![]() ![]() Running a SavedModel in TensorFlow Serving Print("Result after saving and loading:\n", decoded) Running inference from the SavedModel gives the same result as the original model. To customize signature names and output dictionary keys, see Specifying signatures during export. Imported signatures always return dictionaries. loaded = tf.saved_model.load(mobilenet_save_path) You can load the SavedModel back into Python with tf.saved_model.load and see how Admiral Hopper's image is classified. The save-path follows a convention used by TensorFlow Serving where the last path component ( 1/ here) is a version number for your model - it allows tools like Tensorflow Serving to reason about the relative freshness. Tf.saved_model.save(pretrained_model, mobilenet_save_path) mobilenet_save_path = os.path.join(tmpdir, "mobilenet/1/") The top prediction for this image is "military uniform". Print("Result before saving:\n", decoded) Pretrained_model = tf.()ĭecoded = imagenet_labels 1] Imagenet_labels = np.array(open(labels_path).read().splitlines()) Custom models work too, and are covered in detail later. You'll use an image of Grace Hopper as a running example, and a Keras pre-trained image classification model since it's easy to use. Physical_devices = tf.config.list_physical_devices('GPU') The rest of the guide will fill in details and discuss other ways to create SavedModels. The low-level SavedModel format continues to be supported for existing code.įor a quick introduction, this section exports a pre-trained Keras model and serves image classification requests with it. keras format and tf., as demonstrated in the guide here. Creating a SavedModel from Keras Deprecated: For Keras objects, it's recommended to use the new high-level. If you just want to save/load weights during training, refer to the checkpoints guide.Ĭaution: TensorFlow models are code and it is important to be careful with untrusted code.Refer to the keras save and serialize guide. Load: model = tf.saved_model.load(path_to_dir).Save: tf.saved_model.save(model, path_to_dir).This document describes how to use this API in detail. You can save and load a model in the SavedModel format using the following APIs: ![]() It does not require the original model building code to run, which makes it useful for sharing or deploying with TFLite, TensorFlow.js, TensorFlow Serving, or TensorFlow Hub. ![]() A SavedModel contains a complete TensorFlow program, including trained parameters (i.e, tf.Variables) and computation. ![]()
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