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GeezerVison is showing github.com — add predict for logistic regression. · lqdev/TensorFlow.NET@476f8cf

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  • docs/source
    • Graph.md
    • LogisticRegression.md
    • index.rst
  • src/TensorFlowNET.Core
    • Framework
      • meta_graph.py.cs
    • Graphs
      • FreezeGraph.cs
      • Graph.Export.cs
      • graph_io.py.cs
    • Sessions
      • _FetchHandler.cs
    • Train
      • Saving
        • Saver.cs
      • tf.optimizers.cs
  • test/TensorFlowNET.Examples
    • LogisticRegression.cs
    • python
      • logistic_regression.py
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‎docs/source/Graph.md‎

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

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# Chapter. Logistic Regression
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### What is logistic regression?
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The full example is [here](https://github.com/SciSharp/TensorFlow.NET/blob/master/test/TensorFlowNET.Examples/LogisticRegression.cs).
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‎docs/source/index.rst‎

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@@ -26,4 +26,5 @@ Welcome to TensorFlow.NET's documentation!
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Train
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EagerMode
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LinearRegression
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LogisticRegression
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ImageRecognition
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‎src/TensorFlowNET.Core/Framework/meta_graph.py.cs‎

Copy file name to clipboardExpand all lines: src/TensorFlowNET.Core/Framework/meta_graph.py.cs
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@@ -184,7 +184,7 @@ private static MetaGraphDef create_meta_graph_def(MetaInfoDef meta_info_def = nu
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// Adds graph_def or the default.
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if (graph_def == null)
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meta_graph_def.GraphDef = graph._as_graph_def(add_shapes: true);
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meta_graph_def.GraphDef = graph.as_graph_def(add_shapes: true);
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else
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meta_graph_def.GraphDef = graph_def;
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‎src/TensorFlowNET.Core/Graphs/FreezeGraph.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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namespace Tensorflow
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{
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public class FreezeGraph
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{
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public static void freeze_graph(string input_graph,
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string input_saver,
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bool input_binary,
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string input_checkpoint,
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string output_node_names,
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string restore_op_name,
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string filename_tensor_name,
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string output_graph,
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bool clear_devices,
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string initializer_nodes)
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{
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}
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}
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}
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‎src/TensorFlowNET.Core/Graphs/Graph.Export.cs‎

Copy file name to clipboardExpand all lines: src/TensorFlowNET.Core/Graphs/Graph.Export.cs
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@@ -18,7 +18,7 @@ public Buffer ToGraphDef(Status s)
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return buffer;
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}
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public GraphDef _as_graph_def(bool add_shapes = false)
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private GraphDef _as_graph_def(bool add_shapes = false)
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{
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var buffer = ToGraphDef(Status);
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Status.Check();
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return def;
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}
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public GraphDef as_graph_def(bool add_shapes = false)
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=> _as_graph_def(add_shapes);
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}
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}
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‎src/TensorFlowNET.Core/Graphs/graph_io.py.cs‎

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{
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public static string write_graph(Graph graph, string logdir, string name, bool as_text = true)
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{
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var graph_def = graph._as_graph_def();
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var graph_def = graph.as_graph_def();
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string path = Path.Combine(logdir, name);
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if (as_text)
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File.WriteAllText(path, graph_def.ToString());
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‎src/TensorFlowNET.Core/Sessions/_FetchHandler.cs‎

Copy file name to clipboardExpand all lines: src/TensorFlowNET.Core/Sessions/_FetchHandler.cs
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@@ -58,17 +58,24 @@ public NDArray build_results(BaseSession session, NDArray[] tensor_values)
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{
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var value = tensor_values[j];
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j += 1;
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switch (value.dtype.Name)
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if (value.ndim == 2)
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{
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case "Int32":
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full_values.Add(value.Data<int>(0));
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break;
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case "Single":
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full_values.Add(value.Data<float>(0));
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break;
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case "Double":
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full_values.Add(value.Data<double>(0));
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break;
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full_values.Add(value[0]);
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}
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else
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{
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switch (value.dtype.Name)
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{
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case "Int32":
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full_values.Add(value.Data<int>(0));
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break;
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case "Single":
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full_values.Add(value.Data<float>(0));
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break;
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case "Double":
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full_values.Add(value.Data<double>(0));
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break;
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}
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}
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}
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i += 1;
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‎src/TensorFlowNET.Core/Train/Saving/Saver.cs‎

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@@ -251,7 +251,7 @@ public MetaGraphDef export_meta_graph(string filename= "",
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{
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return export_meta_graph(
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filename: filename,
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graph_def: ops.get_default_graph()._as_graph_def(add_shapes: true),
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graph_def: ops.get_default_graph().as_graph_def(add_shapes: true),
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saver_def: _saver_def,
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collection_list: collection_list,
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as_text: as_text,
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‎src/TensorFlowNET.Core/Train/tf.optimizers.cs‎

Copy file name to clipboardExpand all lines: src/TensorFlowNET.Core/Train/tf.optimizers.cs
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@@ -16,7 +16,8 @@ public static class train
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public static Saver Saver() => new Saver();
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public static string write_graph(Graph graph, string logdir, string name, bool as_text = true) => graph_io.write_graph(graph, logdir, name, as_text);
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public static string write_graph(Graph graph, string logdir, string name, bool as_text = true)
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=> graph_io.write_graph(graph, logdir, name, as_text);
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public static Saver import_meta_graph(string meta_graph_or_file,
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bool clear_devices = false,

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