PyTorch-批量训练技巧

来自：https://morvanzhou.github.io/tutorials/machine-learning/torch/3-05-train-on-batch/

import torch
import torch.utils.data as Data

torch.manual_seed(1)

BATCH_SIZE = 8  # 批训练的数据个数

x = torch.linspace(1, 10, 10)  # x data (torch tensor)
y = torch.linspace(10, 1, 10)  # y data (torch tensor)

# 先转换成 torch 能识别的 Dataset
torch_dataset = Data.TensorDataset(data_tensor=x, target_tensor=y)

# 把 dataset 放入 DataLoader
loader = Data.DataLoader(
    dataset=torch_dataset,  # torch TensorDataset format
    batch_size=BATCH_SIZE,  # mini batch size
    shuffle=True,  # 要不要打乱数据 (打乱比较好)
    num_workers=2,  # 多线程来读数据
)

for epoch in range(3):  # 训练所有!整套!数据 3 次
    for step, (batch_x, batch_y) in enumerate(loader):  # 每一步 loader 释放一小批数据用来学习
        # 假设这里就是你训练的地方...

        # 打出来一些数据
        print('批次Epoch: ', epoch, '| Step: ', step, '| 数据batch x: ',
              batch_x.numpy(), '| y: ', batch_y.numpy())

批次Epoch: 0 | Step: 0 | 数据batch x: [ 6. 7. 2. 3. 1. 9. 10. 4.] | y: [ 5. 4. 9. 8. 10. 2. 1. 7.]

批次Epoch: 0 | Step: 1 | 数据batch x: [ 8. 5.] | y: [ 3. 6.]

批次Epoch: 1 | Step: 0 | 数据batch x: [ 3. 4. 2. 9. 10. 1. 7. 8.] | y: [ 8. 7. 9. 2. 1. 10. 4. 3.]

批次Epoch: 1 | Step: 1 | 数据batch x: [ 5. 6.] | y: [ 6. 5.]

批次Epoch: 2 | Step: 0 | 数据batch x: [ 3. 9. 2. 6. 7. 10. 4. 8.] | y: [ 8. 2. 9. 5. 4. 1. 7. 3.]

批次Epoch: 2 | Step: 1 | 数据batch x: [ 1. 5.] | y: [ 10. 6.]