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' ...
