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Remove unused DNNModelPytorch params (#1470)

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* Remove lr_decay and lr_decay_steps params

More flexible way to pass a scheduler (via callable function) is already
supported

* remove lr_decay and lr_decay_steps from mlp workflow configs
This commit is contained in:
Maksim Zayakin
2023-04-28 14:48:40 +05:00
committed by GitHub
parent dceff7b471
commit 9e22e5168b
5 changed files with 0 additions and 18 deletions
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2
examples/benchmarks/MLP/workflow_config_mlp_Alpha158.yaml
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@@ -64,8 +64,6 @@ task:
kwargs: kwargs:
loss: mse loss: mse
lr: 0.002 lr: 0.002
lr_decay: 0.96
lr_decay_steps: 100
optimizer: adam optimizer: adam
max_steps: 8000 max_steps: 8000
batch_size: 8192 batch_size: 8192

2
examples/benchmarks/MLP/workflow_config_mlp_Alpha158_csi500.yaml
View File

@@ -64,8 +64,6 @@ task:
kwargs: kwargs:
loss: mse loss: mse
lr: 0.002 lr: 0.002
lr_decay: 0.96
lr_decay_steps: 100
optimizer: adam optimizer: adam
max_steps: 8000 max_steps: 8000
batch_size: 8192 batch_size: 8192

2
examples/benchmarks/MLP/workflow_config_mlp_Alpha360.yaml
View File

@@ -52,8 +52,6 @@ task:
kwargs: kwargs:
loss: mse loss: mse
lr: 0.002 lr: 0.002
lr_decay: 0.96
lr_decay_steps: 100
optimizer: adam optimizer: adam
max_steps: 8000 max_steps: 8000
batch_size: 4096 batch_size: 4096

2
examples/benchmarks/MLP/workflow_config_mlp_Alpha360_csi500.yaml
View File

@@ -52,8 +52,6 @@ task:
kwargs: kwargs:
loss: mse loss: mse
lr: 0.002 lr: 0.002
lr_decay: 0.96
lr_decay_steps: 100
optimizer: adam optimizer: adam
max_steps: 8000 max_steps: 8000
batch_size: 4096 batch_size: 4096

10
qlib/contrib/model/pytorch_nn.py
View File

@@ -47,10 +47,6 @@ class DNNModelPytorch(Model):
layer sizes layer sizes
lr : float lr : float
learning rate learning rate
lr_decay : float
learning rate decay
lr_decay_steps : int
learning rate decay steps
optimizer : str optimizer : str
optimizer name optimizer name
GPU : int GPU : int
@@ -64,8 +60,6 @@ class DNNModelPytorch(Model):
batch_size=2000, batch_size=2000,
early_stop_rounds=50, early_stop_rounds=50,
eval_steps=20, eval_steps=20,
lr_decay=0.96,
lr_decay_steps=100,
optimizer="gd", optimizer="gd",
loss="mse", loss="mse",
GPU=0, GPU=0,
@@ -93,8 +87,6 @@ class DNNModelPytorch(Model):
self.batch_size = batch_size self.batch_size = batch_size
self.early_stop_rounds = early_stop_rounds self.early_stop_rounds = early_stop_rounds
self.eval_steps = eval_steps self.eval_steps = eval_steps
self.lr_decay = lr_decay
self.lr_decay_steps = lr_decay_steps
self.optimizer = optimizer.lower() self.optimizer = optimizer.lower()
self.loss_type = loss self.loss_type = loss
if isinstance(GPU, str): if isinstance(GPU, str):
@@ -116,8 +108,6 @@ class DNNModelPytorch(Model):
f"\nbatch_size : {batch_size}" f"\nbatch_size : {batch_size}"
f"\nearly_stop_rounds : {early_stop_rounds}" f"\nearly_stop_rounds : {early_stop_rounds}"
f"\neval_steps : {eval_steps}" f"\neval_steps : {eval_steps}"
f"\nlr_decay : {lr_decay}"
f"\nlr_decay_steps : {lr_decay_steps}"
f"\noptimizer : {optimizer}" f"\noptimizer : {optimizer}"
f"\nloss_type : {loss}" f"\nloss_type : {loss}"
f"\nseed : {seed}" f"\nseed : {seed}"