![]() CIFAR-10 images used for classification Classifier Training Finally we can plot the first few images of the training set. The included preprocessing rescales the images into the range between and converts the label from the class index (integers 0 to 10) to a one-hot encoded categorical vector. The above code first downloads the dataset. Visualize_data(x_train, y_train, class_names) Y_test = to_categorical(y_test, num_classes) Y_train = to_categorical(y_train, num_classes) (x_train, y_train), (x_test, y_test) = cifar10.load_data() The data is split into 50k training and 10k test images. It consists of 32x32 pixel images with 10 classes. Because the MNIST dataset is a bit overused and too easy, we use the more challenging CIFAR-10 dataset by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. We are going to build an image classifier. def visualize_data(images, categories, class_names): To plot our dataset, we define a visualization function that takes a dataset and simply plots the first few images. Most importantly for this tutorial, we import the ImageDataGenerator class from the Keras image preprocessing module: import tensorflow as tfįrom import cifar10įrom tensorflow.keras import layers, modelsįrom import ImageDataGeneratorįrom import to_categorical We only need a few things to get started, import tensorflow, keras and a few utility functions for label conversion and plotting. Now it's time to put what we learnt into practice and see what accuracy improvements we get by applying data augmentation! Setup In the previous posts, we learned about the different data augmentation techniques and how to write your own custom data augmentation preprocessing function. ![]() We will also discuss in detail what happened during training and how to spot overfitting. Prevent overfitting and increase accuracy.How to train a Keras model using the ImageDataGenerator class.In this python Colab tutorial you will learn: In Keras, the lightweight tensorflow library, image data augmentation is very easy to include into your training runs and you get a augmented training set in real-time with only a few lines of code. With good data augmentation, you can start experimenting with convolutional neural networks much earlier because you get away with less data. Image data augmentation is very powerful and should be in every deep learning engineer's toolbox! While we could stop the training early or add regularization techniques, it is usually good practice to implement a basic data augmentation in your training routine. In deep learning, we are often limited by the amount of available data and overfitting becomes a real problem. ![]()
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