original_docstring: Given a list of weights and Assign ops, identify the change in weights.
  
      Args:
          weights: list of Variables
          update_ops: list of Assign ops, typically computed using Keras' opt.get_updates()
  
      Returns:
          list of Tensors containing the weight update for each variable
      
docstring: Given a list of weights and Assign ops, identify the change in weights.
docstring_tokens: Given a list of weights and Assign ops identify the change in weights .
code: def extract_weight_changes(weights, update_ops):
      """Given a list of weights and Assign ops, identify the change in weights.
  
      Args:
          weights: list of Variables
          update_ops: list of Assign ops, typically computed using Keras' opt.get_updates()
  
      Returns:
          list of Tensors containing the weight update for each variable
      """
      name_to_var = {v.name: v.value() for v in weights}
      weight_update_ops = list(filter(lambda x: x.op.inputs[0].name in name_to_var, update_ops))
      nonweight_update_ops = list(filter(lambda x: x.op.inputs[0].name not in name_to_var, update_ops))
      # Make sure that all the weight update ops are Assign ops
      for weight in weight_update_ops:
          if weight.op.type != 'Assign':
              raise ValueError('Update op for weight %s is not of type Assign.'%weight.op.inputs[0].name)
      weight_changes = [(new_w.op.inputs[1] - name_to_var[new_w.op.inputs[0].name]) for new_w, old_w in zip(weight_update_ops, weights)]
      # Recreate the update ops, ensuring that we compute the weight changes before updating the weights
      with tf.control_dependencies(weight_changes):
          new_weight_update_ops = [tf.assign(new_w.op.inputs[0], new_w.op.inputs[1]) for new_w in weight_update_ops]
      return weight_changes, tf.group(*(nonweight_update_ops + new_weight_update_ops))
code_tokens: def extract_weight_changes ( weights , update_ops ) : name_to_var = { v . name : v . value ( ) for v in weights } weight_update_ops = list ( filter ( lambda x : x . op . inputs [ 0 ] . name in name_to_var , update_ops ) ) nonweight_update_ops = list ( filter ( lambda x : x . op . inputs [ 0 ] . name not in name_to_var , update_ops ) ) for weight in weight_update_ops : if weight . op . type != 'Assign' : raise ValueError ( 'Update op for weight %s is not of type Assign.' % weight . op . inputs [ 0 ] . name ) weight_changes = [ ( new_w . op . inputs [ 1 ] - name_to_var [ new_w . op . inputs [ 0 ] . name ] ) for new_w , old_w in zip ( weight_update_ops , weights ) ] with tf . control_dependencies ( weight_changes ) : new_weight_update_ops = [ tf . assign ( new_w . op . inputs [ 0 ] , new_w . op . inputs [ 1 ] ) for new_w in weight_update_ops ] return weight_changes , tf . group ( * ( nonweight_update_ops + new_weight_update_ops ) )
short_docstring: Given a list of weights and Assign ops, identify the change in weights.
short_docstring_tokens: Given a list of weights and Assign ops identify the change in weights .
comment: """Given a list of weights and Assign ops, identify the change in weights.
  
      Args:
          weights: list of Variables
          update_ops: list of Assign ops, typically computed using Keras' opt.get_updates()
  
      Returns:
          list of Tensors containing the weight update for each variable
      """$SEP$# Make sure that all the weight update ops are Assign ops$SEP$# Recreate the update ops, ensuring that we compute the weight changes before updating the weights