深度学习——批数据训练

代码与详细注释：

BATCH_SIZE = 5，shuffle=True

import torch
import torch.utils.data as Data

# 添加随机种子以使结果可复现
torch.manual_seed(1)    # reproducible

# 批大小
BATCH_SIZE = 5
# BATCH_SIZE = 8

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=True,               # random shuffle for training
    num_workers=2,              # subprocesses for loading data
)


def show_batch():
    for epoch in range(3):   # 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()

运行结果：

BATCH_SIZE = 5，shuffle=False

import torch
import torch.utils.data as Data

# 添加随机种子以使结果可复现
torch.manual_seed(1)    # reproducible

# 批大小
BATCH_SIZE = 5
# BATCH_SIZE = 8

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,               # random shuffle for training
    num_workers=2,              # subprocesses for loading data
)


def show_batch():
    for epoch in range(3):   # 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()

运行结果：

BATCH_SIZE = 8，shuffle=True

import torch
import torch.utils.data as Data

# 添加随机种子以使结果可复现
torch.manual_seed(1)    # reproducible

# 批大小
# BATCH_SIZE = 5
BATCH_SIZE = 8

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=True,               # random shuffle for training
    num_workers=2,              # subprocesses for loading data
)


def show_batch():
    for epoch in range(3):   # 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()

运行结果：

BATCH_SIZE = 8，shuffle=False

import torch
import torch.utils.data as Data

# 添加随机种子以使结果可复现
torch.manual_seed(1)    # reproducible

# 批大小
# BATCH_SIZE = 5
BATCH_SIZE = 8

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,               # random shuffle for training
    num_workers=2,              # subprocesses for loading data
)


def show_batch():
    for epoch in range(3):   # 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()

运行结果：