Comments (1)
Thank you for your attention. Unfortunately, for the Probing experiments, we validated on the test data directly after each training session without saving the trained classifier. The classifier is a two-layer MLP as follows, which can be easily trained using torch once the data is prepared.
class Net(torch.nn.Module):
def init(self, input_size: int = 64 * 64, num_classes: int = 10):
super(Net,self).init()
self.input_size = input_size
self.hidden_size = max(input_size // 8, 2048)
self.mlp = torch.nn.Sequential(
torch.nn.Flatten(),
torch.nn.Dropout(0.1),
torch.nn.Linear(in_features=self.input_size, out_features=self.hidden_size),
torch.nn.Tanh(),
# torch.nn.ReLU(),
torch.nn.Linear(in_features=self.hidden_size, out_features=num_classes)
)
def forward(self, x):
output = self.mlp(x)
return output
from easynlp.
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