You can visualize the result of applying these layers to an image. If instead you wanted it to be, you would write tf.(1./127.5, offset=-1). Note: The rescaling layer above standardizes pixel values to the range. Resize_and_rescale = tf.keras.Sequential([ You can use the Keras preprocessing layers to resize your images to a consistent shape (with tf.), and to rescale pixel values (with tf.). Use Keras preprocessing layers Resizing and rescaling 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. 02:38:41.776821: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] 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. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. 02:38:35.373466: W tensorflow/compiler/tf2tensorrt/utils/py_:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. 02:38:35.373455: W tensorflow/compiler/xla/stream_executor/platform/default/dso_:64] Could not load dynamic library 'libnvinfer_plugin.so.7' dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory 02:38:35.373345: W tensorflow/compiler/xla/stream_executor/platform/default/dso_:64] Could not load dynamic library 'libnvinfer.so.7' dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory 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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