Method optimizer

modelzoo/features/embedding_variable/deepfm/train.py:224–254

(self)

        return net

    def optimizer(self):
        loss_func = tf.losses.mean_squared_error
        self.predict = tf.squeeze(self.predict)
        loss = tf.math.reduce_mean(loss_func(self.label, self.predict))
        tf.summary.scalar('loss', loss)

        self.global_step = tf.train.get_or_create_global_step()
        if self.optimizer_type == 'adam':
            optimizer = tf.train.AdamOptimizer(
                learning_rate=self.learning_rate,
                beta1=0.9,
                beta2=0.999,
                epsilon=1e-8)
        elif self.optimizer_type == 'adagrad':
            optimizer = tf.train.AdagradOptimizer(
                learning_rate=self.learning_rate,
                initial_accumulator_value=1e-8)
        elif self.optimizer_type == 'adamasync':
            optimizer = tf.train.AdamAsyncOptimizer(
                learning_rate=self.learning_rate,
                beta1=0.9,
                beta2=0.999,
                epsilon=1e-8)
        else:
            raise ValueError('Optimizer type error.')

        update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
        with tf.control_dependencies(update_ops):
            train_op = optimizer.minimize(loss, global_step=self.global_step)

        return train_op, loss


def get_arg_parser():