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@ -38,7 +38,7 @@ depth = 8
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mix_hidden_lr = 1e-3
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IS_SPARSE = True
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PASS_NUM = 1
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PASS_NUM = 2
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BATCH_SIZE = 10
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embedding_name = 'emb'
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@ -196,7 +196,7 @@ def train(use_cuda, save_dirname=None, is_local=True):
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print("second per batch: " + str((time.time(
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) - start_time) / batch_id))
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# Set the threshold low to speed up the CI test
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if float(cost) < 60.0:
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if float(cost) < 80.0:
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if save_dirname is not None:
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# TODO(liuyiqun): Change the target to crf_decode
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fluid.io.save_inference_model(save_dirname, [
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@ -208,6 +208,10 @@ def train(use_cuda, save_dirname=None, is_local=True):
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batch_id = batch_id + 1
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raise RuntimeError(
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"This model should save_inference_model and return, but not reach here, please check!"
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)
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if is_local:
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train_loop(fluid.default_main_program())
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else:
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