def __init__ (self, Neurons, AddBias=False, Name=None):
gobject.GObject.__init__ (self)
ListPool.__init__ (self)
# Unique id
self.Id = Layer.__nextId ()
# Name
self.Name = Name
# The total number of neurons
self.numberOfNeurons = Neurons
# The list of neurons
self.neurons = list ()
self._pool = self.neurons
#
self.hasBiasLayer = False
#
self.biasLayer = None
# Total Number of Weights
self.numberOfWeights = 0
# Total Number Of Bias Weights
self.numberOfBiasWeights = 0
def __init__ (self, Inputs, Outputs, Layers=None, NeuronsPerLayer=None, Name=""):
gobject.GObject.__init__ (self)
ListPool.__init__ (self)
# Name
self.Name = Name
# Are our layers symmetric?
if Layers is not None and NeuronsPerLayer is not None:
self.SymmetricLayers = True
else:
self.SymmetricLayers = False
# The number of inputs
self.NumberOfInputs = Inputs
# The number of outputs (the Neurons).
self.NumberOfOutputs= Outputs
# The number of layers, excluding the Input and Ouput.
self.NumberOfLayers = Layers
# The number of neurons that each layer will have.
self.NumberOfNeuronsPerLayer = NeuronsPerLayer
# Input/Output/Hidden layers.
# Input layer is always at index 0.
# Output layes is always at the end of the list.
self.Layers = list ()
self._pool = self.Layers
# Some flags
self.NetworkCreated = False
# All weights
self.Weights = None
# All inputs
self.Inputs = None
# Output values
self.Output = list ()
# Total number of weights
self.NumberOfWeights = 0
# Does this network has an extra bias layer attached
# to each hidden layer?
self.HasBiasLayer = False
# Store times for different methods. Used for benchmarking.
self.keepTime = dict ()