- 17 1月, 2018 1 次提交
-
-
由 ying 提交于
-
- 16 6月, 2017 1 次提交
-
-
由 xuwei06 提交于
No longer need to use SubsequenceInput. The framework will detect.
-
- 09 12月, 2016 1 次提交
-
-
由 Yi Wang 提交于
-
- 12 11月, 2016 1 次提交
-
-
由 qijun 提交于
-
- 24 10月, 2016 1 次提交
-
-
由 luotao1 提交于
* add maxout layer, including interface and unittest * follow maxout comments * auto setting channels * fix unittest bug in test_RecurrentGradientMachine
-
- 08 10月, 2016 1 次提交
-
-
由 Zrachel 提交于
* add interface and unittest of RecurrentGradientMachine for the function of multiple Subsequence inlinks with unequal token length
-
- 20 9月, 2016 1 次提交
-
-
由 Yu Yang 提交于
* min_pool_size would be infinite by default. * add unittest for min_pool_size * Fix bug in can_over_batch_size * add unittest for can_over_batch_size * Add DEFINE_PROVIDER_EX * Add default value of should_shuffle * When training, the default value of should_shuffle is True. * When testing, the default value of should_shuffle is False. * User a set a provider should_shuffle or not by pass it to `@provider` * should_shuffle can handle a list of value, not just boolean * Add input order mapping by using name * Add unittest * Add check to check input format. * Default is close for speed reason. * User could stop train when check error, or continue train without this train sample. * use deque instead of vector in generators pool, make erase generator faster. * Add chinese/english documentation * Make should shuffle = false in unittest * Add python files to depends.
-
- 14 9月, 2016 1 次提交
-
-
由 xuwei06 提交于
Change-Id: I8e0a8ea6fc2760652d9c76440a539c90860062d3
-
- 29 8月, 2016 1 次提交
-
-
由 zhangjinchao01 提交于
ISSUE=4586495 git-svn-id: https://svn.baidu.com/idl/trunk/paddle@1408 1ad973e4-5ce8-4261-8a94-b56d1f490c56
-