word2vec+LSTM 情感识别，帮忙看看哪出了问题

数据来源是京东新款手机的评论和打分，目前收集到 2500 条数据，但打分小于 5 分的只有不到 40 条

训练模型层次是：嵌入层-》 LSTM-》 Dense-》 Dense-》输出层 嵌入层的初始数据为 word2vec 训练的词向量 输入的训练数据为词索引，标记为打分

部分代码如下：

def main(): x_train = pad_seq() y_train = star() x_train, y_train, x_test, y_test = set_data(x_train, y_train) model = Sequential() model.add(Embedding(input_dim=input_dim+1, output_dim=output_dim, input_length=k, embeddings_initializer=my_init)) model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2, activation='sigmoid')) model.add(Dense(256, activation='relu')) model.add(Dense(128, activation='relu')) model.add(Dense(1,activation='sigmoid')) model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"]) model.fit(x_train, y_train, batch_size=batch_size, epochs=15) score, acc = model.evaluate(x_test, y_test, batch_size=batch_size) print('Test score:', score) print('Test accuracy:', acc) 

然而训练结果是这样的。。。

Epoch 15/15 32/2015 [..............................] - ETA: 0s - loss: -63.2713 - acc: 0.0000e+00 160/2015 [=>............................] - ETA: 0s - loss: -63.4706 - acc: 0.0000e+00 288/2015 [===>..........................] - ETA: 0s - loss: -63.5481 - acc: 0.0000e+00 384/2015 [====>.........................] - ETA: 0s - loss: -63.2713 - acc: 0.0026 480/2015 [======>.......................] - ETA: 0s - loss: -63.3046 - acc: 0.0021 608/2015 [========>.....................] - ETA: 0s - loss: -63.4024 - acc: 0.0016 736/2015 [=========>....................] - ETA: 0s - loss: -63.3796 - acc: 0.0027 864/2015 [===========>..................] - ETA: 0s - loss: -63.3821 - acc: 0.0023 992/2015 [=============>................] - ETA: 0s - loss: -63.3838 - acc: 0.0020 1120/2015 [===============>..............] - ETA: 0s - loss: -63.3852 - acc: 0.0018 1248/2015 [=================>............] - ETA: 0s - loss: -63.3991 - acc: 0.0016 1376/2015 [===================>..........] - ETA: 0s - loss: -63.4104 - acc: 0.0015 1504/2015 [=====================>........] - ETA: 0s - loss: -63.3879 - acc: 0.0020 1632/2015 [=======================>......] - ETA: 0s - loss: -63.4081 - acc: 0.0018 1760/2015 [=========================>....] - ETA: 0s - loss: -63.4344 - acc: 0.0017 1888/2015 [===========================>..] - ETA: 0s - loss: -63.4233 - acc: 0.0021 2015/2015 [==============================] - 1s 470us/step - loss: -63.4214 - acc: 0.0020 32/504 [>.............................] - ETA: 2s 504/504 [==============================] - 0s 412us/step Test score: -63.769539061046785 Test accuracy: 0.0 Process finished with exit code 0 

model 如下：

_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding_1 (Embedding) (None, 20, 50) 49350 _________________________________________________________________ lstm_1 (LSTM) (None, 128) 91648 _________________________________________________________________ dense_1 (Dense) (None, 256) 33024 _________________________________________________________________ dense_2 (Dense) (None, 128) 32896 _________________________________________________________________ dense_3 (Dense) (None, 1) 129 ================================================================= Total params: 207,047 Trainable params: 207,047 Non-trainable params: 0 _________________________________________________________________