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