nanaxdocu.blogg.se - Keras data augmentation example

KERAS DATA AUGMENTATION EXAMPLE GENERATOR
KERAS DATA AUGMENTATION EXAMPLE SERIES

# if we have reached the specified number of examples, break

KERAS DATA AUGMENTATION EXAMPLE GENERATOR

# loop over examples from our image data augmentation generator Save_prefix= "image", save_format= "jpg") ImageGen = aug.flow(image, batch_size= 1, save_to_dir=args, # initialize the total number of images generated thus far # construct the image generator for data augmentation then # load the input image, convert it to a NumPy array, and then Help= "# of training samples to generate") Help= "path to output directory to store augmentation examples")Īp.add_argument( "-t", "-total", type=int, default= 100, # construct the argument parser and parse the argumentsĪp.add_argument( "-i", "-image", required= True,Īp.add_argument( "-o", "-output", required= True, Type 1 Implementation 1įrom import ImageDataGeneratorįrom import img_to_arrayįrom import load_img The final type of data augmentation seeks to combine both dataset generation and in-place augmentation - you may see this type of data augmentation when performing behavioral cloning (like using games and car driving simulators to create training datasets for self-driving.). Combining dataset generation and in-place augmentation.

We call this “in-place” and “on-the-fly” data augmentation because this augmentation is done at training time (i.e., we are not generating these examples ahead of time/prior to training).Ĭ.

The ImageDataGenerator is not returning both the original data and the transformed data - the class only returns the randomly transformed data.

The randomly transformed batch is then returned to the calling function.

KERAS DATA AUGMENTATION EXAMPLE SERIES

The ImageDataGenerator transforms each image in the batch by a series of random translations, rotations, etc.An input batch of images is presented to the ImageDataGenerator.This type of data augmentation is what Keras’ ImageDataGenerator class implements.

(and then applying the type of data augmentation covered in the “Combining data generation and in-place augmentation”.)ī.

A better method – gather additional data or look into methods of behavioral cloning. we cannot expect to train a NN on a small amount of data and the expect it to generalize to data it was never trained on and has never seen before. Our neural network is only as good as the data it was trained on. A problem of this approach – we haven’t exactly increased the ability of our model to generalize. Take the transformed image and write it back out to disk. Randomly transform the original image via a series of random translations, rotations, etc. Dataset generator and data expansion via data augmentation.

There are three types of data augmentation while training deep neural networks: a. Instead, the ImageDataGenerator class accepts the original data, randomly transforms it, and returns only the NEW, transformed data. The Keras ImageDataGenerator class is not an “additive” operation.