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Issue 96: Added missing evals in residual networks #97

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Issue 96: Added missing evals in residual networks #97

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3 changes: 2 additions & 1 deletion tutorials/02-intermediate/deep_residual_network/main-gpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -132,6 +132,7 @@ def forward(self, x):
optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)

# Test
resnet.eval()
correct = 0
total = 0
for images, labels in test_loader:
Expand All @@ -144,4 +145,4 @@ def forward(self, x):
print('Accuracy of the model on the test images: %d %%' % (100 * correct / total))

# Save the Model
torch.save(resnet.state_dict(), 'resnet.pkl')
torch.save(resnet.state_dict(), 'resnet.pkl')
3 changes: 2 additions & 1 deletion tutorials/02-intermediate/deep_residual_network/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -132,6 +132,7 @@ def forward(self, x):
optimizer = torch.optim.Adam(resnet.parameters(), lr=lr)

# Test
resnet.eval()
correct = 0
total = 0
for images, labels in test_loader:
Expand All @@ -144,4 +145,4 @@ def forward(self, x):
print('Accuracy of the model on the test images: %d %%' % (100 * correct / total))

# Save the Model
torch.save(resnet.state_dict(), 'resnet.pkl')
torch.save(resnet.state_dict(), 'resnet.pkl')