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update model

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This commit is contained in:
bxdd
2020-10-04 17:16:39 +08:00
committed by you-n-g
parent 992983ff4c
commit 916d9d363e
3 changed files with 11 additions and 12 deletions
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1
examples/estimator/estimator_config.yaml
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@@ -51,4 +51,3 @@ qlib_data:
# when testing, please modify the following parameters according to the specific environment
provider_uri: "~/.qlib/qlib_data/cn_data"
region: "cn"
redis_port: 1222

3
examples/estimator/estimator_config_dnn.yaml
View File

@@ -48,11 +48,8 @@ backtest:
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
long_short_backtest_args:
topk: 50
qlib_data:
# when testing, please modify the following parameters according to the specific environment
provider_uri: "~/.qlib/qlib_data/cn_data"
region: "cn"
redis_port: 1222

19
qlib/contrib/model/pytorch_nn.py
View File

@@ -47,7 +47,7 @@ class DNNModelPytorch(Model):
self,
input_dim,
output_dim,
layers=(256, 256, 128),
layers=(256, 512, 768, 1024, 768, 512, 256, 128, 64),
lr=0.001,
max_steps=300,
batch_size=2000,
@@ -76,6 +76,7 @@ class DNNModelPytorch(Model):
self.optimizer = optimizer.lower()
self.loss_type = loss
self.visible_GPU = GPU
self.use_gpu = torch.cuda.is_available()
self.logger.info(
"DNN parameters setting:"
@@ -90,7 +91,8 @@ class DNNModelPytorch(Model):
"\noptimizer : {}"
"\nloss_type : {}"
"\neval_steps : {}"
"\nvisible_GPU : {}".format(
"\nvisible_GPU : {}"
"\nuse_GPU : {}".format(
layers,
lr,
max_steps,
@@ -103,6 +105,7 @@ class DNNModelPytorch(Model):
loss,
eval_steps,
GPU,
self.use_gpu,
)
)
@@ -133,7 +136,7 @@ class DNNModelPytorch(Model):
)
self._fitted = False
self.use_gpu = torch.cuda.is_available()
if self.use_gpu:
self.dnn_model.cuda()
@@ -327,20 +330,20 @@ class AverageMeter(object):
class Net(nn.Module):
def __init__(self, input_dim, output_dim, layers=(256, 256, 256), loss="mse"):
def __init__(self, input_dim, output_dim, layers=(256, 512, 768, 512, 256, 128, 64), loss="mse"):
super(Net, self).__init__()
layers = [input_dim] + list(layers)
dnn_layers = []
drop_input = nn.Dropout(0.1)
drop_input = nn.Dropout(0.05)
dnn_layers.append(drop_input)
for i, (input_dim, hidden_units) in enumerate(zip(layers[:-1], layers[1:])):
fc = nn.Linear(input_dim, hidden_units)
activation = nn.ReLU()
bn = nn.BatchNorm1d(hidden_units)
drop = nn.Dropout(0.1)
seq = nn.Sequential(fc, bn, activation, drop)
seq = nn.Sequential(fc, bn, activation)
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)