model.py

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)