tensorflow在文本处理中的使用——Word2Vec预测

代码来源于：tensorflow机器学习实战指南（曾益强 译，2017年9月）——第七章：自然语言处理

代码地址：https://github.com/nfmcclure/tensorflow-cookbook

数据：http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz

问题：加载和使用预训练的嵌套，并使用这些单词嵌套进行情感分析，通过训练线性逻辑回归模型来预测电影的好坏

步骤如下：

• 必要包
• 声明模型参数
• 读取并转换文本数据集，划分训练集和测试集
• 构建图
• 训练

---

step1：必要包

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import random
import os
import pickle
import string
import requests
import collections
import io
import tarfile
import urllib.request
import text_helpers
from nltk.corpus import stopwords
from tensorflow.python.framework import ops
ops.reset_default_graph()

os.chdir(os.path.dirname(os.path.realpath(__file__)))

# Start a graph session
sess = tf.Session()

---

step2：声明模型参数

# Declare model parameters
embedding_size = 200
vocabulary_size = 2000
batch_size = 100
max_words = 100

# Declare stop words
stops = stopwords.words('english') 

---

step3：读取并转换本文数据集，划分训练集和测试集

参考：tensorflow在文本处理中的使用——辅助函数

# Load Data
print('Loading Data')
data_folder_name = 'temp'
texts, target = text_helpers.load_movie_data(data_folder_name)

# Normalize text
print('Normalizing Text Data')
texts = text_helpers.normalize_text(texts, stops)

# Texts must contain at least 3 words
target = [target[ix] for ix, x in enumerate(texts) if len(x.split()) > 2]
texts = [x for x in texts if len(x.split()) > 2]

# Split up data set into train/test
train_indices = np.random.choice(len(target), round(0.8*len(target)), replace=False)
test_indices = np.array(list(set(range(len(target))) - set(train_indices)))
texts_train = [x for ix, x in enumerate(texts) if ix in train_indices]
texts_test = [x for ix, x in enumerate(texts) if ix in test_indices]
target_train = np.array([x for ix, x in enumerate(target) if ix in train_indices])
target_test = np.array([x for ix, x in enumerate(target) if ix in test_indices])

# Load dictionary and embedding matrix加载CBOW嵌套中保存的单词字典
dict_file = os.path.join(data_folder_name, 'movie_vocab.pkl')
word_dictionary = pickle.load(open(dict_file, 'rb'))

# Convert texts to lists of indices根据单词字典将加载的句子转化为数值型numpy数组
text_data_train = np.array(text_helpers.text_to_numbers(texts_train, word_dictionary))
text_data_test = np.array(text_helpers.text_to_numbers(texts_test, word_dictionary))

# Pad/crop movie reviews to specific length电影影评长度不一，不满100维的用0凑满，超过100维的取前100维
text_data_train = np.array([x[0:max_words] for x in [y+[0]*max_words for y in text_data_train]])
text_data_test = np.array([x[0:max_words] for x in [y+[0]*max_words for y in text_data_test]])

---

step4：构建图

print('Creating Model')
# Define Embeddings:创建嵌套变量，用于之后加载CBOW训练好的嵌套向量
embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))

# Define model:
# Create variables for logistic regression变量
A = tf.Variable(tf.random_normal(shape=[embedding_size,1]))
b = tf.Variable(tf.random_normal(shape=[1,1]))

# Initialize placeholders数据占位符
x_data = tf.placeholder(shape=[None, max_words], dtype=tf.int32)
y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)

# Lookup embeddings vectors
embed = tf.nn.embedding_lookup(embeddings, x_data)
# Take average of all word embeddings in documents计算句子中所有单词的平均嵌套
embed_avg = tf.reduce_mean(embed, 1)

# Declare logistic model (sigmoid in loss function)
model_output = tf.add(tf.matmul(embed_avg, A), b)

# Declare loss function (Cross Entropy loss)
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(model_output, y_target))

# Actual Prediction
prediction = tf.round(tf.sigmoid(model_output))
predictions_correct = tf.cast(tf.equal(prediction, y_target), tf.float32)
accuracy = tf.reduce_mean(predictions_correct)

# Declare optimizer
my_opt = tf.train.AdagradOptimizer(0.005)
train_step = my_opt.minimize(loss)

---

step5：训练

# Intitialize Variables
init = tf.initialize_all_variables()
sess.run(init)

# Load model embeddings加载CBOW训练好的嵌套矩阵
model_checkpoint_path = os.path.join(data_folder_name,'cbow_movie_embeddings.ckpt')
saver = tf.train.Saver({"embeddings": embeddings})
saver.restore(sess, model_checkpoint_path)

# Start Logistic Regression
print('Starting Model Training')
train_loss = []
test_loss = []
train_acc = []
test_acc = []
i_data = []
for i in range(10000):
    rand_index = np.random.choice(text_data_train.shape[0], size=batch_size)
    rand_x = text_data_train[rand_index]
    rand_y = np.transpose([target_train[rand_index]])
    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})

    # Only record loss and accuracy every 100 generations
    if (i+1)%100==0:
        i_data.append(i+1)
        train_loss_temp = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})
        train_loss.append(train_loss_temp)

        test_loss_temp = sess.run(loss, feed_dict={x_data: text_data_test, y_target: np.transpose([target_test])})
        test_loss.append(test_loss_temp)

        train_acc_temp = sess.run(accuracy, feed_dict={x_data: rand_x, y_target: rand_y})
        train_acc.append(train_acc_temp)

        test_acc_temp = sess.run(accuracy, feed_dict={x_data: text_data_test, y_target: np.transpose([target_test])})
        test_acc.append(test_acc_temp)
    if (i+1)%500==0:
        acc_and_loss = [i+1, train_loss_temp, test_loss_temp, train_acc_temp, test_acc_temp]
        acc_and_loss = [np.round(x,2) for x in acc_and_loss]
        print('Generation # {}. Train Loss (Test Loss): {:.2f} ({:.2f}). Train Acc (Test Acc): {:.2f} ({:.2f})'.format(*acc_and_loss))

可视化结果展示：

# Plot loss over time
plt.plot(i_data, train_loss, 'k-', label='Train Loss')
plt.plot(i_data, test_loss, 'r--', label='Test Loss', linewidth=4)
plt.title('Cross Entropy Loss per Generation')
plt.xlabel('Generation')
plt.ylabel('Cross Entropy Loss')
plt.legend(loc='upper right')
plt.show()

# Plot train and test accuracy
plt.plot(i_data, train_acc, 'k-', label='Train Set Accuracy')
plt.plot(i_data, test_acc, 'r--', label='Test Set Accuracy', linewidth=4)
plt.title('Train and Test Accuracy')
plt.xlabel('Generation')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.show()