吴裕雄--天生自然神经网络与深度学习实战Python+Keras+TensorFlow：RNN和CNN混合的鸡尾酒疗法提升网络运行效率

from keras.layers import
model = Sequential()
model.add(embedding_layer)
#使用一维卷积网络切割输入数据，参数5表示每各个单词作为切割小段
model.add(layers.Conv1D(32, 5, activation='relu'))
#参数3表示，上层传下来的数据中，从每3个数值中抽取最大值
model.add(layers.MaxPooling1D(3))
#添加一个有记忆性的GRU层，其原理与LSTM相同，运行速度更快，准确率有所降低
model.add(layers.GRU(32, dropout=0.1))

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()