Simple class for non-linear bilinear connect network Modified from https://github.com/jnhwkim/ban-vqa/blob/master/bc.py

以下是一个简单的非线性双线性连接网络的类，修改自 https://github.com/jnhwkim/ban-vqa/blob/master/bc.py：

import torch
import torch.nn as nn

class NonLinearBilinear(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(NonLinearBilinear, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(input_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)

    def forward(self, x1, x2):
        x1 = self.fc1(x1)
        x2 = self.fc2(x2)
        x = torch.mul(x1, x2)
        x = nn.functional.relu(x)
        x = self.fc3(x)
        return x

希望这可以帮助你！

相关问题

用python编写一段代码关于某品牌手机图片爬虫和评论数据分析，并进行数据获取：采集京东某品牌手机评论数据，数据预处理，可视化分析，图形用户界面，数据存取：使用txt/csv/excel等。网址为https://item.jd.com/100011172363.html，

好的，以下是用 Python 实现该任务的代码示例：

import requests
from bs4 import BeautifulSoup
import json
import pandas as pd
import jieba
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from tkinter import *
from tkinter import ttk
from tkinter import filedialog

# 爬取京东某品牌手机的图片
def crawl_images(url):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
    response = requests.get(url, headers=headers)
    html = response.text
    soup = BeautifulSoup(html, 'html.parser')
    img_tags = soup.select('.spec-items img')
    for img_tag in img_tags:
        img_url = 'https:' + img_tag['data-origin']
        img_name = img_url.split('/')[-1]
        with open(img_name, 'wb') as f:
            img = requests.get(img_url, headers=headers).content
            f.write(img)

# 爬取京东某品牌手机的评论数据
def crawl_comments(url):
    comments_url = url.replace('item', 'comment') + '?pageSize=10&callback=fetchJSON_comment98vv18658'
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}
    response = requests.get(comments_url, headers=headers)
    json_str = response.text[len('fetchJSON_comment98vv18658('):-2]
    comments = json.loads(json_str)['comments']
    df = pd.DataFrame(comments)
    df = df[['creationTime', 'content', 'score', 'referenceName']]
    return df

# 对爬取到的数据进行预处理
def data_preprocessing(df):
    # 去除无用信息
    df = df.dropna(subset=['content'])
    df = df[df['content'].str.strip() != '']
    # 去除重复数据
    df = df.drop_duplicates()
    # 中文分词
    df['content'] = df['content'].apply(lambda x: ' '.join(jieba.cut(x)))
    return df

# 可视化分析
def data_visualization(df):
    # 绘制评分分布图
    plt.hist(df['score'], bins=5, alpha=0.5)
    plt.xlabel('Score')
    plt.ylabel('Count')
    plt.title('Score Distribution')
    plt.show()
    
    # 绘制词云图
    wordcloud = WordCloud(background_color='white', width=800, height=600, font_path='msyh.ttc').generate(' '.join(df['content']))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis('off')
    plt.show()

# 图形用户界面
def create_gui():
    def open_file():
        file_path = filedialog.askopenfilename(defaultextension='.csv', filetypes=[('CSV Files', '*.csv'), ('Excel Files', '*.xlsx')])
        if file_path:
            df = pd.read_csv(file_path)
            text.delete('1.0', END)
            text.insert('1.0', df.head().to_string())

    def save_file():
        file_path = filedialog.asksaveasfilename(defaultextension='.csv', filetypes=[('CSV Files', '*.csv'), ('Excel Files', '*.xlsx')])
        if file_path:
            df.to_csv(file_path, index=False)

    def crawl_and_analyze():
        url = url_entry.get()
        comments_df = crawl_comments(url)
        comments_df = data_preprocessing(comments_df)
        data_visualization(comments_df)
        global df
        df = comments_df

    def show_data():
        text.delete('1.0', END)
        text.insert('1.0', df.head().to_string())

    root = Tk()
    root.title('JD Phone Comments Analysis')

    url_label = Label(root, text='URL:')
    url_label.grid(row=0, column=0)

    url_entry = Entry(root, width=50)
    url_entry.grid(row=0, column=1)

    crawl_button = Button(root, text='Crawl and Analyze', command=crawl_and_analyze)
    crawl_button.grid(row=1, column=0, sticky=W, pady=5)

    show_data_button = Button(root, text='Show Data', command=show_data)
    show_data_button.grid(row=1, column=1, sticky=W, pady=5)

    save_button = Button(root, text='Save Data', command=save_file)
    save_button.grid(row=1, column=2, sticky=W, pady=5)

    open_button = Button(root, text='Open Data', command=open_file)
    open_button.grid(row=1, column=3, sticky=W, pady=5)

    text = Text(root, width=80, height=30)
    text.grid(row=2, column=0, columnspan=4)

    root.mainloop()

if __name__ == '__main__':
    url = 'https://item.jd.com/100011172363.html'
    crawl_images(url)
    create_gui()

以上代码中，我们首先定义了 `crawl_images` 和 `crawl_comments` 函数，分别用于爬取京东某品牌手机的图片和评论数据，并返回 `pandas.DataFrame` 类型的数据。然后，我们定义了 `data_preprocessing` 函数，用于对爬取到的数据进行预处理，包括去除无用信息、去除重复数据、中文分词等操作。接着，我们定义了 `data_visualization` 函数，用于绘制评分分布图和评论词云图。最后，我们定义了 `create_gui` 函数，用于创建图形用户界面，方便用户进行数据的查看和操作。在 `main` 函数中，我们首先调用 `crawl_images` 函数爬取京东手机的图片，然后调用 `create_gui` 函数创建图形用户界面。

从http://xc.hfut.edu.cn/1955/list1.htm爬取100条新闻标题（需要翻页），并通过jieba模块分词，并以一张图片图为背景绘制词云图。

好的，我会为您提供相关的代码和步骤：

首先，我们需要安装jieba和wordcloud模块：

!pip install jieba
!pip install wordcloud

然后，我们需要爬取新闻标题并进行分词：

import requests
from bs4 import BeautifulSoup
import jieba

url = "http://xc.hfut.edu.cn/1955/list{}.htm"
headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"}

news_list = []
for i in range(1, 6):  # 爬取前5页的新闻标题
    res = requests.get(url.format(i), headers=headers)
    soup = BeautifulSoup(res.text, "html.parser")
    news = soup.find_all("span", {"class": "news_title"})
    for n in news:
        news_list.append(n.a.string)

# 对新闻标题进行分词
words_list = []
for news in news_list:
    words = jieba.cut(news)
    for word in words:
        words_list.append(word)

接下来，我们使用wordcloud模块生成词云图：

from wordcloud import WordCloud
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np

# 读入背景图片
image = Image.open("background.jpg")
graph = np.array(image)

# 设置停用词
stop_words = ["的", "是", "在", "了", "和", "与", "也", "还", "有", "就", "等", "中", "及", "对", "是"]

# 生成词云图
wc = WordCloud(font_path="msyh.ttc", background_color='white', max_words=2000, mask=graph, stopwords=stop_words,
               max_font_size=200, random_state=42)
wc.generate_from_text(" ".join(words_list))

# 绘制词云图
plt.imshow(wc, interpolation='bilinear')
plt.axis("off")
plt.show()

其中，我们需要提供一张背景图片，可以从网络上下载一张喜欢的图片作为背景。最后的词云图如下所示：


希望这个代码能够帮助到您！