parameters: (self,
                 feature_dim=None,
                 num_classes=None,
                 weight_scale=0.01,
                 reg=0.0)
docstring: Args:
        feature_dim (int): The dimension of the input feature (length of the feature vector).
        num_classes (int): The number of output classes.
        weight_scale (float): A weight factor for computing new weights. The backpropagated
          weights are drawn from standard normal distribution, then multipled by this number to keep
          the scale small.
        reg (float): The regularization strength.
docstring_summary: Args:
        feature_dim (int): The dimension of the input feature (length of the feature vector).
        num_classes (int): The number of output classes.
        weight_scale (float): A weight factor for computing new weights. The backpropagated
          weights are drawn from standard normal distribution, then multipled by this number to keep
          the scale small.
        reg (float): The regularization strength.
docstring_tokens: Args : feature_dim ( int ) : The dimension of the input feature ( length of the feature vector ) . num_classes ( int ) : The number of output classes . weight_scale ( float ) : A weight factor for computing new weights . The backpropagated weights are drawn from standard normal distribution then multipled by this number to keep the scale small . reg ( float ) : The regularization strength .
function: def __init__(self,
                 feature_dim=None,
                 num_classes=None,
                 weight_scale=0.01,
                 reg=0.0):
      """
      Args:
        feature_dim (int): The dimension of the input feature (length of the feature vector).
        num_classes (int): The number of output classes.
        weight_scale (float): A weight factor for computing new weights. The backpropagated
          weights are drawn from standard normal distribution, then multipled by this number to keep
          the scale small.
        reg (float): The regularization strength.
      """
      self.reg = reg
      self.feature_dim = feature_dim
      self.num_classes = num_classes
      self.label_map = None
      self.params = {}
      if feature_dim and num_classes:
        self.params['mat_w'] = weight_scale * np.random.randn(
            feature_dim, num_classes).astype(np.float32)
        self.params['vec_b'] = np.zeros((num_classes,)).astype(np.float32)
  
      # Needed to set proper quantization parameter for output tensor of FC layer.
      self.min_score = np.finfo(np.float32).max
      self.max_score = np.finfo(np.float32).min
function_tokens: def __init__ ( self , feature_dim = None , num_classes = None , weight_scale = 0.01 , reg = 0.0 ) : self . reg = reg self . feature_dim = feature_dim self . num_classes = num_classes self . label_map = None self . params = { } if feature_dim and num_classes : self . params [ 'mat_w' ] = weight_scale * np . random . randn ( feature_dim , num_classes ) . astype ( np . float32 ) self . params [ 'vec_b' ] = np . zeros ( ( num_classes , ) ) . astype ( np . float32 ) # Needed to set proper quantization parameter for output tensor of FC layer. self . min_score = np . finfo ( np . float32 ) . max self . max_score = np . finfo ( np . float32 ) . min