(self, grads_and_vars, global_step=None, name=None)
| 95 | |
| 96 | @HIDE_DOC |
| 97 | def apply_gradients(self, grads_and_vars, global_step=None, name=None): |
| 98 | update_op = super(PostProcessOptimizer, self).apply_gradients( |
| 99 | grads_and_vars, global_step) |
| 100 | ops = [] |
| 101 | with tf.control_dependencies([update_op]): |
| 102 | for _, var in grads_and_vars: |
| 103 | with self._maybe_colocate(var): |
| 104 | op = self._func(var) |
| 105 | if op is not None: |
| 106 | assert isinstance(op, tf.Operation), op |
| 107 | ops.append(op) |
| 108 | update_op = tf.group(update_op, *ops, name=name) |
| 109 | return update_op |
| 110 | |
| 111 | @contextmanager |
| 112 | def _maybe_colocate(self, var): |
nothing calls this directly
no test coverage detected