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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) |