Changing from v0.0.1

This section explains the API changes from v0.0.1.

How to specify the loss and the optimizer

In v0.0.1, the loss function and the optimization algorithm are treated as template parameter of network.

cpp

//v0.0.1
network<mse, adagrad> net;
net.train(x_data, y_label, n_batch, n_epoch);

From v0.1.0, these are treated as parameters of train/fit functions.

cpp

//v0.1.0
network<sequential> net;
adagrad opt;
net.fit<mse>(opt, x_data, y_label, n_batch, n_epoch);

Training API for regression

In v0.0.1, the regression and the classification have the same API:

cpp

//v0.0.1
net.train(x_data, y_data, n_batch, n_epoch); // regression
net.train(x_data, y_label, n_batch, n_epoch); // classification

From v0.1.0, these are separated into fit and train.

cpp

//v0.1.0
net.fit<mse>(opt, x_data, y_data, n_batch, n_epoch); // regression
net.train<mse>(opt, x_data, y_label, n_batch, n_epoch); // classification

The default mode of re-init weights

In v0.0.1, the default mode of weight-initialization in trian function is reset_weights=true.

cpp

//v0.0.1
std::ifstream is("model");
is >> net;
net.train(x_data, y_data, n_batch, n_epoch); // reset loaded weights automatically
net.train(x_data, y_data, n_bacth, n_epoch); // again we lost trained parameter we got the last training

cpp

//v0.1.0
std::ifstream is("model");
is >> net;    
net.trian<mse>(opt, x_data, y_data, n_batch, n_epoch); // hold loaded weights
net.train<mse>(opt, x_data, y_data, n_bacth, n_epoch); // continue training from the last training

// weights are automatically initialized if we don't load models and train yet
net2.train<mse>(opt, x_data, y_data, n_batch, n_epoch);