GDU & TF_COMMON Instructions

• gdu.py is a convenient functions tool for data processing.
• tf_common.py is a high level and convenient wrapper for TensorFlow APIs, include some preveiling blocks.

DEMO1 - CLS: model.py & train.py

DEMO2 - SEQ2SEQ: modelt.py & traint.py

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For using GDU module, you should follow these steps:

data format:

> sequence_source=sequence_label
> e.g.
>   for CHAT, 你好=你也好啊！
>   for CLS, 新华社消息……=2 # (2 is the id of news domain)
>   for TAG, 中华人民站起来了=BEBEBIEE

For import module:

import gdu as g

For reading vocab:

vocab = g.read_vocab(VOCAB_PATH)
reverse_vocab = g.reverse_vacab(vocab)

If your data can be fully loaded in RAM, you could load data as the following:

source_label = g.read_contents(FILE_PATH, replace=' ', split='=')
source, label = g.unzip_tuple(source_label)
index = 0
for i in range(TRAIN_STEP):
    eid, eil, did, dod, dol, need_shuffle, index = g.get_seq2seq_batch(source, label, 256, index, vocab)
    if (need_shuffle):
        source, label = g.unzip_tuple(g.shuffle(source_label))

If your data is too huge and can not be fully loaded into RAM, you can use {multinomial_read} to simulate a global shuffle.

newd = g.tear_to_pieces({'class1':['./data/class1.txt'],\
                         'class2':['./data/class2.txt'],\
                         'class3':['./data/class3.txt'],\
                         'class4':['./data/class4.txt'],\ 
                         'class5':['./data/class5.txt'],\ 
                         'class6':['./data/class6.txt']},\
                         cache=204800)
for i in range(TRAIN_STEP):
    source_label = multinomial_read(newd, cache=1024, replace=' ', split='=')
    source, label = unzip_tuple(source_label)
    index = 0
    need_shuffle = False
    while (need_shuffle == False):
        eid, eil, ld, need_shuffle, index = get_cls_batch(source=source, label=label, batch_size=8, index=index, vocab=vocab, fix_padding=40)

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In tf_common module, we build lots of convenient tensorflow APIs and some preveiling NN blocks. e.g.:

• bahdanau_att, create bahdanau attention wrapper class.
• dselfatt, createt self-attention for dynamic sequence length.
• sselfatt, create self-attention for static sequence length.
• bi_gru, create a bi-direction GRU cell list.
• gelu & ngelu, gelu activation function from BERT and the one with normed operation.
• ...
• ...

You can use tf_common.flist() to show all the functions and its' helping documents.

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Additionally, we have some lovely LOG functions for beautifying you command-line.

• gdu.normal
• gdu.infor
• gdu.warn
• gdu.error

And some more fantastic LOG functions.

• gdu.scolor
• gdu.lrandom
• gdu.rainbow

Some of them will output information like the following:

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Enjoy it and for good luck.