sarcasm_detection_model_CNN_LSTM_DNN_2D.py 文件源码

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

        model.add(Embedding(vocab_size, emb_weights.shape[1], input_length=maxlen, weights=[emb_weights],
                            trainable=trainable))

        model.add(Reshape((maxlen,emb_weights.shape[1],1)))

        model.add(BatchNormalization(momentum=0.9))

        # model.add(Convolution2D(int(hidden_units/8), (5,5), kernel_initializer='he_normal', padding='valid', activation='sigmoid'))
        # model.add(MaxPooling2D((2,2)))
        # model.add(Dropout(0.5))
        #
        # model.add(Convolution2D(int(hidden_units/4), (5,5), kernel_initializer='he_normal', padding='valid', activation='sigmoid'))
        # model.add(MaxPooling2D((2,2)))
        # model.add(Dropout(0.5))


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

        model.add(Flatten())

        # model.add(Dense(int(hidden_units/2), kernel_initializer='he_normal', activation='sigmoid'))
        # model.add(Dropout(0.5))
        model.add(Dense(2,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