Created
September 3, 2017 08:34
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How to use the VGG16 model from Keras Applications trained on ImageNet to make a prediction on an image.
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| from keras.applications.vgg16 import VGG16 | |
| from keras.preprocessing import image | |
| from keras.applications.vgg16 import preprocess_input, decode_predictions | |
| import numpy as np | |
| model = VGG16(weights='imagenet') | |
| img_path = 'Data/Jellyfish.jpg' | |
| img = image.load_img(img_path, target_size=(224, 224)) | |
| img_data = image.img_to_array(img) | |
| img_data = np.expand_dims(img_data, axis=0) | |
| img_data = preprocess_input(img_data) | |
| preds = model.predict(img_data) | |
| # decode the results into a list of tuples (class, description, probability) | |
| print('Predicted:', decode_predictions(preds, top=3)[0]) |
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Hello, do you have any examples to train the VGG16 from scratch, ie with new dataset?