deeper cnn

model.py

@@ -120,14 +120,14 @@ class CNN1D(torch.nn.Module):
     def __init__(self, cfg, num_inputs):
         super(CNN1D, self).__init__()
         self.model = torch.nn.Sequential(
-            *conv_block(num_inputs, 5, 0.01),
-            *conv_block(32, 6, 0.1),
-            *conv_block(64,7,0.01)
+            *conv_block(num_inputs, 6, 0.01),
+            *conv_block(64, 7, 0.01),
+            *conv_block(128,8,0.01)
         )
         self.avg_pool = torch.nn.AdaptiveAvgPool1d(1)
 
         self.fc = nn.Sequential(
-            nn.Linear(128, 32),
+            nn.Linear(256, 32),
             nn.ReLU(),
             nn.Linear(32,cfg["Dataset"]["num_days"])
         )
@@ -148,12 +148,12 @@ class CNN1D(torch.nn.Module):
 def conv_block(in_channels, power, dropout_p):
     return [
         torch.nn.Conv1d(in_channels, 2**power, 16),
-        torch.nn.BatchNorm1d(2**power),
-        torch.nn.ReLU(),
-        torch.nn.Dropout(p=dropout_p),
-        #torch.nn.Conv1d(2**power, 2**power, 8),
         #torch.nn.BatchNorm1d(2**power),
-        #torch.nn.ReLU(),
+        torch.nn.LeakyReLU(),
+        torch.nn.Dropout(p=dropout_p),
+        torch.nn.Conv1d(2**power, 2**power, 8),
+        torch.nn.BatchNorm1d(2**power),
+        torch.nn.LeakyReLU(),
         #torch.nn.Dropout(p=dropout_p),
         torch.nn.MaxPool1d(2, stride = 1)
     ]

optimizers.py

 import torch.optim
 
 def optimizer(cfg, model):
-    result = {"Adam" : torch.optim.Adam(model.parameters(), lr = 1e-2)}
+    result = {"Adam" : torch.optim.Adam(model.parameters(), lr = 1e-3)}
     return result[cfg["Optimizer"]]