def GetLoss(self, get_deriv=False, **kwargs):
    """Computes loss.

    Computes the loss function. Assumes target is in self.data and predictions
    are in self.state.
    Args:
      get_deriv: If True, computes the derivative of the loss function w.r.t the
      inputs to this layer and puts the result 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:
      self.state.subtract(self.data, target=self.deriv)
      error = self.deriv.euclid_norm()**2
      perf.error = error
      if get_deriv:
        self.ComputeDeriv()
    else:
      raise Exception('Unknown loss function for tanh units.')
    return perf
Computes loss.

    Computes the loss function. Assumes target is in self.data and predictions
    are in self.state.
    Args:
      get_deriv: If True, computes the derivative of the loss function w.r.t the
      inputs to this layer and puts the result in self.deriv.
    
deepnet/tanh_layer.py