def build_keyword_model()

in question_generation_model.py [0:0]

• 25 lines of code
• 1 McCabe index (conditional complexity)

    def build_keyword_model(self):
        """
        Build model definition using GloVe embeddings and image, keyword input
        :return:
        """

        self.logger.debug('In keyword model')

        # image feature
        inputs1 = layers.Input(shape=(self.input_shape,))
        fe1 = Dropout(self.dropout)(inputs1)
        fe2 = layers.Dense(self.hidden_units, activation='relu')(fe1)

        # partial question sequence model
        inputs2 = Input(shape=(self.datasets.max_question_len,))
        se1 = Embedding(self.vocab_size, self.embedding_dim, mask_zero=True)(inputs2)
        se2 = Dropout(self.dropout)(se1)
        question_seq_model = LSTM(self.hidden_units)(se2)
        # question_seq_model = Bidirectional(LSTM(self.hidden_units))(se2)
        # question_seq_model = layers.Dense(self.hidden_units, activation='relu')(question_seq_model)
        # se4 = Dropout(self.dropout)(se3)

        # keyword sequence model
        inputs3 = Input(shape=(self.datasets.max_keyword_len,))
        k1 = Embedding(self.vocab_size, self.embedding_dim, mask_zero=True)(inputs3)
        k2 = Dropout(self.dropout)(k1)
        keyword_seq_model = LSTM(self.hidden_units)(k2)

        # decoder (feed forward) model
        decoder1 = Add()([fe2, question_seq_model, keyword_seq_model])

        decoder2 = layers.Dense(self.hidden_units, activation='relu')(decoder1)
        outputs = layers.Dense(self.vocab_size, activation='softmax')(decoder2)

        # merge the two input models
        model = Model(inputs=[inputs1, inputs2, inputs3], outputs=outputs)
        # make embedding layer weights fixed
        model.layers[3].set_weights([self.embedding_matrix])
        model.layers[3].trainable = False

        model.layers[4].set_weights([self.embedding_matrix])
        model.layers[4].trainable = False

        model.summary()
        optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
        # optimizer = SGD()

        #TODO: try different optimizer?
        model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])

        return model