gonnp
Deep learning from scratch using Go. Specializes in natural language processing
Package gonnp is the library of neural network components specialized in natural language processing in Go. You can assemble a neural network with the necessary components.
This component depends on gonum.org/v1/gonum/mat
https://github.com/gonum/gonum/
The number of components will increase in the future.
Affine
MatuMul
Embedding
EmbeddingDot
Relu
Sigmoid
Softmax with Loss
Sigmoid with Loss
SDG
Adam
Unigram Sampler
. ├── layers ---( Package layers impliments various layer for neural network. ) ├── matutil ---( Package matutil has utility functions of gonum matrix. ) ├── models ---( Package models has some of neural netwark models. ) ├── optimizers ---( Package optimizers updates prams (ex. weight, bias ...) using various algorism. ) ├── params ---( Package params has common parametors type. ) ├── store ---( Package store lets you to store trained data. ) ├── testdata │ ├── ptb ---( Package ptb provides load PTB data functions. ) ├── trainer ---( Package trainer impliments shorhand of training for deep lerning. ) └── word ---( Package word is functions of text processing. )
with EmbeddingDot Layers & Negative Sampling
package e2e_testimport ( "fmt" "io/ioutil" "log" "os" "github.com/po3rin/gonnp/matutil" "github.com/po3rin/gonnp/models" "github.com/po3rin/gonnp/optimizers" "github.com/po3rin/gonnp/trainer" "github.com/po3rin/gonnp/word")func main() { windowSize := 5 hiddenSize := 100 batchSize := 100 maxEpoch := 10 // prepare one-hot matrix from text data. file, err := os.Open("../testdata/golang.txt") if err != nil { log.Fatal(err) } defer file.Close() text, err := ioutil.ReadAll(file) if err != nil { log.Fatal(err) } corpus, w2id, id2w := word.PreProcess(string(text)) vocabSize := len(w2id) contexts, target := word.CreateContextsAndTarget(corpus, windowSize) // Inits model model := models.InitCBOW(vocabSize, hiddenSize, windowSize, corpus) // choses optimizer optimizer := optimizers.InitAdam(0.001, 0.9, 0.999) // inits trainer with model & optimizer. trainer := trainer.InitTrainer(model, optimizer) // training !! trainer.Fit(contexts, target, maxEpoch, batchSize) // checks outputs dist := trainer.GetWordDist() w2v := word.GetWord2VecFromDist(dist, id2w) for w, v := range w2v { fmt.Printf("=== %v ===n", w) matutil.PrintMat(v) } }
outputs
=== you === ⎡ -0.983712641282964⎤ ⎢ 0.9633828650811918⎥ ⎢-0.7253396760955725⎥ ⎢-0.9927919148802162⎥ . . .
package mainimport ( "github.com/po3rin/gomnist" "github.com/po3rin/gonnp/models" "github.com/po3rin/gonnp/optimizers" "github.com/po3rin/gonnp/trainer")func main() { model := models.NewTwoLayerNet(784, 100, 10) optimizer := optimizers.InitSDG(0.01) trainer := trainer.InitTrainer(model, optimizer, trainer.EvalInterval(20)) // load MNIST data using github.com/po3rin/gomnist package l := gomnist.NewLoader("./../testdata", gomnist.OneHotLabel(true), gomnist.Normalization(true)) mnist, _ := l.Load() trainer.Fit(mnist.TestData, mnist.TestLabels, 10, 100) }
https://github.com/oreilly-japan/deep-learning-from-scratch-2
Impliments RNN
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