imshow ( image ) # Displaying the figure pyplot. astype ( 'uint8' ) # Plotting the data pyplot. next () # Remember to convert these images to unsigned integers for viewing image = batch. subplot ( 330 + 1 + i ) # generating images in batches batch = it. flow ( samples, batch_size = 1 ) # Preparing the Samples and Plot for displaying output for i in range ( 9 ): # preparing the subplot pyplot. x dataaugmentation(inputs) This model expects data in the range of (-1,1) and not (0,1). inputs keras.Input(shape(150, 150, 3)) After this, apply the data augmentation. datagen = ImageDataGenerator ( rotation_range = 90 ) # Creating an iterator for data augmentation it = datagen. Let’s start by standardizing the size of the input images. # Importing the required libraries from numpy import expand_dims from import load_img from import img_to_array from import ImageDataGenerator from matplotlib import pyplot # Loading desired images img = load_img ( 'Car.jpg' ) # For processing, we are converting the image(s) to an array data = img_to_array ( img ) # Expanding dimension to one sample samples = expand_dims ( data, 0 ) # Calling ImageDataGenerator for creating data augmentation generator. There are mainly five different techniques for applying image augmentation, we will discuss these techniques in the coming section. And it does all this with better memory management so that you can train a huge dataset efficiently with lesser memory consumption. It supports multiple back-ends, including TensorFlow, CNTK and Theano. For more details, have a look at the Keras documentation for the ImageDataGenerator class. Intro Deep Learning with Keras : : CHEAT SHEET Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. It allows you to specify the augmentation parameters, which we will go over in the next steps. But here ImageDataGenerator takes care of this automatically during the training phase. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by Image data augmentation is a technique that can be used to artificially expand. In Keras, there's an easy way to do data augmentation with the class .ImageDataGenerator. Then in that case we would have to manually generate the augmented image as a preprocessing step and include them in our training dataset.
KERAS DATA AUGMENTATION ON EXISTING IMAGE MATRIX GENERATOR
To appreciate this Keras capability of image data generator we need to imagine if this class was not present. Yet, image augmentation is a preprocessing step (you are preparing your dataset. PyTorch offers a much better interface via Torchvision Transforms. TensorFlow 2 (Keras) gives the ImageDataGenerator. Your favorite Deep Learning library probably offers some tools for it. This simply means it can generate augmented images dynamically during the training of the model making the overall mode more robust and accurate. Image augmentation is widely used in practice. The major advantage of the Keras ImageDataGenerator class is its ability to produce real-time image augmentation. The ImageDataGenerator class in Keras is used for implementing image augmentation. What is Image Data Generator (ImageDataGenerator) in Keras?