https://github.com/kuroko1t/gdeep

simple golang deep learning framework
https://github.com/kuroko1t/gdeep

deep-learning deeplearning go golang machine-learning machinelearning

Last synced: 2 months ago
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simple golang deep learning framework

• Host: GitHub
• URL: https://github.com/kuroko1t/gdeep
• Owner: kuroko1t
• License: apache-2.0
• Created: 2018-06-12T15:00:25.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-07-05T03:15:59.000Z (over 3 years ago)
• Last Synced: 2024-11-17T22:34:45.015Z (2 months ago)
• Topics: deep-learning, deeplearning, go, golang, machine-learning, machinelearning
• Language: Go
• Homepage:
• Size: 14 MB
• Stars: 10
• Watchers: 3
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

Awesome Lists containing this project

• awesome-ai-ml-dl - GDeep - Deep learning library written in golang

README

        
# gdeep

deep learning library written by golang. wellcom to contribution!! and still in production..

# Getting Started

```

git clone https://github.com/kuroko1t/gdeep

cd gdeep

go run example/mlpMnist.go

```

[sample](https://github.com/kuroko1t/gdeep/blob/master/example/mlpMnist.go)

## distributed

* go get github.com/kuroko1t/gmpi

* install openmpi > 3.0

# Run

* cpu

```

go run example/mlpMnist.go

```

* distributed

```

mpirun -np 2 -H host1:1,host2:1 go run example/mlpMnist_allreduce.go

```

# Document

https://godoc.org/github.com/kuroko1t/gdeep

# implemented networks

only support 2D input yet

### layer

* Dense

* Dropout

### activation

* Relu

* Sigmoid

### loss

* crossentropy

### optimization

* Sgd

* Momentum

# Sample

* mlp sample

```golang

package main

import (

	"fmt"

	"github.com/kuroko1t/GoMNIST"

	"github.com/kuroko1t/gdeep"

	"github.com/kuroko1t/gmat"

)

func main() {

	train, test, _ := GoMNIST.Load("./data")

	trainDataSize := len(train.ImagesFloatNorm)

	testDataSize := len(test.ImagesFloatNorm)

	batchSize := 128

	inputSize := 784

	hiddenSize := 20

	outputSize := 10

	learningRate := 0.01

	epochNum := 1

	iterationNum := trainDataSize * epochNum / batchSize

	dropout1 := &gdeep.Dropout{}

	dropout2 := &gdeep.Dropout{}

	layer := []gdeep.LayerInterface{}

	gdeep.LayerAdd(&layer, &gdeep.Dense{}, []int{inputSize, hiddenSize})

	gdeep.LayerAdd(&layer, &gdeep.Relu{})

	gdeep.LayerAdd(&layer, dropout1, 0.2)

	gdeep.LayerAdd(&layer, &gdeep.Dense{}, []int{hiddenSize, hiddenSize})

	gdeep.LayerAdd(&layer, &gdeep.Relu{})

	gdeep.LayerAdd(&layer, dropout2, 0.2)

	gdeep.LayerAdd(&layer, &gdeep.Dense{}, []int{hiddenSize, outputSize})

	gdeep.LayerAdd(&layer, &gdeep.SoftmaxWithLoss{})

	momentum := &gdeep.Momentum{learningRate, 0.9}

	iter := 0

	for i := 0; i < iterationNum; i++ {

		if (i+2)*batchSize > trainDataSize {

			iter = 0

		}

		imageBatch := train.ImagesFloatNorm[:][iter*batchSize : (iter+1)*batchSize]

		lagelBatch := train.LabelsOneHot[:][iter*batchSize : (iter+1)*batchSize]

		x := gmat.Make2DInitArray(imageBatch)

		t := gmat.Make2DInitArray(lagelBatch)

		loss := gdeep.Run(layer, momentum, x, t)

		gdeep.AvePrint(loss, "loss")

		iter++

	}

}

```

* distributed

[sample](https://github.com/kuroko1t/gdeep/blob/master/example/mlpMnist_allreduce.go)

* Mnist Test Accuracy

![accuracy](https://raw.githubusercontent.com/kuroko1t/gdeep/master/img/accuracy.png)

# Done

* cpu calc parallelization(dot)

* learning param save and restore

* mlp with gpu

# Todo

* CNN

# License

gdeep is licensed under the Apache License, Version2.0