# Create a callback that saves the model's weightsĬp_callback = tf.(filepath=checkpoint_path, Checkpoint callback usageĬreate a tf. callback that saves weights only during training: checkpoint_path = "training_1/cp.ckpt"Ĭheckpoint_dir = os.path.dirname(checkpoint_path) The tf. callback allows you to continually save the model both during and at the end of training. You can use a trained model without having to retrain it, or pick-up training where you left off in case the training process was interrupted. Start by building a simple sequential model: # Define a simple sequential model To speed up these runs, use the first 1000 examples: (train_images, train_labels), (test_images, test_labels) = tf._data() To demonstrate how to save and load weights, you'll use the MNIST dataset. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. 07:12:15.990676: W tensorflow/compiler/tf2tensorrt/utils/py_:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. 07:12:15.990666: 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 07:12:15.990564: 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
Install and import TensorFlow and dependencies: pip install pyyaml h5py # Required to save models in HDF5 format import os For other approaches, refer to the Using the SavedModel format guide and the Save and load Keras models guide. This guide uses tf.keras-a high-level API to build and train models in TensorFlow. There are different ways to save TensorFlow models depending on the API you're using. See Using TensorFlow Securely for details. Caution: TensorFlow models are code and it is important to be careful with untrusted code. Sharing this data helps others understand how the model works and try it themselves with new data. the trained weights, or parameters, for the model.When publishing research models and techniques, most machine learning practitioners share: Saving also means you can share your model and others can recreate your work. This means a model can resume where it left off and avoid long training times. Model progress can be saved during and after training.
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