pytorch 6 batch_train 批训练操作

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。