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import torch
import torch.nn as nn
class EmotiClassifier(nn.Module):
def __init__(self):
super().__init__()
self.l1 = nn.Sequential(
nn.Conv2d(1, 32, 3),
nn.ReLU(),
nn.BatchNorm2d(32),
nn.MaxPool2d(2),
nn.Dropout(0.2),
nn.Conv2d(32,64, 3),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(2),
nn.Dropout(0.2),
nn.Conv2d(64,128, 3),
nn.ReLU(),
nn.BatchNorm2d(128),
nn.MaxPool2d(2),
nn.Dropout(0.2),
nn.Conv2d(128,256, 3),
nn.ReLU(),
nn.BatchNorm2d(256),
nn.MaxPool2d(2),
nn.Dropout(0.2),
)
self.fc = nn.Sequential(
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 32),
nn.ReLU(),
nn.Linear(32, 7),
)
self.loss = nn.CrossEntropyLoss();
def forward(self, x):
out = self.l1(x);
out = out.view(-1, 256);
out = self.fc(out);
return out
def predict(self, x):
self.eval();
with torch.no_grad():
out = self.forward(x);
return out;
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