CNN_wavelet_MI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification of motion imaging eeg signals based on wavelet transform and convolution neural network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape: (1152, 3, 280)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "MI_Wavelet_CNN\n",
    "@zdl\n",
    "reference paper:https://ieeexplore.ieee.org/document/8585027\n",
    "DataSet BCI Competition III dataSet II \n",
    "MI task,binary classification \n",
    "x_train (1152,3,140)\n",
    "x_test  (1152,3,140)\n",
    "'''\n",
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "# load data \n",
    "dataSetfile = r\"E:/Github_Repositories/BCI_MI_CSP_DNN/graz_data/dataset_BCIcomp1_bandpassfilter.mat\"\n",
    "testLabelfile = r\"E:\\Github_Repositories/BCI_MI_CSP_DNN\\graz_data\\labels_data_set_iii.mat\"\n",
    "dataSet = sio.loadmat(dataSetfile)\n",
    "testLabel = sio.loadmat(testLabelfile)\n",
    "# concat data \n",
    "dataTrain = np.concatenate([dataSet['x_train'],dataSet['x_test']], axis=2)\n",
    "dataLabel = np.concatenate([dataSet['y_train'],testLabel['y_test']])\n",
    "print('data shape:',dataTrain.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c1f85ef9e8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c1f9d5af98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn')\n",
    "sampleRate = 128      # EEG sample rate\n",
    "t = np.linspace(0,dataTrain.shape[0]/sampleRate,dataTrain.shape[0])\n",
    "i = 1\n",
    "C3 = dataTrain[:,0,i]\n",
    "C4 = dataTrain[:,2,i]\n",
    "plt.plot(t, C3, 'r', label='C3')\n",
    "plt.plot(t, C4, 'b', label='C4')\n",
    "plt.xlabel('time /s')\n",
    "plt.ylabel('amplitude')\n",
    "plt.legend(loc='upper right',fontsize='large',frameon=True,edgecolor='blue')   #"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Soft\\Anaconda3\\lib\\site-packages\\scipy\\signal\\_arraytools.py:45: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  b = a[a_slice]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c1f48ccb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2c1f9a80ba8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c1f48a4be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "fir filter\n",
    "butter bandpass filter\n",
    "bandpass [8 30]\n",
    "mu and beta rhythm\n",
    "'''\n",
    "import scipy.signal as signal\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "from scipy.signal import butter, lfilter\n",
    "  \n",
    "def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n",
    "    b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n",
    "    y = signal.filtfilt(b, a, data)\n",
    "    return y\n",
    "\n",
    "def butter_bandpass(lowcut, highcut, fs, order=5):\n",
    "    nyq = 0.5 * fs\n",
    "    low = lowcut / nyq\n",
    "    high = highcut / nyq\n",
    "    b, a = butter(order, [low, high], btype='bandpass')\n",
    "    return b, a\n",
    "\n",
    "# filter test\n",
    "sampRat = 128\n",
    "dt = 1/sampRat\n",
    "T = 10 \n",
    "t = np.linspace(0, T, T*sampRat, endpoint=False)\n",
    "y = 5*np.sin(2*math.pi*t*10)+10*np.sin(2*math.pi*t*35)\n",
    "filterY = butter_bandpass_filter(y, 8,30, sampRat, 4)\n",
    "f = np.linspace(0, sampRat, T*sampRat, endpoint=False)\n",
    "ff = np.fft.fft(y)\n",
    "ff = np.abs(ff)*2/T/sampRat\n",
    "plt.figure()\n",
    "plt.plot(f, ff)\n",
    "plt.title('FFT before filter')\n",
    "plt.show()\n",
    " \n",
    "ff = np.fft.fft(filterY)\n",
    "ff = np.abs(ff)*2/T/sampRat\n",
    "plt.figure()\n",
    "plt.plot(f, ff)\n",
    "plt.title('FFT after filter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "想象右手运动\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Soft\\Anaconda3\\lib\\site-packages\\scipy\\signal\\_arraytools.py:45: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  b = a[a_slice]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c1f79ada90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "EEG FFT compare between filter and filter before\n",
    "'''\n",
    "# FFT transfrom\n",
    "i =50 # index \n",
    "dataFtt = np.fft.fft(dataTrain[:,0,i])  # C3 channel\n",
    "Freq = np.linspace(1/dataTrain.shape[0],sampleRate,dataTrain.shape[0])\n",
    "# plot FFT\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "ax1.plot(Freq, np.abs(dataFtt), color='blue')\n",
    "ax1.set_title('C3 Channel Filter Before')\n",
    "plt.xlabel('Freq (in Hz)')\n",
    "plt.ylabel('Amplitude')\n",
    "\n",
    "filterY = butter_bandpass_filter(dataTrain,8,30,sampleRate, 5)\n",
    "dataFtt = np.fft.fft(filterY[:,0,i])    # C3通道\n",
    "ax2.plot(Freq, np.abs(dataFtt), color='green')\n",
    "ax2.set_title('C3 Channel Filter After')\n",
    "\n",
    "if label == 1:\n",
    "    print('想象左手运动')\n",
    "else:\n",
    "    print('想象右手运动')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c1f9f0a3c8>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c1f786f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# python wavelet transfrom\n",
    "import pywt\n",
    "cA, cD = pywt.dwt(dataTrain[:,0,2], 'db4')    \n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True)\n",
    "ax1.plot(dataTrain[:,0,2])\n",
    "ax2.plot(cA,'-g')\n",
    "ax3.plot(cD,'-r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wavelet Transfrom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bmh', 'classic', 'dark_background', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', '_classic_test']\n",
      "连续小波函数：\n",
      "['cgau1', 'cgau2', 'cgau3', 'cgau4', 'cgau5', 'cgau6', 'cgau7', 'cgau8', 'cmor', 'fbsp', 'gaus1', 'gaus2', 'gaus3', 'gaus4', 'gaus5', 'gaus6', 'gaus7', 'gaus8', 'mexh', 'morl', 'shan']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7bdc3ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f7bf8200b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Wavelet transform generates time-frequency graph\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pywt\n",
    "print(plt.style.available)\n",
    "plt.style.use('classic')\n",
    "wavlist = pywt.wavelist(kind='continuous')\n",
    "print(\"Class of continuous wavelet functions：\")\n",
    "print(wavlist)\n",
    "sampling_rate = 128 \n",
    "t = np.arange(0,9.0,1.0/sampling_rate)\n",
    "wavename = 'morl'    # \"cmorB-C\" where B is the bandwidth and C is the center frequency.\n",
    "# frequencies = pywt.scale2frequency('cmor1.5-0.5', [1, 2, 3, 4]) / (1/sampling_rate)\n",
    "# print(frequencies)\n",
    "totalscal = 64    # scale \n",
    "fc = pywt.central_frequency(wavename) #  central frequency\n",
    "cparam = 2 * fc * totalscal\n",
    "scales = cparam/np.arange(1,totalscal+1)\n",
    "# C3 channel\n",
    "[cwtmatr, frequencies] = pywt.cwt(dataTrain[:,0,3],scales,wavename,1.0/sampling_rate) # continuous wavelet transform\n",
    "fig = plt.figure(1)\n",
    "plt.contourf(t, frequencies, abs(cwtmatr))\n",
    "plt.ylabel(u\"freq(Hz)\")\n",
    "plt.xlabel(u\"time(s)\")\n",
    "plt.colorbar()\n",
    "fig.savefig('C3.png')\n",
    "fig = plt.figure(2)\n",
    "# C4 channel\n",
    "[cwtmatr, frequencies] = pywt.cwt(dataTrain[:,2,3],scales,wavename,1.0/sampling_rate) #连续小波变换\n",
    "plt.contourf(t, frequencies, abs(cwtmatr))\n",
    "plt.ylabel(u\"freq(Hz)\")\n",
    "plt.xlabel(u\"time(s)\")\n",
    "plt.colorbar()\n",
    "fig.savefig('C4.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Soft\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wavelet transfrom completed\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Unrecognized argument type <class 'builtin_function_or_method'> to close",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-10ccadad4a77>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'wavelet transfrom completed'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 50\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     51\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[1;31m#plt.colorbar()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Soft\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mclose\u001b[1;34m(*args)\u001b[0m\n\u001b[0;32m    662\u001b[0m             \u001b[0m_pylab_helpers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGcf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdestroy_fig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    663\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 664\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unrecognized argument type %s to close'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    665\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    666\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'close takes 0 or 1 arguments'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Unrecognized argument type <class 'builtin_function_or_method'> to close"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c1a1ce5550>"
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     "metadata": {},
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     "data": {
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     "data": {
      "text/plain": [
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     "data": {
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     "data": {
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     "output_type": "display_data"
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   "source": [
    "'''\n",
    "Wavelet transfrom \n",
    "Time-frequency diagram combination\n",
    "select 3-6s EEG data every trial\n",
    "'''\n",
    "TimeStart = 3                     # start time\n",
    "TimeEnd = 6                       # end time \n",
    "sampling_rate = 128 \n",
    "samplePointStart=TimeStart*sampleRate\n",
    "samplePointEnd=TimeEnd*sampleRate\n",
    "# data capture 3-6s\n",
    "dataTrainC3 = dataTrain[samplePointStart:samplePointEnd,0,:]\n",
    "dataTrianC4 = dataTrain[samplePointStart:samplePointEnd,2,:]\n",
    "sampling_rate = 128 \n",
    "wavename = 'morl'    # \"cmorB-C\" where B is the bandwidth and C is the center frequency.\n",
    "# frequencies = pywt.scale2frequency('cmor1.5-0.5', [1, 2, 3, 4]) / (1/sampling_rate)\n",
    "# print(frequencies)\n",
    "totalscal = 64    # scale \n",
    "fc = pywt.central_frequency(wavename) #  central frequency\n",
    "cparam = 2 * fc * totalscal\n",
    "scales = cparam/np.arange(1,totalscal+1)\n",
    "t = np.arange(3,6.0,1.0/sampling_rate)\n",
    "# wavelet transfrom \n",
    "for i in range(280):\n",
    "    figureName = str(i)\n",
    "    [cwtmatr3, frequencies3] = pywt.cwt(dataTrainC3[:,i],scales,wavename,1.0/sampling_rate) \n",
    "    [cwtmatr4, frequencies4] = pywt.cwt(dataTrianC4[:,i],scales,wavename,1.0/sampling_rate) \n",
    "    cwtmatr = np.concatenate([abs(cwtmatr3[7:30,:]), abs(cwtmatr4[7:30,:])],axis=0)   # the sequence is C3 then C4\n",
    "    fig = plt.figure()\n",
    "    plt.contourf(cwtmatr)\n",
    "    plt.xticks([])  # remove x\n",
    "    plt.yticks([])  # remove y\n",
    "    plt.axis('off') # remove axis\n",
    "    fig.set_size_inches(800/100.0,600/100.0)#  set pixels width*height\n",
    "    plt.gca().xaxis.set_major_locator(plt.NullLocator())\n",
    "    plt.gca().yaxis.set_major_locator(plt.NullLocator())\n",
    "    plt.subplots_adjust(top=1,bottom=0,left=0,right=1,hspace =0, wspace =0) \n",
    "    plt.margins(0,0)\n",
    "    #plt.savefig(path)\n",
    "    if dataLabel[i][0] == 1:\n",
    "        filepath = r'E:\\wavelet_feature\\morl\\1\\{}.jpg'.format(figureName)\n",
    "    if dataLabel[i][0] == 2:\n",
    "        filepath = r'E:\\wavelet_feature\\morl\\2\\{}.jpg'.format(figureName)\n",
    "    #print(filepath)  \n",
    "    fig.savefig(filepath)\n",
    "    fig.clear()\n",
    "    #print('The {} sample wavelet transform is completed'.format(i))\n",
    "    \n",
    "print('wavelet transfrom completed')\n",
    "plt.close(all)    \n",
    "\n",
    "#plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN model\n",
    "* the network model is smaller with fewer convolutional layers\n",
    "* one dimensional convolution kernel and two dimensional convolution kernel are used\n",
    "* innovatively add batch normalized processing data BN\n",
    "* Keras built the network\n",
    "\n",
    "## train dataset description\n",
    "* X_Train  train data  280 samples\n",
    "* Y_Train  train label\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  CNN Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load feature dataset\n",
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "def get_file(filename):\n",
    "    ''' load time frequency diagram '''\n",
    "    dataTrain = list()\n",
    "    labelTrain = list()\n",
    "    for label in os.listdir(filename):\n",
    "        for pic in os.listdir(filename+label):\n",
    "            dataTrain.append(filename+label+'/'+pic)  # shuffle data\n",
    "            labelTrain.append(int(label))\n",
    "    temp = np.array([dataTrain, labelTrain])          \n",
    "    temp = np.transpose(temp)   # 280*2\n",
    "    np.random.shuffle(temp)\n",
    "    image_list = temp[:,0]\n",
    "    label_list = temp[:,1]\n",
    "    label_list = [int(i) for i in label_list]  \n",
    "    return image_list, label_list\n",
    "\n",
    "pathname = r\"E:/wavelet_feature/morl/\"\n",
    "image_list, label_list = get_file(pathname) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-34-fc39274330eb>:6: FastGFile.__init__ (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.gfile.GFile.\n",
      "(432, 576, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c1a27faf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read figure Demo\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "\n",
    "# read binary data\n",
    "image_raw = tf.gfile.FastGFile(image_list[10],'rb').read()\n",
    "# tf decode\n",
    "img = tf.image.decode_jpeg(image_raw)  #Tensor\n",
    " \n",
    "with tf.Session() as sess:\n",
    "   img_ = img.eval()  # 等价于sess.run(img)\n",
    "   print(img_.shape)\n",
    " \n",
    "plt.figure(1)\n",
    "plt.imshow(img_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_Train shape: (280, 64, 64, 3)\n",
      "Y_Train shape: (280, 2)\n"
     ]
    }
   ],
   "source": [
    "# read train data  figure-->tensor\n",
    "# read_file \n",
    "# decode_jpeg\n",
    "X = np.empty([280,64,64,3])\n",
    "with tf.Session() as sess:\n",
    "    for i in range(len(image_list)):\n",
    "        # 读取图像文件\n",
    "        image_raw_data = tf.gfile.GFile(image_list[i],'rb').read() \n",
    "        # 将图像文件解码为tensor\n",
    "        image_data = tf.image.decode_jpeg(image_raw_data)\n",
    "        # 改变张量的形状\n",
    "        resized = tf.image.resize_images(image_data, [64,64],method=0)  \n",
    "        resized = np.asarray(resized.eval(),dtype='uint8')      # asarray 生成数组 深拷贝不复制对象   .eval()等价于 sess.run(resized)\n",
    "        X[i,:,:,:]=resized  \n",
    "\n",
    "# train data\n",
    "X_Train = X[:,:,:,:]\n",
    "Y = np.subtract(np.array(label_list), 1)\n",
    "Y = Y.reshape([280,1])\n",
    "V = tf.one_hot(Y, depth=2,axis=1,dtype=tf.float32)\n",
    "session = tf.Session()\n",
    "Y_Train = session.run(V)\n",
    "session.close()\n",
    "# resize\n",
    "Y_Train = Y_Train.reshape([280,2])\n",
    "#  normalization\n",
    "X_Train = X_Train/255.\n",
    "print (\"X_Train shape: \" + str(X_Train.shape))\n",
    "print (\"Y_Train shape: \" + str(Y_Train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 64, 64, 16)        784       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 64, 64, 16)        0         \n",
      "_________________________________________________________________\n",
      "maxpool2d_1 (MaxPooling2D)   (None, 8, 8, 16)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 8, 8, 16)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 8, 8, 32)          8224      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "maxpool2d_2 (MaxPooling2D)   (None, 4, 4, 32)          0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 4, 4, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 240)               123120    \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 240)               0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 240)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 2)                 482       \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 2)                 0         \n",
      "=================================================================\n",
      "Total params: 132,610\n",
      "Trainable params: 132,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Keras 序贯模型\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import Conv2D, Conv1D, BatchNormalization, MaxPool2D\n",
    "from keras.optimizers import Adam            # 优化器\n",
    "import keras \n",
    "\n",
    "input_shape = X_Train.shape[1:]\n",
    "num_classes = 2\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(16, (4,4), padding = 'same', input_shape = input_shape, kernel_initializer = 'he_normal', name = 'conv2d_1'))\n",
    "model.add(Activation('relu',name = 'activation_1'))\n",
    "model.add(MaxPool2D(pool_size=(8,8),name = 'maxpool2d_1'))\n",
    "model.add(Dropout(0.25,name = 'dropout_1'))\n",
    "\n",
    "model.add(Conv2D(32, (4,4), padding = 'same',kernel_initializer = 'he_normal', name = 'conv2d_2'))\n",
    "model.add(Activation('relu',name = 'activation_2'))\n",
    "model.add(MaxPool2D(pool_size=(2,2),name = 'maxpool2d_2'))\n",
    "model.add(Dropout(0.25,name = 'dropout_2'))\n",
    "\n",
    "model.add(Flatten(name = 'flatten_1'))\n",
    "model.add(Dense(240,kernel_initializer='he_normal',name='dense_1'))\n",
    "model.add(Activation('relu',name='activation_3'))\n",
    "model.add(Dropout(0.5,name = 'dropout_3'))\n",
    "model.add(Dense(num_classes, kernel_initializer='he_normal',name='dense_2'))\n",
    "model.add(Activation('softmax',name='activation_4'))\n",
    "model.summary()\n",
    "\n",
    "# 优化方法\n",
    "opt = keras.optimizers.Adam(lr=0.0003, decay = 1e-6)\n",
    "# 编译模型\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer=opt, metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train model\n",
    "epochs = 300\n",
    "history = model.fit(X_Train, Y_Train, shuffle=True, batch_size=8, epochs=epochs, verbose=1, validation_split=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'#交叉验证\\n#learning_rate = 0.01,num_epochs = 500, minibatch_size = 16\\nfrom sklearn.model_selection import StratifiedKFold\\nfrom sklearn.metrics import confusion_matrix, classification_report\\nskf=StratifiedKFold(n_splits=5)\\nskf.get_n_splits(X,b)\\nprint(skf)\\nfor train_index,test_index in skf.split(X,b):\\n    #print(\"Train Index:\",train_index,\",Test Index:\",test_index)\\n    X_train,X_test=X[train_index],X[test_index]\\n    y_train,y_test=Y[train_index],Y[test_index]\\n    \\n    X_train = X_train/255.\\n    X_test = X_test/255.\\n    print (\"number of training examples = \" + str(X_train.shape[0]))\\n    print (\"number of test examples = \" + str(X_test.shape[0]))\\n#     print (\"X_train shape: \" + str(X_train.shape))\\n#     print (\"Y_train shape: \" + str(y_train.shape))\\n#     print (\"X_test shape: \" + str(X_test.shape))\\n#     print (\"Y_test shape: \" + str(y_test.shape))\\n    \\n    _, _, parameters = model(X_train, y_train, X_test, y_test)\\n    \\n    \\n    with open(\\'2003.pkl\\',\\'rb\\') as f:\\n            pred=pickle.load(f)\\n            \\n    true=np.argmax(y_test,1)+1\\n    confusion_matrix(true,pred)\\n    print(classification_report(true,pred))\\n'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# cross validation\n",
    "'''\n",
    "#learning_rate = 0.01,num_epochs = 500, minibatch_size = 16\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "skf=StratifiedKFold(n_splits=5)\n",
    "skf.get_n_splits(X,b)\n",
    "print(skf)\n",
    "for train_index,test_index in skf.split(X,b):\n",
    "    #print(\"Train Index:\",train_index,\",Test Index:\",test_index)\n",
    "    X_train,X_test=X[train_index],X[test_index]\n",
    "    y_train,y_test=Y[train_index],Y[test_index]\n",
    "    \n",
    "    X_train = X_train/255.\n",
    "    X_test = X_test/255.\n",
    "    print (\"number of training examples = \" + str(X_train.shape[0]))\n",
    "    print (\"number of test examples = \" + str(X_test.shape[0]))\n",
    "#     print (\"X_train shape: \" + str(X_train.shape))\n",
    "#     print (\"Y_train shape: \" + str(y_train.shape))\n",
    "#     print (\"X_test shape: \" + str(X_test.shape))\n",
    "#     print (\"Y_test shape: \" + str(y_test.shape))\n",
    "    \n",
    "    _, _, parameters = model(X_train, y_train, X_test, y_test)\n",
    "    \n",
    "    \n",
    "    with open('2003.pkl','rb') as f:\n",
    "            pred=pickle.load(f)\n",
    "            \n",
    "    true=np.argmax(y_test,1)+1\n",
    "    confusion_matrix(true,pred)\n",
    "    print(classification_report(true,pred))\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c1aaf76c88>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c1aad49a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c1aaefe470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Visualization of training process\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(0)\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='lower right')\n",
    "plt.ylim(0,1)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(history.history['loss'])\n",
    "\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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