Modern deep learning algorithms, such as the convolutional neural network, or CNN, can learn features that are invariant to their location in the image. In addition, it can be useful to experiment with data augmentation methods in isolation and in concert to see if they result in a measurable improvement to model performance, perhaps with a small prototype dataset, model, and training run. A vertical flip of the photo of a cat does not make sense and would probably not be appropriate given that the model is very unlikely to see a photo of an upside down cat.Īs such, it is clear that the choice of the specific data augmentation techniques used for a training dataset must be chosen carefully and within the context of the training dataset and knowledge of the problem domain. For example, a horizontal flip of a picture of a cat may make sense, because the photo could have been taken from the left or right. This means, variations of the training set images that are likely to be seen by the model. The intent is to expand the training dataset with new, plausible examples. Transforms include a range of operations from the field of image manipulation, such as shifts, flips, zooms, and much more. Image data augmentation is perhaps the most well-known type of data augmentation and involves creating transformed versions of images in the training dataset that belong to the same class as the original image. This is done by applying domain-specific techniques to examples from the training data that create new and different training examples. The performance of deep learning neural networks often improves with the amount of data available.ĭata augmentation is a technique to artificially create new training data from existing training data.
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#How to use keras data augmentation update#
Update Jun/2019: Fixed small typo in API example (thanks Georgios).Update May/2019: Fixed data type for pixel values when plotting.
#How to use keras data augmentation how to#
How to use shift, flip, brightness, and zoom image data augmentation.ĭiscover how to build models for photo classification, object detection, face recognition, and more in my new computer vision book, with 30 step-by-step tutorials and full source code.Image data augmentation is supported in the Keras deep learning library via the ImageDataGenerator class.Image data augmentation is used to expand the training dataset in order to improve the performance and ability of the model to generalize.In this tutorial, you will discover how to use image data augmentation when training deep learning neural networks.Īfter completing this tutorial, you will know: The Keras deep learning neural network library provides the capability to fit models using image data augmentation via the ImageDataGenerator class. Paste it in the notebook in the variable “html_page”.We respect your privacy. Copy a complicated part of the html code. Click right and select “View Page Source”.
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Question 1: Go on any static website online. Question 9: (Optional) Train the CNN coded for you in the notebook (See Appendix below) on any of the pictures you created. flow ( x, batch_size = 1, save_to_dir = 'preview', save_prefix = 'cat', save_format = 'jpeg' ): num_image_generated += 1 if num_image_generated > 20 : break # stop the loop after num_image_generated iterations ) to fit keras' standard shape #Use flow() to apply data augmentation randomly according to the datagenerator #and saves the results to the `preview/` directory num_image_generated = 0 for batch in datagen. #Image preprocessing in Keras from import ImageDataGenerator, array_to_img, img_to_array, load_img datagen = ImageDataGenerator ( rotation_range = 45, width_shift_range = 0.3, height_shift_range = 0.3, shear_range = 0.3, zoom_range = 0.3, horizontal_flip = True, fill_mode = 'nearest' ) img = load_img ( 'iguana.jpg' ) # this is a PIL image x = img_to_array ( img ) # convert image to numpy array x = x.