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	Fix import of 'cyclic_triangular' from thinc
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				|  | @ -14,7 +14,7 @@ from thinc.neural.util import require_gpu | |||
| from wasabi import Printer | ||||
| import srsly | ||||
| from thinc.neural.util import to_categorical | ||||
| from thinc.rates import cyclic_triangular_rate | ||||
| from thinc.rates import cyclic_triangular | ||||
| 
 | ||||
| from ..errors import Errors | ||||
| from ..tokens import Doc | ||||
|  | @ -134,6 +134,7 @@ def pretrain( | |||
|         if isinstance(config[key], Path): | ||||
|             config[key] = str(config[key]) | ||||
|     util.fix_random_seed(seed) | ||||
|     msg = Printer() | ||||
|     if gpu_id != -1: | ||||
|         has_gpu = require_gpu(gpu_id=gpu_id) | ||||
|         msg.info("Using GPU {}".format(gpu_id)) | ||||
|  | @ -234,7 +235,7 @@ def pretrain( | |||
|     min_lr = optimizer.alpha / 3 | ||||
|     max_lr = optimizer.alpha * 2 | ||||
|     period = 10000 | ||||
|     learn_rates = cyclic_triangular_rate(min_lr, max_lr, period) | ||||
|     learn_rates = cyclic_triangular(min_lr, max_lr, period) | ||||
|     for epoch in range(epoch_start, n_iter + epoch_start): | ||||
|         for batch_id, batch in enumerate( | ||||
|             util.minibatch_by_words(((text, None) for text in texts), size=batch_size) | ||||
|  |  | |||
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