def add_cdf_image_summary()

in 3_predict/visualization_utils.py [0:0]

• 19 lines of code
• 1 McCabe index (conditional complexity)

def add_cdf_image_summary(values, name):
  """Adds a tf.summary.image for a CDF plot of the values.

  Normalizes `values` such that they sum to 1, plots the cumulative distribution
  function and creates a tf image summary.

  Args:
    values: a 1-D float32 tensor containing the values.
    name: name for the image summary.
  """
  def cdf_plot(values):
    """Numpy function to plot CDF."""
    normalized_values = values / np.sum(values)
    sorted_values = np.sort(normalized_values)
    cumulative_values = np.cumsum(sorted_values)
    fraction_of_examples = (np.arange(cumulative_values.size, dtype=np.float32)
                            / cumulative_values.size)
    fig = plt.figure(frameon=False)
    ax = fig.add_subplot('111')
    ax.plot(fraction_of_examples, cumulative_values)
    ax.set_ylabel('cumulative normalized values')
    ax.set_xlabel('fraction of examples')
    fig.canvas.draw()
    width, height = fig.get_size_inches() * fig.get_dpi()
    image = np.fromstring(fig.canvas.tostring_rgb(), dtype='uint8').reshape(
        1, int(height), int(width), 3)
    return image
  cdf_plot = tf.py_func(cdf_plot, [values], tf.uint8)
  tf.summary.image(name, cdf_plot)