def ref_hist(inp, nbins=64, offset=(-48)):
 bins = (np.arange((nbins + 1)) + float(offset))
 bins[0] = (- float('Inf'))
 np_inp_log_abs = np.rint(np.log2(np.abs(inp.astype(np.float32))))
 (np_hist, edges) = np.histogram(np_inp_log_abs, density=False, bins=bins)
 if (np_hist.ndim < 2):
  np_hist = np_hist.reshape(1, np_hist.size)
 return np_hist

def ref_hist ( inp , nbins = 64 , offset = ( - 48 ) ) : bins = ( np . arange ( ( nbins + 1 ) ) + float ( offset ) ) bins [ 0 ] = ( - float ( 'Inf' ) ) np_inp_log_abs = np . rint ( np . log2 ( np . abs ( inp . astype ( np . float32 ) ) ) ) ( np_hist , edges ) = np . histogram ( np_inp_log_abs , density = False , bins = bins ) if ( np_hist . ndim < 2 ) : np_hist = np_hist . reshape ( 1 , np_hist . size ) return np_hist
implement a log2 histogram geared towards visualizing neural net parameters .