num_correct = (pred == label).sum().item()
时间: 2024-04-18 10:27:16 浏览: 24
这段代码是用来计算预测值与标签值相等的数量的。首先,将预测值(pred)和标签值(label)进行比较,得到一个布尔数组,其中相等的位置为True,不相等的位置为False。然后,使用sum()函数将所有True的数量相加,最后使用item()函数将结果转换为标量值(scalar)。这样就得到了预测正确的数量(num_correct)。
相关问题
model.eval() with torch.no_grad(): # test total_correct = 0 total_num = 0 for x, label in cifar_test: # [b, 3, 32, 32] # [b] x, label = x.to(device), label.to(device) # [b, 10] logits = model(x) # [b] pred = logits.argmax(dim=1) # [b] vs [b] => scalar tensor correct = torch.eq(pred, label).float().sum().item() total_correct += correct total_num += x.size(0) # print(correct) acc = total_correct / total_num print(epoch, 'test acc:', acc) ———————————————— 逐行解释
这段代码用于在训练过程中对模型进行测试,并打印出测试准确率。以下是对代码逐行的解释:
2. `model.eval()`
将模型设置为评估模式,这将冻结模型中的dropout和batch normalization等操作。
3. `with torch.no_grad():`
使用torch.no_grad()上下文管理器,禁用梯度计算。在测试阶段,我们不需要计算梯度,只需要进行前向传播和评估。
6-19. 进行测试:
- `total_correct = 0` 初始化正确分类的样本数量。
- `total_num = 0` 初始化总样本数量。
- `for x, label in cifar_test:` 对测试集数据进行迭代,每次迭代获取一个批次的输入x和对应的标签label。
- `x, label = x.to(device), label.to(device)` 将输入x和标签label移动到指定的设备(GPU或CPU)上。
- `logits = model(x)` 将输入x传递给模型,得到模型的输出logits。
- `pred = logits.argmax(dim=1)` 获取logits中每个样本预测结果的最大值所对应的索引,即预测的类别。
- `correct = torch.eq(pred, label).float().sum().item()` 计算预测正确的样本数量,将预测结果和真实标签进行比较,并将结果转换为浮点数。
- `total_correct += correct` 累加正确分类的样本数量。
- `total_num += x.size(0)` 累加总样本数量,x.size(0)表示批次中的样本数量。
- `acc = total_correct / total_num` 计算测试准确率,即正确分类的样本数量除以总样本数量。
- `print(epoch, 'test acc:', acc)` 打印出当前训练轮数epoch和测试准确率。
这段代码的主要目的是在训练过程中定期对模型进行测试,并打印出测试准确率。通过计算模型在测试集上的准确率可以评估模型的性能。使用torch.no_grad()上下文管理器可以提高代码的运行效率,同时禁用梯度计算以减少内存占用。
给出一个cnn进行信道估计训练和测试的代码,用pytorch写,程序内包括snr_num,per_snr_num
好的,下面是一个简单的CNN进行信道估计的训练和测试代码,使用PyTorch实现:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import numpy as np
from sklearn.metrics import confusion_matrix, accuracy_score
# 定义CNN模型
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv1d(in_channels=2, out_channels=64, kernel_size=3, padding=1)
self.conv2 = nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3, padding=1)
self.conv3 = nn.Conv1d(in_channels=128, out_channels=256, kernel_size=3, padding=1)
self.fc1 = nn.Linear(in_features=256*8, out_features=512)
self.fc2 = nn.Linear(in_features=512, out_features=32)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(x)
x = nn.functional.max_pool1d(x, kernel_size=2)
x = self.conv2(x)
x = nn.functional.relu(x)
x = nn.functional.max_pool1d(x, kernel_size=2)
x = self.conv3(x)
x = nn.functional.relu(x)
x = nn.functional.max_pool1d(x, kernel_size=2)
x = x.view(-1, 256*8)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.fc2(x)
return x
# 定义数据集类
class ChannelDataset(data.Dataset):
def __init__(self, snr_num, per_snr_num):
self.snr_num = snr_num
self.per_snr_num = per_snr_num
self.data = []
self.labels = []
for i in range(snr_num):
for j in range(per_snr_num):
h_real = np.random.normal(0, 1, [2, 1024])
h_imag = np.random.normal(0, 1, [2, 1024])
y_real = np.random.normal(0, 1, [2, 1024])
y_imag = np.random.normal(0, 1, [2, 1024])
y = np.concatenate((y_real, y_imag), axis=0)
h = np.concatenate((h_real, h_imag), axis=0)
input_data = np.concatenate((y, h), axis=1)
label = i
self.data.append(input_data)
self.labels.append(label)
def __getitem__(self, index):
input_data = self.data[index]
label = self.labels[index]
return torch.from_numpy(input_data).float(), torch.tensor(label).long()
def __len__(self):
return len(self.data)
# 定义训练函数
def train(model, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = nn.functional.cross_entropy(output, target)
loss.backward()
optimizer.step()
if batch_idx % 100 == 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()))
# 定义测试函数
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
y_true = []
y_pred = []
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += nn.functional.cross_entropy(output, target, reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
y_true += target.tolist()
y_pred += pred.tolist()
test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / len(test_loader.dataset)
print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'.format(
test_loss, correct, len(test_loader.dataset), accuracy))
print('Confusion Matrix:\n', confusion_matrix(y_true, y_pred))
print('Accuracy Score:', accuracy_score(y_true, y_pred))
# 设置超参数
batch_size = 64
epochs = 10
lr = 0.001
momentum = 0.9
no_cuda = False
# 设置随机种子
torch.manual_seed(1)
np.random.seed(1)
# 判断是否使用GPU
use_cuda = not no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
# 加载数据集
train_dataset = ChannelDataset(snr_num=10, per_snr_num=500)
test_dataset = ChannelDataset(snr_num=10, per_snr_num=100)
train_loader = data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
# 初始化模型和优化器
model = CNN().to(device)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)
# 训练和测试模型
for epoch in range(1, epochs + 1):
train(model, device, train_loader, optimizer, epoch)
test(model, device, test_loader)
```
这个代码中,我们定义了一个简单的CNN模型来进行信道估计的训练和测试,数据集类ChannelDataset用于生成随机的信道和接收信号,train函数用于训练模型,test函数用于测试模型的准确率和混淆矩阵。在这个代码中,我们使用了PyTorch的自动求导和优化器来进行训练,同时使用了sklearn.metrics包中的函数来计算混淆矩阵和准确率。
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