def loss_fn(): # Use fixed initialization to make the steps deterministic. w = variable_scope.get_variable("w", initializer=[[2.]]) all_vars.append(w) predict = math_ops.matmul(x, w) return losses_impl.mean_squared_error( y, predict, reduction=loss_reduction)
def loss_fn(): # Use fixed initialization to make the steps deterministic. w = variable_scope.get_variable("w", initializer=[[2.]]) all_vars.append(w) predict = math_ops.matmul(x, w) return losses_impl.mean_squared_error( y, predict, reduction=loss_reduction)
def loss_fn(): # Use fixed initialization to make the steps deterministic. predict = math_ops.matmul(x, w) loss = losses_impl.mean_squared_error( y, predict, reduction=loss_reduction) if loss_reduction == losses_impl.Reduction.SUM: return loss return loss / distribution.num_replicas_in_sync
def loss_fn(): # Use fixed initialization to make the steps deterministic. predict = math_ops.matmul(x, w) loss = losses_impl.mean_squared_error( y, predict, reduction=loss_reduction) if loss_reduction == losses_impl.Reduction.SUM: return loss return loss / distribution.num_replicas_in_sync
def loss_fn(): # Use fixed initialization to make the steps deterministic. predict = math_ops.matmul(x, w) return losses_impl.mean_squared_error( y, predict, reduction=loss_reduction)
def loss_fn(): # Use fixed initialization to make the steps deterministic. predict = math_ops.matmul(x, w) return losses_impl.mean_squared_error( y, predict, reduction=loss_reduction)