DeViSE-model.py

model = models.resnet34(pretrained=True).cuda()

# Freeze the base of the network and only train the new custom layers
for param in model.parameters():
    param.requires_grad = False
    
p=0.1

model.fc = nn.Sequential(nn.BatchNorm1d(512),
                         nn.Dropout(p),
                         nn.Linear(in_features=512, out_features=512, bias=True),
                         nn.ReLU(),
                         nn.BatchNorm1d(512),
                         nn.Dropout(p),
                         nn.Linear(in_features=512, out_features=300, bias=True))