def _get_initializer()

in recommenders/models/deeprec/models/base_model.py [0:0]

• 45 lines of code
• 12 McCabe index (conditional complexity)

    def _get_initializer(self):
        if self.hparams.init_method == "tnormal":
            return tf.compat.v1.truncated_normal_initializer(
                stddev=self.hparams.init_value, seed=self.seed
            )
        elif self.hparams.init_method == "uniform":
            return tf.compat.v1.random_uniform_initializer(
                -self.hparams.init_value, self.hparams.init_value, seed=self.seed
            )
        elif self.hparams.init_method == "normal":
            return tf.compat.v1.random_normal_initializer(
                stddev=self.hparams.init_value, seed=self.seed
            )
        elif self.hparams.init_method == "xavier_normal":
            return tf.compat.v1.keras.initializers.VarianceScaling(
                scale=1.0,
                mode="fan_avg",
                distribution=("uniform" if False else "truncated_normal"),
                seed=self.seed,
            )
        elif self.hparams.init_method == "xavier_uniform":
            return tf.compat.v1.keras.initializers.VarianceScaling(
                scale=1.0,
                mode="fan_avg",
                distribution=("uniform" if True else "truncated_normal"),
                seed=self.seed,
            )
        elif self.hparams.init_method == "he_normal":
            return tf.compat.v1.keras.initializers.VarianceScaling(
                scale=2.0,
                mode=("FAN_IN").lower(),
                distribution=("uniform" if False else "truncated_normal"),
                seed=self.seed,
            )
        elif self.hparams.init_method == "he_uniform":
            return tf.compat.v1.keras.initializers.VarianceScaling(
                scale=2.0,
                mode=("FAN_IN").lower(),
                distribution=("uniform" if True else "truncated_normal"),
                seed=self.seed,
            )
        else:
            return tf.compat.v1.truncated_normal_initializer(
                stddev=self.hparams.init_value, seed=self.seed
            )