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: 3 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 7 years ago)
- Default Branch: master
- Last Pushed: 2021-07-05T03:15:59.000Z (over 4 years ago)
- Last Synced: 2025-04-15T19:53:53.937Z (7 months ago)
- Topics: deep-learning, deeplearning, go, golang, machine-learning, machinelearning
- Language: Go
- Homepage:
- Size: 14 MB
- Stars: 10
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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- 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
# 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