Quantization object support:
1. quantize a value with max and min.
2. quantize an array
3. quantize a Tensor[Float]
And for test, there're relative dequantize methods.
* make a layer support different type
* finish equal and make layer suppor different type
* fix conflict
* add Floor L2Loss RandomUniform Rank MatMul SoftMax
* add TruncatedNormal
* fit new code
* make jenkens pass
* Support control flow
* Decouple input/output tensor numeric type from the module numeric type(parameter type).
* FIx unit tests
* while loop api refactor
* fix unit test
* make breeze version configurable as spark 2.1 bump breeze version
* Add scheduler to exclude layers
* remove control-flow change
* remove Schduler and ControlOps
* fix broken unit test
* make a layer support different type
* finish equal and make layer suppor different type
* make code clear
* move zipwith to tensor
* fix conflict
* fix scala style error
* change an implementation for make scala2.10 compile successfully
* support loading a subset of tf graph
* support load model with specified inputs
* add control dependency support in Graph
* fix file reader conflict
* fix bug
* add some comments
* fix control dep bug
* fix doc
* Allow module output multiple tensor in Graph
* fix failed unit test
* meet code review
* meet code review
* more unit test
* add more unit tests
* fix unit tests
* fix style check
* fix style check and unit test
* meet code review
* support nested output
* meet code review
* add boolean, short, int, long, string, char to tensor support
* implement and test LogicalOr and LogicalAnd operations
* add logical not
* change a typo
* refine logicalOr and logicalAnd
* fix scala code style error
* add isInputWithBias and isHiddenWithBias for RNNCell
* default affine = true in BatchNormParams in recurrent layer
* add serializableSpec for recurrent
* addPreprocessInputLayer to support BatchNormalization
* revise bnorm to hiddensize
* add comments
* add BatchNormalization between preTopology and topology
* add BatchNormalization in Recurrent
* recurrent support batchnormalization
* revise sequential to identity in throw new Error
* fix serialize
* add serializeBigDLModule
* return Unit in doSerializeModule
* add version control
* version support
* add python api
* add version control proto
* add unit test
* change function name
* rename function api
* per comments
* refinement to make hierachy more cleaner
* backward compatibility
* add propagateback and generate backward graph
new unit test pass
unit test except resnet pass
unit test pass
support rebuild
support multiple inputs
fix updateGradInput and acc
fix unit test
rename to trainable, add API to set trainable at graph
unit test except inception and resnet pass
unit test pass
code clean
fix conflicts
add unit test for setTrainable, setFreeze, unFreeze
use input buffer
add getSubmodule in AbstractModule
resolve conflicts
update isInputNode
update comment
update setTrainable
fix
fix mixture of graph and container
add setFreeze python API
update python and add doc
meet code review
* meet code review
* fix unit test
* meet code review
* fix code style
* fix inception unit test
* meet code review
* meet code review
* rm cloneNode
* resolve conflicts
* fix reset
* meet code review
* meet code review
* fix
