Use "reduction='elementwise_mean'" instead of "size_average=True"

vinhkhuc · Oct 6, 2018 · 03b7ecf · 03b7ecf
1 parent 9afc6a7
commit 03b7ecf
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Showing 5 changed files with 5 additions and 5 deletions.
diff --git a/2_logistic_regression.py b/2_logistic_regression.py
@@ -52,7 +52,7 @@ def main():
     n_examples, n_features = trX.size()
     n_classes = 10
     model = build_model(n_features, n_classes)
-    loss = torch.nn.CrossEntropyLoss(size_average=True)
+    loss = torch.nn.CrossEntropyLoss(reduction='elementwise_mean')
     optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
     batch_size = 100
 

diff --git a/3_neural_net.py b/3_neural_net.py
@@ -51,7 +51,7 @@ def main():
     n_examples, n_features = trX.size()
     n_classes = 10
     model = build_model(n_features, n_classes)
-    loss = torch.nn.CrossEntropyLoss(size_average=True)
+    loss = torch.nn.CrossEntropyLoss(reduction='elementwise_mean')
     optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
     batch_size = 100
 

diff --git a/4_modern_neural_net.py b/4_modern_neural_net.py
@@ -55,7 +55,7 @@ def main():
     n_examples, n_features = trX.size()
     n_classes = 10
     model = build_model(n_features, n_classes)
-    loss = torch.nn.CrossEntropyLoss(size_average=True)
+    loss = torch.nn.CrossEntropyLoss(reduction='elementwise_mean')
     optimizer = optim.Adam(model.parameters())
     batch_size = 100
 

diff --git a/5_convolutional_net.py b/5_convolutional_net.py
@@ -72,7 +72,7 @@ def main():
     n_examples = len(trX)
     n_classes = 10
     model = ConvNet(output_dim=n_classes)
-    loss = torch.nn.CrossEntropyLoss(size_average=True)
+    loss = torch.nn.CrossEntropyLoss(reduction='elementwise_mean')
     optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
     batch_size = 100
 

diff --git a/6_lstm.py b/6_lstm.py
@@ -73,7 +73,7 @@ def main():
     trY = torch.from_numpy(trY).long()
 
     model = LSTMNet(input_dim, hidden_dim, n_classes)
-    loss = torch.nn.CrossEntropyLoss(size_average=True)
+    loss = torch.nn.CrossEntropyLoss(reduction='elementwise_mean')
     optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
 
     for i in range(epochs):