def GetLoss(self, get_deriv=False, acc_deriv=False, **kwargs):
    """Compute loss and also deriv w.r.t to it if asked for.

    Compute the loss function. Targets should be in self.data, predictions
    should be in self.state.
    Args:
      get_deriv: If True, compute the derivative w.r.t the loss function and put
        it in self.deriv.
    """
    perf = deepnet_pb2.Metrics()
    perf.MergeFrom(self.proto.performance_stats)
    perf.count = self.batchsize
    if self.loss_function == deepnet_pb2.Layer.SQUARED_LOSS:
      target = self.statesize
      self.state.subtract(self.data, target=target)
      error = target.euclid_norm()**2
      perf.error = error
      if acc_deriv:
        self.deriv.add_mult(target, alpha=self.loss_weight)
      else:
        self.deriv.assign(target)
      if get_deriv:
        self.ComputeDeriv()
    else:
      raise Exception('Unknown loss function for ReLU units.')
    return perf
Compute loss and also deriv w.r.t to it if asked for.

    Compute the loss function. Targets should be in self.data, predictions
    should be in self.state.
    Args:
      get_deriv: If True, compute the derivative w.r.t the loss function and put
        it in self.deriv.
    
deepnet/relu_layer.py