def test_word2vec():
""" Test the two word2vec implementations, before running on Stanford Sentiment Treebank """
dataset = type('dummy', (), {})()
def dummySampleTokenIdx():
return random.randint(0, 4)
def getRandomContext(C):
tokens = ["a", "b", "c", "d", "e"]
return tokens[random.randint(0,4)], \
[tokens[random.randint(0,4)] for i in range(2*C)]
dataset.sampleTokenIdx = dummySampleTokenIdx
dataset.getRandomContext = getRandomContext
random.seed(31415)
np.random.seed(9265)
dummy_vectors = normalizeRows(np.random.randn(10,3))
dummy_tokens = dict([("a",0), ("b",1), ("c",2),("d",3),("e",4)])
print("==== Gradient check for skip-gram with naiveSoftmaxLossAndGradient ====")
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(
skipgram, dummy_tokens, vec, dataset, 5, naiveSoftmaxLossAndGradient),
dummy_vectors, "naiveSoftmaxLossAndGradient Gradient")
grad_tests_softmax(skipgram, dummy_tokens, dummy_vectors, dataset)
print("==== Gradient check for skip-gram with negSamplingLossAndGradient ====")
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(
skipgram, dummy_tokens, vec, dataset, 5, negSamplingLossAndGradient),
dummy_vectors, "negSamplingLossAndGradient Gradient")
grad_tests_negsamp(skipgram, dummy_tokens, dummy_vectors, dataset, negSamplingLossAndGradient)
def test_word2vec():
""" Test the two word2vec implementations, before running on Stanford Sentiment Treebank """
dataset, dummy_vectors, dummy_tokens = dummy()
print("==== Gradient check for skip-gram with naiveSoftmaxLossAndGradient ====")
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(
skipgram, dummy_tokens, vec, dataset, 5, negSamplingLossAndGradient),
dummy_vectors, "naiveSoftmaxLossAndGradient Gradient")
print("\n\n\t\t\tSkip-Gram with naiveSoftmaxLossAndGradient\t\t\t")
print("\nYour Result:")
loss, dj_dv, dj_du = skipgram(inputs['test_word2vec']['currentCenterWord'], inputs['test_word2vec']['windowSize'],
inputs['test_word2vec']['outsideWords'],
dummy_tokens, dummy_vectors[:5, :], dummy_vectors[5:, :], dataset,
naiveSoftmaxLossAndGradient)
print(
"Loss: {}\nGradient wrt Center Vectors (dJ/dV):\n {}\nGradient wrt Outside Vectors (dJ/dU):\n {}\n".format(loss,
dj_dv,
dj_du))
print("Expected Result: Value should approximate these:")
print(
"Loss: {}\nGradient wrt Center Vectors (dJ/dV):\n {}\nGradient wrt Outside Vectors (dJ/dU):\n {}\n".format(
outputs['test_word2vec']['loss'],
outputs['test_word2vec']['dj_dv'],
outputs['test_word2vec']['dj_du']))
def test_skipgram():
""" Test skip-gram with naiveSoftmaxLossAndGradient """
dataset, dummy_vectors, dummy_tokens = getDummyObjects()
print("==== Gradient check for skip-gram with naiveSoftmaxLossAndGradient ====")
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(
skipgram, dummy_tokens, vec, dataset, 5, naiveSoftmaxLossAndGradient),
dummy_vectors, "naiveSoftmaxLossAndGradient Gradient")
grad_tests_softmax(skipgram, dummy_tokens, dummy_vectors, dataset)
print("==== Gradient check for skip-gram with negSamplingLossAndGradient ====")
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(
skipgram, dummy_tokens, vec, dataset, 5, negSamplingLossAndGradient),
dummy_vectors, "negSamplingLossAndGradient Gradient")
grad_tests_negsamp(skipgram, dummy_tokens, dummy_vectors, dataset, negSamplingLossAndGradient)
def test_negSamplingLossAndGradient():
""" Test negSamplingLossAndGradient """
dataset, dummy_vectors, dummy_tokens = getDummyObjects()
print("==== Gradient check for negSamplingLossAndGradient ====")
def temp(vec):
loss, gradCenterVec, gradOutsideVecs = negSamplingLossAndGradient(vec, 1, dummy_vectors, dataset)
return loss, gradCenterVec
gradcheck_naive(temp, np.random.randn(3), "negSamplingLossAndGradient gradCenterVec")
centerVec = np.random.randn(3)
def temp(vec):
loss, gradCenterVec, gradOutsideVecs = negSamplingLossAndGradient(centerVec, 1, vec, dataset)
return loss, gradOutsideVecs
gradcheck_naive(temp, dummy_vectors, "negSamplingLossAndGradient gradOutsideVecs")
def test_word2vec():
""" Test the two word2vec implementations, before running on Stanford Sentiment Treebank """
dataset = type('dummy', (), {})()
def dummySampleTokenIdx():
return random.randint(0, 4)
def getRandomContext(C):
tokens = ["a", "b", "c", "d", "e"]
return tokens[random.randint(0,4)], \
[tokens[random.randint(0,4)] for i in range(2*C)]
dataset.sampleTokenIdx = dummySampleTokenIdx
dataset.getRandomContext = getRandomContext
random.seed(31415)
np.random.seed(9265)
dummy_vectors = normalizeRows(np.random.randn(10, 3))
dummy_tokens = dict([("a", 0), ("b", 1), ("c", 2), ("d", 3), ("e", 4)])
print(
"==== Gradient check for skip-gram with naiveSoftmaxLossAndGradient ===="
)
gradcheck_naive(
lambda vec: word2vec_sgd_wrapper(skipgram, dummy_tokens, vec, dataset,
5, naiveSoftmaxLossAndGradient),
dummy_vectors, "naiveSoftmaxLossAndGradient Gradient")
print(
"==== Gradient check for skip-gram with negSamplingLossAndGradient ===="
)
gradcheck_naive(
lambda vec: word2vec_sgd_wrapper(skipgram, dummy_tokens, vec, dataset,
5, negSamplingLossAndGradient),
dummy_vectors, "negSamplingLossAndGradient Gradient")
print("\n=== Results ===")
print("Skip-Gram with naiveSoftmaxLossAndGradient")
print("Your Result:")
print(
"Loss: {}\nGradient wrt Center Vectors (dJ/dV):\n {}\nGradient wrt Outside Vectors (dJ/dU):\n {}\n"
.format(
*skipgram("c", 3, ["a", "b", "e", "d", "b", "c"], dummy_tokens,
dummy_vectors[:5, :], dummy_vectors[5:, :], dataset)))
print("Expected Result: Value should approximate these:")
print("""Loss: 11.16610900153398
Gradient wrt Center Vectors (dJ/dV):
[[ 0. 0. 0. ]
[ 0. 0. 0. ]
[-1.26947339 -1.36873189 2.45158957]
[ 0. 0. 0. ]
[ 0. 0. 0. ]]
Gradient wrt Outside Vectors (dJ/dU):
[[-0.41045956 0.18834851 1.43272264]
[ 0.38202831 -0.17530219 -1.33348241]
[ 0.07009355 -0.03216399 -0.24466386]
[ 0.09472154 -0.04346509 -0.33062865]
[-0.13638384 0.06258276 0.47605228]]
""")
print("Skip-Gram with negSamplingLossAndGradient")
print("Your Result:")
print(
"Loss: {}\nGradient wrt Center Vectors (dJ/dV):\n {}\n Gradient wrt Outside Vectors (dJ/dU):\n {}\n"
.format(*skipgram("c", 1, ["a", "b"], dummy_tokens,
dummy_vectors[:5, :], dummy_vectors[
5:, :], dataset, negSamplingLossAndGradient)))
print("Expected Result: Value should approximate these:")
print("""Loss: 16.15119285363322
Gradient wrt Center Vectors (dJ/dV):
[[ 0. 0. 0. ]
[ 0. 0. 0. ]
[-4.54650789 -1.85942252 0.76397441]
[ 0. 0. 0. ]
[ 0. 0. 0. ]]
Gradient wrt Outside Vectors (dJ/dU):
[[-0.69148188 0.31730185 2.41364029]
[-0.22716495 0.10423969 0.79292674]
[-0.45528438 0.20891737 1.58918512]
[-0.31602611 0.14501561 1.10309954]
[-0.80620296 0.36994417 2.81407799]]
""")
def test_word2vec():
""" Test the two word2vec implementations, before running on Stanford Sentiment Treebank """
dataset = type('dummy', (), {})()
def dummySampleTokenIdx():
return random.randint(0, 4)
def getRandomContext(C):
tokens = ["a", "b", "c", "d", "e"]
return tokens[random.randint(0,4)], \
[tokens[random.randint(0,4)] for i in range(2*C)]
dataset.sampleTokenIdx = dummySampleTokenIdx
dataset.getRandomContext = getRandomContext
random.seed(31415)
np.random.seed(9265)
dummy_vectors = normalizeRows(np.random.randn(10, 3))
dummy_tokens = dict([("a", 0), ("b", 1), ("c", 2), ("d", 3), ("e", 4)])
print(
"==== Gradient check for skip-gram with naiveSoftmaxLossAndGradient ===="
)
gradcheck_naive(
lambda vec: word2vec_sgd_wrapper(skipgram, dummy_tokens, vec, dataset,
5, naiveSoftmaxLossAndGradient),
dummy_vectors, "naiveSoftmaxLossAndGradient Gradient")
print(
"==== Gradient check for skip-gram with negSamplingLossAndGradient ===="
)
gradcheck_naive(
lambda vec: word2vec_sgd_wrapper(skipgram, dummy_tokens, vec, dataset,
5, negSamplingLossAndGradient),
dummy_vectors, "negSamplingLossAndGradient Gradient")
print("\n=== Results ===")
print("Skip-Gram with naiveSoftmaxLossAndGradient")
print("Your Result:")
print(
"Loss: {}\nGradient wrt Center Vectors (dJ/dV):\n {}\nGradient wrt Outside Vectors (dJ/dU):\n {}\n"
.format(
*skipgram("c", 3, ["a", "b", "e", "d", "b", "c"], dummy_tokens,
dummy_vectors[:5, :], dummy_vectors[5:, :], dataset)))
print("Expected Result: Value should approximate these:")
print("""Loss: 11.16610900153398
Gradient wrt Center Vectors (dJ/dV):
[[ 0. 0. 0. ]
[ 0. 0. 0. ]
[-1.26947339 -1.36873189 2.45158957]
[ 0. 0. 0. ]
[ 0. 0. 0. ]]
Gradient wrt Outside Vectors (dJ/dU):
[[-0.41045956 0.18834851 1.43272264]
[ 0.38202831 -0.17530219 -1.33348241]
[ 0.07009355 -0.03216399 -0.24466386]
[ 0.09472154 -0.04346509 -0.33062865]
[-0.13638384 0.06258276 0.47605228]]
""")
print("Skip-Gram with negSamplingLossAndGradient")
print("Your Result:")
print(
"Loss: {}\nGradient wrt Center Vectors (dJ/dV):\n {}\n Gradient wrt Outside Vectors (dJ/dU):\n {}\n"
.format(*skipgram("c", 1, ["a", "b"], dummy_tokens,
dummy_vectors[:5, :], dummy_vectors[
5:, :], dataset, negSamplingLossAndGradient)))
print("Expected Result: Value should approximate these:")
print("""Loss: 14.3018669327
Gradient wrt Center Vectors (dJ/dV):
[[ 0. 0. 0. ]
[ 0. 0. 0. ]
[-3.86035429 -2.8660339 -0.9739887 ]
[ 0. 0. 0. ]
[ 0. 0. 0. ]]
Gradient wrt Outside Vectors (dJ/dU):
[[-0.30559455 0.14022886 1.06668785]
[-0.12708467 0.05831563 0.44359323]
[-0.45528438 0.20891737 1.58918512]
[-0.73739425 0.33836976 2.57389893]
[-0.64496237 0.29595533 2.25126239]]
""")
def test_word2vec():
""" Test the two word2vec implementations, before running on Stanford Sentiment Treebank """
# dummy class declaration/initialization
dataset = type('dummy', (), {})()
def dummySampleTokenIdx():
return random.randint(0, 4)
def getRandomContext(C):
tokens = ["a", "b", "c", "d", "e"]
return tokens[random.randint(0,4)], \
[tokens[random.randint(0,4)] for i in range(2*C)]
# adding variables and methods to the dummy class is allowed!!
dataset.sampleTokenIdx = dummySampleTokenIdx
dataset.getRandomContext = getRandomContext
# Random number generation isn't truly "random". It is deterministic, and the
# sequence it generates is dictated by the seed value you pass into random.seed.
# Typically you just invoke random.seed(), and it uses the current time as the
# seed value, which means whenever you run the script you will get a different
# sequence of values
random.seed(31415)
np.random.seed(9265)
#print(np.random.randn(10,3))
# Each vector should have unit length. Divide by the total vector length.
# normalization of vectors. Search for the why?
dummy_vectors = normalizeRows(np.random.randn(10, 3))
# this is our dummy corpus. words = strings do not matter
dummy_tokens = dict([("a", 0), ("b", 1), ("c", 2), ("d", 3), ("e", 4)])
print(
"==== Gradient check for skip-gram with naiveSoftmaxLossAndGradient ===="
)
gradcheck_naive(
lambda vec: word2vec_sgd_wrapper(skipgram, dummy_tokens, vec, dataset,
5, naiveSoftmaxLossAndGradient),
dummy_vectors, "naiveSoftmaxLossAndGradient Gradient")
print(
"==== Gradient check for skip-gram with negSamplingLossAndGradient ===="
)
gradcheck_naive(
lambda vec: word2vec_sgd_wrapper(skipgram, dummy_tokens, vec, dataset,
5, negSamplingLossAndGradient),
dummy_vectors, "negSamplingLossAndGradient Gradient")
print("\n=== Results ===")
print("Skip-Gram with naiveSoftmaxLossAndGradient")
print("Your Result:")
print(
"Loss: {}\nGradient wrt Center Vectors (dJ/dV):\n {}\nGradient wrt Outside Vectors (dJ/dU):\n {}\n"
.format(
*skipgram("c", 3, ["a", "b", "e", "d", "b", "c"], dummy_tokens,
dummy_vectors[:5, :], dummy_vectors[5:, :], dataset)))
print("Expected Result: Value should approximate these:")
print("""Loss: 11.16610900153398
Gradient wrt Center Vectors (dJ/dV):
[[ 0. 0. 0. ]
[ 0. 0. 0. ]
[-1.26947339 -1.36873189 2.45158957]
[ 0. 0. 0. ]
[ 0. 0. 0. ]]
Gradient wrt Outside Vectors (dJ/dU):
[[-0.41045956 0.18834851 1.43272264]
[ 0.38202831 -0.17530219 -1.33348241]
[ 0.07009355 -0.03216399 -0.24466386]
[ 0.09472154 -0.04346509 -0.33062865]
[-0.13638384 0.06258276 0.47605228]]
""")
print("Skip-Gram with negSamplingLossAndGradient")
print("Your Result:")
print(
"Loss: {}\nGradient wrt Center Vectors (dJ/dV):\n {}\n Gradient wrt Outside Vectors (dJ/dU):\n {}\n"
.format(*skipgram("c", 1, ["a", "b"], dummy_tokens,
dummy_vectors[:5, :], dummy_vectors[
5:, :], dataset, negSamplingLossAndGradient)))
print("Expected Result: Value should approximate these:")
print("""Loss: 16.15119285363322
Gradient wrt Center Vectors (dJ/dV):
[[ 0. 0. 0. ]
[ 0. 0. 0. ]
[-4.54650789 -1.85942252 0.76397441]
[ 0. 0. 0. ]
[ 0. 0. 0. ]]
Gradient wrt Outside Vectors (dJ/dU):
[[-0.69148188 0.31730185 2.41364029]
[-0.22716495 0.10423969 0.79292674]
[-0.45528438 0.20891737 1.58918512]
[-0.31602611 0.14501561 1.10309954]
[-0.80620296 0.36994417 2.81407799]]
""")
#!/usr/bin/env python
"""
Finished@Oct 17, 2019 by @ivan1rufus
The specific implementation of part word2vec model, skipgram model.
Attention points:
1. Gradient of centerVec is only a row vector.
2. In negativeSampleLoss, there may be multiple same negative samples.
3. The way how matrix multiplication is the point.
Bonus:
1. help(gradcheck_naive)
gradcheck_naive(f, x, gradientText)
Gradient check for a function f.
Arguments:
f -- a function that takes a single argument and outputs the
loss and its gradients
x -- the point (numpy array) to check the gradient at
gradientText -- a string detailing some context about the gradient computation
2. help(normalizeRows)
normalizeRows(x)
Row normalization function
Implement a function that normalizes each row of a matrix to have
unit length.
"""
import numpy as np
import random
