You should use `dataset.take(k).cache().repeat()` instead. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. 05:09:18.712477: W tensorflow/core/kernels/data/cache_dataset_ops.cc:768] The calling iterator did not fully read the dataset being cached. Let's retrieve an image from the dataset and use it to demonstrate data augmentation. (train_ds, val_ds, test_ds), metadata = tfds.load( If you would like to learn about other ways of importing data, check out the load images tutorial. For convenience, download the dataset using TensorFlow Datasets. This tutorial uses the tf_flowers dataset. Use the tf.image methods, such as tf.image.flip_left_right, tf.image.rgb_to_grayscale, tf.image.adjust_brightness, tf.image.central_crop, and tf.image.stateless_random*.Use the Keras preprocessing layers, such as tf., tf., tf., and tf.You will learn how to apply data augmentation in two ways: This tutorial demonstrates data augmentation: a technique to increase the diversity of your training set by applying random (but realistic) transformations, such as image rotation.
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