-
-
Notifications
You must be signed in to change notification settings - Fork 8
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
168 changed files
with
998 additions
and
1,136 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -258,4 +258,5 @@ paket-files/ | |
|
|
||
| # Python Tools for Visual Studio (PTVS) | ||
| __pycache__/ | ||
| *.pyc | ||
| *.pyc | ||
| /examples/MNIST/model | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1 +1,68 @@ | ||
| # MxNet-NN | ||
| # MxNet | ||
|
|
||
| Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. | ||
|
|
||
| MXNet is more than a deep learning project. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers. | ||
|
|
||
| mxnetlib is a CSharp binding coving all the Imperative and Symbolic API's with an easy to use interface. Also developed a high level interface to build and train model. | ||
|
|
||
| Setup MxNet for Windows: https://github.com/deepakkumar1984/mxnetlib/wiki/Setup---Windows | ||
|
|
||
| ## Nuget | ||
|
|
||
| Install the package: Install-Package MxNetLib | ||
|
|
||
| https://www.nuget.org/packages/MxNetLib | ||
|
|
||
| ## Symbolic Example | ||
| ```csharp | ||
| model.SetInput(784); | ||
|
|
||
| var x = Symbol.Variable("X"); | ||
| var fc1 = sym.Relu(sym.FullyConnected(x, Symbol.Variable("fc1_w"), 128)); | ||
| var fc2 = sym.Relu(sym.FullyConnected(fc1, Symbol.Variable("fc2_w"), 128)); | ||
| var fc3 = sym.FullyConnected(fc2, Symbol.Variable("fc3_w"), 10); | ||
| var output = sym.SoftmaxOutput(fc3, Symbol.Variable("label"), symbol_name: "model"); | ||
|
|
||
| model.SetDefaultInitializer(new RandomUniform(-1, 1)); | ||
| model.Compile(output, OptimizerRegistry.SGD(), MetricType.Accuracy); | ||
| ``` | ||
|
|
||
| ## High Level API Example | ||
| ```csharp | ||
| model.SetInput(784); | ||
|
|
||
| model.Add(new Dense(128, ActivationType.ReLU, kernalInitializer: new RandomUniform(-1, 1))); | ||
| model.Add(new Dense(128, ActivationType.ReLU, kernalInitializer: new RandomUniform(-1, 1))); | ||
| model.Add(new Dense(10)); | ||
|
|
||
| model.Compile(OptimizerRegistry.SGD(), LossType.SoftmaxCategorialCrossEntropy, MetricType.Accuracy); | ||
| ``` | ||
|
|
||
| ## Train and Inference | ||
|
|
||
| ```csharp | ||
|
|
||
| //Training for 10 epoch | ||
| model.Fit(train, 10, batchSize, val); | ||
|
|
||
| //Load test data | ||
| ImageDataFrame frame = new ImageDataFrame(1, 28, 28); | ||
| frame.LoadImages("test_6.png", "test_4.png", "test_4.png", "test_6.png"); | ||
| NDArray test = frame.ToVariable().Ravel() / 255; | ||
|
|
||
| // Predict | ||
| var prediction = model.Predict(test).Argmax(); | ||
| Console.WriteLine(prediction.ToString()); | ||
| ``` | ||
|
|
||
| ## Saving and Loading model and checkpoint | ||
|
|
||
| ```csharp | ||
| string modelFolder = "../../../model"; | ||
| model.SaveModel(modelFolder); | ||
| model.SaveCheckpoint(modelFolder); | ||
|
|
||
| var loadedModel = Module.LoadModel(modelFolder); | ||
| loadedModel.LoadCheckpoint(modelFolder); | ||
| ``` |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.