Look at the code.
import torch
import as Data
torch.manual_seed(1) # reproducible
# BATCH_SIZE = 5
BATCH_SIZE = 8 # 8 simultaneous data feeds to the network per use
x = (1, 10, 10) # this is x data (torch tensor)
y = (10, 1, 10) # this is y data (torch tensor)
torch_dataset = (x, y)
loader = (
dataset=torch_dataset, # torch TensorDataset format
batch_size=BATCH_SIZE, # mini batch size
shuffle=False, # Set up not to randomly shuffle data random shuffle for training
num_workers=2, # Extract data using two processes, subprocesses for loading data
)
def show_batch():
for epoch in range(3): # Use the entire dataset 3 times, 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 , all data utilized three times
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.]
Addendum: pytorch batch training bugs
Problem Description:
When doing pytorch neural network batch training, it sometimes reports an error
TypeError: batch must contain tensors, numbers, dicts or lists; found <class ''>
Solution:
Step one:
Inspection (focus ！！！！！) :
train_dataset = (train_x, train_y)
train_x, and train_y format, requires the tensor class, my first error was because I passed in variable
The data can be turned into a tensor class in this way:
train_x = (train_x)
Step two:
train_loader = (
dataset=train_dataset,
batch_size=batch_size,
shuffle=True
)
Instantiate a DataLoader object
Step Three:
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)
This way you can batch train
One thing to note: the output of train_loader is a tensor, which needs to be turned into a Variable when training the network
The above is a personal experience, I hope it can give you a reference, and I hope you can support me more.