What is the use of the shuffle=True of dataloader in PyTorch
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Understanding of shuffle=True:
I didn't know the actual effect of shuffle before. Suppose I have some data, such as a batch_size=2, I don't know how to disturb it, which of the following is the specific case?
1. First take the batch in order, and then disrupt the batch, that is, first take a dagger, b, and then disrupt the internal batch.
two。 First disturb, then take the batch.
Proved to be the second shuffle (bool, optional): set to ``True`` to have the data reshuffled at every epoch (default: ``False``). If shuffle: sampler = RandomSampler (dataset) # what you get at this time is the index
Add: simply test how shuffle=True works in pytorch dataloader
Look at the code ~ import sysimport torchimport randomimport argparseimport numpy as npimport pandas as pdimport torch.nn as nnfrom torch.nn import functional as Ffrom torch.optim import lr_schedulerfrom torchvision import datasets, transformsfrom torch.utils.data import TensorDataset, DataLoader, Dataset class DealDataset (Dataset): def _ _ init__ (self): xy = np.loadtxt (open ('. / iris.csv','rb'), delimiter=',', dtype=np.float32) # data = pd.read_csv ("iris.csv" Header=None) # xy = data.values self.x_data = torch.from_numpy (xy [:, 0xy [:]) self.y_data = torch.from_numpy (xy [:, [- 1]]) self.len = xy.shape [0] def _ _ getitem__ (self, index): return self.x_ data [index] Self.y_ data [index] def _ len__ (self): return self.len dealDataset = DealDataset () train_loader2 = DataLoader (dataset=dealDataset, batch_size=2, shuffle=True) # print (dealDataset.x_data) for I, data in enumerate (train_loader2): inputs, labels = data # inputs, labels = Variable (inputs) Variable (labels) print (inputs) # print ("epoch:", epoch, "No.", I, "inputs", inputs.data.size (), "labels", labels.data.size ()) are all the contents of the article "what is the shuffle=True of dataloader in PyTorch?" Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!
