def addHiddenLayer(self, NumberOfNeurons, AddBias=False, AddToStack=True):
if not self.NetworkCreated:
new_layer = NeuronLayer(Neurons=NumberOfNeurons, AddBias=AddBias)
new_layer.connect("layer-created", self.layerCreated)
new_layer.connect("layer-connected", self.layerConnected)
new_layer.connect("layer-disconnected", self.layerDisconnected)
new_layer.createLayer()
if AddToStack:
self._TempLayers.append(new_layer)
else:
return new_layer
def addHiddenLayer (self, NumberOfNeurons, AddBias=False, AddToStack=True):
if not self.NetworkCreated:
new_layer = NeuronLayer (Neurons = NumberOfNeurons, AddBias = AddBias)
new_layer.connect ("layer-created", self.layerCreated)
new_layer.connect ("layer-connected", self.layerConnected)
new_layer.connect ("layer-disconnected", self.layerDisconnected)
new_layer.createLayer ()
if AddToStack:
self._TempLayers.append (new_layer)
else:
return new_layer
"""
"""
# batteries
import sys
sys.path.append("../../src/")
# spiral framework
from spiral.nn import globals
from spiral.nn.generic.layer import NeuronLayer
from spiral.helper import colored
if __name__ == "__main__":
globals.runInDebug = True
input_layer = NeuronLayer(Name="Input Layer", Neurons=3)
input_layer.createLayer()
first_hidden = NeuronLayer(Name="Hidden Layer #1", Neurons=2, AddBias=True)
first_hidden.createLayer()
second_hidden = NeuronLayer(Name="Hidden Layer #2",
Neurons=2,
AddBias=True)
second_hidden.createLayer()
output_layer = NeuronLayer(Name="Output Layer", Neurons=1, AddBias=True)
output_layer.createLayer()
print "*" * 100
input_layer.connectTo(first_hidden)
print "*" * 50
first_hidden.connectTo(second_hidden)
print "*" * 50
def __init__ (self, Neurons, AddBias=False, Name=None):
NeuronLayer.__init__ (self, Neurons, AddBias=False, Name=Name)
# Remove uneeded member variables
del self.biasLayer, self.numberOfBiasWeights
def createNet(self):
""" Create the neurons, the layers and the synapses. """
if not self.NetworkCreated:
# Create input layer
input_layer = NeuronLayer(Neurons=self.NumberOfInputs)
input_layer.createLayer()
# Create the hidden layers
hidden_layers = list()
for i in range(self.NumberOfLayers):
hidden_layer = NeuronLayer(
Neurons=self.NumberOfNeuronsPerLayer)
hidden_layer.createLayer()
hidden_layers.append(hidden_layer)
# Create the output layer
output_layer = NeuronLayer(Neurons=self.NumberOfOutputs)
output_layer.createLayer()
# Now, we must explicity declare the synapses!
# We connect all neurons from input layer to the first hidden layer,
# also we connect the input neuron to the output neuron!
input_layer.connectTo(hidden_layers[0])
input_layer.connectTo(output_layer)
# We connect the neurons of the first hidden layer wih the neurons
# of the second hidden layer.
# Also, we connect the second neuron of this layer (the first hidden)
# to the neuron of the output layer.
hidden_layers[0].connectTo(hidden_layers[1])
hidden_layers[0][1].createSynapse(output_layer.Id, output_layer[0])
# We connect the neurons of the second hidden layer to the output.
hidden_layers[1].connectTo(output_layer)
self.addLayers((input_layer, hidden_layers[0], hidden_layers[1],
output_layer))
self.NetworkCreated = True
def __init__(self, Neurons, AddBias=False, Name=None):
NeuronLayer.__init__(self, Neurons, AddBias=False, Name=Name)
# Remove uneeded member variables
del self.biasLayer, self.numberOfBiasWeights
def createNet (self):
""" Create the neurons, the layers and the synapses. """
if not self.NetworkCreated:
# Create input layer
input_layer = NeuronLayer (Neurons = self.NumberOfInputs)
input_layer.createLayer ()
# Create the hidden layers
hidden_layers = list ()
for i in range (self.NumberOfLayers):
hidden_layer = NeuronLayer (Neurons = self.NumberOfNeuronsPerLayer)
hidden_layer.createLayer ()
hidden_layers.append (hidden_layer)
# Create the output layer
output_layer = NeuronLayer (Neurons = self.NumberOfOutputs)
output_layer.createLayer ()
# Now, we must explicity declare the synapses!
# We connect all neurons from input layer to the first hidden layer,
# also we connect the input neuron to the output neuron!
input_layer.connectTo (hidden_layers[0])
input_layer.connectTo (output_layer)
# We connect the neurons of the first hidden layer wih the neurons
# of the second hidden layer.
# Also, we connect the second neuron of this layer (the first hidden)
# to the neuron of the output layer.
hidden_layers[0].connectTo (hidden_layers[1])
hidden_layers[0][1].createSynapse (output_layer.Id, output_layer[0])
# We connect the neurons of the second hidden layer to the output.
hidden_layers[1].connectTo (output_layer)
self.addLayers ( (input_layer, hidden_layers[0], hidden_layers[1], output_layer) )
self.NetworkCreated = True
# batteries
import sys
sys.path.append ("../../src/")
# spiral framework
from spiral.nn import globals
from spiral.nn.generic.layer import NeuronLayer
from spiral.helper import colored
if __name__ == "__main__":
globals.runInDebug = True
input_layer = NeuronLayer (Name="Input Layer" , Neurons=3)
input_layer.createLayer ()
first_hidden = NeuronLayer (Name="Hidden Layer #1", Neurons=2, AddBias=True)
first_hidden.createLayer ()
second_hidden= NeuronLayer (Name="Hidden Layer #2", Neurons=2, AddBias=True)
second_hidden.createLayer ()
output_layer = NeuronLayer (Name="Output Layer" , Neurons=1, AddBias=True)
output_layer.createLayer ()
print "*" * 100
input_layer.connectTo (first_hidden)
print "*" * 50
first_hidden.connectTo (second_hidden)
print "*" * 50
second_hidden.connectTo (output_layer)
print "*" * 50
