![]() Next, load these images off disk using the helpful tf._dataset_from_directory utility. Here are some roses: roses = list(data_dir.glob('roses/*'))Īnd some tulips: tulips = list(data_dir.glob('tulips/*')) There are 3,670 total images: image_count = len(list(data_dir.glob('*/*.jpg'))) The dataset contains five sub-directories, one per class: flower_photo/ĭata_dir = tf._file('flower_photos', origin=dataset_url, untar=True)Ģ28813984/228813984 - 1s 0us/stepĪfter downloading, you should now have a copy of the dataset available. This tutorial uses a dataset of about 3,700 photos of flowers. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. 02:27:15.406679: W tensorflow/compiler/tf2tensorrt/utils/py_:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. 02:27:15.406662: 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 02:27:15.406562: 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 and other necessary libraries: import matplotlib.pyplot as pltįrom import Sequential In addition, the notebook demonstrates how to convert a saved model to a TensorFlow Lite model for on-device machine learning on mobile, embedded, and IoT devices. Improve the model and repeat the process.This tutorial follows a basic machine learning workflow: ![]() ![]() Identifying overfitting and applying techniques to mitigate it, including data augmentation and dropout.Efficiently loading a dataset off disk.This tutorial shows how to classify images of flowers using a tf.keras.Sequential model and load data using tf._dataset_from_directory. ![]()
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