add predict for logistic regression.

Author: Oceania2018

docs/source/Graph.md

@@ -21,3 +21,61 @@ A typical graph is looks like below:
 
 ![image](../assets/graph_vis_animation.gif)
 
+
+
+### Save Model
+
+Saving the model means saving all the values of the parameters and the graph.
+
+```python
+saver = tf.train.Saver()
+saver.save(sess,'./tensorflowModel.ckpt')
+```
+
+After saving the model there will be four files:
+
+* tensorflowModel.ckpt.meta:
+* tensorflowModel.ckpt.data-00000-of-00001:
+* tensorflowModel.ckpt.index
+* checkpoint
+
+We also created a protocol buffer file .pbtxt. It is human readable if you want to convert it to binary: `as_text: false`.
+
+* tensorflowModel.pbtxt: 
+
+This holds a network of nodes, each representing one operation, connected to each other as inputs and outputs.
+
+
+
+### Freezing the Graph
+
+##### *Why we need it?*
+
+When we need to keep all the values of the variables and the Graph structure in a single file we have to freeze the graph.
+
+```csharp
+from tensorflow.python.tools import freeze_graph
+
+freeze_graph.freeze_graph(input_graph = 'logistic_regression/tensorflowModel.pbtxt', 
+                              input_saver = "", 
+                              input_binary = False, 
+                              input_checkpoint = 'logistic_regression/tensorflowModel.ckpt', 
+                              output_node_names = "Softmax",
+                              restore_op_name = "save/restore_all", 
+                              filename_tensor_name = "save/Const:0",
+                              output_graph = 'frozentensorflowModel.pb', 
+                              clear_devices = True, 
+                              initializer_nodes = "")
+
+```
+
+### Optimizing for Inference
+
+To Reduce the amount of computation needed when the network is used only for inferences we can remove some parts of a graph that are only needed for training. 
+
+
+
+### Restoring the Model
+
+
+

docs/source/LogisticRegression.md

@@ -0,0 +1,7 @@
+# Chapter. Logistic Regression
+
+### What is logistic regression?
+
+
+
+The full example is [here](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/LogisticRegression.cs).

docs/source/index.rst

@@ -26,4 +26,5 @@ Welcome to TensorFlow.NET's documentation!
    Train
    EagerMode
    LinearRegression
+   LogisticRegression
    ImageRecognition

src/TensorFlowNET.Core/Framework/meta_graph.py.cs

@@ -184,7 +184,7 @@ private static MetaGraphDef create_meta_graph_def(MetaInfoDef meta_info_def = nu
 
             // Adds graph_def or the default.
             if (graph_def == null)
-                meta_graph_def.GraphDef = graph._as_graph_def(add_shapes: true);
+                meta_graph_def.GraphDef = graph.as_graph_def(add_shapes: true);
             else
                 meta_graph_def.GraphDef = graph_def;
 

src/TensorFlowNET.Core/Graphs/FreezeGraph.cs

@@ -0,0 +1,23 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public class FreezeGraph
+    {
+        public static void freeze_graph(string input_graph,
+                              string input_saver,
+                              bool input_binary,
+                              string input_checkpoint,
+                              string output_node_names,
+                              string restore_op_name,
+                              string filename_tensor_name,
+                              string output_graph,
+                              bool clear_devices,
+                              string initializer_nodes)
+        {
+
+        }
+    }
+}

src/TensorFlowNET.Core/Graphs/Graph.Export.cs

@@ -18,7 +18,7 @@ public Buffer ToGraphDef(Status s)
             return buffer;
         }
 
-        public GraphDef _as_graph_def(bool add_shapes = false)
+        private GraphDef _as_graph_def(bool add_shapes = false)
         {
             var buffer = ToGraphDef(Status);
             Status.Check();
@@ -30,5 +30,8 @@ public GraphDef _as_graph_def(bool add_shapes = false)
 
             return def;
         }
+
+        public GraphDef as_graph_def(bool add_shapes = false) 
+            => _as_graph_def(add_shapes);
     }
 }

src/TensorFlowNET.Core/Graphs/graph_io.py.cs

@@ -10,7 +10,7 @@ public class graph_io
     {
         public static string write_graph(Graph graph, string logdir, string name, bool as_text = true)
         {
-            var graph_def = graph._as_graph_def();
+            var graph_def = graph.as_graph_def();
             string path = Path.Combine(logdir, name);
             if (as_text)
                 File.WriteAllText(path, graph_def.ToString());

src/TensorFlowNET.Core/Sessions/_FetchHandler.cs

@@ -58,17 +58,24 @@ public NDArray build_results(BaseSession session, NDArray[] tensor_values)
                 {
                     var value = tensor_values[j];
                     j += 1;
-                    switch (value.dtype.Name)
+                    if (value.ndim == 2)
                     {
-                        case "Int32":
-                            full_values.Add(value.Data<int>(0));
-                            break;
-                        case "Single":
-                            full_values.Add(value.Data<float>(0));
-                            break;
-                        case "Double":
-                            full_values.Add(value.Data<double>(0));
-                            break;
+                        full_values.Add(value[0]);
+                    }
+                    else
+                    {
+                        switch (value.dtype.Name)
+                        {
+                            case "Int32":
+                                full_values.Add(value.Data<int>(0));
+                                break;
+                            case "Single":
+                                full_values.Add(value.Data<float>(0));
+                                break;
+                            case "Double":
+                                full_values.Add(value.Data<double>(0));
+                                break;
+                        }
                     }
                 }
                 i += 1;

src/TensorFlowNET.Core/Train/Saving/Saver.cs

@@ -251,7 +251,7 @@ public MetaGraphDef export_meta_graph(string filename= "",
         {
             return export_meta_graph(
                 filename: filename,
-                graph_def: ops.get_default_graph()._as_graph_def(add_shapes: true),
+                graph_def: ops.get_default_graph().as_graph_def(add_shapes: true),
                 saver_def: _saver_def,
                 collection_list: collection_list,
                 as_text: as_text,

src/TensorFlowNET.Core/Train/tf.optimizers.cs

@@ -16,7 +16,8 @@ public static class train
 
             public static Saver Saver() => new Saver();
 
-            public static string write_graph(Graph graph, string logdir, string name, bool as_text = true) => graph_io.write_graph(graph, logdir, name, as_text);
+            public static string write_graph(Graph graph, string logdir, string name, bool as_text = true) 
+                => graph_io.write_graph(graph, logdir, name, as_text);
 
             public static Saver import_meta_graph(string meta_graph_or_file,
                 bool clear_devices = false,

test/TensorFlowNET.Examples/LogisticRegression.cs

@@ -2,6 +2,7 @@
 using NumSharp.Core;
 using System;
 using System.Collections.Generic;
+using System.IO;
 using System.Linq;
 using System.Text;
 using Tensorflow;
@@ -17,7 +18,7 @@ namespace TensorFlowNET.Examples
     public class LogisticRegression : Python, IExample
     {
         private float learning_rate = 0.01f;
-        private int training_epochs = 5;
+        private int training_epochs = 10;
         private int batch_size = 100;
         private int display_step = 1;
 
@@ -78,19 +79,66 @@ public void Run()
                 }
 
                 print("Optimization Finished!");
+                // SaveModel(sess);
 
                 // Test model
                 var correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1));
                 // Calculate accuracy
                 var accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32));
                 float acc = accuracy.eval(new FeedItem(x, mnist.test.images), new FeedItem(y, mnist.test.labels));
                 print($"Accuracy: {acc.ToString("F4")}");
+
+                Predict();
             });
         }
 
         public void PrepareData()
         {
             mnist = MnistDataSet.read_data_sets("logistic_regression", one_hot: true);
         }
+
+        public void SaveModel(Session sess)
+        {
+            var saver = tf.train.Saver();
+            var save_path = saver.save(sess, "logistic_regression/model.ckpt");
+            tf.train.write_graph(sess.graph, "logistic_regression", "model.pbtxt", as_text: true);
+
+            FreezeGraph.freeze_graph(input_graph: "logistic_regression/model.pbtxt",
+                              input_saver: "",
+                              input_binary: false,
+                              input_checkpoint: "logistic_regression/model.ckpt",
+                              output_node_names: "Softmax",
+                              restore_op_name: "save/restore_all",
+                              filename_tensor_name: "save/Const:0",
+                              output_graph: "logistic_regression/model.pb",
+                              clear_devices: true,
+                              initializer_nodes: "");
+        }
+
+        public void Predict()
+        {
+            var graph = new Graph().as_default();
+            graph.Import(Path.Join("logistic_regression", "model.pb"));
+
+            with(tf.Session(graph), sess =>
+            {
+                // restoring the model
+                // var saver = tf.train.import_meta_graph("logistic_regression/tensorflowModel.ckpt.meta");
+                // saver.restore(sess, tf.train.latest_checkpoint('logistic_regression'));
+                var pred = graph.OperationByName("Softmax");
+                var output = pred.outputs[0];
+                var x = graph.OperationByName("Placeholder");
+                var input = x.outputs[0];
+
+                // predict
+                var (batch_xs, batch_ys) = mnist.train.next_batch(10);
+                var results = sess.run(output, new FeedItem(input, batch_xs[np.arange(1)]));
+
+                if (results.argmax() == (batch_ys[0] as NDArray).argmax())
+                    print("predicted OK!");
+                else
+                    throw new ValueError("predict error, maybe 90% accuracy");
+            });
+        }
     }
 }

test/TensorFlowNET.Examples/python/logistic_regression.py

@@ -16,7 +16,7 @@
 
 # Parameters
 learning_rate = 0.01
-training_epochs = 25
+training_epochs = 10
 batch_size = 100
 display_step = 1
 
@@ -67,4 +67,34 @@
     correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
     # Calculate accuracy
     accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
+    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
+
+    # predict
+    # results = sess.run(pred, feed_dict={x: batch_xs[:1]})
+
+    # save model
+    saver = tf.train.Saver()
+    save_path = saver.save(sess, "logistic_regression/model.ckpt")
+    tf.train.write_graph(sess.graph.as_graph_def(),'logistic_regression','model.pbtxt', as_text=True)
+
+    freeze_graph.freeze_graph(input_graph = 'logistic_regression/model.pbtxt', 
+                              input_saver = "", 
+                              input_binary = False, 
+                              input_checkpoint = 'logistic_regression/model.ckpt', 
+                              output_node_names = "Softmax",
+                              restore_op_name = "save/restore_all", 
+                              filename_tensor_name = "save/Const:0",
+                              output_graph = 'logistic_regression/model.pb', 
+                              clear_devices = True, 
+                              initializer_nodes = "")
+
+    # restoring the model
+    saver = tf.train.import_meta_graph('logistic_regression/tensorflowModel.ckpt.meta')
+    saver.restore(sess,tf.train.latest_checkpoint('logistic_regression'))
+
+    # predict
+    # pred = graph._nodes_by_name["Softmax"]
+    # output = pred.outputs[0]
+    # x = graph._nodes_by_name["Placeholder"]
+    # input = x.outputs[0]
+    # results = sess.run(output, feed_dict={input: batch_xs[:1]})