in the Exercise 1 - sentence_to_avg we are asked to :
Initialize the average word vector, should have the same shape as your word vectors.
Use np.zeros and pass in the argument of any word’s word 2 vec’s shape
here is my output :
avg =
[-0.008005 0.56370833 -0.50427333 0.258865 0.55131103 0.03104983
-0.21013718 0.16893933 -0.09590267 0.141784 -0.15708967 0.18525867
0.6495785 0.38371117 0.21102167 0.11301667 0.02613967 0.26037767
0.05820667 -0.01578167 -0.12078833 -0.02471267 0.4128455 0.5152061
0.38756167 -0.898661 -0.535145 0.33501167 0.68806933 -0.2156265
1.797155 0.10476933 -0.36775333 0.750785 0.10282583 0.348925
-0.27262833 0.66768 -0.10706167 -0.283635 0.59580117 0.28747333
-0.3366635 0.23393817 0.34349183 0.178405 0.1166155 -0.076433
0.1445417 0.09808667]
KeyError Traceback (most recent call last)
in
27 print(“\033[92mAll tests passed!”)
28
—> 29 sentence_to_avg_test(sentence_to_avg)
30
31 # END UNIT TEST
in sentence_to_avg_test(target)
15 word_to_vec_map[key] = np.array(word_to_vec_map[key])
16
—> 17 avg = target(“a a_nw c_w a_s”, word_to_vec_map)
18 assert tuple(avg.shape) == tuple(word_to_vec_map[‘a’].shape), “Check the shape of your avg array”
19 assert np.allclose(avg, [1.25, 2.5]), “Check that you are finding the 4 words”
in sentence_to_avg(sentence, word_to_vec_map)
23 # Initialize the average word vector, should have the same shape as your word vectors.
24 # Use np.zeros and pass in the argument of any word’s word 2 vec’s shape
—> 25 avg = np.zeros(word_to_vec_map[“algeria”].shape)
26
27 # Initialize count to 0
KeyError: ‘algeria’
