实验目的】
理解神经网络原理，掌握神经网络前向推理和后向传播方法；
掌握使用pytorch框架训练和推理全连接神经网络模型的编程实现方法。
【实验内容】
1.使用pytorch框架，设计一个全连接神经网络，实现Mnist手写数字字符集的训练与识别。
【实验报告要求】
修改神经网络结构，改变层数观察层数对训练和检测时间，准确度等参数的影响；
修改神经网络的学习率，观察对训练和检测效果的影响；
修改神经网络结构，增强或减少神经元的数量，观察对训练的检测效果的影响。
import torch import torchvision from torch.utils.data import DataLoader
n_epochs = 3 batch_size_train = 64 batch_size_test = 1000 learning_rate = 0.01 momentum = 0.5 log_interval = 10 random_seed = 1 torch.manual_seed(random_seed)
train_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST('./data/', train=True, download=True,
transform=torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
(0.1307,), (0.3081,))
])),
batch_size=batch_size_train, shuffle=True)
test_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST('./data/', train=False, download=True,
transform=torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
(0.1307,), (0.3081,))
])),
batch_size=batch_size_test, shuffle=True)
examples = enumerate(test_loader) batch_idx, (example_data, example_targets) = next(examples) print(example_targets) print(example_data.shape)
import matplotlib.pyplot as plt
fig = plt.figure()
for i in range(6):
plt.subplot(2,3,i+1) #2行3列，依次对每一个图形填充
plt.tight_layout() #自动调整坐标轴标签，刻度，标题
plt.imshow(example_data[i][0], cmap='gray') #数字转化成图形
plt.title("Ground Truth: {}".format(example_targets[i]))
#不显示坐标轴
plt.xticks([])
plt.yticks([])
plt.show()
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x,dim=0)
network = Net()
optimizer = optim.SGD(network.parameters(), lr=learning_rate,
momentum=momentum)
train_losses = [] train_counter = [] test_losses = [] test_counter = [i*len(train_loader.dataset) for i in range(n_epochs + 1)]
def train(epoch):
network.train()
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = network(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
train_losses.append(loss.item())
train_counter.append(
(batch_idx*64) + ((epoch-1)*len(train_loader.dataset)))
torch.save(network.state_dict(), './model.pth')
torch.save(optimizer.state_dict(), './optimizer.pth')
train(1)
def test():
network.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
output = network(data)
test_loss += F.nll_loss(output, target, reduction='sum').item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).sum()
test_loss /= len(test_loader.dataset)
test_losses.append(test_loss)
print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
test()
修改神经网络的学习率，观察对训练和检测效果的影响；
学习率设置太大会造成网络不能收敛，在最优值附近徘徊，也就是说直接跳过最低的地方跳到对称轴另一边，从而忽视了找到最优值的位置，学习率设置太小，网络收敛非常缓慢，会增大找到最优值的时间，也就是说从山坡上像蜗牛一样慢慢地爬下去。虽然设置非常小的学习率是可以到达，但是这很可能会进入局部极值点就收敛，没有真正找到的最优解。
修改神经网络结构，增强或减少神经元的数量，观察对训练的检测效果的影响。
一般条件下，在调参合理的情况下，层数和神经元数越多，正确率越高，但随之对应的是，容易出现过拟合
标签：loss,self,loader,神经网络,train,实验,test,手写,data From： https://www.cnblogs.com/zcqs-001/p/16928992.html