我最近尝试使用word2vec，我训练了模型并获得了分配的所有向量。但是，我不知道如何找到每个向量的值。
我尝试打印模型，但它仅输出它训练的所有矢量。但是，我仍然不明白，我认为向量是基于每个单词的，但是某种程度上所有内容都在一个列表中。
我对word2vec的理解是，每个单词（假设这个W1）都有自己的向量，每个向量代表当前单词（W1）和word2（W2）之间的相似性。由于每个单词都分配有稀疏向量，因此仅W1应该由许多向量组成。但是，当我打印模型时，我仅收到（也许）一个字，但是我不确定这是哪个字。谁能帮我吗？
我的代码：
import collections
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
batch_size = 20
embedding_size = 2
num_sampled = 15
sentences = ["I have something that I want to say to him",
"How are you",
"We can see many stars tonight",
"That's our house",
"sung likes cats",
"she loves dogs",
"Do you know what he has done",
"cats are great companions when they want to be",
"We need to invest in clean, renewable energy",
"women love his man",
"queen love his king",
"girl love his boy",
"The line is too long. Why don't you come back tomorrow",
"man and women roam in park",
"Does it really matter",
"dynasty king remain mortal"]
words = " ".join(sentences).split()
count = collections.Counter(words).most_common()
# Build dictionaries
reverse_dictionary = [i[0] for i in count] #reverse dic, idx -> word
dic = {w: i for i, w in enumerate(reverse_dictionary)} #dic, word -> id
voc_size = len(dic)
data = [dic[word] for word in words]
cbow_pairs = []
for i in range(1, len(data)-1) :
cbow_pairs.append([[data[i-1], data[i+1]], data[i]])
skip_gram_pairs = []
for c in cbow_pairs:
skip_gram_pairs.append([c[1], c[0][0]])
skip_gram_pairs.append([c[1], c[0][1]])
def generate_batch (size):
assert size < len(skip_gram_pairs)
x_data=[]
y_data = []
r = np.random.choice(range(len(skip_gram_pairs)), size, replace=False)
for i in r:
x_data.append(skip_gram_pairs[i][0]) # n dim
y_data.append([skip_gram_pairs[i][1]]) # n, 1 dim
return x_data, y_data
# Input data
train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
# Ops and variables pinned to the CPU because of missing GPU implementation
with tf.device('/cpu:0'):
# Look up embeddings for inputs.
embeddings = tf.Variable(
tf.random_uniform([voc_size, embedding_size], -1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_inputs) # lookup table
# Construct the variables for the NCE loss
nce_weights = tf.Variable(
tf.random_uniform([voc_size, embedding_size],-1.0, 1.0))
nce_biases = tf.Variable(tf.zeros([voc_size]))
# Compute the average NCE loss for the batch.
# This does the magic:
# tf.nn.nce_loss(weights, biases, inputs, labels, num_sampled, num_classes ...)
# It automatically draws negative samples when we evaluate the loss.
loss = tf.reduce_mean(tf.nn.nce_loss(nce_weights, nce_biases, train_labels, embed, num_sampled, voc_size))
# Use the adam optimizer
train_op = tf.train.AdamOptimizer(1e-1).minimize(loss)
# Launch the graph in a session# Launch
with tf.Session() as sess:
# Initializing all variables
tf.global_variables_initializer().run()
for step in range(100):
batch_inputs, batch_labels = generate_batch(batch_size)
_, loss_val = sess.run([train_op, loss],
feed_dict={train_inputs: batch_inputs, train_labels: batch_labels})
# Final embeddings are ready for you to use. Need to normalize for practical use
trained_embeddings = embeddings.eval()
print(trained_embeddings)
当前输出：某种程度上，此输出似乎仅适用于单个单词，而不适用于语料库中的所有单词。
[[-0.751498 -1.4963825 ]
[-0.7022982 -1.4211462 ]
[-1.6240289 -0.96706766]
[-3.2109795 -1.2967492 ]
[-0.8835893 -1.5251521 ]
[-1.4316636 -1.4322135 ]
[-1.8665589 -1.1734825 ]
[-0.4726948 -1.836668 ]
[-0.11171409 -2.0847342 ]
[-1.0599283 -0.9792351 ]
[-1.6748023 -0.9584413 ]
[-0.8855507 -1.3226773 ]
[-0.9565117 -1.5730425 ]
[-1.2891663 -1.1687953 ]
[-0.06940217 -1.7782353 ]
[-0.92220575 -1.8264929 ]
[-3.2258956 -1.105678 ]
[-2.4262347 -0.9806146 ]
[-0.36716968 -2.3782976 ]
[-0.4972397 -1.9926786 ]
[-0.65995616 -1.2129989 ]
[-0.53334516 -1.5244756 ]
[-1.4961753 -0.5592766 ]
[-0.57391864 -1.9852302 ]
[-0.6580112 -1.0749325 ]
[-0.7821078 -1.598069 ]
[-1.264001 -1.002861 ]
[-0.23881587 -2.103974 ]
[-0.3729657 -1.9456012 ]
[-0.9266953 -1.516872 ]
[-1.4948957 -1.1232641 ]
[-1.109361 -1.3108519 ]
[-2.0748782 -0.93853486]
[-2.0241299 -0.8716516 ]
[-0.9448593 -1.0530868 ]
[-1.4578291 -0.57673496]
[-0.31915158 -1.4830168 ]
[-1.2568909 -1.0629684 ]
[-0.50458056 -2.2233846 ]
[-1.2059065 -1.0402468 ]
[-0.17204402 -1.8913956 ]
[-1.5484996 -1.0246676 ]
[-1.7026784 -1.4470854 ]
[-2.114282 -1.2304462 ]
[-1.6737207 -1.2598573 ]
[-0.9031189 -1.8086503 ]
[-1.4084693 -0.9171761 ]
[-1.261698 -1.5333931 ]
[-2.7891722 -0.69629264]
[-2.7634912 -1.0250676 ]
[-2.171037 -1.3402877 ]
[-1.5588827 -1.4741637 ]
[-2.012083 -1.6028976 ]
[-1.4286829 -1.485801 ]
[-0.06908941 -2.370034 ]
[-1.3277153 -1.2935033 ]
[-0.52055264 -1.2549478 ]
[-2.4971442 -0.6335571 ]
[-2.7244987 -0.6136059 ]
[-0.7155211 -1.8717885 ]
[-2.1862056 -0.78832203]
[-2.068198 -0.96536046]
[-0.9023069 -1.6741301 ]
[-0.39895654 -1.584905 ]
[-0.656657 -1.6787726 ]
[ 0.13354267 -2.105389 ]
[-1.248123 -1.7273897 ]
[-0.6168909 -1.3929827 ]
[-0.1866242 -2.0612721 ]
[-2.3246803 -1.1561321 ]
[ 0.88145804 0.35487294]]
示例预期输出：
[-0.751498 -1.4963825]显示这两个向量的值。例如，“如何”或“是”。