We have released Neural Network Libraries v.1.0.15! Added spectral normalization and 3 new models (VGG, DenseNet, NIN)!

# Spotlight

## Spectral Normalization

Spectral normalization (https://arxiv.org/abs/1802.05957) is a novel weight normalization technique to stabilize the training of the discriminator that is computationally light.

```
import nnabla as nn
import nnabla.parametric_functions as PF
b, c, h, w = 4, 64, 32, 32
# Spectrally normalized convolution
apply_w = lambda w: PF.spectral_norm(w, dim=0)
h = nn.Variable.from_numpy_array(np.random.randn(b, c, h, w))
h = PF.convolution(h, with_bias=False, apply_w=apply_w)
```

## New NNabla Models: VGG,DenseNet,Network in Network

3 new models have been added to NNabla Models and can be used with pre-trained parameters!

VGG (https://arxiv.org/abs/1409.1556)

DenseNet (https://arxiv.org/abs/1608.06993)

Network in Network (https://arxiv.org/abs/1312.4400)

# Other Updates

## Function Layers

## Utilities

- [c++][python] Add pre-/post- function callback into graph engine
- Provide eq instead of richcmp for equality check.
- Use np.random.RandomState(313) as default random generator
- modified nbla train data shuffle