parameters: (self, inputDimension, projectionDimension, numPrototypes,
                   numOutputLabels, gamma,
                   W = None, B = None, Z = None)
docstring: Forward computation graph for ProtoNN.
  
          inputDimension: Input data dimension or feature dimension.
          projectionDimension: hyperparameter
          numPrototypes: hyperparameter
          numOutputLabels: The number of output labels or classes
          W, B, Z: Numpy matrices that can be used to initialize
              projection matrix(W), prototype matrix (B) and prototype labels
              matrix (B).
              Expected Dimensions:
                  W   inputDimension (d) x projectionDimension (d_cap)
                  B   projectionDimension (d_cap) x numPrototypes (m)
                  Z   numOutputLabels (L) x numPrototypes (m)
docstring_summary: Forward computation graph for ProtoNN.
docstring_tokens: Forward computation graph for ProtoNN .
function: def __init__(self, inputDimension, projectionDimension, numPrototypes,
                   numOutputLabels, gamma,
                   W = None, B = None, Z = None):
          '''
          Forward computation graph for ProtoNN.
  
          inputDimension: Input data dimension or feature dimension.
          projectionDimension: hyperparameter
          numPrototypes: hyperparameter
          numOutputLabels: The number of output labels or classes
          W, B, Z: Numpy matrices that can be used to initialize
              projection matrix(W), prototype matrix (B) and prototype labels
              matrix (B).
              Expected Dimensions:
                  W   inputDimension (d) x projectionDimension (d_cap)
                  B   projectionDimension (d_cap) x numPrototypes (m)
                  Z   numOutputLabels (L) x numPrototypes (m)
          '''
          with tf.name_scope('protoNN') as ns:
              self.__nscope = ns
          self.__d = inputDimension
          self.__d_cap = projectionDimension
          self.__m = numPrototypes
          self.__L = numOutputLabels
  
          self.__inW = W
          self.__inB = B
          self.__inZ = Z
          self.__inGamma = gamma
          self.W, self.B, self.Z = None, None, None
          self.gamma = None
  
          self.__validInit = False
          self.__initWBZ()
          self.__initGamma()
          self.__validateInit()
          self.protoNNOut = None
          self.predictions = None
          self.accuracy = None
function_tokens: def __init__ ( self , inputDimension , projectionDimension , numPrototypes , numOutputLabels , gamma , W = None , B = None , Z = None ) : with tf . name_scope ( 'protoNN' ) as ns : self . __nscope = ns self . __d = inputDimension self . __d_cap = projectionDimension self . __m = numPrototypes self . __L = numOutputLabels self . __inW = W self . __inB = B self . __inZ = Z self . __inGamma = gamma self . W , self . B , self . Z = None , None , None self . gamma = None self . __validInit = False self . __initWBZ ( ) self . __initGamma ( ) self . __validateInit ( ) self . protoNNOut = None self . predictions = None self . accuracy = None