b50695e45350a8a4d11bd60ba1801d6ae93eddbf,src/garage/tf/regressors/continuous_mlp_regressor.py,ContinuousMLPRegressor,fit,#ContinuousMLPRegressor#Any#Any#,116

Before Change

    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.compat.v1.get_default_session().run(
                tf.group(
                    tf.compat.v1.assign(self.x_mean_var, new_mean),
                    tf.compat.v1.assign(self.x_std_var, new_std),
                ))
        inputs = [xs, ys]
        loss_before = self.optimizer.loss(inputs)
        if self.name:
            prefix = self.name + "/"
        else:
            prefix = ""
        tabular.record(prefix + "LossBefore", loss_before)
        self.optimizer.optimize(inputs)
        loss_after = self.optimizer.loss(inputs)
        tabular.record(prefix + "LossAfter", loss_after)
        tabular.record(prefix + "dLoss", loss_before - loss_after)

    def predict(self, xs):
        return self.f_predict(np.asarray(xs))

After Change

            with tf.name_scope("update_opt"):
                self._optimizer.update_opt(**optimizer_args)

    def fit(self, xs, ys):
        Fit with input data xs and label ys.
        if self._normalize_inputs:
            // recompute normalizing constants for inputs
            self.model.networks["default"].x_mean.load(
                np.mean(xs, axis=0, keepdims=True))
            self.model.networks["default"].x_std.load(
                np.std(xs, axis=0, keepdims=True) + 1e-8)

        inputs = [xs, ys]
        loss_before = self._optimizer.loss(inputs)
        tabular.record("{}/LossBefore".format(self._name), loss_before)
        self._optimizer.optimize(inputs)
        loss_after = self._optimizer.loss(inputs)
        tabular.record("{}/LossAfter".format(self._name), loss_after)
        tabular.record("{}/dLoss".format(self._name), loss_before - loss_after)

    def predict(self, xs):
        

Instances