imshow ( image ) # Displaying the figure pyplot. astype ( 'uint8' ) # Plotting the data pyplot. Random crop is a data augmentation technique wherein we. For more on data augmentation, read our introductory post to this series. Data augmentation is the practice of using data we already have to create new training examples to help our machine learning models generalize better. next () # Remember to convert these images to unsigned integers for viewing image = batch. This is where data augmentation comes into play. 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. datagen = ImageDataGenerator ( rotation_range = 90 ) # Creating an iterator for data augmentation it = datagen. # 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. This class allows you to: configure random transformations and normalization operations to be done on your image data during training instantiate generators of augmented image batches (and their labels) via. And it does all this with better memory management so that you can train a huge dataset efficiently with lesser memory consumption. In Keras this can be done via the class. But here ImageDataGenerator takes care of this automatically during the training phase. 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 GENERATOR#
To appreciate this Keras capability of image data generator we need to imagine if this class was not present. This simply means it can generate augmented images dynamically during the training of the model making the overall mode more robust and accurate. 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 if we need to plug in custom augmentation operations in the augmentation pipeline? Added on top of it, what if we need to fix the probability at which the augmentation operations would get applied? Data augmentation pipelines are quite central behind the success of recent works like SimCLR, Augmix, etc.What is Image Data Generator (ImageDataGenerator) in Keras? Let’s now think about situations where we may need to use a combination of the image ops of TensorFlow and the layers we just saw. Note that, performance might get slightly affected when going with this approach since the GPUs will be utilized to run the preprocessing layers as well. A fully worked out example is available here. Now, model should be good to go with - model.fit(train_ds. Input ( shape = ( IMG_SHAPE, IMG_SHAPE, 3 )) x = data_augmentation ( inputs ) # Apply random data augmentation x = feature_extractor_model ( x, training = False ) x = GlobalAveragePooling2D ()( x ) x = Dropout ( 0.2 )( x ) outputs = Dense ( 1 )( x ) model = Model ( inputs, outputs ) # You define an input layer with pre-defined shapes inputs = keras.