{"id":1135,"date":"2019-04-13T15:14:49","date_gmt":"2019-04-13T18:14:49","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1135"},"modified":"2019-04-13T15:14:49","modified_gmt":"2019-04-13T18:14:49","slug":"vol15-no1-art3","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art3\/","title":{"rendered":"QUALITATIVE ANALYSIS OF DEEP LEARNING FRAMEWORKS"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> QUALITATIVE ANALYSIS OF DEEP LEARNING FRAMEWORKS<\/p>\n<p><strong>Autor:<\/strong>\u00a0Matheus Gutoski, Leandro Takeshi Hattori, Nelson Marcelo Romero Aquino, Helen Cristina Mattos Senefonte, Manass\u00e9s Ribeiro, Andr\u00e9 Eug\u00eanio Lazzaretti, Heitor Silv\u00e9rio Lopes<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> Deep learning methods are becoming more popular for complex pattern recognition applications. As result, many frameworks have appeared aiming to facilitate the development of such applications. However, choosing a suitable framework may not be an easy task for new users. In this paper, a qualitative evaluation of four of the most popular Deep Learning frameworks is provided, including: Caffe, Torch, Lasagne and TensorFlow. A printed character recognition task was used as case study, and a Convolutional Neural Network was implemented for this purpose. The analysis focus on issues that are important for the development process and encompasses nine qualitative dimensions, showing the strengths and weaknesses of each framework. It is expected that this analysis can be useful for guiding new users in the area.<\/p>\n<p>Palavras-chave: Deep Learning, Caffe, Torch, Lasagne, TensorFlow, Convolutional Neural Networks.<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 8<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/LNLM-vol15-no1-art3\">10.21528\/LNLM-vol15-no1-art3<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/03\/vol15-no1-art3.pdf\">vol15-no1-art3.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/03\/vol15-no1-art3.bib\">vol15-no1-art3.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: QUALITATIVE ANALYSIS OF DEEP LEARNING FRAMEWORKS Autor:\u00a0Matheus Gutoski, Leandro Takeshi Hattori, Nelson Marcelo Romero Aquino, Helen Cristina Mattos Senefonte, Manass\u00e9s Ribeiro, Andr\u00e9 Eug\u00eanio Lazzaretti, Heitor Silv\u00e9rio Lopes Resumo: Deep learning methods are becoming more popular for complex pattern recognition <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art3\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":992,"menu_order":2,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1135","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>QUALITATIVE ANALYSIS OF DEEP LEARNING FRAMEWORKS - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art3\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"QUALITATIVE ANALYSIS OF DEEP LEARNING FRAMEWORKS - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: QUALITATIVE ANALYSIS OF DEEP LEARNING FRAMEWORKS Autor:\u00a0Matheus Gutoski, Leandro Takeshi Hattori, Nelson Marcelo Romero Aquino, Helen Cristina Mattos Senefonte, Manass\u00e9s Ribeiro, Andr\u00e9 Eug\u00eanio Lazzaretti, Heitor Silv\u00e9rio Lopes Resumo: Deep learning methods are becoming more popular for complex pattern recognition Read More ...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art3\/\" \/>\n<meta property=\"og:site_name\" content=\"Learning and NonLinear Models\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minuto\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art3\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art3\/\",\"name\":\"QUALITATIVE ANALYSIS OF DEEP LEARNING FRAMEWORKS - Learning and NonLinear Models\",\"isPartOf\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\"},\"datePublished\":\"2019-04-13T18:14:49+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art3\/#breadcrumb\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art3\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol15-no1\/vol15-no1-art3\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Browse issues\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning &#038; 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