ff5da2b2bfd03234519927b966923e8baecf6616,slm_lab/agent/net/convnet.py,ConvNet,training_step,#ConvNet#Any#Any#,123

Before Change

        """
        Takes a single training step: one forwards and one backwards pass
        """
        return super(ConvNet, self).training_step(x, y)

    def eval(self, x):
        """
        Completes one feedforward step, ensuring net is set to evaluation model

After Change

        out = self(x)
        loss = self.loss_fn(out, y)
        loss.backward()
        if self.clamp_grad:
            torch.nn.utils.clip_grad_norm(
                self.conv_model.parameters(), self.clamp_grad_val)
            torch.nn.utils.clip_grad_norm(
                self.dense_model.parameters(), self.clamp_grad_val)
        self.optim.step()
        return loss

    def wrap_eval(self, x):

Instances