title: Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Deep Forest Ð½Ð° GPU Ð´Ð»Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Implementation of the Deep Forest model on a GPU for image processing
Creators: name: Ð ÑÐ¶ÐµÐ½ÐºÐ¾, ÐÐ¸ÐºÑÐ¾Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: deep forest
gpu
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
random ferns
classification

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¹ Deep Forest Ð´Ð»Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ñ ÑÐµÐ»ÑÑ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ñ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ð¹. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ: 1. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð´Ð»Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Deep Forest Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ CUDA NVIDIA; 2. Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°; 3. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ñ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ð¼Ð¸ ÑÑÐµÐ¹Ð¼Ð²Ð¾ÑÐºÐ°Ð¼Ð¸ Ð´Ð»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ (gcForest, VGG) Ð¿Ð¾ ÐºÑÐ¸ÑÐµÑÐ¸ÑÐ¼ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð¸ ÐºÐ°ÑÐµÑÑÐ²Ð° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸; 4. ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð´Ð»Ñ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð¼ÐµÑÑ, Ð² ÐºÐ¾ÑÐ¾ÑÑÑ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ñ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐ¸Ðµ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ; Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð° Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Deep Forest, Ð° ÑÐ°ÐºÐ¶Ðµ ÐµÐ³Ð¾ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ. ÐÑÐ¾ÑÐµÐ´ÑÑÑ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¿Ð°ÑÐ°Ð»Ð»ÐµÐ»ÑÐ½ÑÑ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹ Ð½Ð° GPU. ÐÐ»Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð° Ð¾ÑÐµÐ½ÐºÐ° ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð¸ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ð² ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ð¸ Ñ Ð¸ÑÑÐ¾Ð´Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»ÑÑ, Ð° ÑÐ°ÐºÐ¶Ðµ Ñ ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ð¼Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°Ð¼Ð¸ ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ LeNet-5, VGG-5. ÐÐ¾ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼ Ð¾ÑÐµÐ½ÐºÐ¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð¾, ÑÑÐ¾ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÐ¼ÑÐ¼Ð¸ Ð¼Ð¾Ð´ÐµÐ»ÑÐ¼Ð¸ Ð¸Ð¼ÐµÐµÑ Ð¼ÐµÐ½ÑÑÑÑ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ (1-6% Ð¿Ð¾ Ð¼ÐµÑÑÐ¸ÐºÐµ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸) Ð¸ Ð±Ð¾Ð»ÑÑÑÑ ÑÐºÐ¾ÑÐ¾ÑÑÑ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ (Ð² 4-12 ÑÐ°Ð· Ð±ÑÑÑÑÐµÐµ).
description: This work is devoted to the study and implementation of Deep Forest modifications for the problem of image classification in order to increase the speed of learning and predictions. Tasks that were solved in the course of work: 1. Development of a modification for the Deep Forest algorithm using the CUDA NVIDIA architecture; 2. Implementation of the developed algorithm; 3. Assessment of the implemented model with existing frameworks for image classification (gcForest, VGG) according to performance criteria and classification quality; 4. Research of the developed algorithm to identify places where further improvements are possible; The paper presents a modification of the Deep Forest algorithm, as well as its implementation. The training and prediction procedures are implemented using parallel computing on the GPU. For the developed algorithm, the prediction quality and performance were assessed in comparison with the original model, as well as with the existing algorithms of convolutional neural networks LeNet-5, VGG-5. Based on the results of the assessment, it was found that the developed algorithm, in comparison with the models under consideration, has a lower prediction accuracy (1-6% in terms of the accuracy metric) and a higher learning rate (4-12 times faster).