98b18cadf44257fde77b36ada1729feaab3aed97,examples/word_language_model/train.py,,,#,13

Before Change

    iterator_train__use_cuda=use_cuda,
    iterator_test__use_cuda=use_cuda)

params = [
    {
        "lr": [20],
        "iterator_train__bptt": [5, 10],
    },
]

pl = GridSearchCV(trainer, params)
pl.fit(corpus.train[:1000], corpus.train[:1000])

After Change

import data
import learner

parser = argparse.ArgumentParser(description="PyTorch PennTreeBank RNN/LSTM Language Model")
parser.add_argument("--data", type=str, default="./data/penn",
                    help="location of the data corpus")
parser.add_argument("--bptt", type=int, default=35,
                    help="sequence length")
parser.add_argument("--batch_size", type=int, default=20, metavar="N",
                    help="batch size")
parser.add_argument("--seed", type=int, default=1111,
                    help="random seed")
parser.add_argument("--no-cuda", dest="cuda", action="store_false",
                    help="use CUDA")
parser.add_argument("--save", type=str,  default="model.pt",
                    help="path to save the final model")
args = parser.parse_args()


class LRAnnealing(inferno.callbacks.Callback):
    def on_epoch_end(self, net, **kwargs):

Instances