Beyond handling individual arrays, deep learning frameworks support the serialization and deserialization of ordered collections of tensors. A programmer can supply a list containing multiple tensors to the save function to store them together in a single file, and later use the load function to retrieve the entire list back into memory while preserving the sequential order of the underlying tensors.

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Deep learning frameworks provide dedicated functions to serialize and deserialize individual tensors to and from persistent storage. By directly invoking framework-specific functions such as load and save, a practitioner can write a tensor's data to a file by providing a target file name and the corresponding tensor variable. The serialized data can subsequently be read from the stored file back into memory when needed.

Saving and Loading Individual Tensors

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Saving and Loading Lists of Tensors

Deep learning I/O functions possess the capability to serialize and deserialize dictionaries that map string keys to tensor values. This technique is highly practical in model development, as it provides a structured and convenient mechanism to seamlessly read or write all the named weights and parameters within a complex neural network architecture in a single operation.

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