The article discusses the advantages of using shared layers within the functional API for deep learning models. Shared layers help to encode inputs from similar spaces, allowing for efficient information sharing across multiple inputs. This reduces the data training requirements. The article also outlines how to reuse instances of layers, emphasizing that the static structure of the graph allows easy access to node activations for tasks like feature extraction. Examples illustrate usage, particularly with the VGG19 model, showcasing the benefits of enhancing model architectures through the functional API.
Shared layers are layer instances that are reused multiple times in the same model -- they learn features that correspond to multiple paths in the graph-of-layers.
To share a layer in the functional API, call the same layer instance multiple times, enabling sharing of information across different inputs and benefiting model training.
Because the graph of layers you are manipulating is a static data structure, it can be accessed and inspected, allowing for plotting functional models as images.
Accessing the activations of intermediate layers is useful for feature extraction, enhancing the functional API's versatility in deep learning model architectures.
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