/******************************************************************************* * Copyright (c) 2019-2020 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://www.apache.org/licenses/LICENSE-2.0. * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the * License for the specific language governing permissions and limitations * under the License. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // @author Oleh Semeniv (oleg.semeniv@gmail.com) // #ifndef LIBND4J_HEADERS_UPDATERS_H #define LIBND4J_HEADERS_UPDATERS_H #include #include #include #include #include namespace sd { namespace ops { /** * SGD updater * Input arrays: * 0 - input array with gradients. * Optional: * 1 - scalar learning rate value * Optional: * T args * 0 - scalar learning rate value */ #if NOT_EXCLUDED(OP_sgd_updater) DECLARE_CONFIGURABLE_OP(sgd_updater, 1, 1, true, 0, 0); #endif /** * RmsPropUpdater updater * Input arrays: * 0 - input array with gradients. * 1 - Initial state * Optional: * 2 - scalar learning rate value * 3 - scalar rms decay * 4 - epsilon * Optional: * T args * 0 - scalar learning rate value * 1 - scalar rms decay * 2 - epsilon */ #if NOT_EXCLUDED(OP_rms_prop_updater) DECLARE_CONFIGURABLE_OP(rms_prop_updater, 2, 2, true, 0, 0); #endif // AdaGrad /* Input arrays : * 0 - input array with gradients. * 1 - historical grad state * Optional : * 2 - scalar learning rate value * 3 - epsilon * Optional: * T args * 0 - scalar learning rate value * 1 - epsilon */ #if NOT_EXCLUDED(OP_ada_grad_updater) DECLARE_CONFIGURABLE_OP(ada_grad_updater, 2, 2, true, 0, 0); #endif // AdaMax /* Input arrays : * 0 - input array with gradients. * 1 - gradient state V * 2 - gradient state M * Optional : * 3 - scalar learning rate value * 4 - beta 1 value * 5 - beta 2 value * 6 - epsilon * Optional: * T args * 0 - scalar learning rate value * 1 - beta 1 value * 2 - beta 2 value * 3 - epsilon * Optional: * I args * 0 - iteration */ #if NOT_EXCLUDED(OP_ada_max_updater) DECLARE_CONFIGURABLE_OP(ada_max_updater, 3, 3, true, 0, 0); #endif // Nesterov's momentum /* Input arrays : * 0 - input array with gradients. * 1 - V grad state * Optional : * 2 - scalar learning rate value * 3 - scalar momentum value * Optional: * T args * 0 - learning rate value * 1 - momentum value */ #if NOT_EXCLUDED(OP_nesterovs_updater) DECLARE_CONFIGURABLE_OP(nesterovs_updater, 2, 2, true, 0, 0); #endif // Adam /* Input arrays : * 0 - input array with gradients. * 1 - gradient state V * 2 - gradient state M * Optional : * 3 - scalar learning rate value * 4 - beta 1 value * 5 - beta 2 value * 6 - epsilon * Optional: * T args * 0 - scalar learning rate value * 1 - beta 1 value * 2 - beta 2 value * 3 - epsilon * Optional: * I args * 0 - iteration */ #if NOT_EXCLUDED(OP_adam_updater) DECLARE_CONFIGURABLE_OP(adam_updater, 3, 3, true, 0, 0); #endif // AdaDelta /* Input arrays : * 0 - input array with gradients. * 1 - gradient state V * 2 - gradient state M * Optional : * 3 - rho value * 6 - epsilon * Optional: * T args * 0 - rho * 1 - epsilon */ #if NOT_EXCLUDED(OP_ada_delta_updater) DECLARE_CONFIGURABLE_OP(ada_delta_updater, 3, 3, true, 0, 0); #endif // Nadam /* Input arrays : * 0 - input array with gradients. * 1 - gradient state V * 2 - gradient state M * Optional : * 3 - scalar learning rate value * 4 - beta 1 value * 5 - beta 2 value * 6 - epsilon * Optional: * T args * 0 - scalar learning rate value * 1 - beta 1 value * 2 - beta 2 value * 3 - epsilon * Optional: * I args * 0 - iteration */ #if NOT_EXCLUDED(OP_nadam_updater) DECLARE_CONFIGURABLE_OP(nadam_updater, 3, 3, true, 0, 0); #endif // AmsGrad /* Input arrays : * 0 - input array with gradients. * 1 - gradient state V - sqrd gradients * 2 - gradient state M - moving avg * 3 - gradient state H - max * Optional : * 4 - scalar learning rate value * 5 - beta 1 value * 6 - beta 2 value * 7 - epsilon * Optional: * T args * 0 - scalar learning rate value * 1 - beta 1 value * 2 - beta 2 value * 3 - epsilon * Optional: * I args * 0 - iteration */ #if NOT_EXCLUDED(OP_ams_grad_updater) DECLARE_CONFIGURABLE_OP(ams_grad_updater, 4, 4, true, 0, 0); #endif } } #endif