def plot()

in tools/tensorflow_docs/plots/__init__.py [0:0]

• 33 lines of code
• 7 McCabe index (conditional complexity)

  def plot(self, histories, metric=None, smoothing_std=None):
    """Plots a {name: history} dictionary of Keras histories.

    Colors are assigned to the name-key, and maintained from call to call.
    Training metrics are shown as a solid line, validation metrics dashed.

    Args:
      histories: {name: history} a dictionary of Keras histories.
      metric: which metric to plot from all the histories.
      smoothing_std: the standard deviation of the smoothing kernel applied
        before plotting. The units are in array-indices.
    """
    if metric is None:
      metric = self.metric
    if smoothing_std is None:
      smoothing_std = self.smoothing_std

    for name, history in histories.items():
      # Remember name->color associations.
      if name in self.color_table:
        color = self.color_table[name]
      else:
        color = COLOR_CYCLE[len(self.color_table) % len(COLOR_CYCLE)]
        self.color_table[name] = color

      train_value = history.history[metric]
      val_value = history.history['val_' + metric]
      if smoothing_std is not None:
        train_value = _smooth(train_value, std=smoothing_std)
        val_value = _smooth(val_value, std=smoothing_std)

      plt.plot(
          history.epoch,
          train_value,
          color=color,
          label=name.title() + ' Train')
      plt.plot(
          history.epoch,
          val_value,
          '--',
          label=name.title() + ' Val',
          color=color)

    plt.xlabel('Epochs')
    plt.ylabel(metric.replace('_', ' ').title())
    plt.legend()

    plt.xlim(
        [0, max([history.epoch[-1] for name, history in histories.items()])])
    plt.grid(True)