From 03b7ecfe8079f9b4b6c7ff7e82068162687ca42c Mon Sep 17 00:00:00 2001 From: vinhkhuc Date: Fri, 5 Oct 2018 19:54:40 -0700 Subject: [PATCH] Use "reduction='elementwise_mean'" instead of "size_average=True" --- 2_logistic_regression.py | 2 +- 3_neural_net.py | 2 +- 4_modern_neural_net.py | 2 +- 5_convolutional_net.py | 2 +- 6_lstm.py | 2 +- 5 files changed, 5 insertions(+), 5 deletions(-) diff --git a/2_logistic_regression.py b/2_logistic_regression.py index f4ff9d4..0494090 100644 --- 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 index e725d64..b8db316 100644 --- 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 index 4f330c2..84f970e 100644 --- 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 index f53c511..d877961 100644 --- 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 index b8ccb52..6b9f44e 100644 --- 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):