pytorch 6 batch_train 批训练

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.]

END