![]() Note: For featurewise_center, featurewise_std_normalization, zca_whitening, one must fit the data to calculate the mean, standard deviation, and principal components. This should be used with featurewise_center=True, otherwise, this will give you a warning and automatically set featurewise_center=True. You need to fit the training data to calculate the principal components. For maths behind this, refer to this StackOverflow question. In short, this strengthens the high-frequency components in the image. Zca_whitening: This is a preprocessing method which tries to remove the redundancy from the data while keeping its structure intact, unlike PCA. Samplewise_std_normalization: In this, we divide each input image by its standard deviation. Since the image mean is a local statistic that can be calculated from the image itself, there is no need for calling the fit method. So, in this, we set the mean pixel value of each image to be zero. Samplewise_center: Sample-wise means of a single image. deviation of 1 or in short Gaussian Distribution. Thus, featurewise center and std_normalization together known as standardization tends to make the mean of the data to be 0 and std. To prevent this, one can calculate the mean from a smaller sample.įeaturewise_std_normalization: In this, we divide each image by the standard deviation of the entire dataset. ![]() For this, you have to load the entire training dataset which may significantly kill your memory if the dataset is large.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |