Keras - 损失和val_loss增加

我在Keras使用LSTM进行神经网络训练聊天机器人 .

contextTrain, contextTest, utteranceTrain, utteranceTest = train_test_split(context, utterance, test_size=0.1, random_state=1)
model = Sequential()
model.add(LSTM(input_shape=contextTrain.shape[1:], return_sequences=True, units=300, activation="sigmoid", kernel_initializer="glorot_normal", recurrent_initializer="glorot_normal"))
model.add(LSTM(return_sequences=True, units=300, activation="sigmoid", kernel_initializer="glorot_normal", recurrent_initializer="glorot_normal"))
model.compile(loss="cosine_proximity", optimizer="adam", metrics=["accuracy"])
model.fit(contextTrain, utteranceTrain, epochs=5000, validation_data=(contextTest, utteranceTest), callbacks=[ModelCheckpoint("model{epoch:02d}.h5", monitor='val_acc', save_best_only=true, mode='max')])

上下文和话语是具有形状的numpy数组，例如（1000,10,300）或（10000,10,300） . 第一个LSTM的Input_shape应为（10,300） . 大小300的向量是由Word2vec模型创建的单词嵌入表示的单词 . 因此，在此示例中，输入数据是这些向量中的10个 .

最大的问题是在训练期间损失和val_loss几乎都在稳步增加 .

Epoch 1/5000
900/900 [==============================] - 18s 20ms/step - loss: -0.5855 - acc: 0.0220 - val_loss: -0.6527 - val_acc: 0.0260
Epoch 2/5000
900/900 [==============================] - 13s 14ms/step - loss: -0.6299 - acc: 0.0239 - val_loss: -0.6673 - val_acc: 0.0240
Epoch 3/5000
900/900 [==============================] - 12s 14ms/step - loss: -0.6387 - acc: 0.0213 - val_loss: -0.6764 - val_acc: 0.0160
Epoch 4/5000
900/900 [==============================] - 12s 13ms/step - loss: -0.6457 - acc: 0.0229 - val_loss: -0.6821 - val_acc: 0.0240
Epoch 5/5000
900/900 [==============================] - 12s 14ms/step - loss: -0.6497 - acc: 0.0274 - val_loss: -0.6873 - val_acc: 0.0230
Epoch 6/5000
900/900 [==============================] - 14s 15ms/step - loss: -0.6507 - acc: 0.0276 - val_loss: -0.6874 - val_acc: 0.0240
Epoch 7/5000
900/900 [==============================] - 15s 16ms/step - loss: -0.6517 - acc: 0.0279 - val_loss: -0.6877 - val_acc: 0.0260
Epoch 8/5000
900/900 [==============================] - 14s 16ms/step - loss: -0.6526 - acc: 0.0272 - val_loss: -0.6875 - val_acc: 0.0230
Epoch 9/5000
900/900 [==============================] - 14s 16ms/step - loss: -0.6530 - acc: 0.0274 - val_loss: -0.6879 - val_acc: 0.0240
Epoch 10/5000
900/900 [==============================] - 14s 15ms/step - loss: -0.6530 - acc: 0.0278 - val_loss: -0.6871 - val_acc: 0.0230

损失和val_loss改变这种方式而不是减少的可能原因是什么？神经网络，训练数据还是其他什么问题？

如果您需要任何进一步的信息，我会提供 .

谢谢你的回复