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add arg weight_decay

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This commit is contained in:
bxdd
2020-12-09 00:27:54 +08:00
committed by you-n-g
parent 2873813562
commit 56e579e20f
2 changed files with 8 additions and 3 deletions
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1
examples/benchmarks/MLP/workflow_config_mlp_Alpha158.yaml
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@@ -67,6 +67,7 @@ task:
max_steps: 8000 max_steps: 8000
batch_size: 8192 batch_size: 8192
GPU: 0 GPU: 0
weight_decay: 0.0002
dataset: dataset:
class: DatasetH class: DatasetH
module_path: qlib.data.dataset module_path: qlib.data.dataset

10
qlib/contrib/model/pytorch_nn.py
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@@ -62,6 +62,7 @@ class DNNModelPytorch(Model):
loss="mse", loss="mse",
GPU="0", GPU="0",
seed=None, seed=None,
weight_decay=0.0,
**kwargs **kwargs
): ):
# Set logger. # Set logger.
@@ -82,6 +83,7 @@ class DNNModelPytorch(Model):
self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu") self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
self.use_GPU = torch.cuda.is_available() self.use_GPU = torch.cuda.is_available()
self.seed = seed self.seed = seed
self.weight_decay = weight_decay
self.logger.info( self.logger.info(
"DNN parameters setting:" "DNN parameters setting:"
@@ -98,7 +100,8 @@ class DNNModelPytorch(Model):
"\neval_steps : {}" "\neval_steps : {}"
"\nseed : {}" "\nseed : {}"
"\nvisible_GPU : {}" "\nvisible_GPU : {}"
"\nuse_GPU : {}".format( "\nuse_GPU : {}"
"\nweight_decay : {}".format(
layers, layers,
lr, lr,
max_steps, max_steps,
@@ -113,6 +116,7 @@ class DNNModelPytorch(Model):
seed, seed,
GPU, GPU,
self.use_GPU, self.use_GPU,
weight_decay
) )
) )
@@ -126,9 +130,9 @@ class DNNModelPytorch(Model):
self.dnn_model = Net(input_dim, output_dim, layers, loss=self.loss_type) self.dnn_model = Net(input_dim, output_dim, layers, loss=self.loss_type)
if optimizer.lower() == "adam": if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.dnn_model.parameters(), lr=self.lr, weight_decay=2e-4) self.train_optimizer = optim.Adam(self.dnn_model.parameters(), lr=self.lr, weight_decay=self.weight_decay)
elif optimizer.lower() == "gd": elif optimizer.lower() == "gd":
self.train_optimizer = optim.SGD(self.dnn_model.parameters(), lr=self.lr, weight_decay=2e-4) self.train_optimizer = optim.SGD(self.dnn_model.parameters(), lr=self.lr, weight_decay=self.weight_decay)
else: else:
raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) raise NotImplementedError("optimizer {} is not supported!".format(optimizer))