deterministic_mlp_regressor.py 文件源码

def predict_sym(self, xs):
        return L.get_output(self.l_out, xs)

    # def fit(self, xs, ys):
    #     if self._normalize_inputs:
    #         # recompute normalizing constants for inputs
    #         new_mean = np.mean(xs, axis=0, keepdims=True)
    #         new_std = np.std(xs, axis=0, keepdims=True) + 1e-8
    #         tf.get_default_session().run(tf.group(
    #             tf.assign(self._x_mean_var, new_mean),
    #             tf.assign(self._x_std_var, new_std),
    #         ))
    #         inputs = [xs, ys]
    #     loss_before = self._optimizer.loss(inputs)
    #     if self._name:
    #         prefix = self._name + "_"
    #     else:
    #         prefix = ""
    #     logger.record_tabular(prefix + 'LossBefore', loss_before)
    #     self._optimizer.optimize(inputs)
    #     loss_after = self._optimizer.loss(inputs)
    #     logger.record_tabular(prefix + 'LossAfter', loss_after)
    #     logger.record_tabular(prefix + 'dLoss', loss_before - loss_after)