吴裕雄--天生自然神经网络与深度学习实战Python+Keras+TensorFlow：LSTM网络层详解及其应用

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()