preprocess_input(x) is a function that applies some preprocessing to an input image before feeding it to a neural network model. It is part of the keras.applications module and is typically used when working with pre-trained models such as ResNet50.
The preprocess_input function takes a single argument x, which is a NumPy array representing an image. The function applies a series of transformations to the image, such as normalizing the pixel values to be between -1 and 1 and zero-centering the data.
The purpose of this preprocessing step is to ensure that the input image is in the same format as the images used to train the pre-trained model. This is important because the pre-trained model has learned to recognize patterns in images of a specific format, and if the input images are not in that format, the model's performance may suffer.
Once the input image has been preprocessed, it can be passed through the neural network model to obtain a prediction.
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