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- SAVING EXTERNAL JAVASCRIPT USING EDITPAD LITE CODE
- SAVING EXTERNAL JAVASCRIPT USING EDITPAD LITE DOWNLOAD
Scheme: file:// await model.save('file:///path/to/my-model') This APIĪffords greater flexiblity in controlling HTTP requests.įor example: await model.save(tf.io.browserHTTPRequest( These aspects of the requests from save by replacing the URL string argument in tf.io.browserHTTPRequest. for authentication or if you want to specify a folder that the model should be saved in). Often you will have to pass more arguments or request headers to your HTTP server (e.g.
SAVING EXTERNAL JAVASCRIPT USING EDITPAD LITE CODE
This api doc contains a Python code snippet that demonstrates how one may use the flask web framework to handle the request originated from save. Note that the name of the two files will always be exactly as specified above (the name is built in to the function).
SAVING EXTERNAL JAVASCRIPT USING EDITPAD LITE DOWNLOAD
This will cause the browser to download the model files to the user's machine.
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Scheme: downloads:// await model.save('downloads://my-model') Like local storage it persists between refreshes, it also tends to have larger limits on the size of objects stored. This saves a model to the browser's IndexedDB storage. Scheme: indexeddb:// await model.save('indexeddb://my-model') Each browser also sets their own limit on how much data can be stored in local storage for a given domain. This will persist between refreshes, though local storage can be cleared by users or the browser itself if space becomes a concern. This saves a model under the name my-model in the browser's local storage. Scheme: localstorage:// await model.save('localstorage://my-model') Saving a model in node.js does not prevent it from being loaded in the browser.īelow we will examine the different schemes available.
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Weights: These are binary files that store the weights of a given model in an efficient format. It contains references to the models's weights which are stored externally. Topology: This is a file describing the architecture of a model (i.e. Loading these models are covered in the following two tutorials:īoth provide a function model.save that allow you to save the We can also import TensorFlow Python models. This tutorial will focus on saving and loading TensorFlow.js models (identifiable by JSON files). Layers api is that the models created with it are serializable and this is what we will explore in this tutorial. These may be models you have trained yourself or those trained by others. The Layers API or converted from existing TensorFlow models. TensorFlow.js provides functionality for saving and loading models that have been created with