def Show(self, train=False):
    """Displays useful statistics about the model."""
    if not self.proto.hyperparams.enable_display:
      return
    f = 1
    if self.hyperparams.dropout and not train:
      f = 1 / (1 - self.hyperparams.dropout_prob)
    if self.is_input:
      visualize.display_hidden(self.data.asarray(), self.fig, title=self.name)
      #visualize.display_w(self.neg_sample.asarray(), 28, 10, self.state.shape[1]/10, self.fig, title=self.name, vmax=1, vmin=0)
      #visualize.show_hist(self.params['bias'].asarray(), self.fig)
    else:
      visualize.display_hidden(f*self.state.asarray(), self.fig, title=self.name)
      #visualize.show_hist(self.params['bias'].asarray(), self.fig)
      """
      plt.figure(self.fig)
      plt.clf()
      plt.subplot(1, 3, 1)
      plt.title('pos_probabilities')
      plt.imshow(self.pos_state.asarray(), cmap = plt.cm.gray, interpolation = 'nearest', vmax=1, vmin=0)
      plt.subplot(1, 3, 2)
      plt.title('neg_probabilities')
      plt.imshow(self.neg_state.asarray(), cmap = plt.cm.gray, interpolation = 'nearest', vmax=1, vmin=0)
      plt.subplot(1, 3, 3)
      plt.title('neg_samples')
      plt.imshow(self.neg_sample.asarray(), cmap = plt.cm.gray, interpolation = 'nearest', vmax=1, vmin=0)
      plt.suptitle(self.name)
      plt.draw()
      """
      #visualize.display_w(self.neg_sample.asarray(), 1, 1, self.state.shape[1], self.fig, title=self.name)
Displays useful statistics about the model.
deepnet/layer.py