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) |