Deep Learning Quick Reference

With GloVe vectors

Now let's compare that to the code that includes pretrained GloVe vectors encoded in a 2D matrix:

sequence_input = Input(shape=(sequence_length,), dtype='int32')embedding_layer = Embedding(input_dim=vocab_size,                            output_dim=embedding_dim,                            weights=[embedding_matrix],                            input_length=sequence_length,                            trainable=False,                            name="embedding")(sequence_input)

For the most part, this code looks equivalent. There are two key differences:

• We initialize the layer weights to be contained in the GloVe matrix that we assembled with weights=[embedding_matrix].
• We also set the layer to trainable=False. This will prevent us from updating our weights. You may wish to fine tune the weights in a similar way to how we fine tuned the CNN we built in ...