batch_set = MiniBatches(dataset, batch_size=32)
if __name__ == '__main__':
ap = ArgumentParser()
ap.add_argument("--model", default=os.path.join(os.path.dirname(__file__), "models", "sequence_adding_100_2.gz"))
args = ap.parse_args()
model = NeuralRegressor(input_dim=2, input_tensor=3, clip_value=3.)
model.stack_layer(IRNN(hidden_size=100, output_size=1, input_type="sequence",
output_type="one", output_activation="linear"))
if os.path.exists(args.model):
model.load_params(args.model)
conf = TrainerConfig()
conf.learning_rate = LearningRateAnnealer.learning_rate(0.01)
conf.max_norm = 1
conf.patience = 50
conf.gradient_tolerance = 5
trainer = SGDTrainer(model, conf)
annealer = LearningRateAnnealer(trainer, patience=20)
trainer.run(batch_set, controllers=[annealer])
model.save_params(args.model)
print "Identity matrix weight:"
print model.first_layer().W_h.get_value().diagonal()
