- class brainpy.Sequential(*modules_as_tuple, name=None, mode=None, **modules_as_dict)#
A sequential input-output module.
Modules will be added to it in the order they are passed in the constructor. Alternatively, an
dictof modules can be passed in. The
Sequentialaccepts 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
Sequentialprovides 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
Sequentialapplies to each of the modules it stores (which are each a registered submodule of the
What’s the difference between a
Containeris exactly what it sounds like–a container to store
DynamicalSystems! On the other hand, the layers in a
Sequentialare connected in a cascading way.
>>> 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)))
Update function of a sequential model.