tensorflow_probability/examples/bayesian_neural_network.py
def plot_weight_posteriors(names, qm_vals, qs_vals, fname):
  """Save a PNG plot with histograms of weight means and stddevs.

  Args:
    names: A Python `iterable` of `str` variable names.
    qm_vals: A Python `iterable`, the same length as `names`,
      whose elements are Numpy `array`s, of any shape, containing
      posterior means of weight varibles.
    qs_vals: A Python `iterable`, the same length as `names`,
      whose elements are Numpy `array`s, of any shape, containing
      posterior standard deviations of weight varibles.
    fname: Python `str` filename to save the plot to.
  """
  fig = figure.Figure(figsize=(6, 3))
  canvas = backend_agg.FigureCanvasAgg(fig)

  ax = fig.add_subplot(1, 2, 1)
  for n, qm in zip(names, qm_vals):
    sns.distplot(qm.flatten(), ax=ax, label=n)
  ax.set_title("weight means")
  ax.set_xlim([-1.5, 1.5])
  ax.legend()

  ax = fig.add_subplot(1, 2, 2)
  for n, qs in zip(names, qs_vals):
    sns.distplot(qs.flatten(), ax=ax)
  ax.set_title("weight stddevs")
  ax.set_xlim([0, 1.])

  fig.tight_layout()
  canvas.print_figure(fname, format="png")
  print("saved {}".format(fname))
def plot_weight_posteriors(names, qm_vals, qs_vals, fname):
  fig = figure.Figure(figsize=(6, 3))
  canvas = backend_agg.FigureCanvasAgg(fig)

  ax = fig.add_subplot(1, 2, 1)
  for n, qm in zip(names, qm_vals):
    sns.distplot(qm.flatten(), ax=ax, label=n)
  ax.set_title("weight means")
  ax.set_xlim([-1.5, 1.5])
  ax.legend()

  ax = fig.add_subplot(1, 2, 2)
  for n, qs in zip(names, qs_vals):
    sns.distplot(qs.flatten(), ax=ax)
  ax.set_title("weight stddevs")
  ax.set_xlim([0, 1.])

  fig.tight_layout()
  canvas.print_figure(fname, format="png")
  print("saved {}".format(fname))
def plot_weight_posteriors ( names , qm_vals , qs_vals , fname ) : fig = figure . Figure ( figsize = ( 6 , 3 ) ) canvas = backend_agg . FigureCanvasAgg ( fig ) ax = fig . add_subplot ( 1 , 2 , 1 ) for n , qm in zip ( names , qm_vals ) : sns . distplot ( qm . flatten ( ) , ax = ax , label = n ) ax . set_title ( "weight means" ) ax . set_xlim ( [ - 1.5 , 1.5 ] ) ax . legend ( ) ax = fig . add_subplot ( 1 , 2 , 2 ) for n , qs in zip ( names , qs_vals ) : sns . distplot ( qs . flatten ( ) , ax = ax ) ax . set_title ( "weight stddevs" ) ax . set_xlim ( [ 0 , 1. ] ) fig . tight_layout ( ) canvas . print_figure ( fname , format = "png" ) print ( "saved {}" . format ( fname ) )
Save a PNG plot with histograms of weight means and stddevs.

  Args:
    names: A Python `iterable` of `str` variable names.
    qm_vals: A Python `iterable`, the same length as `names`,
      whose elements are Numpy `array`s, of any shape, containing
      posterior means of weight varibles.
    qs_vals: A Python `iterable`, the same length as `names`,
      whose elements are Numpy `array`s, of any shape, containing
      posterior standard deviations of weight varibles.
    fname: Python `str` filename to save the plot to.
Save a PNG plot with histograms of weight means and stddevs .