product-titles-classifiy-text-cnn.py

from keras.layers import Embedding
word_index = tokenizer.word_index
nb_words = min(MAX_NB_WORDS, len(word_index))+1

embedding_matrix = np.zeros((nb_words, EMBEDDING_DIM))
for word, i in word_index.items():
    if word in word2vec.vocab:
        embedding_matrix[i] = word2vec.word_vec(word)
print('Null word embeddings: %d' % np.sum(np.sum(embedding_matrix, axis=1) == 0))

embedding_layer = Embedding(embedding_matrix.shape[0], # or len(word_index) + 1
                            embedding_matrix.shape[1], # or EMBEDDING_DIM,
                            weights=[embedding_matrix],
                            input_length=MAX_SEQUENCE_LENGTH,
                            trainable=False)