listing deep learning for computer vision preprocessing an input image for vgg from keras preprocessing import image from keras applications vgg import preprocess_inputdecode_predictions import numpy as np img_path '/users/fchollet/downloads/creative_commons_elephant jpgimg image load_img(img_pathtarget_size=( ) image img_to_array(imgfloat numpy array of shape ( np expand_dims(xaxis= preprocess_input(xpython imaging library (pilimage of size adds dimension to transform the array into batch of size ( preprocesses the batch (this does channel-wise color normalizationlocal path to the target image you can now run the pretrained network on the image and decode its prediction vector back to human-readable formatpreds model predict(xprint('predicted:'decode_predictions(predstop= )[ ]predicted:'[( ' ' 'african_elephant' )( ' ' 'tusker' )( ' ' 'indian_elephant' )the top three classes predicted for this image are as followsafrican elephant (with probabilitytusker (with probabilityindian elephant (with probabilitythe network has recognized the image as containing an undetermined quantity of african elephants the entry in the prediction vector that was maximally activated is the one corresponding to the "african elephantclassat index np argmax(preds[ ] to visualize which parts of the image are the most african elephant-likelet' set up the grad-cam process listing set ing up the grad-cam algorit hm african elephantentry in the prediction vector african_e lephant_output model output[: last_conv_layer model get_layer('block _conv 'licensed to output feature map of the block _conv layerthe last convolutional layer in vgg