Method get_params

nets/model.py:230–245

(self, weight_decay=0.0)

            return wav_lst

    def get_params(self, weight_decay=0.0):
            # add L2 penalty
        weights, biases = [], []
        for name, param in self.named_parameters():
            if 'bias' in name:
                biases += [param]
            else:
                weights += [param]
        params = [{
                     'params': weights,
                     'weight_decay': weight_decay,
                 }, {
                     'params': biases,
                     'weight_decay': 0.0,
                 }]
        return params

if __name__ == '__main__':
    import os