【Pytorch基础】方法的使用

import torch import torch.utils.data as Data torch.manual_seed(1) # reproducible BATCH_SIZE = 5 # 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(x, 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(\'Epoch: \', epoch, \'| Step: \', step, \'| batch x: \', batch_x.numpy(), \'| batch y: \', batch_y.numpy()) """ Epoch: 0 | Step: 0 | batch x: [ 6. 7. 2. 3. 1.] | batch y: [ 5. 4. 9. 8. 10.] Epoch: 0 | Step: 1 | batch x: [ 9. 10. 4. 8. 5.] | batch y: [ 2. 1. 7. 3. 6.] Epoch: 1 | Step: 0 | batch x: [ 3. 4. 2. 9. 10.] | batch y: [ 8. 7. 9. 2. 1.] Epoch: 1 | Step: 1 | batch x: [ 1. 7. 8. 5. 6.] | batch y: [ 10. 4. 3. 6. 5.] Epoch: 2 | Step: 0 | batch x: [ 3. 9. 2. 6. 7.] | batch y: [ 8. 2. 9. 5. 4.] Epoch: 2 | Step: 1 | batch x: [ 10. 4. 8. 1. 5.] | batch y: [ 1. 7. 3. 10. 6.] """ “” 如果改变batch大小每次迭代数据不够batch,则函数就会把剩下的数据输出。 “”