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Sequential

Sequential#

class brainpy.Sequential(*modules_as_tuple, name=None, mode=None, **modules_as_dict)[source]#

A sequential input-output module.

Modules will be added to it in the order they are passed in the constructor. Alternatively, an dict of modules can be passed in. The update() method of Sequential accepts any input and forwards it to the first module it contains. It then “chains” outputs to inputs sequentially for each subsequent module, finally returning the output of the last module.

The value a Sequential provides over manually calling a sequence of modules is that it allows treating the whole container as a single module, such that performing a transformation on the Sequential applies to each of the modules it stores (which are each a registered submodule of the Sequential).

What’s the difference between a Sequential and a Container? A Container is exactly what it sounds like–a container to store DynamicalSystem s! On the other hand, the layers in a Sequential are connected in a cascading way.

Examples

>>> import brainpy as bp
>>> import brainpy.math as bm
>>>
>>> # composing ANN models
>>> l = bp.Sequential(bp.layers.Dense(100, 10),
>>>                   bm.relu,
>>>                   bp.layers.Dense(10, 2))
>>> l(bm.random.random((256, 100)))
>>>
>>> # Using Sequential with Dict. This is functionally the
>>> # same as the above code
>>> l = bp.Sequential(l1=bp.layers.Dense(100, 10),
>>>                   l2=bm.relu,
>>>                   l3=bp.layers.Dense(10, 2))
>>> l(bm.random.random((256, 100)))
Parameters:

  • modules_as_tuple – The children modules.

  • modules_as_dict – The children modules.

  • name (Optional[str]) – The object name.

  • mode (Optional[Mode]) – The object computing context/mode. Default is None.

update(x)[source]#

Update function of a sequential model.

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