(self, num_classes, binarize=False, epsilon=1e-6)
Arguments:
            num_classes (int): Number of different output classes.
            binarize (bool, optional): If True will attempt to convert the model
                                       outputs to a one-hot encoding (in place).
                                       Defaults to False.
            epsilon (float, optional): Smoothing to apply to avoid division by zero.
                                       Defaults to 1e-6.
Arguments:
            num_classes (int): Number of different output classes.
            binarize (bool, optional): If True will attempt to convert the model
                                       outputs to a one-hot encoding (in place).
                                       Defaults to False.
            epsilon (float, optional): Smoothing to apply to avoid division by zero.
                                       Defaults to 1e-6.
Arguments : num_classes ( int ) : Number of different output classes . binarize ( bool optional ) : If True will attempt to convert the model outputs to a one - hot encoding ( in place ) . Defaults to False . epsilon ( float optional ) : Smoothing to apply to avoid division by zero . Defaults to 1e - 6 .
def __init__(self, num_classes, binarize=False, epsilon=1e-6):
        """
        Arguments:
            num_classes (int): Number of different output classes.
            binarize (bool, optional): If True will attempt to convert the model
                                       outputs to a one-hot encoding (in place).
                                       Defaults to False.
            epsilon (float, optional): Smoothing to apply to avoid division by zero.
                                       Defaults to 1e-6.
        """
        self.outputs = self.be.empty((num_classes, 2))
        self.token_stats = self.be.empty((num_classes, 3))
        self.metric_names = ['Precision', 'Recall']
        if binarize:
            self.bin_buf = self.be.iobuf(1, dtype=np.int32)
        else:
            self.bin_buf = None
        self.eps = epsilon
def __init__ ( self , num_classes , binarize = False , epsilon = 1e-6 ) : self . outputs = self . be . empty ( ( num_classes , 2 ) ) self . token_stats = self . be . empty ( ( num_classes , 3 ) ) self . metric_names = [ 'Precision' , 'Recall' ] if binarize : self . bin_buf = self . be . iobuf ( 1 , dtype = np . int32 ) else : self . bin_buf = None self . eps = epsilon