load_word_embeddings.py

# Load in embeddings
glove_vectors = '/home/ubuntu/.keras/datasets/glove.6B.100d.txt'
glove = np.loadtxt(glove_vectors, dtype='str', comments=None)

# Extract the vectors and words
vectors = glove[:, 1:].astype('float')
words = glove[:, 0]

# Create lookup of words to vectors
word_lookup = {word: vector for word, vector in zip(words, vectors)}

# New matrix to hold word embeddings
embedding_matrix = np.zeros((num_words, vectors.shape[1]))

for i, word in enumerate(word_idx.keys()):
    # Look up the word embedding
    vector = word_lookup.get(word, None)

    # Record in matrix
    if vector is not None:
        embedding_matrix[i + 1, :] = vector