Pytorch利用dataloader定义batch数目为16，建立一个全连接神经网络，并以f1评估模型

以下是利用dataloader定义batch数目为16，建立一个全连接神经网络，并以f1评估模型的示例代码：

import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset, BatchSampler
from sklearn.metrics import f1_score

# 定义数据集
tensor_data = torch.randn(14807, 24)
tensor_label = torch.randint(0, 2, (14807,))

# 定义BatchSampler
batch_size = 16
batch_sampler = BatchSampler(range(len(tensor_data)), batch_size=batch_size, drop_last=False)

# 定义数据集和dataloader
dataset = TensorDataset(tensor_data, tensor_label)
dataloader = DataLoader(dataset, batch_sampler=batch_sampler)

# 定义全连接神经网络
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(24, 32)
        self.fc2 = nn.Linear(32, 2)

    def forward(self, x):
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return x

net = Net()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练神经网络
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(dataloader, 0):
        inputs, labels = data

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 1000 == 999:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 1000))
            running_loss = 0.0

print('Finished Training')

# 测试神经网络并计算f1-score
net.eval()
y_true = []
y_pred = []
for data in dataloader:
    inputs, labels = data
    outputs = net(inputs)
    _, predicted = torch.max(outputs.data, 1)
    y_true += labels.tolist()
    y_pred += predicted.tolist()

f1 = f1_score(y_true, y_pred)
print('f1-score: %.3f' % f1)

以上代码中，在训练神经网络后，需要将神经网络设置为评估模式，即使用`net.eval()`。然后，遍历测试数据集，对于每个batch的数据，将输出的预测值和真实标签保存在列表中，最后计算f1-score。