parent
a61e37950d
commit
8101c31d01
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package com.xht.springboot.Config;
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import org.springframework.context.annotation.Configuration;
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import org.springframework.web.servlet.config.annotation.CorsRegistry;
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import org.springframework.web.servlet.config.annotation.WebMvcConfigurer;
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@Configuration
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public class CrossOriginConfig implements WebMvcConfigurer {
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@Override
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public void addCorsMappings(CorsRegistry registry) {
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registry.addMapping("/**")
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.allowedOrigins("*")
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.allowedMethods("*");
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}
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}
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@ -1,14 +0,0 @@
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package com.xht.springboot.Control;
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import org.springframework.stereotype.Controller;
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import org.springframework.web.bind.annotation.RequestMapping;
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@Controller
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public class GetSpiderInformation
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{
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@RequestMapping("/index")
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public String pagespider()
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{
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return "index";
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}
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}
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@ -1,89 +0,0 @@
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import requests
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from PIL import Image
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from selenium import webdriver
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from selenium.webdriver import ActionChains
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from selenium.webdriver.common.by import By
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import time
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from lxml import etree
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def analyse(driver , input_imagexpath , input_anlxpath , imagepath):
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# 图片按钮
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loc1 = driver.find_element(By.XPATH, input_imagexpath).send_keys(imagepath)
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# 分析按钮
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loc2 = driver.find_element(By.XPATH, input_anlxpath)
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ActionChains(driver).click(loc1).perform()
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time.sleep(2)
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ActionChains(driver).click(loc2).perform()
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time.sleep(2)
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def go_in(driver , input_user , input_password , input_upload):
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# 用户名
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loc3 = driver.find_element(By.XPATH, input_user).send_keys("xht")
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# 密码
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loc4 = driver.find_element(By.XPATH, input_password).send_keys("Xht@20021213")
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# 点击进入
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loc5 = driver.find_element(By.XPATH, input_upload)
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ActionChains(driver).click(loc3).perform()
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time.sleep(2)
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ActionChains(driver).click(loc4).perform()
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time.sleep(2)
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ActionChains(driver).click(loc5).perform()
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time.sleep(2)
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def get_information(driver , url):
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input_res = []
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for i in range(2 , 7):
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if i == 5:
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continue
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else:
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# / html / body / p[3] / text()[2]
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t = "/html/body/p[" + str(i) + "]"
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input_res.append(t)
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loc_res = []
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for i in input_res:
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temp = driver.find_element(By.XPATH , i).text
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loc_res.append(temp)
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return loc_res
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def get_picture(driver , output_pic , imagename):
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return driver.find_element(By.XPATH , output_pic).screenshot(imagename)
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if __name__ == "__main__":
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# driver.get("http://mammo.neuralrad.com:5300/upload")
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option = webdriver.EdgeOptions()
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option.add_argument("--headless")
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option.add_argument("--disable-gpu")
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option.add_argument("--disable-software-rasterizer")
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url = "http://mammo.neuralrad.com:5300/"
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driver = webdriver.Edge(options=option)
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imagepath = "D:\\pro_of_program\\Java\\test\\SOB_B_A-14-22549AB-400-006.png"
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imagename = "SOB_B_A-14-22549AB-400-006.png"
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driver.get(url)
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input_imagexpath = "/html/body/section[2]/form/p/input[1]"
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input_anlxpath = "/html/body/section[2]/form/p/input[2]"
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input_user = "/html/body/section[1]/div/header/div/div[2]/a[1]/div/p/input"
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input_password = "/html/body/section[1]/div/header/div/div[2]/a[2]/div/p/input"
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input_upload = "/html/body/section[2]/div/div/h1/a"
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output_picture = "/html/body/img"
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go_in(driver , input_user , input_password , input_upload)
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analyse(driver , input_imagexpath , input_anlxpath , imagepath)
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# context 解析结果
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context = get_information(driver , url)
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for i in context:
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print(i)
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# 获取图片
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picture = get_picture(driver , output_picture , imagename)
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# print(picture)
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@ -1,35 +0,0 @@
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package com.xht.springboot.pythonspider;
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import org.python.util.PythonInterpreter;
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import java.io.BufferedReader;
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import java.io.IOException;
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import java.io.InputStreamReader;
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import java.util.ArrayList;
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public class PythonSpider
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{
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public static ArrayList<String> Spider()
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{
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ArrayList<String> res = new ArrayList<>();
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PythonInterpreter Pyit = new PythonInterpreter();
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Process proc;
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String path = "src/main/java/com/xht/springboot/pythonspider/CancerSpider/spider.py";
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path = "python " + path;
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try {
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proc = Runtime.getRuntime().exec(path);
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BufferedReader in = new BufferedReader(new InputStreamReader(proc.getInputStream()));
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String line = null;
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while((line = in.readLine()) != null)
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res.add(line);
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in.close();
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proc.waitFor();
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}
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catch (IOException e) {
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e.printStackTrace();
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} catch (InterruptedException e) {
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throw new RuntimeException(e);
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}
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return res;
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}
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}
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package org.example;
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import org.python.core.PyFunction;
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import org.python.core.PyObject;
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import org.python.core.PyString;
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import org.python.util.PythonInterpreter;
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import javax.imageio.IIOException;
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import java.io.BufferedReader;
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import java.io.File;
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import java.io.IOException;
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import java.io.InputStreamReader;
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import java.util.ArrayList;
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import java.util.Arrays;
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import org.example.runmodel.*;
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public class Main {
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public static void main(String[] args) throws IOException {
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String dir_pic = "E:\\Git project\\medicine\\src\\medicine\\springboot\\src\\main\\resources\\upload\\cancerpictures";
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String res = runmodel.get_percent(dir_pic);
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System.out.println(res);
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}
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}
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package org.example;
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import java.io.BufferedReader;
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import java.io.IOException;
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import java.io.InputStreamReader;
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public class runmodel {
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public static String get_percent(String dir_pic) throws IOException {
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// 自行设置相对路径即可
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String dir_model = "test_model/src/main/java/org/example/model";
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String py_dir = "test_model/src/main/java/org/example/python/output_result.py";
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/**
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* py_dir output_result文件所在位置
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* dir_pic 图片位置
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* dir_model 模型的位置
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*/
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String[] path = new String[]{"python " , py_dir , dir_pic , dir_model};
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Process proc = Runtime.getRuntime().exec(path);
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BufferedReader in = new BufferedReader(new InputStreamReader(proc.getInputStream()));
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String line = null;
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String temp = null;
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while ((line = in.readLine()) != null) temp = line;
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assert temp != null;
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String[] t = temp.split(",");
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return "良性：" + t[0] + " 恶性：" + t[1];
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}
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}
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<?xml version="1.0" encoding="UTF-8"?>
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<module org.jetbrains.idea.maven.project.MavenProjectsManager.isMavenModule="true" type="JAVA_MODULE" version="4">
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<component name="NewModuleRootManager" LANGUAGE_LEVEL="JDK_11">
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<output url="file://$MODULE_DIR$/target/classes" />
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<output-test url="file://$MODULE_DIR$/target/test-classes" />
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<content url="file://$MODULE_DIR$">
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<sourceFolder url="file://$MODULE_DIR$/src/main/java" isTestSource="false" />
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<sourceFolder url="file://$MODULE_DIR$/src/main/resources" type="java-resource" />
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<sourceFolder url="file://$MODULE_DIR$/src/test/java" isTestSource="true" />
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<excludeFolder url="file://$MODULE_DIR$/target" />
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</content>
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<orderEntry type="inheritedJdk" />
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<orderEntry type="sourceFolder" forTests="false" />
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<orderEntry type="library" name="Maven: org.python:jython-standalone:2.7.3" level="project" />
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</component>
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<component name="SonarLintModuleSettings">
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<option name="uniqueId" value="ab6080dd-8568-4336-bc91-3676458afc54" />
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</component>
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</module>
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# Default ignored files
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/shelf/
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/workspace.xml
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# Editor-based HTTP Client requests
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/httpRequests/
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# Datasource local storage ignored files
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/dataSources/
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/dataSources.local.xml
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<component name="InspectionProjectProfileManager">
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<settings>
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<option name="USE_PROJECT_PROFILE" value="false" />
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<version value="1.0" />
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</settings>
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</component>
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectRootManager" version="2" project-jdk-name="D:\Python 3.11\python.exe" project-jdk-type="Python SDK" />
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</project>
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/train_cancer.iml" filepath="$PROJECT_DIR$/.idea/train_cancer.iml" />
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</modules>
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</component>
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</project>
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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<orderEntry type="inheritedJdk" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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</module>
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import os
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path = '../picture/malignant'
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dir = os.listdir(path)
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x = 211
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y = 438
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for i in range(len(dir)):
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if str(y) in dir[i]:
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os.rename(path + '/' + dir[i] , path + '/' + "malignant (" + str(x) + ").png")
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x += 1
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y += 1
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import cv2
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from PIL import Image
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import numpy as np
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def load_single(dir , size):
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read = lambda i: np.asarray(Image.open(i).convert("RGB"))
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path = dir
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img = read(path)
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return np.array(cv2.resize(img, (size, size)))
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node {
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input: "root"
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device: "_tf_keras_sequential"
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}
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from loader_picture import data_loader
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import numpy as np
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from matplotlib import pyplot as plt
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from train_model.modeling import reduce_study_rate
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from train_model.modeling import modeling
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from train_model.data_gen import data_output
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# 载入图片
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load = data_loader.Loader()
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# 获取模型
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models = modeling.breast_train_test()
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# 展现模型
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models.model.summary()
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# data
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data = data_output.gen_data()
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# 降低学习率
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reduces = reduce_study_rate.reduce()
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reduces.train()
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# 训练+评估
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history = models.model.fit(
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data.tr_gen.flow(load.train_of_x , load.train_of_y , batch_size=data.batch),
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steps_per_epoch = load.train_of_x.shape[0] / data.batch,
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# 训练20次
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epochs=20,
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validation_data=(load.val_of_x , load.val_of_y),
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callbacks=[reduces.learn_control , reduces.checkpoint]
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)
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6.092087723175155e-09 1.0
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0.9989112615585327 0.001088793040253222
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0.9935709238052368 0.006429135799407959
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0.9997281432151794 0.00027184083592146635
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3.0335837436723523e-05 0.9999697208404541
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0.999747097492218 0.0002529561170376837
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0.008596532978117466 0.9914035201072693
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0.999934196472168 6.57897544442676e-05
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0.9989705085754395 0.0010294488165527582
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0.5978875756263733 0.4021124839782715
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3.6031291529070586e-05 0.9999639987945557
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3.7862635053897975e-06 0.9999961853027344
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0.9993938207626343 0.0006061863387003541
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8.81914729689015e-06 0.9999911785125732
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0.3917083740234375 0.6082916259765625
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0.9998810291290283 0.00011889902816619724
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0.6820005178451538 0.3179994821548462
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0.9987319111824036 0.0012680714717134833
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0.00011924829595955089 0.9998807907104492
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0.9993540644645691 0.0006459243595600128
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0.9996631145477295 0.0003369428450241685
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0.48353785276412964 0.5164620876312256
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3.497324087220477e-06 0.999996542930603
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0.9905140399932861 0.009485905058681965
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0.9999887943267822 1.1235452802793588e-05
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0.04187602177262306 0.9581239223480225
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0.9910897016525269 0.008910246193408966
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0.999971866607666 2.8184229449834675e-05
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0.9999890327453613 1.0908943295362405e-05
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0.8602142333984375 0.1397857666015625
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0.9999949932098389 5.041587883169996e-06
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0.9978411197662354 0.002158836927264929
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0.6723547577857971 0.3276452124118805
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0.9998103976249695 0.00018961272144224495
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||||
0.9999525547027588 4.740445001516491e-05
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||||
1.4190926833634876e-07 0.9999998807907104
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||||
0.7048918008804321 0.29510819911956787
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1.4167305835144361e-06 0.9999985694885254
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||||
0.00013711843348573893 0.9998629093170166
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||||
0.9996042847633362 0.00039568531792610884
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||||
0.7229845523834229 0.2770155072212219
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||||
0.999221682548523 0.000778281013481319
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||||
0.00015150148828979582 0.999848484992981
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||||
0.9998829364776611 0.00011702731717377901
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||||
0.7218467593193054 0.2781532406806946
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||||
0.15666170418262482 0.8433382511138916
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||||
0.9998588562011719 0.0001410984550602734
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||||
0.7082615494728088 0.29173845052719116
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||||
0.02286633290350437 0.9771337509155273
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||||
0.1373814195394516 0.8626185059547424
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||||
0.8673439621925354 0.13265608251094818
|
||||
0.9999347925186157 6.523412594106048e-05
|
||||
0.00019901152700185776 0.999800980091095
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||||
0.9999953508377075 4.663339950639056e-06
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||||
0.9997709393501282 0.00022907997481524944
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||||
0.9986693859100342 0.001330674858763814
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0.353473961353302 0.646526038646698
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||||
0.003105917014181614 0.996894121170044
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||||
0.9999948740005493 5.0977191676793154e-06
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||||
0.9999982118606567 1.7473138314016978e-06
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||||
0.9951081871986389 0.004891766235232353
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||||
0.9999829530715942 1.6995192709146068e-05
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||||
0.9996659755706787 0.0003340912808198482
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3.4974067375515006e-07 0.9999996423721313
|
||||
0.0729290321469307 0.9270709753036499
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||||
0.9704300761222839 0.029569925740361214
|
||||
1.4645347157227206e-09 1.0
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||||
3.1297126952267718e-06 0.9999969005584717
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||||
0.6081261038780212 0.39187392592430115
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||||
0.44829297065734863 0.5517070293426514
|
||||
0.7791184186935425 0.2208816111087799
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||||
0.7523340582847595 0.24766597151756287
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0.025663873180747032 0.9743360877037048
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||||
0.9983474016189575 0.0016525561222806573
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||||
0.9999810457229614 1.8939881556434557e-05
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0.9934049248695374 0.006595127750188112
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0.9381039142608643 0.061896029859781265
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0.9972034692764282 0.002796591492369771
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0.002707625972107053 0.9972923398017883
|
||||
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||||
0.7748852372169495 0.22511470317840576
|
||||
0.9990695118904114 0.0009304761188104749
|
||||
0.995189905166626 0.004810101818293333
|
||||
0.9996241331100464 0.0003758403763640672
|
||||
0.9999953508377075 4.590831395034911e-06
|
||||
0.9997206330299377 0.0002793869352899492
|
||||
0.030857346951961517 0.9691426753997803
|
||||
0.9999920129776001 7.935722351248842e-06
|
||||
0.9986991882324219 0.0013007957022637129
|
||||
0.9632222056388855 0.03677775710821152
|
||||
0.9999549388885498 4.506126788328402e-05
|
||||
0.9833811521530151 0.016618827357888222
|
||||
0.9945966005325317 0.005403310991823673
|
||||
0.9999295473098755 7.049996929708868e-05
|
||||
0.8726794719696045 0.1273205429315567
|
||||
0.5480396151542664 0.45196038484573364
|
||||
0.9999912977218628 8.708932909939904e-06
|
||||
3.044563356979779e-07 0.9999996423721313
|
||||
0.4884990453720093 0.511500895023346
|
||||
0.9999558925628662 4.410284964251332e-05
|
||||
0.0013927229447290301 0.998607337474823
|
||||
0.009765226393938065 0.9902348518371582
|
||||
0.9685745239257812 0.03142549470067024
|
||||
0.9254651665687561 0.0745348185300827
|
||||
0.3337121903896332 0.6662877798080444
|
||||
0.9999887943267822 1.119600437959889e-05
|
||||
0.9906603693962097 0.009339618496596813
|
||||
0.9685745239257812 0.03142549470067024
|
||||
0.006740563083440065 0.9932593703269958
|
||||
0.9999961853027344 3.85885050491197e-06
|
||||
3.629901357271592e-07 0.9999996423721313
|
||||
0.934252142906189 0.06574781984090805
|
||||
0.007116943132132292 0.9928830862045288
|
||||
7.037731393211288e-07 0.9999992847442627
|
||||
0.999893069267273 0.00010697087418520823
|
||||
0.9997791647911072 0.00022086691751610488
|
||||
0.09022868424654007 0.9097712635993958
|
||||
0.09035655111074448 0.9096434116363525
|
||||
0.6130415797233582 0.3869584798812866
|
||||
0.03604685515165329 0.9639530777931213
|
||||
0.9997678399085999 0.00023215077817440033
|
||||
0.8518919944763184 0.14810799062252045
|
||||
0.9921340942382812 0.007865877822041512
|
||||
0.9982209801673889 0.0017789960838854313
|
||||
0.4990655183792114 0.5009344816207886
|
||||
0.9998867511749268 0.00011325316881993786
|
||||
0.9972509741783142 0.002748970640823245
|
||||
0.9990136623382568 0.0009862740989774466
|
||||
0.04258143529295921 0.9574185013771057
|
||||
0.9979773163795471 0.002022656612098217
|
||||
0.00170273391995579 0.9982972741127014
|
||||
0.9992154836654663 0.0007844708161428571
|
||||
0.9999994039535522 5.988696329950471e-07
|
||||
0.29590702056884766 0.7040929794311523
|
||||
0.9998394250869751 0.0001605670986464247
|
||||
0.9999637603759766 3.619980998337269e-05
|
||||
0.9999910593032837 8.895711289369501e-06
|
||||
0.9202988743782043 0.07970114797353745
|
||||
0.0006385140586644411 0.9993614554405212
|
||||
0.999945878982544 5.410108860814944e-05
|
||||
0.9620537161827087 0.03794630244374275
|
||||
2.8449434466892853e-05 0.9999715089797974
|
||||
0.008708189241588116 0.9912918210029602
|
||||
0.999786913394928 0.0002130416687577963
|
||||
0.9994038343429565 0.0005961833521723747
|
||||
0.08757160604000092 0.9124283194541931
|
||||
0.2788959741592407 0.721104085445404
|
||||
0.9998784065246582 0.0001216340679093264
|
||||
0.000586844515055418 0.9994131326675415
|
||||
0.9932354092597961 0.006764642894268036
|
||||
0.5138823390007019 0.4861176908016205
|
||||
0.9999340772628784 6.58786520943977e-05
|
||||
0.9999055862426758 9.441948350286111e-05
|
||||
0.058314643800258636 0.9416853189468384
|
||||
3.1827673296902503e-07 0.9999996423721313
|
||||
0.9062193632125854 0.09378059953451157
|
||||
0.3755261301994324 0.6244738698005676
|
||||
0.9990484118461609 0.0009515314595773816
|
||||
4.8617998515965155e-09 1.0
|
||||
0.9998499155044556 0.0001500763464719057
|
||||
0.9525101184844971 0.047489847987890244
|
||||
8.557045475754421e-06 0.9999914169311523
|
||||
0.6955783367156982 0.30442163348197937
|
||||
0.0013446572702378035 0.9986553192138672
|
||||
0.9748040437698364 0.025195982307195663
|
||||
0.41111892461776733 0.5888810753822327
|
||||
0.9934592247009277 0.006540720816701651
|
||||
0.9999262094497681 7.374441338470206e-05
|
||||
0.00020276627037674189 0.9997972846031189
|
||||
0.9937252402305603 0.006274723447859287
|
||||
0.00010733706585597247 0.9998925924301147
|
||||
0.9928871989250183 0.007112863473594189
|
||||
0.0602891631424427 0.9397108554840088
|
||||
0.9996383190155029 0.00036165796336717904
|
||||
0.0011588835623115301 0.9988411068916321
|
||||
0.9999188184738159 8.113295916700736e-05
|
||||
0.9908154010772705 0.009184620343148708
|
||||
0.522026002407074 0.47797396779060364
|
||||
0.9999701976776123 2.9817896574968472e-05
|
||||
0.9991376399993896 0.0008623311878181994
|
||||
0.9985169768333435 0.0014830041909590364
|
||||
0.9996399879455566 0.0003599420888349414
|
||||
0.9999990463256836 9.134150218415016e-07
|
||||
0.640516459941864 0.359483540058136
|
||||
0.9618650674819946 0.038134992122650146
|
||||
0.9396104216575623 0.06038954481482506
|
||||
0.999846339225769 0.00015368135063908994
|
||||
0.9973353743553162 0.0026645760517567396
|
||||
0.07111228257417679 0.9288877844810486
|
||||
0.9820839762687683 0.017916034907102585
|
||||
0.9932542443275452 0.006745814345777035
|
||||
0.903160572052002 0.09683939814567566
|
||||
7.4447594755611135e-09 1.0
|
||||
0.0007499365019612014 0.9992499947547913
|
||||
0.9993971586227417 0.0006028888747096062
|
||||
0.5567896366119385 0.4432104229927063
|
||||
5.036302354710642e-06 0.9999949932098389
|
||||
0.9275308847427368 0.07246915251016617
|
||||
1.5821095075807534e-05 0.9999841451644897
|
||||
0.9999226331710815 7.732493395451456e-05
|
||||
0.0025079061742872 0.9974920749664307
|
||||
0.9999935626983643 6.464079433499137e-06
|
||||
2.063066162349969e-08 1.0
|
||||
0.999344527721405 0.0006554379360750318
|
||||
0.999762237071991 0.00023780007904861122
|
||||
0.5607470273971558 0.43925294280052185
|
||||
0.004888234660029411 0.9951117634773254
|
||||
0.9999599456787109 4.00401622755453e-05
|
||||
0.9981156587600708 0.0018843263387680054
|
||||
0.9999803304672241 1.9677449017763138e-05
|
||||
0.008506403304636478 0.9914935827255249
|
||||
0.9981905817985535 0.0018094099359586835
|
||||
0.9999939203262329 6.022808065608842e-06
|
||||
0.9997492432594299 0.0002507257158868015
|
||||
0.8678835034370422 0.13211651146411896
|
||||
0.04346703737974167 0.9565330147743225
|
||||
0.00040210483712144196 0.99959796667099
|
||||
0.9017825126647949 0.09821751713752747
|
||||
0.9939233660697937 0.0060766092501580715
|
||||
5.989947453599598e-07 0.9999994039535522
|
||||
0.9997304081916809 0.0002695649745874107
|
||||
3.269677506523294e-07 0.9999996423721313
|
||||
0.9969833493232727 0.0030166786164045334
|
||||
0.9517912268638611 0.04820884019136429
|
||||
0.9999783039093018 2.1741187083534896e-05
|
||||
9.318217780673876e-05 0.9999067783355713
|
||||
0.9998562335968018 0.00014380061475094408
|
||||
0.3769559860229492 0.623043954372406
|
||||
0.9998469352722168 0.0001530707668280229
|
||||
0.9999862909317017 1.3655897419084795e-05
|
||||
0.9993302822113037 0.0006697573116980493
|
||||
0.15087568759918213 0.8491243124008179
|
||||
0.3585548996925354 0.6414450407028198
|
||||
0.9995833039283752 0.0004167572478763759
|
||||
0.9699705839157104 0.030029406771063805
|
||||
0.9976567029953003 0.0023432616144418716
|
||||
0.9995654225349426 0.0004345984198153019
|
||||
0.9999468326568604 5.319152114680037e-05
|
||||
0.01776832714676857 0.9822316765785217
|
||||
0.9999912977218628 8.687638910487294e-06
|
||||
0.9995306730270386 0.0004693038354162127
|
||||
0.9969593286514282 0.0030407337471842766
|
||||
0.7958576083183289 0.20414242148399353
|
||||
0.7064720988273621 0.2935279309749603
|
||||
0.9999697208404541 3.022661985596642e-05
|
||||
3.67343527614139e-05 0.9999632835388184
|
||||
0.9659992456436157 0.0340007022023201
|
||||
1.5184506310106372e-06 0.9999984502792358
|
||||
0.999897837638855 0.00010216181544819847
|
||||
0.08395311236381531 0.9160469174385071
|
||||
0.15092000365257263 0.849079966545105
|
||||
0.9279717206954956 0.07202835381031036
|
||||
0.9999750852584839 2.48734049819177e-05
|
||||
0.9203755259513855 0.07962450385093689
|
||||
4.529081252258038e-06 0.9999954700469971
|
||||
0.9543004035949707 0.0456995815038681
|
||||
0.8035893440246582 0.1964106261730194
|
||||
0.9665433168411255 0.033456698060035706
|
||||
0.9999411106109619 5.8903253375319764e-05
|
||||
2.718790312883357e-07 0.9999997615814209
|
||||
0.22823651134967804 0.7717635035514832
|
||||
0.9616555571556091 0.03834441676735878
|
||||
0.9989759922027588 0.0010240200208500028
|
||||
0.9999855756759644 1.4466210814134683e-05
|
||||
0.9992571473121643 0.0007428252720274031
|
||||
4.4184514990774915e-05 0.9999557733535767
|
||||
0.9813732504844666 0.018626734614372253
|
||||
0.9996235370635986 0.00037640007212758064
|
||||
0.45522207021713257 0.5447779297828674
|
||||
0.9984334111213684 0.0015666189137846231
|
||||
0.5999354124069214 0.4000645875930786
|
||||
0.9992015957832336 0.0007983882678672671
|
||||
1.1163784847667557e-06 0.999998927116394
|
||||
0.9999932050704956 6.819630016252631e-06
|
||||
0.9733205437660217 0.026679448783397675
|
||||
0.9996452331542969 0.0003547593660186976
|
||||
0.9831674695014954 0.01683255285024643
|
||||
0.6404561996459961 0.3595438301563263
|
||||
0.9756172895431519 0.024382784962654114
|
||||
5.3901224816854665e-08 1.0
|
||||
0.9999752044677734 2.4776625650702044e-05
|
||||
6.151201523607597e-05 0.9999384880065918
|
||||
0.06782747805118561 0.9321725964546204
|
||||
0.8198803067207336 0.18011964857578278
|
||||
0.7196502685546875 0.2803496718406677
|
||||
0.9816163778305054 0.018383584916591644
|
||||
0.10275167971849442 0.8972483277320862
|
||||
0.0002488196187186986 0.9997511506080627
|
||||
8.342409273609519e-05 0.9999165534973145
|
||||
0.9998685121536255 0.00013146884157322347
|
||||
0.9984714388847351 0.0015285988338291645
|
||||
0.9258694648742676 0.07413050532341003
|
||||
1.120878323490615e-08 1.0
|
||||
0.9015117287635803 0.09848826378583908
|
||||
0.0031838531140238047 0.9968162178993225
|
||||
0.9999969005584717 3.1560648494632915e-06
|
||||
0.9999675750732422 3.247032145736739e-05
|
||||
0.999393105506897 0.000606913585215807
|
||||
0.9986691474914551 0.0013309227069839835
|
||||
0.8089115619659424 0.19108842313289642
|
||||
0.9996803998947144 0.0003196683246642351
|
||||
0.3002210855484009 0.6997789144515991
|
||||
0.9999908208847046 9.12171890377067e-06
|
||||
0.9135971665382385 0.08640281111001968
|
||||
0.9970927238464355 0.0029073168989270926
|
||||
0.06237632781267166 0.9376236796379089
|
||||
0.9997960925102234 0.0002039155806414783
|
||||
0.9999871253967285 1.286438691749936e-05
|
||||
0.8027787804603577 0.19722121953964233
|
||||
1.0446061793345507e-08 1.0
|
||||
0.9908953905105591 0.009104611352086067
|
||||
0.3253200054168701 0.6746799945831299
|
||||
0.999521017074585 0.0004789357481058687
|
||||
7.699640036662458e-08 0.9999998807907104
|
||||
0.9891263246536255 0.010873646475374699
|
||||
0.9063988327980042 0.09360110759735107
|
||||
0.7809692025184631 0.21903081238269806
|
||||
0.999873161315918 0.0001268410123884678
|
||||
0.008283156901597977 0.9917168021202087
|
||||
0.9999409914016724 5.894730566069484e-05
|
||||
0.9855729937553406 0.014427030459046364
|
||||
0.9714321494102478 0.02856782265007496
|
||||
0.9999531507492065 4.679941412177868e-05
|
||||
0.9885430335998535 0.01145696360617876
|
||||
0.989084780216217 0.01091520581394434
|
||||
0.6579383015632629 0.34206169843673706
|
||||
0.993756890296936 0.006243090145289898
|
||||
3.4057687781086088e-09 1.0
|
||||
0.9917623996734619 0.008237648755311966
|
||||
0.9420074224472046 0.05799262225627899
|
||||
0.9658148288726807 0.03418519347906113
|
||||
0.962051510810852 0.03794848546385765
|
||||
0.9975982308387756 0.0024017158430069685
|
||||
0.725468099117279 0.27453184127807617
|
||||
0.9995902180671692 0.0004097826895304024
|
||||
0.027873143553733826 0.972126841545105
|
||||
0.9987267851829529 0.0012732003815472126
|
||||
0.9999923706054688 7.612093213538174e-06
|
||||
0.9996806383132935 0.00031932350248098373
|
||||
0.9975622892379761 0.0024377263616770506
|
||||
0.9914565682411194 0.008543377742171288
|
||||
0.850246012210846 0.14975394308567047
|
||||
0.00613927049562335 0.9938607215881348
|
||||
0.04651568830013275 0.953484296798706
|
||||
0.8069931268692017 0.19300685822963715
|
||||
0.011340790428221226 0.9886592030525208
|
||||
0.5788864493370056 0.4211135506629944
|
||||
0.06980060786008835 0.930199384689331
|
||||
0.9999834299087524 1.6547259292565286e-05
|
||||
0.9645374417304993 0.03546259179711342
|
||||
0.9999163150787354 8.368112321477383e-05
|
||||
Binary file not shown.
@ -1,23 +0,0 @@
|
||||
from keras.preprocessing.image import ImageDataGenerator
|
||||
|
||||
class gen_data:
|
||||
def __init__(self):
|
||||
# batch 表示训练样本数
|
||||
# 这里推测20一组为好
|
||||
# 过大会过拟合
|
||||
# self.batch = 20
|
||||
# self.batch = 16
|
||||
self.batch = 32
|
||||
# keras 提供的数据生成器
|
||||
'''
|
||||
zoom_range 随机缩放的幅度
|
||||
rotation_range 数据提升时图片随机转动的角度
|
||||
horizontal_flip 图片随机水平翻转
|
||||
vertical_flip 图片竖直翻转
|
||||
'''
|
||||
self.tr_gen = ImageDataGenerator(
|
||||
zoom_range=2,
|
||||
rotation_range=90,
|
||||
horizontal_flip=True,
|
||||
vertical_flip=True
|
||||
)
|
||||
@ -1,10 +0,0 @@
|
||||
import tensorflow as tf
|
||||
|
||||
a = tf.test.is_built_with_cuda() # 判断CUDA是否可以用
|
||||
b = tf.test.is_gpu_available(
|
||||
cuda_only=False,
|
||||
min_cuda_compute_capability=None
|
||||
) # 判断GPU是否可以用
|
||||
|
||||
print(a) # 显示True表示CUDA可用
|
||||
print(b) # 显示True表示GPU可用
|
||||
Binary file not shown.
Binary file not shown.
@ -1,24 +0,0 @@
|
||||
# 降低学习率
|
||||
from keras.src.callbacks import ReduceLROnPlateau
|
||||
from tensorflow.python.keras.callbacks import ModelCheckpoint
|
||||
|
||||
class reduce:
|
||||
def train(self):
|
||||
# 控制学习率
|
||||
self.learn_control = ReduceLROnPlateau(
|
||||
monitor='val_accuracy',
|
||||
patience=5,
|
||||
verbose=1,
|
||||
factor=0.2,
|
||||
min_lr=1e-7
|
||||
)
|
||||
|
||||
path = "D:\\pro_of_program\\Python\\train_cancer\\train_model\\third_model"
|
||||
# 保存模型只保存最优解
|
||||
self.checkpoint = ModelCheckpoint(
|
||||
filepath=path,
|
||||
monitor='val_accuracy',
|
||||
verbose=1,
|
||||
save_best_only=True,
|
||||
mode='max'
|
||||
)
|
||||
@ -1,30 +0,0 @@
|
||||
from loader_picture import data_loader
|
||||
import numpy as np
|
||||
from matplotlib import pyplot as plt
|
||||
|
||||
from train_model.modeling import reduce_study_rate
|
||||
from train_model.modeling import modeling
|
||||
from train_model.data_gen import data_output
|
||||
|
||||
# 载入图片
|
||||
load = data_loader.Loader()
|
||||
# 获取模型
|
||||
models = modeling.breast_train_test()
|
||||
# 展现模型
|
||||
models.model.summary()
|
||||
# data
|
||||
data = data_output.gen_data()
|
||||
|
||||
# 降低学习率
|
||||
reduces = reduce_study_rate.reduce()
|
||||
reduces.train()
|
||||
|
||||
# 训练+评估
|
||||
history = models.model.fit(
|
||||
data.tr_gen.flow(load.train_of_x , load.train_of_y , batch_size=data.batch),
|
||||
steps_per_epoch = load.train_of_x.shape[0] / data.batch,
|
||||
# 训练30次
|
||||
epochs=50,
|
||||
validation_data=(load.val_of_x , load.val_of_y),
|
||||
callbacks=[reduces.learn_control , reduces.checkpoint]
|
||||
)
|
||||
File diff suppressed because one or more lines are too long
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Reference in new issue