From 05c08214e33cadac54966250b47b7903d175c404 Mon Sep 17 00:00:00 2001 From: yangyaming Date: Mon, 8 Jan 2018 10:50:40 +0800 Subject: [PATCH] Bug fix when inserting fill_zeros_like_op. --- python/paddle/v2/fluid/backward.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/python/paddle/v2/fluid/backward.py b/python/paddle/v2/fluid/backward.py index 88fe19da5..66a7f7375 100644 --- a/python/paddle/v2/fluid/backward.py +++ b/python/paddle/v2/fluid/backward.py @@ -7,7 +7,7 @@ __all__ = ['append_backward'] def _rename_arg_(op_descs, old_name, new_name, begin_idx=None, end_idx=None): """ - Traverse all ops in op_descs[begin_idx : end_idx], + Traverse all ops in op_descs[begin_idx : end_idx], if any op has inputs/outputs named "old_name", rename it as 'new_name' """ if begin_idx is None: @@ -162,7 +162,7 @@ def _remove_no_grad_branch_(op_descs, no_grad_set): if core.grad_var_suffix() in arg and arg in no_grad_set: to_insert.append((_create_op_desc_("fill_zeros_like", { "X": [_strip_grad_suffix_(arg)] - }, {"Y": [arg]}, {}), idx)) + }, {"Out": [arg]}, {}), idx)) map(lambda p: op_descs.insert(p[1], p[0]), reversed(to_insert)) @@ -182,7 +182,7 @@ def _append_backward_ops_(target, target(Variable): the target variable of forward pass block(Block): the block where forward ops are target_block(Block): the block which is going to hold new generated grad ops - no_grad_dict(dict): + no_grad_dict(dict): key(int) block index val(set) a set of varibale names. These varibales have no gradient grad_to_var(dict)(output argument): @@ -276,8 +276,8 @@ def append_backward(loss, parameter_list=None, no_grad_set=None): loss(Variable): The variable generated by cost function. parameter_list(list): Parameters that need to be updated by optimizer. If None, it means all parameters need to be updated. - no_grad_set(set): Variables that have no gradients in Block 0. - If None, the set will be generated inside the function and + no_grad_set(set): Variables that have no gradients in Block 0. + If None, the set will be generated inside the function and contains all variables with `step_gradient=True` from all blocks. Return: -- GitLab