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model.py

@@ -0,0 +1,64 @@
+import torch.nn as nn
+
+# ckp_02
+class MajorClassifier(nn.Module):
+    def __init__(self, input_size=768, output_size=9):
+        super(MajorClassifier, self).__init__()
+        self.model = nn.Sequential(
+            nn.Linear(input_size, 512),
+            nn.ReLU(),
+            nn.Linear(512, 512),
+            nn.ReLU(),
+            nn.Linear(512, 256),
+            nn.ReLU(),
+            nn.Linear(256, 128),
+            nn.ReLU(),
+            nn.Linear(128, 64),
+            nn.ReLU(),
+            nn.Linear(64, output_size),
+        )
+    
+    def forward(self, x):
+        return self.model(x)
+
+# class MajorClassifier(nn.Module):
+#     def __init__(self, input_size=768, output_size=9, dropout_prob=0.1):
+#         super(MajorClassifier, self).__init__()
+#         self.model = nn.Sequential(
+#             nn.Linear(input_size, 512),
+#             nn.BatchNorm1d(512),
+#             nn.ReLU(),
+#             nn.Dropout(dropout_prob),
+#             nn.Linear(512, 512),
+#             nn.BatchNorm1d(512),
+#             nn.ReLU(),
+#             nn.Dropout(dropout_prob),
+#             nn.Linear(512, 256),
+#             nn.BatchNorm1d(256),
+#             nn.ReLU(),
+#             nn.Dropout(dropout_prob),
+#             nn.Linear(256, 256),
+#             nn.BatchNorm1d(256),
+#             nn.ReLU(),
+#             nn.Dropout(dropout_prob),
+#             nn.Linear(256, 128),
+#             nn.BatchNorm1d(128),
+#             nn.ReLU(),
+#             nn.Dropout(dropout_prob),
+#             nn.Linear(128, 128),
+#             nn.BatchNorm1d(128),
+#             nn.ReLU(),
+#             nn.Dropout(dropout_prob),
+#             nn.Linear(128, 64),
+#             nn.BatchNorm1d(64),
+#             nn.ReLU(),
+#             nn.Dropout(dropout_prob),
+#             nn.Linear(64, 64),
+#             nn.BatchNorm1d(64),
+#             nn.ReLU(),
+#             nn.Dropout(dropout_prob),
+#             nn.Linear(64, output_size),
+#         )
+    
+#     def forward(self, x):
+#         return self.model(x)