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text_classification.py
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290 lines (245 loc) · 9.44 KB
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#!/usr/bin/env python
# _*_ coding: utf-8 _*_
"""
@author : lightnine
@site : https://ligntnine.github.io
采用深度学习进行文本分类，参考：https://www.tensorflow.org/tutorials/keras/basic_text_classification?hl=zh-cn
使用keras包
@version : 1.0
@file : text_classification.py
@software : PyCharm
@time : 2019/1/11 15:28
"""
import tensorflow as tf
from tensorflow import keras
from tensorflow.python.keras.preprocessing.sequence import _remove_long_seq
import numpy as np
import json
print(tf.__version__)
imdb = keras.datasets.imdb
def read_imdb_data(path='imdb.npz',
num_words=None,
skip_top=0,
maxlen=None,
seed=113,
start_char=1,
oov_char=2,
index_from=3,
**kwargs):
"""
仿照keras.datasets.imdb.load_data()函数加载已经存在的数据
:return:
"""
# imdb = keras.datasets.imdb
# num_words 参数保留训练数据中出现频次在前 10000 位的字词
# (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
with np.load(path) as f:
x_train, labels_train = f['x_train'], f['y_train']
x_test, labels_test = f['x_test'], f['y_test']
# 分别对训练集和测试集进行混淆
np.random.seed(seed)
indices = np.arange(len(x_train))
np.random.shuffle(indices)
x_train = x_train[indices]
labels_train = labels_train[indices]
indices = np.arange(len(x_test))
np.random.shuffle(indices)
x_test = x_test[indices]
labels_test = labels_test[indices]
xs = np.concatenate([x_train, x_test])
labels = np.concatenate([labels_train, labels_test])
if start_char is not None:
xs = [[start_char] + [w + index_from for w in x] for x in xs]
elif index_from:
xs = [[w + index_from for w in x] for x in xs]
if maxlen:
xs, labels = _remove_long_seq(maxlen, xs, labels)
if not xs:
raise ValueError('After filtering for sequences shorter than maxlen=' +
str(maxlen) + ', no sequence was kept. '
'Increase maxlen.')
if not num_words:
num_words = max([max(x) for x in xs])
# by convention, use 2 as OOV word
# reserve 'index_from' (=3 by default) characters:
# 0 (padding), 1 (start), 2 (OOV)
if oov_char is not None:
xs = [
[w if (skip_top <= w < num_words) else oov_char for w in x] for x in xs
]
else:
xs = [[w for w in x if skip_top <= w < num_words] for x in xs]
idx = len(x_train)
x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])
return (x_train, y_train), (x_test, y_test)
def explore_data(data_x, data_y):
"""
探索数据
:param data_x:
:param data_y:
:return:
"""
print("训练数据量：{},标签：{}".format(len(data_x), len(data_y)))
print("第一条影评:" + str(data_x[0]))
print("前两条影评长度分别为{},{}".format(len(data_x[0]), len(data_x[1])))
# 单词到数字的映射
word_index_path = "./data/imdb/imdb_word_index.json"
word_index = get_word_index(path=word_index_path)
print("word_index 数量:" + str(len(word_index)))
word_index = {k: (v + 3) for k, v in word_index.items()}
# word_index中字词的最小编号是1
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2 # unknown
word_index["<UNUSED>"] = 3
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
text_0 = decode_review(reverse_word_index, data_x[0])
print("第一条文本：{}".format(text_0))
return word_index, reverse_word_index
def get_word_index(path):
"""
仿照keras.datasets.imdb.get_word_index()函数，获取单词和数字之间的映射
:param path:
:return:
"""
with open(path) as f:
return json.load(f)
def decode_review(reverse_word_index, text):
"""
根据单词数字的映射关系将数字解码为对应的文本
:param reverse_word_index:
:param text:
:return:
"""
return ' '.join([reverse_word_index.get(i, '?') for i in text])
def prepare_data(train_data, test_data, word_index):
"""
准备数据
将文本补充为同样长度的
:param train_data:
:param test_data:
:param word_index:
:return:
"""
# 填充序列，如果超过256，则在序列后面添加word_index['<PAD>']直到达到256
train_data = keras.preprocessing.sequence.pad_sequences(train_data,
value=word_index["<PAD>"],
padding='post',
maxlen=256)
test_data = keras.preprocessing.sequence.pad_sequences(test_data,
value=word_index["<PAD>"],
padding='post',
maxlen=256)
print("after pad process, length of train_data:" + str(len(train_data[0])))
print("after pad process, length of train_data:" + str(len(train_data[1])))
print("the first data:{}".format(train_data[0]))
return train_data, test_data
def build_model():
"""
构建模型
:return:
"""
# 单词总数大小
vocab_size = 10000
# 经过embedding之后的维度，官网例子是16
embed_size = 16
model = keras.Sequential()
# Embedding 将单词进行降维处理，生成词向量。16是输出的词向量的维度，vocab_size是输入序列的维度
# Embedding输出的维度是(batch, 10000, 16)
model.add(keras.layers.Embedding(vocab_size, embed_size))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(embed_size, activation=tf.nn.relu))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))
model.summary()
model.compile(optimizer=tf.train.AdamOptimizer(), loss='binary_crossentropy',
metrics=['accuracy'])
return model
def train_model(model, train_x, train_y, val_x, val_y, show_process=True):
"""
训练模型
:param model:
:param train_x:
:param train_y:
:param val_x:
:param val_y:
:param show_process:是否显示训练过程的图像
:return:
"""
history = model.fit(train_x, train_y, epochs=40, batch_size=512,
validation_data=(val_x, val_y), verbose=1)
# 查看训练过程中指标变化情况
history_dict = history.history
print("训练过程中的指标:" + str(history_dict.keys()))
if show_process:
show_train_process(history)
return model
def show_train_process(history):
"""
显示训练过程中准确率和损失的变化
:param history:
:return:
"""
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label="Validation loss")
plt.title("Training and Validation loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend()
# 生成第二张图
plt.figure()
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title("training and validation acc")
plt.xlabel("epochs")
plt.ylabel("acc")
plt.legend()
plt.show()
def evaluate_model(model, test_x, test_y):
results = model.evaluate(test_x, test_y)
print("评估结果:")
print(results)
def split_train_to_valid(train_x, train_y):
"""
从训练集中获取验证集
:param train_x:
:param train_y:
:return: 返回训练集和验证集
"""
x_val = train_x[:10000]
partial_x_train = train_x[10000:]
y_val = train_y[:10000]
partial_y_train = train_y[10000:]
return (partial_x_train, partial_y_train), (x_val, y_val)
if __name__ == '__main__':
"""
整个程序的流程如下：
1. 下载imdb数据集
2. 探索数据
- 将整数转换为字词
3. 准备数据
4. 构建模型
- 隐藏单元
- 损失函数和优化器
5. 创建验证集
6. 训练模型
7. 评估模型
8. 创建准确率和损失随时间变化的图
"""
# 注意这里数据处理的过程
imdb_path = "./data/imdb/imdb.npz"
# num_words指定了训练集中出现次数排在前10000次的单词才会入选，其他字词会被舍弃
(train_x, train_y), (test_x, test_y) = read_imdb_data(path=imdb_path, num_words=10000)
word_index, reverse_word_index = explore_data(train_x, train_y)
train_x, test_x = prepare_data(train_x, test_x, word_index)
(train_x, train_y), (val_x, val_y) = split_train_to_valid(train_x, train_y)
model = build_model()
model = train_model(model, train_x, train_y, val_x, val_y, show_process=True)
evaluate_model(model, test_x, test_y)