pdfcoffee

Autoencoders
The embedding is generated into a matrix of shape (VOCAB_SIZE, EMBED_SIZE)
where each row represents the GloVe embedding for a word in our vocabulary.
The PAD and UNK rows (0 and 1 respectively) are populated with zeros and random
uniform values respectively:
EMBED_SIZE = 50
def lookup_word2id(word):
try:
return word2id[word]
except KeyError:
return word2id["UNK"]
def load_glove_vectors(glove_file, word2id, embed_size):
embedding = np.zeros((len(word2id), embed_size))
fglove = open(glove_file, "rb")
for line in fglove:
cols = line.strip().split()
word = cols[0]
if embed_size == 0:
embed_size = len(cols) - 1
if word2id.has_key(word):
vec = np.array([float(v) for v in cols[1:]])
embedding[lookup_word2id(word)] = vec
embedding[word2id["PAD"]] = np.zeros((embed_size))
embedding[word2id["UNK"]] = np.random.uniform(-1, 1, embed_size)
return embedding
Next, we use these functions to generate embeddings:
sent_wids = [[lookup_word2id(w) for w in s.split()] for s in sents]
sent_wids = sequence.pad_sequences(sent_wids, SEQUENCE_LEN)
# load glove vectors into weight matrix
embeddings = load_glove_vectors(os.path.join(DATA_DIR, "glove.6B.{:d}
d.txt".format(EMBED_SIZE)), word2id, EMBED_SIZE)
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