多分類任務中不同隱藏層層數對實驗結果的影響(使用GPU)
阿新 • • 發佈:2022-03-09
1 匯入包
import torch
import torch.nn as nn
import numpy as np
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader,TensorDataset
2 匯入資料
train_dataset = torchvision.datasets.MNIST('../Dataset/MNIST/',download = True,train = True,transform = transforms.ToTensor() )
test_dataset = torchvision.datasets.MNIST('../Dataset/MNIST/',download = True,train = False,transform = transforms.ToTensor() )
train_x = train_dataset.data.cuda().type(torch.float32)
train_y = train_dataset.targets.cuda()
test_x = test_dataset.data.cuda().type(torch.float32)
test_y = test_dataset.targets.cuda()
batch_size = 64
train_data = TensorDataset(train_x,train_y)
train_iter = DataLoader(
dataset = train_data,
shuffle = True,
batch_size = batch_size
)
test_data = TensorDataset(test_x,test_y)
test_iter = DataLoader(
dataset = test_data,
shuffle = True,
batch_size = batch_size
)
3 定義模型
class flatten(nn.Module):
def __init__(self):
super(flatten,self).__init__()
def forward(self,x):
return x.view(x.shape[0],784)
class Linear1(nn.Module ):
def __init__(self,num_input,num_hidden,num_output):
super(Linear1,self).__init__()
self.linear1 = nn.Linear(num_input,num_hidden)
self.linear2 = nn.Linear(num_hidden,num_output)
self.flatten = flatten()
self.relu = nn.ReLU()
def forward(self,input):
out = self.flatten(input)
out = self.relu(self.linear1(out))
out = self.linear2(out)
return out
class Linear2(nn.Module ):
def __init__(self,num_input,num_hidden1,num_hidden2,num_output):
super(Linear1,self).__init__()
self.linear1 = nn.Linear(num_input,num_hidden1)
self.linear2 = nn.Linear(num_hidden1,num_hidden2)
self.linear3 = nn.Linear(num_hidden2,num_output)
self.flatten = flatten()
self.relu = nn.ReLU()
def forward(self,input):
out = self.flatten(input)
out = self.relu(self.linear1(out))
out = self.relu(self.linear2(out))
out = self.linear3(out)
return out
class Linear3(nn.Module ):
def __init__(self,num_input,num_hidden1,num_hidden2,num_hidden3,num_output):
super(Linear1,self).__init__()
self.linear1 = nn.Linear(num_input,num_hidden1)
self.linear2 = nn.Linear(num_hidden1,num_hidden2)
self.linear3 = nn.Linear(num_hidden2,num_hidden3)
self.linear4 = nn.Linear(num_hidden3,num_output)
self.flatten = flatten()
self.relu = nn.ReLU()
def forward(self,input):
out = self.flatten(input)
out = self.relu(self.linear1(out))
out = self.relu(self.linear2(out))
out = self.relu(self.linear3(out))
out = self.linear4(out)
return out
4 定義損失函式和優化器
num_input,num_hidden,num_output = 784,256,10
lr = 0.001
net = Linear1(num_input,num_hidden,num_output).cuda()
loss = nn.CrossEntropyLoss(reduction='mean')
# optimizer = torch.optim.Adam(net.parameters(),lr = lr)
optimizer = torch.optim.SGD(net.parameters(),lr = lr)
5 定義訓練模型
def train(net,train_iter,test_iter,loss,num_epochs,batch_size,optimizer):
train_ls ,test_ls, train_acc,test_acc = [],[],[],[]
for epoch in range(num_epochs):
train_ls_sum,train_acc_sum,n = 0,0,0
for x,y in train_iter:
y_pred = net(x)
l = loss(y_pred,y)
optimizer.zero_grad()
l.backward()
optimizer.step()
train_ls_sum +=l
train_acc_sum += (y_pred.argmax(dim = 1) == y).sum().item()
n += x.shape[0]
train_ls.append(train_ls_sum)
train_acc.append(train_acc_sum/n)
test_ls_sum,test_acc_sum ,n = 0,0,0
for x,y in test_iter:
y_pred = net(x)
l = loss(y_pred,y)
test_ls_sum +=l
test_acc_sum += (y_pred.argmax(dim = 1) == y).sum().item()
n += x.shape[0]
test_ls.append(test_ls_sum)
test_acc.append(test_acc_sum/n)
print('epoch: %d, train loss: %f, test loss: %f , train acc: %f, test acc: %f '
%(epoch+1,train_ls[-1],test_ls[-1],train_acc[-1],test_acc[-1]))
return train_ls,test_ls
6 開始訓練
num_epochs = 40
train_ls,test_ls = train(net,train_iter,test_iter,loss,num_epochs,batch_size,optimizer)