def connectTo(self, otherLayer):
for i in range(otherLayer.getLayerSize()):
neuron = otherLayer[i]
BiasNeuron = Neuron(i, self.Id)
BiasNeuron.createSynapse(otherLayer.Id, neuron)
self.neurons.append(BiasNeuron)
self.numberOfWeights += 1
self.emit("bias-layer-connected")
def createLayer (self):
if len (self) == 0:
# Create the neurons
for i in range (self.numberOfNeurons):
self.neurons.append ( Neuron (
Id = i,
LayerId = self.Id,
)
)
# Create the bias layer
if self.hasBiasLayer:
self.biasLayer.connect ("bias-layer-connected", self.biasLayerConnected)
self.biasLayer.connectTo (self)
self.emit ("layer-created")
Notice, that the number of inputs for each neuron is calculated dynamically,
and changed whenever a new synapse-connection is created.
"""
# batteries
import sys
sys.path.append("../../src/")
# spiral framework
from spiral.nn import globals
from spiral.nn.generic import Neuron
from spiral.helper import colored
if __name__ == "__main__":
globals.runInDebug = False
n1 = Neuron(Id=1, LayerId=0)
n2 = Neuron(Id=2, LayerId=0)
n3 = Neuron(Id=3, LayerId=0)
n4 = Neuron(Id=4, LayerId=1)
n5 = Neuron(Id=5, LayerId=1)
n6 = Neuron(Id=6, LayerId=2)
n7 = Neuron(Id=7, LayerId=2)
n8 = Neuron(Id=8, LayerId=3)
# Create the synapses for input layer
n1.createSynapse(1, n4)
n1.createSynapse(1, n5)
n2.createSynapse(1, n4)
connects with the hidden layer`s two neurons, which in turn, they
connect with the output layer`s neuron.
"""
# batteries
import sys
sys.path.append("../../src/")
# spiral framework
from spiral.nn import globals
from spiral.nn.generic import Neuron
from spiral.helper import colored
if __name__ == "__main__":
globals.runInDebug = True
n1 = Neuron(Id=1, LayerId=0, Name="Input")
n1.setAutoNotify(True)
n2 = Neuron(Id=2, LayerId=1)
n3 = Neuron(Id=3, LayerId=1)
n4 = Neuron(Id=4, LayerId=2, Name="Output")
# Create the synapses
n1.createSynapse(1, n2)
n1.createSynapse(1, n3)
n2.createSynapse(2, n4)
n3.createSynapse(2, n4)
print "*" * 40
# Put the weights
