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 __init__ (self, Id, LayerId):
Neuron.__init__ (self, Id, LayerId)
# Input value
self.input = None
# The associated feature map.
self.featureMap = list ()
# The number of inputs also define the length of the feature map.
self.numberOfInputs = 0
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 __init__(self, Id, LayerId):
Neuron.__init__(self, Id, LayerId)
# Input value
self.input = None
# The associated feature map.
self.featureMap = list()
# The number of inputs also define the length of the feature map.
self.numberOfInputs = 0
def neuronInputsUpdated(self, object, property):
"""
Pass the input value to all synapses except the input bidisynapses.
"""
if self.numberOfSynapses > 0:
Neuron.neuronInputsUpdated(self, object, property)
for layer in self.outputBidiSynapses.values():
for neuron, bidisynapse in layer.iteritems():
bidisynapse.putInput(self.input)
for layer in self.inputBidiSynapses.values():
for neuron, inputSynapse in layer.iteritems():
inputSynapse.putInput(self.input)
def neuronInputsUpdated (self, object, property):
"""
Pass the input value to all synapses except the input bidisynapses.
"""
if self.numberOfSynapses > 0:
Neuron.neuronInputsUpdated (self, object, property)
for layer in self.outputBidiSynapses.values ():
for neuron, bidisynapse in layer.iteritems ():
bidisynapse.putInput (self.input)
for layer in self.inputBidiSynapses.values ():
for neuron, inputSynapse in layer.iteritems ():
inputSynapse.putInput (self.input)
def __init__(self, Id, LayerId, Name=None):
Neuron.__init__(self, Id, LayerId, Name)
# Bidirectional Synapses
self.inputBidiSynapses = dict()
self.outputBidiSynapses = dict()
# Total number of bidirectional synapses
self.numberOfBidiSynapses = 0
self.numberOfInputBidiSynapses = 0
self.numberOfOutputBidiSynapses = 0
# The current number of number of synapses.
# that have "fired".
self.numberOfBidiSynapsesFired = 0
self.numberOfInputBidiSynapsesFired = 0
self.numberOfOutputBidiSynapsesFired = 0
# The current bidi synapses that have
# successfuly fired (an in "input"), so
# that we release the lock ;)
self.indexOfFiredBidiSynapses = list()
def __init__ (self, Id, LayerId, Name=None):
Neuron.__init__ (self, Id, LayerId, Name)
# Bidirectional Synapses
self.inputBidiSynapses = dict ()
self.outputBidiSynapses= dict ()
# Total number of bidirectional synapses
self.numberOfBidiSynapses = 0
self.numberOfInputBidiSynapses = 0
self.numberOfOutputBidiSynapses= 0
# The current number of number of synapses.
# that have "fired".
self.numberOfBidiSynapsesFired = 0
self.numberOfInputBidiSynapsesFired = 0
self.numberOfOutputBidiSynapsesFired= 0
# The current bidi synapses that have
# successfuly fired (an in "input"), so
# that we release the lock ;)
self.indexOfFiredBidiSynapses = list ()
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")
# 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
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
# 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, Name="Input")
n2 = Neuron (Id=2, LayerId=1)
n3 = Neuron (Id=3, LayerId=2, Name="Output")
# Create the synapses
n1.createSynapse (1, n2)
n1.createSynapse (1, n3)
n2.createSynapse (2, n3)
# Put the weights
n1.putWeights ((0.5, 3.0))
n2.putWeights ((-.75,))
# Insert the inputs
n1.putInput (1.0)
Input layer connects with the first hidden layer and the output layer as well.
The "network" is really simple since it is composed only of three neurons.
"""
# 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, Name="Input")
n2 = Neuron(Id=2, LayerId=1)
n3 = Neuron(Id=3, LayerId=2, Name="Output")
# Create the synapses
n1.createSynapse(1, n2)
n1.createSynapse(1, n3)
n2.createSynapse(2, n3)
# Put the weights
n1.putWeights((0.5, 3.0))
n2.putWeights((-.75, ))
# Insert the inputs
n1.putInput(1.0)
