From 916d9d363e9e6e038d5019097ba5394d804b72ce Mon Sep 17 00:00:00 2001
From: bxdd <bxddream@gmail.com>
Date: Sun, 4 Oct 2020 17:16:39 +0800
Subject: [PATCH] update model

---
 examples/estimator/estimator_config.yaml     |  1 -
 examples/estimator/estimator_config_dnn.yaml |  3 ---
 qlib/contrib/model/pytorch_nn.py             | 19 +++++++++++--------
 3 files changed, 11 insertions(+), 12 deletions(-)

diff --git a/examples/estimator/estimator_config.yaml b/examples/estimator/estimator_config.yaml
index e5941cefe..7b532ca40 100644
--- a/examples/estimator/estimator_config.yaml
+++ b/examples/estimator/estimator_config.yaml
@@ -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
diff --git a/examples/estimator/estimator_config_dnn.yaml b/examples/estimator/estimator_config_dnn.yaml
index d5651f6b9..a4a9d18ff 100644
--- a/examples/estimator/estimator_config_dnn.yaml
+++ b/examples/estimator/estimator_config_dnn.yaml
@@ -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
diff --git a/qlib/contrib/model/pytorch_nn.py b/qlib/contrib/model/pytorch_nn.py
index 84b184104..b27664848 100644
--- a/qlib/contrib/model/pytorch_nn.py
+++ b/qlib/contrib/model/pytorch_nn.py
@@ -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)