{"id":3316,"date":"2024-03-13T06:45:25","date_gmt":"2024-03-13T06:45:25","guid":{"rendered":"https:\/\/www.silicloud.com\/blog\/how-to-utilize-pre-trained-models-in-keras\/"},"modified":"2025-07-30T14:27:36","modified_gmt":"2025-07-30T14:27:36","slug":"how-to-utilize-pre-trained-models-in-keras","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/blog\/how-to-utilize-pre-trained-models-in-keras\/","title":{"rendered":"Keras Pre-trained Models: A Guide"},"content":{"rendered":"

There are two ways to use pre-trained models in Keras: either by utilizing pre-trained models already provided in Keras (such as VGG16, ResNet50, InceptionV3, etc.) or by using models trained in other deep learning frameworks (like TensorFlow, PyTorch).<\/p>\n

    \n
  1. Utilize the pre-trained models provided by Keras.<\/li>\n<\/ol>\n
    from<\/span> keras.applications.vgg16 import<\/span> VGG16\r\nfrom<\/span> keras.applications.vgg16 import<\/span> preprocess_input, decode_predictions\r\nfrom<\/span> keras.preprocessing import<\/span> image\r\nimport<\/span> numpy as<\/span> np\r\n\r\n# \u52a0\u8f7d\u9884\u8bad\u7ec3\u7684VGG16\u6a21\u578b<\/span>\r\nmodel = VGG16(weights='imagenet'<\/span>)\r\n\r\n# \u52a0\u8f7d\u8981\u9884\u6d4b\u7684\u56fe\u7247\uff0c\u5e76\u8fdb\u884c\u9884\u5904\u7406<\/span>\r\nimg_path = 'path_to_your_image.jpg'<\/span>\r\nimg = image.load_img(img_path, target_size=(224<\/span>, 224<\/span>))\r\nx = image.img_to_array(img)\r\nx = np.expand_dims(x, axis=0<\/span>)\r\nx = preprocess_input(x)\r\n\r\n# \u9884\u6d4b\u56fe\u50cf\u7684\u7c7b\u522b<\/span>\r\npreds = model.predict(x)\r\npredictions = decode_predictions(preds, top=3<\/span>)[0<\/span>]\r\nfor<\/span> i, (imagenetID, label, score) in<\/span> enumerate<\/span>(predictions):\r\n    print<\/span>(\"{}. {}: {:.2f}%\"<\/span>.format<\/span>(i + 1<\/span>, label, score * 100<\/span>))\r\n<\/code><\/pre>\n
      \n
    1. Utilize models trained in other deep learning frameworks.<\/li>\n<\/ol>\n
      import<\/span> tensorflow as<\/span> tf\r\nfrom<\/span> keras.applications.vgg16 import<\/span> preprocess_input\r\nfrom<\/span> keras.preprocessing import<\/span> image\r\nimport<\/span> numpy as<\/span> np\r\n\r\n# \u52a0\u8f7d\u5176\u4ed6\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u4e2d\u8bad\u7ec3\u597d\u7684\u6a21\u578b<\/span>\r\nmodel = tf.keras.models.load_model('path_to_your_model.h5'<\/span>)\r\n\r\n# \u52a0\u8f7d\u8981\u9884\u6d4b\u7684\u56fe\u7247\uff0c\u5e76\u8fdb\u884c\u9884\u5904\u7406<\/span>\r\nimg_path = 'path_to_your_image.jpg'<\/span>\r\nimg = image.load_img(img_path, target_size=(224<\/span>, 224<\/span>))\r\nx = image.img_to_array(img)\r\nx = np.expand_dims(x, axis=0<\/span>)\r\nx = preprocess_input(x)\r\n\r\n# \u9884\u6d4b\u56fe\u50cf\u7684\u7c7b\u522b<\/span>\r\npreds = model.predict(x)\r\n<\/code><\/pre>\n
      You can use pre-trained models in Keras for tasks such as image classification and object detection using the two methods mentioned above.<\/p>\n","protected":false},"excerpt":{"rendered":"
      There are two ways to use pre-trained models in Keras: either by utilizing pre-trained models already provided in Keras (such as VGG16, ResNet50, InceptionV3, etc.) or by using models trained in other deep learning frameworks (like TensorFlow, PyTorch). Utilize the pre-trained models provided by Keras. from keras.applications.vgg16 import VGG16 from keras.applications.vgg16 import preprocess_input, decode_predictions from […]<\/p>\n","protected":false},"author":11,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[960,1251,1218,1292,1291],"class_list":["post-3316","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-deep-learning","tag-keras","tag-pre-trained-models","tag-resnet50","tag-vgg16"],"yoast_head":"\nKeras Pre-trained Models: A Guide - Blog - Silicon Cloud<\/title>\n<meta name=\"description\" content=\"Learn how to use pre-trained models in Keras effectively. 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