How can l load my best model as a feature extractor/evaluator?

6

I’ve created a small code snippet using a forward hook to store one activation from fc2:

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.cl1 = nn.Linear(25, 60)
        self.cl2 = nn.Linear(60, 16)
        self.fc1 = nn.Linear(16, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)
        
    def forward(self, x):
        x = F.relu(self.cl1(x))
        x = F.relu(self.cl2(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.log_softmax(self.fc3(x), dim=1)
        return x


activation = {}
def get_activation(name):
    def hook(model, input, output):
        activation[name] = output.detach()
    return hook


model = MyModel()
model.fc2.register_forward_hook(get_activation('fc2'))
x = torch.randn(1, 25)
output = model(x)
print(activation['fc2'])
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