wehub-resource-sync
133 lines
3.9 KiB
Python
133 lines
3.9 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
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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 numpy as np
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SEED = 2020
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def build_fake_sentence(seed):
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random = np.random.RandomState(seed)
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sentence_len = random.randint(5, 15)
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token_ids = [random.randint(0, 1000) for _ in range(sentence_len - 1)]
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return token_ids
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def get_data_iter(batch_size, mode='train', cache_num=20):
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self_random = np.random.RandomState(SEED)
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def to_pad_np(data, source=False):
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max_len = 0
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bs = min(batch_size, len(data))
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for ele in data:
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if len(ele) > max_len:
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max_len = len(ele)
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ids = np.ones((bs, max_len), dtype='int64') * 2
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mask = np.zeros((bs), dtype='int32')
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for i, ele in enumerate(data):
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ids[i, : len(ele)] = ele
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if not source:
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mask[i] = len(ele) - 1
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else:
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mask[i] = len(ele)
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return ids, mask
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b_src = []
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if mode != "train":
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cache_num = 1
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data_len = 1000
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for j in range(data_len):
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if len(b_src) == batch_size * cache_num:
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if mode == 'infer':
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new_cache = b_src
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else:
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new_cache = sorted(b_src, key=lambda k: len(k[0]))
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for i in range(cache_num):
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batch_data = new_cache[i * batch_size : (i + 1) * batch_size]
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src_cache = [w[0] for w in batch_data]
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tar_cache = [w[1] for w in batch_data]
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src_ids, src_mask = to_pad_np(src_cache, source=True)
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tar_ids, tar_mask = to_pad_np(tar_cache)
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yield (src_ids, src_mask, tar_ids, tar_mask)
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b_src = []
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src_seed = self_random.randint(0, data_len)
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tar_seed = self_random.randint(0, data_len)
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src_data = build_fake_sentence(src_seed)
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tar_data = build_fake_sentence(tar_seed)
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b_src.append((src_data, tar_data))
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if len(b_src) == batch_size * cache_num or mode == 'infer':
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if mode == 'infer':
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new_cache = b_src
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else:
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new_cache = sorted(b_src, key=lambda k: len(k[0]))
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for i in range(cache_num):
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batch_end = min(len(new_cache), (i + 1) * batch_size)
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batch_data = new_cache[i * batch_size : batch_end]
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src_cache = [w[0] for w in batch_data]
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tar_cache = [w[1] for w in batch_data]
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src_ids, src_mask = to_pad_np(src_cache, source=True)
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tar_ids, tar_mask = to_pad_np(tar_cache)
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yield (src_ids, src_mask, tar_ids, tar_mask)
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class Seq2SeqModelHyperParams:
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# Whether use attention model
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attention = False
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# learning rate for optimizer
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learning_rate = 0.01
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# layers number of encoder and decoder
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num_layers = 2
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# hidden size of encoder and decoder
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hidden_size = 8
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src_vocab_size = 1000
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tar_vocab_size = 1000
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batch_size = 8
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max_epoch = 12
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# max length for source and target sentence
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max_len = 30
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# drop probability
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dropout = 0.0
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# init scale for parameter
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init_scale = 0.1
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# max grad norm for global norm clip
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max_grad_norm = 5.0
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# model path for model to save
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base_model_path = "dy2stat/model/base_seq2seq"
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attn_model_path = "dy2stat/model/attn_seq2seq"
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# reload model to inference
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reload_model = "model/epoch_0.pdparams"
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beam_size = 4
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max_seq_len = 3
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