def __init__(self, layerSize):
"""
"""
# Properly inherit the AbstractLayer
AbstractLayer.__init__(self)
# A ReLU layer has an input port and output port
self.input = InputPort(layerSize)
self.output = OutputPort(layerSize)
def __init__(self, layerSize):
"""
An absolute value layer can be connected to several inputs
"""
# Properly inherit the AbstractLayer
AbstractLayer.__init__(self)
# A sigmoid layer has an input port and output port
self.input = InputPort(layerSize)
self.output = OutputPort(layerSize)
def __init__(self, size, initialHistory=np.zeros((0, 0))):
"""
Create a History layer
"""
AbstractLayer.__init__(self)
self.layerSize = size
self.input = InputPort(self.layerSize)
self.output = OutputPort(self.layerSize)
self.history = []
self.output.value = np.copy(initialHistory)
self.initialHistory = initialHistory
def __init__(self, inputSize, outputSizes):
"""
Create a layer which splits the input into the provided output sizes
"""
# Properly inherit the AbstractLayer
AbstractLayer.__init__(self)
# Should probably have an assertion that the output sizes add up to the
# input sizes
# A sigmoid layer has an input port and output port
self.input = InputPort(inputSize)
self.outputPorts = []
for size in outputSizes:
self.outputPorts.append(OutputPort(size))
def __init__(self, size, initialHistory=gpu.zeros((0, 0))):
"""
Create a History layer
"""
AbstractLayer.__init__(self)
self.layerSize = size
self.input = InputPort(self.layerSize)
self.output = OutputPort(self.layerSize)
self.history = []
self.output.value = gpu.garray(np.copy(
initialHistory.as_numpy_array()))
self.initialHistory = initialHistory
def __init__(self, layerSize, initialHistory):
"""
A delay layer can be connected to several inputs
"""
# Properly inherit the AbstractLayer
AbstractLayer.__init__(self)
# A delay layer has an input port and output port
self.input = InputPort(layerSize)
self.output = OutputPort(layerSize)
# A delay layer has a history, which propagates forward
# when step is called
self.initial_history = initialHistory
self.history = np.zeros((1, layerSize))
self.current_step = 0
