![]() ![]() WARNING: All log messages before absl::InitializeLog() is called are written to STDERR # are in place to discourage outdated usage, and can be ignored. # These warnings (and similar warnings throughout this notebook) ![]() # This may generate warnings related to saving the state of the optimizer. Validation_data=(test_images, test_labels),Ĭallbacks=) # Pass callback to training # 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. 04:46:18.798424: E external/local_xla/xla/stream_executor/cuda/cuda_:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 04:46:18.796965: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 04:46:18.796910: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 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. For more advanced saving or serialization workflows, especially those involving custom objects, please refer to the Save and load Keras models guide. keras format used in this tutorial is recommended for saving Keras objects, as it provides robust, efficient name-based saving that is often easier to debug than low-level or legacy formats. 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.
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