[git] ignore vscode configs and multiple vim backup files

ignore vscode configs and multiple vim backup files

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[ini] update ini bias init and flatten as feature

bias init name is changes to bias_init_zero to make it more readable
flatten is now a layer feature externally rather than as a new layer itself

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

Prepare bn layer tc

This PR add a python function that generates bn_layer forward/backward
pass.

Return value's order & contents are subject to change for the time
being.

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>
**How to evaluate:**
Run python function

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

Restructure inner data inside Tensor

**Changes proposed in this PR:**
- Change Tensor structure to enable sharing between Tensor
- Refactor Tensor ctors
- Add copy/move ctors & assignment operators

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

Add exception to TensorDim::setTensorDim

**Prerequisite**
- Add capi exception wrapper (later pr)

**Changes proposed in this PR:**
- Add `std::invalid_argument` to `TensorDim::setTensorDim`
- Fix `fc_layer` positive test does not have unit declared.
- Fix tests accordingly.
- Add TensorDim test (positive && negative)

**Todo**
- ~Add TensorDim test (positive && negative)~

See also: #233

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

Add exception boundary to capi

**Changes proposed in this PR:**
- Add a functor that returns `errno` to corresponding `exception`

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[activation] Add missing config for activation layer

Add missing initialization and setting input/output dimension for activation layer
Also updated setting previous dimension to be from the last computation layer

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

Add ml-api-common to capi

**Changes proposed in this PR:**
- Add capi-ml-common-devel to spec file
- Add `ml-api-common.h` for dummy
- Change error code accordingly

Resolves #75

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[ Pooling2D ] unittest cases for backwarding

This PR provides unittest cased for backwarding about global_max &
global average

. global_max : forwarding / backwarding
. global_average : forwarding /backwarding

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[ini] Update ini parsing and format

Update the parsing and format of ini input file
Remove declaring the layers at the top of the ini file unnecessarily
Adding corresponding bug fixes and updates to unittests

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[unittest] generate fc unittests

Added generate forward unittests data for fully connected layer, and corresponding unittests
Added minor bug fixes as well

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[genInput] Update generation of input for fc

Update generation of input for fc layer for forward
backward is also supported, however not yet used

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[genInput] Make input data reproducible

Make input data generation reproducible with fixed seeding

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[optimizer] Bug fix

Tensor copy constructor and copy assigment operator creates a copy of the vector
This led to bug in optimizer which updated the copy of the weight than the weight itself
Fixed by refernce of weight in optimizer

Resolves #241

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[ Pooling2D ] global max / global average

This PR provides global max / global average.
. forwarding global_max / global_average
. backwarding global_max / global_average

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[layer] Support get weights

Support getWeights functionality equivalent to getGradients

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[gradients] Get gradient of each layer

Added function to get gradient of each layer
Each layer is responsible to fill in the list containing the gradients

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

Add tensor save / read test

This Pr adds tensor save / read test.

Not directly related to the PR though, save & read better
need error handling

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[optimizer] Optimizer to manage list of weights

Update optimizer to handle list of weights than weights and bias inidividually
as layers like RNN, LSTM will have more than just (w,b)

V2:
calculate changed to apply_gradient as it applies gradients
applied for conv layer as well

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[ Conv2D ] fix calculate parameter of optimizer

fix calculate function parameters

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[activation] Derivative for activation

The derivative of softmax has been hand crafted to be different from others
Refer to https://github.com/nnstreamer/nntrainer/blob/2a650512813db6ce3bba828b5790066fbc655f14/nntrainer/src/fc_layer.cpp#L265 for original implementation
Softmax requires softmax(x) as input for derivative while other activations require x as input for derivative_

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

Add stride and contiguous flag to tensor

**Changes proposed in this PR:**
- Add stride
- Add checking is countigous flag to tensor

This patch is an anchor for #217

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[ Conv2D ] backwarding

This PR provides backwarding of Convolution 2D Layer

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

Fix optimizer signature

optimizer signature now does not have `init_zero`

This quick fix deletes init_zero from `fc_layer::backward`

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[bias] Bias updation missing for sgd

Bias updatation fixed for sgd where it only happened when bias was initialized with 0
For adam, bias updataion was happening twice

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[fc_layer] Add the deleted statement

Add the deleted statement about weight update from fc layer
Resolves #221

cc. @zhoonit

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[tensor/tensor_dim] Added equal comparison operation

Added equal comparison operation
Currently its based on fixed epsilon
Needs updation to use variable number of bits based on exponentiation as done in gtest

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

Move weight_decay handling from opt to layer

**Changes proposed in this PR:**
- remove weight_decay from `Optimizer::calculate` signature
- apply weight decay to fc_layer.cpp

please note that conv2d_layer::backwarding also need to handle weight
decay after this PR is merged.

Resolves #213

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

Attach activation layer to neuralnet.cpp

**Changes proposed in this PR:**
- strip activation function related members in `layer`
- init activation property as `activation_layer`

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

resolves #153

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[Fix/jni] Change tflite-dependency

There seems a problem with building tensorflow. This PR propose to use
prebuilt tensorflow-lite instead of building one.

**Changes proposed in this PR:**
- Change `Applications/android.mk` to use prebuilt library
- Change `prepare_tflite.sh`
- Bump tflite to 1.13.1 as suggested in #20

Resolves #207

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

Make .clang-format competible with version 6

Clang-format 6 is widely used. However,
`AllowAllConstructorInitializersOnNextLine` added from #203 is supported
from clang-format 9.

This Pr reverts use of `AllowAllConstructorInitializersOnNextLine`
while having similar linting style.

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[meson] Arrange file order

Arrange files order for easier access

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[loss] Combine softmax with cross entropy

Softmax is combined with cross entropy
Cross entropy exists on its as an option but isnt supported

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[loss] Combined cross entropy with sigmoid

Combined cross-entropy with sigmoid version for loss because of its higher stability
Note that this happens internally and is not exposed outside

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[Proposal] clang format for initializer list

preivious initialization list was hard to read when it gets too long.

eg) layers initialization list

before:
```cpp
  Layer()
    : last_layer(false), init_zero(false), type(LAYER_UNKNOWN),
      activation(NULL), activation_prime(NULL), activation_type(ACT_UNKNOWN),
      bn_follow(false), weight_decay(), weight_ini_type(WEIGHT_UNKNOWN) {}
```

after:
```cpp
  Layer()
    : last_layer(false),
      init_zero(false),
      type(LAYER_UNKNOWN),
      activation(NULL),
      activation_prime(NULL),
      activation_type(ACT_UNKNOWN),
      bn_follow(false),
      weight_decay(),
      weight_ini_type(WEIGHT_UNKNOWN) {}
```

**Self evaluation:**
1. Build test: [ ]Passed [ ]Failed [x]Skipped
2. Run test: [ ]Passed [ ]Failed [x]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[ Pooling2D ] backwarding

This PR provides backwarding process of Pooling 2D.
. backwarding for max pooling 2D
. backwarding for average pooling 2D
. backwarding global_max, global_averge is NYI.

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

Add test for activation layer(Wait for #187)

**Changes proposed in this PR:**
- Add test for activation layer

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

Separate activation to layer

**Changes proposed in this PR:**
- add activation_layer.[h|cpp]
- add test to activation_layer

See also #153, #152

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

Fix bug in `Tensor::setValue`

`memset` can't be used to initialize a float array as explained
[here](https://stackoverflow.com/questions/1040070/initializing-a-float-array-with-memset)

**Changes proposed in this PR:**
- change `memset` to `std::fill`

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[warning fix] unsigned int compare with int warning

warning fix of comparing unsigned int values with signed values

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[parse] Parse unknown properties

Properties exposed to the users and internal are different (losslayer, etc)
Hence using `*_string.size() - 1` for unknown cases will cause bugs in parse_util
Replaced with its own individual unknown value

V2:
Combined all individual layer properties into common properties in layer.h

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[ Flatten ] backwarding

This PR includes back propagation of Flatten Layer.
. backwarding Flatten Layer.
. batch(), channel(), width(), height(), setDim() of tensor
. unit test of flatten layer

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[bugfix] Bug fix with git merge

Git merged new commits causing build errors
Need urgent merge

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[neuralnet] Handle adding layer with compiled model

This PR handles the case when a layer is added after a model has been compiled
Currently it has been set to give out error

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[API] simplify API

Remove exposed function "model_construct_with_conf" as then "compile_with_conf" looks strange without any config
Rather, just keep one "model_construct" and "compile_with_conf" can take the config file
Updated corresponding unittests

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[save/load] save/load the optimizer parameters

save and load the optimizer parameters as well for continued training
Add additive option in neural to continue training from previous training

Resolves #172

V2:
setOptimizer() bug fix to be called with set for only fc layer and not other layers
now setOptimizer() for fc_layer is unique compared to virtual defined by its parent
added continued training property for optimizer
added getSize in tensor
save optimizer type to verify that the loaded optimizer values can be used sensibly
if not loading, move the file seek ahead

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[fc_layer] Initialization bug fix

Add missing initialization of unit at object construction

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[layer] Added loss layer

Added loss layer
This is added by the framework and is hidden from the user
This separates all the cost/loss related extra work from the layers
However, Loss and cost is now available for and from all layers

Resolves #101

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[ Flatten ] forwarding / copy for Flatten Layer

This PR provides the fowarding/copy function of Flatten Layer
. implement forwarding function
. implement copy function

Resolves:

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[init] Making random deterministic

Adding determinism to the random number generators in the library
DataBuffer has multiple threads but single thread of train/valid/test
which run in sequence in my understanding

Resolves #167

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[ Layer copy ] copy function for conv2d & pooling

This PR fixs the copy member function of covn2d and pooling layer.
. include copy of layer varaibles for conv2d
. implemnet copy of pooling2d layer

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[neuralnet] Iteration bug fix in learning rate

learning rate is decayed using the iteration
however current implementation was using epoch count

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[Docs] Update readme.md prerequisites

**Changes proposed in this PR:**
- Update readme.md prerequisites to include gcc version

**Self evaluation:**
1. Build test: [ ]Passed [ ]Failed [X]Skipped
2. Run test: [ ]Passed [ ]Failed [X]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

Optimize optimzer::calculate

**Changes proposed in this PR:**
- Optimze `optimzer::calculate` with new add_i and applyIf

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

Add `LazyTensor::applyIf`

This PR propose `LazyTensor::applyIf` for more control flow.

Because of the problem proposed in http://wg21.link/P0834R0
macro to wrap a function need to be used for the function.

**Changes proposed in this PR:**
- add `LazyTensor::applyIf` semantics
- add `LazyTensor::applyIf` tests

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[ Flatten ] Skeleton of Flatten Layer

This PR provides skeleton code for flatten layer.
- Header & implementation of flatten layer

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[ Pooling2D ] forwarding pooling 2D layer

This PR includs forwarding process of pooling 2d layer.
in which,
. implementation of forwarding
. unit test code for forwarding
. input / ouput generation of pooling 2D ( max pooling only )
. move zero_pad to util_func.h

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[ Bug ] setActivation for input layer

Input Layer does not have activation property

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[ Pooling2D ] initialize pooling 2d layer

This PR includes initialization of pooling 2d layer.

. check input dimension
. set intput / output dimemsion
. allocate hidden layer

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[Tensor] Change Add_i to have alpha signature

**Changes proposed in this PR:**
- `Add_i(Tensor &T, float alpha)` for coefficient multiplication
- Change test accordingly
- Optimize `blas` implementation for `multiply_i`

See also: #166

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[util] Simplify sigmoidPrime

Simplified sigmoid prime which IMO might also be more efficient

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[layer] object initialization bugfix

Added bugfix to object initialization

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[ Pooling2D ] Set Property for Pooling 2D Layer

This PR provides seting property for pooling 2d layer.
which is,
  . stride, padding, pooling_size, pooling type

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
a2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

Refactor: delete Tensor::mat2vec()

This method is initially proposed to flatten and clone mat to vector.

However, `Tensor::getData()` can cover most of it's purpose, it seems
no longer in need.

**Changes proposed in this PR:**
- Delete `Tensor::mat2vec()`.

See also:
https://github.com/nnstreamer/nntrainer/pull/149#discussion_r439172191

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[ Pooling2D ] Pooling 2D Layer

This PR provides skeleton of pooling 2d layer

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[bug] error macros not defined

Corrected some of the error state macros which were wrongly defined

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[bugfix] Ignoring error in training/validation

Errors in forward and backward propagation were ignored.
Added error handling for them.

Further, model was being saved after validation.
Errors in validation would result in no model.
Changed to saving of model after training than after validation.

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[layers] set status in operations

Set status in forward and backward operations

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

[meson] Remove duplicated bits from test/meson

**Changes proposed in this PR:**
- Add conv2d_unittest to be unzipped for testing
- Add foreach loop to handle duplicated control flow

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[loss] Added missing weight decay loss

Added missing weight decay loss into final loss for intermediate layers

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

Fix numeric limits in `Tensor`

**Changes proposed in this PR:**
- Add `static constexpr min / max` to Tensor
- Change normalization to use predefined method for accelaration

Resolves #141

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[LazyTensor] Fix memcopy happening on lambda func

Fix unintential memcopy occurring in capture clause in lambda

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

Add blas operation to `Tensor::l2norm`

Meanwhile, current implementation (plain version) of `l2norm` is not
effcient and have potential overflow problem.

**Changes proposed in this PR:**
- Add blas operation to `Tensor::l2norm`
- Add fixme

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[ Unit Test ] Conv2D Forwarding Unit Test

This PR provides Conv2D Forwarding Unit Test.
. update python script to generate random input / kernel / golden Data
. update unit test code for conv2d & evaluate results

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[tensor] Added axis dimension to average

Current implementation of average() is confusing when compared with sum().
Both sum() and average() do not take axis, and perform similar operations.
However, the semantics of their output is different -
- sum() acts over the dimensions other than batch size
- average() acts over the batch size

To avoid this confusion, average is provided with axis argument with 0 as default.
This is now analogous to sum(axis) than sum()
Further, sum() is renamed to sum_by_batch() as thats what it does and is different from sum(axis)

V2:
Applied the above for lazy_tensor

Minor updates to TensorDim are also made

Signed-off-by: Parichay Kapoor <pk.kapoor@samsung.com>

Add openmp parellelism to l2norm()

**Changes proposed in this PR:**
- Fix:`meson -Denable-blas=false` has no dep found error
- Fix:sum() function bug in `USE_BLAS` is not defined
- Add openmp dependency
- Apply openmp to l2norm function

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

Optimizing concept for Tensor operation

**Changes proposed in this PR:**
- `i_operation` are used to calculate without memcopy

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[ Conv2D ] forwarding calculation

This PR provides forwarding calculation of conv2d layer

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

Register Tensor ops to lazy tensor

**Changes proposed in this PR:**
- Register tensor ops to `LazyTensor`
- Add test fixture to `LazyTensor`
- Add tests to `LazyTensor`
- Change `Tensor::apply` signature to permit closures

**Changes proposed in V2:**
- Delete and fix unnecessary comments
- Change `Tensor::getData` signature

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[ Test ] Input Generator

This PR provides layer input generator to evalute the layer
calculation.

[ To Do ]
Golden Test Result Generation should be added.

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[ CI ] Add Build Dependency flatbuffers-dev

for ubuntu, add flatbuffers-dev

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[ Conv2d ] save & read unittest

Add save & read unit test

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[ Conv2d ] save & read weight from file

This PR provides read & save Kenel and Bias

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[Tensor] Add divide_i / multiply_i (Tensor)

**Changes proposed in this PR:**
- Add `divide_i(Tensor)`
- Add `multiply_i(Tensor)`

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[Tensor] Add `subtract_i(Tensor)` operator

**Changes proposed in this PR:**
- Add subtract_i(Tensor) operator for a memcpyless operation
- Lint unformated lines

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[ Refactor ] Remove unused function in layer class

There is no need to exist the initialize(...) in layer class.
This PR removes this unused function.

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[ Conv2D ] initialize Conv2DLayer

During initialize,
 1. set input & output dimension
 2. set Tensor for Kernel and bias
 setInputDimension() function should be called before this is called.

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

Add multiply_i / divide_i operator

**Changes proposed in this PR:**
- Add `multiply_i(float)`
- Add `divide_i(float)`

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[Tensor] Add `subtract_i(float)` operator

**Changes proposed in this PR:**
- Add subtract_i operator
- Revise subtract operator
- Change precision in add_i / subtract_i test

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

Add LazyTensor to support lazy evaluation

This PR propose 2 concepts.

1. Propose `*_i` type of operation to `Tensor`. Which mutates target
tensor instead of memcopying the tensor.(511c)
2. Add chaining for lazy & memcopyless operation.

For example Tensor could do the operation in such manner:

```cpp
Tensor t;
t.chain() /* Initial memcpy happens to gaurantee immutability */
 .add_i(x)
 .multiply_i(y) /* NYI */
 .divide_i(y) /* NYI */
 .run()
```

operation are delayed until `.run()` is called. This is done by
monadic object named `DefferedTensor`.

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>
[Tensor] Add add_i for memcpyless operation

This pr propose `*_i()` operation to Tensor to support memcpyless
operation.

**Changes proposed in this PR:**
- Add add_i for memcpyless operation

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[Tensor] Add `add_i(Tensor T)` operator

**Changes proposed in this PR:**
- add_i(Tensor T) operator for memcopyless operation

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[ Refactor ] using input dimesion and output dimenstion.

Currently each layer handle just one TensorDim for Weight. However,
there are more complicata cases to use. Therefore each layer have
TensorDim instance to specify input and output activation dimension.
Original dim variable of layer is use to define weight dimension.

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[ Refactor ] Modify to deal with 3D Tensor

Until now, nntrainer cannot handle 3D Tensor including channel. Only
supported 2D Tensor which is batch, height, width.
From this PR, nntrainer can handle 3D Tensor include channel. But more
optimization is required.

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[ Layer ] Set Property for Conv2D Layer

. parse and set Convolution 2D Property
   0. input shape : string
   1. bias zero : bool
   4. activation : string (type)
   6. weight_decay : string (type)
   7. weight_decay_lambda : float
   9. filter : int
   10. kernel_size : ( n , m )
   11. stride : ( n, m )
   12, padding : valid | same

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[ Layer ] Draft of Conv2D Layer

This is the first draft of 2d convolution layer

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

Reduce duplicated function call

**Changes proposed in this PR:**
- Reduce `average()` call in `Optimizer::calculate
- Delete setZero call after construction `Tensor`
- Reduce indexing and multiplication in few loops

This PR results in better performance including roughly 60% decrease
in time spent in `Tensor::average()` in
Classification example(checked in vtune).

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[ Refector ] Manage unit test with seperate file

unit test code is too big to manage. So make sepearte file for each
class / classes.
. unittest_nntrainer_internal
  : test neural network / optimizer
. unittest_nntrainer_tensor
  : test tensor/tensorDim
. unittest_nntrainer_layer
  : test layers

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[Example] Add exit when naviframe is empty

**Changes proposed in this PR:**
- Add and use _on_back_pressed_cb when back button is pressed
- Change `view_routes_to` signiture to return naviframe item.
- Delete unused functions

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>

[ Bug ] possible corruption by double erase

There is possible double erase vector due to the duplicate random
number generation.
In this PR, remove to use random number and erase just once.

**Self evaluation:**
1. Build test:   [X]Passed [ ]Failed [ ]Skipped
2. Run test:     [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>

[ Refactor ] Layer Method for weight initialization

Cannot use weight initiatlization method for other layer.
In this PR, Move it into Layer class to use for other layer.

**Self evaluation:**
1. Build test:  [X]Passed [ ]Failed [ ]Skipped
2. Run test:  [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: jijoong.moon <jijoong.moon@samsung.com>