train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, num_workers=num_workers)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, num_workers=num_workers)
Pytorch dataloader helps load data in batches such as images.
Flavors of Pytorch Model Initialization
Functional
import torch.nn as nn
import torch.nn.functional as F
Object Oriented OOP
class Autoencoder(nn.Module):
def __init__(self, encoding_dim):
super(Autoencoder, self).__init__()
## encoder ##
## decoder ##
def forward(self, x):
# define feedforward behavior
# and scale the *output* layer with a sigmoid activation function
return x
Best Practice:
Pytorch do sanity check load checkpoint and make sure everything worked. Imshow() the output image make sure it is the desired output. Do sanity check, visual check.
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