Source code for akida.fully_connected

from akida.core import (Layer, FullyConnectedParams, DataProcessingParams,
                        NumNeuronsParams, WeightBitsParams, LearningParams,

[docs]class FullyConnected(Layer): """This is used for most processing purposes, since any neuron in the layer can be connected to any input channel. Outputs are returned from FullyConnected layers as a list of events, that is, as a triplet of x, y and feature values. However, FullyConnected models by definition have no intrinsic spatial organization. Thus, all output events have x and y values of zero with only the f value being meaningful – corresponding to the index of the event-generating neuron. Note that each neuron can only generate a single event for each packet of inputs processed. Args: units (int): number of units. name (str, optional): name of the layer. weights_bits (int, optional): number of bits used to quantize weights. activation (bool, optional): enable or disable activation function. threshold (int, optional): threshold for neurons to fire or generate an event. act_step (float, optional): length of the potential quantization intervals. act_bits (int, optional): number of bits used to quantize the neuron response. """ def __init__(self, units, name="", weights_bits=1, activation=True, threshold=0, act_step=1, act_bits=1): try: params = FullyConnectedParams( DataProcessingParams( NumNeuronsParams(units), WeightBitsParams(weights_bits), LearningParams(), ActivationsParams(activation, threshold, act_step, act_bits))) # Call parent constructor to initialize C++ bindings # Note that we invoke directly __init__ instead of using super, as # specified in pybind documentation Layer.__init__(self, params, name) except: self = None raise