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arshjat/1.9.py

Created January 1, 2019 10:42
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1.9.py
word_index = tk.word_index
nb_words = min(max_features, len(word_index))
embedding_matrix = np.zeros((nb_words + 1, embed_size))
for word, i in word_index.items():
if i >= max_features: continue
embedding_vector = embedding_index.get(word)
if embedding_vector is not None: embedding_matrix[i] = embedding_vector
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