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Change BCELoss in MLP model (#756)

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cuicorey
2021-12-20 19:03:33 +08:00
committed by GitHub
parent 2aca74cd21
commit 350fbe91c9
1 changed files with 3 additions and 11 deletions
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14
qlib/contrib/model/pytorch_nn.py
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@@ -267,7 +267,7 @@ class DNNModelPytorch(Model):
loss = torch.mul(sqr_loss, w).mean()
return loss
elif loss_type == "binary":
loss = nn.BCELoss(weight=w)
loss = nn.BCEWithLogitsLoss(weight=w)
return loss(pred, target)
else:
raise NotImplementedError("loss {} is not supported!".format(loss_type))
@@ -334,16 +334,8 @@ class Net(nn.Module):
dnn_layers.append(seq)
drop_input = nn.Dropout(0.05)
dnn_layers.append(drop_input)
if loss == "mse":
fc = nn.Linear(hidden_units, output_dim)
dnn_layers.append(fc)
elif loss == "binary":
fc = nn.Linear(hidden_units, output_dim)
sigmoid = nn.Sigmoid()
dnn_layers.append(nn.Sequential(fc, sigmoid))
else:
raise NotImplementedError("loss {} is not supported!".format(loss))
fc = nn.Linear(hidden_units, output_dim)
dnn_layers.append(fc)
# optimizer
self.dnn_layers = nn.ModuleList(dnn_layers)
self._weight_init()