-
Notifications
You must be signed in to change notification settings - Fork 15
/
c5_word2vec.py
194 lines (129 loc) · 6.6 KB
/
c5_word2vec.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
#!/usr/bin/env python
import numpy as np
import random
from c1_softmax import softmax
from c2_gradcheck import gradcheck_naive
from c3_sigmoid import sigmoid, sigmoid_grad
#Each element is divided by square root of square sum of relative row
def normalizeRows(x):
N = x.shape[0]
x /= np.sqrt(np.sum(x**2, axis=1)).reshape((N,1)) + 1e-30
return x
def test_normalize_rows():
print "Testing normalizeRows..."
x = normalizeRows(np.array([[3.0, 4.0],[1, 2]]))
print x
assert (np.amax(np.fabs(x - np.array([[0.6,0.8],[0.4472136,0.89442719]]))) <= 1e-6)
print ""
def softmaxCostAndGradient(predicted, target, outputVectors, dataset):
""" Softmax cost function for word2vec models """
probabilities = softmax(predicted.dot(outputVectors.T)) #难怪我看不懂，predicted.dot(outputVectors.T) 这个没懂啥意思
cost = -np.log(probabilities[target])
delta = probabilities
delta[target] -= 1
N = delta.shape[0] #delta.shape = (5,)
D = predicted.shape[0] #predicted.shape = (3,)
grad = delta.reshape((N, 1)) * predicted.reshape((1, D))
gradPred = (delta.reshape((1, N)).dot(outputVectors)).flatten()
return cost, gradPred, grad
def negSamplingCostAndGradient(predicted, target, outputVectors, dataset, K=10):
""" Negative sampling cost function for word2vec models """
grad = np.zeros(outputVectors.shape)
gradPred = np.zeros(predicted.shape)
indices = [target]
for k in xrange(K):
newidx = dataset.sampleTokenIdx()
while newidx == target:
newidx = dataset.sampleTokenIdx()
indices += [newidx]
labels = np.array([1] + [-1 for k in xrange(K)])
vecs = outputVectors[indices, :]
t = sigmoid(vecs.dot(predicted) * labels)
cost = -np.sum(np.log(t))
delta = labels * (t-1)
gradPred = delta.reshape((1, K+1)).dot(vecs).flatten()
gradtemp = delta.reshape((K+1, 1)).dot(predicted.reshape(1, predicted.shape[0]))
for k in xrange(K+1):
grad[indices[k]] += gradtemp[k, :]
return cost, gradPred, grad
def skipgram(currentWord, C, contextWords, tokens, inputVectors, outputVectors,
dataset, word2vecCostAndGradient = softmaxCostAndGradient):
""" Skip-gram model in word2vec """
currentI = tokens[currentWord] #the order of this center word in the whole vocabulary
predicted = inputVectors[currentI, :] #turn this word to vector representation
cost = 0.0
gradIn = np.zeros(inputVectors.shape)
gradOut = np.zeros(outputVectors.shape)
for cwd in contextWords: #contextWords is of 2C length
idx = tokens[cwd]
cc, gp, gg = word2vecCostAndGradient(predicted, idx, outputVectors, dataset)
cost += cc #final cost/gradient is the 'sum' of result calculated by each word in context
gradOut += gg
gradIn[currentI, :] += gp
return cost, gradIn, gradOut
def cbow(currentWord, C, contextWords, tokens, inputVectors, outputVectors,
dataset, word2vecCostAndGradient = softmaxCostAndGradient):
""" CBOW model in word2vec """
cost = 0
gradIn = np.zeros(inputVectors.shape)
gradOut = np.zeros(outputVectors.shape)
D = inputVectors.shape[1]
predicted = np.zeros((D, ))
indices = [tokens[cwd] for cwd in contextWords]
for idx in indices:
predicted += inputVectors[idx, :]
cost, gp, gradOut = word2vecCostAndGradient(predicted, tokens[currentWord], outputVectors, dataset)
gradIn = np.zeros(inputVectors.shape)
for idx in indices:
gradIn[idx, :] += gp
return cost, gradIn, gradOut
def word2vec_sgd_wrapper(word2vecModel, tokens, wordVectors, dataset, C, word2vecCostAndGradient = softmaxCostAndGradient):
batchsize = 50
cost = 0.0
grad = np.zeros(wordVectors.shape) #each element in wordVectors has a gradient
N = wordVectors.shape[0]
inputVectors = wordVectors[:N/2, :]
outputVectors = wordVectors[N/2:, :]
for i in xrange(batchsize): #train word2vecModel for 50 times
C1 = random.randint(1, C)
centerword, context = dataset.getRandomContext(C1) #randomly choose 1 word, and generate a context of it
if word2vecModel == skipgram:
denom = 1
else:
denom = 1
c, gin, gout = word2vecModel(centerword, C1, context, tokens, inputVectors, outputVectors, dataset, word2vecCostAndGradient)
cost += c / batchsize / denom #calculate the average
grad[:N/2, :] += gin / batchsize / denom
grad[N/2:, :] += gout / batchsize / denom
return cost, grad #在run里，sgd返回的是wordvectors，但sgd返回的东西是由wrapper决定的，难道wrapper的gra就是那个wordvectors吗
#应该是的，W1 W2 就是词向量的矩阵，但是我给忘了原理
def test_word2vec():
dataset = type('dummy', (), {})() #create a dynamic object and then add attributes to it
def dummySampleTokenIdx(): #generate 1 integer between (0,4)
return random.randint(0, 4)
def getRandomContext(C): #getRandomContext(3) = ('d', ['d', 'd', 'd', 'e', 'a', 'd'])
tokens = ["a", "b", "c", "d", "e"]
return tokens[random.randint(0,4)], [tokens[random.randint(0,4)] \
for i in xrange(2*C)]
dataset.sampleTokenIdx = dummySampleTokenIdx #add two methods to dataset
dataset.getRandomContext = getRandomContext
random.seed(31415)
np.random.seed(9265) #can be called again to re-seed the generator
#in this test, this wordvectors matrix is randomly generated,
#but in real training, this matrix is a well trained data
dummy_vectors = normalizeRows(np.random.randn(10,3)) #generate matrix in shape=(10,3),
dummy_tokens = dict([("a",0), ("b",1), ("c",2), ("d",3), ("e",4)]) #{'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4}
print "==== Gradient check for skip-gram ===="
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(skipgram, dummy_tokens, vec, dataset, 5), dummy_vectors) #vec is dummy_vectors
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(skipgram, dummy_tokens, vec, dataset, 5, negSamplingCostAndGradient), dummy_vectors)
print "\n==== Gradient check for CBOW ===="
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(cbow, dummy_tokens, vec, dataset, 5), dummy_vectors)
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(cbow, dummy_tokens, vec, dataset, 5, negSamplingCostAndGradient), dummy_vectors)
print "\n=== Results ==="
print skipgram("c", 3, ["a", "b", "e", "d", "b", "c"], dummy_tokens, dummy_vectors[:5, :], dummy_vectors[5:, :], dataset)
print skipgram("c", 1, ["a", "b"], dummy_tokens, dummy_vectors[:5, :], dummy_vectors[5:, :], dataset, negSamplingCostAndGradient)
print cbow("a", 2, ["a", "b", "c", "a"], dummy_tokens, dummy_vectors[:5, :], dummy_vectors[5:, :], dataset)
print cbow("a", 2, ["a", "b", "a", "c"], dummy_tokens, dummy_vectors[:5,:], dummy_vectors[5:,:], dataset, negSamplingCostAndGradient)
if __name__ == "__main__":
test_normalize_rows()
test_word2vec()