Released v1.0.13! Provides C++ API for Building Computational Graph!

Thursday, March 14, 2019

News

Posted by shin

This week, we have released nnabla packages v1.0.13! The main features updated from the previous version are described below:

Spotlight

C++ computation graph building APIs

We have implemented C++ API to design neural networks in a Python-like way.
Our Python-like C++ API handles the function-layer instantiation, composition of layers, and parameter creations.

Users can refer to the sample code in the following directory.

nnabla/examples/cpp/mnist_collection

 

 

C++ API’s Usage compared to Python:

  • name space
namespace f = nbla::functions;
namespace pf = nbla::parametric_functions;
import nnabla.functions as F
import nnabla.parameteric_functions as PF

 

  • graph design
auto h = pf::convolution(x, 1, 16, {5, 5}, parameters["conv1"]);
h = f::max_pooling(h, {2, 2}, {2, 2}, true, {0, 0});
h = f::relu(h, false);
h = pf::convolution(h, 1, 16, {5, 5}, parameters["conv2"]);
h = f::max_pooling(h, {2, 2}, {2, 2}, true, {0, 0});
h = f::relu(h, false);
h = pf::affine(h, 1, 50, parameters["affine3"]);
h = f::relu(h, false);
h = pf::affine(h, 1, 10, parameters["affine4"]);
h = PF.convolution(x, 16, (5, 5), name='conv1')
h = F.max_pooling(h, (2, 2))
h = F.relu(h, inplace=True)
h = PF.convolution(h, 16, (5, 5), name='conv2')
h = F.max_pooling(h, (2, 2))
h = F.relu(h, inplace=True)
h = PF.affine(h, 50, name='fc3')
h = F.relu(h, inplace=True)
h = PF.affine(h, 10, name='fc4')

 

Other Features

Functions

Utilities

Documentation