/
simple_trainer.py
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/
simple_trainer.py
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# -*- coding:utf-8 -*-
# @FileName :run_classifier.py
# @Time :2021/4/14 19:45
# @Author :huanghui
import numpy as np
from tfbert.data import Dataset
from tfbert.models import create_word_embeddings, dropout, create_initializer
from tfbert.models.loss import cross_entropy_loss
from tfbert.models.layers import conv2d_layer, max_pooling_layer
import tensorflow.compat.v1 as tf
from tfbert import SimplerTrainer, ProgressBar, set_seed
import pandas as pd
from collections import Counter
from tqdm import tqdm, trange
import platform
from sklearn.metrics import accuracy_score
if platform.system() == 'Windows':
bar_fn = ProgressBar
else:
bar_fn = tqdm
set_seed(42)
class TextCNN:
def __init__(self,
max_seq_length,
vocab_size,
is_training,
input_ids,
label_ids):
embedding, _ = create_word_embeddings(
input_ids=input_ids, vocab_size=vocab_size, embedding_size=300
)
embedding = tf.expand_dims(embedding, -1)
pooled_outputs = []
for i, filter_size in enumerate([2, 3, 5]):
with tf.variable_scope("conv_{}".format(filter_size)):
filter_shape = [filter_size, 300, 1, 128]
h = conv2d_layer(embedding, filter_shape)
pooled = max_pooling_layer(h, ksize=[1, max_seq_length - filter_size + 1, 1, 1])
pooled_outputs.append(pooled)
conv_output = tf.concat(pooled_outputs, 3)
conv_output = tf.reshape(conv_output, [-1, 128 * 3])
with tf.variable_scope("classifier"):
# dropout = get_dropout_prob(is_training, dropout_prob=dropout)
if is_training:
conv_output = dropout(conv_output, dropout_prob=0.3)
self.logits = tf.layers.dense(
conv_output,
5,
kernel_initializer=create_initializer(0.02),
name='logits'
)
if label_ids is not None:
self.loss = cross_entropy_loss(self.logits, label_ids, 5)
def get_model_fn(is_training, vocab_size):
def model_fn():
input_ids = tf.placeholder(shape=[None, 32], dtype=tf.int64, name='input_ids')
if is_training:
label_ids = tf.placeholder(shape=[None], dtype=tf.int64, name='label_ids')
else:
label_ids = None
model = TextCNN(
32, vocab_size,
is_training=is_training,
input_ids=input_ids,
label_ids=label_ids)
inputs = {'input_ids': input_ids}
outputs = {"logits": model.logits}
if is_training:
outputs['loss'] = model.loss
inputs['label_ids'] = label_ids
outputs['label_ids'] = label_ids
return inputs, outputs
return model_fn
def create_vocab(train_file, dev_file):
datas = pd.read_csv(train_file, encoding='utf-8', sep='\t').values.tolist()
datas.extend(
pd.read_csv(dev_file, encoding='utf-8', sep='\t').values.tolist()
)
words = []
for data in datas:
words.extend(list(data[1]))
words = [word.strip() for word in words if word.strip()]
counter = Counter(words)
words = counter.most_common(5000)
vocabs = ["<PAD>", "<UNK>"] + [word[0] for word in words]
vocab2id = dict(zip(vocabs, range(len(vocabs))))
return vocab2id
def load_dataset(filename, is_training, batch_size, max_seq_length, vocab2id, label2id):
data = pd.read_csv(filename, encoding='utf-8', sep='\t').values.tolist()
examples = []
for d in data:
label, text = d
id_ = list(map(lambda x: vocab2id[x] if x in vocab2id else vocab2id['<UNK>'], list(text)))
id_ = id_[:max_seq_length]
id_ += [vocab2id["<PAD>"]] * (max_seq_length - len(id_))
examples.append({'input_ids': id_, 'label_ids': label2id[label]})
dataset = Dataset(examples,
is_training=is_training,
batch_size=batch_size,
drop_last=is_training,
buffer_size=len(examples),
max_length=max_seq_length)
dataset.format_as(['input_ids', 'label_ids'])
return dataset
max_length = 32
batch_size = 32
data_dir = "D:/python/data/data/classification"
vocab2id = create_vocab(data_dir + "/train.csv", data_dir + "/dev.csv")
label2id = {'体育': 0, '娱乐': 1, '家居': 2, '房产': 3, '教育': 4}
train_dataset = load_dataset(
data_dir + "/train.csv",
True, batch_size, max_length, vocab2id, label2id
)
dev_dataset = load_dataset(
data_dir + "/dev.csv",
False, batch_size, max_length, vocab2id, label2id
)
trainer = SimplerTrainer(
optimizer_type='adamw',
learning_rate=5e-5
)
trainer.build_model(model_fn=get_model_fn(True, len(vocab2id)))
trainer.compile()
trainer.init_variables()
best_score = 0
for epoch in trange(5):
epoch_iter = bar_fn(train_dataset)
for d in epoch_iter:
loss = trainer.train_step(d)
epoch_iter.set_description(desc='epoch {} ,loss {:.4f}'.format(epoch + 1, loss))
epoch_iter.close()
outputs = trainer.predict(dev_dataset.get_all_features(), output_names=['logits', 'label_ids'])
y_true, y_pred = outputs['label_ids'], np.argmax(outputs['logits'], axis=-1)
score = accuracy_score(y_true, y_pred)
if score > best_score:
best_score = score
trainer.save_pretrained('output')
print()
print(score)