{"id":12049,"date":"2022-09-29T11:01:40","date_gmt":"2022-09-29T05:31:40","guid":{"rendered":"https:\/\/jassweb.com\/solved\/solved-python-tflearn-machine-learning-optimiser-loss-and-parameters\/"},"modified":"2022-09-29T11:01:40","modified_gmt":"2022-09-29T05:31:40","slug":"solved-python-tflearn-machine-learning-optimiser-loss-and-parameters","status":"publish","type":"post","link":"https:\/\/jassweb.com\/solved\/solved-python-tflearn-machine-learning-optimiser-loss-and-parameters\/","title":{"rendered":"[Solved] Python Tflearn machine learning Optimiser, loss and parameters"},"content":{"rendered":"

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What you are trying to do is called time series prediction (given data at time t-n, t-(n+1) … t-1<\/em>: predict the state at time t<\/em>) and is generally a task for a recurrent neural network. Here<\/a> is the great blog post by Andrej Karpathy about the topic that you should have a look at. <\/p>\n

About your two questions:<\/p>\n

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  1. \n

    This is hard to answer since the question of what optimizer to use highly depends on the input data. Generally speaking the network will converge no matter what optimizer you use. The time it takes to converge will differ however. Adaptive learning-rate methods, like Adagrad, Adadelta, and Adam tend to achieve convergence slightly faster. Here<\/a> is a good write-up of the different optimizers. <\/p>\n<\/li>\n

  2. \n

    Basic neural networks (MLPs) don’t do well with time series prediction. That would be an explanation for the low accuracy. However I don’t know why the loss would be 0.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n

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    5<\/span> <\/p><\/div>\n<\/div>\n

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    solved Python Tflearn machine learning Optimiser, loss and parameters <\/p>\n","protected":false},"excerpt":{"rendered":"

    [ad_1] What you are trying to do is called time series prediction (given data at time t-n, t-(n+1) … t-1: predict the state at time t) and is generally a task for a recurrent neural network. Here is the great blog post by Andrej Karpathy about the topic that you should have a look at. … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[320],"tags":[1173,349,2624,3285],"class_list":["post-12049","post","type-post","status-publish","format-standard","hentry","category-solved","tag-machine-learning","tag-python","tag-tensorflow","tag-tflearn"],"yoast_head":"\n[Solved] Python Tflearn machine learning Optimiser, loss and parameters - JassWeb<\/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:\/\/jassweb.com\/solved\/solved-python-tflearn-machine-learning-optimiser-loss-and-parameters\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"[Solved] Python Tflearn machine learning Optimiser, loss and parameters - JassWeb\" \/>\n<meta property=\"og:description\" content=\"[ad_1] What you are trying to do is called time series prediction (given data at time t-n, t-(n+1) … t-1: predict the state at time t) and is generally a task for a recurrent neural network. 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