#NEWLINE# def __init__( self , inputNames , inputDim , outputDim , lazyInit = True , initRange = 1.0 , initSeed = 2 , intConversion = False , needInit = True , initWeights = 0 , sparse = False , learningRate = 0.0 , learningRateAnnealConst = 0.0 , momentum = 0.0 , deltaMomentum = 0.0 , weightClip = 0.0 , gradientClip = 0.0 , weightRegConst = 0.0 , outputdEdX = False , name = None ) : #NEWLINE# #INDENT# Stage . __init__ ( self , name = name , inputNames = inputNames , learningRate = learningRate , outputDim = outputDim , learningRateAnnealConst = learningRateAnnealConst , momentum = momentum , deltaMomentum = deltaMomentum , weightClip = weightClip , gradientClip = gradientClip , weightRegConst = weightRegConst , gpu = False , outputdEdX = outputdEdX ) #NEWLINE# self . outputDim = outputDim #NEWLINE# self . inputDim = inputDim #NEWLINE# self . initRange = initRange #NEWLINE# self . random = np . random . RandomState ( initSeed ) #NEWLINE# self . needInit = needInit #NEWLINE# self . intConversion = intConversion #NEWLINE# if needInit : #NEWLINE# #INDENT# if lazyInit : #NEWLINE# #INDENT# self . W = None #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# self . initWeights ( ) #NEWLINE# #UNINDENT# #UNINDENT# else : #NEWLINE# #INDENT# self . W = initWeights #NEWLINE# if ( use_gpu and ( self . W . dtype != np . float32 ) ) : #NEWLINE# #INDENT# self . W = self . W . astype ( 'float32' ) #NEWLINE# #UNINDENT# #UNINDENT# self . X = 0 #NEWLINE# self . Y = 0 #NEWLINE# self . sparse = sparse #NEWLINE# self . dEdW = 0.0