def forward(self, input):
           H1 = self.hidden_layer_1(input)
           H1 = self.relu(H1)
           final_inputs = self.output(H1)
  
        optimizer = torch.optim.SGD(self.parameters(), lr =lr,momentum=momentum)

        weights = np.load('./w0.npy')
        bias = np.load('./b0.npy')

        self.hidden_layer_1.weight.data = torch.from_numpy(weights).float()
        self.hidden_layer_1.bias.data = torch.from_numpy(bias).float()

        # freeze
        for param in self.hidden_layer_1.parameters():

          param.requires_grad = False

I am trying to freeze the layer but the loss is still changing after epochs.