前言
最近学习了一新概念，叫科学发现和科技发明，科学发现是高于科技发明的，而这个说法我觉得还是挺有道理的，我们总说中国的科技不如欧美，但我们实际感觉上，不论建筑，硬件还是软件，理论，我们都已经高于欧美了，那为什么还说我们不如欧美呢？
科学发现是高于科技发明就很好的解释了这个问题，即，我们的在线支付，建筑行业等等，这些都是科技发明，而不是科学发现，而科学发现是引领科技发明的，而欧美在科学发现上远远领先我们，科技发明上虽然领先的不多，但也有很多大幅领先的，比如chatgpt。
说这些的主要目的是想说明，软件开发也是科技发明，所以这个行业的高手，再高的水平，也就那么回事。
也就是说，即便你是清北的，一旦你进入科技发明的队伍，那也就那么回事了。
神经网络并不难，我的这个系列文章就证明了，你完全不会python，完全没学过算法，一样可以在短时间内学会。我个人感觉，一周到一个月之内，都能学会。
所以，会的不必高人一等的看别人，不会的也不用觉得人家是高水平。
本文内容
本文主要介绍结合神经网络进行机器人开发。
准备工作
运行代码前，我们需要先下载nltk包。
首先安装nltk的包。
pip install nltk
然后下载nltk工具，编写一个py文件，写代码如下：
import nltk
nltk.download()
然后使用管理员打开cmd，运行这个py文件。
C:\Project\python_test\github\PythonTest\venv\Scripts\python.exe C:\Project\python_test\github\PythonTest\robot_nltk\dlnltk.py
然后弹出界面如下，修改保存地址：
PS：有资料说可以直接运行 nltk.download('punkt') ，下载我们需要的指定的包，但我没下载成功，我还是全部下载了。
# nltk.download('punkt') #是 NLTK (Natural Language Toolkit) 库中的一个命令，用来下载名为 'punkt' 的资源，通常用于 分词（Tokenization）
# nltk.download('popular') #命令会下载 NLTK 中大部分常用的资源,比punkt的资源更多
代码编写
编写model
首先编写一个NeuralNet(model.py)如下：
import torch.nn as nn
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.l1 = nn.Linear(input_size, hidden_size)
self.l2 = nn.Linear(hidden_size, hidden_size)
self.l3 = nn.Linear(hidden_size, num_classes)
self.relu = nn.ReLU()
def forward(self, x):
out = self.l1(x)
out = self.relu(out)
out = self.l2(out)
out = self.relu(out)
out = self.l3(out)
# no activation and no softmax at the end
return out
然后编写一个工具nltk_utils.py如下：
import numpy as np
import nltk
from nltk.stem.porter import PorterStemmer
stemmer = PorterStemmer()
def tokenize(sentence):
return nltk.word_tokenize(sentence)
def stem(word):
return stemmer.stem(word.lower())
def bag_of_words(tokenized_sentence, words):
sentence_words = [stem(word) for word in tokenized_sentence]
bag = np.zeros(len(words), dtype=np.float32)
for idx, w in enumerate(words):
if w in sentence_words:
bag[idx] = 1
return bag
a="How long does shipping take?"
print(a)
a = tokenize(a)
print(a)
这个文件可以直接运行，测试工具内函数的应用。
词干化和token化
词干化就是把单词提取成词干。逻辑如下：
words =["0rganize","organizes", "organizing"]
stemmed_words =[stem(w) for w in words]
print(stemmed_words)
过程如下图：
token化就是把单词转换成token。
下面这段代码就是测试token化。
a="How long does shipping take?"
print(a)
a = tokenize(a)
print(a)
token化的逻辑大致如下：
编写测试数据
编写json文件intents.json（英文版）
{
"intents": [
{
"tag": "greeting",
"patterns": [
"Hi",
"Hey",
"How are you",
"Is anyone there?",
"Hello",
"Good day"
],
"responses": [
"Hey :-)",
"Hello, thanks for visiting",
"Hi there, what can I do for you?",
"Hi there, how can I help?"
]
},
{
"tag": "goodbye",
"patterns": ["Bye", "See you later", "Goodbye"],
"responses": [
"See you later, thanks for visiting",
"Have a nice day",
"Bye! Come back again soon."
]
},
{
"tag": "thanks",
"patterns": ["Thanks", "Thank you", "That's helpful", "Thank's a lot!"],
"responses": ["Happy to help!", "Any time!", "My pleasure"]
},
{
"tag": "items",
"patterns": [
"Which items do you have?",
"What kinds of items are there?",
"What do you sell?"
],
"responses": [
"We sell coffee and tea",
"We have coffee and tea"
]
},
{
"tag": "payments",
"patterns": [
"Do you take credit cards?",
"Do you accept Mastercard?",
"Can I pay with Paypal?",
"Are you cash only?"
],
"responses": [
"We accept VISA, Mastercard and Paypal",
"We accept most major credit cards, and Paypal"
]
},
{
"tag": "delivery",
"patterns": [
"How long does delivery take?",
"How long does shipping take?",
"When do I get my delivery?"
],
"responses": [
"Delivery takes 2-4 days",
"Shipping takes 2-4 days"
]
},
{
"tag": "funny",
"patterns": [
"Tell me a joke!",
"Tell me something funny!",
"Do you know a joke?"
],
"responses": [
"Why did the hipster burn his mouth? He drank the coffee before it was cool.",
"What did the buffalo say when his son left for college? Bison."
]
}
]
}
intents_cn.json中文版数据。
{
"intents": [
{
"tag": "greeting",
"patterns": [
"你好",
"嗨",
"您好",
"有谁在吗?",
"你好呀",
"早上好",
"下午好",
"晚上好"
],
"responses": [
"你好！有什么我可以帮忙的吗？",
"您好！感谢您的光临。",
"嗨！有什么我可以为您效劳的吗？",
"早上好！今天怎么样？"
]
},
{
"tag": "goodbye",
"patterns": [
"再见",
"拜拜",
"下次见",
"保重",
"晚安"
],
"responses": [
"再见！希望很快能再次见到你。",
"拜拜！祝你有个愉快的一天。",
"保重！下次见。",
"晚安，祝你做个好梦！"
]
},
{
"tag": "thanks",
"patterns": [
"谢谢",
"感谢",
"多谢",
"非常感谢"
],
"responses": [
"不客气！很高兴能帮到你。",
"没问题！随时为您服务。",
"别客气！希望能帮到您。",
"很高兴能帮忙！"
]
},
{
"tag": "help",
"patterns": [
"你能帮我做什么?",
"你能做什么?",
"你能帮助我吗?",
"我需要帮助",
"能帮我一下吗?"
],
"responses": [
"我可以帮您回答问题、提供信息，或者进行简单的任务。",
"我能帮助您查询信息、安排任务等。",
"您可以问我问题，或者让我做一些简单的事情。",
"请告诉我您需要的帮助！"
]
},
{
"tag": "weather",
"patterns": [
"今天天气怎么样?",
"今天的天气如何?",
"天气预报是什么?",
"外面冷吗?",
"天气好不好?"
],
"responses": [
"今天的天气很好，适合外出！",
"今天天气有点冷，记得穿暖和点。",
"今天天气晴朗，适合去散步。",
"天气晴，温度适宜，非常适合外出。"
]
},
{
"tag": "about",
"patterns": [
"你是什么?",
"你是谁?",
"你是做什么的?",
"你能做些什么?"
],
"responses": [
"我是一个聊天机器人，可以回答您的问题和帮助您解决问题。",
"我是一个智能助手，帮助您完成各种任务。",
"我是一个虚拟助手，可以处理简单的任务和查询。",
"我可以帮助您获取信息，或者做一些简单的任务。"
]
}
]
}
训练数据
训练数据逻辑如下：
import numpy as np
import random
import json
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from nltk_utils import bag_of_words, tokenize, stem
from model import NeuralNet
with open('intents_cn.json', 'r', encoding='utf-8') as f:
intents = json.load(f)
all_words = []
tags = []
xy = []
# loop through each sentence in our intents patterns
for intent in intents['intents']:
tag = intent['tag']
# add to tag list
tags.append(tag)
for pattern in intent['patterns']:
# tokenize each word in the sentence
w = tokenize(pattern)
# add to our words list
all_words.extend(w)
# add to xy pair
xy.append((w, tag))
# stem and lower each word
ignore_words = ['?', '.', '!']
all_words = [stem(w) for w in all_words if w not in ignore_words]
# remove duplicates and sort
all_words = sorted(set(all_words))
tags = sorted(set(tags))
print(len(xy), "patterns")
print(len(tags), "tags:", tags)
print(len(all_words), "unique stemmed words:", all_words)
# create training data
X_train = []
y_train = []
for (pattern_sentence, tag) in xy:
# X: bag of words for each pattern_sentence
bag = bag_of_words(pattern_sentence, all_words)
X_train.append(bag)
# y: PyTorch CrossEntropyLoss needs only class labels, not one-hot
label = tags.index(tag)
y_train.append(label)
X_train = np.array(X_train)
y_train = np.array(y_train)
# Hyper-parameters
num_epochs = 1000
batch_size = 8
learning_rate = 0.001
input_size = len(X_train[0])
hidden_size = 8
output_size = len(tags)
print(input_size, output_size)
class ChatDataset(Dataset):
def __init__(self):
self.n_samples = len(X_train)
self.x_data = X_train
self.y_data = y_train
# support indexing such that dataset[i] can be used to get i-th sample
def __getitem__(self, index):
return self.x_data[index], self.y_data[index]
# we can call len(dataset) to return the size
def __len__(self):
return self.n_samples
dataset = ChatDataset()
train_loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = NeuralNet(input_size, hidden_size, output_size).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Train the model
for epoch in range(num_epochs):
for (words, labels) in train_loader:
words = words.to(device)
labels = labels.to(dtype=torch.long).to(device)
# Forward pass
outputs = model(words)
# if y would be one-hot, we must apply
# labels = torch.max(labels, 1)[1]
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (epoch+1) % 100 == 0:
print (f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
print(f'final loss: {loss.item():.4f}')
data = {
"model_state": model.state_dict(),
"input_size": input_size,
"hidden_size": hidden_size,
"output_size": output_size,
"all_words": all_words,
"tags": tags
}
FILE = "data.pth"
torch.save(data, FILE)
print(f'training complete. file saved to {FILE}')
编写使用聊天
编写使用聊天代码如下：
import random
import json
import torch
from model import NeuralNet
from nltk_utils import bag_of_words, tokenize
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
with open('intents_cn.json', 'r',encoding='utf-8') as json_data:
intents = json.load(json_data)
FILE = "data.pth"
data = torch.load(FILE)
input_size = data["input_size"]
hidden_size = data["hidden_size"]
output_size = data["output_size"]
all_words = data['all_words']
tags = data['tags']
model_state = data["model_state"]
model = NeuralNet(input_size, hidden_size, output_size).to(device)
model.load_state_dict(model_state)
model.eval()
bot_name = "电脑"
print("Let's chat! (type 'quit' to exit)")
while True:
# sentence = "do you use credit cards?"
sentence = input("我:")
if sentence == "quit":
break
sentence = tokenize(sentence)
X = bag_of_words(sentence, all_words)
X = X.reshape(1, X.shape[0])
X = torch.from_numpy(X).to(device)
output = model(X)
_, predicted = torch.max(output, dim=1)
tag = tags[predicted.item()]
probs = torch.softmax(output, dim=1)
prob = probs[0][predicted.item()]
if prob.item() > 0.75:
for intent in intents['intents']:
if tag == intent["tag"]:
print(f"{bot_name}: {random.choice(intent['responses'])}")
else:
print(f"{bot_name}: 我不知道")
运行效果如下：
传送门：
零基础学习人工智能---Python---Pytorch学习---全集
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