{"id":1687,"date":"2020-10-22T18:44:43","date_gmt":"2020-10-22T18:44:43","guid":{"rendered":"https:\/\/aix.web.tr\/?p=1687"},"modified":"2024-04-22T14:12:41","modified_gmt":"2024-04-22T14:12:41","slug":"beyin-tumoru-mri-cnn","status":"publish","type":"post","link":"https:\/\/aix.web.tr\/en\/beyin-tumoru-mri-cnn\/","title":{"rendered":"Brain Tumor MRI CNN"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"1687\" class=\"elementor elementor-1687\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8c5d489 elementor-section-stretched elementor-hidden-tablet elementor-hidden-phone elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8c5d489\" 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-3772bdf\" data-id=\"3772bdf\" 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-acb5c02 elementor-widget elementor-widget-shortcode\" data-id=\"acb5c02\" 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\" 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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\" 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\/>\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-18bc35c elementor-section-stretched elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"18bc35c\" 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-ea3e574\" data-id=\"ea3e574\" 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-134f21c elementor-widget elementor-widget-heading\" data-id=\"134f21c\" 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\">Beyin T\u00fcm\u00f6r\u00fc MRI CNN<\/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-c685558 elementor-section-content-middle elementor-section-stretched elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c685558\" 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-e43129a\" data-id=\"e43129a\" 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-8b3b9b5 elementor-widget elementor-widget-heading\" data-id=\"8b3b9b5\" 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\">Ama\u00e7<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5b71c33 elementor-widget elementor-widget-text-editor\" data-id=\"5b71c33\" 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>MRI taramalar\u0131nda beyin t\u00fcm\u00f6rlerini tespit etmek i\u00e7in Evri\u015fimli Sinir A\u011flar\u0131n\u0131 kullan\u0131m\u0131. Umar\u0131m yap\u0131lan bu \u00e7al\u0131\u015fma gelecekte t\u0131bbi g\u00f6r\u00fcnt\u00fcleme te\u015fhisini otomatik hale getirilebilir veya en az\u0131ndan doktorlara ikinci bir g\u00f6r\u00fc\u015f sa\u011flayabilir.<\/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-bf55b80 elementor-widget elementor-widget-heading\" data-id=\"bf55b80\" 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 ve \u00c7\u0131kt\u0131lar<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b68f0ef elementor-widget elementor-widget-text-editor\" data-id=\"b68f0ef\" 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=\"input\"><div class=\"prompt input_prompt\">Girdi [1]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"kn\">import<\/span> <span class=\"nn\"><a name=\"kln-1\"><\/a><a class=\"indexed_symbol\">keras<\/a><\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">keras.models<\/span> <span class=\"kn\">import<\/span> <span class=\"n\"><a name=\"kln-2\"><\/a><a class=\"indexed_symbol\">Sequential<\/a><\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">keras.layers<\/span> <span class=\"kn\">import<\/span> <span class=\"n\"><a name=\"kln-3\"><\/a><a class=\"indexed_symbol\">Dense<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">Dropout<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">Flatten<\/a><\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">keras.layers<\/span> <span class=\"kn\">import<\/span> <span class=\"n\"><a name=\"kln-4\"><\/a><a class=\"indexed_symbol\">Conv2D<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">MaxPooling2D<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">Conv3D<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">BatchNormalization<\/a><\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">keras<\/span> <span class=\"kn\">import<\/span> <span class=\"n\">backend<\/span> <span class=\"k\">as<\/span> <span class=\"n\"><a name=\"kln-5\"><\/a><a class=\"indexed_symbol\">K<\/a><\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\"><a name=\"kln-6\"><\/a><a class=\"indexed_symbol\">os<\/a><\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">PIL<\/span> <span class=\"kn\">import<\/span> <span class=\"n\"><a name=\"kln-7\"><\/a><a class=\"indexed_symbol\">Image<\/a><\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">numpy<\/span> <span class=\"k\">as<\/span> <span class=\"nn\"><a name=\"kln-8\"><\/a><a class=\"indexed_symbol\">np<\/a><\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.model_selection<\/span> <span class=\"kn\">import<\/span> <span class=\"n\"><a name=\"kln-9\"><\/a><a class=\"indexed_symbol\">train_test_split<\/a><\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">sklearn.preprocessing<\/span> <span class=\"kn\">import<\/span> <span class=\"n\"><a name=\"kln-10\"><\/a><a class=\"indexed_symbol\">OneHotEncoder<\/a><\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">matplotlib.pyplot<\/span> <span class=\"k\">as<\/span> <span class=\"nn\"><a name=\"kln-11\"><\/a><a class=\"indexed_symbol\">plt<\/a><\/span>\n<span class=\"kn\">from<\/span> <span class=\"nn\">matplotlib.pyplot<\/span> <span class=\"kn\">import<\/span> <span class=\"n\"><a name=\"kln-12\"><\/a><a class=\"indexed_symbol\">imshow<\/a><\/span>\n<span class=\"kn\">import<\/span> <span class=\"nn\">pandas<\/span> <span class=\"k\">as<\/span> <span class=\"nn\"><a name=\"kln-13\"><\/a><a class=\"indexed_symbol\">pd<\/a><\/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_stderr output_text\"><pre>TensorFlow backend kullan\u0131m\u0131.\n<\/pre><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"prompt input_prompt\">Girdi [2]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"n\"><a name=\"kln-14\"><\/a><a class=\"indexed_symbol\">os<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">listdir<\/a><\/span><span class=\"p\">(<\/span><span class=\"s1\">'..\/input\/brain-mri-images-for-brain-tumor-detection'<\/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 output_prompt\">\u00c7\u0131kt\u0131 [2]:<\/div><div class=\"output_text output_subarea output_execute_result\"><pre>['no', 'brain_tumor_dataset', 'yes']<\/pre><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"prompt input_prompt\">Girdi [3]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"n\"><a name=\"kln-15\"><\/a><a class=\"indexed_symbol\">enc<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">OneHotEncoder<\/a><\/span><span class=\"p\">()<\/span>\n<span class=\"n\"><a name=\"kln-16\"><\/a><a class=\"indexed_symbol\">enc<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">fit<\/a><\/span><span class=\"p\">([[<\/span><span class=\"mi\">0<\/span><span class=\"p\">],<\/span> <span class=\"p\">[<\/span><span class=\"mi\">1<\/span><span class=\"p\">]])<\/span> \n<span class=\"k\">def<\/span> <span class=\"nf\"><a name=\"kln-17\"><\/a><a class=\"indexed_symbol\">names<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">number<\/a><\/span><span class=\"p\">):<\/span>\n    <span class=\"k\">if<\/span><span class=\"p\">(<\/span><span class=\"n\"><a name=\"kln-18\"><\/a><a class=\"indexed_symbol\">number<\/a><\/span> <span class=\"o\">==<\/span> <span class=\"mi\">0<\/span><span class=\"p\">):<\/span>\n        <span class=\"k\">return<\/span> <span class=\"s1\">'Tumor'<\/span>\n    <span class=\"k\">else<\/span><span class=\"p\">:<\/span>\n        <span class=\"k\">return<\/span> <span class=\"s1\">'Normal'<\/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_stderr output_text\"><pre>\/opt\/conda\/lib\/python3.6\/site-packages\/sklearn\/preprocessing\/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\nIf you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\nIn case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n  warnings.warn(msg, FutureWarning)\n<\/pre><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"prompt input_prompt\">Girdi [4]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"n\"><a name=\"kln-22\"><\/a><a class=\"indexed_symbol\">data<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n<span class=\"n\"><a name=\"kln-23\"><\/a><a class=\"indexed_symbol\">paths<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n<span class=\"n\"><a name=\"kln-24\"><\/a><a class=\"indexed_symbol\">ans<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n<span class=\"k\">for<\/span> <span class=\"n\"><a name=\"kln-25\"><\/a><a class=\"indexed_symbol\">r<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">d<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">f<\/a><\/span> <span class=\"ow\">in<\/span> <span class=\"n\"><a class=\"indexed_symbol\">os<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\">walk<\/span><span class=\"p\">(<\/span><span class=\"sa\">r<\/span><span class=\"s1\">'..\/input\/brain-mri-images-for-brain-tumor-detection\/yes'<\/span><span class=\"p\">):<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\"><a name=\"kln-26\"><\/a><a class=\"indexed_symbol\">file<\/a><\/span> <span class=\"ow\">in<\/span> <span class=\"n\"><a class=\"indexed_symbol\">f<\/a><\/span><span class=\"p\">:<\/span>\n        <span class=\"k\">if<\/span> <span class=\"s1\">'.jpg'<\/span> <span class=\"ow\">in<\/span> <span class=\"n\"><a name=\"kln-27\"><\/a><a class=\"indexed_symbol\">file<\/a><\/span><span class=\"p\">:<\/span>\n            <span class=\"n\"><a name=\"kln-28\"><\/a><a class=\"indexed_symbol\">paths<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">os<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">path<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">join<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">r<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">file<\/a><\/span><span class=\"p\">))<\/span>\n\n<span class=\"k\">for<\/span> <span class=\"n\"><a name=\"kln-30\"><\/a><a class=\"indexed_symbol\">path<\/a><\/span> <span class=\"ow\">in<\/span> <span class=\"n\"><a class=\"indexed_symbol\">paths<\/a><\/span><span class=\"p\">:<\/span>\n    <span class=\"n\"><a name=\"kln-31\"><\/a><a class=\"indexed_symbol\">img<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">Image<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">open<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">path<\/a><\/span><span class=\"p\">)<\/span>\n    <span class=\"n\"><a name=\"kln-32\"><\/a><a class=\"indexed_symbol\">x<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">img<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">resize<\/a><\/span><span class=\"p\">((<\/span><span class=\"mi\">128<\/span><span class=\"p\">,<\/span><span class=\"mi\">128<\/span><span class=\"p\">))<\/span>\n    <span class=\"n\"><a name=\"kln-33\"><\/a><a class=\"indexed_symbol\">x<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">np<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">array<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">x<\/a><\/span><span class=\"p\">)<\/span>\n    <span class=\"k\">if<\/span><span class=\"p\">(<\/span><span class=\"n\"><a name=\"kln-34\"><\/a><a class=\"indexed_symbol\">x<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">shape<\/a><\/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        <span class=\"n\"><a name=\"kln-35\"><\/a><a class=\"indexed_symbol\">data<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">np<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">array<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">x<\/a><\/span><span class=\"p\">))<\/span>\n        <span class=\"n\"><a name=\"kln-36\"><\/a><a class=\"indexed_symbol\">ans<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">enc<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">transform<\/a><\/span><span class=\"p\">([[<\/span><span class=\"mi\">0<\/span><span class=\"p\">]])<\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">toarray<\/a><\/span><span class=\"p\">())<\/span>\n<\/pre><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"prompt input_prompt\">Girdi [5]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"n\"><a name=\"kln-37\"><\/a><a class=\"indexed_symbol\">paths<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"p\">[]<\/span>\n<span class=\"k\">for<\/span> <span class=\"n\"><a name=\"kln-38\"><\/a><a class=\"indexed_symbol\">r<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">d<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">f<\/a><\/span> <span class=\"ow\">in<\/span> <span class=\"n\"><a class=\"indexed_symbol\">os<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\">walk<\/span><span class=\"p\">(<\/span><span class=\"sa\">r<\/span><span class=\"s2\">\"..\/input\/brain-mri-images-for-brain-tumor-detection\/no\"<\/span><span class=\"p\">):<\/span>\n    <span class=\"k\">for<\/span> <span class=\"n\"><a name=\"kln-39\"><\/a><a class=\"indexed_symbol\">file<\/a><\/span> <span class=\"ow\">in<\/span> <span class=\"n\"><a class=\"indexed_symbol\">f<\/a><\/span><span class=\"p\">:<\/span>\n        <span class=\"k\">if<\/span> <span class=\"s1\">'.jpg'<\/span> <span class=\"ow\">in<\/span> <span class=\"n\"><a name=\"kln-40\"><\/a><a class=\"indexed_symbol\">file<\/a><\/span><span class=\"p\">:<\/span>\n            <span class=\"n\"><a name=\"kln-41\"><\/a><a class=\"indexed_symbol\">paths<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">os<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">path<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">join<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">r<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">file<\/a><\/span><span class=\"p\">))<\/span>\n\n<span class=\"k\">for<\/span> <span class=\"n\"><a name=\"kln-43\"><\/a><a class=\"indexed_symbol\">path<\/a><\/span> <span class=\"ow\">in<\/span> <span class=\"n\"><a class=\"indexed_symbol\">paths<\/a><\/span><span class=\"p\">:<\/span>\n    <span class=\"n\"><a name=\"kln-44\"><\/a><a class=\"indexed_symbol\">img<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">Image<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">open<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">path<\/a><\/span><span class=\"p\">)<\/span>\n    <span class=\"n\"><a name=\"kln-45\"><\/a><a class=\"indexed_symbol\">x<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">img<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">resize<\/a><\/span><span class=\"p\">((<\/span><span class=\"mi\">128<\/span><span class=\"p\">,<\/span><span class=\"mi\">128<\/span><span class=\"p\">))<\/span>\n    <span class=\"n\"><a name=\"kln-46\"><\/a><a class=\"indexed_symbol\">x<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">np<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">array<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">x<\/a><\/span><span class=\"p\">)<\/span>\n    <span class=\"k\">if<\/span><span class=\"p\">(<\/span><span class=\"n\"><a name=\"kln-47\"><\/a><a class=\"indexed_symbol\">x<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">shape<\/a><\/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        <span class=\"n\"><a name=\"kln-48\"><\/a><a class=\"indexed_symbol\">data<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">np<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">array<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">x<\/a><\/span><span class=\"p\">))<\/span>\n        <span class=\"n\"><a name=\"kln-49\"><\/a><a class=\"indexed_symbol\">ans<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\">append<\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">enc<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">transform<\/a><\/span><span class=\"p\">([[<\/span><span class=\"mi\">1<\/span><span class=\"p\">]])<\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">toarray<\/a><\/span><span class=\"p\">())<\/span>\n<\/pre><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"prompt input_prompt\">Girdi [6]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"n\"><a name=\"kln-50\"><\/a><a class=\"indexed_symbol\">data<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">np<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">array<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">data<\/a><\/span><span class=\"p\">)<\/span>\n<span class=\"n\"><a name=\"kln-51\"><\/a><a class=\"indexed_symbol\">data<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\">shape<\/span>\n<\/pre><\/div><\/div><\/div><\/div><div class=\"output_wrapper\"><div class=\"output\"><div class=\"output_area\"><div class=\"prompt output_prompt\">\u00c7\u0131kt\u0131 [6]:<\/div><div class=\"output_text output_subarea output_execute_result\"><pre>(139, 128, 128, 3)<\/pre><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"prompt input_prompt\">Girdi [7]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"n\"><a name=\"kln-52\"><\/a><a class=\"indexed_symbol\">ans<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">np<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">array<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">ans<\/a><\/span><span class=\"p\">)<\/span>\n<span class=\"n\"><a name=\"kln-53\"><\/a><a class=\"indexed_symbol\">ans<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">ans<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\">reshape<\/span><span class=\"p\">(<\/span><span class=\"mi\">139<\/span><span class=\"p\">,<\/span><span class=\"mi\">2<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"prompt input_prompt\">Girdi [8]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"n\"><a name=\"kln-54\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">Sequential<\/a><\/span><span class=\"p\">()<\/span>\n\n<span class=\"n\"><a name=\"kln-56\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">Conv2D<\/a><\/span><span class=\"p\">(<\/span><span class=\"mi\">32<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_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> <span class=\"n\">input_shape<\/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> <span class=\"n\">padding<\/span> <span class=\"o\">=<\/span> <span class=\"s1\">'Same'<\/span><span class=\"p\">))<\/span>\n<span class=\"n\"><a name=\"kln-57\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">Conv2D<\/a><\/span><span class=\"p\">(<\/span><span class=\"mi\">32<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_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>  <span class=\"n\">activation<\/span> <span class=\"o\">=<\/span><span class=\"s1\">'selu'<\/span><span class=\"p\">,<\/span> <span class=\"n\">padding<\/span> <span class=\"o\">=<\/span> <span class=\"s1\">'Same'<\/span><span class=\"p\">))<\/span>\n\n\n<span class=\"n\"><a name=\"kln-60\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">BatchNormalization<\/a><\/span><span class=\"p\">())<\/span>\n<span class=\"n\"><a name=\"kln-61\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">MaxPooling2D<\/a><\/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<span class=\"n\"><a name=\"kln-62\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">Dropout<\/a><\/span><span class=\"p\">(<\/span><span class=\"mf\">0.25<\/span><span class=\"p\">))<\/span>\n\n<span class=\"n\"><a name=\"kln-64\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">Conv2D<\/a><\/span><span class=\"p\">(<\/span><span class=\"mi\">64<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_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> <span class=\"n\">activation<\/span> <span class=\"o\">=<\/span><span class=\"s1\">'selu'<\/span><span class=\"p\">,<\/span> <span class=\"n\">padding<\/span> <span class=\"o\">=<\/span> <span class=\"s1\">'Same'<\/span><span class=\"p\">))<\/span>\n<span class=\"n\"><a name=\"kln-65\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">Conv2D<\/a><\/span><span class=\"p\">(<\/span><span class=\"mi\">64<\/span><span class=\"p\">,<\/span> <span class=\"n\">kernel_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> <span class=\"n\">activation<\/span> <span class=\"o\">=<\/span><span class=\"s1\">'selu'<\/span><span class=\"p\">,<\/span> <span class=\"n\">padding<\/span> <span class=\"o\">=<\/span> <span class=\"s1\">'Same'<\/span><span class=\"p\">))<\/span>\n\n<span class=\"n\"><a name=\"kln-67\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">BatchNormalization<\/a><\/span><span class=\"p\">())<\/span>\n<span class=\"n\"><a name=\"kln-68\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">MaxPooling2D<\/a><\/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> <span class=\"n\">strides<\/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<span class=\"n\"><a name=\"kln-69\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">Dropout<\/a><\/span><span class=\"p\">(<\/span><span class=\"mf\">0.25<\/span><span class=\"p\">))<\/span>\n\n<span class=\"n\"><a name=\"kln-71\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">Flatten<\/a><\/span><span class=\"p\">())<\/span>\n\n<span class=\"n\"><a name=\"kln-73\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">Dense<\/a><\/span><span class=\"p\">(<\/span><span class=\"mi\">512<\/span><span class=\"p\">,<\/span> <span class=\"n\">activation<\/span><span class=\"o\">=<\/span><span class=\"s1\">'relu'<\/span><span class=\"p\">))<\/span>\n<span class=\"n\"><a name=\"kln-74\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">Dropout<\/a><\/span><span class=\"p\">(<\/span><span class=\"mf\">0.5<\/span><span class=\"p\">))<\/span>\n<span class=\"n\"><a name=\"kln-75\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">add<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">Dense<\/a><\/span><span class=\"p\">(<\/span><span class=\"mi\">2<\/span><span class=\"p\">,<\/span> <span class=\"n\">activation<\/span><span class=\"o\">=<\/span><span class=\"s1\">'softmax'<\/span><span class=\"p\">))<\/span>\n\n<span class=\"n\"><a name=\"kln-77\"><\/a><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">compile<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\">loss<\/span> <span class=\"o\">=<\/span> <span class=\"s2\">\"categorical_crossentropy\"<\/span><span class=\"p\">,<\/span> <span class=\"n\">optimizer<\/span><span class=\"o\">=<\/span><span class=\"s1\">'Adamax'<\/span><span class=\"p\">)<\/span>\n<span class=\"nb\"><a name=\"kln-78\"><\/a><a class=\"indexed_symbol\">print<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">summary<\/a><\/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>Model: \"sequential_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_1 (Conv2D)            (None, 128, 128, 32)      416       \n_________________________________________________________________\nconv2d_2 (Conv2D)            (None, 128, 128, 32)      4128      \n_________________________________________________________________\nbatch_normalization_1 (Batch (None, 128, 128, 32)      128       \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 64, 64, 32)        0         \n_________________________________________________________________\ndropout_1 (Dropout)          (None, 64, 64, 32)        0         \n_________________________________________________________________\nconv2d_3 (Conv2D)            (None, 64, 64, 64)        8256      \n_________________________________________________________________\nconv2d_4 (Conv2D)            (None, 64, 64, 64)        16448     \n_________________________________________________________________\nbatch_normalization_2 (Batch (None, 64, 64, 64)        256       \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 32, 32, 64)        0         \n_________________________________________________________________\ndropout_2 (Dropout)          (None, 32, 32, 64)        0         \n_________________________________________________________________\nflatten_1 (Flatten)          (None, 65536)             0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 512)               33554944  \n_________________________________________________________________\ndropout_3 (Dropout)          (None, 512)               0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 2)                 1026      \n=================================================================\nTotal params: 33,585,602\nTrainable params: 33,585,410\nNon-trainable params: 192\n_________________________________________________________________\nNone\n<\/pre><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"prompt input_prompt\">Girdi [9]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"n\"><a name=\"kln-79\"><\/a><a class=\"indexed_symbol\">x_train<\/a><\/span><span class=\"p\">,<\/span><span class=\"n\"><a class=\"indexed_symbol\">x_test<\/a><\/span><span class=\"p\">,<\/span><span class=\"n\"><a class=\"indexed_symbol\">y_train<\/a><\/span><span class=\"p\">,<\/span><span class=\"n\"><a class=\"indexed_symbol\">y_test<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">train_test_split<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">data<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">ans<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\">test_size<\/span><span class=\"o\">=<\/span><span class=\"mf\">0.2<\/span><span class=\"p\">,<\/span> <span class=\"n\">shuffle<\/span><span class=\"o\">=<\/span><span class=\"kc\"><a class=\"indexed_symbol\">True<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\">random_state<\/span><span class=\"o\">=<\/span><span class=\"mi\">69<\/span><span class=\"p\">)<\/span>\n<\/pre><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"prompt input_prompt\">Girdi [10]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"n\"><a name=\"kln-80\"><\/a><a class=\"indexed_symbol\">history<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">fit<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">x_train<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">y_train<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\">epochs<\/span><span class=\"o\">=<\/span><span class=\"mi\">30<\/span><span class=\"p\">,<\/span> <span class=\"n\">batch_size<\/span><span class=\"o\">=<\/span><span class=\"mi\">40<\/span><span class=\"p\">,<\/span> <span class=\"n\">verbose<\/span><span class=\"o\">=<\/span><span class=\"mi\">1<\/span><span class=\"p\">,<\/span><span class=\"n\">validation_data<\/span><span class=\"o\">=<\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">x_test<\/a><\/span><span class=\"p\">,<\/span> <span class=\"n\"><a class=\"indexed_symbol\">y_test<\/a><\/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>Train on 111 samples, validate on 28 samples\nEpoch 1\/30\n111\/111 [==============================] - 5s 49ms\/step - loss: 97.9745 - val_loss: 277.9749\nEpoch 2\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 86.0070 - val_loss: 161.2324\nEpoch 3\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 44.8761 - val_loss: 22.8152\nEpoch 4\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 22.8710 - val_loss: 13.1432\nEpoch 5\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 12.6436 - val_loss: 25.8424\nEpoch 6\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 8.0254 - val_loss: 15.9823\nEpoch 7\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 5.6897 - val_loss: 6.6303\nEpoch 8\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 2.4485 - val_loss: 4.8793\nEpoch 9\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.9813 - val_loss: 4.9282\nEpoch 10\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 1.8591 - val_loss: 5.8082\nEpoch 11\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 1.8579 - val_loss: 4.9097\nEpoch 12\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.9552 - val_loss: 4.0293\nEpoch 13\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.6528 - val_loss: 3.9330\nEpoch 14\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.4592 - val_loss: 4.3758\nEpoch 15\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.3196 - val_loss: 5.1238\nEpoch 16\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.0131 - val_loss: 5.3396\nEpoch 17\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.1137 - val_loss: 4.8185\nEpoch 18\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.1940 - val_loss: 3.8550\nEpoch 19\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.1083 - val_loss: 3.0845\nEpoch 20\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.0706 - val_loss: 3.0981\nEpoch 21\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.1765 - val_loss: 3.4054\nEpoch 22\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.3862 - val_loss: 3.5916\nEpoch 23\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.0132 - val_loss: 3.7866\nEpoch 24\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.0541 - val_loss: 3.7140\nEpoch 25\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.1772 - val_loss: 2.8360\nEpoch 26\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.0022 - val_loss: 2.1522\nEpoch 27\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 6.7226e-04 - val_loss: 2.1731\nEpoch 28\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.1826 - val_loss: 2.2111\nEpoch 29\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.0385 - val_loss: 2.5520\nEpoch 30\/30\n111\/111 [==============================] - 0s 2ms\/step - loss: 0.0615 - val_loss: 3.0602\n<\/pre><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"prompt input_prompt\">Girdi [11]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"c1\"># Plot training &amp; validation loss values<\/span>\n<span class=\"n\"><a name=\"kln-82\"><\/a><a class=\"indexed_symbol\">plt<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">plot<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">history<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">history<\/a><\/span><span class=\"p\">[<\/span><span class=\"s1\">'loss'<\/span><span class=\"p\">])<\/span>\n<span class=\"n\"><a name=\"kln-83\"><\/a><a class=\"indexed_symbol\">plt<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">plot<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">history<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">history<\/a><\/span><span class=\"p\">[<\/span><span class=\"s1\">'val_loss'<\/span><span class=\"p\">])<\/span>\n<span class=\"n\"><a name=\"kln-84\"><\/a><a class=\"indexed_symbol\">plt<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">title<\/a><\/span><span class=\"p\">(<\/span><span class=\"s1\">'Model Loss'<\/span><span class=\"p\">)<\/span>\n<span class=\"n\"><a name=\"kln-85\"><\/a><a class=\"indexed_symbol\">plt<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">ylabel<\/a><\/span><span class=\"p\">(<\/span><span class=\"s1\">'Loss'<\/span><span class=\"p\">)<\/span>\n<span class=\"n\"><a name=\"kln-86\"><\/a><a class=\"indexed_symbol\">plt<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">xlabel<\/a><\/span><span class=\"p\">(<\/span><span class=\"s1\">'Epoch'<\/span><span class=\"p\">)<\/span>\n<span class=\"n\"><a name=\"kln-87\"><\/a><a class=\"indexed_symbol\">plt<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">legend<\/a><\/span><span class=\"p\">([<\/span><span class=\"s1\">'Test'<\/span><span class=\"p\">,<\/span> <span class=\"s1\">'Validation'<\/span><span class=\"p\">],<\/span> <span class=\"n\">loc<\/span><span class=\"o\">=<\/span><span class=\"s1\">'upper right'<\/span><span class=\"p\">)<\/span>\n<span class=\"n\"><a name=\"kln-88\"><\/a><a class=\"indexed_symbol\">plt<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">show<\/a><\/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_png output_subarea \"><img decoding=\"async\" class=\"alignnone size-medium wp-image-1688\" src=\"https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/10\/results___10_0-300x213.png\" alt=\"\" width=\"300\" height=\"213\" srcset=\"https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/10\/results___10_0-300x213.png 300w, https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/10\/results___10_0.png 392w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"prompt input_prompt\">Girdi [12]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"n\"><a name=\"kln-89\"><\/a><a class=\"indexed_symbol\">img<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">Image<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">open<\/a><\/span><span class=\"p\">(<\/span><span class=\"sa\">r<\/span><span class=\"s2\">\"..\/input\/brain-mri-images-for-brain-tumor-detection\/no\/N17.jpg\"<\/span><span class=\"p\">)<\/span>\n<span class=\"n\"><a name=\"kln-90\"><\/a><a class=\"indexed_symbol\">x<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">np<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">array<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">img<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">resize<\/a><\/span><span class=\"p\">((<\/span><span class=\"mi\">128<\/span><span class=\"p\">,<\/span><span class=\"mi\">128<\/span><span class=\"p\">)))<\/span>\n<span class=\"n\"><a name=\"kln-91\"><\/a><a class=\"indexed_symbol\">x<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">x<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">reshape<\/a><\/span><span class=\"p\">(<\/span><span class=\"mi\">1<\/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<span class=\"n\"><a name=\"kln-92\"><\/a><a class=\"indexed_symbol\">answ<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">predict_on_batch<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">x<\/a><\/span><span class=\"p\">)<\/span>\n<span class=\"n\"><a name=\"kln-93\"><\/a><a class=\"indexed_symbol\">classification<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">np<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">where<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">answ<\/a><\/span> <span class=\"o\">==<\/span> <span class=\"n\"><a class=\"indexed_symbol\">np<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">amax<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">answ<\/a><\/span><span class=\"p\">))[<\/span><span class=\"mi\">1<\/span><span class=\"p\">][<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span>\n<span class=\"n\"><a name=\"kln-94\"><\/a><a class=\"indexed_symbol\">imshow<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">img<\/a><\/span><span class=\"p\">)<\/span>\n<span class=\"nb\"><a name=\"kln-95\"><\/a><a class=\"indexed_symbol\">print<\/a><\/span><span class=\"p\">(<\/span><span class=\"nb\"><a class=\"indexed_symbol\">str<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">answ<\/a><\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">][<\/span><span class=\"n\"><a class=\"indexed_symbol\">classification<\/a><\/span><span class=\"p\">]<\/span><span class=\"o\">*<\/span><span class=\"mi\">100<\/span><span class=\"p\">)<\/span> <span class=\"o\">+<\/span> <span class=\"s1\">'% Confidence This Is '<\/span> <span class=\"o\">+<\/span> <span class=\"n\"><a class=\"indexed_symbol\">names<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">classification<\/a><\/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>100.0% Bu Normal\n<\/pre><\/div><\/div><div class=\"output_area\"><div class=\"prompt\">\u00a0<\/div><div class=\"output_png output_subarea \"><img decoding=\"async\" class=\"alignnone size-full wp-image-1690\" src=\"https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/10\/results___11_1.png\" alt=\"\" width=\"237\" height=\"252\" \/><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"\"><div class=\"cell border-box-sizing code_cell rendered\"><div class=\"input\"><div class=\"prompt input_prompt\">Girdi [13]:<\/div><div class=\"inner_cell\"><div class=\"input_area\"><div class=\" highlight hl-lexer_wrapper\"><pre><span class=\"n\"><a name=\"kln-96\"><\/a><a class=\"indexed_symbol\">img<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">Image<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">open<\/a><\/span><span class=\"p\">(<\/span><span class=\"sa\">r<\/span><span class=\"s2\">\"..\/input\/brain-mri-images-for-brain-tumor-detection\/yes\/Y3.jpg\"<\/span><span class=\"p\">)<\/span>\n<span class=\"n\"><a name=\"kln-97\"><\/a><a class=\"indexed_symbol\">x<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">np<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">array<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">img<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">resize<\/a><\/span><span class=\"p\">((<\/span><span class=\"mi\">128<\/span><span class=\"p\">,<\/span><span class=\"mi\">128<\/span><span class=\"p\">)))<\/span>\n<span class=\"n\"><a name=\"kln-98\"><\/a><a class=\"indexed_symbol\">x<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">x<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">reshape<\/a><\/span><span class=\"p\">(<\/span><span class=\"mi\">1<\/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<span class=\"n\"><a name=\"kln-99\"><\/a><a class=\"indexed_symbol\">answ<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">model<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">predict_on_batch<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">x<\/a><\/span><span class=\"p\">)<\/span>\n<span class=\"n\"><a name=\"kln-100\"><\/a><a class=\"indexed_symbol\">classification<\/a><\/span> <span class=\"o\">=<\/span> <span class=\"n\"><a class=\"indexed_symbol\">np<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">where<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">answ<\/a><\/span> <span class=\"o\">==<\/span> <span class=\"n\"><a class=\"indexed_symbol\">np<\/a><\/span><span class=\"o\">.<\/span><span class=\"n\"><a class=\"indexed_symbol\">amax<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">answ<\/a><\/span><span class=\"p\">))[<\/span><span class=\"mi\">1<\/span><span class=\"p\">][<\/span><span class=\"mi\">0<\/span><span class=\"p\">]<\/span>\n<span class=\"n\"><a name=\"kln-101\"><\/a><a class=\"indexed_symbol\">imshow<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">img<\/a><\/span><span class=\"p\">)<\/span>\n<span class=\"nb\"><a name=\"kln-102\"><\/a><a class=\"indexed_symbol\">print<\/a><\/span><span class=\"p\">(<\/span><span class=\"nb\"><a class=\"indexed_symbol\">str<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">answ<\/a><\/span><span class=\"p\">[<\/span><span class=\"mi\">0<\/span><span class=\"p\">][<\/span><span class=\"n\"><a class=\"indexed_symbol\">classification<\/a><\/span><span class=\"p\">]<\/span><span class=\"o\">*<\/span><span class=\"mi\">100<\/span><span class=\"p\">)<\/span> <span class=\"o\">+<\/span> <span class=\"s1\">'% Confidence This Is A '<\/span> <span class=\"o\">+<\/span> <span class=\"n\"><a class=\"indexed_symbol\">names<\/a><\/span><span class=\"p\">(<\/span><span class=\"n\"><a class=\"indexed_symbol\">classification<\/a><\/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>100.0% Bu Bir T\u00fcm\u00f6r\n<\/pre><\/div><\/div><div class=\"output_area\"><div class=\"prompt\">\u00a0<\/div><div class=\"output_png output_subarea \"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1691\" src=\"https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/10\/results___12_1.png\" alt=\"\" width=\"234\" height=\"252\" \/><\/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-7e5b073 elementor-widget elementor-widget-heading\" data-id=\"7e5b073\" 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\">Sonu\u00e7lar<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dcc73f1 elementor-widget elementor-widget-image\" data-id=\"dcc73f1\" data-element_type=\"widget\" data-e-type=\"widget\" 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<img loading=\"lazy\" decoding=\"async\" width=\"743\" height=\"614\" src=\"https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/10\/sonuc.png\" class=\"attachment-large size-large wp-image-1692\" alt=\"\" srcset=\"https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/10\/sonuc.png 743w, https:\/\/aix.web.tr\/wp-content\/uploads\/2020\/10\/sonuc-300x248.png 300w\" sizes=\"(max-width: 743px) 100vw, 743px\" \/>\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\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4d169517 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4d169517\" 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-5301a4bf\" data-id=\"5301a4bf\" 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-c8db532 elementor-widget elementor-widget-heading\" data-id=\"c8db532\" 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-3bad6cb elementor-align-left elementor-widget elementor-widget-button\" data-id=\"3bad6cb\" 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\/navoneel\/brain-mri-images-for-brain-tumor-detection\" 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-af9c366 elementor-widget elementor-widget-heading\" data-id=\"af9c366\" 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\">Kaggle Notebook<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4153949 elementor-align-left elementor-widget elementor-widget-button\" data-id=\"4153949\" 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\/function9\/brain-tumor-mri-cnn-classification-with-keras\" 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=\"far 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<div class=\"elementor-element elementor-element-f0c7ecd elementor-widget elementor-widget-heading\" data-id=\"f0c7ecd\" 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-da2dc50 elementor-align-left elementor-widget elementor-widget-button\" data-id=\"da2dc50\" 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\/Brain-Tumor-MRI-CNN\/blob\/master\/Brain%20Tumor%20CNN.ipynb\" 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=\"far fa-file-code\"><\/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>Beyin T\u00fcm\u00f6r\u00fc MRI CNN Ama\u00e7 MRI taramalar\u0131nda beyin t\u00fcm\u00f6rlerini tespit etmek i\u00e7in Evri\u015fimli Sinir A\u011flar\u0131n\u0131 kullan\u0131m\u0131. Umar\u0131m yap\u0131lan bu \u00e7al\u0131\u015fma gelecekte t\u0131bbi g\u00f6r\u00fcnt\u00fcleme te\u015fhisini otomatik&hellip;<\/p>","protected":false},"author":1,"featured_media":1691,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-1687","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\/1687","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=1687"}],"version-history":[{"count":0,"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/posts\/1687\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/media\/1691"}],"wp:attachment":[{"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/media?parent=1687"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/categories?post=1687"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aix.web.tr\/en\/wp-json\/wp\/v2\/tags?post=1687"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}