Pytorch多層感知機(jī)的實(shí)現(xiàn)方法

這篇文章主要講解了“Pytorch多層感知機(jī)的實(shí)現(xiàn)方法”，文中的講解內(nèi)容簡單清晰，易于學(xué)習(xí)與理解，下面請大家跟著小編的思路慢慢深入，一起來研究和學(xué)習(xí)“Pytorch多層感知機(jī)的實(shí)現(xiàn)方法”吧！

import torch
from torch import nn
from torch.nn import init
import numpy as np
import sys
import torchvision
from torchvision import transforms

num_inputs=784
num_outputs=10
num_hiddens=256

mnist_train = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=True, download=True, transform=transforms.ToTensor())
mnist_test = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=False, download=True, transform=transforms.ToTensor())
batch_size = 256

train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True)
test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False)

def evalute_accuracy(data_iter,net):
    acc_sum,n=0.0,0
    for X,y in data_iter:
        acc_sum+=(net(X).argmax(dim=1)==y).float().sum().item()
        n+=y.shape[0]
    return acc_sum/n

def train(net,train_iter,test_iter,loss,num_epochs,batch_size,params=None,lr=None,optimizer=None):
    for epoch in range(num_epochs):
        train_l_sum,train_acc_sum,n=0.0,0.0,0
        for X,y in train_iter:
            y_hat=net(X)
            l=loss(y_hat,y).sum()

            if optimizer is not None:
                optimizer.zero_grad()
            elif params is not None and params[0].grad is not None:
                for param in params:
                    param.grad.data.zero_()
            l.backward()
            optimizer.step()  # “softmax回歸的簡潔實(shí)現(xiàn)”一節(jié)將用到
            train_l_sum+=l.item()
            train_acc_sum+=(y_hat.argmax(dim=1)==y).sum().item()
            n+=y.shape[0]
        test_acc=evalute_accuracy(test_iter,net);
        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'
              % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc))

class Faltten(nn.Module):
    def __init__(self):
        super(Faltten, self).__init__()
    def forward(self,x):
        return x.view(x.shape[0],-1)

net =nn.Sequential(
    Faltten(),
    nn.Linear(num_inputs,num_hiddens),
    nn.ReLU(),
    nn.Linear(num_hiddens,num_outputs)
)

for params in net.parameters():
    init.normal_(params,mean=0,std=0.01)

batch_size=256
loss=torch.nn.CrossEntropyLoss()

optimizer=torch.optim.SGD(net.parameters(),lr=0.5)

num_epochs=5

train(net,train_iter,test_iter,loss,num_epochs,batch_size,None,None,optimizer)

感謝各位的閱讀，以上就是“Pytorch多層感知機(jī)的實(shí)現(xiàn)方法”的內(nèi)容了，經(jīng)過本文的學(xué)習(xí)后，相信大家對(duì)Pytorch多層感知機(jī)的實(shí)現(xiàn)方法這一問題有了更深刻的體會(huì)，具體使用情況還需要大家實(shí)踐驗(yàn)證。這里是億速云，小編將為大家推送更多相關(guān)知識(shí)點(diǎn)的文章，歡迎關(guān)注！