spaCy and Keras embedding layers

fake_news-embeddings.py

# Imports
from keras.initializers import Constant
import spacy
from keras.layers import Embedding

def spacy_embedding(tokenizer, maxlen = 500, show_progress = False):

    # Load the spacy pipeline
    nlp = spacy.load("en_core_web_sm")
    # Get vocab size of tokenizer
    vocab_size = len(tokenizer.word_index) + 1

    # Get the number of embedding dimensions SpaCy uses
    embedding_dim = nlp("any_word").vector.shape[0]
    # Create a matrix to use in embedding layer
    embedding_matrix = np.zeros((vocab_size, embedding_dim))

    # Iterate through our vocabulary, mapping words to spacy embedding
    # this will take a while to run
    for i, word in enumerate(tokenizer.word_index):
        embedding_matrix[i] = nlp(word).vector
        # Show progress if desired
        if show_progress:
            if i % 10000 == 0 and i > 0:
                print(round(i*100/vocab_size, 3), "% complete")


    # Load the embedding matrix as the weights matrix for the embedding layer
    # Set trainable to False as the layer is already "learned"
    Embedding_layer = Embedding(
        vocab_size,
        embedding_dim,
        input_length = maxlen,
        embeddings_initializer=Constant(embedding_matrix),
        trainable=False,
        name = "spacy_embedding")

    return Embedding_layer


def keras_embedding(tokenizer, embedding_dim = 256, maxlen = 500):

    # Get vocab size of tokenizer
    vocab_size = len(tokenizer.word_index) + 1

    # Load the embedding matrix as the weights matrix for the embedding layer
    # Set trainable to False as the layer is already "learned"
    Embedding_layer = Embedding(
        vocab_size,
        embedding_dim,
        input_length = maxlen,
        name = "keras_embedding")

    return Embedding_layer


# Generate the embeddings
embed_dict = dict()
embed_dict["spacy"] = spacy_embedding(tokenizer, show_progress = True, maxlen = 500)
embed_dict["keras"] = keras_embedding(tokenizer, maxlen = 500)