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聊天機器人(ChatRobot)的概念我們并不陌生，也許你曾經在百無聊賴之下和Siri打情罵俏過，亦或是閑暇之余與小愛同學談笑風生，無論如何，我們都得承認，人工智能已經深入了我們的生活。目前市面上提供三方api的機器人不勝枚舉：微軟小冰、圖靈機器人、騰訊閑聊、青云客機器人等等，只要我們想，就隨時可以在App端或者web應用上進行接入。但是，這些應用的底層到底如何實現的？在沒有網絡接入的情況下，我們能不能像美劇《西部世界》(Westworld)里面描繪的那樣，機器人只需要存儲在本地的“心智球”就可以和人類溝通交流，如果你不僅僅滿足于當一個“調包俠”，請跟隨我們的旅程，本次我們將首度使用深度學習庫Keras/TensorFlow打造屬于自己的本地聊天機器人，不依賴任何三方接口與網絡。

首先安裝相關依賴：

pip3 install Tensorflow
pip3 install Keras
pip3 install nltk
pip3 install pandas

然后撰寫腳本test_bot.py導入需要的庫：

import nltk
import ssl
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.optimizers import SGD
import pandas as pd
import pickle
import random

這里有一個坑，就是自然語言分析庫NLTK會報一個錯誤：

Resource punkt not found

正常情況下，只要加上一行下載器代碼即可

import nltk
nltk.download('punkt')

但是由于學術上網的原因，很難通過Python/ target=_blank class=infotextkey>Python下載器正常下載，所以我們玩一次曲線救國，手動自己下載壓縮包：

https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/packages/tokenizers/punkt.zip

解壓之后，放在你的用戶目錄下即可：

C:Usersliuyuetokenizersnltk_datapunkt

ok，言歸正傳，開發聊天機器人所面對的最主要挑戰是對用戶輸入信息進行分類，以及能夠識別人類的正確意圖（這個可以用機器學習解決，但是太復雜，我偷懶了，所以用的深度學習Keras）。第二就是怎樣保持語境，也就是分析和跟蹤上下文，通常情況下，我們不太需要對用戶意圖進行分類，只需要把用戶輸入的信息當作聊天機器人問題的答案即可，所這里我們使用Keras深度學習庫用于構建分類模型。

聊天機器人的意向和需要學習的模式都定義在一個簡單的變量中。不需要動輒上T的語料庫。我們知道如果玩機器人的，手里沒有語料庫，就會被人嘲笑，但是我們的目標只是為某一個特定的語境建立一個特定聊天機器人。所以分類模型作為小詞匯量創建，它僅僅將能夠識別為訓練提供的一小組模式。

說白了就是，所謂的機器學習，就是你重復的教機器做某一件或幾件正確的事情，在訓練中，你不停的演示怎么做是正確的，然后期望機器在學習中能夠舉一反三，只不過這次我們不教它很多事情，只一件，用來測試它的反應而已，是不是有點像你在家里訓練你的寵物狗？只不過狗子可沒法和你聊天。

這里的意向數據變量我就簡單舉個例子，如果愿意，你可以用語料庫對變量進行無限擴充：

intents = {"intents": [
        {"tag": "打招呼",
         "patterns": ["你好", "您好", "請問", "有人嗎", "師傅","不好意思","美女","帥哥","靚妹","hi"],
         "responses": ["您好", "又是您啊", "吃了么您內","您有事嗎"],
         "context": [""]
        },
        {"tag": "告別",
         "patterns": ["再見", "拜拜", "88", "回見", "回頭見"],
         "responses": ["再見", "一路順風", "下次見", "拜拜了您內"],
         "context": [""]
        },
   ]
}

可以看到，我插入了兩個語境標簽，打招呼和告別，包括用戶輸入信息以及機器回應數據。

在開始分類模型訓練之前，我們需要先建立詞匯。模式經過處理后建立詞匯庫。每一個詞都會有詞干產生通用詞根，這將有助于能夠匹配更多用戶輸入的組合。

for intent in intents['intents']:
    for pattern in intent['patterns']:
        # tokenize each word in the sentence
        w = nltk.word_tokenize(pattern)
        # add to our words list
        words.extend(w)
        # add to documents in our corpus
        documents.append((w, intent['tag']))
        # add to our classes list
        if intent['tag'] not in classes:
            classes.append(intent['tag'])

words = [stemmer.stem(w.lower()) for w in words if w not in ignore_words]
words = sorted(list(set(words)))

classes = sorted(list(set(classes)))

print (len(classes), "語境", classes)

print (len(words), "詞數", words)

輸出：

2 語境 ['告別', '打招呼']
14 詞數 ['88', '不好意思', '你好', '再見', '回頭見', '回見', '帥哥', '師傅', '您好', '拜拜', '有人嗎', '美女', '請問', '靚妹']

訓練不會根據詞匯來分析，因為詞匯對于機器來說是沒有任何意義的，這也是很多中文分詞庫所陷入的誤區，其實機器并不理解你輸入的到底是英文還是中文，我們只需要將單詞或者中文轉化為包含0/1的數組的詞袋。數組長度將等于詞匯量大小，當當前模式中的一個單詞或詞匯位于給定位置時，將設置為1。

# create our training data
training = []
# create an empty array for our output
output_empty = [0] * len(classes)
# training set, bag of words for each sentence
for doc in documents:
    # initialize our bag of words
    bag = []

    pattern_words = doc[0]
   
    pattern_words = [stemmer.stem(word.lower()) for word in pattern_words]

    for w in words:
        bag.append(1) if w in pattern_words else bag.append(0)
    
 
    output_row = list(output_empty)
    output_row[classes.index(doc[1])] = 1
    
    training.append([bag, output_row])

random.shuffle(training)
training = np.array(training)

train_x = list(training[:,0])
train_y = list(training[:,1])

我們開始進行數據訓練，模型是用Keras建立的，基于三層。由于數據基數小，分類輸出將是多類數組，這將有助于識別編碼意圖。使用softmax激活來產生多類分類輸出（結果返回一個0/1的數組：[1,0,0,...,0]--這個數組可以識別編碼意圖）。

model = Sequential()
model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(train_y[0]), activation='softmax'))


sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])


model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)

這塊是以200次迭代的方式執行訓練，批處理量為5個，因為我的測試數據樣本小，所以100次也可以，這不是重點。

開始訓練：

14/14 [==============================] - 0s 32ms/step - loss: 0.7305 - acc: 0.5000
Epoch 2/200
14/14 [==============================] - 0s 391us/step - loss: 0.7458 - acc: 0.4286
Epoch 3/200
14/14 [==============================] - 0s 390us/step - loss: 0.7086 - acc: 0.3571
Epoch 4/200
14/14 [==============================] - 0s 395us/step - loss: 0.6941 - acc: 0.6429
Epoch 5/200
14/14 [==============================] - 0s 426us/step - loss: 0.6358 - acc: 0.7143
Epoch 6/200
14/14 [==============================] - 0s 356us/step - loss: 0.6287 - acc: 0.5714
Epoch 7/200
14/14 [==============================] - 0s 366us/step - loss: 0.6457 - acc: 0.6429
Epoch 8/200
14/14 [==============================] - 0s 899us/step - loss: 0.6336 - acc: 0.6429
Epoch 9/200
14/14 [==============================] - 0s 464us/step - loss: 0.5815 - acc: 0.6429
Epoch 10/200
14/14 [==============================] - 0s 408us/step - loss: 0.5895 - acc: 0.6429
Epoch 11/200
14/14 [==============================] - 0s 548us/step - loss: 0.6050 - acc: 0.6429
Epoch 12/200
14/14 [==============================] - 0s 468us/step - loss: 0.6254 - acc: 0.6429
Epoch 13/200
14/14 [==============================] - 0s 388us/step - loss: 0.4990 - acc: 0.7857
Epoch 14/200
14/14 [==============================] - 0s 392us/step - loss: 0.5880 - acc: 0.7143
Epoch 15/200
14/14 [==============================] - 0s 370us/step - loss: 0.5118 - acc: 0.8571
Epoch 16/200
14/14 [==============================] - 0s 457us/step - loss: 0.5579 - acc: 0.7143
Epoch 17/200
14/14 [==============================] - 0s 432us/step - loss: 0.4535 - acc: 0.7857
Epoch 18/200
14/14 [==============================] - 0s 357us/step - loss: 0.4367 - acc: 0.7857
Epoch 19/200
14/14 [==============================] - 0s 384us/step - loss: 0.4751 - acc: 0.7857
Epoch 20/200
14/14 [==============================] - 0s 346us/step - loss: 0.4404 - acc: 0.9286
Epoch 21/200
14/14 [==============================] - 0s 500us/step - loss: 0.4325 - acc: 0.8571
Epoch 22/200
14/14 [==============================] - 0s 400us/step - loss: 0.4104 - acc: 0.9286
Epoch 23/200
14/14 [==============================] - 0s 738us/step - loss: 0.4296 - acc: 0.7857
Epoch 24/200
14/14 [==============================] - 0s 387us/step - loss: 0.3706 - acc: 0.9286
Epoch 25/200
14/14 [==============================] - 0s 430us/step - loss: 0.4213 - acc: 0.8571
Epoch 26/200
14/14 [==============================] - 0s 351us/step - loss: 0.2867 - acc: 1.0000
Epoch 27/200
14/14 [==============================] - 0s 3ms/step - loss: 0.2903 - acc: 1.0000
Epoch 28/200
14/14 [==============================] - 0s 366us/step - loss: 0.3010 - acc: 0.9286
Epoch 29/200
14/14 [==============================] - 0s 404us/step - loss: 0.2466 - acc: 0.9286
Epoch 30/200
14/14 [==============================] - 0s 428us/step - loss: 0.3035 - acc: 0.7857
Epoch 31/200
14/14 [==============================] - 0s 407us/step - loss: 0.2075 - acc: 1.0000
Epoch 32/200
14/14 [==============================] - 0s 457us/step - loss: 0.2167 - acc: 0.9286
Epoch 33/200
14/14 [==============================] - 0s 613us/step - loss: 0.1266 - acc: 1.0000
Epoch 34/200
14/14 [==============================] - 0s 534us/step - loss: 0.2906 - acc: 0.9286
Epoch 35/200
14/14 [==============================] - 0s 463us/step - loss: 0.2560 - acc: 0.9286
Epoch 36/200
14/14 [==============================] - 0s 500us/step - loss: 0.1686 - acc: 1.0000
Epoch 37/200
14/14 [==============================] - 0s 387us/step - loss: 0.0922 - acc: 1.0000
Epoch 38/200
14/14 [==============================] - 0s 430us/step - loss: 0.1620 - acc: 1.0000
Epoch 39/200
14/14 [==============================] - 0s 371us/step - loss: 0.1104 - acc: 1.0000
Epoch 40/200
14/14 [==============================] - 0s 488us/step - loss: 0.1330 - acc: 1.0000
Epoch 41/200
14/14 [==============================] - 0s 381us/step - loss: 0.1322 - acc: 1.0000
Epoch 42/200
14/14 [==============================] - 0s 462us/step - loss: 0.0575 - acc: 1.0000
Epoch 43/200
14/14 [==============================] - 0s 1ms/step - loss: 0.1137 - acc: 1.0000
Epoch 44/200
14/14 [==============================] - 0s 450us/step - loss: 0.0245 - acc: 1.0000
Epoch 45/200
14/14 [==============================] - 0s 470us/step - loss: 0.1824 - acc: 1.0000
Epoch 46/200
14/14 [==============================] - 0s 444us/step - loss: 0.0822 - acc: 1.0000
Epoch 47/200
14/14 [==============================] - 0s 436us/step - loss: 0.0939 - acc: 1.0000
Epoch 48/200
14/14 [==============================] - 0s 396us/step - loss: 0.0288 - acc: 1.0000
Epoch 49/200
14/14 [==============================] - 0s 580us/step - loss: 0.1367 - acc: 0.9286
Epoch 50/200
14/14 [==============================] - 0s 351us/step - loss: 0.0363 - acc: 1.0000
Epoch 51/200
14/14 [==============================] - 0s 379us/step - loss: 0.0272 - acc: 1.0000
Epoch 52/200
14/14 [==============================] - 0s 358us/step - loss: 0.0712 - acc: 1.0000
Epoch 53/200
14/14 [==============================] - 0s 4ms/step - loss: 0.0426 - acc: 1.0000
Epoch 54/200
14/14 [==============================] - 0s 370us/step - loss: 0.0430 - acc: 1.0000
Epoch 55/200
14/14 [==============================] - 0s 368us/step - loss: 0.0292 - acc: 1.0000
Epoch 56/200
14/14 [==============================] - 0s 494us/step - loss: 0.0777 - acc: 1.0000
Epoch 57/200
14/14 [==============================] - 0s 356us/step - loss: 0.0496 - acc: 1.0000
Epoch 58/200
14/14 [==============================] - 0s 427us/step - loss: 0.1485 - acc: 1.0000
Epoch 59/200
14/14 [==============================] - 0s 381us/step - loss: 0.1006 - acc: 1.0000
Epoch 60/200
14/14 [==============================] - 0s 421us/step - loss: 0.0183 - acc: 1.0000
Epoch 61/200
14/14 [==============================] - 0s 344us/step - loss: 0.0788 - acc: 0.9286
Epoch 62/200
14/14 [==============================] - 0s 529us/step - loss: 0.0176 - acc: 1.0000

ok，200次之后，現在模型已經訓練好了，現在聲明一個方法用來進行詞袋轉換：

def clean_up_sentence(sentence):
    # tokenize the pattern - split words into array
    sentence_words = nltk.word_tokenize(sentence)
    # stem each word - create short form for word
    sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
    return sentence_words

def bow(sentence, words, show_details=True):
    # tokenize the pattern
    sentence_words = clean_up_sentence(sentence)
    # bag of words - matrix of N words, vocabulary matrix
    bag = [0]*len(words)  
    for s in sentence_words:
        for i,w in enumerate(words):
            if w == s: 
                # assign 1 if current word is in the vocabulary position
                bag[i] = 1
                if show_details:
                    print ("found in bag: %s" % w)
    return(np.array(bag))

測試一下，看看是否可以命中詞袋：

p = bow("你好", words)
print (p)

返回值：

found in bag: 你好
[0 0 1 0 0 0 0 0 0 0 0 0 0 0]

很明顯匹配成功，詞已入袋。

在我們打包模型之前，可以使用model.predict函數對用戶輸入進行分類測試，并根據計算出的概率返回用戶意圖（可以返回多個意圖，根據概率倒序輸出）：

def classify_local(sentence):
    ERROR_THRESHOLD = 0.25
    
    # generate probabilities from the model
    input_data = pd.DataFrame([bow(sentence, words)], dtype=float, index=['input'])
    results = model.predict([input_data])[0]
    # filter out predictions below a threshold, and provide intent index
    results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD]
    # sort by strength of probability
    results.sort(key=lambda x: x[1], reverse=True)
    return_list = []
    for r in results:
        return_list.append((classes[r[0]], str(r[1])))
    # return tuple of intent and probability
    
    return return_list

測試一下：

print(classify_local('您好'))

返回值：

found in bag: 您好
[('打招呼', '0.999913')]
liuyue:mytornado liuyue$

再測：

print(classify_local('88'))

返回值：

found in bag: 88
[('告別', '0.9995449')]

完美，匹配出打招呼的語境標簽，如果愿意，可以多測試幾個，完善模型。

測試完成之后，我們可以將訓練好的模型打包，這樣每次調用之前就不用訓練了：

model.save("./v3u.h5")

這里分類模型會在根目錄產出，文件名為v3u.h5，將它保存好，一會兒會用到。

接下來，我們來搭建一個聊天機器人的API，這里我們使用目前非常火的框架Fastapi，將模型文件放入到項目的目錄之后，編寫main.py:

import random
import uvicorn
from fastapi import FastAPI
app = FastAPI()


def classify_local(sentence):
    ERROR_THRESHOLD = 0.25
    
    # generate probabilities from the model
    input_data = pd.DataFrame([bow(sentence, words)], dtype=float, index=['input'])
    results = model.predict([input_data])[0]
    # filter out predictions below a threshold, and provide intent index
    results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD]
    # sort by strength of probability
    results.sort(key=lambda x: x[1], reverse=True)
    return_list = []
    for r in results:
        return_list.append((classes[r[0]], str(r[1])))
    # return tuple of intent and probability
    
    return return_list

@app.get('/')
async def root(word: str = None):
    
    from keras.models import model_from_json,load_model
    model = load_model("./v3u.h5")

    wordlist = classify_local(word)
    a = ""
    for intent in intents['intents']:
        if intent['tag'] == wordlist[0][0]:
            a = random.choice(intent['responses'])



    return {'message':a}

if __name__ == "__main__":
    uvicorn.run(app, host="127.0.0.1", port=8000)

這里的：

from keras.models import model_from_json,load_model
    model = load_model("./v3u.h5")

用來導入剛才訓練好的模型庫，隨后啟動服務：

uvicorn main:app --reload

效果是這樣的：

結語：毫無疑問，科技改變生活，聊天機器人可以讓我們沒有佳人相伴的情況下，也可以聽聞鶯啼燕語，相信不久的將來，笑語盈盈、衣香鬢影的“機械姬”亦能伴吾等于清風明月之下。