reading_word_embedding_model.py

def emb_mat(nb_words):
    EMBEDDING_FILE="glove.6B.100d.txt"
    def get_coefs(word,*arr): 
        return word, np.asarray(arr, dtype='float32')
    embeddings_index = dict(get_coefs(*o.strip().split()) for o in open(EMBEDDING_FILE, encoding="utf8"))

    all_embs = np.stack(embeddings_index.values())
    emb_mean,emb_std = all_embs.mean(), all_embs.std()
    emb_mean,emb_std

    embedding_matrix = np.random.normal(emb_mean, emb_std, (nb_words+1, embed_size))
    for word, i in word_index.items():
        if (i >= max_features) or i==nb_words:
            continue
        embedding_vector = embeddings_index.get(word) #here we will get embedding for each word from GloVe
        if embedding_vector is not None:
            embedding_matrix[i] = embedding_vector
            
    return embedding_matrix