cellAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实验1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(29700,)\n",
      "(300,)\n"
     ]
    }
   ],
   "source": [
    "train_1=np.load(\"/home/mzhang/dcn/record/1_train_loss.npy\")\n",
    "valid_1=np.load(\"/home/mzhang/dcn/record/1_valid_loss.npy\")\n",
    "print(train_1.shape)\n",
    "print(valid_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VZrYymDbGzJ42s03Bn6NTlr87aNMGM/t4yvSLg/VsNrP7LPWBv+HV/pCZ7TGz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\nEZEkhYKIiCQpFEREJOn/AxDHOX9uN8iDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbcb884d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch=range(1,29701,1)\n",
    "plt.plot(epoch,train_1,label=\"train_1\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cell--DCN--全用--256--2scope--4层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26557,)\n",
      "(269,)\n"
     ]
    }
   ],
   "source": [
    "train_dcn_2=np.load(\"/home/mzhang/dcn/record/DCN_256_4layer_train_loss.npy\")\n",
    "valid_dcn_2=np.load(\"/home/mzhang/dcn/record/DCN_256_4layer_valid_loss.npy\")\n",
    "print(train_dcn_2.shape)\n",
    "print(valid_dcn_2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d3mOB7SmYWQRYA5wJ1ACLgYvc/c2Edc4Bvk00FD4J3OTun+xsu9pTEBHpulT3\nFIJ89tEkoNrd17l7AzAfmN5mnenAXR61CCgxs8y8bEFEJAcEGQojgY0J8zWxtq6uIyIiaZIVT0k1\ns5lmVmVmVbW1XbthS0REUhdkKGwCRifMj4q1dXUd3H2eu1e6e2VZWVmvFyoiIlFBhsJi4FgzqzCz\nQmAG8EibdR4BvmZRk4Ed7v5+gDWJiEgnArsk1d2bzOxqYCHRS1LvcPeVZjYrtnwusIDolUfVRC9J\nvTSoekRE5OACvU/B3RcQ/eJPbJubMO3AVUHWICIiqcuKE80iIpIeWfeYCzOrBd7t5suHA8lvUe1b\ncqGf6mPfkQv9zIQ+HunuB71SJ+tCoSfMrCqVO/qyXS70U33sO3Khn9nURx0+EhGROIWCiIjE5Voo\nzAu7gDTJhX6qj31HLvQza/qYU+cURESkc7m2pyAiIp3ImVAws6lmttrMqs1sdtj1dJWZrTez181s\nmZlVxdqGmdnfzGxt7N+hCetfF+vrajM7O6H95Nh2qs3sZktl4NyAmNkdZrbFzN5IaOu1PplZkZnd\nF2t/xczK09m/hPqS9fNnZrYp9nkui40tcmBZVvXTzEab2TNm9qaZrTSza2Ltfeqz7KSffeazBKID\nU/f1H6KP2XgbOAooBJYDY8Ouq4t9WA8Mb9P2a2B2bHo28O+x6bGxPhYBFbG+R2LLXgUmAwY8BkwL\nsU+nAx8H3giiT8CVwNzY9Azgvgzq58+AHyRZN+v6CRwGfDw2PYjo4Fpj+9pn2Uk/+8xn6e45s6eQ\nyoA/2Wg68MfY9B+B8xLa57v7fnd/h+izpSZZdACjwe6+yKP/6+5KeE3aufvzwNY2zb3Zp8Rt/Rn4\nbBh7Rh30syNZ1093f99jw+i6+y7gLaLjovSpz7KTfnYkK/uZK6HQFwbzceBJM1tiZjNjbSP8o6fK\nfgCMiE131N+Rsem27ZmkN/sUf427NwE7gNJgyu6Wb5vZitjhpQOHVrK6n7HDHScBr9CHP8s2/YQ+\n9FnmSij0Bae5+0RgGnCVmZ2euDD2F0efupSsL/Ypwe+JHs6cCLwP/CbccnrOzAYCDwDfdfedicv6\n0meZpJ996rPMlVBIaTCfTObum2L/bgEeInpIbHNsV5TYv1tiq3fU302x6bbtmaQ3+xR/jZnlA0OA\nusAq7wJ33+zuze7eAvwn0c8TsrSfZlZA9IvyHnd/MNbc5z7LZP3sa59lroRCKgP+ZCwzG2Bmgw5M\nA2cBbxBBslhiAAABCUlEQVTtw9djq30deDg2/QgwI3YlQwVwLPBqbFd+p5lNjh2n/FrCazJFb/Yp\ncVsXAE/H/mIN3YEvy5gvEv08IQv7GavnduAtd/+/CYv61GfZUT/70mcJ5MbVR7Hf6TlErxZ4G/hx\n2PV0sfajiF7FsBxYeaB+oscanwLWAk8CwxJe8+NYX1eTcIURUEn0P+3bwO+I3cAYUr/uJbq73Uj0\nuOo3e7NPQD/gfqIn+F4Fjsqgft4NvA6sIPpFcFi29hM4jeihoRXAstjPOX3ts+ykn33ms3R33dEs\nIiIfyZXDRyIikgKFgoiIxCkUREQkTqEgIiJxCgUREYlTKIiISJxCQURE4hQKIiIS9z+CemEYNvNN\nQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6eff674b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch=range(1,26558,1)\n",
    "plt.plot(epoch,train_dcn_2,label=\"train_dcn_2\")\n",
    "#plt.plot(epoch,train_dcn_4,label=\"train_dcn_4\")\n",
    "#plt.plot(epoch,train_unet[0:9020],label=\"train_unet\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ITonjypk5pCUGCAYdb79fR1FGItWN7VTWt/HR2TmYGc45KhvaOFjTwtyCVFLj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\nRq0osbAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6f01e5e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch=range(1,26901,100)\n",
    "plt.plot(epoch,valid_dcn_2,label=\"valid_dcn_2\")\n",
    "#plt.plot(epoch,valid_dcn_4,label=\"valid_dcn_4\")\n",
    "#plt.plot(epoch,valid_unet[0:92],label=\"valid_unet\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cell--DCN--2scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12376,)\n",
      "(125,)\n",
      "(9020,)\n",
      "(92,)\n",
      "(19800,)\n",
      "(200,)\n"
     ]
    }
   ],
   "source": [
    "train_dcn_2=np.load(\"/home/mzhang/dcn_2/record/DCN_all_train_loss.npy\")\n",
    "valid_dcn_2=np.load(\"/home/mzhang/dcn_2/record/DCN_all_valid_loss.npy\")\n",
    "print(train_dcn_2.shape)\n",
    "print(valid_dcn_2.shape)\n",
    "train_dcn_4=np.load(\"/home/mzhang/dcn_3/record/DCN_all_4scope_train_loss.npy\")\n",
    "valid_dcn_4=np.load(\"/home/mzhang/dcn_3/record/DCN_all_4scope_valid_loss.npy\")\n",
    "print(train_dcn_4.shape)\n",
    "print(valid_dcn_4.shape)\n",
    "train_unet=np.load(\"/home/mzhang/dcn/record/UNet_128_train_loss.npy\")\n",
    "valid_unet=np.load(\"/home/mzhang/dcn/record/UNet_128_valid_loss.npy\")\n",
    "print(train_unet.shape)\n",
    "print(valid_unet.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QVGYbY4wBC4VaJzI8jF9f1IPzct4krtBDq8JCbi/jyed9J/bR440efLz94yBV\naYyprSwUaql1j15eNH3LsQx+nXGcmQd+KnWdP/73j7y/9X0bsMcYE5BoOR6MqkkSExM1Odm1/ead\n7vhBePnnkHH6KaQeCR3KXPXj0R8T3zjeocKMMTWNiKwsT/9ydqRQmzVsBTd+yDeFJYe+LtsVH1xB\njzd6kFdYem+sxhh3sVCo7Vp0ZN+V77Aofupps4dmZrGmHA+79X27Lz3e6MH29O1OVWiMqUXs9FFd\n8v3LHEzPpPk3fyIcb+JniTCifRuOhIeXufr9ifdzY7cbHS/TGBN8dvrIjfrdSqtL7ybyocOsG/s9\nT+dfQz1VlpTjtlWAp5KfYvORzQ4XaYypySwU6qLwSC44vzPnjfkzD+bfQiSwdHcKwzKziCjjyHDM\nx2Po8UYPhs0fRmZ+ZnDqNcbUGBYKddioXm24+raHAWju8TD90GGW7tlHt9yyx3c+nH2YIfOGsCxl\nmdNlGmNqEAuFOkxE6NOhKcenHi6a19jjYd7+n3hz/8Ey1y/wFDD588nM3zyf6aumsyFtA6rKspRl\nFHgKnCzdGBMijl5oFpEk4FkgHHhFVZ8osfx8YBbQB3hQVZ8ua5t2obmSTqSyd/c22v/rVJcX6WFh\nvNKkEW80blTuzQw/ezj/3v1vpvSawphOY2gW0wwRcaJiY0w1Ku+FZsdCQUTCgS3AcCAFWAFcp6ob\nirU5CzgbGA0ctVAIgrwsiIhhw1f/outXEwH4OjaGaFWWxcYyq0n5AwLg3r73Mr77eNanradrs64W\nEMbUUDXh7qN+wDZV3aGqecA8YFTxBqp6SFVXADaOZLBE1YOwMKK6jSQ+Zzb/kz+ewdk5JObkcmt6\nxbu/mLZyGp/v/pyx/zeWD7d/6EDBxphgcvJIYQyQpKoTfO/HAf1VdYqfto8AJ+xIIbg2HzzOeWc1\nIFwgY9EjNPr+GQC2REayIyqSc/Lyubpd6wpv9+GBD3NR24toVb9VdZdsjKmkmnCkUG1EZKKIJItI\ncmpq2SOPmfLp3Koh4WECIjQa+WeSx23mnw0m0yk/n6TMLDrl57Nm5x4GZmdXaLuPfvsow98dbhej\njamFnAyFfUD7Yu/b+eZVmKrOVNVEVU2Mi4urluLMmRLPbcXt9/0Vz5g3iuaFAS8dTGVBygEeOlyx\n4T17v9WbwXMHczyv7FHijDE1Q4SD214BdBSRBLxhMBb4jYOfZ6pJWPfREDEHohvCG1cgQKf8fDrl\n53NpZha2qzlKAAALkUlEQVSx6iE1PJxHWzTj29jYUreVkZfBoLmDSGyZyIP9H6RNgzbUi6wXnC9i\njKkwp29JHQk8g/eW1NdU9S8iMglAVWeISCsgGWgEeIATQFdVzQi0TbumEGRHdsL+H2DDh+xc/x0J\ncuC0xaujoxjXpuLXDlrXb81fhvyFC1tdWF2VGmNKEfJbUp1ioRBahS8MJDx1wxnz90eE83qjRsxt\n3LDC21x01SLqR9anQWQDIsMjq6NMY0wJFgrGGVlHYNlT0O1X/PDyHfQO23pGk69jY7j7rBbkhFX8\nklXfln154ZIXiAqPIjLMAsKY6mKhYJyXlwnr34c9y+GHt05bpHjPB4YD957Vgp9nZvGHs1pU+CPe\nufwd2jZoS2xELFHhUdVStjFuZKFgguu7l2DR7zl+726+mn4rlxd85rfZA3HNWdigfpU+6raetzGl\n9xmPuxhjSmGhYELvkcYBF6WHhXF9m5Z0y81jZUw0hyIqdiPc0muX0jy2eVUrNMY1LBRMjZP3/hSi\n1rzld9kJEQbGt/e7zJ/IsEhu63gNreO6ceW5V1ZXicbUWRYKpuYpyIOVr0PCRWw/BufOHnDa4u2R\nETQu9BCJki9Crgj3x7VgXUx0mZtuEtaZD66aRYOYKKLDy25vjNtYKJjaobAA9nzDTx8+TMv0H/w2\nyRIhWpUTYWEMObtduTY7oOXFTP/FUxzLPWZ9MBmDhYKpjfIyYf0HkLICz/GDZA+8h/pvXHpak0Kg\nV0KHSm1+UJtBvDT8pUqtq6rke/LtDihTa1komLqhsICsH+bjaX4eUR9MJOrYTr6oF8tdLavQB5YK\n3aMHc0WnWxjds9sZ3W4UepQDx7Jp1/TU/GkrpzHrx1msvGGlBYOplSwUTN3l8ZCfsY8TH/2OjZEX\nsOzQa8xuUvX+lM7TOPL3n8/BKA9HM7vTgE4su38YYWEw6J0+AEztN5XrWyTCtiXQfxKEhVf5c40J\nBgsF4z4nUnnt27/xv/v/zV9TD/PHuIo/LFcebfIL+HvqYbrk5hEF/L3Znxk/sANxYRnQoBV0Tipz\nG8YEm4WCcb1j2UfIOLyR9s27cOSjOzm4YzG/blvxQYPK4/X9P9E3NxeAgglfEtGqO0TYaSZTc1go\nGBPIqjcpzEon7ewRfH/wK9784Sk2Rlf/D3irggIuzspme1gLEpoN4I7GbWjRqR9EN4a2fUjPKSQ2\nKpzoCDsFZZxnoWBMJczfMIfEowfYnbGbY3u/5aHonKJlzQoLORJevT/gfXJy2BAVxesHDjE991qi\n21/OfVf0JyL/GNuz6nNhfDNiopwb9iQrr4CsvEJaNLBnO+o6CwVjqsnKn1ZyQdwFRIR5f5yP5x1n\n7X/+yusnDpIVLuzbs5+0+pUaVLBCrj92nOGZWWSEhzE4K5v0X80lO2MXZ4d7oMFZ0HE4xDat0Dbj\np35SNP3J2GZ0O7EculwJzc+t7vJNiFkoGBMiBZmp5G/5lHfyD/G/G97Eg4IE//+zeh4P/zh0mMaF\nHt5vWJ+mhR5uysggQuFIkz7UO7KZLz29ydB6jI9YHHA7mRf/iVXNRrAmLYKzYgoZ2jKHs8JPQOP2\n9PjocgDmXjaX7i26n1qpsAAkDCrRffoZ8rJg6V+h80gozIMOAyGi5hzZHDiWTcrRbM5uVo+cvT9w\ndPc6LmjfDNr09oZ0vWahLhGwUDCmZirM59jx40TUi2bXho9YdOxH6mem8v6RdRwoPBHq6kp1dYa3\nvrML8pnWLPARSd/sHPrl5HLFiRNsiYqiVUEh3fLyOBYmRChEqlJdV3A8Zw8hbPd/oeMvIX2390d4\nz7dowzbI8f3QoCVE1oORT0P2UWgQB/k5EBkDu76G1j3h+5e9bTJS4Kyu0HscrJkHq9+GuPOhz03e\ndTsM8G6/MA+y0yEimpQVH9Fu02tl1qkSjmghAPnn/IKIhCF4flpPeKvuHNcY6h/8jrDUTTDgdgo7\nDKHA4yH6o9vh8mmQewJ+eBt6XgvxF1U6aC0UjKnl8nMyiAA8UfVZuncp7SLPp354GCuOJbNm33cs\n2PNhqEs0QfbFnhTiHjpaqXUtFIwxABzJOsGutAzOalRAWuYh3tw4G48niiN5R1l1+NtQl2cqaN1N\n6yq1XnlDwbnbGrxFJAHP4h2A6xVVfaLEcvEtHwlkATer6ionazLGbZrVa0Czeg0AaNe4A/9oU+bv\nQpUVegrxqIfM/ExyCnOICY9h+YHl3rG4oxqwLnUdA1oP4WjOMZpGN2ZXxi72Ht+DRyHtxAly9TjL\nUr6ha7MenN3wXJbtX8KhnD20jujH9pzPiYtpy6GcFOrruWTKDppHtSMtdx+Ip6iG+noembLN8e8a\nTHNGznP8Mxw7UhCRcGALMBxIAVYA16nqhmJtRgJ34g2F/sCzqtq/tO3akYIxxlRceY8UquHWgID6\nAdtUdYeq5gHzgFEl2owC3lSv5UATEXHmkVNjjDFlcjIU2gJ7i71P8c2raBtEZKKIJItIcmpqarUX\naowxxsvJUKg2qjpTVRNVNTEurgpdJhtjjCmVk6GwDyg+6G4737yKtjHGGBMkTobCCqCjiCSISBQw\nFvioRJuPgBvFawBwTFUPOFiTMcaYUjh2S6qqFojIFGAx3ltSX1PV9SIyybd8BrAQ751H2/Dekjre\nqXqMMcaUzdHnFFR1Id4f/uLzZhSbVmCykzUYY4wpv1pxodkYY0xw1LpuLkQkFdhdydVbAIersZza\nzvbHKbYvTmf745S6si/OVtUyb9+sdaFQFSKSXJ4n+tzC9scpti9OZ/vjFLftCzt9ZIwxpoiFgjHG\nmCJuC4WZoS6ghrH9cYrti9PZ/jjFVfvCVdcUjDHGlM5tRwrGGGNK4ZpQEJEkEdksIttEZGqo63GC\niLQXkS9FZIOIrBeRu3zzm4nIv0Vkq++fTYut8wffPtksIr8sNr+viKzzLZvuGxCp1hGRcBH5QUT+\nz/fezfuiiYi8KyKbRGSjiAx06/4QkXt8/4/8KCJzRSTGrfviDKpa5194u9nYDpwDRAFrgK6hrsuB\n79ka6OObboh3kKOuwN+Bqb75U4EnfdNdffsiGkjw7aNw37LvgQGAAIuAEaH+fpXcJ/cCc4D/8713\n8754A5jgm44Cmrhxf+Dtnn8nEOt7Px+42Y37wt/LLUcK5Rnwp9ZT1QPqG85UVY8DG/H+DzAK7w8C\nvn+O9k2PAuapaq6q7sTbB1U/30BHjVR1uXr/y3+z2Dq1hoi0Ay4DXik22637ojHwM+BVAFXNU9V0\nXLo/8HbxEysiEUA9YD/u3RencUsolGswn7pEROKB3sB3QEs91fvsQaClbzrQfmnrmy45v7Z5Bvg9\n4Ck2z637IgFIBWb5Tqe9IiL1ceH+UNV9wNPAHuAA3t6ZP8OF+8Ift4SCq4hIA2ABcLeqZhRf5vuL\nps7fciYilwOHVHVloDZu2Rc+EUAf4J+q2hvIxHuKpIhb9ofvWsEovEHZBqgvIjcUb+OWfeGPW0LB\nNYP5iEgk3kCYrarv+Wb/dHLsa98/D/nmB9ov+3zTJefXJoOBK0VkF97ThT8Xkbdx574A71+xKar6\nne/9u3hDwo374xfATlVNVdV84D1gEO7cF2dwSyiUZ8CfWs9358OrwEZVnVZs0UfATb7pm4APi80f\nKyLRIpIAdAS+9x1CZ4jIAN82byy2Tq2gqn9Q1XaqGo/33/cXqnoDLtwXAKp6ENgrIp19sy4BNuDO\n/bEHGCAi9Xzf4RK819/cuC/OFOor3cF64R3MZwveOwceDHU9Dn3HIXgPedcCq32vkUBz4HNgK7AE\naFZsnQd9+2Qzxe6cABKBH33Lnsf3oGNtfAFDOXX3kWv3BdALSPb99/EB0NSt+wP4M7DJ9z3ewntn\nkSv3RcmXPdFsjDGmiFtOHxljjCkHCwVjjDFFLBSMMcYUsVAwxhhTxELBGGNMEQsFY4wxRSwUjDHG\nFLFQMMYYU+T/A37g+bJXsLlYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6f01e67198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch=range(1,9021,1)\n",
    "plt.plot(epoch,train_dcn_2[0:9020],label=\"train_dcn_2\")\n",
    "plt.plot(epoch,train_dcn_4,label=\"train_dcn_4\")\n",
    "plt.plot(epoch,train_unet[0:9020],label=\"train_unet\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0848944\n",
      "0.0865845\n",
      "0.0875991\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(np.mean(train_dcn_2[0:9020]))\n",
    "print(np.mean(train_dcn_4))\n",
    "print(np.mean(train_unet[0:9020]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3r2VN2Zpol6eUigGWhYIxJgDcBswH8oFZxpj1IjJNRKZFmt0HpANPishqEVlh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\ndQz3uSiIS+K9+Hr+E+9s2Dv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6eff739588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch=range(1,9201,100)\n",
    "plt.plot(epoch,valid_dcn_2[0:92],label=\"valid_dcn_2\")\n",
    "plt.plot(epoch,valid_dcn_4,label=\"valid_dcn_4\")\n",
    "plt.plot(epoch,valid_unet[0:92],label=\"valid_unet\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0899267\n",
      "0.0922566\n",
      "0.0928682\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(valid_dcn_2[0:92]))\n",
    "print(np.mean(valid_dcn_4))\n",
    "print(np.mean(valid_unet[0:92]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 下面用来预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "down  0  shape  (5, 64, 64, 32)\n",
      "down  1  shape  (5, 32, 32, 64)\n",
      "down  2  shape  (5, 16, 16, 128)\n",
      "bottom shape (5, 16, 16, 128)\n",
      "up  2  shape  (5, 32, 32, 64)\n",
      "up  1  shape  (5, 64, 64, 32)\n",
      "up  0  shape  (5, 128, 128, 2)\n",
      "---->predicting  10601\n",
      "INFO:tensorflow:Restoring parameters from /home/mzhang/dcn_2/modeldir/model-10601\n",
      "values-----> 0.0189509 0.954896 0.0\n",
      "values-----> 0.0161123 0.938636 0.0\n",
      "values-----> 0.0190951 0.942176 0.0\n",
      "values-----> 0.0158038 0.936329 0.0\n",
      "----->saving predictions\n",
      "(4, 5, 128, 128)\n",
      "predict_loss [0.01749056]\n",
      "predict_accuracy [0.94300896]\n",
      "predict_m_iou [0.0]\n",
      "program total running time 0.7885817833333334\n"
     ]
    }
   ],
   "source": [
    "%run main.py --action=predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from PIL import Image\n",
    "import h5py\n",
    "import numpy as np\n",
    "np.set_printoptions(threshold=np.inf) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIAAAACACAIAAABMXPacAAAARElEQVR4nO3BAQEAAACAkP6v7ggK\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAYwIAAAWMWdQAA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=128x128 at 0x7F0ECE7144E0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 原图\n",
    "path=\"/home/mzhang/dcn_2/samples/0.png\"\n",
    "img=Image.open(path)\n",
    "#img=img.resize([256,256],Image.BILINEAR)\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 128, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img=np.array(img)\n",
    "img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 128, 128)\n"
     ]
    }
   ],
   "source": [
    "pred=np.load(\"/home/mzhang/dcn_2/pred.npy\")\n",
    "print(pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(5):\n",
    "    for j in range(128):\n",
    "        for k in range(128):\n",
    "            if pred[i,j,k]!=0:\n",
    "                print(pred[i,j,k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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