sarcasm_detection_model_CNN_LSTM_DNN.py 文件源码

def _build_network(self, vocab_size, maxlen, embedding_dimension=256, hidden_units=256, trainable=False):
        print('Build model...')
        model = Sequential()

        model.add(
            Embedding(vocab_size, embedding_dimension, input_length=maxlen, embeddings_initializer='glorot_normal'))

        model.add(Convolution1D(hidden_units, 3, kernel_initializer='he_normal', padding='valid', activation='sigmoid',
                                input_shape=(1, maxlen)))
        # model.add(MaxPooling1D(pool_size=3))
        model.add(Convolution1D(hidden_units, 3, kernel_initializer='he_normal', padding='valid', activation='sigmoid',
                                input_shape=(1, maxlen - 2)))
        # model.add(MaxPooling1D(pool_size=3))

        # model.add(Dropout(0.25))

        model.add(LSTM(hidden_units, kernel_initializer='he_normal', activation='sigmoid', dropout=0.5,
                       return_sequences=True))
        model.add(LSTM(hidden_units, kernel_initializer='he_normal', activation='sigmoid', dropout=0.5))

        model.add(Dense(hidden_units, kernel_initializer='he_normal', activation='sigmoid'))
        model.add(Dense(2))
        model.add(Activation('softmax'))
        adam = Adam(lr=0.0001)
        model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
        print('No of parameter:', model.count_params())

        print(model.summary())
        return model