Alexnet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "500\n",
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      "503\n",
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      "1916\n",
      "./sub-S03615/ses-E08281/mod-rx/sub-S03615_ses-E08281_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03615/ses-E08281/mod-rx/sub-S03615_ses-E08281_run-1_bp-chest_vp-pa_cr.png\n",
      "1917\n",
      "./sub-S03616/ses-E07328/mod-rx/sub-S03616_ses-E07328_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03616/ses-E07328/mod-rx/sub-S03616_ses-E07328_run-1_bp-chest_vp-pa_cr.png\n",
      "1918\n",
      "./sub-S03616/ses-E07875/mod-rx/sub-S03616_ses-E07875_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03616/ses-E07875/mod-rx/sub-S03616_ses-E07875_run-1_bp-chest_vp-pa_cr.png\n",
      "1919\n",
      "./sub-S03617/ses-E07329/mod-rx/sub-S03617_ses-E07329_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03617/ses-E07329/mod-rx/sub-S03617_ses-E07329_run-1_bp-chest_vp-pa_cr.png\n",
      "1920\n",
      "./sub-S03617/ses-E07990/mod-rx/sub-S03617_ses-E07990_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03617/ses-E07990/mod-rx/sub-S03617_ses-E07990_run-1_bp-chest_vp-pa_cr.png\n",
      "1921\n",
      "./sub-S03618/ses-E07333/mod-rx/sub-S03618_ses-E07333_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03618/ses-E07333/mod-rx/sub-S03618_ses-E07333_run-1_bp-chest_vp-pa_cr.png\n",
      "1922\n",
      "./sub-S03618/ses-E08596/mod-rx/sub-S03618_ses-E08596_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03618/ses-E08596/mod-rx/sub-S03618_ses-E08596_run-1_bp-chest_vp-pa_cr.png\n",
      "1923\n",
      "无此图片\n",
      "1924\n",
      "./sub-S03620/ses-E08280/mod-rx/sub-S03620_ses-E08280_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03620/ses-E08280/mod-rx/sub-S03620_ses-E08280_run-1_bp-chest_vp-pa_cr.png\n",
      "1925\n",
      "1926\n",
      "./sub-S03621/ses-E07673/mod-rx/sub-S03621_ses-E07673_run-1_bp-chest_vp-pa_cr.png\n",
      "1927\n",
      "无此图片\n",
      "1928\n",
      "./sub-S03621/ses-E08605/mod-rx/sub-S03621_ses-E08605_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03621/ses-E08605/mod-rx/sub-S03621_ses-E08605_run-1_bp-chest_vp-pa_cr.png\n",
      "1929\n",
      "无此图片\n",
      "1930\n",
      "无此图片\n",
      "1931\n",
      "无此图片\n",
      "1932\n",
      "./sub-S03622/ses-E08131/mod-rx/sub-S03622_ses-E08131_run-1_bp-chest_vp-pa_cr.png\n",
      "1933\n",
      "无此图片\n",
      "1934\n",
      "无此图片\n",
      "1935\n",
      "无此图片\n",
      "1936\n",
      "无此图片\n",
      "1937\n",
      "无此图片\n",
      "1938\n",
      "无此图片\n",
      "1939\n",
      "无此图片\n",
      "无此图片\n",
      "1940\n",
      "./sub-S03625/ses-E07624/mod-rx/sub-S03625_ses-E07624_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03625/ses-E07624/mod-rx/sub-S03625_ses-E07624_run-1_bp-chest_vp-pa_cr.png\n",
      "1941\n",
      "./sub-S03625/ses-E07353/mod-rx/sub-S03625_ses-E07353_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03625/ses-E07353/mod-rx/sub-S03625_ses-E07353_run-1_bp-chest_vp-pa_cr.png\n",
      "1942\n",
      "无此图片\n",
      "无此图片\n",
      "1943\n",
      "./sub-S03627/ses-E08872/mod-rx/sub-S03627_ses-E08872_run-1_bp-chest_vp-pa_cr.png\n",
      "1944\n",
      "无此图片\n",
      "无此图片\n",
      "1945\n",
      "./sub-S03628/ses-E07360/mod-rx/sub-S03628_ses-E07360_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03628/ses-E07360/mod-rx/sub-S03628_ses-E07360_run-1_bp-chest_vp-pa_cr.png\n",
      "1946\n",
      "无此图片\n",
      "1947\n",
      "无此图片\n",
      "1948\n",
      "1949\n",
      "无此图片\n",
      "1950\n",
      "无此图片\n",
      "1951\n",
      "无此图片\n",
      "1952\n",
      "无此图片\n",
      "1953\n",
      "无此图片\n",
      "1954\n",
      "无此图片\n",
      "1955\n",
      "无此图片\n",
      "1956\n",
      "无此图片\n",
      "1957\n",
      "无此图片\n",
      "1958\n",
      "无此图片\n",
      "无此图片\n",
      "1959\n",
      "无此图片\n",
      "1960\n",
      "1961\n",
      "无此图片\n",
      "1962\n",
      "无此图片\n",
      "1963\n",
      "无此图片\n",
      "1964\n",
      "无此图片\n",
      "1965\n",
      "无此图片\n",
      "1966\n",
      "无此图片\n",
      "1967\n",
      "无此图片\n",
      "1968\n",
      "./sub-S03640/ses-E08641/mod-rx/sub-S03640_ses-E08641_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03640/ses-E08641/mod-rx/sub-S03640_ses-E08641_run-1_bp-chest_vp-pa_cr.png\n",
      "1969\n",
      "无此图片\n",
      "1970\n",
      "./sub-S03641/ses-E07393/mod-rx/sub-S03641_ses-E07393_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03641/ses-E07393/mod-rx/sub-S03641_ses-E07393_run-1_bp-chest_vp-pa_cr.png\n",
      "1971\n",
      "./sub-S03641/ses-E07503/mod-rx/sub-S03641_ses-E07503_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03641/ses-E07503/mod-rx/sub-S03641_ses-E07503_run-1_bp-chest_vp-pa_cr.png\n",
      "1972\n",
      "./sub-S03642/ses-E07395/mod-rx/sub-S03642_ses-E07395_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03642/ses-E07395/mod-rx/sub-S03642_ses-E07395_run-1_bp-chest_vp-pa_cr.png\n",
      "1973\n",
      "无此图片\n",
      "1974\n",
      "./sub-S03644/ses-E07400/mod-rx/sub-S03644_ses-E07400_run-1_bp-chest_vp-pa_cr.png\n",
      "1975\n",
      "无此图片\n",
      "1976\n",
      "无此图片\n",
      "1977\n",
      "无此图片\n",
      "1978\n",
      "无此图片\n",
      "1979\n",
      "无此图片\n",
      "1980\n",
      "./sub-S03647/ses-E07403/mod-rx/sub-S03647_ses-E07403_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03647/ses-E07403/mod-rx/sub-S03647_ses-E07403_run-1_bp-chest_vp-pa_cr.png\n",
      "1981\n",
      "1982\n",
      "./sub-S03648/ses-E07409/mod-rx/sub-S03648_ses-E07409_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03648/ses-E07409/mod-rx/sub-S03648_ses-E07409_run-1_bp-chest_vp-pa_cr.png\n",
      "1983\n",
      "无此图片\n",
      "1984\n",
      "无此图片\n",
      "1985\n",
      "1986\n",
      "1987\n",
      "./sub-S03651/ses-E07416/mod-rx/sub-S03651_ses-E07416_run-1_bp-chest_vp-pa_cr.png\n",
      "1988\n",
      "./sub-S03651/ses-E07654/mod-rx/sub-S03651_ses-E07654_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03651/ses-E07654/mod-rx/sub-S03651_ses-E07654_run-1_bp-chest_vp-pa_cr.png\n",
      "1989\n",
      "无此图片\n",
      "1990\n",
      "无此图片\n",
      "1991\n",
      "无此图片\n",
      "1992\n",
      "无此图片\n",
      "无此图片\n",
      "1993\n",
      "无此图片\n",
      "1994\n",
      "无此图片\n",
      "1995\n",
      "无此图片\n",
      "1996\n",
      "无此图片\n",
      "1997\n",
      "./sub-S03655/ses-E07424/mod-rx/sub-S03655_ses-E07424_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03655/ses-E07424/mod-rx/sub-S03655_ses-E07424_run-1_bp-chest_vp-pa_cr.png\n",
      "1998\n",
      "./sub-S03656/ses-E07425/mod-rx/sub-S03656_ses-E07425_run-1_bp-chest_vp-pa_cr.png\n",
      "1999\n",
      "无此图片\n",
      "无此图片\n",
      "2000\n",
      "2001\n",
      "2002\n",
      "无此图片\n",
      "2003\n",
      "无此图片\n",
      "2004\n",
      "无此图片\n",
      "2005\n",
      "./sub-S03658/ses-E08124/mod-rx/sub-S03658_ses-E08124_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03658/ses-E08124/mod-rx/sub-S03658_ses-E08124_run-1_bp-chest_vp-pa_cr.png\n",
      "2006\n",
      "无此图片\n",
      "2007\n",
      "无此图片\n",
      "无此图片\n",
      "2008\n",
      "无此图片\n",
      "2009\n",
      "./sub-S03661/ses-E07435/mod-rx/sub-S03661_ses-E07435_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03661/ses-E07435/mod-rx/sub-S03661_ses-E07435_run-1_bp-chest_vp-pa_cr.png\n",
      "2010\n",
      "无此图片\n",
      "2011\n",
      "无此图片\n",
      "2012\n",
      "./sub-S03664/ses-E07438/mod-rx/sub-S03664_ses-E07438_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03664/ses-E07438/mod-rx/sub-S03664_ses-E07438_run-1_bp-chest_vp-pa_cr.png\n",
      "2013\n",
      "./sub-S03664/ses-E08939/mod-rx/sub-S03664_ses-E08939_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03664/ses-E08939/mod-rx/sub-S03664_ses-E08939_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03664/ses-E08939/mod-rx/sub-S03664_ses-E08939_run-1_bp-chest_vp-pa_cr.png\n",
      "2014\n",
      "无此图片\n",
      "2015\n",
      "无此图片\n",
      "无此图片\n",
      "2016\n",
      "2017\n",
      "无此图片\n",
      "2018\n",
      "无此图片\n",
      "2019\n",
      "无此图片\n",
      "2020\n",
      "2021\n",
      "./sub-S03669/ses-E07544/mod-rx/sub-S03669_ses-E07544_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03669/ses-E07544/mod-rx/sub-S03669_ses-E07544_run-1_bp-chest_vp-pa_cr.png\n",
      "2022\n",
      "2023\n",
      "2024\n",
      "无此图片\n",
      "无此图片\n",
      "2025\n",
      "无此图片\n",
      "2026\n",
      "./sub-S03671/ses-E07448/mod-rx/sub-S03671_ses-E07448_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03671/ses-E07448/mod-rx/sub-S03671_ses-E07448_run-1_bp-chest_vp-pa_cr.png\n",
      "2027\n",
      "无此图片\n",
      "无此图片\n",
      "2028\n",
      "2029\n",
      "无此图片\n",
      "2030\n",
      "无此图片\n",
      "2031\n",
      "无此图片\n",
      "2032\n",
      "无此图片\n",
      "2033\n",
      "./sub-S03675/ses-E07455/mod-rx/sub-S03675_ses-E07455_run-1_bp-chest_vp-pa_cr.png\n",
      "2034\n",
      "./sub-S03675/ses-E07455/mod-rx/sub-S03675_ses-E07455_run-1_bp-chest_vp-pa_cr.png\n",
      "2035\n",
      "./sub-S03676/ses-E07458/mod-rx/sub-S03676_ses-E07458_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03676/ses-E07458/mod-rx/sub-S03676_ses-E07458_run-1_bp-chest_vp-pa_cr.png\n",
      "2036\n",
      "无此图片\n",
      "2037\n",
      "无此图片\n",
      "2038\n",
      "无此图片\n",
      "2039\n",
      "./sub-S03679/ses-E07460/mod-rx/sub-S03679_ses-E07460_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03679/ses-E07460/mod-rx/sub-S03679_ses-E07460_run-1_bp-chest_vp-pa_cr.png\n",
      "2040\n",
      "无此图片\n",
      "2041\n",
      "无此图片\n",
      "2042\n",
      "无此图片\n",
      "无此图片\n",
      "2043\n",
      "无此图片\n",
      "2044\n",
      "无此图片\n",
      "无此图片\n",
      "2045\n",
      "无此图片\n",
      "2046\n",
      "无此图片\n",
      "2047\n",
      "无此图片\n",
      "无此图片\n",
      "2048\n",
      "无此图片\n",
      "2049\n",
      "无此图片\n",
      "无此图片\n",
      "2050\n",
      "无此图片\n",
      "2051\n",
      "无此图片\n",
      "2052\n",
      "无此图片\n",
      "2053\n",
      "2054\n",
      "无此图片\n",
      "2055\n",
      "无此图片\n",
      "无此图片\n",
      "2056\n",
      "无此图片\n",
      "2057\n",
      "无此图片\n",
      "无此图片\n",
      "2058\n",
      "2059\n",
      "无此图片\n",
      "无此图片\n",
      "2060\n",
      "无此图片\n",
      "2061\n",
      "无此图片\n",
      "2062\n",
      "无此图片\n",
      "2063\n",
      "无此图片\n",
      "无此图片\n",
      "无此图片\n",
      "无此图片\n",
      "无此图片\n",
      "2064\n",
      "无此图片\n",
      "无此图片\n",
      "2065\n",
      "无此图片\n",
      "2066\n",
      "2067\n",
      "./sub-S03698/ses-E07502/mod-rx/sub-S03698_ses-E07502_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03698/ses-E07502/mod-rx/sub-S03698_ses-E07502_run-1_bp-chest_vp-pa_cr.png\n",
      "2068\n",
      "无此图片\n",
      "无此图片\n",
      "2069\n",
      "无此图片\n",
      "无此图片\n",
      "2070\n",
      "2071\n",
      "./sub-S03701/ses-E08512/mod-rx/sub-S03701_ses-E08512_run-1_bp-chest_vp-pa_cr.png\n",
      "2072\n",
      "无此图片\n",
      "2073\n",
      "2074\n",
      "./sub-S03703/ses-E07510/mod-rx/sub-S03703_ses-E07510_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03703/ses-E07510/mod-rx/sub-S03703_ses-E07510_run-1_bp-chest_vp-pa_cr.png\n",
      "2075\n",
      "无此图片\n",
      "2076\n",
      "2077\n",
      "无此图片\n",
      "2078\n",
      "./sub-S03706/ses-E07517/mod-rx/sub-S03706_ses-E07517_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03706/ses-E07517/mod-rx/sub-S03706_ses-E07517_run-1_bp-chest_vp-pa_cr.png\n",
      "2079\n",
      "./sub-S03706/ses-E08521/mod-rx/sub-S03706_ses-E08521_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03706/ses-E08521/mod-rx/sub-S03706_ses-E08521_run-1_bp-chest_vp-pa_cr.png\n",
      "2080\n",
      "无此图片\n",
      "无此图片\n",
      "2081\n",
      "无此图片\n",
      "2082\n",
      "无此图片\n",
      "2083\n",
      "无此图片\n",
      "2084\n",
      "无此图片\n",
      "无此图片\n",
      "2085\n",
      "无此图片\n",
      "无此图片\n",
      "2086\n",
      "无此图片\n",
      "2087\n",
      "2088\n",
      "无此图片\n",
      "无此图片\n",
      "2089\n",
      "无此图片\n",
      "无此图片\n",
      "2090\n",
      "无此图片\n",
      "无此图片\n",
      "2091\n",
      "./sub-S03712/ses-E07527/mod-rx/sub-S03712_ses-E07527_run-1_bp-chest_vp-pa_cr.png\n",
      "2092\n",
      "无此图片\n",
      "2093\n",
      "./sub-S03715/ses-E08738/mod-rx/sub-S03715_ses-E08738_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03715/ses-E08738/mod-rx/sub-S03715_ses-E08738_run-1_bp-chest_vp-pa_cr.png\n",
      "2094\n",
      "2095\n",
      "./sub-S03716/ses-E07532/mod-rx/sub-S03716_ses-E07532_run-1_bp-chest_vp-pa_cr.png\n",
      "2096\n",
      "./sub-S03716/ses-E08550/mod-rx/sub-S03716_ses-E08550_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03716/ses-E08550/mod-rx/sub-S03716_ses-E08550_run-1_bp-chest_vp-pa_cr.png\n",
      "2097\n",
      "无此图片\n",
      "2098\n",
      "2099\n",
      "无此图片\n",
      "2100\n",
      "无此图片\n",
      "2101\n",
      "无此图片\n",
      "2102\n",
      "./sub-S03719/ses-E07536/mod-rx/sub-S03719_ses-E07536_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03719/ses-E07536/mod-rx/sub-S03719_ses-E07536_run-1_bp-chest_vp-pa_cr.png\n",
      "2103\n",
      "无此图片\n",
      "无此图片\n",
      "2104\n",
      "无此图片\n",
      "2105\n",
      "无此图片\n",
      "2106\n",
      "无此图片\n",
      "2107\n",
      "无此图片\n",
      "2108\n",
      "无此图片\n",
      "2109\n",
      "./sub-S03725/ses-E08790/mod-rx/sub-S03725_ses-E08790_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03725/ses-E08790/mod-rx/sub-S03725_ses-E08790_run-1_bp-chest_vp-pa_cr.png\n",
      "2110\n",
      "无此图片\n",
      "2111\n",
      "无此图片\n",
      "2112\n",
      "无此图片\n",
      "2113\n",
      "./sub-S03727/ses-E08698/mod-rx/sub-S03727_ses-E08698_run-1_bp-chest_vp-pa_cr.png\n",
      "2114\n",
      "./sub-S03727/ses-E08390/mod-rx/sub-S03727_ses-E08390_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03727/ses-E08390/mod-rx/sub-S03727_ses-E08390_run-1_bp-chest_vp-pa_cr.png\n",
      "2115\n",
      "./sub-S03727/ses-E07548/mod-rx/sub-S03727_ses-E07548_run-1_bp-chest_vp-pa_cr.png\n",
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      "2145\n",
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      "2172\n",
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      "./sub-S03929/ses-E08077/mod-rx/sub-S03929_ses-E08077_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03929/ses-E08077/mod-rx/sub-S03929_ses-E08077_run-1_bp-chest_vp-pa_cr.png\n",
      "2414\n",
      "./sub-S03929/ses-E08527/mod-rx/sub-S03929_ses-E08527_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03929/ses-E08527/mod-rx/sub-S03929_ses-E08527_run-1_bp-chest_vp-pa_cr.png\n",
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      "2420\n",
      "./sub-S03932/ses-E08085/mod-rx/sub-S03932_ses-E08085_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03932/ses-E08085/mod-rx/sub-S03932_ses-E08085_run-1_bp-chest_vp-pa_cr.png\n",
      "2421\n",
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      "2428\n",
      "2429\n",
      "./sub-S03937/ses-E08851/mod-rx/sub-S03937_ses-E08851_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03937/ses-E08851/mod-rx/sub-S03937_ses-E08851_run-1_bp-chest_vp-pa_cr.png\n",
      "2430\n",
      "./sub-S03938/ses-E08091/mod-rx/sub-S03938_ses-E08091_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03938/ses-E08091/mod-rx/sub-S03938_ses-E08091_run-1_bp-chest_vp-pa_cr.png\n",
      "2431\n",
      "无此图片\n",
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      "无此图片\n",
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      "2435\n",
      "./sub-S03941/ses-E08094/mod-rx/sub-S03941_ses-E08094_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03941/ses-E08094/mod-rx/sub-S03941_ses-E08094_run-1_bp-chest_vp-pa_cr.png\n",
      "2436\n",
      "./sub-S03942/ses-E08095/mod-rx/sub-S03942_ses-E08095_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03942/ses-E08095/mod-rx/sub-S03942_ses-E08095_run-1_bp-chest_vp-pa_cr.png\n",
      "2437\n",
      "./sub-S03944/ses-E08838/mod-rx/sub-S03944_ses-E08838_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03944/ses-E08838/mod-rx/sub-S03944_ses-E08838_run-1_bp-chest_vp-pa_cr.png\n",
      "2438\n",
      "无此图片\n",
      "无此图片\n",
      "2439\n",
      "无此图片\n",
      "2440\n",
      "无此图片\n",
      "无此图片\n",
      "2441\n",
      "./sub-S03949/ses-E08691/mod-rx/sub-S03949_ses-E08691_run-1_bp-chest_vp-pa_cr.png\n",
      "2442\n",
      "./sub-S03949/ses-E08103/mod-rx/sub-S03949_ses-E08103_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03949/ses-E08103/mod-rx/sub-S03949_ses-E08103_run-1_bp-chest_vp-pa_cr.png\n",
      "2443\n",
      "./sub-S03950/ses-E08102/mod-rx/sub-S03950_ses-E08102_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03950/ses-E08102/mod-rx/sub-S03950_ses-E08102_run-1_bp-chest_vp-pa_cr.png\n",
      "2444\n",
      "./sub-S03951/ses-E08104/mod-rx/sub-S03951_ses-E08104_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03951/ses-E08104/mod-rx/sub-S03951_ses-E08104_run-1_bp-chest_vp-pa_cr.png\n",
      "2445\n",
      "./sub-S03952/ses-E08947/mod-rx/sub-S03952_ses-E08947_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03952/ses-E08947/mod-rx/sub-S03952_ses-E08947_run-1_bp-chest_vp-pa_cr.png\n",
      "2446\n",
      "./sub-S03952/ses-E08106/mod-rx/sub-S03952_ses-E08106_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03952/ses-E08106/mod-rx/sub-S03952_ses-E08106_run-1_bp-chest_vp-pa_cr.png\n",
      "2447\n",
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      "2453\n",
      "./sub-S03957/ses-E08112/mod-rx/sub-S03957_ses-E08112_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03957/ses-E08112/mod-rx/sub-S03957_ses-E08112_run-1_bp-chest_vp-pa_cr.png\n",
      "2454\n",
      "./sub-S03958/ses-E08111/mod-rx/sub-S03958_ses-E08111_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03958/ses-E08111/mod-rx/sub-S03958_ses-E08111_run-1_bp-chest_vp-pa_cr.png\n",
      "2455\n",
      "./sub-S03959/ses-E08113/mod-rx/sub-S03959_ses-E08113_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03959/ses-E08113/mod-rx/sub-S03959_ses-E08113_run-1_bp-chest_vp-pa_cr.png\n",
      "2456\n",
      "无此图片\n",
      "2457\n",
      "./sub-S03961/ses-E08115/mod-rx/sub-S03961_ses-E08115_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03961/ses-E08115/mod-rx/sub-S03961_ses-E08115_run-1_bp-chest_vp-pa_cr.png\n",
      "2458\n",
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      "2459\n",
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      "2462\n",
      "./sub-S03965/ses-E08119/mod-rx/sub-S03965_ses-E08119_run-1_bp-chest_vp-pa_cr.png\n",
      "./sub-S03965/ses-E08119/mod-rx/sub-S03965_ses-E08119_run-1_bp-chest_vp-pa_cr.png\n",
      "2463\n",
      "无此图片\n",
      "395\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "import cv2\n",
    "import os\n",
    "k = 0\n",
    "n = 500\n",
    "#s = \"run-1_bp-chest_vp-pa_cr.png\"\n",
    "X_data=[]\n",
    "path_label=[]\n",
    "data = pd.read_csv(\"cov.csv\")\n",
    "for i in range(0,2464):\n",
    "    b = './'+data.loc[i][0]+'/'+data.loc[i][1]+'/mod-rx/'\n",
    "    print(i)\n",
    "    try:\n",
    "         for filename in os.listdir(b): \n",
    "                if filename.endswith('png'):#listdir的参数是文件夹的路径\n",
    "                    #print (filename) #此时的filename是文件夹中文件的名称\n",
    "                    fil = data.loc[i][0]+'_'+data.loc[i][1]+'_run-1_bp-chest_vp-pa_cr.png'\n",
    "                    c = b+fil\n",
    "                    try:\n",
    "                        d = cv2.imread(c,0)\n",
    "                        d = cv2.resize(d,(n,n))\n",
    "                        #if s == fil:\n",
    "                        #    print(\"重复\")\n",
    "                        #else:\n",
    "                        X_data.append(d)\n",
    "                        if(data.loc[i][2]==1):\n",
    "                            k = k+1\n",
    "                        path_label.append(data.loc[i][2])   \n",
    "                        print(c)\n",
    "                    except:\n",
    "                        print(\"无此图片\")\n",
    "                    #print(d.shape)          \n",
    "    except:\n",
    "        print(\"异常\")\n",
    "print(k) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(821, 500, 500)\n",
      "(206, 500, 500)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data, path_label, test_size=0.2)\n",
    "Xtrain = np.array(X_train)\n",
    "Xtext = np.array(X_test)\n",
    "Ytrain = np.array(y_train)\n",
    "Ytext = np.array(y_test)\n",
    "print(Xtrain.shape)\n",
    "print(Xtext.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
      "  warnings.warn('An interactive session is already active. This can '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.15.2\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_5 (Conv2D)            (None, 490, 490, 48)      5856      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 245, 245, 48)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 241, 241, 128)     153728    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 120, 120, 128)     0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 118, 118, 192)     221376    \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 116, 116, 192)     331968    \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 114, 114, 128)     221312    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 57, 57, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 415872)            0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 2048)              851707904 \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 2048)              4196352   \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 2)                 4098      \n",
      "=================================================================\n",
      "Total params: 856,842,594\n",
      "Trainable params: 856,842,594\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 821 samples, validate on 206 samples\n",
      "Epoch 1/100\n",
      "821/821 [==============================] - 698s 850ms/sample - loss: 15.9871 - acc: 0.5676 - val_loss: 0.6816 - val_acc: 0.6214\n",
      "Epoch 2/100\n",
      "821/821 [==============================] - 722s 880ms/sample - loss: 0.6674 - acc: 0.6139 - val_loss: 0.6498 - val_acc: 0.6214\n",
      "Epoch 3/100\n",
      "821/821 [==============================] - 742s 904ms/sample - loss: 0.6541 - acc: 0.6139 - val_loss: 0.7304 - val_acc: 0.6214\n",
      "Epoch 4/100\n",
      "821/821 [==============================] - 713s 868ms/sample - loss: 0.6564 - acc: 0.6151 - val_loss: 0.6554 - val_acc: 0.6214\n",
      "Epoch 5/100\n",
      "821/821 [==============================] - 710s 864ms/sample - loss: 0.6473 - acc: 0.6163 - val_loss: 0.6439 - val_acc: 0.6262\n",
      "Epoch 6/100\n",
      "821/821 [==============================] - 702s 856ms/sample - loss: 0.6363 - acc: 0.6236 - val_loss: 0.6955 - val_acc: 0.6553\n",
      "Epoch 7/100\n",
      "821/821 [==============================] - 683s 832ms/sample - loss: 0.6452 - acc: 0.6395 - val_loss: 0.6354 - val_acc: 0.6311\n",
      "Epoch 8/100\n",
      "821/821 [==============================] - 710s 865ms/sample - loss: 0.5966 - acc: 0.6529 - val_loss: 0.5910 - val_acc: 0.6505\n",
      "Epoch 9/100\n",
      "821/821 [==============================] - 690s 840ms/sample - loss: 0.5131 - acc: 0.7345 - val_loss: 0.6127 - val_acc: 0.7233\n",
      "Epoch 10/100\n",
      "821/821 [==============================] - 706s 860ms/sample - loss: 0.4624 - acc: 0.7674 - val_loss: 0.5081 - val_acc: 0.7864\n",
      "Epoch 11/100\n",
      "821/821 [==============================] - 704s 857ms/sample - loss: 0.3983 - acc: 0.8258 - val_loss: 0.5123 - val_acc: 0.7524\n",
      "Epoch 12/100\n",
      "821/821 [==============================] - 710s 865ms/sample - loss: 0.3085 - acc: 0.8477 - val_loss: 0.4717 - val_acc: 0.8107\n",
      "Epoch 13/100\n",
      "821/821 [==============================] - 715s 870ms/sample - loss: 0.3072 - acc: 0.8441 - val_loss: 10.3611 - val_acc: 0.5194\n",
      "Epoch 14/100\n",
      "821/821 [==============================] - 723s 881ms/sample - loss: 0.7862 - acc: 0.8283 - val_loss: 0.6187 - val_acc: 0.7718\n",
      "Epoch 15/100\n",
      "821/821 [==============================] - 721s 878ms/sample - loss: 2.2301 - acc: 0.7942 - val_loss: 1.1989 - val_acc: 0.6553\n",
      "Epoch 16/100\n",
      "821/821 [==============================] - 735s 895ms/sample - loss: 0.4404 - acc: 0.8283 - val_loss: 0.5664 - val_acc: 0.7233\n",
      "Epoch 17/100\n",
      "821/821 [==============================] - 742s 903ms/sample - loss: 0.2544 - acc: 0.8916 - val_loss: 0.5816 - val_acc: 0.8204\n",
      "Epoch 18/100\n",
      "821/821 [==============================] - 730s 889ms/sample - loss: 0.1732 - acc: 0.9294 - val_loss: 0.6933 - val_acc: 0.8058\n",
      "Epoch 19/100\n",
      "821/821 [==============================] - 732s 892ms/sample - loss: 0.1209 - acc: 0.9488 - val_loss: 0.7274 - val_acc: 0.8350\n",
      "Epoch 20/100\n",
      "821/821 [==============================] - 742s 903ms/sample - loss: 0.0963 - acc: 0.9610 - val_loss: 0.8099 - val_acc: 0.8689\n",
      "Epoch 21/100\n",
      "821/821 [==============================] - 735s 895ms/sample - loss: 0.0710 - acc: 0.9744 - val_loss: 1.0639 - val_acc: 0.8786\n",
      "Epoch 22/100\n",
      "821/821 [==============================] - 730s 890ms/sample - loss: 0.1133 - acc: 0.9708 - val_loss: 1.2401 - val_acc: 0.8738\n",
      "Epoch 23/100\n",
      "821/821 [==============================] - 730s 889ms/sample - loss: 0.0934 - acc: 0.9683 - val_loss: 1.1552 - val_acc: 0.8592\n",
      "Epoch 24/100\n",
      "821/821 [==============================] - 726s 884ms/sample - loss: 0.0954 - acc: 0.9659 - val_loss: 0.8316 - val_acc: 0.8738\n",
      "Epoch 25/100\n",
      "821/821 [==============================] - 751s 915ms/sample - loss: 0.0695 - acc: 0.9708 - val_loss: 1.6697 - val_acc: 0.8932\n",
      "Epoch 26/100\n",
      "821/821 [==============================] - 762s 929ms/sample - loss: 0.0758 - acc: 0.9732 - val_loss: 1.1815 - val_acc: 0.8738\n",
      "Epoch 27/100\n",
      "821/821 [==============================] - 749s 913ms/sample - loss: 0.0608 - acc: 0.9793 - val_loss: 1.2495 - val_acc: 0.8738\n",
      "Epoch 28/100\n",
      "821/821 [==============================] - 737s 898ms/sample - loss: 0.0780 - acc: 0.9781 - val_loss: 1.1473 - val_acc: 0.8932\n",
      "Epoch 29/100\n",
      "821/821 [==============================] - 791s 964ms/sample - loss: 0.1079 - acc: 0.9659 - val_loss: 1.0762 - val_acc: 0.8252\n",
      "Epoch 30/100\n",
      "821/821 [==============================] - 732s 891ms/sample - loss: 0.1467 - acc: 0.9647 - val_loss: 1.0865 - val_acc: 0.8544\n",
      "Epoch 31/100\n",
      "821/821 [==============================] - 703s 856ms/sample - loss: 0.1477 - acc: 0.9671 - val_loss: 0.7717 - val_acc: 0.8689\n",
      "Epoch 32/100\n",
      "821/821 [==============================] - 709s 864ms/sample - loss: 0.0609 - acc: 0.9769 - val_loss: 0.9350 - val_acc: 0.8738\n",
      "Epoch 33/100\n",
      "821/821 [==============================] - 690s 841ms/sample - loss: 0.0445 - acc: 0.9829 - val_loss: 1.0464 - val_acc: 0.8835\n",
      "Epoch 34/100\n",
      "821/821 [==============================] - 692s 843ms/sample - loss: 0.0467 - acc: 0.9829 - val_loss: 1.1093 - val_acc: 0.8883\n",
      "Epoch 35/100\n",
      "821/821 [==============================] - 723s 880ms/sample - loss: 0.0395 - acc: 0.9842 - val_loss: 1.2141 - val_acc: 0.8932\n",
      "Epoch 36/100\n",
      "821/821 [==============================] - 706s 860ms/sample - loss: 0.0397 - acc: 0.9842 - val_loss: 1.2003 - val_acc: 0.8981\n",
      "Epoch 37/100\n",
      "821/821 [==============================] - 725s 883ms/sample - loss: 0.0388 - acc: 0.9842 - val_loss: 1.2370 - val_acc: 0.8981\n",
      "Epoch 38/100\n",
      "821/821 [==============================] - 718s 875ms/sample - loss: 0.0388 - acc: 0.9842 - val_loss: 1.3316 - val_acc: 0.8932\n",
      "Epoch 39/100\n",
      "821/821 [==============================] - 715s 871ms/sample - loss: 0.0390 - acc: 0.9842 - val_loss: 1.3268 - val_acc: 0.8932\n",
      "Epoch 40/100\n",
      "821/821 [==============================] - 670s 816ms/sample - loss: 0.0376 - acc: 0.9842 - val_loss: 1.3133 - val_acc: 0.8981\n",
      "Epoch 41/100\n",
      "821/821 [==============================] - 635s 773ms/sample - loss: 0.0379 - acc: 0.9842 - val_loss: 1.2989 - val_acc: 0.9078\n",
      "Epoch 42/100\n",
      "821/821 [==============================] - 647s 788ms/sample - loss: 0.0416 - acc: 0.9829 - val_loss: 1.4225 - val_acc: 0.8786\n",
      "Epoch 43/100\n",
      "821/821 [==============================] - 652s 794ms/sample - loss: 0.0939 - acc: 0.9659 - val_loss: 1.3368 - val_acc: 0.8932\n",
      "Epoch 44/100\n",
      "821/821 [==============================] - 650s 792ms/sample - loss: 0.0750 - acc: 0.9781 - val_loss: 1.4118 - val_acc: 0.8738\n",
      "Epoch 45/100\n",
      "821/821 [==============================] - 649s 791ms/sample - loss: 0.0675 - acc: 0.9793 - val_loss: 0.9761 - val_acc: 0.8883\n",
      "Epoch 46/100\n",
      "821/821 [==============================] - 655s 798ms/sample - loss: 0.1070 - acc: 0.9720 - val_loss: 0.8965 - val_acc: 0.8786\n",
      "Epoch 47/100\n",
      "821/821 [==============================] - 656s 799ms/sample - loss: 0.0813 - acc: 0.9708 - val_loss: 1.0189 - val_acc: 0.8883\n",
      "Epoch 48/100\n",
      "821/821 [==============================] - 666s 811ms/sample - loss: 0.0541 - acc: 0.9805 - val_loss: 0.9847 - val_acc: 0.8981\n",
      "Epoch 49/100\n",
      "821/821 [==============================] - 673s 819ms/sample - loss: 0.0448 - acc: 0.9829 - val_loss: 0.9443 - val_acc: 0.8738\n",
      "Epoch 50/100\n",
      "821/821 [==============================] - 671s 817ms/sample - loss: 0.0420 - acc: 0.9842 - val_loss: 1.0752 - val_acc: 0.8786\n",
      "Epoch 51/100\n",
      "821/821 [==============================] - 650s 792ms/sample - loss: 0.0431 - acc: 0.9817 - val_loss: 1.5682 - val_acc: 0.8786\n",
      "Epoch 52/100\n",
      "821/821 [==============================] - 642s 782ms/sample - loss: 0.0397 - acc: 0.9842 - val_loss: 1.6450 - val_acc: 0.8883\n",
      "Epoch 53/100\n",
      "821/821 [==============================] - 653s 796ms/sample - loss: 0.0386 - acc: 0.9842 - val_loss: 1.7375 - val_acc: 0.8786\n",
      "Epoch 54/100\n",
      "821/821 [==============================] - 662s 806ms/sample - loss: 0.2123 - acc: 0.9781 - val_loss: 1.6508 - val_acc: 0.8738\n",
      "Epoch 55/100\n",
      "821/821 [==============================] - 664s 809ms/sample - loss: 0.1211 - acc: 0.9574 - val_loss: 1.0473 - val_acc: 0.8786\n",
      "Epoch 56/100\n",
      "821/821 [==============================] - 652s 795ms/sample - loss: 0.1023 - acc: 0.9647 - val_loss: 1.3874 - val_acc: 0.8689\n",
      "Epoch 57/100\n",
      "821/821 [==============================] - 652s 795ms/sample - loss: 0.1094 - acc: 0.9598 - val_loss: 0.8109 - val_acc: 0.8981\n",
      "Epoch 58/100\n",
      "821/821 [==============================] - 650s 791ms/sample - loss: 0.0675 - acc: 0.9732 - val_loss: 1.3901 - val_acc: 0.8786\n",
      "Epoch 59/100\n",
      "821/821 [==============================] - 658s 801ms/sample - loss: 0.0491 - acc: 0.9793 - val_loss: 1.6248 - val_acc: 0.9126\n",
      "Epoch 60/100\n",
      "821/821 [==============================] - 665s 810ms/sample - loss: 0.0432 - acc: 0.9829 - val_loss: 1.7745 - val_acc: 0.8883\n",
      "Epoch 61/100\n",
      "821/821 [==============================] - 586s 714ms/sample - loss: 0.0434 - acc: 0.9829 - val_loss: 1.6437 - val_acc: 0.8932\n",
      "Epoch 62/100\n",
      "821/821 [==============================] - 588s 717ms/sample - loss: 0.0428 - acc: 0.9829 - val_loss: 1.6774 - val_acc: 0.9126\n",
      "Epoch 63/100\n",
      "821/821 [==============================] - 591s 720ms/sample - loss: 0.0420 - acc: 0.9829 - val_loss: 1.5936 - val_acc: 0.8932\n",
      "Epoch 64/100\n",
      "821/821 [==============================] - 587s 715ms/sample - loss: 0.0416 - acc: 0.9829 - val_loss: 1.6544 - val_acc: 0.8932\n",
      "Epoch 65/100\n",
      "821/821 [==============================] - 586s 714ms/sample - loss: 0.0434 - acc: 0.9829 - val_loss: 1.6866 - val_acc: 0.8932\n",
      "Epoch 66/100\n",
      "821/821 [==============================] - 572s 697ms/sample - loss: 0.0403 - acc: 0.9829 - val_loss: 1.7107 - val_acc: 0.8932\n",
      "Epoch 67/100\n",
      "821/821 [==============================] - 573s 697ms/sample - loss: 0.0415 - acc: 0.9829 - val_loss: 1.7259 - val_acc: 0.8932\n",
      "Epoch 68/100\n",
      "821/821 [==============================] - 573s 698ms/sample - loss: 0.0415 - acc: 0.9829 - val_loss: 1.7287 - val_acc: 0.8932\n",
      "Epoch 69/100\n",
      "821/821 [==============================] - 580s 706ms/sample - loss: 0.0413 - acc: 0.9829 - val_loss: 1.7352 - val_acc: 0.8932\n",
      "Epoch 70/100\n",
      "821/821 [==============================] - 578s 704ms/sample - loss: 0.0419 - acc: 0.9829 - val_loss: 1.7348 - val_acc: 0.8932\n",
      "Epoch 71/100\n",
      "821/821 [==============================] - 573s 698ms/sample - loss: 0.0417 - acc: 0.9829 - val_loss: 1.7499 - val_acc: 0.8932\n",
      "Epoch 72/100\n",
      "821/821 [==============================] - 559s 680ms/sample - loss: 0.0421 - acc: 0.9829 - val_loss: 1.7602 - val_acc: 0.8981\n",
      "Epoch 73/100\n",
      "821/821 [==============================] - 548s 668ms/sample - loss: 0.0410 - acc: 0.9829 - val_loss: 1.7693 - val_acc: 0.8932\n",
      "Epoch 74/100\n",
      "821/821 [==============================] - 551s 671ms/sample - loss: 0.0419 - acc: 0.9829 - val_loss: 1.7947 - val_acc: 0.8981\n",
      "Epoch 75/100\n",
      "821/821 [==============================] - 580s 707ms/sample - loss: 0.0409 - acc: 0.9829 - val_loss: 1.8313 - val_acc: 0.9029\n",
      "Epoch 76/100\n",
      "821/821 [==============================] - 567s 690ms/sample - loss: 0.0412 - acc: 0.9829 - val_loss: 1.8346 - val_acc: 0.9029\n",
      "Epoch 77/100\n",
      "821/821 [==============================] - 574s 699ms/sample - loss: 0.0413 - acc: 0.9829 - val_loss: 1.8548 - val_acc: 0.9029\n",
      "Epoch 78/100\n",
      "821/821 [==============================] - 567s 690ms/sample - loss: 0.0416 - acc: 0.9829 - val_loss: 1.8496 - val_acc: 0.9029\n",
      "Epoch 79/100\n",
      "821/821 [==============================] - 568s 692ms/sample - loss: 0.0409 - acc: 0.9829 - val_loss: 1.8596 - val_acc: 0.9029\n",
      "Epoch 80/100\n",
      "821/821 [==============================] - 572s 697ms/sample - loss: 0.0421 - acc: 0.9829 - val_loss: 1.8751 - val_acc: 0.9029\n",
      "Epoch 81/100\n",
      "821/821 [==============================] - 578s 704ms/sample - loss: 0.0409 - acc: 0.9829 - val_loss: 1.8820 - val_acc: 0.9029\n",
      "Epoch 82/100\n",
      "821/821 [==============================] - 579s 705ms/sample - loss: 0.0417 - acc: 0.9829 - val_loss: 1.8941 - val_acc: 0.9029\n",
      "Epoch 83/100\n",
      "821/821 [==============================] - 569s 694ms/sample - loss: 0.0405 - acc: 0.9829 - val_loss: 1.9208 - val_acc: 0.9029\n",
      "Epoch 84/100\n",
      "821/821 [==============================] - 569s 693ms/sample - loss: 0.0406 - acc: 0.9829 - val_loss: 1.9453 - val_acc: 0.9078\n",
      "Epoch 85/100\n",
      "821/821 [==============================] - 570s 694ms/sample - loss: 0.0406 - acc: 0.9842 - val_loss: 1.9595 - val_acc: 0.9078\n",
      "Epoch 86/100\n",
      "821/821 [==============================] - 568s 692ms/sample - loss: 0.0407 - acc: 0.9842 - val_loss: 1.9727 - val_acc: 0.8981\n",
      "Epoch 87/100\n",
      "821/821 [==============================] - 572s 696ms/sample - loss: 0.0405 - acc: 0.9842 - val_loss: 2.0018 - val_acc: 0.8981\n",
      "Epoch 88/100\n",
      "821/821 [==============================] - 572s 696ms/sample - loss: 0.0404 - acc: 0.9842 - val_loss: 2.0089 - val_acc: 0.8981\n",
      "Epoch 89/100\n",
      "821/821 [==============================] - 585s 713ms/sample - loss: 0.0406 - acc: 0.9842 - val_loss: 2.0236 - val_acc: 0.8981\n",
      "Epoch 90/100\n",
      "821/821 [==============================] - 586s 713ms/sample - loss: 0.0397 - acc: 0.9842 - val_loss: 2.0417 - val_acc: 0.8981\n",
      "Epoch 91/100\n",
      "821/821 [==============================] - 580s 707ms/sample - loss: 0.0393 - acc: 0.9842 - val_loss: 2.0523 - val_acc: 0.8981\n",
      "Epoch 92/100\n",
      "821/821 [==============================] - 575s 701ms/sample - loss: 0.0404 - acc: 0.9842 - val_loss: 2.0653 - val_acc: 0.8981\n",
      "Epoch 93/100\n",
      "821/821 [==============================] - 574s 699ms/sample - loss: 0.0391 - acc: 0.9842 - val_loss: 2.0445 - val_acc: 0.8981\n",
      "Epoch 94/100\n",
      "821/821 [==============================] - 569s 693ms/sample - loss: 0.0396 - acc: 0.9842 - val_loss: 2.0582 - val_acc: 0.8981\n",
      "Epoch 95/100\n",
      "821/821 [==============================] - 553s 673ms/sample - loss: 0.0399 - acc: 0.9842 - val_loss: 2.0615 - val_acc: 0.8981\n",
      "Epoch 96/100\n",
      "821/821 [==============================] - 551s 671ms/sample - loss: 0.0392 - acc: 0.9842 - val_loss: 2.0794 - val_acc: 0.8981\n",
      "Epoch 97/100\n",
      "821/821 [==============================] - 559s 681ms/sample - loss: 0.0393 - acc: 0.9842 - val_loss: 2.0993 - val_acc: 0.8981\n",
      "Epoch 98/100\n",
      "821/821 [==============================] - 566s 689ms/sample - loss: 0.0394 - acc: 0.9842 - val_loss: 2.1107 - val_acc: 0.8981\n",
      "Epoch 99/100\n",
      "821/821 [==============================] - 556s 677ms/sample - loss: 0.0388 - acc: 0.9842 - val_loss: 2.1255 - val_acc: 0.8981\n",
      "Epoch 100/100\n",
      "821/821 [==============================] - 559s 681ms/sample - loss: 0.0400 - acc: 0.9842 - val_loss: 2.1356 - val_acc: 0.8981\n",
      "206/206 [==============================] - 9s 42ms/sample - loss: 2.1356 - acc: 0.8981\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow.compat.v1 import ConfigProto\n",
    "from tensorflow.compat.v1 import InteractiveSession\n",
    "from sklearn.metrics import classification_report\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "config = ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "sess = InteractiveSession(config=config)\n",
    "print(tf.__version__)\n",
    "Xtrain = Xtrain.reshape(821, n, n, 1)\n",
    "#Xtrain = Xtrain / 255.0\n",
    "Xtext = Xtext.reshape(206, n, n, 1)\n",
    "#Xtext = Xtext / 255.0\n",
    "#X_test1 = X_test1.reshape(628, 2170, 2036, 1)\n",
    "#X_test1 = X_test1 / 255.0\n",
    "#mnist = tf.keras.datasets.fashion_mnist\n",
    "#(training_images, training_labels), (test_images, test_labels) = mnist.load_data()\n",
    "#training_images=training_images.reshape(60000, 28, 28, 1)\n",
    "#training_images=training_images / 255.0\n",
    "#test_images = test_images.reshape(10000, 28, 28, 1)\n",
    "#test_images=test_images/255.0\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Conv2D(48, (11,11), activation='relu', input_shape=(n, n, 1)),\n",
    "  tf.keras.layers.MaxPooling2D(2, 2),\n",
    "  tf.keras.layers.Conv2D(128, (5,5), activation='relu'),\n",
    "  tf.keras.layers.MaxPooling2D(2, 2),\n",
    "  tf.keras.layers.Conv2D(192, (3,3), activation='relu'),\n",
    "  tf.keras.layers.Conv2D(192, (3,3), activation='relu'),\n",
    "  tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
    "  tf.keras.layers.MaxPooling2D(2,2),\n",
    "  #tf.keras.layers.BatchNormalization(),\n",
    "  #tf.keras.layers.BatchNormalization(),\n",
    "  tf.keras.layers.Flatten(),\n",
    "  tf.keras.layers.Dense(2048, activation='relu'),\n",
    "  tf.keras.layers.Dropout(0.25),\n",
    "  tf.keras.layers.Dense(2048, activation='relu'),\n",
    "  tf.keras.layers.Dropout(0.25),\n",
    "  tf.keras.layers.Dense(2, activation='softmax')\n",
    "])\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "model.summary()\n",
    "model.fit(Xtrain, Ytrain, batch_size=50, epochs=100,validation_data=(Xtext, Ytext))\n",
    "test_loss = model.evaluate(Xtext, Ytext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.94      0.92       128\n",
      "           1       0.89      0.83      0.86        78\n",
      "\n",
      "    accuracy                           0.90       206\n",
      "   macro avg       0.90      0.89      0.89       206\n",
      "weighted avg       0.90      0.90      0.90       206\n",
      "\n",
      "[[ 0.00000000e+00 -2.07423163e-03]\n",
      " [ 0.00000000e+00 -8.03434197e-03]\n",
      " [ 0.00000000e+00 -6.99069351e-03]\n",
      " [ 0.00000000e+00 -1.07003271e-03]\n",
      " [ 0.00000000e+00 -5.37602277e-03]\n",
      " [ 1.00000000e+00  9.90709424e-01]\n",
      " [ 1.00000000e+00  9.92357314e-01]\n",
      " [ 0.00000000e+00  9.96836364e-01]\n",
      " [ 0.00000000e+00 -5.36956266e-03]\n",
      " [ 0.00000000e+00 -3.44338967e-03]\n",
      " [ 0.00000000e+00 -3.10262176e-03]\n",
      " [ 0.00000000e+00  1.82748854e-01]\n",
      " [ 1.00000000e+00  9.99130905e-01]\n",
      " [ 1.00000000e+00  9.91822541e-01]\n",
      " [ 1.00000000e+00 -7.46230921e-03]\n",
      " [ 1.00000000e+00  9.98602450e-01]\n",
      " [ 1.00000000e+00  9.96187568e-01]\n",
      " [ 0.00000000e+00  2.57301964e-02]\n",
      " [ 0.00000000e+00 -5.31929825e-03]\n",
      " [ 0.00000000e+00 -1.21962430e-03]\n",
      " [ 0.00000000e+00  1.84522018e-01]\n",
      " [ 0.00000000e+00  1.82033345e-01]\n",
      " [ 1.00000000e+00  9.92543340e-01]\n",
      " [ 0.00000000e+00 -4.49214596e-03]\n",
      " [ 0.00000000e+00 -5.43839950e-03]\n",
      " [ 0.00000000e+00 -2.73297937e-03]\n",
      " [ 0.00000000e+00 -9.52212326e-03]\n",
      " [ 1.00000000e+00  9.99782145e-01]\n",
      " [ 1.00000000e+00  9.99712467e-01]\n",
      " [ 1.00000000e+00  9.91884530e-01]\n",
      " [ 1.00000000e+00  9.96029615e-01]\n",
      " [ 0.00000000e+00  9.97100115e-01]\n",
      " [ 1.00000000e+00  9.98966217e-01]\n",
      " [ 0.00000000e+00 -9.66791995e-03]\n",
      " [ 1.00000000e+00  9.95414972e-01]\n",
      " [ 0.00000000e+00 -3.09693930e-03]\n",
      " [ 1.00000000e+00  9.99321580e-01]\n",
      " [ 0.00000000e+00  1.03881113e-01]\n",
      " [ 1.00000000e+00  9.27620605e-02]\n",
      " [ 0.00000000e+00 -7.67668290e-03]\n",
      " [ 0.00000000e+00 -7.21452432e-03]\n",
      " [ 0.00000000e+00 -1.79199874e-03]\n",
      " [ 1.00000000e+00  9.94121552e-01]\n",
      " [ 1.00000000e+00  9.92193639e-01]\n",
      " [ 0.00000000e+00  1.80590168e-01]\n",
      " [ 1.00000000e+00  9.97207046e-01]\n",
      " [ 0.00000000e+00  1.82058886e-01]\n",
      " [ 1.00000000e+00  9.97317314e-01]\n",
      " [ 1.00000000e+00  9.97662902e-01]\n",
      " [ 0.00000000e+00 -1.51152676e-03]\n",
      " [ 0.00000000e+00  9.42019999e-01]\n",
      " [ 0.00000000e+00 -2.91287492e-04]\n",
      " [ 0.00000000e+00 -4.88695735e-03]\n",
      " [ 0.00000000e+00 -9.91829671e-03]\n",
      " [ 1.00000000e+00 -7.87798315e-03]\n",
      " [ 0.00000000e+00 -4.50816331e-03]\n",
      " [ 1.00000000e+00  9.92781520e-01]\n",
      " [ 0.00000000e+00  1.03720963e-01]\n",
      " [ 0.00000000e+00 -2.45609903e-03]\n",
      " [ 1.00000000e+00  9.92623568e-01]\n",
      " [ 0.00000000e+00 -1.69039262e-03]\n",
      " [ 0.00000000e+00 -8.58870894e-03]\n",
      " [ 0.00000000e+00 -8.51137005e-03]\n",
      " [ 0.00000000e+00 -1.87031669e-03]\n",
      " [ 0.00000000e+00 -9.26194806e-03]\n",
      " [ 0.00000000e+00 -9.21701454e-03]\n",
      " [ 0.00000000e+00 -3.47248395e-03]\n",
      " [ 0.00000000e+00 -2.61989329e-03]\n",
      " [ 0.00000000e+00  1.84174672e-01]\n",
      " [ 0.00000000e+00 -1.30136241e-03]\n",
      " [ 1.00000000e+00  9.97255623e-01]\n",
      " [ 0.00000000e+00  9.88542438e-01]\n",
      " [ 0.00000000e+00 -7.95282237e-03]\n",
      " [ 1.00000000e+00  9.91356611e-01]\n",
      " [ 1.00000000e+00 -8.67738202e-03]\n",
      " [ 0.00000000e+00  1.82986856e-01]\n",
      " [ 1.00000000e+00  9.99013662e-01]\n",
      " [ 1.00000000e+00  9.99542415e-01]\n",
      " [ 0.00000000e+00 -7.64233666e-03]\n",
      " [ 0.00000000e+00 -8.13875906e-03]\n",
      " [ 1.00000000e+00  9.91295040e-01]\n",
      " [ 0.00000000e+00 -1.40342780e-03]\n",
      " [ 1.00000000e+00 -5.12428395e-03]\n",
      " [ 1.00000000e+00  9.97138262e-01]\n",
      " [ 0.00000000e+00 -4.64119343e-03]\n",
      " [ 0.00000000e+00  9.92065787e-01]\n",
      " [ 1.00000000e+00 -4.96953144e-04]\n",
      " [ 0.00000000e+00 -3.25497729e-03]\n",
      " [ 0.00000000e+00  1.83944672e-01]\n",
      " [ 1.00000000e+00  9.97244775e-01]\n",
      " [ 1.00000000e+00  9.99028444e-01]\n",
      " [ 0.00000000e+00 -7.33675435e-03]\n",
      " [ 1.00000000e+00  9.99163270e-01]\n",
      " [ 0.00000000e+00 -6.10548188e-04]\n",
      " [ 0.00000000e+00 -3.24289873e-03]\n",
      " [ 0.00000000e+00 -7.35238893e-03]\n",
      " [ 1.00000000e+00  9.92535830e-01]\n",
      " [ 0.00000000e+00  1.88620836e-01]\n",
      " [ 1.00000000e+00  9.99953985e-01]\n",
      " [ 1.00000000e+00  9.98275340e-01]\n",
      " [ 1.00000000e+00  1.87427610e-01]\n",
      " [ 1.00000000e+00  9.90952849e-01]\n",
      " [ 0.00000000e+00 -8.13632179e-03]\n",
      " [ 0.00000000e+00 -4.20203712e-03]\n",
      " [ 1.00000000e+00  9.95266080e-01]\n",
      " [ 1.00000000e+00  9.97205198e-01]\n",
      " [ 1.00000000e+00  9.90452290e-01]\n",
      " [ 0.00000000e+00 -3.68866976e-03]\n",
      " [ 0.00000000e+00  1.88071162e-01]\n",
      " [ 1.00000000e+00  9.97549474e-01]\n",
      " [ 0.00000000e+00 -9.41698626e-03]\n",
      " [ 1.00000000e+00  9.97654974e-01]\n",
      " [ 1.00000000e+00 -1.46685122e-03]\n",
      " [ 1.00000000e+00  9.96701658e-01]\n",
      " [ 0.00000000e+00 -8.11696146e-03]\n",
      " [ 0.00000000e+00 -5.37142111e-03]\n",
      " [ 1.00000000e+00  1.84004411e-01]\n",
      " [ 0.00000000e+00  9.97971773e-01]\n",
      " [ 1.00000000e+00  9.98696089e-01]\n",
      " [ 1.00000000e+00  9.90962207e-01]\n",
      " [ 0.00000000e+00 -1.18942698e-03]\n",
      " [ 0.00000000e+00 -4.96395538e-03]\n",
      " [ 0.00000000e+00  1.82604551e-01]\n",
      " [ 0.00000000e+00 -4.49575426e-04]\n",
      " [ 0.00000000e+00 -2.76937033e-03]\n",
      " [ 0.00000000e+00 -5.30930981e-03]\n",
      " [ 0.00000000e+00 -7.46196276e-03]\n",
      " [ 0.00000000e+00 -5.11298841e-03]\n",
      " [ 0.00000000e+00 -3.73179100e-05]\n",
      " [ 1.00000000e+00  9.91111577e-01]\n",
      " [ 1.00000000e+00  9.94779170e-01]\n",
      " [ 1.00000000e+00  9.97783363e-01]\n",
      " [ 1.00000000e+00  9.90082979e-01]\n",
      " [ 0.00000000e+00 -9.95867793e-03]\n",
      " [ 1.00000000e+00  9.92184758e-01]\n",
      " [ 1.00000000e+00  8.92482474e-02]\n",
      " [ 0.00000000e+00 -3.35464627e-03]\n",
      " [ 0.00000000e+00 -4.80601157e-04]\n",
      " [ 0.00000000e+00  1.83670089e-01]\n",
      " [ 1.00000000e+00  9.96794760e-01]\n",
      " [ 0.00000000e+00 -5.14962338e-03]\n",
      " [ 1.00000000e+00  9.99608934e-01]\n",
      " [ 1.00000000e+00  9.97504532e-01]\n",
      " [ 0.00000000e+00 -1.83028588e-03]\n",
      " [ 0.00000000e+00 -3.43028572e-03]\n",
      " [ 0.00000000e+00 -3.82427848e-03]\n",
      " [ 1.00000000e+00  9.99278307e-01]\n",
      " [ 0.00000000e+00  9.91293907e-01]\n",
      " [ 0.00000000e+00 -1.81029225e-03]\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics \n",
    "from sklearn.metrics import roc_curve, auc  \n",
    "import numpy as np  \n",
    "from numpy import interp\n",
    "y = model.predict(Xtext)\n",
    "print (metrics.classification_report(Ytext,y[:, 1].round()))  \n",
    "import random\n",
    "for i in range(len(y)):\n",
    "    a = random.uniform(0,0.01)\n",
    "    y[i,1] = y[i,1] - a\n",
    "    #print(y[i,1])\n",
    "mean_tpr = 0.0  \n",
    "mean_fpr = np.linspace(0, 1, 100)  \n",
    "all_tpr = [] \n",
    "probas_ = y \n",
    "fpr, tpr, thresholds = roc_curve(Ytext, probas_[:, 1])  \n",
    "copy = np.random.uniform(1,100,(len(Ytext),2)) \n",
    "for i in range(len(y)):\n",
    "    copy[i,0] = Ytext[i]\n",
    "    copy[i,1] =probas_[i,1]\n",
    "print(copy)\n",
    "mean_tpr += interp(mean_fpr, fpr, tpr) #对mean_tpr在mean_fpr处进行插值，通过scipy包调用interp()函数  \n",
    "mean_tpr[0] = 0.0  #初始处为0  \n",
    "roc_auc = auc(fpr, tpr)\n",
    "plt.plot(fpr, tpr, lw=1, label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc)) \n",
    "plt.xlim([-0.05, 1.05])  \n",
    "plt.ylim([-0.05, 1.05])  \n",
    "plt.xlabel('False Positive Rate')  \n",
    "plt.ylabel('True Positive Rate')  \n",
    "plt.title('SVM')  \n",
    "plt.legend(loc=\"lower right\")  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "name = ['True','pre']\n",
    "test = pd.DataFrame(columns=name,data=copy)\n",
    "test.to_csv('Alexnet.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}