fail in hl_activetype; also fix the hasinputsset in submodel

* set mixedlayer output size according to input operator
* change from num_channel to num_channels for conv_operator (the old one is
really misleading because all the others are num_channels)

* also changed the arg name in projections.py

* some tiny fixs

* use VLOG(3)

Fix #342

* Add elementwise math operations
This allows use to use expressions like: y=log(1+exp(x))
Also added unittests for ActivationFunction
* Enforce keyword arguments for non-positional arguments
* Add LogActivation to doc

* Add benchmark for PaddlePaddle, tensorflow and caffe

* ConvProjection to reduce memory for goolenet

* Add unit test for ConvProjection.
1. unit test in test_LayerGrad.
2. compare the ConvPorjection and CudnnConvLayer, also compare the concat_layer+img_conv_layer and concat_layer_conv_projection.

* Reduce cudnn_conv memory and add benchmark document.
1. Use TmpMatrix as the workspace in cudnn_conv to reduce gpu memory. It reduce lots of memory.
2. Add benchmark document.
3. fix smallnet_mnist_cifar.py in paddle.

* Add job=time and refine cudnn_conv to reduce gpu memroy and speed up

* Refine cudnn_conv and shared biases operation in concat_layer and mixed_layer.

* follow comments

* follow comments

* Use unique_ptr to prevent memory leaks in CudnnConvLayer.

* fix interface bug of block_expand_layer and add unittest

* auto compute num_channels

* default value of num_channels is None

* adjust input order of block_expand

* Python trainer API and demo

* Adding missing PaddleAPIPrivate.h

* Adding api_train.sh

* More comments

* Bump up patch version to 0b3

* add maxout layer, including interface and unittest

* follow maxout comments

* auto setting channels

* fix unittest bug in test_RecurrentGradientMachine

* Follow #223
* Fix #222

* Fix bug in yield dictionary in DataProvider.
* Also make virtualenv work in Paddle.

* Init commit for doing FAQ

* Add speed up training

* Add graphviz to ci

* Add shared paramter

* Tiny refine

* add interface and unittest for nce layer

* follow comments

* support rectangle padding, stride, window and input for PoolProjection

* Follow comments.
1. Remove start
2. refine img_pool_a/b.conf for test_NetworkCompare
3. Split unit test

* Modify the test in img_layers.py

* add interface and unittest of RecurrentGradientMachine for the function of multiple Subsequence inlinks with unequal token length

* refine sparse momentum api and unittest
* fix unittests bug

* Fix lots of trainer_config_helpers bug, and complete unittest for `layers.py`

* Correctly handling multiple inputs and integer inputs for recurrent_group

* Fix ScatterAgentLayer for generation

* Revert sequence_(nest)_rnn.conf

* Add `device` parameter to ExtraAttr in trainer_config_helpers.
* add unittest for it.

* add type compatible check for ParamAttr

* fix bug in dotmul_operator's api and anotation
* update rnn document
* remove redundant info of projection and operator in layers.py

* remove unnecessary field set in ParameterConfig, Evaluators, etc

* split dotmul_projection and dotmul_operator
* bug fix in outputsize checking for mixed layer

* 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.

Sequences should be sorted according to the number of subsequences they have.

Sequences should be sorted according to the number of subsequences they have.