{"product_id":"cnn-kernel-performance-analysis-based-on-kernel-size-of-convolutional-layers-in-a-network","title":"CNN KERNEL : Performance analysis based on kernel size of Convolutional Layers in a network","description":"\u003cp\u003eIn This Book, I Perform An Experimental Review On Twelve Similar Types Of Convolutional Neural Network Architecture But The Different Sizes Of Kernels For The Filters. For This Experiment, I Select Twelve Different Sizes Of The Kernel For Twelve Convolutional Neural Network Models, The Size Of Kernels Are  (12, 12), (11, 11), (10, 10), (9, 9), (8, 8), (7, 7), (6, 6), (5, 5), (4, 4), (3, 3), (2, 2), And (1, 1). For This Experiment, I Use The Flowers Recognition Dataset. I Use 77 Batches (Batch Size = 45) Per Epoch And 10 Epochs Per Experimental Fold. After Analyzing The Results, I Found That According To The Performance, Kernel_Size (2, 2) And (3, 3) Are The Best Selection For The TwoDimensional Convolutional Layer In The Convolutional Neural Networks. The Goal Of This Experiment Is To Help The Developer To Understand And Select The Perfect Size Of The Kernel For Filter During TwoDimensional Image Processing By Using The TwoDimensional Convolutional (Conv2D) Layer [11] Of Cnns.\u003c\/p\u003e","brand":"notionpress.com","offers":[{"title":"Default Title","offer_id":45904317513926,"sku":"DADAX1685380522","price":4.84,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0695\/9389\/1014\/files\/61SeH4CMKCL.jpg?v=1780383625","url":"https:\/\/ergodemedia.com\/products\/cnn-kernel-performance-analysis-based-on-kernel-size-of-convolutional-layers-in-a-network","provider":"Ergodemedia","version":"1.0","type":"link"}