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import torch
import torch.nn as nn
# Prédit 33% environ partout (dans le cas 3 classes)
# class EmotionClassifier(nn.Module):
# def __init__(self, feature_dim, num_labels):
# super(EmotionClassifier, self).__init__()
# self.fc1 = nn.Linear(feature_dim, 256)
# self.relu = nn.ReLU()
# self.dropout = nn.Dropout(0.3)
# self.fc2 = nn.Linear(256, num_labels)
# def forward(self, x):
# x = self.fc1(x)
# x = self.relu(x)
# x = self.dropout(x)
# return self.fc2(x)
class EmotionClassifier(nn.Module):
def __init__(self, feature_dim, num_labels=3):
super(EmotionClassifier, self).__init__()
self.fc = nn.Linear(feature_dim, num_labels)
self.dropout = nn.Dropout(0.3) # Evite l'overfitting
def forward(self, x):
pooled_output = torch.mean(x, dim=1) # Moyenne des features audio
pooled_output = self.dropout(pooled_output) # Dropout avant classification
logits = self.fc(pooled_output)
return logits
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