关于算法:stack-overflow-问题分类

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本教程的目标是率领大家学会如何给 stack overflow 上的问题进行打标签

首先咱们须要导入要用到的函数库

import matplotlib.pyplot as plt
import os
import re
import shutil
import string

import numpy as np
import tensorflow as tf

from tensorflow.keras import layers
from tensorflow.keras import losses
from tensorflow.keras import preprocessing
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization

接下来咱们看下 stack overflow 数据集，该数据集有 4 个类别标签，别离是 csharp、java、javascript、python，每个类别有 2000 个样本，数据集下载地址: http://storage.googleapis.com…

下一步是加载数据集，咱们用的是 tf.keras.preprocessing.text_dataset_from_directory()，要求的数据寄存构造如下图所示

main_directory/
...class_a/
......a_text_1.txt
......a_text_2.txt
...class_b/
......b_text_1.txt
......b_text_2.txt

在开始训练前，咱们须要把数据集划分成训练集、验证集、测试集，不过咱们看下目录能够发现，曾经存在训练集和测试集，那么还缺验证集，这个能够用 validation_split 从训练集里划分进去，代码如下所示

batch_size = 32
seed = 42

raw_train_ds = tf.keras.preprocessing.text_dataset_from_directory(
    'stack_overflow/train',
    batch_size=batch_size,
    validation_split=0.2,
    subset='training',
    seed=seed
)

raw_val_ds = tf.keras.preprocessing.text_dataset_from_directory(
    'stack_overflow/train',
    batch_size=batch_size,
    validation_split=0.2,
    subset='validation',
    seed=seed
)

raw_test_ds = tf.keras.preprocessing.text_dataset_from_directory(
    'stack_overflow/test',
    batch_size=batch_size
)

在开始训练之前咱们还须要对数据进行一些解决，能够通过调用 tf.keras.layers.experimental.preprocessing.TextVectorization 来进行数据的 standardize , tokenize , and vectorize

standardize: 用于移除 remove punctuation or HTML elements

tokenize: 把 strings 切分成 tokens

vectorize: 把 tokens 转化成 numbers，而后能够送入神经网络

def custom_standardization(input_data):
    lowercase = tf.strings.lower(input_data)
    stripped_html = tf.strings.regex_replace(lowercase, '<br />', ' ')
    return tf.strings.regex_replace(stripped_html,
                                    '[%s]' % re.escape(string.punctuation),
                                    '')

max_features = 10000
sequence_length = 125

vectorize_layer = TextVectorization(
    standardize=custom_standardization,
    max_tokens=max_features,
    output_mode='int',
    output_sequence_length=sequence_length
)

train_text = raw_train_ds.map(lambda x, y: x)
vectorize_layer.adapt(train_text)

def vectorize_text(text, label):
    text = tf.expand_dims(text, -1)
    return vectorize_layer(text), label

咱们能够一起看下解决过后的数据长什么样子，如下图所示

到这一步，咱们还须要对数据进行最初一步解决，而后就能够开始训练了

train_ds = raw_train_ds.map(vectorize_text)
val_ds = raw_val_ds.map(vectorize_text)
test_ds = raw_test_ds.map(vectorize_text)

AUTOTUNE = tf.data.experimental.AUTOTUNE

train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
test_ds = test_ds.cache().prefetch(buffer_size=AUTOTUNE)

接下来咱们开始搭建模型

embedding_dim = 16

model = tf.keras.Sequential([layers.Embedding(max_features + 1, embedding_dim),
    layers.Dropout(0.2),
    layers.GlobalAveragePooling1D(),
    layers.Dropout(0.2),
    layers.Dense(4)
])

model.summary()

model.compile(loss=losses.SparseCategoricalCrossentropy(from_logits=True),
              optimizer='adam',
              metrics=['accuracy'])

开始模型训练

epochs = 20
history = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=epochs
)

绘制训练后果图

history_dict = history.history
history_dict.keys()

acc = history_dict['accuracy']
val_acc = history_dict['val_accuracy']
loss = history_dict['loss']
val_loss = history_dict['val_loss']

epochs = range(1, len(acc) + 1)

# "bo" is for "blue dot"
plt.plot(epochs, loss, 'bo', label='Training loss')
# b is for "solid blue line"
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.show()

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')

plt.show()

咱们来剖析下，下面的两个图，第一个图反馈的是训练损失值和验证损失值的曲线，咱们发现模型过拟合了，针对这种状况咱们能够用 tf.keras.callbacks.EarlyStopping 来解决，只有在模型的验证损失值不再降落的中央，进行训练就好

训练完模型之后，咱们能够对样本进行预测，比方 examples 外面有 3 个样本，别离截取自 stack overflow 数据集，对于预测成果，大家能够自行测试

代码地址: https://codechina.csdn.net/cs…

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