看代码吧~
import torch
import torch.utils.data as Data
torch.manual_seed(1) # reproducible
# BATCH_SIZE = 5
BATCH_SIZE = 8 # 每次使用8个数据同时传入网路
x = torch.linspace(1, 10, 10) # this is x data (torch tensor)
y = torch.linspace(10, 1, 10) # this is y data (torch tensor)
torch_dataset = Data.TensorDataset(x, y)
loader = Data.DataLoader(
dataset=torch_dataset, # torch TensorDataset format
batch_size=BATCH_SIZE, # mini batch size
shuffle=False, # 设置不随机打乱数据 random shuffle for training
num_workers=2, # 使用两个进程提取数据，subprocesses for loading data
)
def show_batch():
for epoch in range(3): # 全部的数据使用3遍，train entire dataset 3 times
for step, (batch_x, batch_y) in enumerate(loader): # for each training step
# train your data...
print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',
batch_x.numpy(), '| batch y: ', batch_y.numpy())
if __name__ == '__main__':
show_batch()
BATCH_SIZE = 8 , 所有数据利用三次
Epoch: 0 | Step: 0 | batch x: [1. 2. 3. 4. 5. 6. 7. 8.] | batch y: [10. 9. 8. 7. 6. 5. 4. 3.] Epoch: 0 | Step: 1 | batch x: [ 9. 10.] | batch y: [2. 1.] Epoch: 1 | Step: 0 | batch x: [1. 2. 3. 4. 5. 6. 7. 8.] | batch y: [10. 9. 8. 7. 6. 5. 4. 3.] Epoch: 1 | Step: 1 | batch x: [ 9. 10.] | batch y: [2. 1.] Epoch: 2 | Step: 0 | batch x: [1. 2. 3. 4. 5. 6. 7. 8.] | batch y: [10. 9. 8. 7. 6. 5. 4. 3.] Epoch: 2 | Step: 1 | batch x: [ 9. 10.] | batch y: [2. 1.]
补充：pytorch批训练bug
问题描述：
在进行pytorch神经网络批训练的时候，有时会出现报错
TypeError: batch must contain tensors, numbers, dicts or lists; found <class 'torch.autograd.variable.Variable'>
解决办法：
第一步：
检查(重点！！！！！)：
train_dataset = Data.TensorDataset(train_x, train_y)
train_x，和train_y格式，要求是tensor类，我第一次出错就是因为传入的是variable
可以这样将数据变为tensor类：
train_x = torch.FloatTensor(train_x)
第二步：
train_loader = Data.DataLoader(
dataset=train_dataset,
batch_size=batch_size,
shuffle=True
)
实例化一个DataLoader对象
第三步：
for epoch in range(epochs):
for step, (batch_x, batch_y) in enumerate(train_loader):
batch_x, batch_y = Variable(batch_x), Variable(batch_y)
这样就可以批训练了
需要注意的是：train_loader输出的是tensor，在训练网络时，需要变成Variable
以上为个人经验，希望能给大家一个参考，也希望大家多多支持。
