* memory init

* add env

* refine anounce

* Add check for Nan

* Debug

* Add env for cc_test

* Add env for py_test and nv_test

* Remove py_test env

* Add env for py_test

* serial test_recognize_digits

* Test FLAGS_init_allocated_mem function for unit test

* Init allocated mem for op unit test

* Add env for all unit test

* Fix mean iou op.

* Fix crop layer test.

* Fix unitest

* fix unitest.

* Add fill_zeros_array op. This op is used for zero-filling an LoDTensorArray.

* merge fill_zeros_array_op with fill_zeros_like_op

* add unit_test for fill_zeros_like for array

* bnorm+relu fuse for mkldnn

* separate fuse_relu function

* bug fix

* proper while range in inference_transpiler

* description fix

* review fix

* review fix

* unit test for fwd batch norm+relu MKLDNN fuse

* optimize bipartite_match_op in large scale input

* Speed depthwise transposed conv2d.

* change return_numpy[PE] default value

* Remove convert to numpy in unit test

* elementwise_add with bcast: Brian's implementation by Brian added, with default bcasts

* elementwise_add with bcast: GetExpectedKernelType added to elementwise_op

* elementwise_add with bcast: use_mkldnn attribute added

* elementwise_add with bcast: changes after review and some formatting

* elementwise_add with bcast: changes after style check

* elementwise_add with bcast: changes after style check cont.

* elementwise_add with bcast: MKLDNN unittests added

* elementwise_add with bcast: original unittests with use_mkldnn flag

* elementwise_add with bcast: handling of MKLDNN format corrected

* elementwise_add with bcast: setting MKLDNN format turned into lambda

* elementwise_add with bcast: MKDNN format setting turned into separate function

* elementwise_add with bcast: condition for choosing MKLDNN simplified

* elementwise_add with bcast: fix for MKLDNN format set incorrectly in bcasts

* elementwise_add with bcast: changes in unittests for broadcasts

* elementwise_add with bcast: fixes in unittests regarding dimensions

* elementwise_add with bcast: bring back correct format setting in mklml grad path

* elementwise_add with bcast: fixed compilation error

* 1. Remove PYTHON_FLAGS from paddle_build.sh in paddlepaddle/paddle:latest-dev

* 1. Add PYTHON_FLAGS back
2. Change SIGKILL to SIGINT and SIGTERM

* 1. Add setup.py.in back

* 1. add pip install open-cv in Dockerfile to avoid libusb_exit hanging up which is caused by the opencv-python package missing

* 1. Add the && \ to line above

* 1. Remove the notice comment

This reverts commit 4d8e8ee2, reversing
changes made to d6a9f005.

* Make the CUDA kernel of concat correct and fix unit tests.

* add lod_tensor util and modify pybind

* refind pybind LoDTensor API and modify LoDTensor and DataFeeder test

* fix test error

* fix detection map op test

* fix reorder_lod_tensor test

* fix seq_concat_op

* fix chunk evel op test

* fix target assign op

* fix warp ctc op

* address comments step 1: reverse reset_lod op

* step 2: modify op test

* add warning message

* remove has_valid_lod

* add back has_valid_lod

* address comments

* add exception catching trial

* Implement a bilinear initializer for transposed convolution.

* Update some error message.

* Add mean_iou op.

* Add unitest for mean iou op.

* Add optional collections of confusion matrix and mean_iou.

* Fix cuda kernel.

* Refine code.
1. Merge computing in GPU to two kernel.
2. Use wrong array and correct array instead of confusion matrix.

* Add python api and fix cuda kernel.

* Fix comments.

* Small fix.

* Small fix.

* Add normalization operator.
1. Refine the raw norm_op and let it more general to support to normalize Tensor along any axis.
2. There is a bug in l2_normalize API, which lacks sqrt after `reduce_sum`.
3. Use norm_op to refine the l2_normalize API.
4. Fix bug in test_normalization_wrapper.py.

* Add slice op.

* Remove using from header file and fix doc.

* Fix doc

* Small fix.

* 1. add weight decay feature into fluid benchmark test
2. add learning rate decay feature into fluid benchmark test
3. add L1&L2 regularization feature into fluid benchmark test
4. add error clipping feature into fluid benchmark test
5. add gradient clipping feature into fluid benchmark test

* Add some document to README.md under benchmark/fluid/ repo

* Add model_base.py

* Fix bugs in test_listen_and_serv_op

* 1. remove args out of fluid_benchmark.py
2. remove lr_decay, regularization, clipping out of fluid_benchmark.py

* add async_mode description to doc and remove the clipping description out

* for restart build

* to restart build

* remove optimization args from args.py

* 1. remove optimization from models
2. fix bug in test_listen_and_serv_op

* change the name retry_times to left_time

* change retry_times to the pserver start left time

* Expose maxout API.

* Fix code style.