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_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)