Spaces:
Sleeping
Sleeping
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; | |