这是一个不使用api,只使用numpy的方法,当然反向传播得用pytorch.
import numpy as np
from collections import defaultdict
class word2vec():
def __init__(self):
self.n = settings['n']
self.lr = settings['learning_rate']
self.epochs = settings['epochs']
self.window = settings['window_size']
def generate_training_data(self, settings, corpus):
"""
得到训练数据
"""
#defaultdict(int) 一个字典，当所访问的键不存在时，用int类型实例化一个默认值
word_counts = defaultdict(int)
#遍历语料库corpus
for row in corpus:
for word in row:
#统计每个单词出现的次数
word_counts[word] += 1
# 词汇表的长度
self.v_count = len(word_counts.keys())
# 在词汇表中的单词组成的列表
self.words_list = list(word_counts.keys())
# 以词汇表中单词为key，索引为value的字典数据
self.word_index = dict((word, i) for i, word in enumerate(self.words_list))
#以索引为key，以词汇表中单词为value的字典数据
self.index_word = dict((i, word) for i, word in enumerate(self.words_list))
training_data = []
for sentence in corpus:
sent_len = len(sentence)
for i, word in enumerate(sentence):
w_target = self.word2onehot(sentence[i])
w_context = []
for j in range(i - self.window, i + self.window):
if j != i and j <= sent_len - 1 and j >= 0:
w_context.append(self.word2onehot(sentence[j]))
training_data.append([w_target, w_context])
return np.array(training_data)
def word2onehot(self, word):
#将词用onehot编码
word_vec = [0 for i in range(0, self.v_count)]
word_index = self.word_index[word]
word_vec[word_index] = 1
return word_vec
def train(self, training_data):
#随机化参数w1,w2
self.w1 = np.random.uniform(-1, 1, (self.v_count, self.n))
self.w2 = np.random.uniform(-1, 1, (self.n, self.v_count))
for i in range(self.epochs):
self.loss = 0
# w_t 是表示目标词的one-hot向量
#w_t -> w_target,w_c ->w_context
for w_t, w_c in training_data:
#前向传播
y_pred, h, u = self.forward(w_t)
#计算误差
EI = np.sum([np.subtract(y_pred, word) for word in w_c], axis=0)#误差求和.
#反向传播，更新参数
self.backprop(EI, h, w_t)
#计算总损失
self.loss += -np.sum([u[word.index(1)] for word in w_c]) + len(w_c) * np.log(np.sum(np.exp(u)))
print('Epoch:', i, "Loss:", self.loss)
def forward(self, x):
"""
前向传播
"""
h = np.dot(self.w1.T, x)
u = np.dot(self.w2.T, h)
y_c = self.softmax(u)
return y_c, h, u
def softmax(self, x):
"""
"""
e_x = np.exp(x - np.max(x))
return e_x / np.sum(e_x)
def backprop(self, e, h, x):
d1_dw2 = np.outer(h, e)
d1_dw1 = np.outer(x, np.dot(self.w2, e.T))
self.w1 = self.w1 - (self.lr * d1_dw1)
self.w2 = self.w2 - (self.lr * d1_dw2)
def word_vec(self, word):
"""
获取词向量
通过获取词的索引直接在权重向量中找
"""
w_index = self.word_index[word]
v_w = self.w1[w_index]
return v_w
def vec_sim(self, word, top_n):
"""
找相似的词
"""
v_w1 = self.word_vec(word)
word_sim = {}
for i in range(self.v_count):
v_w2 = self.w1[i]
theta_sum = np.dot(v_w1, v_w2)
#np.linalg.norm(v_w1) 求范数 默认为2范数，即平方和的二次开方
theta_den = np.linalg.norm(v_w1) * np.linalg.norm(v_w2)
theta = theta_sum / theta_den
word = self.index_word[i]
word_sim[word] = theta
words_sorted = sorted(word_sim.items(), key=lambda kv: kv[1], reverse=True)
for word, sim in words_sorted[:top_n]:
print(word, sim)
def get_w(self):
w1 = self.w1
return w1
#超参数
settings = {
'window_size': 2, #窗口尺寸 m
#单词嵌入(word embedding)的维度,维度也是隐藏层的大小。
'n': 10,
'epochs': 50, #表示遍历整个样本的次数。在每个epoch中，我们循环通过一遍训练集的样本。
'learning_rate':0.01 #学习率
}
#数据准备
text = "natural language processing and machine learning is fun and exciting"
#按照单词间的空格对我们的语料库进行分词
corpus = [[word.lower() for word in text.split()]]
print(corpus)
#初始化一个word2vec对象
w2v = word2vec()
training_data = w2v.generate_training_data(settings,corpus)
#训练
w2v.train(training_data)
# 获取词的向量
word = "machine"
vec = w2v.word_vec(word)
print(word, vec)
# 找相似的词
w2v.vec_sim("machine", 3)
这是别人的,写的挺好,我忘了谁的了,如果知道是谁的,我就挂上他的链接
