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pytorch实现softmax多分类（MNIST）

#导⼊各种库

import torch

as nn

测井车functional as F

import torch.optim as optim

电缆保护管HDPEfrom torchvision import datasets, transforms

from torch.utils.data import DataLoader

# Training settings

batch_size = 64

#数据集的处理

#图⽚变换：转换成Tensor，标准化

transform = transforms.Compose([ transforms.ToTensor(),

transforms.Normalize((0.1307, ),(0.3081, ))])

#创建训练数据集

train_dataset = datasets.MNIST(root='./mnist_data/',

train=True, download=True,

transform=transform)

#导⼊训练数据集

train_loader = DataLoader(train_dataset,

shuffle=True,

batch_size=batch_size)

#创建测试数据集

test_dataset = datasets.MNIST(root='./mnist_data/',

train=False,

download=True,

transform=transform)

#导⼊测试数据集

test_loader = DataLoader(test_dataset,

shuffle=False,

batch_size=batch_size)

class Net(nn.Module):

def __init__(self):

super(Net, self).__init__()

self.l1 = Linear(784, 512)

self.l2 = Linear(512, 256)

self.l3 = Linear(256, 128)

self.l4 = Linear(128, 64)

self.l5 = Linear(64, 10)

def forward(self, x):

# Flatten the data (n, 1, 28, 28) --> (n, 784)

x = x.view(-1, 784)

x = F.relu(self.l1(x))

x = F.relu(self.l2(x))

x = F.relu(self.l3(x))

x = F.relu(self.l4(x))

return F.log_softmax(self.l5(x), dim=1)

model = Net()

criterion = CrossEntropyLoss()

optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

def train(epoch):

# 每次输⼊barch_idx个数据

for batch_idx, data in enumerate(train_loader):

inputs, target = data

<_grad()

output = model(inputs)

# loss

loss = criterion(output, target)

loss.backward()

# update

各种卧式注塑机射咀

optimizer.step()

if batch_idx % 200 == 0: #len(data)=64，理解为batch-size,len(train_loader.dataset)=60000总样本数，len(train_loader)是有多少个loader，理解为共有多少个iterations print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(

Train Epoch: 5 [0/60000 (0%)] Loss: 0.112639

Train Epoch: 5 [12800/60000 (21%)] Loss: 0.130508

Train Epoch: 5 [25600/60000 (43%)] Loss: 0.107424

Train Epoch: 5 [38400/60000 (64%)] Loss: 0.023164

Train Epoch: 5 [51200/60000 (85%)] Loss: 0.075948

Test set: Average loss: 0.0017, Accuracy: 9673/10000 (97%)

Train Epoch: 6 [0/60000 (0%)] Loss: 0.037222低频放大器

Train Epoch: 6 [12800/60000 (21%)] Loss: 0.033421

Train Epoch: 6 [25600/60000 (43%)] Loss: 0.019519

Train Epoch: 6 [38400/60000 (64%)] Loss: 0.229080

Train Epoch: 6 [51200/60000 (85%)] Loss: 0.012769

Test set: Average loss: 0.0015, Accuracy: 9698/10000 (97%)

rake接收机Train Epoch: 7 [0/60000 (0%)] Loss: 0.024760

Train Epoch: 7 [12800/60000 (21%)] Loss: 0.043735

Train Epoch: 7 [25600/60000 (43%)] Loss: 0.025668

Train Epoch: 7 [38400/60000 (64%)] Loss: 0.030804

Train Epoch: 7 [51200/60000 (85%)] Loss: 0.048212

Test set: Average loss: 0.0014, Accuracy: 9743/10000 (97%)

Train Epoch: 8 [0/60000 (0%)] Loss: 0.061919

Train Epoch: 8 [12800/60000 (21%)] Loss: 0.047970

Train Epoch: 8 [25600/60000 (43%)] Loss: 0.088634滑动水口

Train Epoch: 8 [38400/60000 (64%)] Loss: 0.069933

Train Epoch: 8 [51200/60000 (85%)] Loss: 0.010266

Test set: Average loss: 0.0013, Accuracy: 9739/10000 (97%)

Train Epoch: 9 [0/60000 (0%)] Loss: 0.024968

Train Epoch: 9 [12800/60000 (21%)] Loss: 0.024904

Train Epoch: 9 [25600/60000 (43%)] Loss: 0.071306

Train Epoch: 9 [38400/60000 (64%)] Loss: 0.003656

Train Epoch: 9 [51200/60000 (85%)] Loss: 0.023971

Test set: Average loss: 0.0012, Accuracy: 9768/10000 (98%)

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