class_docstr: NASNet Cell class that is used as a 'layer' in image architectures.
  
  Args:
    num_conv_filters: The number of filters for each convolution operation.
    operations: List of operations that are performed in the NASNet Cell in
      order.
    used_hiddenstates: Binary array that signals if the hiddenstate was used
      within the cell. This is used to determine what outputs of the cell
      should be concatenated together.
    hiddenstate_indices: Determines what hiddenstates should be combined
      together with the specified operations to create the NASNet cell.
class_docstr_tok: ["NASNet","Cell","class","that","is","used","as","a","'layer","'","in","image","architectures",".","Args",":","num_conv_filters",":","The","number","of","filters","for","each","convolution","operation",".","operations",":","List","of","operations","that","are","performed","in","the","NASNet","Cell","in","order",".","used_hiddenstates",":","Binary","array","that","signals","if","the","hiddenstate","was","used","within","the","cell",".","This","is","used","to","determine","what","outputs","of","the","cell","should","be","concatenated","together",".","hiddenstate_indices",":","Determines","what","hiddenstates","should","be","combined","together","with","the","specified","operations","to","create","the","NASNet","cell","."]
docstr: Apply drop_path regularization to net.
docstr_tok: ["Apply","drop_path","regularization","to","net","."]
code: def _apply_drop_path(self, net):
      """Apply drop_path regularization to net."""
      drop_path_keep_prob = self._drop_path_keep_prob
      if drop_path_keep_prob < 1.0:
          # Scale keep prob by layer number
          assert self._cell_num != -1
          # The added 2 is for the reduction cells
          num_cells = self._total_num_cells
          layer_ratio = (self._cell_num + 1) / float(num_cells)
          with tf.device("/cpu:0"):
              tf.summary.scalar("layer_ratio", layer_ratio)
          drop_path_keep_prob = 1 - layer_ratio * (
              1 - drop_path_keep_prob
          )
          # Decrease the keep probability over time
          current_step = tf.cast(
              tf.train.get_or_create_global_step(), tf.float32
          )
          drop_path_burn_in_steps = self._total_training_steps
          current_ratio = current_step / drop_path_burn_in_steps
          current_ratio = tf.minimum(1.0, current_ratio)
          with tf.device("/cpu:0"):
              tf.summary.scalar("current_ratio", current_ratio)
          drop_path_keep_prob = 1 - current_ratio * (
              1 - drop_path_keep_prob
          )
          with tf.device("/cpu:0"):
              tf.summary.scalar(
                  "drop_path_keep_prob", drop_path_keep_prob
              )
          net = drop_path(net, drop_path_keep_prob)
      return net
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
label_desc: Use image node_id 7 for calling the NasNetABaseCell obj's underlying member method code with example usage: obj._apply_drop_path(net) and returns: net