sarcasm_detection_model_CNN_LSTM_DNN_word2vec.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(Convolution1D(emb_weights.shape[1], 3, kernel_initializer='he_normal', padding='valid',
                                activation='sigmoid',
                                input_shape=(1, maxlen)))
        # model.add(MaxPooling1D(pool_size=3))

        model.add(Convolution1D(emb_weights.shape[1], 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, 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