(self, tag, values, bins, step=None)
Saves histogram of values.

    Args:
      tag: str: label for this data
      values: ndarray: will be flattened by this routine
      bins: number of bins in histogram, or array of bins for np.histogram
      step: int: training step
Saves histogram of values.
Saves histogram of values .
def histogram(self, tag, values, bins, step=None):
    """Saves histogram of values.

    Args:
      tag: str: label for this data
      values: ndarray: will be flattened by this routine
      bins: number of bins in histogram, or array of bins for np.histogram
      step: int: training step
    """
    if step is None:
      step = self._step
    else:
      self._step = step
    values = np.array(values)
    bins = np.array(bins)
    values = np.reshape(values, -1)
    counts, limits = np.histogram(values, bins=bins)
    # boundary logic
    cum_counts = np.cumsum(np.greater(counts, 0, dtype=np.int32))
    start, end = np.searchsorted(
        cum_counts, [0, cum_counts[-1] - 1], side='right')
    start, end = int(start), int(end) + 1
    counts = (
        counts[start -
               1:end] if start > 0 else np.concatenate([[0], counts[:end]]))
    limits = limits[start:end + 1]
    sum_sq = values.dot(values)
    histo = tf.compat.v1.HistogramProto(
        min=values.min(),
        max=values.max(),
        num=len(values),
        sum=values.sum(),
        sum_squares=sum_sq,
        bucket_limit=limits.tolist(),
        bucket=counts.tolist())
    summary = tf.compat.v1.Summary(
        value=[tf.compat.v1.Summary.Value(tag=tag, histo=histo)])
    self.add_summary(summary, step)
def histogram ( self , tag , values , bins , step = None ) : if step is None : step = self . _step else : self . _step = step values = np . array ( values ) bins = np . array ( bins ) values = np . reshape ( values , - 1 ) counts , limits = np . histogram ( values , bins = bins ) # boundary logic cum_counts = np . cumsum ( np . greater ( counts , 0 , dtype = np . int32 ) ) start , end = np . searchsorted ( cum_counts , [ 0 , cum_counts [ - 1 ] - 1 ] , side = 'right' ) start , end = int ( start ) , int ( end ) + 1 counts = ( counts [ start - 1 : end ] if start > 0 else np . concatenate ( [ [ 0 ] , counts [ : end ] ] ) ) limits = limits [ start : end + 1 ] sum_sq = values . dot ( values ) histo = tf . compat . v1 . HistogramProto ( min = values . min ( ) , max = values . max ( ) , num = len ( values ) , sum = values . sum ( ) , sum_squares = sum_sq , bucket_limit = limits . tolist ( ) , bucket = counts . tolist ( ) ) summary = tf . compat . v1 . Summary ( value = [ tf . compat . v1 . Summary . Value ( tag = tag , histo = histo ) ] ) self . add_summary ( summary , step )