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minor refactoring for batch size in deep residual network

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Liang Yi
2018-06-03 01:20:51 +08:00
parent 3008d88222
commit 7aac81272a
1 changed files with 3 additions and 2 deletions
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5
tutorials/02-intermediate/deep_residual_network/main.py
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@@ -16,6 +16,7 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Hyper-parameters
num_epochs = 80
batch_size = 100
learning_rate = 0.001
# Image preprocessing modules
@@ -37,11 +38,11 @@ test_dataset = torchvision.datasets.CIFAR10(root='../../data/',
# Data loader
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=100,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=100,
batch_size=batch_size,
shuffle=False)
# 3x3 convolution