(self, tag, values, step, bins=1000)
Log a histogram of the tensor of values.
Log a histogram of the tensor of values.
Log a histogram of the tensor of values .
def histo_summary(self, tag, values, step, bins=1000):
        """Log a histogram of the tensor of values."""

        # Create a histogram using numpy
        counts, bin_edges = np.histogram(values, bins=bins)

        # Fill the fields of the histogram proto
        hist = tf.HistogramProto()
        hist.min = float(np.min(values))
        hist.max = float(np.max(values))
        hist.num = int(np.prod(values.shape))
        hist.sum = float(np.sum(values))
        hist.sum_squares = float(np.sum(values ** 2))

        # Drop the start of the first bin
        bin_edges = bin_edges[1:]

        # Add bin edges and counts
        for edge in bin_edges:
            hist.bucket_limit.append(edge)
        for c in counts:
            hist.bucket.append(c)

        # Create and write Summary
        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
        self.writer.add_summary(summary, step)
        self.writer.flush()
def histo_summary ( self , tag , values , step , bins = 1000 ) : # Create a histogram using numpy counts , bin_edges = np . histogram ( values , bins = bins ) # Fill the fields of the histogram proto hist = tf . HistogramProto ( ) hist . min = float ( np . min ( values ) ) hist . max = float ( np . max ( values ) ) hist . num = int ( np . prod ( values . shape ) ) hist . sum = float ( np . sum ( values ) ) hist . sum_squares = float ( np . sum ( values ** 2 ) ) # Drop the start of the first bin bin_edges = bin_edges [ 1 : ] # Add bin edges and counts for edge in bin_edges : hist . bucket_limit . append ( edge ) for c in counts : hist . bucket . append ( c ) # Create and write Summary summary = tf . Summary ( value = [ tf . Summary . Value ( tag = tag , histo = hist ) ] ) self . writer . add_summary ( summary , step ) self . writer . flush ( )