多分類一種比較常用的做法是在最後一層加softmax歸一化，值最大的維度所對應的位置則作為該樣本對應的類。本文采用PyTorch框架，選用經典影象資料集mnist學習一波多分類。

MNIST資料集

MNIST 資料集(手寫數字資料集)來自美國國家標準與技術研究所,National Institute of Standards and Technology (NIST). 訓練集 (training set) 由來自 250 個不同人手寫的數字構成,其中 50% 是高中學生,50% 來自人口普查局 (the Census Bureau) 的工作人員. 測試集(test set) 也是同樣比例的手寫數字資料。MNIST資料集下載地址:http://yann.lecun.com/exdb/mnist/。手寫數字的MNIST資料庫包括60,000個的訓練集樣本，以及10,000個測試集樣本。

其中：

train-images-idx3-ubyte.gz (訓練資料集圖片)

train-labels-idx1-ubyte.gz （訓練資料集標記類別）

t10k-images-idx3-ubyte.gz: （測試資料集）

t10k-labels-idx1-ubyte.gz（測試資料集標記類別）

MNIST資料集是經典影象資料集，包括10個類別(0到9)。每一張圖片拉成向量表示，如下圖784維向量作為第一層輸入特徵。

Softmax分類

softmax函式的本質就是將一個K 維的任意實數向量壓縮（對映）成另一個K維的實數向量，其中向量中的每個元素取值都介於（0，1）之間，並且壓縮後的K個值相加等於1(變成了概率分佈)。在選用Softmax做多分類時，可以根據值的大小來進行多分類的任務，如取權重最大的一維。softmax介紹和公式網上很多，這裡不介紹了。下面使用Pytorch定義一個多層網路(4個隱藏層，最後一層softmax概率歸一化)，輸出層為10正好對應10類。

PyTorch實戰

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets,transforms
from torch.autograd import Variable

# Training settings
batch_size = 64

# MNIST Dataset
train_dataset = datasets.MNIST(root='./mnist_data/',train=True,transform=transforms.ToTensor(),download=True)

test_dataset = datasets.MNIST(root='./mnist_data/',train=False,transform=transforms.ToTensor())

# Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,shuffle=False)
class Net(nn.Module):
  def __init__(self):
    super(Net,self).__init__()
    self.l1 = nn.Linear(784,520)
    self.l2 = nn.Linear(520,320)
    self.l3 = nn.Linear(320,240)
    self.l4 = nn.Linear(240,120)
    self.l5 = nn.Linear(120,10)

  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)
    #return self.l5(x)
model = Net()
optimizer = optim.SGD(model.parameters(),lr=0.01,momentum=0.5)
def train(epoch):

  # 每次輸入barch_idx個數據
  for batch_idx,(data,target) in enumerate(train_loader):
    data,target = Variable(data),Variable(target)

    optimizer.zero_grad()
    output = model(data)
    # loss
    loss = F.nll_loss(output,target)
    loss.backward()
    # update
    optimizer.step()
    if batch_idx % 200 == 0:
      print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
        epoch,batch_idx * len(data),len(train_loader.dataset),100. * batch_idx / len(train_loader),loss.data[0]))
def test():
  test_loss = 0
  correct = 0
  # 測試集
  for data,target in test_loader:
    data,target = Variable(data,volatile=True),Variable(target)
    output = model(data)
    # sum up batch loss
    test_loss += F.nll_loss(output,target).data[0]
    # get the index of the max
    pred = output.data.max(1,keepdim=True)[1]
    correct += pred.eq(target.data.view_as(pred)).cpu().sum()

  test_loss /= len(test_loader.dataset)
  print('\nTest set: Average loss: {:.4f},Accuracy: {}/{} ({:.0f}%)\n'.format(
    test_loss,correct,len(test_loader.dataset),100. * correct / len(test_loader.dataset)))

for epoch in range(1,6):
  train(epoch)
  test()

輸出結果：
Train Epoch: 1 [0/60000 (0%)]	Loss: 2.292192
Train Epoch: 1 [12800/60000 (21%)]	Loss: 2.289466
Train Epoch: 1 [25600/60000 (43%)]	Loss: 2.294221
Train Epoch: 1 [38400/60000 (64%)]	Loss: 2.169656
Train Epoch: 1 [51200/60000 (85%)]	Loss: 1.561276

Test set: Average loss: 0.0163,Accuracy: 6698/10000 (67%)

Train Epoch: 2 [0/60000 (0%)]	Loss: 0.993218
Train Epoch: 2 [12800/60000 (21%)]	Loss: 0.859608
Train Epoch: 2 [25600/60000 (43%)]	Loss: 0.499748
Train Epoch: 2 [38400/60000 (64%)]	Loss: 0.422055
Train Epoch: 2 [51200/60000 (85%)]	Loss: 0.413933

Test set: Average loss: 0.0065,Accuracy: 8797/10000 (88%)

Train Epoch: 3 [0/60000 (0%)]	Loss: 0.465154
Train Epoch: 3 [12800/60000 (21%)]	Loss: 0.321842
Train Epoch: 3 [25600/60000 (43%)]	Loss: 0.187147
Train Epoch: 3 [38400/60000 (64%)]	Loss: 0.469552
Train Epoch: 3 [51200/60000 (85%)]	Loss: 0.270332

Test set: Average loss: 0.0045,Accuracy: 9137/10000 (91%)

Train Epoch: 4 [0/60000 (0%)]	Loss: 0.197497
Train Epoch: 4 [12800/60000 (21%)]	Loss: 0.234830
Train Epoch: 4 [25600/60000 (43%)]	Loss: 0.260302
Train Epoch: 4 [38400/60000 (64%)]	Loss: 0.219375
Train Epoch: 4 [51200/60000 (85%)]	Loss: 0.292754

Test set: Average loss: 0.0037,Accuracy: 9277/10000 (93%)

Train Epoch: 5 [0/60000 (0%)]	Loss: 0.183354
Train Epoch: 5 [12800/60000 (21%)]	Loss: 0.207930
Train Epoch: 5 [25600/60000 (43%)]	Loss: 0.138435
Train Epoch: 5 [38400/60000 (64%)]	Loss: 0.120214
Train Epoch: 5 [51200/60000 (85%)]	Loss: 0.266199

Test set: Average loss: 0.0026,Accuracy: 9506/10000 (95%)
Process finished with exit code 0

隨著訓練迭代次數的增加，測試集的精確度還是有很大提高的。並且當迭代次數為5時，使用這種簡單的網路可以達到95%的精確度。

以上這篇PyTorch: Softmax多分類實戰操作就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。