The input layer takes a shape argument that is a tuple that indicates the dimensionality of the input data. Unlike the Sequential model, you must create and define a standalone Input layer that specifies the shape of input data. Let’s look at the three unique aspects of Keras functional API in turn: 1. Models are defined by creating instances of layers and connecting them directly to each other in pairs, then defining a Model that specifies the layers to act as the input and output to the model. More than that, it allows you to define ad hoc acyclic network graphs. It specifically allows you to define multiple input or output models as well as models that share layers. The Keras functional API provides a more flexible way for defining models. The Sequential model API is great for developing deep learning models in most situations, but it also has some limitations.įor example, it is not straightforward to define models that may have multiple different input sources, produce multiple output destinations or models that re-use layers. The Sequential model API is a way of creating deep learning models where an instance of the Sequential class is created and model layers are created and added to it.įor example, the layers can be defined and passed to the Sequential as an array: ![]() If you are new to Keras or deep learning, see this step-by-step Keras tutorial.
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