![]() ![]() # Normalize pixel values to be between 0 and 1 (train_images, train_labels), (test_images, test_labels) = _data() The classes are mutually exclusive and there is no overlap between them. The dataset is divided into 50,000 training images and 10,000 testing images. The CIFAR10 dataset contains 60,000 color images in 10 classes, with 6,000 images in each class. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. 02:35:18.952748: W tensorflow/compiler/tf2tensorrt/utils/py_:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. 02:35:18.952732: 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 02:35:18.952623: 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 ![]() Import TensorFlow import tensorflow as tfįrom tensorflow.keras import datasets, layers, models Because this tutorial uses the Keras Sequential API, creating and training your model will take just a few lines of code. This tutorial demonstrates training a simple Convolutional Neural Network (CNN) to classify CIFAR images. ![]()
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