wehub-resource-sync
65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import paddle.nn as nn
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from args import parse_args
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from data import create_train_loader
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from seq2seq_attn import CrossEntropyCriterion, Seq2SeqAttnModel
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from paddlenlp.metrics import Perplexity
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def do_train(args):
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paddle.set_device(args.device)
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# Define dataloader
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train_loader, eval_loader, src_vocab_size, tgt_vocab_size, eos_id = create_train_loader(args)
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model = paddle.Model(
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Seq2SeqAttnModel(
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src_vocab_size, tgt_vocab_size, args.hidden_size, args.hidden_size, args.num_layers, args.dropout, eos_id
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)
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)
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grad_clip = nn.ClipGradByGlobalNorm(args.max_grad_norm)
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optimizer = paddle.optimizer.Adam(
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learning_rate=args.learning_rate, parameters=model.parameters(), grad_clip=grad_clip
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)
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ppl_metric = Perplexity()
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model.prepare(optimizer, CrossEntropyCriterion(), ppl_metric)
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print(args)
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if args.init_from_ckpt:
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model.load(args.init_from_ckpt)
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print("Loaded checkpoint from %s" % args.init_from_ckpt)
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benchmark_logger = paddle.callbacks.ProgBarLogger(log_freq=args.log_freq, verbose=3)
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model.fit(
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train_data=train_loader,
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eval_data=eval_loader,
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epochs=args.max_epoch,
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eval_freq=1,
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save_freq=1,
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save_dir=args.model_path,
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callbacks=[benchmark_logger],
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)
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if __name__ == "__main__":
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args = parse_args()
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do_train(args)
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