![]() ![]() It accepts the paths to the training and validation data, image size to resize to, and batch size as parameters. The prepare_batches function starting from line 6 prepares the training and validation data batches. We need matplotlib for plotting of accuracy and loss graphs and tensorflow for preparing the dataset. Valid_batches = valid_datagen.flow_from_directory( ![]() Train_batches = train_datagen.flow_from_directory( Let’s start with the import statements and the first function. These will go into the utils.py file.ĭownload the Source Code for this Tutorial We will start with writing a few helper functions. The Python files will stay inside the src directory. Let’s write the code in each file in the following order: We have three Python files and will write the code for each in their respective sections. You can install/upgrade your TensorFlow version here. This tutorial is based on TensorFlow version 2.6.2. And the src directory holds the three Python files that we need for the tutorial.įor now, you can ignore the monkey_labels.txt file as we do not need that in this tutorial.The outputs directory will contain the accuracy and loss graphs.The input directory contains the training and validation data inside the respective class directories.Let’s check out the directory structure for the tutorial now. There are about 1400 images in total in the dataset.īefore moving ahead in the tutorial, I recommend that you download the dataset if you intend to execute the code locally in your system. The training and validation images are inside the respective class folders which are named from n0 to n9. The other columns show the common name, the Latin name, number of training, and validation images. Monkey species dataset details.Īs you can see we have the Label column from n0 to n9. The dataset contains images of 10 different monkey species.įigure 3. In this tutorial, we will be using the 10 Monkey Species dataset from Kaggle. You can find all the preprocessing and augmentation layers here. ![]() tf.: Let’s us reshape the input into another shape.tf.: A preprocessing layer which rescales input values to a new range.There are a few which can only act as image preprocessing layers. tf.: Randomly varies image width during training.Īll of the above layers can act as both preprocessing and augmentation layers during training.tf.: Randomly varies image height during training.tf.: For random cropping of the image.tf.: For adjusting the contrast of the image during training.tf.: Randomly zooms the image during training.tf.: Randomly rotates the image during training.tf.: For randomly flipping the image horizontally and vertically during training.These include (without getting into the technical details): As this happens within the tf.keras.layers module, if we train our model on the GPU, then the augmentation will also happen on the GPU.Īs of now, we have a pretty decent collection of image augmentation that we can carry out using tf.keras.layers. Now, with tf.keras.layers some of the image augmentation techniques can be applied on the fly just before being fed into the neural network. With the recent versions of TensorFlow, we are able to offload much of this CPU processing part onto the GPU. This mainly happens because the augmentations take place on the CPU. When we have the right GPU and a good model in place, we do not want that the preprocessing should slow down the training. This is not desirable when dealing with huge datasets. It can even increase the per epoch training time by two-fold at times. Image Data Augmentation using TensorFlow and KerasĪs we know, image augmentation with the TensorFlow ImageDataGenerator can be very slow. Next, we will jump into the coding part of the tutorial.Then we will get to know the dataset and directory structure for this tutorial.What new thing can we do to improve performance?.How is it generally done using TensorFlow and Keras?.We will start with a short discussion on image data augmentation.We will cover the following topics in this tutorial. ![]()
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