Golang implements rnn

In recent years, deep learning technology has been widely used in the field of computer science. Among them, recurrent neural network (RNN) is an important structure, which plays a key role in natural language processing, speech recognition and other fields.

For Golang developers, implementing RNN in this language is an important task. Therefore, this article will explain in detail the implementation of RNN technology in Golang. This article will discuss the following aspects:

• What is RNN
• The structure of RNN
• RNN technology implemented by Golang
• Example code
• Summary

What is RNN

A recurrent neural network is a neural network with a cyclic structure. Compared with other neural networks, RNN can handle sequence type data. For example, natural language, time domain signals, etc.

The structure of RNN

The structure of RNN is very special. It differs from other neural networks in that each neuron receives input from the output of the previous neuron. In other words, RNN retains the previously calculated state when processing sequence data.

Specifically, the structure of RNN is as shown in the figure.

[Picture]

It can be seen that RNN mainly contains three parts: input layer, hidden layer and output layer. Among them, the input layer is used to receive external data, while the hidden layer is used to calculate and edit the current state. Finally, the output layer outputs the final result.

RNN technology implemented by Golang

To use Golang to implement RNN, we first need to understand the concurrent programming and neural network programming technology in the Go language.

For concurrent programming, Go provides goroutine and channel related features. Goroutine is a lightweight thread in Go language. It consumes very little memory resources and runs very efficiently. Channel is a synchronous communication technology that can be used to transfer data between different goroutines.

For neural network programming technology, we need to understand how to build neural network models and how to use optimizers and loss functions.

The specific steps are as follows:

1. Define the structure and parameters of RNN

In Golang, we define RNN as a structure. Specifically, we need to define the size of the input and output, the size of the hidden layer, the size of the state, etc.

2. Define forward propagation and back propagation algorithms

RNN’s forward propagation algorithm calculates the results of the previous state and the current input and passes it to the next layer status. The purpose of the backpropagation algorithm is to calculate the loss and update the weights according to different optimizers.

In Golang, we can use the chain rule to implement the backpropagation algorithm. The specific implementation method is to first derive the loss function and then update the weight according to the corresponding formula.

3. Define loss function and optimizer

Cross entropy is a common loss function, and Adagrad is a common optimizer. In Golang, we can use the math package in the standard library to define these functions.

Sample code

The following is a simple sample code that demonstrates how to use Golang to implement a simple RNN model.

package main

import (
    "fmt"
    "math"
)

func sigmoid(x float64) float64 {
    //sigmoid 激活函数
    return 1 / (1 + math.Exp(-x))
}

type RNN struct {
    //RNN模型定义
    InputDim, HiddenDim, OutputDim, StateDim int
    InputWeight, HiddenWeight, OutputWeight [][]float64
}

func NewRNN(inputDim, hiddenDim, outputDim, stateDim int) *RNN {
    rnn := &RNN{}
    rnn.InputDim = inputDim
    rnn.HiddenDim = hiddenDim
    rnn.OutputDim = outputDim
    rnn.StateDim = stateDim
    rnn.InputWeight = make([][]float64, inputDim)
    for i := range rnn.InputWeight {
        rnn.InputWeight[i] = make([]float64, hiddenDim)
    }
    rnn.HiddenWeight = make([][]float64, hiddenDim)
    for i := range rnn.HiddenWeight {
        rnn.HiddenWeight[i] = make([]float64, hiddenDim)
    }
    rnn.OutputWeight = make([][]float64, hiddenDim)
    for i := range rnn.OutputWeight {
        rnn.OutputWeight[i] = make([]float64, outputDim)
    }
    return rnn
}

func (rnn *RNN) Forward(input []float64) ([]float64, [][]float64) {
    h := make([]float64, rnn.HiddenDim)
    state := make([]float64, rnn.StateDim)
    output := make([]float64, rnn.OutputDim)
    //前向传播
    for i := 0; i < rnn.HiddenDim; i++ {
        for j := 0; j < rnn.InputDim; j++ {
            h[i] += input[j] * rnn.InputWeight[j][i]
        }
        for j := 0; j < rnn.HiddenDim; j++ {
            h[i] += state[j] * rnn.HiddenWeight[j][i]
        }
        h[i] = sigmoid(h[i])
    }
    for i := 0; i < rnn.OutputDim; i++ {
        for j := 0; j < rnn.HiddenDim; j++ {
            output[i] += h[j] * rnn.OutputWeight[j][i]
        }
    }
    return output, [][]float64{nil, nil, nil}
}

func (rnn *RNN) Backward(input []float64, target []float64) [][]float64 {
    h := make([]float64, rnn.HiddenDim)
    state := make([]float64, rnn.StateDim)
    output := make([]float64, rnn.OutputDim)
    delta := make([]float64, rnn.OutputDim)
    deltaH := make([]float64, rnn.HiddenDim)
    //计算损失
    loss := 0.0
    for i := 0; i < rnn.OutputDim; i++ {
        loss += math.Pow(target[i]-output[i], 2)
        delta[i] = target[i] - output[i]
    }
    gradInput := make([]float64, rnn.InputDim)
    gradInputWeight := make([][]float64, rnn.InputDim)
    for i := range gradInputWeight {
        gradInputWeight[i] = make([]float64, rnn.HiddenDim)
    }
    gradHiddenWeight := make([][]float64, rnn.HiddenDim)
    for i := range gradHiddenWeight {
        gradHiddenWeight[i] = make([]float64, rnn.HiddenDim)
    }
    gradOutputWeight := make([][]float64, rnn.HiddenDim)
    for i := range gradOutputWeight {
        gradOutputWeight[i] = make([]float64, rnn.OutputDim)
    }
    //反向传播
    for i := 0; i < rnn.OutputDim; i++ {
        for j := 0; j < rnn.HiddenDim; j++ {
            gradOutputWeight[j][i] = h[j] * delta[i]
            deltaH[j] += delta[i] * rnn.OutputWeight[j][i]
        }
    }
    for i := 0; i < rnn.HiddenDim; i++ {
        deltaH[i] *= h[i] * (1 - h[i])
        for j := 0; j < rnn.HiddenDim; j++ {
            gradHiddenWeight[j][i] = state[j] * deltaH[i]
            if i == 0 {
                gradInput[j] = input[j] * deltaH[0]
                for k := 0; k < rnn.HiddenDim; k++ {
                    gradInputWeight[j][k] = input[j] * deltaH[0] * h[k]
                }
            }
        }
        for j := 0; j < rnn.StateDim; j++ {
            state[j] = deltaH[i] * rnn.HiddenWeight[j][i]
        }
    }
    return [][]float64{gradInput, gradInputWeight, gradHiddenWeight, gradOutputWeight}
}

func main() {
    //定义RNN模型
    rnn := NewRNN(2, 2, 1, 2)
    rnn.InputWeight[0][0] = 0.5
    rnn.InputWeight[0][1] = 0.2
    rnn.InputWeight[1][0] = 0.1
    rnn.InputWeight[1][1] = 0.3
    rnn.HiddenWeight[0][0] = 0.4
    rnn.HiddenWeight[0][1] = 0.4
    rnn.HiddenWeight[1][0] = 0.5
    rnn.HiddenWeight[1][1] = 0.5
    rnn.OutputWeight[0][0] = 0.6
    rnn.OutputWeight[1][0] = 0.7
    //前向传播和反向传播
    output, _ := rnn.Forward([]float64{0.2, 0.4})
    fmt.Println("Output:", output)
    grad := rnn.Backward([]float64{0.2, 0.4}, []float64{0.9})
    fmt.Println("Grad:", grad)
}

Summary

This article introduces the technology of Golang to implement RNN model. The steps from the basic structure and use of RNN to Golang implementation are explained. At the same time, we also introduce sample code so that developers can refer to it for practice. Today, Golang has become a popular programming language. It is believed that driven by the era of big data, Golang's technical contribution to implementing RNN models will become greater and greater.

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