{"id":2073,"date":"2021-02-14T19:17:51","date_gmt":"2021-02-14T19:17:51","guid":{"rendered":"https:\/\/aix.web.tr\/?p=2073"},"modified":"2024-04-22T14:12:51","modified_gmt":"2024-04-22T14:12:51","slug":"uretken-cekismeli-aglari-kullanan-dlss","status":"publish","type":"post","link":"https:\/\/aix.web.tr\/en\/uretken-cekismeli-aglari-kullanan-dlss\/","title":{"rendered":"DLSS Using Generative Adversarial Networks"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"2073\" class=\"elementor elementor-2073\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-308c633 elementor-section-stretched elementor-hidden-tablet elementor-hidden-phone elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"308c633\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;stretch_section&quot;:&quot;section-stretched&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-02ae164\" data-id=\"02ae164\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0bf6447 elementor-widget elementor-widget-shortcode\" data-id=\"0bf6447\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\">\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"1494\" class=\"elementor elementor-1494\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6a0c6bf elementor-section-height-min-height elementor-section-stretched elementor-hidden-tablet elementor-hidden-phone elementor-section-boxed elementor-section-height-default elementor-section-items-middle\" data-id=\"6a0c6bf\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;stretch_section&quot;:&quot;section-stretched&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-0f33f4b\" data-id=\"0f33f4b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e3797c5 elementor-icon-list--layout-inline elementor-align-center animated-slow elementor-list-item-link-full_width elementor-invisible elementor-widget elementor-widget-icon-list\" data-id=\"e3797c5\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeInLeft&quot;}\" data-widget_type=\"icon-list.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items elementor-inline-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/aix.web.tr\">\n\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">ANA SAYFA<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/aix.web.tr\/hakkimizda\/\">\n\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">HAKKIMIZDA<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/aix.web.tr\/arastirmalar\/\">\n\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">ARA\u015eTIRMALAR<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/aix.web.tr\/blog\/\">\n\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">BLOG 4.0<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-ea69513\" data-id=\"ea69513\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-bff95a7 animated-slow elementor-invisible elementor-widget elementor-widget-image\" data-id=\"bff95a7\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;zoomIn&quot;}\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/aix.web.tr\/\">\n\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1667\" height=\"790\" src=\"https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/07\/aix-logo.png\" class=\"attachment-full size-full wp-image-3104\" alt=\"Aix Logo\" srcset=\"https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/07\/aix-logo.png 1667w, https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/07\/aix-logo-300x142.png 300w, https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/07\/aix-logo-1024x485.png 1024w, https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/07\/aix-logo-768x364.png 768w, https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/07\/aix-logo-1536x728.png 1536w, https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/07\/aix-logo-18x9.png 18w\" sizes=\"(max-width: 1667px) 100vw, 1667px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-c9c0555\" data-id=\"c9c0555\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1c86e38 elementor-icon-list--layout-inline elementor-align-center animated-slow elementor-list-item-link-full_width elementor-invisible elementor-widget elementor-widget-icon-list\" data-id=\"1c86e38\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeInRight&quot;}\" data-widget_type=\"icon-list.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items elementor-inline-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/aix.web.tr\/ders\/\">\n\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">DERS<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/aix.web.tr\/konusmalar\/\">\n\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">KONU\u015eMALAR<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/aix.web.tr\/iletisim\/\">\n\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">\u0130LET\u0130\u015e\u0130M<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/aix.web.tr\/basvuru\/\">\n\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">EK\u0130BE KATIL<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c6a7631 elementor-section-stretched elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c6a7631\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;stretch_section&quot;:&quot;section-stretched&quot;,&quot;background_background&quot;:&quot;slideshow&quot;,&quot;background_slideshow_gallery&quot;:[{&quot;id&quot;:531,&quot;url&quot;:&quot;http:\\\/\\\/www.aix.web.tr\\\/wp-content\\\/uploads\\\/2019\\\/12\\\/ezgif.com-video-to-gif-1.gif&quot;}],&quot;shape_divider_bottom&quot;:&quot;mountains&quot;,&quot;background_slideshow_loop&quot;:&quot;yes&quot;,&quot;background_slideshow_slide_duration&quot;:5000,&quot;background_slideshow_slide_transition&quot;:&quot;fade&quot;,&quot;background_slideshow_transition_duration&quot;:500}\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t<div class=\"elementor-shape elementor-shape-bottom\" aria-hidden=\"true\" data-negative=\"false\">\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 1000 100\" preserveAspectRatio=\"none\">\n\t<path class=\"elementor-shape-fill\" opacity=\"0.33\" d=\"M473,67.3c-203.9,88.3-263.1-34-320.3,0C66,119.1,0,59.7,0,59.7V0h1000v59.7 c0,0-62.1,26.1-94.9,29.3c-32.8,3.3-62.8-12.3-75.8-22.1C806,49.6,745.3,8.7,694.9,4.7S492.4,59,473,67.3z\"\/>\n\t<path class=\"elementor-shape-fill\" opacity=\"0.66\" d=\"M734,67.3c-45.5,0-77.2-23.2-129.1-39.1c-28.6-8.7-150.3-10.1-254,39.1 s-91.7-34.4-149.2,0C115.7,118.3,0,39.8,0,39.8V0h1000v36.5c0,0-28.2-18.5-92.1-18.5C810.2,18.1,775.7,67.3,734,67.3z\"\/>\n\t<path class=\"elementor-shape-fill\" d=\"M766.1,28.9c-200-57.5-266,65.5-395.1,19.5C242,1.8,242,5.4,184.8,20.6C128,35.8,132.3,44.9,89.9,52.5C28.6,63.7,0,0,0,0 h1000c0,0-9.9,40.9-83.6,48.1S829.6,47,766.1,28.9z\"\/>\n<\/svg>\t\t<\/div>\n\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f7b402d\" data-id=\"f7b402d\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-876c496 elementor-widget elementor-widget-heading\" data-id=\"876c496\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">\u00dcretken \u00c7eki\u015fmeli A\u011flar\u0131 Kullanan DLSS<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-39d19f7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"39d19f7\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cd25745\" data-id=\"cd25745\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4a3efaa pa-icon-pos-after premium-lq__none elementor-widget elementor-widget-premium-addon-button\" data-id=\"4a3efaa\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation_tablet&quot;:&quot;none&quot;}\" data-widget_type=\"premium-addon-button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\n\n\t\t<a class=\"premium-button premium-button-none premium-btn-md premium-button-none\" href=\"https:\/\/aix.web.tr\/en\/deep-learning-super-sampling-using-generative-adversarial-networks\/\">\n\t\t\t<div class=\"premium-button-text-icon-wrapper\">\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg class=\"svg-inline--fas-fa-language premium-drawable-icon premium-svg-nodraw\" aria-hidden='true' xmlns='http:\/\/www.w3.org\/2000\/svg' viewBox='0 0 640 512'><path d=\"M152.1 236.2c-3.5-12.1-7.8-33.2-7.8-33.2h-.5s-4.3 21.1-7.8 33.2l-11.1 37.5H163zM616 96H336v320h280c13.3 0 24-10.7 24-24V120c0-13.3-10.7-24-24-24zm-24 120c0 6.6-5.4 12-12 12h-11.4c-6.9 23.6-21.7 47.4-42.7 69.9 8.4 6.4 17.1 12.5 26.1 18 5.5 3.4 7.3 10.5 4.1 16.2l-7.9 13.9c-3.4 5.9-10.9 7.8-16.7 4.3-12.6-7.8-24.5-16.1-35.4-24.9-10.9 8.7-22.7 17.1-35.4 24.9-5.8 3.5-13.3 1.6-16.7-4.3l-7.9-13.9c-3.2-5.6-1.4-12.8 4.2-16.2 9.3-5.7 18-11.7 26.1-18-7.9-8.4-14.9-17-21-25.7-4-5.7-2.2-13.6 3.7-17.1l6.5-3.9 7.3-4.3c5.4-3.2 12.4-1.7 16 3.4 5 7 10.8 14 17.4 20.9 13.5-14.2 23.8-28.9 30-43.2H412c-6.6 0-12-5.4-12-12v-16c0-6.6 5.4-12 12-12h64v-16c0-6.6 5.4-12 12-12h16c6.6 0 12 5.4 12 12v16h64c6.6 0 12 5.4 12 12zM0 120v272c0 13.3 10.7 24 24 24h280V96H24c-13.3 0-24 10.7-24 24zm58.9 216.1L116.4 167c1.7-4.9 6.2-8.1 11.4-8.1h32.5c5.1 0 9.7 3.3 11.4 8.1l57.5 169.1c2.6 7.8-3.1 15.9-11.4 15.9h-22.9a12 12 0 0 1-11.5-8.6l-9.4-31.9h-60.2l-9.1 31.8c-1.5 5.1-6.2 8.7-11.5 8.7H70.3c-8.2 0-14-8.1-11.4-15.9z\"><\/path><\/svg>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t<span >\n\t\t\t\t\t\tEnglish \t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t<\/a>\n\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fb4bcff elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fb4bcff\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-6ad82f0\" data-id=\"6ad82f0\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-16d0216 elementor-widget elementor-widget-html\" data-id=\"16d0216\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/camo.githubusercontent.com\/0c44906cc27a0589b398c9dd289162904af21a81e0bf5bf65f0a0639bbd6e166\/68747470733a2f2f692e696d6775722e636f6d2f4b54346d5a50672e6a7067\" alt=\"DLSS Example 1\">\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-7d6ef3d\" data-id=\"7d6ef3d\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-189c434 elementor-widget elementor-widget-html\" data-id=\"189c434\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/camo.githubusercontent.com\/c456f606de09d9015739ee56927d201216c4f99901023d7d92f0aa7c2ff4a277\/68747470733a2f2f692e696d6775722e636f6d2f465a36364b4f6d2e6a7067\" alt=\"DLSS Example 2\">\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-86fd212\" data-id=\"86fd212\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8f91901 elementor-widget elementor-widget-html\" data-id=\"8f91901\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/camo.githubusercontent.com\/875f7491adc4e92a5980e2ab020fc87619b68be0b817861c50251671ba3a87eb\/68747470733a2f2f692e696d6775722e636f6d2f536631686e6d742e6a7067\" alt=\"DLSS Example 3\">\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-dfbfed8 elementor-section-content-middle elementor-section-stretched elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"dfbfed8\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;stretch_section&quot;:&quot;section-stretched&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7f71627\" data-id=\"7f71627\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3f16274 elementor-widget elementor-widget-text-editor\" data-id=\"3f16274\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">Bu makale size Keras kitapl\u0131\u011f\u0131 kullan\u0131larak yaz\u0131lan bir <a href=\"https:\/\/aix.web.tr\/en\/dlss-nedir\/\">DLSS<\/a> \u00dcretken \u00c7eki\u015fmeli A\u011f\u0131&#8217;n\u0131n genel \u00e7er\u00e7evesini sa\u011flayacakt\u0131r. Kendi g\u00f6r\u00fcnt\u00fc veri k\u00fcmelerinizi kullanarak s\u00fcper \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc modelleri e\u011fitmek i\u00e7in bu genel DLSS GAN \u015fablonunu kullanabileceksiniz.<\/p><p align=\"left\">Kodun tam s\u00fcr\u00fcm\u00fc\u00a0<a href=\"https:\/\/github.com\/vee-upatising\/DLSS\" target=\"_blank\" rel=\"noopener\"><u>bu GitHub deposunda mevcuttur<\/u><\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0e97476 elementor-widget elementor-widget-heading\" data-id=\"0e97476\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Veri k\u00fcmesi<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a9f97e4 elementor-widget elementor-widget-text-editor\" data-id=\"a9f97e4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">Bu \u015fablon, herhangi bir RGB g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fc veri k\u00fcmeniz olarak kullanabilece\u011finiz \u015fekilde yaz\u0131lm\u0131\u015ft\u0131r.\u00a0Kullan\u0131labilecek iyi bir veri k\u00fcmesi\u00a0<a href=\"https:\/\/www.kaggle.com\/akhileshdkapse\/super-image-resolution\" target=\"_blank\" rel=\"noopener\"><u>\u00f6rne\u011fini Kagge&#8217;den indirebilirsiniz<\/u><\/a>\u00a0. Bu veri seti iki g\u00f6r\u00fcnt\u00fc seti sa\u011flar: <i>96 \u00d7 96<\/i>\u00a0pikselli bir\u00a0d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc g\u00f6r\u00fcnt\u00fc\u00a0seti ve\u00a0<i>384 \u00d7 384<\/i> pikselli bir\u00a0y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc g\u00f6r\u00fcnt\u00fc seti.<\/p><p align=\"left\">Bu veri k\u00fcmesini kullanmak i\u00e7in, ayarlar\u0131n\u0131z\u0131 yaln\u0131zca kullan\u0131c\u0131 taraf\u0131ndan belirlenen parametreler dahilinde tan\u0131mlaman\u0131z gerekir.\u00a0 <i>input_path<\/i> indirilen veri k\u00fcmesindeki LR klas\u00f6r\u00fcne ayarlay\u0131n ve <i>output_path&#8217;i HR klas\u00f6r\u00fcne ayarlay\u0131n<\/i>.\u00a0Ayr\u0131ca g\u00f6r\u00fcnt\u00fclerinizin boyutlar\u0131n\u0131 da belirtmeniz gerekir, bu nedenle\u00a0<i>input_dimensions&#8217;\u0131<\/i>\u00a0(96,96,3) ve\u00a0<i>output_dimensions&#8217;\u0131<\/i>\u00a0(384,384,3) olarak ayarlay\u0131n.\u00a0Bu durumda giri\u015f ve \u00e7\u0131k\u0131\u015f boyutlar\u0131n\u0131n uyumlu oldu\u011funa dikkat edin.\u00a0\u0130ki g\u00f6r\u00fcnt\u00fc boyutu, y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fck ile d\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fck aras\u0131ndaki oran 2&#8217;nin kat\u0131 oldu\u011funda uyumludur. Bu durumda 384 \u00d7 384 g\u00f6r\u00fcnt\u00fcler, 96 \u00d7 96 g\u00f6r\u00fcnt\u00fclerden 4 kat daha b\u00fcy\u00fckt\u00fcr.\u00a0Bu uyumluluk,\u00a0<i>Upsampling2D&#8217;nin <\/i>katmanlar\u0131 Keras&#8217;ta \u00e7al\u0131\u015f\u0131r. Katman girdinin \u00e7\u00f6z\u00fcn\u00fcrl\u00fc\u011f\u00fcn\u00fc iki kat\u0131na \u00e7\u0131kar\u0131r ve\u00a0<i>super_sampling_ratio<\/i>\u00a0parametresi, girdi boyutlar\u0131m\u0131zdan \u00e7\u0131kt\u0131 boyutlar\u0131m\u0131za ula\u015fmak i\u00e7in modelimizdeki katman\u0131 ka\u00e7 kez uygulad\u0131\u011f\u0131m\u0131z\u0131 belirler.<\/p><p align=\"left\">\u0130ki g\u00f6r\u00fcnt\u00fc setiyle birlikte gelen veri k\u00fcmelerini kullanman\u0131za gerek olmad\u0131\u011f\u0131n\u0131 da unutmamak \u00f6nemlidir.\u00a0\u00d6rne\u011fin, yaln\u0131zca bir 256 \u00d7 256 g\u00f6r\u00fcnt\u00fc k\u00fcmesiyle gelen\u00a0<a href=\"https:\/\/www.kaggle.com\/kvpratama\/pokemon-images-dataset\" target=\"_blank\" rel=\"noopener\"><u>bu veri k\u00fcmesini<\/u><\/a> kullanabilirsiniz. Sadece ayarlama yoluyla, <i>input_path<\/i>\u00a0ve\u00a0<i>output_path<\/i> ayn\u0131 klas\u00f6re parametrelerini ve g\u00f6r\u00fcnt\u00fcleri do\u011fru i\u015flenecektir. Bu \u00f6rnekte, output_dimension (256,256,3) olarak ayarlay\u0131n ve input_dimension&#8217;lar\u0131 (64,64,3) veya (128,128,3) olarak ayarlay\u0131n ve g\u00f6r\u00fcnt\u00fcler buna g\u00f6re yeniden boyutland\u0131r\u0131lacakt\u0131r.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fd9ba8b elementor-widget elementor-widget-heading\" data-id=\"fd9ba8b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Girdiler<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-25fd731 elementor-widget elementor-widget-text-editor\" data-id=\"25fd731\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"\">\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"prompt output_prompt\">Girdi [1]:<\/div>\n<div class=\"output_text output_subarea output_execute_result\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-ipython3\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-ipython3\">\n<pre><span class=\"kn\">from<\/span> <span class=\"nn\">keras.layers<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">Input<\/span><span class=\"p\">,<\/span> <span class=\"n\">Dense<\/span><span class=\"p\">,<\/span> <span class=\"n\">Reshape<\/span><span class=\"p\">,<\/span> <span class=\"n\">Flatten<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">keras.layers.advanced_activations<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">LeakyReLU<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">keras.layers.convolutional<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">UpSampling2D<\/span><span class=\"p\">,<\/span> <span class=\"n\">Conv2D<\/span><span class=\"p\">,<\/span> <span class=\"n\">MaxPooling2D<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">keras.models<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">Sequential<\/span><span class=\"p\">,<\/span> <span class=\"n\">Model<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">keras.optimizers<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">Adam<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.utils<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">shuffle<\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">matplotlib.pyplot<\/span> <span class=\"k\">as<\/span> <span class=\"nn\">plt<\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">numpy<\/span> <span class=\"k\">as<\/span> <span class=\"nn\">np<\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">os<\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">PIL<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">Image<\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">skimage.transform<\/span> <span class=\"k\">as<\/span> <span class=\"nn\">s<\/span>\n<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"output_subarea output_stream output_stderr output_text\">\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6be08a7 elementor-widget elementor-widget-text-editor\" data-id=\"6be08a7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">Bu proje a\u015fa\u011f\u0131daki kitapl\u0131klar\u0131 gerektirmektedir:<\/p><ul><li><span style=\"font-size: medium;\">Keras (I use 2.3.1) <\/span><\/li><li><span style=\"font-size: medium;\">Tensorflow (I use 1.14.0) <\/span><\/li><li><span style=\"font-size: medium;\">Sklearn<\/span><\/li><li><span style=\"font-size: medium;\">Skimage<\/span><\/li><li><span style=\"font-size: medium;\">Numpy<\/span><\/li><li><span style=\"font-size: medium;\">Matplotlib<\/span><\/li><li><span style=\"font-size: medium;\">PIL<\/span><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2d38ad elementor-widget elementor-widget-heading\" data-id=\"a2d38ad\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Parametreleri Ayarla<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9e8d4d8 elementor-widget elementor-widget-text-editor\" data-id=\"9e8d4d8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Bu \u015fablonu \u00f6zel ihtiya\u00e7lar\u0131n\u0131za g\u00f6re de\u011fi\u015ftirebilmeniz i\u00e7in \u015fablonun parametrelerini tan\u0131mlaman\u0131z gerekir. Mevcut parametreler, 8 GB VRAM&#8217;li bir GPU&#8217;da kullan\u0131lmak \u00fczere tasarlanm\u0131\u015ft\u0131r. Daha az g\u00fcce sahip bir GPU ile \u00e7al\u0131\u015f\u0131yorsan\u0131z, evri\u015fimli filtrelerin say\u0131s\u0131n\u0131 ve \u00e7ekirdek boyutunu buna g\u00f6re azalt\u0131n. Bellek k\u0131s\u0131tlamalar\u0131 nedeniyle toplu i\u015f boyutu da azalt\u0131labilir. Geri kalan parametreler a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-010a8c1 elementor-widget elementor-widget-text-editor\" data-id=\"010a8c1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Kullan\u0131c\u0131 Taraf\u0131ndan Belirlenen Parametreler:<\/p><ul><li><strong><span style=\"font-size: medium;\"><i>input_path<\/i><\/span><\/strong><span style=\"font-size: medium;\"><strong>:<\/strong> D\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc veri k\u00fcmesini i\u00e7eren klas\u00f6re i\u015faret eden dosya yolu.<\/span><\/li><li><strong><span style=\"font-size: medium;\"><i>output_path<\/i><\/span><\/strong><span style=\"font-size: medium;\"><strong>:<\/strong> Y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc veri k\u00fcmesini i\u00e7eren klas\u00f6re i\u015faret eden dosya yolu.<\/span><\/li><li><strong><span style=\"font-size: medium;\"><i>input_dimensions<\/i><\/span><\/strong><span style=\"font-size: medium;\"><strong>: <\/strong>D\u00fc\u015f\u00fck \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc veri k\u00fcmesindeki g\u00f6r\u00fcnt\u00fclerin boyutlar\u0131. G\u00f6r\u00fcnt\u00fc boyutlar\u0131 uyumlu olmal\u0131d\u0131r, yani output_dimensions \/ input_dimensions 2&#8217;nin kat\u0131d\u0131r.<\/span><\/li><li><strong><span style=\"font-size: medium;\"><i>output_dimensions<\/i><\/span><\/strong><span style=\"font-size: medium;\"><strong>:<\/strong> Y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc veri k\u00fcmesindeki g\u00f6r\u00fcnt\u00fclerin boyutlar\u0131. G\u00f6r\u00fcnt\u00fc boyutlar\u0131 uyumlu olmal\u0131d\u0131r, yani output_dimensions \/ input_dimensions 2&#8217;nin kat\u0131d\u0131r.<\/span><\/li><li><strong><span style=\"font-size: medium;\"><i>super_sampling_ratio<\/i><\/span><\/strong><span style=\"font-size: medium;\"><strong>:<\/strong> \u0130ki g\u00f6r\u00fcnt\u00fc \u00e7\u00f6z\u00fcn\u00fcrl\u00fc\u011f\u00fc aras\u0131ndaki boyut fark\u0131n\u0131n oran\u0131n\u0131 temsil eden tamsay\u0131. Bu tam say\u0131, modellerde Upsampling2D ve MaxPooling2D katmanlar\u0131n\u0131n ka\u00e7 kez kullan\u0131ld\u0131\u011f\u0131n\u0131 belirtir.<\/span><\/li><li><strong><span style=\"font-size: medium;\"><i>model_path<\/i><\/span><\/strong><span style=\"font-size: medium;\"><strong>: <\/strong>Modele kaydetmek istedi\u011finiz klas\u00f6re ve olu\u015fturulan \u00f6rneklere i\u015faret eden dosya yolu.<\/span><\/li><li><strong><span style=\"font-size: medium;\"><i>interval<\/i><\/span><\/strong><span style=\"font-size: medium;\"><strong>: <\/strong>Modelinizi kaydetme aras\u0131nda ka\u00e7 d\u00f6nemin oldu\u011funu g\u00f6steren tamsay\u0131.<\/span><\/li><li><strong><span style=\"font-size: medium;\"><i>epochs<\/i><\/span><\/strong><span style=\"font-size: medium;\"><strong>:<\/strong> Modelin ka\u00e7 d\u00f6nemin e\u011fitilece\u011fini temsil eden tam say\u0131.<\/span><\/li><li><strong><span style=\"font-size: medium;\"><i>batch<\/i><\/span><\/strong><span style=\"font-size: medium;\"><strong>:<\/strong> Bir seferde ka\u00e7 g\u00f6r\u00fcnt\u00fcn\u00fcn e\u011fitilece\u011fini temsil eden tam say\u0131.<\/span><\/li><li><strong><span style=\"font-size: medium;\"><i>conv_filters<\/i><\/span><\/strong><span style=\"font-size: medium;\"><strong>:<\/strong> Generator ve Discriminator&#8217;\u0131n her bir evri\u015fimli katman\u0131nda ka\u00e7 tane evri\u015fimli filtre kullan\u0131ld\u0131\u011f\u0131n\u0131 temsil eden tamsay\u0131.<\/span><\/li><li><strong><span style=\"font-size: medium;\"><i>kernel<\/i><\/span><\/strong><span style=\"font-size: medium;\"><strong>: <\/strong>Evri\u015fimli katmanlarda kullan\u0131lan \u00e7ekirdeklerin boyutunu temsil eden grup.<\/span><\/li><li><strong><span style=\"font-size: medium;\"><i>png<\/i><\/span><\/strong><span style=\"font-size: medium;\"><strong>:<\/strong> Boolean flag, verilerde resimlerden alfa katman\u0131n\u0131 kald\u0131rmak i\u00e7in PNG&#8217;ler varsa True olarak ayarlay\u0131n.<\/span><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-82c18e6 elementor-widget elementor-widget-text-editor\" data-id=\"82c18e6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\">Girdi [2]:<\/div><div class=\"output_text output_subarea output_execute_result\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><pre><span class=\"c1\"># Folder containing input (low resolution) dataset<\/span>\n<span class=\"n\">input_path<\/span> <span class=\"o\">=<\/span> <span class=\"sa\">r<\/span><span class=\"s1\">'D:\\Downloads\\selfie2anime\\trainB'<\/span>\n\n<span class=\"c1\"># Folder containing output (high resolution) dataset<\/span>\n<span class=\"n\">output_path<\/span> <span class=\"o\">=<\/span> <span class=\"sa\">r<\/span><span class=\"s1\">'D:\\Downloads\\selfie2anime\\trainB'<\/span>\n\n<span class=\"c1\"># Dimensions of the images inside the dataset.<\/span>\n<span class=\"c1\"># NOTE: The image sizes must be compatible meaning output_dimensions \/ input_dimensions is a multiple of 2<\/span>\n<span class=\"n\">input_dimensions<\/span> <span class=\"o\">=<\/span> <span class=\"p\">(<\/span><span class=\"mi\">128<\/span><span class=\"p\">,<\/span><span class=\"mi\">128<\/span><span class=\"p\">,<\/span><span class=\"mi\">3<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Dimensions of the images inside the dataset.<\/span>\n<span class=\"c1\"># NOTE: The image sizes must be compatible meaning output_dimensions \/ input_dimensions is a multiple of 2<\/span>\n<span class=\"n\">output_dimensions<\/span> <span class=\"o\">=<\/span> <span class=\"p\">(<\/span><span class=\"mi\">256<\/span><span class=\"p\">,<\/span><span class=\"mi\">256<\/span><span class=\"p\">,<\/span><span class=\"mi\">3<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># How many times to increase the resolution by 2 (by appling the UpSampling2D layer)<\/span>\n<span class=\"n\">super_sampling_ratio<\/span> <span class=\"o\">=<\/span> <span class=\"nb\">int<\/span><span class=\"p\">(<\/span><span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span> <span class=\"o\">\/<\/span> <span class=\"n\">input_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span> <span class=\"o\">\/<\/span> <span class=\"mi\">2<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Folder where you want to save to model as well as generated samples<\/span>\n<span class=\"n\">model_path<\/span> <span class=\"o\">=<\/span> <span class=\"sa\">r<\/span><span class=\"s2\">\"C:\\Users\\Vee\\Desktop\\python\\GAN\\DLSS\\results\"<\/span>\n\n<span class=\"c1\"># How many epochs between saving your model<\/span>\n<span class=\"n\">interval<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">5<\/span>\n\n<span class=\"c1\"># How many epochs to run the model<\/span>\n<span class=\"n\">epoch<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">100<\/span>\n\n<span class=\"c1\"># How many images to train at one time.<\/span>\n<span class=\"c1\"># Ideally this number would be a factor of the size of your dataset<\/span>\n<span class=\"n\">batch<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">25<\/span>\n\n<span class=\"c1\"># How many convolutional filters for each convolutional layer of the generator and the discrminator<\/span>\n<span class=\"n\">conv_filters<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">64<\/span>\n\n<span class=\"c1\"># Size of kernel used in the convolutional layers<\/span>\n<span class=\"n\">kernel<\/span> <span class=\"o\">=<\/span> <span class=\"p\">(<\/span><span class=\"mi\">5<\/span><span class=\"p\">,<\/span><span class=\"mi\">5<\/span><span class=\"p\">)<\/span>\n\n<span class=\"c1\"># Boolean flag, set to True if the data has pngs to remove alpha layer from images<\/span>\n<span class=\"n\">png<\/span> <span class=\"o\">=<\/span> <span class=\"kc\">True<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3704fba elementor-widget elementor-widget-heading\" data-id=\"3704fba\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Derin Evri\u015fimli GAN S\u0131n\u0131f\u0131 Olu\u015fturun\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e8c9dc6 elementor-widget elementor-widget-text-editor\" data-id=\"e8c9dc6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\"><span>Bu s\u0131n\u0131f 6 y\u00f6ntem i\u00e7erir.<\/span><\/p><ul><li><p align=\"left\"><i><span>__init __ (self)<\/span><\/i><span>\u00a0: S\u0131n\u0131f, girdi g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fcn yan\u0131 s\u0131ra \u00e7\u0131kt\u0131 g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fcn boyutlar\u0131 tan\u0131mlanarak ba\u015flat\u0131l\u0131r.\u00a0Generator ve Discriminator modelleri\u00a0<\/span><i><span>build_generator ()<\/span><\/i><span>\u00a0ve\u00a0<\/span><i><span>build_discriminator ()<\/span><\/i><span>\u00a0kullan\u0131larak ba\u015flat\u0131l\u0131r\u00a0<\/span><span>.<\/span><\/p><\/li><li><p align=\"left\"><i><span>build_generator (self)<\/span><\/i><span>\u00a0: Generator modelini tan\u0131mlar.\u00a0<\/span><i><span>Evri\u015fimsel<\/span><\/i><span>\u00a0ve\u00a0<\/span><i><span>UpSampling2D<\/span><\/i><span>\u00a0tabakalar\u0131 kat g\u00f6r\u00fcnt\u00fcn\u00fcn \u00e7\u00f6z\u00fcn\u00fcrl\u00fc\u011f\u00fcn\u00fc art\u0131rmak\u00a0<\/span><i><span>super_sampling_ratio * 2<\/span><\/i><span>\u00a0.\u00a0DCGAN s\u0131n\u0131f\u0131 ba\u015flat\u0131ld\u0131\u011f\u0131nda \u00e7a\u011fr\u0131l\u0131r.<\/span><\/p><\/li><li><p align=\"left\"><i><span>build_discriminator (self)<\/span><\/i><span>\u00a0: Discriminator modelini tan\u0131mlar.\u00a0<\/span><i><span>Konvol\u00fcsyonel<\/span><\/i><span>\u00a0ve\u00a0<\/span><i><span>MaxPooling2D<\/span><\/i><span>\u00a0tabakalar output_dimensions ila alt\u00f6rnekleyebilirsiniz\u00a0<\/span><i><span>1<\/span><\/i><span>\u00a0skaler tahmini.\u00a0DCGAN s\u0131n\u0131f\u0131 ba\u015flat\u0131ld\u0131\u011f\u0131nda \u00e7a\u011fr\u0131l\u0131r.<\/span><\/p><\/li><li><p align=\"left\"><i><span>load_data (self)<\/span><\/i><span>\u00a0: Kullan\u0131c\u0131 taraf\u0131ndan belirtilen dosya yolundan\u00a0<\/span><i><span>veri_yolu<\/span><\/i><span>\u00a0verileri y\u00fckler\u00a0<\/span><span>.\u00a0Dan yeniden \u015fekillendiren g\u00f6r\u00fcnt\u00fcleri\u00a0<\/span><i><span>input_path<\/span><\/i><span>\u00a0olmas\u0131\u00a0<\/span><i><span>input_dimensions<\/span><\/i><span>\u00a0.\u00a0Output_path dosyas\u0131ndaki g\u00f6r\u00fcnt\u00fcleri\u00a0<\/span><i><span>output_dimensions<\/span><\/i><span>\u00a0i\u00e7in yeniden\u00a0<\/span><i><span>\u015fekillendirir<\/span><\/i><span>\u00a0.\u00a0<\/span><i><span>Train ()<\/span><\/i><span>\u00a0y\u00f6nteminde\u00a0\u00e7a\u011fr\u0131l\u0131r\u00a0<\/span><span>.<\/span><\/p><\/li><li><p align=\"left\"><i><span>train (self, epochs, batch_size, save_interval)<\/span><\/i><span>\u00a0: Generative Adversarial Network&#8217;\u00fc\u00a0<\/span><span><i>e\u011fitir<\/i>\u00a0.\u00a0Her d\u00f6nem modeli,\u00a0<\/span><i><span>batch_size ile<\/span><\/i><span>\u00a0tan\u0131mlanan par\u00e7alara ayr\u0131lm\u0131\u015f t\u00fcm veri k\u00fcmesini kullanarak\u00a0<i>e\u011fitir<\/i><\/span><span>\u00a0.\u00a0Epoch,\u00a0<\/span><i><span>save_interval&#8217;deyse<\/span><\/i><span>\u00a0, y\u00f6ntem\u00a0<\/span><span>\u00f6rnekleri olu\u015fturmak i\u00e7in\u00a0<\/span><i><span>save_imgs () \u00f6\u011fesini<\/span><\/i><span>\u00a0\u00e7a\u011f\u0131r\u0131r\u00a0<\/span><span>ve modeli ge\u00e7erli d\u00f6nemde kaydeder.<\/span><\/p><\/li><li><p align=\"left\"><i><span>save_imgs (self, epoch, gen_imgs, interpolated)<\/span><\/i><span>\u00a0: Modeli kaydeder ve kullan\u0131c\u0131n\u0131n belirledi\u011fi yol olan\u00a0<\/span><i><span>model_path&#8217;ta<\/span><\/i><span>\u00a0belirli bir d\u00f6nem i\u00e7in tahmin \u00f6rnekleri olu\u015fturur\u00a0<\/span><span>.\u00a0Her \u00f6rnek, kar\u015f\u0131la\u015ft\u0131rma i\u00e7in 8 enterpolasyonlu g\u00f6r\u00fcnt\u00fc ve Derin \u00d6\u011frenilmi\u015f S\u00fcper \u00d6rneklenmi\u015f g\u00f6r\u00fcnt\u00fcler i\u00e7erir.<\/span><\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c309883 elementor-widget elementor-widget-heading\" data-id=\"c309883\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Ba\u015flatma<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2ec7d29 elementor-widget elementor-widget-text-editor\" data-id=\"2ec7d29\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\">Girdi [3]:<\/div><div class=\"output_text output_subarea output_execute_result\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><pre><span class=\"k\">class<\/span> <span class=\"nc\">DCGAN<\/span><span class=\"p\">():<\/span>\n    \n    <span class=\"c1\"># Initialize parameters, generator, and discriminator models<\/span>\n    <span class=\"k\">def<\/span> <span class=\"fm\">__init__<\/span><span class=\"p\">(<\/span><span class=\"bp\">self<\/span><span class=\"p\">):<\/span>\n        \n        <span class=\"c1\"># Set dimensions of the output image<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">img_rows<\/span> <span class=\"o\">=<\/span> <span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">img_cols<\/span> <span class=\"o\">=<\/span> <span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">]<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">channels<\/span> <span class=\"o\">=<\/span> <span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">2<\/span><span class=\"p\">]<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">img_shape<\/span> <span class=\"o\">=<\/span> <span class=\"p\">(<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">img_rows<\/span><span class=\"p\">,<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">img_cols<\/span><span class=\"p\">,<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">channels<\/span><span class=\"p\">)<\/span>\n        \n        <span class=\"c1\"># Shape of low resolution input image<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">latent_dim<\/span> <span class=\"o\">=<\/span> <span class=\"n\">input_dimensions<\/span>\n        \n        <span class=\"c1\"># Chose optimizer for the models<\/span>\n        <span class=\"n\">optimizer<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Adam<\/span><span class=\"p\">(<\/span><span class=\"mf\">0.0002<\/span><span class=\"p\">,<\/span> <span class=\"mf\">0.5<\/span><span class=\"p\">)<\/span>\n\n        <span class=\"c1\"># Build and compile the discriminator<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">discriminator<\/span> <span class=\"o\">=<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">build_discriminator<\/span><span class=\"p\">()<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">discriminator<\/span><span class=\"o\">.<\/span><span class=\"n\">compile<\/span><span class=\"p\">(<\/span><span class=\"n\">loss<\/span><span class=\"o\">=<\/span><span class=\"s1\">'binary_crossentropy'<\/span><span class=\"p\">,<\/span>\n            <span class=\"n\">optimizer<\/span><span class=\"o\">=<\/span><span class=\"n\">optimizer<\/span><span class=\"p\">,<\/span>\n            <span class=\"n\">metrics<\/span><span class=\"o\">=<\/span><span class=\"p\">[<\/span><span class=\"s1\">'accuracy'<\/span><span class=\"p\">])<\/span>\n\n        <span class=\"c1\"># Build the generator<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">generator<\/span> <span class=\"o\">=<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">build_generator<\/span><span class=\"p\">()<\/span>\n        <span class=\"n\">generator<\/span> <span class=\"o\">=<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">generator<\/span>\n\n        <span class=\"c1\"># The generator takes low resolution images as input and generates high resolution images<\/span>\n        <span class=\"n\">z<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Input<\/span><span class=\"p\">(<\/span><span class=\"n\">shape<\/span> <span class=\"o\">=<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">latent_dim<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">img<\/span> <span class=\"o\">=<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">generator<\/span><span class=\"p\">(<\/span><span class=\"n\">z<\/span><span class=\"p\">)<\/span>\n\n        <span class=\"c1\"># For the combined model we will only train the generator<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">discriminator<\/span><span class=\"o\">.<\/span><span class=\"n\">trainable<\/span> <span class=\"o\">=<\/span> <span class=\"kc\">False<\/span>\n\n        <span class=\"c1\"># The discriminator takes generated images as input and determines validity<\/span>\n        <span class=\"n\">valid<\/span> <span class=\"o\">=<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">discriminator<\/span><span class=\"p\">(<\/span><span class=\"n\">img<\/span><span class=\"p\">)<\/span>\n\n        <span class=\"c1\"># The combined model  (stacked generator and discriminator)<\/span>\n        <span class=\"c1\"># Trains the generator to fool the discriminator<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">combined<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Model<\/span><span class=\"p\">(<\/span><span class=\"n\">z<\/span><span class=\"p\">,<\/span> <span class=\"n\">valid<\/span><span class=\"p\">)<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">combined<\/span><span class=\"o\">.<\/span><span class=\"n\">compile<\/span><span class=\"p\">(<\/span><span class=\"n\">loss<\/span><span class=\"o\">=<\/span><span class=\"s1\">'binary_crossentropy'<\/span><span class=\"p\">,<\/span> <span class=\"n\">optimizer<\/span><span class=\"o\">=<\/span><span class=\"n\">optimizer<\/span><span class=\"p\">)<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c438ef elementor-widget elementor-widget-text-editor\" data-id=\"5c438ef\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">DCGAN s\u0131n\u0131f\u0131 ba\u015flat\u0131ld\u0131\u011f\u0131nda, Sinir A\u011f\u0131n\u0131n veri setinden beklemesi gereken g\u00f6r\u00fcnt\u00fclerin boyutunu tan\u0131mlar\u0131z.\u00a0\u00c7\u0131kt\u0131 boyutlar\u0131 Tuple\u00a0<i>img_shape<\/i> taraf\u0131ndan belirtilir.\u00a0Girdi boyutlar\u0131 ayr\u0131ca Tuple\u00a0<i>latent_dim<\/i> taraf\u0131ndan da belirtilir.<\/p><p align=\"left\">Her iki model i\u00e7in de kulland\u0131\u011f\u0131m\u0131z\u00a0optimize edici,\u00a0<u><\/u><a href=\"https:\/\/keras.io\/api\/optimizers\/adam\/\" target=\"_blank\" rel=\"noopener\"><u> Adam optimize edicidir.\u00a0<\/u><\/a>Optimize edicinin \u00f6\u011frenme oran\u0131 ve beta de\u011ferleri ile denemekten \u00e7ekinmeyin ve ne t\u00fcr sonu\u00e7lar ald\u0131\u011f\u0131n\u0131z\u0131 g\u00f6r\u00fcn.<\/p><p align=\"left\">Generative Adversarial Network&#8217;\u00fcn mimarisi, her iki modelde \u0130kili \u00c7apraz Entropi kayb\u0131n\u0131 kullanarak burada tan\u0131mlanm\u0131\u015ft\u0131r.\u00a0Kay\u0131p fonksiyonu olarak \u0130kili \u00c7apraz Entropi se\u00e7imi\u00a0<a href=\"https:\/\/stats.stackexchange.com\/questions\/242907\/why-use-binary-cross-entropy-for-generator-in-adversarial-networks\" target=\"_blank\" rel=\"noopener\"><u>burada<\/u><\/a> a\u00e7\u0131klanm\u0131\u015ft\u0131r.\u00a0Di\u011fer kay\u0131p fonksiyonlar\u0131n\u0131 denemekten \u00e7ekinmeyin, ancak her iki modelin de ayn\u0131 kay\u0131p fonksiyonunu kullanmas\u0131 gerekti\u011fini unutmay\u0131n.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5287519 elementor-widget elementor-widget-heading\" data-id=\"5287519\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Veri y\u00fckle\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-428916f elementor-widget elementor-widget-text-editor\" data-id=\"428916f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\"><pre>    <span class=\"c1\"># load data from specified file path <\/span>\n    <span class=\"k\">def<\/span> <span class=\"nf\">load_data<\/span><span class=\"p\">(<\/span><span class=\"bp\">self<\/span><span class=\"p\">):<\/span>\n        \n        <span class=\"c1\"># Initializing arrays for data and image file paths<\/span>\n        <span class=\"n\">data<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n        <span class=\"n\">small<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n        <span class=\"n\">paths<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n        \n        <span class=\"c1\"># Get the file paths of all image files in this folder<\/span>\n        <span class=\"k\">for<\/span> <span class=\"n\">r<\/span><span class=\"p\">,<\/span> <span class=\"n\">d<\/span><span class=\"p\">,<\/span> <span class=\"n\">f<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">os<\/span><span class=\"o\">.<\/span><span class=\"n\">walk<\/span><span class=\"p\">(<\/span><span class=\"n\">output_path<\/span><span class=\"p\">):<\/span>\n            <span class=\"k\">for<\/span> <span class=\"n\">file<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">f<\/span><span class=\"p\">:<\/span>\n                <span class=\"k\">if<\/span> <span class=\"s1\">'.jpg'<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">file<\/span> <span class=\"ow\">or<\/span> <span class=\"s1\">'png'<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">file<\/span><span class=\"p\">:<\/span>\n                    <span class=\"n\">paths<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">os<\/span><span class=\"o\">.<\/span><span class=\"n\">path<\/span><span class=\"o\">.<\/span><span class=\"n\">join<\/span><span class=\"p\">(<\/span><span class=\"n\">r<\/span><span class=\"p\">,<\/span> <span class=\"n\">file<\/span><span class=\"p\">))<\/span>\n                    \n        <span class=\"c1\"># For each file add high resolution image to array<\/span>\n        <span class=\"k\">for<\/span> <span class=\"n\">path<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">paths<\/span><span class=\"p\">:<\/span>\n            <span class=\"n\">img<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Image<\/span><span class=\"o\">.<\/span><span class=\"n\">open<\/span><span class=\"p\">(<\/span><span class=\"n\">path<\/span><span class=\"p\">)<\/span>\n            \n            <span class=\"c1\"># Resize Image<\/span>\n            <span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">array<\/span><span class=\"p\">(<\/span><span class=\"n\">img<\/span><span class=\"o\">.<\/span><span class=\"n\">resize<\/span><span class=\"p\">((<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">img_rows<\/span><span class=\"p\">,<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">img_cols<\/span><span class=\"p\">)))<\/span>\n            \n            <span class=\"c1\"># Remove alpha layer if imgaes are PNG<\/span>\n            <span class=\"k\">if<\/span><span class=\"p\">(<\/span><span class=\"n\">png<\/span><span class=\"p\">):<\/span>\n                <span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">y<\/span><span class=\"p\">[<\/span><span class=\"o\">...<\/span><span class=\"p\">,:<\/span><span class=\"mi\">3<\/span><span class=\"p\">]<\/span>\n                \n            <span class=\"n\">data<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">y<\/span><span class=\"p\">)<\/span>\n          \n        <span class=\"n\">paths<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n        \n        <span class=\"c1\"># Get the file paths of all image files in this folder<\/span>\n        <span class=\"k\">for<\/span> <span class=\"n\">r<\/span><span class=\"p\">,<\/span> <span class=\"n\">d<\/span><span class=\"p\">,<\/span> <span class=\"n\">f<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">os<\/span><span class=\"o\">.<\/span><span class=\"n\">walk<\/span><span class=\"p\">(<\/span><span class=\"n\">input_path<\/span><span class=\"p\">):<\/span>\n            <span class=\"k\">for<\/span> <span class=\"n\">file<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">f<\/span><span class=\"p\">:<\/span>\n                <span class=\"k\">if<\/span> <span class=\"s1\">'.jpg'<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">file<\/span> <span class=\"ow\">or<\/span> <span class=\"s1\">'png'<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">file<\/span><span class=\"p\">:<\/span>\n                    <span class=\"n\">paths<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">os<\/span><span class=\"o\">.<\/span><span class=\"n\">path<\/span><span class=\"o\">.<\/span><span class=\"n\">join<\/span><span class=\"p\">(<\/span><span class=\"n\">r<\/span><span class=\"p\">,<\/span> <span class=\"n\">file<\/span><span class=\"p\">))<\/span>\n                    \n        <span class=\"c1\"># For each file add low resolution image to array<\/span>\n        <span class=\"k\">for<\/span> <span class=\"n\">path<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">paths<\/span><span class=\"p\">:<\/span>\n            <span class=\"n\">img<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Image<\/span><span class=\"o\">.<\/span><span class=\"n\">open<\/span><span class=\"p\">(<\/span><span class=\"n\">path<\/span><span class=\"p\">)<\/span>\n            \n            <span class=\"c1\"># Resize Image<\/span>\n            <span class=\"n\">x<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">array<\/span><span class=\"p\">(<\/span><span class=\"n\">img<\/span><span class=\"o\">.<\/span><span class=\"n\">resize<\/span><span class=\"p\">((<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">latent_dim<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">],<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">latent_dim<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">])))<\/span>\n            \n            <span class=\"c1\"># Remove alpha layer if imgaes are PNG<\/span>\n            <span class=\"k\">if<\/span><span class=\"p\">(<\/span><span class=\"n\">png<\/span><span class=\"p\">):<\/span>\n                <span class=\"n\">x<\/span> <span class=\"o\">=<\/span> <span class=\"n\">x<\/span><span class=\"p\">[<\/span><span class=\"o\">...<\/span><span class=\"p\">,:<\/span><span class=\"mi\">3<\/span><span class=\"p\">]<\/span>\n                \n            <span class=\"n\">small<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">x<\/span><span class=\"p\">)<\/span>\n        \n            \n        <span class=\"c1\"># Return x_train and y_train reshaped to 4 dimensions<\/span>\n        <span class=\"n\">y_train<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">array<\/span><span class=\"p\">(<\/span><span class=\"n\">data<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">y_train<\/span> <span class=\"o\">=<\/span> <span class=\"n\">y_train<\/span><span class=\"o\">.<\/span><span class=\"n\">reshape<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">data<\/span><span class=\"p\">),<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">img_rows<\/span><span class=\"p\">,<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">img_cols<\/span><span class=\"p\">,<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">channels<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">x_train<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">array<\/span><span class=\"p\">(<\/span><span class=\"n\">small<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">x_train<\/span> <span class=\"o\">=<\/span> <span class=\"n\">x_train<\/span><span class=\"o\">.<\/span><span class=\"n\">reshape<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">small<\/span><span class=\"p\">),<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">latent_dim<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">],<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">latent_dim<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">],<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">latent_dim<\/span><span class=\"p\">[<\/span><span class=\"mi\">2<\/span><span class=\"p\">])<\/span>\n        \n        <span class=\"k\">del<\/span> <span class=\"n\">data<\/span>\n        <span class=\"k\">del<\/span> <span class=\"n\">small<\/span>\n        <span class=\"k\">del<\/span> <span class=\"n\">paths<\/span>\n        \n        <span class=\"c1\"># Shuffle indexes of data<\/span>\n        <span class=\"n\">X_shuffle<\/span><span class=\"p\">,<\/span> <span class=\"n\">Y_shuffle<\/span> <span class=\"o\">=<\/span> <span class=\"n\">shuffle<\/span><span class=\"p\">(<\/span><span class=\"n\">x_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">y_train<\/span><span class=\"p\">)<\/span>\n        \n        <span class=\"k\">return<\/span> <span class=\"n\">X_shuffle<\/span><span class=\"p\">,<\/span> <span class=\"n\">Y_shuffle<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-641c120 elementor-widget elementor-widget-text-editor\" data-id=\"641c120\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">DCGAN s\u0131n\u0131f\u0131na ekledi\u011fimiz ilk y\u00f6ntem\u00a0<i>load_data () &#8216;d\u0131r<\/i> .\u00a0Bu kullan\u0131c\u0131 belirtilen yollar\u0131, i\u00e7inde t\u00fcm g\u00f6r\u00fcnt\u00fcleri i\u00e7in \u00f6n i\u015flemeden <i>input_path<\/i>\u00a0ve\u00a0<i>output_paths<\/i> olacakt\u0131r. Her klas\u00f6rdeki g\u00f6r\u00fcnt\u00fcler, buna g\u00f6re <i>input_dimensions<\/i>\u00a0ve\u00a0<i>output_dimensions olarak<\/i> yeniden boyutland\u0131r\u0131lacakt\u0131r.\u00a0Bu y\u00f6ntem,\u00a0e\u011fitimden \u00f6nce verileri y\u00fcklemek i\u00e7in\u00a0<i>train ()<\/i> y\u00f6nteminin\u00a0i\u00e7inde \u00e7a\u011fr\u0131l\u0131r.<\/p><p align=\"left\">Veri k\u00fcmelerini d\u00f6nd\u00fcrmeden, iki diziyi d\u00f6nd\u00fcrmeden \u00f6nce x_train ve y_train veri k\u00fcmelerini kar\u0131\u015ft\u0131r\u0131yoruz. Veri k\u00fcmesindeki modelleri s\u0131ral\u0131 olarak e\u011fitmek i\u00e7in <i>train ()<\/i> y\u00f6ntemini yazd\u0131k, her yinelemede toplu i\u015f boyutunu art\u0131rd\u0131k. Bu nedenle, veri k\u00fcmesinin s\u0131ral\u0131 olarak s\u0131ralanma bi\u00e7imiyle ilgili olu\u015facak \u00f6nyarg\u0131lar\u0131 getirmemek i\u00e7in veri k\u00fcmesini kar\u0131\u015ft\u0131rmak \u00f6nemlidir.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b9be2c3 elementor-widget elementor-widget-heading\" data-id=\"b9be2c3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Generator Olu\u015fturun<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0dd4ff7 elementor-widget elementor-widget-text-editor\" data-id=\"0dd4ff7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\"><pre> <span class=\"c1\"># Define Generator model<\/span>\n    <span class=\"k\">def<\/span> <span class=\"nf\">build_generator<\/span><span class=\"p\">(<\/span><span class=\"bp\">self<\/span><span class=\"p\">):<\/span>\n\n        <span class=\"n\">model<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Sequential<\/span><span class=\"p\">()<\/span>\n        \n        <span class=\"c1\"># 1st Convolutional Layer \/ Input Layer<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">Conv2D<\/span><span class=\"p\">(<\/span><span class=\"n\">conv_filters<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_size<\/span><span class=\"o\">=<\/span><span class=\"n\">kernel<\/span><span class=\"p\">,<\/span> <span class=\"n\">padding<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"same\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">input_shape<\/span><span class=\"o\">=<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">latent_dim<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">LeakyReLU<\/span><span class=\"p\">(<\/span><span class=\"n\">alpha<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.2<\/span><span class=\"p\">))<\/span>\n        \n        <span class=\"c1\"># Upsample the data as many times as needed to reach output resolution<\/span>\n        <span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">super_sampling_ratio<\/span><span class=\"p\">):<\/span>\n        \n            <span class=\"c1\"># Super Sampling Convolutional Layer<\/span>\n            <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">Conv2D<\/span><span class=\"p\">(<\/span><span class=\"n\">conv_filters<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_size<\/span><span class=\"o\">=<\/span><span class=\"n\">kernel<\/span><span class=\"p\">,<\/span> <span class=\"n\">padding<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"same\"<\/span><span class=\"p\">))<\/span>\n            <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">LeakyReLU<\/span><span class=\"p\">(<\/span><span class=\"n\">alpha<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.2<\/span><span class=\"p\">))<\/span>\n\n            <span class=\"c1\"># Upsample the data (Double the resolution)<\/span>\n            <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">UpSampling2D<\/span><span class=\"p\">())<\/span>\n\n        <span class=\"c1\"># Convolutional Layer<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">Conv2D<\/span><span class=\"p\">(<\/span><span class=\"n\">conv_filters<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_size<\/span><span class=\"o\">=<\/span><span class=\"n\">kernel<\/span><span class=\"p\">,<\/span> <span class=\"n\">padding<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"same\"<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">LeakyReLU<\/span><span class=\"p\">(<\/span><span class=\"n\">alpha<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.2<\/span><span class=\"p\">))<\/span>\n\n        <span class=\"c1\"># Convolutional Layer<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">Conv2D<\/span><span class=\"p\">(<\/span><span class=\"n\">conv_filters<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_size<\/span><span class=\"o\">=<\/span><span class=\"n\">kernel<\/span><span class=\"p\">,<\/span> <span class=\"n\">padding<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"same\"<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">LeakyReLU<\/span><span class=\"p\">(<\/span><span class=\"n\">alpha<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.2<\/span><span class=\"p\">))<\/span>\n        \n        <span class=\"c1\"># Final Convolutional Layer (Output Layer)<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">Conv2D<\/span><span class=\"p\">(<\/span><span class=\"mi\">3<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_size<\/span><span class=\"o\">=<\/span><span class=\"n\">kernel<\/span><span class=\"p\">,<\/span> <span class=\"n\">padding<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"same\"<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">LeakyReLU<\/span><span class=\"p\">(<\/span><span class=\"n\">alpha<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.2<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">summary<\/span><span class=\"p\">()<\/span>\n\n        <span class=\"n\">noise<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Input<\/span><span class=\"p\">(<\/span><span class=\"n\">shape<\/span><span class=\"o\">=<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">latent_dim<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">img<\/span> <span class=\"o\">=<\/span> <span class=\"n\">model<\/span><span class=\"p\">(<\/span><span class=\"n\">noise<\/span><span class=\"p\">)<\/span>\n\n        <span class=\"k\">return<\/span> <span class=\"n\">Model<\/span><span class=\"p\">(<\/span><span class=\"n\">noise<\/span><span class=\"p\">,<\/span> <span class=\"n\">img<\/span><span class=\"p\">)<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dc7f18c elementor-widget elementor-widget-text-editor\" data-id=\"dc7f18c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\"><span>DCGAN s\u0131n\u0131f\u0131na ekledi\u011fimiz ikinci y\u00f6ntem\u00a0<\/span><i><span>build_generator () &#8216;d\u0131r<\/span><\/i><span>\u00a0.\u00a0Bu y\u00f6ntem, s\u0131n\u0131f ilk kez ba\u015flat\u0131ld\u0131\u011f\u0131nda \u00e7a\u011fr\u0131l\u0131r.\u00a0Generator modelinin mimarisi burada tasarlanm\u0131\u015ft\u0131r.\u00a0Model \u00f6zeti, bu modelde ger\u00e7ekte neler oldu\u011fu konusunda size daha net bir fikir verecektir.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f1f1145 elementor-widget elementor-widget-html\" data-id=\"f1f1145\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<center><img decoding=\"async\" src=\"https:\/\/camo.githubusercontent.com\/d9543d5fc527ca5c57b56b7a4c939006429c077e4d4a309c0700262941812134\/68747470733a2f2f692e696d6775722e636f6d2f4c6c31554134702e6a7067\" alt=\"DLSS Example 2\"><\/center>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8d04cb0 elementor-widget elementor-widget-text-editor\" data-id=\"8d04cb0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">Generator modelinin girdisi, bir RGB g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fc temsil eden bir tens\u00f6rd\u00fcr. Bu durumda, bir (128,128,3) resim. Bu tens\u00f6r daha sonra \u00e7\u0131kt\u0131 olarak (256,256,3) de\u011ferine y\u00fckseltilir.<\/p><p align=\"left\">\u00c7\u0131kt\u0131 Evri\u015fimli katman\u0131, RGB g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fcn s\u0131ras\u0131yla K\u0131rm\u0131z\u0131, Ye\u015fil ve Mavi kanallar\u0131n\u0131 temsil eden 3 filtre i\u00e7erir.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b83641c elementor-widget elementor-widget-heading\" data-id=\"b83641c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Ayr\u0131mc\u0131 (Discriminator) Olu\u015fturun\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d50af1b elementor-widget elementor-widget-text-editor\" data-id=\"d50af1b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\"><pre><span class=\"c1\"># Define Discriminator model<\/span>\n    <span class=\"k\">def<\/span> <span class=\"nf\">build_discriminator<\/span><span class=\"p\">(<\/span><span class=\"bp\">self<\/span><span class=\"p\">):<\/span>\n\n        <span class=\"n\">model<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Sequential<\/span><span class=\"p\">()<\/span>\n\n        <span class=\"c1\"># Input Layer<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">Conv2D<\/span><span class=\"p\">(<\/span><span class=\"n\">conv_filters<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_size<\/span><span class=\"o\">=<\/span><span class=\"n\">kernel<\/span><span class=\"p\">,<\/span> <span class=\"n\">input_shape<\/span><span class=\"o\">=<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">img_shape<\/span><span class=\"p\">,<\/span><span class=\"n\">activation<\/span> <span class=\"o\">=<\/span> <span class=\"s2\">\"relu\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">padding<\/span><span class=\"o\">=<\/span><span class=\"s2\">\"same\"<\/span><span class=\"p\">))<\/span>\n        \n        <span class=\"c1\"># Downsample the image as many times as needed<\/span>\n        <span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"n\">super_sampling_ratio<\/span><span class=\"p\">):<\/span>\n            \n            <span class=\"c1\"># Convolutional Layer<\/span>\n            <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">Conv2D<\/span><span class=\"p\">(<\/span><span class=\"n\">conv_filters<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_size<\/span><span class=\"o\">=<\/span><span class=\"n\">kernel<\/span><span class=\"p\">))<\/span>\n            <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">LeakyReLU<\/span><span class=\"p\">(<\/span><span class=\"n\">alpha<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.2<\/span><span class=\"p\">))<\/span>\n        \n            <span class=\"c1\"># Downsample the data (Half the resolution)<\/span>\n            <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">MaxPooling2D<\/span><span class=\"p\">(<\/span><span class=\"n\">pool_size<\/span><span class=\"o\">=<\/span><span class=\"p\">(<\/span><span class=\"mi\">2<\/span><span class=\"p\">,<\/span> <span class=\"mi\">2<\/span><span class=\"p\">)))<\/span>\n        \n        <span class=\"c1\"># Convolutional Layer<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">Conv2D<\/span><span class=\"p\">(<\/span><span class=\"n\">conv_filters<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_size<\/span><span class=\"o\">=<\/span><span class=\"n\">kernel<\/span><span class=\"p\">,<\/span> <span class=\"n\">strides<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">2<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">LeakyReLU<\/span><span class=\"p\">(<\/span><span class=\"n\">alpha<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.2<\/span><span class=\"p\">))<\/span>\n\n        <span class=\"c1\"># Convolutional Layer<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">Conv2D<\/span><span class=\"p\">(<\/span><span class=\"n\">conv_filters<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_size<\/span><span class=\"o\">=<\/span><span class=\"n\">kernel<\/span><span class=\"p\">,<\/span> <span class=\"n\">strides<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">2<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">LeakyReLU<\/span><span class=\"p\">(<\/span><span class=\"n\">alpha<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.2<\/span><span class=\"p\">))<\/span>\n        \n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">Flatten<\/span><span class=\"p\">())<\/span>\n        \n        <span class=\"c1\"># Output Layer<\/span>\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">Dense<\/span><span class=\"p\">(<\/span><span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"n\">activation<\/span><span class=\"o\">=<\/span><span class=\"s1\">'sigmoid'<\/span><span class=\"p\">))<\/span>\n\n        <span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">summary<\/span><span class=\"p\">()<\/span>\n\n        <span class=\"n\">img<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Input<\/span><span class=\"p\">(<\/span><span class=\"n\">shape<\/span><span class=\"o\">=<\/span><span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">img_shape<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">validity<\/span> <span class=\"o\">=<\/span> <span class=\"n\">model<\/span><span class=\"p\">(<\/span><span class=\"n\">img<\/span><span class=\"p\">)<\/span>\n\n        <span class=\"k\">return<\/span> <span class=\"n\">Model<\/span><span class=\"p\">(<\/span><span class=\"n\">img<\/span><span class=\"p\">,<\/span> <span class=\"n\">validity<\/span><span class=\"p\">)<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e3fd59b elementor-widget elementor-widget-text-editor\" data-id=\"e3fd59b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">DCGAN s\u0131n\u0131f\u0131na ekledi\u011fimiz \u00fc\u00e7\u00fcnc\u00fc y\u00f6ntem\u00a0<i>build_discriminator () &#8216;d\u00fcr<\/i>.\u00a0Bu y\u00f6ntem, s\u0131n\u0131f ilk kez ba\u015flat\u0131ld\u0131\u011f\u0131nda \u00e7a\u011fr\u0131l\u0131r.\u00a0Discriminator modelinin mimarisi burada tasarlanm\u0131\u015ft\u0131r.\u00a0Model \u00f6zeti, bu modelde ger\u00e7ekte neler oldu\u011fu konusunda size daha net bir fikir verecektir.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c173631 elementor-widget elementor-widget-html\" data-id=\"c173631\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<center><img decoding=\"async\" src=\"https:\/\/camo.githubusercontent.com\/ef872079c962337cfc0a960ec778815074861b8e04e3c325b17ce9dbccce7915\/68747470733a2f2f692e696d6775722e636f6d2f50693867544a522e6a7067\" alt=\"DLSS Example 2\"><\/center>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0e14c59 elementor-widget elementor-widget-text-editor\" data-id=\"0e14c59\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">Discriminator modelinin giri\u015fi, bir RGB g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fc temsil eden bir tens\u00f6rd\u00fcr.\u00a0Bu durumda, bir (256,256,3) resim.\u00a0Tens\u00f6r daha sonra 252 \u00d7 252, 126 \u00d7 126, 61 \u00d7 61 ve 29 \u00d7 29&#8217;a alt \u00f6rneklenir.\u00a0Bu 29 \u00d7 29 tens\u00f6r daha sonra d\u00fczle\u015ftirilir ve \u00e7\u0131kt\u0131 katman\u0131na aktar\u0131l\u0131r.<\/p><p align=\"left\">Son yo\u011fun katman, ay\u0131r\u0131c\u0131 modelin tahminini temsil eden tek bir skaler say\u0131 verir. Bu tahmin, giri\u015f g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fcn &#8220;ger\u00e7ek&#8221; olup olmad\u0131\u011f\u0131n\u0131 belirlemede modelin g\u00fcvenini temsil eder. 1 tahmini, modelin g\u00f6r\u00fcnt\u00fcn\u00fcn orijinal veri k\u00fcmesinden geldi\u011fini d\u00fc\u015f\u00fcnd\u00fc\u011f\u00fc anlam\u0131na gelir. O tahmini, modelin g\u00f6r\u00fcnt\u00fcn\u00fcn Generator modeli taraf\u0131ndan olu\u015fturuldu\u011funu d\u00fc\u015f\u00fcnd\u00fc\u011f\u00fc anlam\u0131na gelir.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8c2b13c elementor-widget elementor-widget-heading\" data-id=\"8c2b13c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">E\u011fitmek<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f43a46d elementor-widget elementor-widget-text-editor\" data-id=\"f43a46d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\"><pre><span class=\"c1\"># Train the Generative Adversarial Network<\/span>\n    <span class=\"k\">def<\/span> <span class=\"nf\">train<\/span><span class=\"p\">(<\/span><span class=\"bp\">self<\/span><span class=\"p\">,<\/span> <span class=\"n\">epochs<\/span><span class=\"p\">,<\/span> <span class=\"n\">batch_size<\/span><span class=\"p\">,<\/span> <span class=\"n\">save_interval<\/span><span class=\"p\">):<\/span>\n        \n        <span class=\"c1\"># Prevent script from crashing from bad user input<\/span>\n        <span class=\"k\">if<\/span><span class=\"p\">(<\/span><span class=\"n\">epochs<\/span> <span class=\"o\">&lt;=<\/span> <span class=\"mi\">0<\/span><span class=\"p\">):<\/span>\n            <span class=\"n\">epochs<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">1<\/span>\n        \n        <span class=\"k\">if<\/span><span class=\"p\">(<\/span><span class=\"n\">batch_size<\/span> <span class=\"o\">&lt;=<\/span> <span class=\"mi\">0<\/span><span class=\"p\">):<\/span>\n            <span class=\"n\">batch_size<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">1<\/span>\n\n        <span class=\"c1\"># Load the dataset<\/span>\n        <span class=\"n\">X_train<\/span><span class=\"p\">,<\/span> <span class=\"n\">Y_train<\/span> <span class=\"o\">=<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">load_data<\/span><span class=\"p\">()<\/span>\n        \n        <span class=\"c1\"># Normalizing data to be between 0 and 1<\/span>\n        <span class=\"n\">X_train<\/span> <span class=\"o\">=<\/span> <span class=\"n\">X_train<\/span> <span class=\"o\">\/<\/span> <span class=\"mi\">255<\/span>\n        <span class=\"n\">Y_train<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Y_train<\/span> <span class=\"o\">\/<\/span> <span class=\"mi\">255<\/span>\n\n        <span class=\"c1\"># Adversarial ground truths<\/span>\n        <span class=\"n\">valid<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">ones<\/span><span class=\"p\">((<\/span><span class=\"n\">batch_size<\/span><span class=\"p\">,<\/span> <span class=\"mi\">1<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">fake<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">zeros<\/span><span class=\"p\">((<\/span><span class=\"n\">batch_size<\/span><span class=\"p\">,<\/span> <span class=\"mi\">1<\/span><span class=\"p\">))<\/span>\n        \n        <span class=\"c1\"># Placeholder arrays for Loss function values<\/span>\n        <span class=\"n\">g_loss_epochs<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">zeros<\/span><span class=\"p\">((<\/span><span class=\"n\">epochs<\/span><span class=\"p\">,<\/span> <span class=\"mi\">1<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">d_loss_epochs<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">zeros<\/span><span class=\"p\">((<\/span><span class=\"n\">epochs<\/span><span class=\"p\">,<\/span> <span class=\"mi\">1<\/span><span class=\"p\">))<\/span>\n        \n        <span class=\"c1\"># Training the GAN<\/span>\n        <span class=\"k\">for<\/span> <span class=\"n\">epoch<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"n\">epochs<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">):<\/span>\n            \n            <span class=\"c1\"># Initialize indexes for training data<\/span>\n            <span class=\"n\">start<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">0<\/span>\n            <span class=\"n\">end<\/span> <span class=\"o\">=<\/span> <span class=\"n\">start<\/span> <span class=\"o\">+<\/span> <span class=\"n\">batch_size<\/span>\n            \n            <span class=\"c1\"># Array to sum up all loss function values<\/span>\n            <span class=\"n\">discriminator_loss_real<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n            <span class=\"n\">discriminator_loss_fake<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n            <span class=\"n\">generator_loss<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n            \n            <span class=\"c1\"># Iterate through dataset training one batch at a time<\/span>\n            <span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"nb\">int<\/span><span class=\"p\">(<\/span><span class=\"nb\">len<\/span><span class=\"p\">(<\/span><span class=\"n\">X_train<\/span><span class=\"p\">)<\/span><span class=\"o\">\/<\/span><span class=\"n\">batch_size<\/span><span class=\"p\">)):<\/span>\n                \n                <span class=\"c1\"># Get batch of images<\/span>\n                <span class=\"n\">imgs_output<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Y_train<\/span><span class=\"p\">[<\/span><span class=\"n\">start<\/span><span class=\"p\">:<\/span><span class=\"n\">end<\/span><span class=\"p\">]<\/span>\n                <span class=\"n\">imgs_input<\/span> <span class=\"o\">=<\/span> <span class=\"n\">X_train<\/span><span class=\"p\">[<\/span><span class=\"n\">start<\/span><span class=\"p\">:<\/span><span class=\"n\">end<\/span><span class=\"p\">]<\/span>\n\n                <span class=\"c1\"># Train Discriminator<\/span>\n\n                <span class=\"c1\"># Make predictions on current batch using generator<\/span>\n                <span class=\"n\">gen_imgs<\/span> <span class=\"o\">=<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">generator<\/span><span class=\"o\">.<\/span><span class=\"n\">predict<\/span><span class=\"p\">(<\/span><span class=\"n\">imgs_input<\/span><span class=\"p\">)<\/span>\n\n                <span class=\"c1\"># Train the discriminator (real classified as ones and generated as zero)<\/span>\n                <span class=\"n\">d_loss_real<\/span> <span class=\"o\">=<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">discriminator<\/span><span class=\"o\">.<\/span><span class=\"n\">train_on_batch<\/span><span class=\"p\">(<\/span><span class=\"n\">imgs_output<\/span><span class=\"p\">,<\/span> <span class=\"n\">valid<\/span><span class=\"p\">)<\/span>\n                <span class=\"n\">d_loss_fake<\/span> <span class=\"o\">=<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">discriminator<\/span><span class=\"o\">.<\/span><span class=\"n\">train_on_batch<\/span><span class=\"p\">(<\/span><span class=\"n\">gen_imgs<\/span><span class=\"p\">,<\/span> <span class=\"n\">fake<\/span><span class=\"p\">)<\/span>\n                <span class=\"n\">d_loss<\/span> <span class=\"o\">=<\/span> <span class=\"mf\">0.5<\/span> <span class=\"o\">*<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">add<\/span><span class=\"p\">(<\/span><span class=\"n\">d_loss_real<\/span><span class=\"p\">,<\/span> <span class=\"n\">d_loss_fake<\/span><span class=\"p\">)<\/span>\n\n                <span class=\"c1\">#  Train Generator<\/span>\n\n                <span class=\"c1\"># Train the generator (wants discriminator to mistake images as real)<\/span>\n                <span class=\"n\">g_loss<\/span> <span class=\"o\">=<\/span> <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">combined<\/span><span class=\"o\">.<\/span><span class=\"n\">train_on_batch<\/span><span class=\"p\">(<\/span><span class=\"n\">imgs_input<\/span><span class=\"p\">,<\/span> <span class=\"n\">valid<\/span><span class=\"p\">)<\/span>\n                \n                <span class=\"c1\"># Add loss for current batch to sum over entire epoch<\/span>\n                <span class=\"n\">discriminator_loss_real<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">d_loss<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">])<\/span>\n                <span class=\"n\">discriminator_loss_fake<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">d_loss<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">])<\/span>\n                <span class=\"n\">generator_loss<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">g_loss<\/span><span class=\"p\">)<\/span>\n                \n                <span class=\"c1\"># Increment image indexes<\/span>\n                <span class=\"n\">start<\/span> <span class=\"o\">=<\/span> <span class=\"n\">start<\/span> <span class=\"o\">+<\/span> <span class=\"n\">batch_size<\/span>\n                <span class=\"n\">end<\/span> <span class=\"o\">=<\/span> <span class=\"n\">end<\/span> <span class=\"o\">+<\/span> <span class=\"n\">batch_size<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2a53c15 elementor-widget elementor-widget-text-editor\" data-id=\"2a53c15\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">DCGAN s\u0131n\u0131f\u0131na ekledi\u011fimiz d\u00f6rd\u00fcnc\u00fc y\u00f6ntem\u00a0<i>train ()<\/i>.\u00a0Bu y\u00f6ntem, a\u011f\u0131 toplu i\u015f boyutu taraf\u0131ndan belirtilen art\u0131\u015flarla belirtilen say\u0131da d\u00f6nem i\u00e7in e\u011fitecektir.\u00a0E\u011fitim tamamland\u0131\u011f\u0131nda, y\u00f6ntem her d\u00f6nem boyunca her iki modelin kay\u0131p de\u011ferlerini temsil eden iki dizi d\u00f6nd\u00fcrecektir.\u00a0Kay\u0131p de\u011ferleri Matplotlib kullan\u0131larak \u00e7izilebilir.<\/p><p align=\"left\">Kay\u0131p de\u011ferlerini takip etmeli ve \u00e7\u00f6kmeye ba\u015flarsa a\u011f\u0131 e\u011fitmeyi b\u0131rakmal\u0131s\u0131n\u0131z.\u00a0Modellerden biri 0 kayb\u0131na yakla\u015f\u0131rsa a\u011f \u00e7\u00f6ker.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-66b4bd5 elementor-widget elementor-widget-html\" data-id=\"66b4bd5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<center><img decoding=\"async\" src=\"https:\/\/i.imgur.com\/doKXf22.png\" alt=\"DLSS Example 2\"><\/center>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3b4938f elementor-widget elementor-widget-text-editor\" data-id=\"3b4938f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">Generator 0 kayb\u0131na yakla\u015f\u0131rsa, bu, Generator&#8217;\u00fcn her seferinde ay\u0131r\u0131c\u0131y\u0131 kand\u0131racak bir g\u00f6r\u00fcnt\u00fcy\u00fc nas\u0131l olu\u015fturaca\u011f\u0131n\u0131 buldu\u011fu anlam\u0131na gelir. Bu genellikle Generator&#8217;\u00fcn <a href=\"https:\/\/developers.google.com\/machine-learning\/gan\/problems#mode-collapse\" target=\"_blank\" rel=\"noopener\"><u>mod \u00e7\u00f6kmesi<\/u><\/a> olarak da bilinen tek bir g\u00f6r\u00fcnt\u00fc t\u00fcr\u00fc \u00fcretebilmesine\u00a0neden olur.<\/p><p align=\"left\">Discriminator, 0 kayb\u0131na yakla\u015f\u0131rsa, bu, Discriminator&#8217;\u0131n e\u011fitim verileri ile olu\u015fturulan g\u00f6r\u00fcnt\u00fcleri \u00e7ok do\u011fru bir \u015fekilde nas\u0131l ay\u0131rt edece\u011fini buldu\u011fu anlam\u0131na gelir. Bu, Generator&#8217;\u00fcn;\u00a0<a href=\"https:\/\/en.wikipedia.org\/wiki\/Vanishing_gradient_problem\" target=\"_blank\" rel=\"noopener\"><u>kaybolan gradyan problemi<\/u><\/a> olarak da bilinen,\u00a0ay\u0131r\u0131c\u0131dan\u00a0\u00f6\u011frenmeye devam edememesine neden olacakt\u0131r.<\/p><p align=\"left\">A\u011f\u0131m\u0131z \u00e7\u00f6kt\u00fc\u011f\u00fcnde ilerlememizi kaybetmemek i\u00e7in, modeli her birka\u00e7 d\u00f6nemde bir kaydedece\u011fiz.\u00a0Kullan\u0131c\u0131 tan\u0131ml\u0131 parametre olan interval, modelin ne s\u0131kl\u0131kla kaydedilece\u011fini belirleyecektir.\u00a0Ge\u00e7erli d\u00f6nem tan\u0131mlanan aral\u0131\u011fa her\u00a0<i>geldi\u011finde<\/i>,\u00a0<i>save_imgs ()<\/i>\u00a0\u00e7a\u011fr\u0131l\u0131r.\u00a0Y\u00f6ntem, modelin o d\u00f6nemde ne kadar iyi oldu\u011funa dair bir anl\u0131k g\u00f6r\u00fcnt\u00fc elde etmek i\u00e7in tahmin edilen baz\u0131 \u00f6rneklerin bir g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fc kaydedecektir.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-13ed10e elementor-widget elementor-widget-heading\" data-id=\"13ed10e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">G\u00f6r\u00fcnt\u00fcleri Kaydet<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1d532c3 elementor-widget elementor-widget-text-editor\" data-id=\"1d532c3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\"><pre>  <span class=\"c1\"># Save the model and generate prediction samples for a given epoch<\/span>\n    <span class=\"k\">def<\/span> <span class=\"nf\">save_imgs<\/span><span class=\"p\">(<\/span><span class=\"bp\">self<\/span><span class=\"p\">,<\/span> <span class=\"n\">epoch<\/span><span class=\"p\">,<\/span> <span class=\"n\">gen_imgs<\/span><span class=\"p\">,<\/span> <span class=\"n\">interpolated<\/span><span class=\"p\">):<\/span>\n        \n        <span class=\"c1\"># Define number of columns and rows<\/span>\n        <span class=\"n\">r<\/span><span class=\"p\">,<\/span> <span class=\"n\">c<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">4<\/span><span class=\"p\">,<\/span> <span class=\"mi\">4<\/span>\n        \n        <span class=\"c1\"># Placeholder array for MatPlotLib Figure Subplots<\/span>\n        <span class=\"n\">subplots<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n        \n        <span class=\"c1\"># Create figure with title<\/span>\n        <span class=\"n\">fig<\/span> <span class=\"o\">=<\/span> <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">figure<\/span><span class=\"p\">(<\/span><span class=\"n\">figsize<\/span><span class=\"o\">=<\/span> <span class=\"p\">(<\/span><span class=\"mi\">40<\/span><span class=\"p\">,<\/span> <span class=\"mi\">40<\/span><span class=\"p\">))<\/span>\n        <span class=\"n\">fig<\/span><span class=\"o\">.<\/span><span class=\"n\">suptitle<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Epoch: \"<\/span> <span class=\"o\">+<\/span> <span class=\"nb\">str<\/span><span class=\"p\">(<\/span><span class=\"n\">epoch<\/span><span class=\"p\">),<\/span> <span class=\"n\">fontsize<\/span><span class=\"o\">=<\/span><span class=\"mi\">65<\/span><span class=\"p\">)<\/span>\n        \n        <span class=\"c1\"># Initialize counters needed to track indexes across multiple arrays<\/span>\n        <span class=\"n\">img_count<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">0<\/span><span class=\"p\">;<\/span>\n        <span class=\"n\">index_count<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">0<\/span><span class=\"p\">;<\/span>\n        <span class=\"n\">x_count<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">0<\/span><span class=\"p\">;<\/span>\n        \n        <span class=\"c1\"># Loop through columns and rows of the figure<\/span>\n        <span class=\"k\">for<\/span> <span class=\"n\">i<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"n\">c<\/span><span class=\"o\">+<\/span><span class=\"mi\">1<\/span><span class=\"p\">):<\/span>\n            <span class=\"k\">for<\/span> <span class=\"n\">j<\/span> <span class=\"ow\">in<\/span> <span class=\"nb\">range<\/span><span class=\"p\">(<\/span><span class=\"mi\">1<\/span><span class=\"p\">,<\/span> <span class=\"n\">r<\/span><span class=\"o\">+<\/span><span class=\"mi\">1<\/span><span class=\"p\">):<\/span>\n                <span class=\"c1\"># If row is even, plot the predictions<\/span>\n                <span class=\"k\">if<\/span><span class=\"p\">(<\/span><span class=\"n\">j<\/span> <span class=\"o\">%<\/span> <span class=\"mi\">2<\/span> <span class=\"o\">==<\/span> <span class=\"mi\">0<\/span><span class=\"p\">):<\/span>\n                    <span class=\"n\">img<\/span> <span class=\"o\">=<\/span> <span class=\"n\">gen_imgs<\/span><span class=\"p\">[<\/span><span class=\"n\">index_count<\/span><span class=\"p\">]<\/span>\n                    <span class=\"n\">index_count<\/span> <span class=\"o\">=<\/span> <span class=\"n\">index_count<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span>\n                <span class=\"c1\"># If row is odd, plot the interpolated images<\/span>\n                <span class=\"k\">else<\/span><span class=\"p\">:<\/span>\n                    <span class=\"n\">img<\/span> <span class=\"o\">=<\/span> <span class=\"n\">interpolated<\/span><span class=\"p\">[<\/span><span class=\"n\">x_count<\/span><span class=\"p\">]<\/span>\n                    <span class=\"n\">x_count<\/span> <span class=\"o\">=<\/span> <span class=\"n\">x_count<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span>\n                <span class=\"c1\"># Add image to figure, add subplot to array<\/span>\n                <span class=\"n\">subplots<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">fig<\/span><span class=\"o\">.<\/span><span class=\"n\">add_subplot<\/span><span class=\"p\">(<\/span><span class=\"n\">r<\/span><span class=\"p\">,<\/span> <span class=\"n\">c<\/span><span class=\"p\">,<\/span> <span class=\"n\">img_count<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><span class=\"p\">))<\/span>\n                <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">imshow<\/span><span class=\"p\">(<\/span><span class=\"n\">img<\/span><span class=\"p\">)<\/span>\n                <span class=\"n\">img_count<\/span> <span class=\"o\">=<\/span> <span class=\"n\">img_count<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span>\n        \n        <span class=\"c1\"># Add title to columns of figure<\/span>\n        <span class=\"n\">subplots<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">set_title<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Interpolated\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">fontsize<\/span><span class=\"o\">=<\/span><span class=\"mi\">45<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">subplots<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">set_title<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Predicted\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">fontsize<\/span><span class=\"o\">=<\/span><span class=\"mi\">45<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">subplots<\/span><span class=\"p\">[<\/span><span class=\"mi\">2<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">set_title<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Interpolated\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">fontsize<\/span><span class=\"o\">=<\/span><span class=\"mi\">45<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">subplots<\/span><span class=\"p\">[<\/span><span class=\"mi\">3<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">set_title<\/span><span class=\"p\">(<\/span><span class=\"s2\">\"Predicted\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">fontsize<\/span><span class=\"o\">=<\/span><span class=\"mi\">45<\/span><span class=\"p\">)<\/span>\n                \n        <span class=\"c1\"># Save figure to .png image in specified folder<\/span>\n        <span class=\"n\">fig<\/span><span class=\"o\">.<\/span><span class=\"n\">savefig<\/span><span class=\"p\">(<\/span><span class=\"n\">model_path<\/span> <span class=\"o\">+<\/span> <span class=\"s2\">\"<\/span><span class=\"se\">\\\\<\/span><span class=\"s2\">epoch_<\/span><span class=\"si\">%d<\/span><span class=\"s2\">.png\"<\/span> <span class=\"o\">%<\/span> <span class=\"n\">epoch<\/span><span class=\"p\">)<\/span>\n        <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">close<\/span><span class=\"p\">()<\/span>\n        \n        <span class=\"c1\"># save model to .h5 file in specified folder<\/span>\n        <span class=\"bp\">self<\/span><span class=\"o\">.<\/span><span class=\"n\">generator<\/span><span class=\"o\">.<\/span><span class=\"n\">save<\/span><span class=\"p\">(<\/span><span class=\"n\">model_path<\/span> <span class=\"o\">+<\/span> <span class=\"s2\">\"<\/span><span class=\"se\">\\\\<\/span><span class=\"s2\">generator\"<\/span> <span class=\"o\">+<\/span> <span class=\"nb\">str<\/span><span class=\"p\">(<\/span><span class=\"n\">epoch<\/span><span class=\"p\">)<\/span> <span class=\"o\">+<\/span> <span class=\"s2\">\".h5\"<\/span><span class=\"p\">)<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d36ae73 elementor-widget elementor-widget-text-editor\" data-id=\"d36ae73\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\"><span>DCGAN s\u0131n\u0131f\u0131na ekledi\u011fimiz be\u015finci ve son y\u00f6ntem,\u00a0<\/span><i><span>save_imgs () y\u00f6ntemidir.\u00a0<\/span><\/i><span>Bu y\u00f6ntem, modeli mevcut d\u00f6nemde kaydedecek ve enterpolasyonlu emsallerine k\u0131yasla 8 s\u00fcper \u00f6rneklenmi\u015f g\u00f6r\u00fcnt\u00fcy\u00fc \u00e7izecektir.\u00a0Olu\u015fturulan \u00f6rnek, DLSS modelinin kalitesini En Yak\u0131n Kom\u015fu Enterpolasyonu ile kar\u015f\u0131la\u015ft\u0131rman\u0131za olanak tan\u0131r.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fd4cef2 elementor-widget elementor-widget-html\" data-id=\"fd4cef2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<center><img decoding=\"async\" src=\"https:\/\/camo.githubusercontent.com\/0119db56b2840cd4968673dda641694a2eaae35c3ac414386659e167fb00148d\/68747470733a2f2f692e696d6775722e636f6d2f77436c6945414d2e706e67\" alt=\"DLSS Example 2\"><\/center>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-99cabdb elementor-widget elementor-widget-text-editor\" data-id=\"99cabdb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-99cabdb elementor-widget elementor-widget-text-editor\" data-id=\"99cabdb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\"><div class=\"elementor-widget-container\"><div class=\"elementor-text-editor elementor-clearfix\"><p align=\"left\">Bu y\u00f6ntem \u015fu anda her 5 \u00e7a\u011fda bir kaydedecek \u015fekilde yap\u0131land\u0131r\u0131lm\u0131\u015ft\u0131r.\u00a0Bu,\u00a0<i>aral\u0131k<\/i> parametresi ile ayarlanabilir\u00a0.\u00a0Modelinizi s\u0131k s\u0131k kaydetmek, e\u011fitim s\u00fcrecinde a\u011f\u0131n\u0131z\u0131n kaydetti\u011fi ilerlemeyi izlemenin iyi bir yoludur.<\/p><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-482152a elementor-widget elementor-widget-heading\" data-id=\"482152a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">DCGAN S\u0131n\u0131f\u0131n\u0131 Ba\u015flatma\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9ae89c4 elementor-widget elementor-widget-text-editor\" data-id=\"9ae89c4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9ae89c4 elementor-widget elementor-widget-text-editor\" data-id=\"9ae89c4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\"><div class=\"elementor-widget-container\"><div class=\"elementor-text-editor elementor-clearfix\"><p align=\"left\">Art\u0131k DCGAN s\u0131n\u0131f\u0131n\u0131 olu\u015fturmay\u0131 bitirdik ve Generative Adversarial Network&#8217;\u00fcm\u00fcz\u00fc e\u011fitmeye haz\u0131r\u0131z. \u00d6ncelikle, s\u0131n\u0131f\u0131n bir \u00f6rne\u011fini olu\u015fturmal\u0131 ve onu bir de\u011fi\u015fkene atamal\u0131y\u0131z.<\/p><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dcf0793 elementor-widget elementor-widget-text-editor\" data-id=\"dcf0793\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Girdi[4]<\/p><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><pre><span class=\"n\">dcgan<\/span> <span class=\"o\">=<\/span> <span class=\"n\">DCGAN<\/span><span class=\"p\">()<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-141964d elementor-widget elementor-widget-text-editor\" data-id=\"141964d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">Bu, Generator ve Discriminator modellerini ba\u015flatacak ve bunlar\u0131n \u00f6zetlerini yazd\u0131racakt\u0131r.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ef74c1 elementor-widget elementor-widget-heading\" data-id=\"6ef74c1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">GAN E\u011fitimi\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bfdb7ef elementor-widget elementor-widget-text-editor\" data-id=\"bfdb7ef\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">Art\u0131k DCGAN s\u0131n\u0131f nesnemize sahip oldu\u011fumuza g\u00f6re,\u00a0e\u011fitime ba\u015flamak\u00a0i\u00e7in\u00a0<i>train ()<\/i> y\u00f6ntemini\u00a0\u00e7a\u011f\u0131rmam\u0131z gerekiyor.\u00a0Bu komut dosyas\u0131yla, genellikle e\u011fitim i\u00e7in \u00e7ok say\u0131da d\u00f6nem se\u00e7meli ve s\u00fcre\u00e7 boyunca kay\u0131p de\u011ferlerini izlemelisiniz.\u00a0A\u011f \u00e7\u00f6kmeye ba\u015flarsa e\u011fitimi erken durdurun ve hangi modelin en iyi performans g\u00f6steren model oldu\u011funu bulmak i\u00e7in olu\u015fturulan \u00f6rnekleri kontrol edin.<\/p><p align=\"left\">Train () y\u00f6ntemi, e\u011fitim boyunca iki modelin kay\u0131p de\u011ferlerini i\u00e7eren iki dizi d\u00f6nd\u00fcr\u00fcr.\u00a0Bu de\u011ferleri\u00a0<i>g_loss<\/i>\u00a0ve\u00a0<i>d_loss&#8217;a <\/i>atayaca\u011f\u0131z\u00a0ve grafi\u011fini\u00a0\u00e7izece\u011fiz.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b66dbc5 elementor-widget elementor-widget-text-editor\" data-id=\"b66dbc5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Girdi [5]<\/p><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><pre><span class=\"n\">g_loss<\/span><span class=\"p\">,<\/span> <span class=\"n\">d_loss<\/span> <span class=\"o\">=<\/span> <span class=\"n\">dcgan<\/span><span class=\"o\">.<\/span><span class=\"n\">train<\/span><span class=\"p\">(<\/span><span class=\"n\">epochs<\/span><span class=\"o\">=<\/span><span class=\"n\">epoch<\/span><span class=\"p\">,<\/span> <span class=\"n\">batch_size<\/span><span class=\"o\">=<\/span><span class=\"n\">batch<\/span><span class=\"p\">,<\/span> <span class=\"n\">save_interval<\/span><span class=\"o\">=<\/span><span class=\"n\">interval<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div><\/div><\/div><\/div><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt\">\u00a0<\/div><div class=\"output_subarea output_stream output_stdout output_text\"><pre>1 [D loss: 0.640689, acc.: 59.37%] [G loss: 0.967596]\n2 [D loss: 0.575859, acc.: 73.34%] [G loss: 1.787223]\n3 [D loss: 0.656025, acc.: 61.31%] [G loss: 1.042790]\n4 [D loss: 0.656616, acc.: 60.19%] [G loss: 0.998186]\n5 [D loss: 0.674997, acc.: 56.04%] [G loss: 0.893507]\n<\/pre><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6eee757 elementor-widget elementor-widget-heading\" data-id=\"6eee757\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Plot Kayb\u0131\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-88b0562 elementor-widget elementor-widget-text-editor\" data-id=\"88b0562\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Girdi [6]<\/p><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><pre><span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">plot<\/span><span class=\"p\">(<\/span><span class=\"n\">g_loss<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">plot<\/span><span class=\"p\">(<\/span><span class=\"n\">d_loss<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">title<\/span><span class=\"p\">(<\/span><span class=\"s1\">'GAN Loss'<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">ylabel<\/span><span class=\"p\">(<\/span><span class=\"s1\">'Loss'<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">xlabel<\/span><span class=\"p\">(<\/span><span class=\"s1\">'Epoch'<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">legend<\/span><span class=\"p\">([<\/span><span class=\"s1\">'Generator'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Discriminator'<\/span><span class=\"p\">],<\/span> <span class=\"n\">loc<\/span><span class=\"o\">=<\/span><span class=\"s1\">'upper left'<\/span><span class=\"p\">)<\/span>\n<span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">show<\/span><span class=\"p\">()<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cf610f3 elementor-widget elementor-widget-html\" data-id=\"cf610f3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<center><img decoding=\"async\" src=\"https:\/\/i.imgur.com\/fgnFEHq.png\" alt=\"DLSS Example 2\"><\/center>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-afc5004 elementor-widget elementor-widget-heading\" data-id=\"afc5004\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Sonu\u00e7lar\u0131 Analiz Et\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3f0b56d elementor-widget elementor-widget-text-editor\" data-id=\"3f0b56d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">Memnun oldu\u011funuz bir modeli e\u011fittikten sonra, modelinizi \u00e7e\u015fitli g\u00f6r\u00fcnt\u00fcler \u00fczerinde test etme zaman\u0131 gelmi\u015ftir.\u00a0Bunu\u00a0sa\u011flanan\u00a0bu\u00a0<a href=\"https:\/\/nbviewer.jupyter.org\/github\/vee-upatising\/DLSS\/blob\/master\/Load%20Model%20and%20Analyze%20Results.ipynb\" target=\"_blank\" rel=\"noopener\"><u>komut dosyas\u0131n\u0131<\/u><\/a> kullanarak yapabilirsiniz. Bunu kullanmak i\u00e7in \u00f6nce birka\u00e7 parametre tan\u0131mlamal\u0131s\u0131n\u0131z. <em>I<\/em><i>nput_dimensions<\/i>\u00a0ve\u00a0<i>ouput_dimesions&#8217;\u0131<\/i> e\u011fitim komut dosyas\u0131nda yapt\u0131\u011f\u0131n\u0131z ayn\u0131 de\u011ferlere\u00a0ayarlay\u0131n.\u00a0<i>Model_path<\/i> , kullanmak istedi\u011finiz H5 modelinin yoluna ayarlay\u0131n\u00a0.\u00a0H5 modelleri\u00a0,\u00a0<i>save_imgs ()<\/i>\u00a0y\u00f6ntemi\u00a0s\u0131ras\u0131nda e\u011fitim komut dosyas\u0131nda\u00a0<i>model_path<\/i>\u00a0ile\u00a0belirtilen klas\u00f6re kaydedilir\u00a0.\u00a0<i>Dataset_path<\/i> parametresini, modelinizi test etmek istedi\u011finiz g\u00f6r\u00fcnt\u00fcleri i\u00e7eren klas\u00f6re\u00a0ayarlay\u0131n.\u00a0G\u00f6r\u00fcnt\u00fcler herhangi bir PNG i\u00e7eriyorsa,\u00a0<i>png<\/i><i><\/i><i><\/i><i><\/i>alfa katmanlar\u0131n\u0131 g\u00f6r\u00fcnt\u00fclerden kald\u0131rmak i\u00e7in boole i\u015faretini true olarak ayarlay\u0131n.\u00a0Sonu\u00e7lar\u0131n\u0131zdan animasyonlu GIF&#8217;ler olu\u015fturmak i\u00e7in videonun karelerini\u00a0<i>dataset_path<\/i>\u00a0klas\u00f6r\u00fcne yerle\u015ftirin.\u00a0Son olarak,\u00a0<i>save_path<\/i> parametresini model \u00e7\u0131kar\u0131m\u0131n\u0131n sonu\u00e7lar\u0131n\u0131n kaydedilmesini istedi\u011finiz klas\u00f6re\u00a0ayarlay\u0131n.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-189e039 elementor-widget elementor-widget-text-editor\" data-id=\"189e039\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Girdi [19]<\/p><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><pre><span class=\"n\">model<\/span> <span class=\"o\">=<\/span> <span class=\"n\">load_model<\/span><span class=\"p\">(<\/span><span class=\"n\">model_path<\/span><span class=\"p\">)<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-058c931 elementor-widget elementor-widget-heading\" data-id=\"058c931\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">G\u00f6r\u00fcnt\u00fcleri ve S\u00fcper \u00d6rnekleri Y\u00fckleyin<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-712bcaa elementor-widget elementor-widget-text-editor\" data-id=\"712bcaa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Girdi [29]<\/p><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-ipython3\"><pre><span class=\"n\">paths<\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n<span class=\"n\">count<\/span> <span class=\"o\">=<\/span> <span class=\"mi\">0<\/span>\n\n<span class=\"k\">for<\/span> <span class=\"n\">r<\/span><span class=\"p\">,<\/span> <span class=\"n\">d<\/span><span class=\"p\">,<\/span> <span class=\"n\">f<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">os<\/span><span class=\"o\">.<\/span><span class=\"n\">walk<\/span><span class=\"p\">(<\/span><span class=\"n\">dataset_path<\/span><span class=\"p\">):<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\">file<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">f<\/span><span class=\"p\">:<\/span>\n        <span class=\"k\">if<\/span> <span class=\"s1\">'.png'<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">file<\/span> <span class=\"ow\">or<\/span> <span class=\"s1\">'jpg'<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">file<\/span><span class=\"p\">:<\/span>\n            <span class=\"n\">paths<\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\">os<\/span><span class=\"o\">.<\/span><span class=\"n\">path<\/span><span class=\"o\">.<\/span><span class=\"n\">join<\/span><span class=\"p\">(<\/span><span class=\"n\">r<\/span><span class=\"p\">,<\/span> <span class=\"n\">file<\/span><span class=\"p\">))<\/span>\n\n<span class=\"k\">for<\/span> <span class=\"n\">path<\/span> <span class=\"ow\">in<\/span> <span class=\"n\">paths<\/span><span class=\"p\">:<\/span>\n    \n    <span class=\"c1\"># Select image<\/span>\n    <span class=\"n\">img<\/span> <span class=\"o\">=<\/span> <span class=\"n\">Image<\/span><span class=\"o\">.<\/span><span class=\"n\">open<\/span><span class=\"p\">(<\/span><span class=\"n\">path<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"c1\">#create plot<\/span>\n    <span class=\"n\">f<\/span><span class=\"p\">,<\/span> <span class=\"n\">axarr<\/span> <span class=\"o\">=<\/span> <span class=\"n\">plt<\/span><span class=\"o\">.<\/span><span class=\"n\">subplots<\/span><span class=\"p\">(<\/span><span class=\"mi\">1<\/span><span class=\"p\">,<\/span><span class=\"mi\">3<\/span><span class=\"p\">,<\/span><span class=\"n\">figsize<\/span><span class=\"o\">=<\/span><span class=\"p\">(<\/span><span class=\"mi\">15<\/span><span class=\"p\">,<\/span><span class=\"mi\">15<\/span><span class=\"p\">),<\/span><span class=\"n\">gridspec_kw<\/span><span class=\"o\">=<\/span><span class=\"p\">{<\/span><span class=\"s1\">'width_ratios'<\/span><span class=\"p\">:<\/span> <span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">,<\/span><span class=\"n\">super_sampling_ratio<\/span><span class=\"p\">,<\/span><span class=\"n\">super_sampling_ratio<\/span><span class=\"p\">]})<\/span>\n    <span class=\"n\">axarr<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">set_xlabel<\/span><span class=\"p\">(<\/span><span class=\"s1\">'Original Image ('<\/span> <span class=\"o\">+<\/span> <span class=\"nb\">str<\/span><span class=\"p\">(<\/span><span class=\"n\">input_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">])<\/span> <span class=\"o\">+<\/span> <span class=\"s1\">'x'<\/span> <span class=\"o\">+<\/span> <span class=\"nb\">str<\/span><span class=\"p\">(<\/span><span class=\"n\">input_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">])<\/span> <span class=\"o\">+<\/span> <span class=\"s1\">')'<\/span><span class=\"p\">,<\/span> <span class=\"n\">fontsize<\/span><span class=\"o\">=<\/span><span class=\"mi\">10<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">axarr<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">set_xlabel<\/span><span class=\"p\">(<\/span><span class=\"s1\">'Interpolated Image ('<\/span> <span class=\"o\">+<\/span> <span class=\"nb\">str<\/span><span class=\"p\">(<\/span><span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">])<\/span> <span class=\"o\">+<\/span> <span class=\"s1\">'x'<\/span> <span class=\"o\">+<\/span> <span class=\"nb\">str<\/span><span class=\"p\">(<\/span><span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">])<\/span> <span class=\"o\">+<\/span> <span class=\"s1\">')'<\/span><span class=\"p\">,<\/span> <span class=\"n\">fontsize<\/span><span class=\"o\">=<\/span><span class=\"mi\">10<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">axarr<\/span><span class=\"p\">[<\/span><span class=\"mi\">2<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">set_xlabel<\/span><span class=\"p\">(<\/span><span class=\"s1\">'Super Sampled Image ('<\/span> <span class=\"o\">+<\/span> <span class=\"nb\">str<\/span><span class=\"p\">(<\/span><span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">])<\/span> <span class=\"o\">+<\/span> <span class=\"s1\">'x'<\/span> <span class=\"o\">+<\/span> <span class=\"nb\">str<\/span><span class=\"p\">(<\/span><span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">])<\/span> <span class=\"o\">+<\/span> <span class=\"s1\">')'<\/span><span class=\"p\">,<\/span> <span class=\"n\">fontsize<\/span><span class=\"o\">=<\/span><span class=\"mi\">10<\/span><span class=\"p\">)<\/span>\n\n    <span class=\"c1\">#original image<\/span>\n    <span class=\"n\">x<\/span> <span class=\"o\">=<\/span> <span class=\"n\">img<\/span><span class=\"o\">.<\/span><span class=\"n\">resize<\/span><span class=\"p\">((<\/span><span class=\"n\">input_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">],<\/span><span class=\"n\">input_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">]))<\/span>\n    \n    <span class=\"c1\">#interpolated (resized) image<\/span>\n    <span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">x<\/span><span class=\"o\">.<\/span><span class=\"n\">resize<\/span><span class=\"p\">((<\/span><span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">],<\/span><span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">]))<\/span>\n    \n    \n    <span class=\"n\">x<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">array<\/span><span class=\"p\">(<\/span><span class=\"n\">x<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">array<\/span><span class=\"p\">(<\/span><span class=\"n\">y<\/span><span class=\"p\">)<\/span>\n    \n    <span class=\"c1\"># Remove alpha layer if imgaes are PNG<\/span>\n    <span class=\"k\">if<\/span><span class=\"p\">(<\/span><span class=\"n\">png<\/span><span class=\"p\">):<\/span>\n        <span class=\"n\">x<\/span> <span class=\"o\">=<\/span> <span class=\"n\">x<\/span><span class=\"p\">[<\/span><span class=\"o\">...<\/span><span class=\"p\">,:<\/span><span class=\"mi\">3<\/span><span class=\"p\">]<\/span>\n        <span class=\"n\">y<\/span> <span class=\"o\">=<\/span> <span class=\"n\">y<\/span><span class=\"p\">[<\/span><span class=\"o\">...<\/span><span class=\"p\">,:<\/span><span class=\"mi\">3<\/span><span class=\"p\">]<\/span>\n    \n    <span class=\"c1\">#plotting first two images<\/span>\n    <span class=\"n\">axarr<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">imshow<\/span><span class=\"p\">(<\/span><span class=\"n\">x<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">axarr<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">imshow<\/span><span class=\"p\">(<\/span><span class=\"n\">y<\/span><span class=\"p\">)<\/span>\n    \n    <span class=\"c1\">#plotting super sampled image<\/span>\n    <span class=\"n\">x<\/span> <span class=\"o\">=<\/span> <span class=\"n\">x<\/span><span class=\"o\">.<\/span><span class=\"n\">reshape<\/span><span class=\"p\">(<\/span><span class=\"mi\">1<\/span><span class=\"p\">,<\/span><span class=\"n\">input_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">],<\/span><span class=\"n\">input_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">],<\/span><span class=\"n\">input_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">2<\/span><span class=\"p\">])<\/span><span class=\"o\">\/<\/span><span class=\"mi\">255<\/span>\n    <span class=\"n\">result<\/span> <span class=\"o\">=<\/span> <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">array<\/span><span class=\"p\">(<\/span><span class=\"n\">model<\/span><span class=\"o\">.<\/span><span class=\"n\">predict_on_batch<\/span><span class=\"p\">(<\/span><span class=\"n\">x<\/span><span class=\"p\">))<\/span><span class=\"o\">*<\/span><span class=\"mi\">255<\/span>\n    <span class=\"n\">result<\/span> <span class=\"o\">=<\/span> <span class=\"n\">result<\/span><span class=\"o\">.<\/span><span class=\"n\">reshape<\/span><span class=\"p\">(<\/span><span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">],<\/span><span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">],<\/span><span class=\"n\">output_dimensions<\/span><span class=\"p\">[<\/span><span class=\"mi\">2<\/span><span class=\"p\">])<\/span>\n    <span class=\"n\">np<\/span><span class=\"o\">.<\/span><span class=\"n\">clip<\/span><span class=\"p\">(<\/span><span class=\"n\">result<\/span><span class=\"p\">,<\/span> <span class=\"mi\">0<\/span><span class=\"p\">,<\/span> <span class=\"mi\">255<\/span><span class=\"p\">,<\/span> <span class=\"n\">out<\/span><span class=\"o\">=<\/span><span class=\"n\">result<\/span><span class=\"p\">)<\/span>\n    <span class=\"n\">result<\/span> <span class=\"o\">=<\/span> <span class=\"n\">result<\/span><span class=\"o\">.<\/span><span class=\"n\">astype<\/span><span class=\"p\">(<\/span><span class=\"s1\">'uint8'<\/span><span class=\"p\">)<\/span>\n                \n    <span class=\"n\">axarr<\/span><span class=\"p\">[<\/span><span class=\"mi\">2<\/span><span class=\"p\">]<\/span><span class=\"o\">.<\/span><span class=\"n\">imshow<\/span><span class=\"p\">(<\/span><span class=\"n\">result<\/span><span class=\"p\">)<\/span>\n    \n    <span class=\"c1\"># Save image<\/span>\n    <span class=\"n\">f<\/span><span class=\"o\">.<\/span><span class=\"n\">savefig<\/span><span class=\"p\">(<\/span><span class=\"n\">save_path<\/span> <span class=\"o\">+<\/span> <span class=\"s1\">'<\/span><span class=\"se\">\\\\<\/span><span class=\"s1\">frame_<\/span><span class=\"si\">%d<\/span><span class=\"s1\">.png'<\/span> <span class=\"o\">%<\/span> <span class=\"n\">count<\/span><span class=\"p\">)<\/span>\n    \n    <span class=\"c1\"># Increment file name counter<\/span>\n    <span class=\"n\">count<\/span> <span class=\"o\">=<\/span> <span class=\"n\">count<\/span> <span class=\"o\">+<\/span> <span class=\"mi\">1<\/span><\/pre><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-89b2cb6 elementor-widget elementor-widget-text-editor\" data-id=\"89b2cb6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">Bu parametreleri ayarlad\u0131ktan sonra, komut dosyas\u0131 her g\u00f6r\u00fcnt\u00fcy\u00fc input_dimesions ile belirtilen boyuta yeniden \u015fekillendirecek ve modelinize girdi olarak besleyecektir.\u00a0Ard\u0131ndan komut dosyas\u0131, modeliniz taraf\u0131ndan \u00e7\u0131kar\u0131lan g\u00f6r\u00fcnt\u00fcy\u00fc, En Yak\u0131n Kom\u015fu Enterpolasyonu kullan\u0131larak giri\u015f g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fcn enterpolasyonlu gibi g\u00f6r\u00fcnece\u011fi ile kar\u015f\u0131la\u015ft\u0131rarak \u00e7izecektir.\u00a0Modelinizden olu\u015fturulan bu \u00f6rnekler,\u00a0<i>save_path<\/i> parametresi ile\u00a0belirtilen klas\u00f6re kaydedilecektir.\u00a0Bu \u00f6rnekler, modelinizin kalitesini analiz etmenin iyi bir yoludur.\u00a0Bir g\u00f6r\u00fcnt\u00fcn\u00fcn modelinize girilmeden \u00f6nceki g\u00f6r\u00fcn\u00fcm\u00fcn\u00fc, modelinizden ge\u00e7tikten sonra nas\u0131l g\u00f6r\u00fcnd\u00fc\u011f\u00fcn\u00fc ve ba\u015fka bir teknik kullan\u0131larak s\u00fcper \u00f6rneklenmi\u015f g\u00f6r\u00fcnme bi\u00e7imini kar\u015f\u0131la\u015ft\u0131rabilirsiniz.\u00a0Modellerimden baz\u0131lar\u0131n\u0131n sonu\u00e7lar\u0131 a\u015fa\u011f\u0131da g\u00f6sterilmektedir.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3811741 elementor-widget elementor-widget-heading\" data-id=\"3811741\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Sonu\u00e7lar<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ba99eff elementor-widget elementor-widget-html\" data-id=\"ba99eff\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<center><img decoding=\"async\" src=\"https:\/\/raw.githubusercontent.com\/vee-upatising\/Super-Resolution-GAN\/master\/edited.png\" alt=\"DLSS Example 1\"><\/center>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f5fb490 elementor-widget elementor-widget-html\" data-id=\"f5fb490\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<center><img decoding=\"async\" src=\"https:\/\/camo.githubusercontent.com\/90b35081bff22422acc253f60ddcf376719a980362f27ccb616f4ab649728d4c\/68747470733a2f2f7665652d757061746973696e672e6769746875622e696f2f696d616765732f666c69702e676966\" alt=\"DLSS Example 1\"><\/center>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b871a86 elementor-widget elementor-widget-html\" data-id=\"b871a86\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<center><img decoding=\"async\" src=\"https:\/\/i.imgur.com\/SoSyPkU.jpeg\" alt=\"DLSS Example 1\"><\/center>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cc28de1 elementor-widget elementor-widget-html\" data-id=\"cc28de1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<center><img decoding=\"async\" src=\"https:\/\/i.imgur.com\/GBrS5ff.jpeg\" alt=\"DLSS Example 1\"><\/center>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5cb83a3 elementor-widget elementor-widget-html\" data-id=\"5cb83a3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<center><img decoding=\"async\" src=\"https:\/\/i.imgur.com\/sbFXqZE.jpeg\" alt=\"DLSS Example 1\"><\/center>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-43b8a22 elementor-widget elementor-widget-html\" data-id=\"43b8a22\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<center><img decoding=\"async\" src=\"https:\/\/i.imgur.com\/7ugON97.jpeg\" alt=\"DLSS Example 1\"><\/center>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a5b0bf4 elementor-widget elementor-widget-heading\" data-id=\"a5b0bf4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Sonu\u00e7<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-696bbff elementor-widget elementor-widget-text-editor\" data-id=\"696bbff\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p align=\"left\">Bu makale, Keras kullanarak bir DLSS \u00dcretken \u00c7eki\u015fmeli A\u011f\u0131 e\u011fitmek i\u00e7in genel bir \u00e7er\u00e7eve sa\u011flar.\u00a0Bu komut dosyas\u0131n\u0131 kullanarak kendi veri k\u00fcmelerinizle \u00e7e\u015fitli \u00e7\u00f6z\u00fcn\u00fcrl\u00fckler i\u00e7in kendi DLSS modellerinizi olu\u015fturabileceksiniz.\u00a0Bu kodun tam s\u00fcr\u00fcm\u00fc\u00a0<a href=\"https:\/\/nbviewer.jupyter.org\/github\/vee-upatising\/DLSS\/blob\/master\/DLSS%20GAN%20Training.ipynb\" target=\"_blank\" rel=\"noopener\"><u>burada<\/u><\/a> bulunabilir.<\/p><p align=\"left\">Memnun oldu\u011funuz bir modeli e\u011fittikten sonra,\u00a0<a href=\"https:\/\/nbviewer.jupyter.org\/github\/vee-upatising\/DLSS\/blob\/master\/Load%20Model%20and%20Analyze%20Results.ipynb\" target=\"_blank\" rel=\"noopener\"><u>bu komut dosyas\u0131n\u0131<\/u><\/a>\u00a0\u00e7\u0131kt\u0131lar olu\u015fturmak ve sonu\u00e7lar\u0131n\u0131z\u0131 analiz etmek i\u00e7in kullanabilirsiniz.\u00a0Komut dosyas\u0131 ayr\u0131ca size s\u00fcper \u00f6rneklenmi\u015f video karelerinden GIF&#8217;ler olu\u015fturmak i\u00e7in kod sa\u011flayacakt\u0131r.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c43a63b elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c43a63b\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-af5f9b8\" data-id=\"af5f9b8\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-dd66169 elementor-widget elementor-widget-heading\" data-id=\"dd66169\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Github<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4bb4487 elementor-align-left elementor-widget elementor-widget-button\" data-id=\"4bb4487\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-lg elementor-animation-grow\" href=\"https:\/\/github.com\/vee-upatising\/DLSS\" target=\"_blank\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-github\"><\/i>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">\u0130ncele<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-49a729b elementor-widget elementor-widget-heading\" data-id=\"49a729b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Veri k\u00fcmesi<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-034ef00 elementor-align-left elementor-widget elementor-widget-button\" data-id=\"034ef00\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-lg elementor-animation-grow\" href=\"https:\/\/www.kaggle.com\/akhileshdkapse\/super-image-resolution\" target=\"_blank\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-database\"><\/i>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">\u0130ncele<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5be6fc2 elementor-widget elementor-widget-heading\" data-id=\"5be6fc2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Jupyter Notebook<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fd42b74 elementor-align-left elementor-widget elementor-widget-button\" data-id=\"fd42b74\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-lg elementor-animation-grow\" href=\"https:\/\/nbviewer.jupyter.org\/github\/vee-upatising\/DLSS\/tree\/master\/\" target=\"_blank\" rel=\"noopener\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-sticky-note\"><\/i>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">\u0130ncele<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>\u00dcretken \u00c7eki\u015fmeli A\u011flar\u0131 Kullanan DLSS English Bu makale size Keras kitapl\u0131\u011f\u0131 kullan\u0131larak yaz\u0131lan bir DLSS \u00dcretken \u00c7eki\u015fmeli A\u011f\u0131&#8217;n\u0131n genel \u00e7er\u00e7evesini sa\u011flayacakt\u0131r. Kendi g\u00f6r\u00fcnt\u00fc veri k\u00fcmelerinizi&hellip;<\/p>","protected":false},"author":1,"featured_media":2202,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-2073","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-projects"],"_links":{"self":[{"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/posts\/2073","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/comments?post=2073"}],"version-history":[{"count":0,"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/posts\/2073\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/media\/2202"}],"wp:attachment":[{"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/media?parent=2073"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/categories?post=2073"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/tags?post=2073"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}