 0)
+                        Console.WriteLine($"Step {step} loss: {loss_value}");
+                }
+                Console.WriteLine($"Final loss: {loss_value}");
+            });
+
+            return loss_value;
+        }
+
+        public void PrepareData()
+        {
+            data = new float[,]
+            {
+                {1, 0 },
+                {1, 1 },
+                {0, 0 },
+                {0, 1 }
+            };
+
+            if (ImportGraph)
+            {
+                // download graph meta data
+                string url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/xor.meta";
+                Web.Download(url, "graph", "xor.meta");
+            }
+        }
+    }
+}

test/TensorFlowNET.Examples/Text/DataHelpers.cs → test/TensorFlowNET.Examples/TextProcess/DataHelpers.cs

@@ -43,8 +43,8 @@ namespace TensorFlowNET.Examples.CnnTextClassification
                         x[i][j] = char_dict["<pad>"];
                     else
                         x[i][j] = char_dict.ContainsKey(content[j].ToString()) ? char_dict[content[j].ToString()] : char_dict["<unk>"];
-                }
-
+                }
+
                 y[i] = int.Parse(parts[0]);
             }
 

test/TensorFlowNET.Examples/Text/cnn_models/ITextClassificationModel.cs → test/TensorFlowNET.Examples/TextProcess/cnn_models/ITextClassificationModel.cs

@@ -1,14 +1,14 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow;
-
-namespace TensorFlowNET.Examples.Text.cnn_models
-{
-    interface ITextClassificationModel
-    {
-        Tensor is_training { get;  }
-        Tensor x { get;}
-        Tensor y { get; }
-    }
-}
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow;
+
+namespace TensorFlowNET.Examples.Text.cnn_models
+{
+    interface ITextClassificationModel
+    {
+        Tensor is_training { get;  }
+        Tensor x { get;}
+        Tensor y { get; }
+    }
+}

test/TensorFlowNET.UnitTest/ExamplesTests/ExamplesTest.cs

@@ -61,14 +61,6 @@ namespace TensorFlowNET.ExamplesTests
             new LogisticRegression() { Enabled = true, training_epochs=10, train_size = 500, validation_size = 100, test_size = 100 }.Run();
         }
 
-        [Ignore]
-        [TestMethod]
-        public void MetaGraph()
-        {
-            tf.Graph().as_default();
-            new MetaGraph() { Enabled = true }.Run();
-        }
-
         [Ignore]
         [TestMethod]
         public void NaiveBayesClassifier()

test/TensorFlowNET.UnitTest/GraphTest.cs

@@ -5,6 +5,7 @@ using System.Runtime.InteropServices;
 using System.Text;
 using Tensorflow;
 using Buffer = Tensorflow.Buffer;
+using static Tensorflow.Python;
 
 namespace TensorFlowNET.UnitTest
 {
@@ -417,6 +418,19 @@ namespace TensorFlowNET.UnitTest
 
         }
 
-
+        public void ImportGraphMeta()
+        {
+            var dir = "my-save-dir/";
+            with(tf.Session(), sess =>
+            {
+                var new_saver = tf.train.import_meta_graph(dir + "my-model-10000.meta");
+                new_saver.restore(sess, dir + "my-model-10000");
+                var labels = tf.constant(0, dtype: tf.int32, shape: new int[] { 100 }, name: "labels");
+                var batch_size = tf.size(labels);
+                var logits = (tf.get_collection("logits") as List<ITensorOrOperation>)[0] as Tensor;
+                var loss = tf.losses.sparse_softmax_cross_entropy(labels: labels,
+                                                logits: logits);
+            });
+        }
     }
 }