from keras.layers import LSTM

model = Sequential()

model.add(embedding_layer)

model.add(LSTM(32))

#当结果是输出多个分类的概率时，用softmax激活函数,它将为30个分类提供不同的可能性概率值

model.add(layers.Dense(len(int_category), activation='softmax'))

#对于输出多个分类结果，最好的损失函数是categorical_crossentropy

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

history = model.fit(x_train, y_train, epochs=20, validation_data=(x_val, y_val), batch_size=512)

acc = history.history['acc']

val_acc = history.history['val_acc']

loss = history.history['loss']

val_loss = history.history['val_loss']

epochs = range(1, len(acc) + 1)

plt.title('Training and validation accuracy')

plt.plot(epochs, acc, 'red', label='Training acc')

plt.plot(epochs, val_acc, 'blue', label='Validation acc')

plt.legend()

plt.show()