Land-segmentation-images.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import sklearn as sk\n",
    "import pandas as pd\n",
    "import pywt\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn import neighbors\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(610, 340, 103)\n"
     ]
    }
   ],
   "source": [
    "hsi_data=sio.loadmat('PaviaU.mat')\n",
    "PaviaU_data=hsi_data[\"paviaU\"]\n",
    "dim=PaviaU_data.shape\n",
    "print(dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(207400, 1)\n",
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "hsi_gt=sio.loadmat(\"PaviaU_gt.mat\")\n",
    "hsi_gt_arr=hsi_gt['paviaU_gt']\n",
    "y=np.reshape(hsi_gt_arr,(dim[0]*dim[1],1))\n",
    "print(y.shape)\n",
    "print(np.unique(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(207400, 103)\n"
     ]
    }
   ],
   "source": [
    "twoD_dim=(dim[0]*dim[1],dim[2])\n",
    "twoD_hsi_data=np.zeros(twoD_dim)\n",
    "k=0;\n",
    "for i in range(0,dim[0]):\n",
    "    for j in range(0,dim[1]):\n",
    "        twoD_hsi_data[k,:]=PaviaU_data[i,j,:]\n",
    "        k=k+1\n",
    "    \n",
    "print(twoD_hsi_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "twoD_hsi_data_100=twoD_hsi_data[:,0:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "twoD_dim=twoD_hsi_data_100.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(207400, 100)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twoD_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(207400, 400)\n"
     ]
    }
   ],
   "source": [
    "db1=pywt.Wavelet('db1')\n",
    "s=(twoD_dim[0],4*twoD_dim[1])\n",
    "coefsi=np.zeros(s)\n",
    "db1=pywt.Wavelet('db1')\n",
    "for i in range(0,twoD_dim[0]):\n",
    "    temp=pywt.swt(twoD_hsi_data_100[i,:],db1,level=2,axis=-1)\n",
    "    coefsi[i,:]=np.ndarray.flatten(np.array(temp))\n",
    "  \n",
    "print(np.array(coefsi).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "s=(610,340,400)\n",
    "Coefsi_3d_paviaU=np.zeros(s)\n",
    "for i in range(0,400):\n",
    "  Coefsi_3d_paviaU[:,:,i]=np.reshape(coefsi[:,i],(610,340))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sio.savemat(\"Coefsi_3d_paviaU.mat\",{\"coefsi_3d_paviaU\":Coefsi_3d_paviaU})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadPaviaUData():\n",
    "\n",
    "    data = sio.loadmat(os.path.join(\n",
    "                      'Coefsi_3d_paviaU.mat'))['coefsi_3d_paviaU']\n",
    "    labels = sio.loadmat(os.path.join(\n",
    "                        'PaviaU_gt.mat'))['paviaU_gt']\n",
    "    \n",
    "    return data, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  split data to Train and Test Set\n",
    "def splitTrainTestSet(X, y, classnum=9, testRatio=0.20):\n",
    "#     X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "#                                test_size=testRatio, random_state=345, stratify=y)\n",
    "    ss=StratifiedShuffleSplit(n_splits=classnum, test_size=testRatio, \n",
    "                              train_size=1-testRatio, random_state=0)\n",
    "    \n",
    "    for train_index, test_index in ss.split(X, y):\n",
    "        print(\"TRAIN:\", train_index, \"TEST:\", 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",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  over sample \n",
    "def oversampleWeakClasses(X, y):\n",
    "    uniqueLabels, labelCounts = np.unique(y, return_counts=True)\n",
    "    maxCount = np.max(labelCounts)\n",
    "    labelInverseRatios = maxCount / labelCounts  \n",
    "    # repeat for every label and concat\n",
    "    newX = X[y == uniqueLabels[0], :, :, :].repeat(round(labelInverseRatios[0]), \n",
    "                                                   axis=0)\n",
    "    newY = y[y == uniqueLabels[0]].repeat(round(labelInverseRatios[0]), axis=0)\n",
    "    for label, labelInverseRatio in zip(uniqueLabels[1:], labelInverseRatios[1:]):\n",
    "        cX = X[y== label,:,:,:].repeat(round(labelInverseRatio), axis=0)\n",
    "        cY = y[y == label].repeat(round(labelInverseRatio), axis=0)\n",
    "        newX = np.concatenate((newX, cX))\n",
    "        newY = np.concatenate((newY, cY))\n",
    "    np.random.seed(seed=42)\n",
    "    rand_perm = np.random.permutation(newY.shape[0])\n",
    "    newX = newX[rand_perm, :, :, :]\n",
    "    newY = newY[rand_perm]\n",
    "    return newX, newY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standartizeData(X):\n",
    "    newX = np.reshape(X, (-1, X.shape[2]))\n",
    "    scaler = preprocessing.StandardScaler().fit(newX)  \n",
    "    newX = scaler.transform(newX)\n",
    "    newX = np.reshape(newX, (X.shape[0],X.shape[1],X.shape[2]))\n",
    "    return newX, scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  apply PCA preprocessing for data sets\n",
    "def applyPCA(X, numComponents=100):\n",
    "    newX = np.reshape(X, (-1, X.shape[2]))\n",
    "    pca = PCA(n_components=numComponents, whiten=True)\n",
    "    newX = pca.fit_transform(newX)\n",
    "    newX = np.reshape(newX, (X.shape[0],X.shape[1], numComponents))\n",
    "    return newX, pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  pad zeros to dataset\n",
    "def padWithZeros(X, margin=2):\n",
    "    newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2* margin, \n",
    "                     X.shape[2]))\n",
    "    x_offset = margin\n",
    "    y_offset = margin\n",
    "    newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + \n",
    "         y_offset, :] = X\n",
    "    return newX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  create Patches for dataset\n",
    "def createPatches(X, y, windowSize=5, removeZeroLabels = True):\n",
    "    margin = int((windowSize - 1) / 2)\n",
    "    zeroPaddedX = padWithZeros(X, margin=margin)\n",
    "    # split patches\n",
    "    patchesData = np.zeros((X.shape[0] * X.shape[1], windowSize, \n",
    "                            windowSize, X.shape[2]))\n",
    "    patchesLabels = np.zeros((X.shape[0] * X.shape[1]))\n",
    "    patchIndex = 0\n",
    "    for r in range(margin, zeroPaddedX.shape[0] - margin):\n",
    "        for c in range(margin, zeroPaddedX.shape[1] - margin):\n",
    "            patch = zeroPaddedX[r - margin:r + margin + 1, c - \n",
    "                                margin:c + margin + 1]   \n",
    "            patchesData[patchIndex, :, :, :] = patch\n",
    "            patchesLabels[patchIndex] = y[r-margin, c-margin]\n",
    "            patchIndex = patchIndex + 1\n",
    "    if removeZeroLabels:\n",
    "        patchesData = patchesData[patchesLabels>0,:,:,:]\n",
    "        patchesLabels = patchesLabels[patchesLabels>0]\n",
    "        patchesLabels -= 1\n",
    "    return patchesData, patchesLabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Augment Data\n",
    "def AugmentData(X_train):\n",
    "    for i in range(int(X_train.shape[0]/2)):\n",
    "        patch = X_train[i,:,:,:]\n",
    "        num = random.randint(0,2)\n",
    "        if (num == 0):\n",
    "            \n",
    "            flipped_patch = np.flipud(patch)\n",
    "        if (num == 1):\n",
    "            \n",
    "            flipped_patch = np.fliplr(patch)\n",
    "        if (num == 2):\n",
    "            \n",
    "            no = random.randrange(-180,180,100)\n",
    "            flipped_patch = scipy.ndimage.interpolation.rotate(patch, \n",
    "                            no,axes=(1, 0), reshape=False, output=None, \n",
    "                            order=3, mode='constant', cval=0.0, prefilter=False)\n",
    "        \n",
    "    patch2 = flipped_patch\n",
    "    X_train[i,:,:,:] = patch2\n",
    "    \n",
    "    return X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def savePreprocessedData(path, X_trainPatches, X_testPatches, y_trainPatches, \n",
    "                         y_testPatches, X_all, y_all, windowSize, wasPCAapplied = False, \n",
    "                         numPCAComponents = 0, testRatio = 0.20):\n",
    "    \n",
    "    \n",
    "\n",
    "    if wasPCAapplied:\n",
    "        with open(os.path.join(\"XtrainWindowSize\") + str(windowSize) + \n",
    "                  \"PCA\" + str(numPCAComponents) + \"testRatio\" + str(testRatio) + \n",
    "                  \".npy\", 'bw') as outfile:\n",
    "            np.save(outfile, X_trainPatches)\n",
    "        with open(os.path.join( \"XtestWindowSize\") + str(windowSize) + \n",
    "                  \"PCA\" + str(numPCAComponents) + \"testRatio\" + str(testRatio) + \n",
    "                  \".npy\", 'bw') as outfile:\n",
    "            np.save(outfile, X_testPatches)\n",
    "        with open(os.path.join( \"ytrainWindowSize\") + str(windowSize) + \n",
    "                  \"PCA\" + str(numPCAComponents) + \"testRatio\" + str(testRatio) + \n",
    "                  \".npy\", 'bw') as outfile:\n",
    "            np.save(outfile, y_trainPatches)\n",
    "        with open(os.path.join(\"ytestWindowSize\") + str(windowSize) + \n",
    "                  \"PCA\" + str(numPCAComponents) + \"testRatio\" + str(testRatio) + \n",
    "                  \".npy\", 'bw') as outfile:\n",
    "            np.save(outfile, y_testPatches)\n",
    "        \n",
    "        with open(os.path.join( \"XAllWindowSize\") + str(windowSize) + \n",
    "                  \"PCA\" + str(numPCAComponents) + \"testRatio\" + str(testRatio) + \n",
    "                  \".npy\", 'bw') as outfile:\n",
    "            np.save(outfile, X_all)\n",
    "        with open(os.path.join(\"yAllWindowSize\") + str(windowSize) + \n",
    "                  \"PCA\" + str(numPCAComponents) + \"testRatio\" + str(testRatio) + \n",
    "                  \".npy\", 'bw') as outfile:\n",
    "            np.save(outfile, y_all)\n",
    "    else:\n",
    "        with open(os.path.join(\"preXtrainWindowSize\") + str(windowSize) + \n",
    "                  \".npy\", 'bw') as outfile:\n",
    "            np.save(outfile, X_trainPatches)\n",
    "        with open(os.path.join( \"preXtestWindowSize\") + str(windowSize) + \n",
    "                  \".npy\", 'bw') as outfile:\n",
    "            np.save(outfile, X_testPatches)\n",
    "        with open(os.path.join( \"preytrainWindowSize\") + str(windowSize) + \n",
    "                  \".npy\", 'bw') as outfile:\n",
    "            np.save(outfile, y_trainPatches)\n",
    "        with open(os.path.join( \"preytestWindowSize\") + str(windowSize) + \n",
    "                  \".npy\", 'bw') as outfile:\n",
    "            np.save(outfile, y_testPatches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Global Variables\n",
    "# The number of principal components to be retained in the PCA algorithm, \n",
    "# the number of retained features  n\n",
    "numComponents = 100\n",
    "# Patches windows size\n",
    "windowSize = 5\n",
    "# The proportion of Test sets\n",
    "testRatio = 0.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# Load dataset from file and apply PCA\n",
    "#X, y = loadHSIData()\n",
    "X,y = loadPaviaUData()\n",
    "X, pca = applyPCA(X, numComponents=numComponents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: spectral in /mnt/disks/user/anaconda3/lib/python3.6/site-packages (0.19)\n",
      "Requirement already satisfied: numpy in /mnt/disks/user/anaconda3/lib/python3.6/site-packages (from spectral) (1.14.5)\n",
      "\u001b[33mYou are using pip version 18.0, however version 18.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install spectral\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "import scipy.io as sio\n",
    "from sklearn.model_selection import train_test_split, StratifiedShuffleSplit\n",
    "import os\n",
    "\n",
    "from sklearn import preprocessing\n",
    "import os\n",
    "import random\n",
    "from random import shuffle\n",
    "from skimage.transform import rotate\n",
    "import scipy.ndimage\n",
    "from spectral import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: [ 2475 33602  8009 ... 32223  6300 29867] TEST: [24728  2652 35792 ...  4622 21961 29396]\n",
      "TRAIN: [36369 12882  6050 ... 39972 10721 30970] TEST: [37602  7784 38717 ... 14002  8215 10662]\n",
      "TRAIN: [22466 31881 18882 ... 22731 35414 41382] TEST: [31511 27080 33684 ... 33476  5349 14252]\n",
      "TRAIN: [40356 32247  1521 ... 19131  8670 21446] TEST: [32246 13943 24532 ... 34180  4199 16107]\n",
      "TRAIN: [ 4351  1683 38375 ... 15984 16866 25166] TEST: [23052  7459 36072 ... 15326 24838 29557]\n",
      "TRAIN: [26657 37260 15698 ...  2228 27529 38144] TEST: [27261 33650 19373 ... 41071  4361 23936]\n",
      "TRAIN: [41113   945  8880 ... 30640  2288 23600] TEST: [26159 31612 21923 ... 39952  1296  2860]\n",
      "TRAIN: [13509 12503 41168 ... 41952 21861 32637] TEST: [21903 37226 21085 ... 28017 26957  3374]\n",
      "TRAIN: [39232 22557 37499 ... 25640  8807  4697] TEST: [ 1698  7910 10687 ... 42628 38116 32493]\n",
      "TRAIN: [ 2475 33602  8009 ... 32223  6300 29867] TEST: [24728  2652 35792 ...  4622 21961 29396]\n",
      "TRAIN: [36369 12882  6050 ... 39972 10721 30970] TEST: [37602  7784 38717 ... 14002  8215 10662]\n",
      "TRAIN: [22466 31881 18882 ... 22731 35414 41382] TEST: [31511 27080 33684 ... 33476  5349 14252]\n",
      "TRAIN: [40356 32247  1521 ... 19131  8670 21446] TEST: [32246 13943 24532 ... 34180  4199 16107]\n",
      "TRAIN: [ 4351  1683 38375 ... 15984 16866 25166] TEST: [23052  7459 36072 ... 15326 24838 29557]\n",
      "TRAIN: [26657 37260 15698 ...  2228 27529 38144] TEST: [27261 33650 19373 ... 41071  4361 23936]\n",
      "TRAIN: [41113   945  8880 ... 30640  2288 23600] TEST: [26159 31612 21923 ... 39952  1296  2860]\n",
      "TRAIN: [13509 12503 41168 ... 41952 21861 32637] TEST: [21903 37226 21085 ... 28017 26957  3374]\n",
      "TRAIN: [39232 22557 37499 ... 25640  8807  4697] TEST: [ 1698  7910 10687 ... 42628 38116 32493]\n"
     ]
    }
   ],
   "source": [
    "# Preprocess Data\n",
    "XPatches, yPatches = createPatches(X, y, windowSize=windowSize)\n",
    "X_train, X_test, y_train, y_test = splitTrainTestSet(XPatches, yPatches,y.max()-y.min(), testRatio) \n",
    "                                              # Preprocess Data\n",
    "XPatches, yPatches = createPatches(X, y, windowSize=windowSize)\n",
    "X_train, X_test, y_train, y_test = splitTrainTestSet(XPatches, yPatches, \n",
    "                                                     y.max()-y.min(), testRatio)\n",
    "X_all = np.append(X_train, X_test, axis=0)\n",
    "y_all = np.append(y_train, y_test, axis=0)\n",
    "X_train, y_train = oversampleWeakClasses(X_train, y_train)\n",
    "# X_all, y_all = oversampleWeakClasses(X_all, y_all)\n",
    "X_train = AugmentData(X_train)\n",
    "# X_all = AugmentData(X_all)       y.max()-y.min(), testRatio)\n",
    "X_all = np.append(X_train, X_test, axis=0)\n",
    "y_all = np.append(y_train, y_test, axis=0)\n",
    "X_train, y_train = oversampleWeakClasses(X_train, y_train)\n",
    "# X_all, y_all = oversampleWeakClasses(X_all, y_all)\n",
    "X_train = AugmentData(X_train)\n",
    "# X_all = AugmentData(X_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save Preprocessed Data to file\n",
    "savePreprocessedData('predata', X_train, X_test, y_train, y_test, \n",
    "                     X_all, y_all, windowSize = windowSize, \n",
    "                     wasPCAapplied=True, numPCAComponents = numComponents, \n",
    "                     testRatio = testRatio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Import the necessary libraries\n",
    "import numpy as np\n",
    "import scipy\n",
    "import os\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.optimizers import SGD\n",
    "from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint\n",
    "from keras import backend as K\n",
    "K.set_image_dim_ordering('th')\n",
    "from keras.utils import np_utils\n",
    "from keras.layers import LeakyReLU\n",
    "#from sklearn.cross_validation import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Global Variables\n",
    "# The number of principal components to be retained in the PCA algorithm, \n",
    "# the number of retained features  n\n",
    "numPCAcomponents = 100\n",
    "# Patches windows size\n",
    "windowSize = 5\n",
    "# The proportion of Test sets\n",
    "testRatio = 0.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load Preprocessed data from file\n",
    "X_train = np.load(\"XtrainWindowSize\" \n",
    "                  + str(windowSize) + \"PCA\" + str(numPCAcomponents) + \n",
    "                  \"testRatio\" + str(testRatio)  + \".npy\")\n",
    "y_train = np.load(\"ytrainWindowSize\" \n",
    "                  + str(windowSize) + \"PCA\" + str(numPCAcomponents) + \n",
    "                  \"testRatio\" + str(testRatio) + \".npy\")\n",
    "X_test = np.load(\"XtestWindowSize\" \n",
    "                  + str(windowSize) + \"PCA\" + str(numPCAcomponents) + \n",
    "                 \"testRatio\" + str(testRatio)  + \".npy\")\n",
    "y_test = np.load(\"ytestWindowSize\" \n",
    "                  + str(windowSize) + \"PCA\" + str(numPCAcomponents) + \n",
    "                 \"testRatio\" + str(testRatio) + \".npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 5, 5)\n"
     ]
    }
   ],
   "source": [
    "# Reshape data into (numberofsumples, channels, height, width)\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[3], \n",
    "                               X_train.shape[1], X_train.shape[2]))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[3], \n",
    "                             X_test.shape[1], X_test.shape[2]))\n",
    "\n",
    "# convert class labels to on-hot encoding\n",
    "y_train = np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)\n",
    "\n",
    "# Define the input shape \n",
    "input_shape= X_train[0].shape\n",
    "print(input_shape)\n",
    "\n",
    "# number of filters\n",
    "C1 = 3*numPCAcomponents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model structure\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(C1, (3, 3), activation='relu', input_shape=input_shape))\n",
    "model.add(Conv2D(3*C1, (3, 3), activation='relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(6*numPCAcomponents, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(9, activation='softmax'))\n",
    "\n",
    "# Define optimization and train method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define optimization and train method\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.9, patience=25, \n",
    "                              min_lr=0.000001, verbose=1)\n",
    "checkpointer = ModelCheckpoint(filepath=\"checkpoint.hdf5\", verbose=1, \n",
    "                              save_best_only=False)\n",
    "sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=sgd, \n",
    "                              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 136588 samples, validate on 8556 samples\n",
      "Epoch 1/100\n",
      "136588/136588 [==============================] - 62s 455us/step - loss: 0.2010 - acc: 0.9328 - val_loss: 0.0576 - val_acc: 0.9795\n",
      "\n",
      "Epoch 00001: saving model to checkpoint.hdf5\n",
      "Epoch 2/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 0.0134 - acc: 0.9972 - val_loss: 0.0555 - val_acc: 0.9820\n",
      "\n",
      "Epoch 00002: saving model to checkpoint.hdf5\n",
      "Epoch 3/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 0.0048 - acc: 0.9992 - val_loss: 0.0585 - val_acc: 0.9832\n",
      "\n",
      "Epoch 00003: saving model to checkpoint.hdf5\n",
      "Epoch 4/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 0.0029 - acc: 0.9996 - val_loss: 0.0578 - val_acc: 0.9835\n",
      "\n",
      "Epoch 00004: saving model to checkpoint.hdf5\n",
      "Epoch 5/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 0.0017 - acc: 0.9998 - val_loss: 0.0587 - val_acc: 0.9836\n",
      "\n",
      "Epoch 00005: saving model to checkpoint.hdf5\n",
      "Epoch 6/100\n",
      "136588/136588 [==============================] - 52s 383us/step - loss: 0.0014 - acc: 0.9998 - val_loss: 0.0620 - val_acc: 0.9831\n",
      "\n",
      "Epoch 00006: saving model to checkpoint.hdf5\n",
      "Epoch 7/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 0.0011 - acc: 0.9999 - val_loss: 0.0645 - val_acc: 0.9840\n",
      "\n",
      "Epoch 00007: saving model to checkpoint.hdf5\n",
      "Epoch 8/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 8.4648e-04 - acc: 0.9999 - val_loss: 0.0631 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00008: saving model to checkpoint.hdf5\n",
      "Epoch 9/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 6.9060e-04 - acc: 0.9999 - val_loss: 0.0637 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00009: saving model to checkpoint.hdf5\n",
      "Epoch 10/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 5.7072e-04 - acc: 0.9999 - val_loss: 0.0660 - val_acc: 0.9838\n",
      "\n",
      "Epoch 00010: saving model to checkpoint.hdf5\n",
      "Epoch 11/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 6.1876e-04 - acc: 0.9999 - val_loss: 0.0644 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00011: saving model to checkpoint.hdf5\n",
      "Epoch 12/100\n",
      "136588/136588 [==============================] - 53s 385us/step - loss: 4.7275e-04 - acc: 1.0000 - val_loss: 0.0654 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00012: saving model to checkpoint.hdf5\n",
      "Epoch 13/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 4.0369e-04 - acc: 1.0000 - val_loss: 0.0683 - val_acc: 0.9841\n",
      "\n",
      "Epoch 00013: saving model to checkpoint.hdf5\n",
      "Epoch 14/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 3.6619e-04 - acc: 1.0000 - val_loss: 0.0670 - val_acc: 0.9845\n",
      "\n",
      "Epoch 00014: saving model to checkpoint.hdf5\n",
      "Epoch 15/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 3.5447e-04 - acc: 1.0000 - val_loss: 0.0661 - val_acc: 0.9845\n",
      "\n",
      "Epoch 00015: saving model to checkpoint.hdf5\n",
      "Epoch 16/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 3.8439e-04 - acc: 1.0000 - val_loss: 0.0684 - val_acc: 0.9852\n",
      "\n",
      "Epoch 00016: saving model to checkpoint.hdf5\n",
      "Epoch 17/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 2.7752e-04 - acc: 1.0000 - val_loss: 0.0678 - val_acc: 0.9848\n",
      "\n",
      "Epoch 00017: saving model to checkpoint.hdf5\n",
      "Epoch 18/100\n",
      "136588/136588 [==============================] - 52s 384us/step - loss: 2.4145e-04 - acc: 1.0000 - val_loss: 0.0680 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00018: saving model to checkpoint.hdf5\n",
      "Epoch 19/100\n",
      "136588/136588 [==============================] - 52s 383us/step - loss: 2.7652e-04 - acc: 1.0000 - val_loss: 0.0685 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00019: saving model to checkpoint.hdf5\n",
      "Epoch 20/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 2.1220e-04 - acc: 1.0000 - val_loss: 0.0691 - val_acc: 0.9849\n",
      "\n",
      "Epoch 00020: saving model to checkpoint.hdf5\n",
      "Epoch 21/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 2.1306e-04 - acc: 1.0000 - val_loss: 0.0705 - val_acc: 0.9845\n",
      "\n",
      "Epoch 00021: saving model to checkpoint.hdf5\n",
      "Epoch 22/100\n",
      "136588/136588 [==============================] - 52s 383us/step - loss: 2.3133e-04 - acc: 1.0000 - val_loss: 0.0702 - val_acc: 0.9845\n",
      "\n",
      "Epoch 00022: saving model to checkpoint.hdf5\n",
      "Epoch 23/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 1.7033e-04 - acc: 1.0000 - val_loss: 0.0698 - val_acc: 0.9849\n",
      "\n",
      "Epoch 00023: saving model to checkpoint.hdf5\n",
      "Epoch 24/100\n",
      "136588/136588 [==============================] - 52s 383us/step - loss: 1.7228e-04 - acc: 1.0000 - val_loss: 0.0715 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00024: saving model to checkpoint.hdf5\n",
      "Epoch 25/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 1.8226e-04 - acc: 1.0000 - val_loss: 0.0698 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00025: saving model to checkpoint.hdf5\n",
      "Epoch 26/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 1.5968e-04 - acc: 1.0000 - val_loss: 0.0705 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00026: saving model to checkpoint.hdf5\n",
      "Epoch 27/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 1.7895e-04 - acc: 1.0000 - val_loss: 0.0719 - val_acc: 0.9845\n",
      "\n",
      "Epoch 00027: saving model to checkpoint.hdf5\n",
      "Epoch 28/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 1.4419e-04 - acc: 1.0000 - val_loss: 0.0708 - val_acc: 0.9852\n",
      "\n",
      "Epoch 00028: saving model to checkpoint.hdf5\n",
      "Epoch 29/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 1.3240e-04 - acc: 1.0000 - val_loss: 0.0745 - val_acc: 0.9841\n",
      "\n",
      "Epoch 00029: saving model to checkpoint.hdf5\n",
      "Epoch 30/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 1.1949e-04 - acc: 1.0000 - val_loss: 0.0742 - val_acc: 0.9841\n",
      "\n",
      "Epoch 00030: saving model to checkpoint.hdf5\n",
      "Epoch 31/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 1.1363e-04 - acc: 1.0000 - val_loss: 0.0730 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00031: saving model to checkpoint.hdf5\n",
      "Epoch 32/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 1.1255e-04 - acc: 1.0000 - val_loss: 0.0740 - val_acc: 0.9845\n",
      "\n",
      "Epoch 00032: saving model to checkpoint.hdf5\n",
      "Epoch 33/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 1.2060e-04 - acc: 1.0000 - val_loss: 0.0726 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00033: saving model to checkpoint.hdf5\n",
      "Epoch 34/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 1.2117e-04 - acc: 1.0000 - val_loss: 0.0721 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00034: saving model to checkpoint.hdf5\n",
      "Epoch 35/100\n",
      "136588/136588 [==============================] - 52s 384us/step - loss: 1.0701e-04 - acc: 1.0000 - val_loss: 0.0721 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00035: saving model to checkpoint.hdf5\n",
      "Epoch 36/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 9.8293e-05 - acc: 1.0000 - val_loss: 0.0745 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00036: saving model to checkpoint.hdf5\n",
      "Epoch 37/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 1.1493e-04 - acc: 1.0000 - val_loss: 0.0744 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00037: saving model to checkpoint.hdf5\n",
      "Epoch 38/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 1.1573e-04 - acc: 1.0000 - val_loss: 0.0743 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00038: saving model to checkpoint.hdf5\n",
      "Epoch 39/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 9.7520e-05 - acc: 1.0000 - val_loss: 0.0750 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00039: saving model to checkpoint.hdf5\n",
      "Epoch 40/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 9.6712e-05 - acc: 1.0000 - val_loss: 0.0741 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00040: saving model to checkpoint.hdf5\n",
      "Epoch 41/100\n",
      "136588/136588 [==============================] - 53s 385us/step - loss: 1.1661e-04 - acc: 1.0000 - val_loss: 0.0752 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00041: ReduceLROnPlateau reducing learning rate to 0.0009000000427477062.\n",
      "\n",
      "Epoch 00041: saving model to checkpoint.hdf5\n",
      "Epoch 42/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 8.3908e-05 - acc: 1.0000 - val_loss: 0.0754 - val_acc: 0.9842\n",
      "\n",
      "Epoch 00042: saving model to checkpoint.hdf5\n",
      "Epoch 43/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "136588/136588 [==============================] - 52s 381us/step - loss: 7.9207e-05 - acc: 1.0000 - val_loss: 0.0750 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00043: saving model to checkpoint.hdf5\n",
      "Epoch 44/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 8.1701e-05 - acc: 1.0000 - val_loss: 0.0744 - val_acc: 0.9849\n",
      "\n",
      "Epoch 00044: saving model to checkpoint.hdf5\n",
      "Epoch 45/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 8.2507e-05 - acc: 1.0000 - val_loss: 0.0753 - val_acc: 0.9849\n",
      "\n",
      "Epoch 00045: saving model to checkpoint.hdf5\n",
      "Epoch 46/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 7.9984e-05 - acc: 1.0000 - val_loss: 0.0750 - val_acc: 0.9852\n",
      "\n",
      "Epoch 00046: saving model to checkpoint.hdf5\n",
      "Epoch 47/100\n",
      "136588/136588 [==============================] - 53s 386us/step - loss: 7.3502e-05 - acc: 1.0000 - val_loss: 0.0760 - val_acc: 0.9845\n",
      "\n",
      "Epoch 00047: saving model to checkpoint.hdf5\n",
      "Epoch 48/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 6.4819e-05 - acc: 1.0000 - val_loss: 0.0754 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00048: saving model to checkpoint.hdf5\n",
      "Epoch 49/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 8.0647e-05 - acc: 1.0000 - val_loss: 0.0764 - val_acc: 0.9848\n",
      "\n",
      "Epoch 00049: saving model to checkpoint.hdf5\n",
      "Epoch 50/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 6.4541e-05 - acc: 1.0000 - val_loss: 0.0765 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00050: saving model to checkpoint.hdf5\n",
      "Epoch 51/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 6.5740e-05 - acc: 1.0000 - val_loss: 0.0770 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00051: saving model to checkpoint.hdf5\n",
      "Epoch 52/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 7.2538e-05 - acc: 1.0000 - val_loss: 0.0766 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00052: saving model to checkpoint.hdf5\n",
      "Epoch 53/100\n",
      "136588/136588 [==============================] - 52s 384us/step - loss: 8.1957e-05 - acc: 1.0000 - val_loss: 0.0759 - val_acc: 0.9853\n",
      "\n",
      "Epoch 00053: saving model to checkpoint.hdf5\n",
      "Epoch 54/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 6.4747e-05 - acc: 1.0000 - val_loss: 0.0765 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00054: saving model to checkpoint.hdf5\n",
      "Epoch 55/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 7.3194e-05 - acc: 1.0000 - val_loss: 0.0766 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00055: saving model to checkpoint.hdf5\n",
      "Epoch 56/100\n",
      "136588/136588 [==============================] - 52s 383us/step - loss: 6.7721e-05 - acc: 1.0000 - val_loss: 0.0768 - val_acc: 0.9848\n",
      "\n",
      "Epoch 00056: saving model to checkpoint.hdf5\n",
      "Epoch 57/100\n",
      "136588/136588 [==============================] - 52s 379us/step - loss: 9.5778e-05 - acc: 1.0000 - val_loss: 0.0770 - val_acc: 0.9848\n",
      "\n",
      "Epoch 00057: saving model to checkpoint.hdf5\n",
      "Epoch 58/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 7.4821e-05 - acc: 1.0000 - val_loss: 0.0786 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00058: saving model to checkpoint.hdf5\n",
      "Epoch 59/100\n",
      "136588/136588 [==============================] - 52s 384us/step - loss: 6.2142e-05 - acc: 1.0000 - val_loss: 0.0779 - val_acc: 0.9845\n",
      "\n",
      "Epoch 00059: saving model to checkpoint.hdf5\n",
      "Epoch 60/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 6.6562e-05 - acc: 1.0000 - val_loss: 0.0785 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00060: saving model to checkpoint.hdf5\n",
      "Epoch 61/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 6.1211e-05 - acc: 1.0000 - val_loss: 0.0775 - val_acc: 0.9849\n",
      "\n",
      "Epoch 00061: saving model to checkpoint.hdf5\n",
      "Epoch 62/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 6.0342e-05 - acc: 1.0000 - val_loss: 0.0782 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00062: saving model to checkpoint.hdf5\n",
      "Epoch 63/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 5.1968e-05 - acc: 1.0000 - val_loss: 0.0774 - val_acc: 0.9848\n",
      "\n",
      "Epoch 00063: saving model to checkpoint.hdf5\n",
      "Epoch 64/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 5.0442e-05 - acc: 1.0000 - val_loss: 0.0774 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00064: saving model to checkpoint.hdf5\n",
      "Epoch 65/100\n",
      "136588/136588 [==============================] - 52s 384us/step - loss: 5.5934e-05 - acc: 1.0000 - val_loss: 0.0781 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00065: saving model to checkpoint.hdf5\n",
      "Epoch 66/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 4.6957e-05 - acc: 1.0000 - val_loss: 0.0784 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00066: saving model to checkpoint.hdf5\n",
      "Epoch 67/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 5.1590e-05 - acc: 1.0000 - val_loss: 0.0763 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00067: saving model to checkpoint.hdf5\n",
      "Epoch 68/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 6.3270e-05 - acc: 1.0000 - val_loss: 0.0785 - val_acc: 0.9845\n",
      "\n",
      "Epoch 00068: saving model to checkpoint.hdf5\n",
      "Epoch 69/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 5.7306e-05 - acc: 1.0000 - val_loss: 0.0772 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00069: saving model to checkpoint.hdf5\n",
      "Epoch 70/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 5.0468e-05 - acc: 1.0000 - val_loss: 0.0773 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00070: saving model to checkpoint.hdf5\n",
      "Epoch 71/100\n",
      "136588/136588 [==============================] - 52s 384us/step - loss: 4.5065e-05 - acc: 1.0000 - val_loss: 0.0775 - val_acc: 0.9848\n",
      "\n",
      "Epoch 00071: saving model to checkpoint.hdf5\n",
      "Epoch 72/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 5.2791e-05 - acc: 1.0000 - val_loss: 0.0789 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00072: saving model to checkpoint.hdf5\n",
      "Epoch 73/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 5.2874e-05 - acc: 1.0000 - val_loss: 0.0781 - val_acc: 0.9845\n",
      "\n",
      "Epoch 00073: saving model to checkpoint.hdf5\n",
      "Epoch 74/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 5.0217e-05 - acc: 1.0000 - val_loss: 0.0797 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00074: saving model to checkpoint.hdf5\n",
      "Epoch 75/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 4.6792e-05 - acc: 1.0000 - val_loss: 0.0799 - val_acc: 0.9845\n",
      "\n",
      "Epoch 00075: saving model to checkpoint.hdf5\n",
      "Epoch 76/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 5.0517e-05 - acc: 1.0000 - val_loss: 0.0779 - val_acc: 0.9849\n",
      "\n",
      "Epoch 00076: saving model to checkpoint.hdf5\n",
      "Epoch 77/100\n",
      "136588/136588 [==============================] - 53s 385us/step - loss: 5.6464e-05 - acc: 1.0000 - val_loss: 0.0790 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00077: saving model to checkpoint.hdf5\n",
      "Epoch 78/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 4.2832e-05 - acc: 1.0000 - val_loss: 0.0794 - val_acc: 0.9843\n",
      "\n",
      "Epoch 00078: ReduceLROnPlateau reducing learning rate to 0.0008100000384729356.\n",
      "\n",
      "Epoch 00078: saving model to checkpoint.hdf5\n",
      "Epoch 79/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 5.0727e-05 - acc: 1.0000 - val_loss: 0.0797 - val_acc: 0.9842\n",
      "\n",
      "Epoch 00079: saving model to checkpoint.hdf5\n",
      "Epoch 80/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 4.4002e-05 - acc: 1.0000 - val_loss: 0.0772 - val_acc: 0.9852\n",
      "\n",
      "Epoch 00080: saving model to checkpoint.hdf5\n",
      "Epoch 81/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 5.5478e-05 - acc: 1.0000 - val_loss: 0.0779 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00081: saving model to checkpoint.hdf5\n",
      "Epoch 82/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 4.0882e-05 - acc: 1.0000 - val_loss: 0.0783 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00082: saving model to checkpoint.hdf5\n",
      "Epoch 83/100\n",
      "136588/136588 [==============================] - 53s 385us/step - loss: 4.0998e-05 - acc: 1.0000 - val_loss: 0.0788 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00083: saving model to checkpoint.hdf5\n",
      "Epoch 84/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 5.1432e-05 - acc: 1.0000 - val_loss: 0.0801 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00084: saving model to checkpoint.hdf5\n",
      "Epoch 85/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "136588/136588 [==============================] - 52s 381us/step - loss: 4.3066e-05 - acc: 1.0000 - val_loss: 0.0798 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00085: saving model to checkpoint.hdf5\n",
      "Epoch 86/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 3.4209e-05 - acc: 1.0000 - val_loss: 0.0795 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00086: saving model to checkpoint.hdf5\n",
      "Epoch 87/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 3.5813e-05 - acc: 1.0000 - val_loss: 0.0797 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00087: saving model to checkpoint.hdf5\n",
      "Epoch 88/100\n",
      "136588/136588 [==============================] - 52s 379us/step - loss: 6.2306e-05 - acc: 1.0000 - val_loss: 0.0781 - val_acc: 0.9852\n",
      "\n",
      "Epoch 00088: saving model to checkpoint.hdf5\n",
      "Epoch 89/100\n",
      "136588/136588 [==============================] - 52s 384us/step - loss: 5.8371e-05 - acc: 1.0000 - val_loss: 0.0788 - val_acc: 0.9850\n",
      "\n",
      "Epoch 00089: saving model to checkpoint.hdf5\n",
      "Epoch 90/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 3.6668e-05 - acc: 1.0000 - val_loss: 0.0802 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00090: saving model to checkpoint.hdf5\n",
      "Epoch 91/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 4.0129e-05 - acc: 1.0000 - val_loss: 0.0793 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00091: saving model to checkpoint.hdf5\n",
      "Epoch 92/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 3.4875e-05 - acc: 1.0000 - val_loss: 0.0791 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00092: saving model to checkpoint.hdf5\n",
      "Epoch 93/100\n",
      "136588/136588 [==============================] - 52s 379us/step - loss: 3.9388e-05 - acc: 1.0000 - val_loss: 0.0793 - val_acc: 0.9848\n",
      "\n",
      "Epoch 00093: saving model to checkpoint.hdf5\n",
      "Epoch 94/100\n",
      "136588/136588 [==============================] - 52s 380us/step - loss: 4.6802e-05 - acc: 1.0000 - val_loss: 0.0832 - val_acc: 0.9842\n",
      "\n",
      "Epoch 00094: saving model to checkpoint.hdf5\n",
      "Epoch 95/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 4.2107e-05 - acc: 1.0000 - val_loss: 0.0800 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00095: saving model to checkpoint.hdf5\n",
      "Epoch 96/100\n",
      "136588/136588 [==============================] - 51s 377us/step - loss: 3.4455e-05 - acc: 1.0000 - val_loss: 0.0792 - val_acc: 0.9847\n",
      "\n",
      "Epoch 00096: saving model to checkpoint.hdf5\n",
      "Epoch 97/100\n",
      "136588/136588 [==============================] - 52s 382us/step - loss: 3.6282e-05 - acc: 1.0000 - val_loss: 0.0793 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00097: saving model to checkpoint.hdf5\n",
      "Epoch 98/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 3.9547e-05 - acc: 1.0000 - val_loss: 0.0792 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00098: saving model to checkpoint.hdf5\n",
      "Epoch 99/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 3.9247e-05 - acc: 1.0000 - val_loss: 0.0795 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00099: saving model to checkpoint.hdf5\n",
      "Epoch 100/100\n",
      "136588/136588 [==============================] - 52s 381us/step - loss: 1.4082e-04 - acc: 1.0000 - val_loss: 0.0792 - val_acc: 0.9846\n",
      "\n",
      "Epoch 00100: saving model to checkpoint.hdf5\n"
     ]
    }
   ],
   "source": [
    "# Start to train model \n",
    "history = model.fit(X_train, y_train, \n",
    "                    batch_size=32, \n",
    "                    epochs=100, \n",
    "                    verbose=1, \n",
    "                    validation_data=(X_test, y_test),\n",
    "                    callbacks=[reduce_lr, checkpointer],\n",
    "                    shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model with h5py\n",
    "import h5py\n",
    "from keras.models import load_model\n",
    "model.save('paviaU_model_epochs100.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import cross_val_score\n",
    "from sklearn.cross_validation import KFold\n",
    "def baseline_model():\n",
    "        model = Sequential()\n",
    "\n",
    "        model.add(Conv2D(C1, (3, 3), activation='relu', input_shape=input_shape))\n",
    "        model.add(Conv2D(3*C1, (3, 3), activation='relu'))\n",
    "        model.add(Dropout(0.25))\n",
    "\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(6*numPCAcomponents, activation='relu'))\n",
    "        model.add(Dropout(0.5))\n",
    "        model.add(Dense(9, activation='softmax'))\n",
    "\n",
    "kfold = KFold(n_splits=10, random_state=seed)\n",
    "results = cross_val_score(pipeline, X_train,y_train, cv=kfold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# summarize history for accuracy\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.grid(True)\n",
    "plt.legend(['train', 'test'], loc='upper left') \n",
    "plt.savefig(\"model_accuracy_100_paviou.svg\")\n",
    "plt.show()\n",
    "\n",
    "# summarize history for loss \n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(True)\n",
    "plt.legend(['train', 'test'], loc='upper left') \n",
    "plt.savefig(\"model_loss_100_paviou.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools\n",
    "import scipy.io as sio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the model evaluation report, \n",
    "# include classification report, confusion matrix, Test_Loss, Test_accuracy\n",
    "target_names = ['Water','Trees','Asphalt','Self-Blocking Bricks','Bitumen','Tiles','Shadows','Meadows','Bare Soil']\n",
    "def reports(X_test,y_test):\n",
    "    Y_pred = model.predict(X_test)\n",
    "    y_pred = np.argmax(Y_pred, axis=1)\n",
    "\n",
    "    classification = classification_report(np.argmax(y_test, axis=1), y_pred, target_names=target_names)\n",
    "    confusion = confusion_matrix(np.argmax(y_test, axis=1), y_pred)\n",
    "    score = model.evaluate(X_test, y_test, batch_size=32)\n",
    "    Test_Loss = score[0]*100\n",
    "    Test_accuracy = score[1]*100\n",
    "    return classification, confusion, Test_Loss, Test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8556/8556 [==============================] - 1s 150us/step\n",
      "Test loss 7.919281568153916 (%)\n",
      "Test accuracy 98.45722300140253 (%)\n",
      "classification result: \n",
      "                      precision    recall  f1-score   support\n",
      "\n",
      "               Water       0.99      0.99      0.99      1326\n",
      "               Trees       1.00      1.00      1.00      3730\n",
      "             Asphalt       0.94      0.86      0.90       420\n",
      "Self-Blocking Bricks       1.00      0.99      0.99       613\n",
      "             Bitumen       1.00      1.00      1.00       269\n",
      "               Tiles       0.99      1.00      0.99      1006\n",
      "             Shadows       0.98      1.00      0.99       266\n",
      "             Meadows       0.91      0.95      0.93       737\n",
      "           Bare Soil       1.00      1.00      1.00       189\n",
      "\n",
      "         avg / total       0.98      0.98      0.98      8556\n",
      "\n",
      "confusion matrix: \n",
      "[[1312    0    0    0    0    0    5    9    0]\n",
      " [   0 3720    0    1    0    9    0    0    0]\n",
      " [   0    1  363    0    0    0    0   56    0]\n",
      " [   0    2    0  607    0    3    0    1    0]\n",
      " [   0    0    0    0  269    0    0    0    0]\n",
      " [   0    5    0    0    0 1001    0    0    0]\n",
      " [   0    0    0    0    0    0  265    1    0]\n",
      " [  15    0   23    0    0    0    1  698    0]\n",
      " [   0    0    0    0    0    0    0    0  189]]\n"
     ]
    }
   ],
   "source": [
    "# load the model architecture and weights\n",
    "model = load_model('paviaU_model_epochs100.h5')\n",
    "\n",
    "# calculate result, loss, accuray and confusion matrix\n",
    "classification, confusion, Test_loss, Test_accuracy = reports(X_test,y_test)\n",
    "classification = str(classification)\n",
    "confusion_str = str(confusion)\n",
    "\n",
    "# show result and save to file\n",
    "print('Test loss {} (%)'.format(Test_loss))\n",
    "print('Test accuracy {} (%)'.format(Test_accuracy))\n",
    "print(\"classification result: \")\n",
    "print('{}'.format(classification))\n",
    "print(\"confusion matrix: \")\n",
    "print('{}'.format(confusion_str))\n",
    "file_name =\"WindowSize\" + str(windowSize) + \"PCA\" + str(numPCAcomponents) + \"testRatio\" + str(testRatio) +\".txt\"\n",
    "with open(file_name, 'w') as x_file:\n",
    "    x_file.write('Test loss {} (%)'.format(Test_loss))\n",
    "    x_file.write('\\n')\n",
    "    x_file.write('Test accuracy {} (%)'.format(Test_accuracy))\n",
    "    x_file.write('\\n')\n",
    "    x_file.write('\\n')\n",
    "    x_file.write(\" classification result: \\n\")\n",
    "    x_file.write('{}'.format(classification))\n",
    "    x_file.write('\\n')\n",
    "    x_file.write(\" confusion matrix: \\n\")\n",
    "    x_file.write('{}'.format(confusion_str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[1312    0    0    0    0    0    5    9    0]\n",
      " [   0 3720    0    1    0    9    0    0    0]\n",
      " [   0    1  363    0    0    0    0   56    0]\n",
      " [   0    2    0  607    0    3    0    1    0]\n",
      " [   0    0    0    0  269    0    0    0    0]\n",
      " [   0    5    0    0    0 1001    0    0    0]\n",
      " [   0    0    0    0    0    0  265    1    0]\n",
      " [  15    0   23    0    0    0    1  698    0]\n",
      " [   0    0    0    0    0    0    0    0  189]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n",
      "[[9.89441931e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 3.77073906e-03 6.78733032e-03\n",
      "  0.00000000e+00]\n",
      " [0.00000000e+00 9.97319035e-01 0.00000000e+00 2.68096515e-04\n",
      "  0.00000000e+00 2.41286863e-03 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00]\n",
      " [0.00000000e+00 2.38095238e-03 8.64285714e-01 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 0.00000000e+00 1.33333333e-01\n",
      "  0.00000000e+00]\n",
      " [0.00000000e+00 3.26264274e-03 0.00000000e+00 9.90212072e-01\n",
      "  0.00000000e+00 4.89396411e-03 0.00000000e+00 1.63132137e-03\n",
      "  0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  1.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00]\n",
      " [0.00000000e+00 4.97017893e-03 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 9.95029821e-01 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 9.96240602e-01 3.75939850e-03\n",
      "  0.00000000e+00]\n",
      " [2.03527815e-02 0.00000000e+00 3.12075984e-02 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 1.35685210e-03 9.47082768e-01\n",
      "  0.00000000e+00]\n",
      " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      "  1.00000000e+00]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.get_cmap(\"Blues\")):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    Normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    if normalize:\n",
    "        cm = Normalized\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(Normalized, interpolation='nearest', cmap=cmap)\n",
    "    plt.colorbar()\n",
    "    plt.title(title)\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=90)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.4f' if normalize else 'd'\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        thresh = cm[i].max() / 2.\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plot_confusion_matrix(confusion, classes=target_names, normalize=False, \n",
    "                      title='Confusion matrix, without normalization')\n",
    "plt.savefig(\"confusion_matrix_without_normalization.svg\")\n",
    "plt.show()\n",
    "plt.figure(figsize=(15,15))\n",
    "plot_confusion_matrix(confusion, classes=target_names, normalize=True, \n",
    "                      title='Normalized confusion matrix')\n",
    "plt.savefig(\"confusion_matrix_with_normalization.svg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the original image\n",
    "X, y = loadPaviaUData()\n",
    "X, pca = applyPCA(X, numComponents=numPCAcomponents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Patch(data,height_index,width_index):\n",
    "    #transpose_array = data.transpose((2,0,1))\n",
    "    #print transpose_array.shape\n",
    "    height_slice = slice(height_index, height_index+PATCH_SIZE)\n",
    "    width_slice = slice(width_index, width_index+PATCH_SIZE)\n",
    "    patch = data[height_slice, width_slice, :]\n",
    "    \n",
    "    return patch\n",
    "height = y.shape[0]\n",
    "width = y.shape[1]\n",
    "PATCH_SIZE = 5\n",
    "numComponents = 30\n",
    "\n",
    "# calculate the predicted image\n",
    "outputs = np.zeros((height,width))\n",
    "for i in range(height-PATCH_SIZE+1):\n",
    "    for j in range(width-PATCH_SIZE+1):\n",
    "        p = int(PATCH_SIZE/2)\n",
    "        # print(y[i+p][j+p])\n",
    "        # target = int(y[i+PATCH_SIZE/2, j+PATCH_SIZE/2])\n",
    "        target = y[i+p][j+p]\n",
    "        if target == 0 :\n",
    "            continue\n",
    "        else :\n",
    "            image_patch=Patch(X,i,j)\n",
    "            # print (image_patch.shape)\n",
    "            X_test_image = image_patch.reshape(1,image_patch.shape[2],image_patch.shape[0],image_patch.shape[1]).astype('float32')                                   \n",
    "            prediction = (model.predict_classes(X_test_image))                         \n",
    "            outputs[i+p][j+p] = prediction+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ground_truth = imshow(classes=y, figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_image = imshow(classes=outputs.astype(int), figsize=(10, 10))"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}