As before, we will show the entire neural network structure here. Note that this structure is for the version of the model that includes GloVe vectors:

def build_model(vocab_size, embedding_dim, sequence_length, embedding_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)
    x = Conv1D(128, 5, activation='relu')(embedding_layer)
    x = MaxPooling1D(5)(x)
    x = Conv1D(128, 5, activation='relu')(x)
    x = MaxPooling1D(5)(x)
    x = Conv1D(128, 5, activation='relu')(x)
    x = GlobalMaxPooling1D()(x)
    x = Dense(128, activation='relu')(x)
    preds = Dense(20, activation='softmax')(x)
    model = Model(sequence_input, preds)
    model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
    return model

I'm using adam, categorical_crossentropy, and accuracy here again. While there are many new topics presented in this chapter, hopefully it's somewhat comforting to see what remains constant.