def _compose_save_list_secure(self, *pairs):
     with tf.name_scope('secure_save_list'):
         save_list = list()
         for pair in pairs:
             variables = flatten(pair[0])
             new_values = flatten(pair[1])
             s_list = self._recurrent_assign_loop_with_dependencies(
                 variables, new_values, [])
             save_list.extend(s_list)
         return save_list
 def _compose_save_list(self,
                        *pairs):
     #print('start')
     with tf.name_scope('save_list'):
         save_list = list()
         for pair in pairs:
             #print('pair:', pair)
             variables = flatten(pair[0])
             #print(variables)
             new_values = flatten(pair[1])
             for variable, value in zip(variables, new_values):
                 name = self._extract_op_name(variable.name)
                 save_list.append(tf.assign(variable, value, name='assign_save_%s' % name))
         return save_list
 def _compose_reset_list(self, *args):
     with tf.name_scope('reset_list'):
         reset_list = list()
         flattened = flatten(args)
         for variable in flattened:
             shape = variable.get_shape().as_list()
             name = self._extract_op_name(variable.name)
             reset_list.append(tf.assign(variable, tf.zeros(shape), name='assign_reset_%s' % name))
         return reset_list
 def _compose_randomize_list(self, *args):
     with tf.name_scope('randomize_list'):
         randomize_list = list()
         flattened = flatten(args)
         for variable in flattened:
             shape = variable.get_shape().as_list()
             name = self._extract_op_name(variable.name)
             assign_tensor = tf.truncated_normal(shape, stddev=1.)
             randomize_list.append(tf.assign(variable, assign_tensor, name='assign_reset_%s' % name))
         return randomize_list
Beispiel #5
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 def _compose_reset_list(self, saved_last_sample_predictions, *args):
     with tf.name_scope('reset_list'):
         reset_list = list()
         name = self._extract_op_name(saved_last_sample_predictions.name)
         reset_list.append(
             tf.assign(saved_last_sample_predictions,
                       np.tile(
                           char_2_base_vec(
                               self._character_positions_in_vocabulary,
                               self._replica_delimiter), (1, 1)),
                       name='assign_reset_%s' % name))
         flattened = flatten(args)
         for variable in flattened:
             shape = variable.get_shape().as_list()
             name = self._extract_op_name(variable.name)
             reset_list.append(
                 tf.assign(variable,
                           tf.zeros(shape),
                           name='assign_reset_%s' % name))
         return reset_list