笔记5：TensorDataset、DataLoader及数据集划分

TensorDataset

转自：https://www.cnblogs.com/miraclepbc/p/14333299.html

导入相关包

from torch.utils.data import TensorDataset

特征与标签合并

HRdataset = TensorDataset(X, Y)

模型训练

for epoch in range(epochs):
    for i in range(num_batch):
        x, y = HRdataset[i * batch_size: i * batch_size + batch_size]
        y_pred = model(x)
        loss = loss_func(y_pred, y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    with torch.no_grad():
        print('epoch: ', epoch, 'loss: ', loss_func(model(X), Y).data.item())

DataLoader

导入相关包

from torch.utils.data import DataLoader

加载数据

HR_ds = TensorDataset(X, Y)
HR_dl = DataLoader(HR_ds, batch_size = batch_size, shuffle = True)

模型训练

for epoch in range(epochs):
    for x, y in HR_dl:
        y_pred = model(x)
        loss = loss_func(y_pred, y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    with torch.no_grad():
        print('epoch: ', epoch, 'loss: ', loss_func(model(X), Y).data.item())

划分数据集

导入相关包

from sklearn.model_selection import train_test_split

划分数据集

train_x, test_x, train_y, test_y = train_test_split(X_data, Y_data)

• 默认3:1

包装数据

train_x = torch.from_numpy(train_x).type(torch.float32)
test_x = torch.from_numpy(test_x).type(torch.float32)
train_y = torch.from_numpy(train_y).type(torch.float32)
test_y = torch.from_numpy(test_y).type(torch.float32)

train_ds = TensorDataset(train_x, train_y)
train_dl = DataLoader(train_ds, batch_size = batch_size, shuffle = True)
test_ds = TensorDataset(test_x, test_y)
test_dl = DataLoader(test_ds, batch_size = batch_size)

定义准确率

def accuracy(y_pred, y_true):
    return ((y_pred.data.numpy() > 0.5).astype('int') == y_true.numpy()).mean()

模型训练

for epoch in range(epochs):
    for x, y in train_dl:
        y_pred = model(x)
        loss = loss_func(y_pred, y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    with torch.no_grad():
        epoch_accuracy = accuracy(model(train_x), train_y)
        epoch_loss = loss_func(model(train_x), train_y).data
        epoch_test_accuracy = accuracy(model(test_x), test_y)
        epoch_test_loss = loss_func(model(test_x), test_y).data
        print('epoch: ', epoch, 'loss: ', round(epoch_loss.item(), 3), 'accuracy: ', round(epoch_accuracy.item(), 3),
              'test_loss: ', round(epoch_test_loss.item(), 3), 'test_accuracy: ', round(epoch_test_accuracy.item(), 3))