Kaivalya3 (copy).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from skimage import io, morphology, img_as_bool, segmentation\n",
    "from scipy import ndimage as ndi\n",
    "from scipy.ndimage.morphology import binary_fill_holes\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img_shape = (64,64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pad_resize(img):\n",
    "    top = int((224 - img.shape[0])/2)\n",
    "    left = int((224 - img.shape[1])/2)\n",
    "    bottom = 224 - img.shape[0] - top\n",
    "    right = 224 - img.shape[1] - left\n",
    "    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=255)\n",
    "    img = img/255.\n",
    "    img = cv2.resize(img, img_shape) #KADD\n",
    "    return img\n",
    "\n",
    "def skeletonize(img):\n",
    "    size = np.size(img)\n",
    "    skel = np.zeros(img.shape,np.uint8)\n",
    "    img = cv2.bitwise_not(img)\n",
    "#     element = cv2.getStructuringElement(cv2.MORPH_CROSS,(1,1))\n",
    "#     done = 0\n",
    "#     while( done < 1 ):\n",
    "#         eroded = cv2.erode(img,element)\n",
    "#         temp = cv2.dilate(eroded,element)\n",
    "#         temp = cv2.subtract(img,temp)\n",
    "#         skel = cv2.bitwise_or(skel,temp)\n",
    "#         img = eroded.copy()\n",
    "\n",
    "#         zeros = size - cv2.countNonZero(img)\n",
    "#         if zeros==size:#cv2.countNonZero(img) * 1 >= 0:#\n",
    "#             done += 1\n",
    "#     img = skel\n",
    "    kernel = np.ones((3,3),np.uint8)\n",
    "    erosion = cv2.erode(img,kernel,iterations = 2)\n",
    "    img = cv2.bitwise_not(erosion)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 Done\n",
      "page6_1_1_2340_2375_2379.png dropped!\n",
      "200 Done\n",
      "page7_3_0_2319_2366_2367.png dropped!\n",
      "300 Done\n",
      "page0_8_8_2330_2375_2379.png dropped!\n",
      "400 Done\n",
      "page6_10_6_2325_2366_2380.png dropped!\n",
      "page0_9_15_2332_2375_2379.png dropped!\n",
      "500 Done\n",
      "600 Done\n",
      "page3_19_7_2357_2364_2417.png dropped!\n",
      "700 Done\n",
      "page3_18_4_2332_2366_2379.png dropped!\n",
      "page3_12_0_2357_2364_2416.png dropped!\n",
      "800 Done\n",
      "900 Done\n",
      "page7_3_13_2319_2366_2367.png dropped!\n",
      "1000 Done\n",
      "1100 Done\n",
      "1200 Done\n",
      "1300 Done\n",
      "1400 Done\n",
      "page5_7_8_2325_2367_2388.png dropped!\n",
      "1500 Done\n",
      "page2_4_20_2346_2366_2390.png dropped!\n",
      "1600 Done\n",
      "1700 Done\n",
      "1800 Done\n"
     ]
    }
   ],
   "source": [
    "PATH = '../train_images_modified'\n",
    "images = []\n",
    "base_class = []\n",
    "matra_class = []\n",
    "dot_class = []\n",
    "total_class = []\n",
    "for filename in os.listdir(PATH):\n",
    "    if filename.endswith(\".png\"):\n",
    "        img = cv2.imread(os.path.join(PATH,filename),0)\n",
    "        blur = cv2.GaussianBlur(img,(9,9),0)# KADD\n",
    "        a,img = cv2.threshold(blur,127,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)#KEDIT\n",
    "#         kernel = np.ones((5,5),np.uint8)\n",
    "#         img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)\n",
    "#         plt.imshow(img, cmap='gray')\n",
    "#         plt.show()\n",
    "#        img = skeletonize(img)\n",
    "        img = pad_resize(img)\n",
    "        blur = cv2.GaussianBlur(img,(9,9),0)# KADD\n",
    "        char_arr = filename[:-4].split('_')[3:]\n",
    "        if(len(char_arr)>0):\n",
    "            char_arr = [int(i) for i in char_arr]\n",
    "            base = [i for i in char_arr if (i>=2308 and i<=2361) or (i==2384) or (i>=2392 and i<=2401) or (i>=2404 and i!=2416 and i!=2417)]  \n",
    "            matra = [i for i in char_arr if i>=2362 and i<=2391 and i!=2364 and i!=2362]\n",
    "            dot = [i for i in char_arr if i==2306 or i==2416 or i==2362 or i==2364 or i==2417]\n",
    "            if(len(matra)>1 or len(dot)>1 or len(base)>1 or (len(matra)==0 and len(dot)==0 and len(char_arr)==2)):\n",
    "                print(filename+\" dropped!\")\n",
    "            else:\n",
    "                images.append(img)\n",
    "                base_class.append(base[0])\n",
    "                if(len(matra)==1):\n",
    "                    matra_class.append(matra[0])\n",
    "                else:\n",
    "                    matra_class.append(0)\n",
    "\n",
    "                if len(dot)==1:\n",
    "                    if dot[0] == 2364 or dot[0] == 2416:\n",
    "                        dot_class.append(2)\n",
    "                    else:\n",
    "                        dot_class.append(1)\n",
    "                else:\n",
    "                    dot_class.append(0)\n",
    "\n",
    "                if len(images)%100==0:\n",
    "                    print(\"{} Done\".format(len(images)))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NEuTEN0uQE98sQU58swQ58c0S5MQ3S5AT3yxBpRJf0hpJ35b0Q0knJH1C0uWSDkg6md2u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\nFZfi0g1eNxkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc02f4c5ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1884\n",
      "1884\n"
     ]
    }
   ],
   "source": [
    "plt.imshow(images[10],\"gray\")\n",
    "plt.show()\n",
    "print(len(images))\n",
    "print(len(dot_class))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2392, 2353, 2384, 2415, 2310, 2414]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(base_class))\n",
    "freq = {i:base_class.count(i) for i in base_class}\n",
    "[i for i in freq.keys() if freq[i]==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1884, 64, 64)\n"
     ]
    }
   ],
   "source": [
    "images = np.array(images)\n",
    "print(images.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1884, 53)\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.utils import np_utils\n",
    "\n",
    "le = LabelEncoder()\n",
    "le.fit(base_class)\n",
    "y_labeled = le.transform(base_class)\n",
    "y_train = np_utils.to_categorical(y_labeled)\n",
    "print(y_train.shape)\n",
    "#print(le)\n",
    "#le.get_params()\n",
    "#for i in range(len(topr)):\n",
    "#    if base_class[i] == 2405:\n",
    "#        print('{} {}'.format(topr[i], base_class[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1884, 64, 64, 1)\n"
     ]
    }
   ],
   "source": [
    "x_train = np.reshape(images,(-1,img_shape[0],img_shape[1],1))\n",
    "print(x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout,Conv2D,MaxPooling2D,Flatten\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "base_model = Sequential()\n",
    "\n",
    "base_model.add(Conv2D(16,(3,3),input_shape = (img_shape[0],img_shape[1],1),activation = 'relu'))\n",
    "base_model.add(Conv2D(16,(3,3),activation = 'relu'))\n",
    "base_model.add(MaxPooling2D())\n",
    "base_model.add(Conv2D(32,(3,3),activation = 'relu'))\n",
    "base_model.add(Conv2D(32,(3,3),activation = 'relu'))\n",
    "base_model.add(MaxPooling2D())\n",
    "base_model.add(Conv2D(64,(3,3),activation = 'relu'))\n",
    "base_model.add(Conv2D(64,(3,3),activation = 'relu'))\n",
    "base_model.add(MaxPooling2D())\n",
    "base_model.add(MaxPooling2D())\n",
    "base_model.add(Dropout(0.5))\n",
    "base_model.add(Flatten())\n",
    "base_model.add(Dense(256,activation = 'sigmoid'))\n",
    "base_model.add(Dropout(0.25))\n",
    "base_model.add(Dense(len(set(base_class)),activation = 'softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 62, 62, 16)        160       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 60, 60, 16)        2320      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 30, 30, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 28, 28, 32)        4640      \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 26, 26, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 9, 9, 64)          36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 2, 2, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 2, 2, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 53)                13621     \n",
      "=================================================================\n",
      "Total params: 151,205\n",
      "Trainable params: 151,205\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras.backend as K\n",
    "\n",
    "def f1_score(theta):\n",
    "    def score(y_true, y_pred):\n",
    "\n",
    "        y_thresh = K.cast(K.greater(y_pred,theta),K.floatx())\n",
    "\n",
    "        true_pos =  K.sum(y_true * y_thresh)\n",
    "        false_pos = K.sum(y_true * (1. - y_thresh))\n",
    "        false_neg = K.sum((1. - y_true) * y_thresh)\n",
    "\n",
    "        precision = true_pos / (true_pos + false_pos)\n",
    "        recall = true_pos / (true_pos + false_neg)\n",
    "        \n",
    "        f1_score_val = 2 * (precision * recall) / (precision + recall)\n",
    "        return f1_score_val\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "base_model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy', metrics=['categorical_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# augmentation code here ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1318 samples, validate on 566 samples\n",
      "Epoch 1/30\n",
      "1318/1318 [==============================] - 15s 12ms/step - loss: 3.5758 - categorical_accuracy: 0.0804 - val_loss: 3.3588 - val_categorical_accuracy: 0.1166\n",
      "Epoch 2/30\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 3.4012 - categorical_accuracy: 0.0751 - val_loss: 3.3592 - val_categorical_accuracy: 0.1166\n",
      "Epoch 3/30\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 3.3684 - categorical_accuracy: 0.0941 - val_loss: 3.3246 - val_categorical_accuracy: 0.0601\n",
      "Epoch 4/30\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 3.1904 - categorical_accuracy: 0.1715 - val_loss: 2.8285 - val_categorical_accuracy: 0.2809\n",
      "Epoch 5/30\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 2.6526 - categorical_accuracy: 0.3225 - val_loss: 2.1116 - val_categorical_accuracy: 0.4717\n",
      "Epoch 6/30\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 2.0719 - categorical_accuracy: 0.4659 - val_loss: 1.6779 - val_categorical_accuracy: 0.5724\n",
      "Epoch 7/30\n",
      "1318/1318 [==============================] - 16s 12ms/step - loss: 1.7060 - categorical_accuracy: 0.5615 - val_loss: 1.3818 - val_categorical_accuracy: 0.6254\n",
      "Epoch 8/30\n",
      "1318/1318 [==============================] - 16s 12ms/step - loss: 1.4833 - categorical_accuracy: 0.6138 - val_loss: 1.1687 - val_categorical_accuracy: 0.6943\n",
      "Epoch 9/30\n",
      "1318/1318 [==============================] - 17s 13ms/step - loss: 1.3086 - categorical_accuracy: 0.6540 - val_loss: 1.0845 - val_categorical_accuracy: 0.6943\n",
      "Epoch 10/30\n",
      "1318/1318 [==============================] - 16s 12ms/step - loss: 1.1963 - categorical_accuracy: 0.6866 - val_loss: 0.9341 - val_categorical_accuracy: 0.7438\n",
      "Epoch 11/30\n",
      "1318/1318 [==============================] - 16s 12ms/step - loss: 1.0379 - categorical_accuracy: 0.7178 - val_loss: 0.9000 - val_categorical_accuracy: 0.7580\n",
      "Epoch 12/30\n",
      "1318/1318 [==============================] - 15s 12ms/step - loss: 0.9694 - categorical_accuracy: 0.7428 - val_loss: 0.8421 - val_categorical_accuracy: 0.7686\n",
      "Epoch 13/30\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.9024 - categorical_accuracy: 0.7618 - val_loss: 0.7962 - val_categorical_accuracy: 0.7933\n",
      "Epoch 14/30\n",
      "1318/1318 [==============================] - 15s 12ms/step - loss: 0.8695 - categorical_accuracy: 0.7564 - val_loss: 0.7934 - val_categorical_accuracy: 0.7739\n",
      "Epoch 15/30\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.7788 - categorical_accuracy: 0.7853 - val_loss: 0.7134 - val_categorical_accuracy: 0.8039\n",
      "Epoch 16/30\n",
      "1318/1318 [==============================] - 16s 12ms/step - loss: 0.7419 - categorical_accuracy: 0.7853 - val_loss: 0.7859 - val_categorical_accuracy: 0.7650\n",
      "Epoch 17/30\n",
      "1318/1318 [==============================] - 14s 11ms/step - loss: 0.7268 - categorical_accuracy: 0.7959 - val_loss: 0.6849 - val_categorical_accuracy: 0.8198\n",
      "Epoch 18/30\n",
      "1318/1318 [==============================] - 14s 11ms/step - loss: 0.6898 - categorical_accuracy: 0.8065 - val_loss: 0.6308 - val_categorical_accuracy: 0.8357\n",
      "Epoch 19/30\n",
      "1318/1318 [==============================] - 14s 11ms/step - loss: 0.6244 - categorical_accuracy: 0.8171 - val_loss: 0.6611 - val_categorical_accuracy: 0.8322\n",
      "Epoch 20/30\n",
      "1318/1318 [==============================] - 14s 11ms/step - loss: 0.6118 - categorical_accuracy: 0.8300 - val_loss: 0.6012 - val_categorical_accuracy: 0.8322\n",
      "Epoch 21/30\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.5393 - categorical_accuracy: 0.8460 - val_loss: 0.6063 - val_categorical_accuracy: 0.8339\n",
      "Epoch 22/30\n",
      "1318/1318 [==============================] - 17s 13ms/step - loss: 0.5547 - categorical_accuracy: 0.8414 - val_loss: 0.5998 - val_categorical_accuracy: 0.8357\n",
      "Epoch 23/30\n",
      "1318/1318 [==============================] - 15s 12ms/step - loss: 0.5532 - categorical_accuracy: 0.8293 - val_loss: 0.5886 - val_categorical_accuracy: 0.8410\n",
      "Epoch 24/30\n",
      "1318/1318 [==============================] - 16s 12ms/step - loss: 0.4982 - categorical_accuracy: 0.8566 - val_loss: 0.5417 - val_categorical_accuracy: 0.8569\n",
      "Epoch 25/30\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.4495 - categorical_accuracy: 0.8703 - val_loss: 0.5571 - val_categorical_accuracy: 0.8481\n",
      "Epoch 26/30\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.4498 - categorical_accuracy: 0.8718 - val_loss: 0.5368 - val_categorical_accuracy: 0.8587\n",
      "Epoch 27/30\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3939 - categorical_accuracy: 0.8885 - val_loss: 0.5439 - val_categorical_accuracy: 0.8410\n",
      "Epoch 28/30\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.4219 - categorical_accuracy: 0.8786 - val_loss: 0.5447 - val_categorical_accuracy: 0.8569\n",
      "Epoch 29/30\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3665 - categorical_accuracy: 0.8976 - val_loss: 0.5234 - val_categorical_accuracy: 0.8657\n",
      "Epoch 30/30\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.4067 - categorical_accuracy: 0.8816 - val_loss: 0.5217 - val_categorical_accuracy: 0.8569\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fc01afbf898>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_model.fit(x_train,y_train,epochs=30,batch_size=32,validation_split=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1318 samples, validate on 566 samples\n",
      "Epoch 1/5\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3258 - categorical_accuracy: 0.9150 - val_loss: 0.4969 - val_categorical_accuracy: 0.8622\n",
      "Epoch 2/5\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3028 - categorical_accuracy: 0.9127 - val_loss: 0.4976 - val_categorical_accuracy: 0.8640\n",
      "Epoch 3/5\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.2956 - categorical_accuracy: 0.9135 - val_loss: 0.4924 - val_categorical_accuracy: 0.8640\n",
      "Epoch 4/5\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3053 - categorical_accuracy: 0.9181 - val_loss: 0.4912 - val_categorical_accuracy: 0.8640\n",
      "Epoch 5/5\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.2908 - categorical_accuracy: 0.9203 - val_loss: 0.4852 - val_categorical_accuracy: 0.8657\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fc01971f518>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_adam = keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
    "base_model.compile(optimizer=new_adam,\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['categorical_accuracy'])\n",
    "base_model.fit(x_train,y_train,epochs=5,batch_size=32,validation_split=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new_adam = keras.optimizers.Adam(lr=0.00005, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
    "# base_model.compile(optimizer=new_adam,\n",
    "#               loss='categorical_crossentropy',\n",
    "#               metrics=['categorical_accuracy'])\n",
    "# base_model.fit(x_train,y_train,epochs=30,batch_size=32,validation_split=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1884/1884 [==============================] - 5s 3ms/step\n",
      "confusion matrix for (53, 53) features (base):\n",
      "[[ 1  0  0 ...,  0  0  0]\n",
      " [ 0 15  0 ...,  0  0  0]\n",
      " [ 0  0 31 ...,  0  0  0]\n",
      " ..., \n",
      " [ 0  0  0 ...,  0  0  0]\n",
      " [ 0  1  0 ...,  0  3  0]\n",
      " [ 0  0  0 ...,  0  0  3]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfe441c0f0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YfoZ+efcv9dC/PKSH/uUhlZ1Qpu9/4fsqLS11fV88baDhb0Osxrwr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\n0oCSARpePVyjPjRKZ3zoDA0qH6Sjhx2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df4akL5rZZd24jY4b2JzFsso05/fHhocNNLrX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r7Blj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\nB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfe48c8a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "y_pred = base_model.predict_classes(x_train)\n",
    "y_original = np.argmax(y_train, axis=1)\n",
    "\n",
    "cm = confusion_matrix(y_original, y_pred)\n",
    "print('confusion matrix for {} features (base):'.format(cm.shape))\n",
    "print(cm)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,20))\n",
    "sns.heatmap(cm, cmap=sns.light_palette('purple', n_colors=500, as_cmap=True), annot=True, linewidths = 5, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DOT CLASS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train_dot = np_utils.to_categorical(dot_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1318 samples, validate on 566 samples\n",
      "Epoch 1/20\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.4652 - categorical_accuracy: 0.8809 - val_loss: 0.2726 - val_categorical_accuracy: 0.9382\n",
      "Epoch 2/20\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.4182 - categorical_accuracy: 0.9014 - val_loss: 0.2696 - val_categorical_accuracy: 0.9382\n",
      "Epoch 3/20\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3942 - categorical_accuracy: 0.9029 - val_loss: 0.2681 - val_categorical_accuracy: 0.9382\n",
      "Epoch 4/20\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3956 - categorical_accuracy: 0.9044 - val_loss: 0.2665 - val_categorical_accuracy: 0.9382\n",
      "Epoch 5/20\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3926 - categorical_accuracy: 0.9044 - val_loss: 0.2665 - val_categorical_accuracy: 0.9382\n",
      "Epoch 6/20\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3852 - categorical_accuracy: 0.9052 - val_loss: 0.2667 - val_categorical_accuracy: 0.9382\n",
      "Epoch 7/20\n",
      "1318/1318 [==============================] - 14s 10ms/step - loss: 0.3891 - categorical_accuracy: 0.9044 - val_loss: 0.2669 - val_categorical_accuracy: 0.9382\n",
      "Epoch 8/20\n",
      "1318/1318 [==============================] - 14s 10ms/step - loss: 0.3870 - categorical_accuracy: 0.9052 - val_loss: 0.2675 - val_categorical_accuracy: 0.9382\n",
      "Epoch 9/20\n",
      "1318/1318 [==============================] - 14s 10ms/step - loss: 0.3793 - categorical_accuracy: 0.9052 - val_loss: 0.2676 - val_categorical_accuracy: 0.9382\n",
      "Epoch 10/20\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3860 - categorical_accuracy: 0.9052 - val_loss: 0.2689 - val_categorical_accuracy: 0.9382\n",
      "Epoch 11/20\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3673 - categorical_accuracy: 0.9052 - val_loss: 0.2670 - val_categorical_accuracy: 0.9382\n",
      "Epoch 12/20\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3715 - categorical_accuracy: 0.9052 - val_loss: 0.2674 - val_categorical_accuracy: 0.9382\n",
      "Epoch 13/20\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3858 - categorical_accuracy: 0.9052 - val_loss: 0.2697 - val_categorical_accuracy: 0.9382\n",
      "Epoch 14/20\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3685 - categorical_accuracy: 0.9052 - val_loss: 0.2712 - val_categorical_accuracy: 0.9382\n",
      "Epoch 15/20\n",
      "1318/1318 [==============================] - 13s 10ms/step - loss: 0.3783 - categorical_accuracy: 0.9052 - val_loss: 0.2686 - val_categorical_accuracy: 0.9382\n",
      "Epoch 16/20\n",
      "1318/1318 [==============================] - 14s 11ms/step - loss: 0.3713 - categorical_accuracy: 0.9052 - val_loss: 0.2719 - val_categorical_accuracy: 0.9382\n",
      "Epoch 17/20\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.3653 - categorical_accuracy: 0.9052 - val_loss: 0.2682 - val_categorical_accuracy: 0.9382\n",
      "Epoch 18/20\n",
      "1318/1318 [==============================] - 19s 15ms/step - loss: 0.3766 - categorical_accuracy: 0.9052 - val_loss: 0.2698 - val_categorical_accuracy: 0.9382\n",
      "Epoch 19/20\n",
      "1318/1318 [==============================] - 21s 16ms/step - loss: 0.3731 - categorical_accuracy: 0.9052 - val_loss: 0.2681 - val_categorical_accuracy: 0.9382\n",
      "Epoch 20/20\n",
      "1318/1318 [==============================] - 25s 19ms/step - loss: 0.3701 - categorical_accuracy: 0.9052 - val_loss: 0.2712 - val_categorical_accuracy: 0.9382\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fbfe32baa20>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_model = Sequential()\n",
    "\n",
    "dot_model.add(Conv2D(16,(3,3),input_shape = (img_shape[0],img_shape[1],1),activation = 'relu'))\n",
    "dot_model.add(Conv2D(16,(3,3),activation = 'relu'))\n",
    "dot_model.add(MaxPooling2D())\n",
    "dot_model.add(Conv2D(32,(3,3),activation = 'relu'))\n",
    "dot_model.add(Conv2D(32,(3,3),activation = 'relu'))\n",
    "dot_model.add(MaxPooling2D())\n",
    "dot_model.add(Conv2D(64,(3,3),activation = 'relu'))\n",
    "dot_model.add(Conv2D(64,(3,3),activation = 'relu'))\n",
    "dot_model.add(MaxPooling2D())\n",
    "dot_model.add(Flatten())\n",
    "dot_model.add(Dense(64,activation = 'sigmoid'))\n",
    "dot_model.add(Dropout(0.5))\n",
    "dot_model.add(Dense(len(set(dot_class)),activation = 'softmax'))\n",
    "\n",
    "dot_model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['categorical_accuracy'])\n",
    "\n",
    "dot_model.fit(x_train,y_train_dot,epochs=20,batch_size=32,validation_split=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1318 samples, validate on 566 samples\n",
      "Epoch 1/5\n",
      "1318/1318 [==============================] - 20s 15ms/step - loss: 0.3688 - categorical_accuracy: 0.9052 - val_loss: 0.2688 - val_categorical_accuracy: 0.9382\n",
      "Epoch 2/5\n",
      "1318/1318 [==============================] - 16s 12ms/step - loss: 0.3799 - categorical_accuracy: 0.9052 - val_loss: 0.2708 - val_categorical_accuracy: 0.9382\n",
      "Epoch 3/5\n",
      "1318/1318 [==============================] - 17s 13ms/step - loss: 0.3679 - categorical_accuracy: 0.9052 - val_loss: 0.2723 - val_categorical_accuracy: 0.9382\n",
      "Epoch 4/5\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.3661 - categorical_accuracy: 0.9052 - val_loss: 0.2706 - val_categorical_accuracy: 0.9382\n",
      "Epoch 5/5\n",
      "1318/1318 [==============================] - 14s 11ms/step - loss: 0.3640 - categorical_accuracy: 0.9052 - val_loss: 0.2699 - val_categorical_accuracy: 0.9382\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fbfe42e0320>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_adam = keras.optimizers.Adam(lr=0.0005, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
    "dot_model.compile(optimizer=new_adam,\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['categorical_accuracy'])\n",
    "dot_model.fit(x_train,y_train_dot,epochs=5,batch_size=64,validation_split=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1884/1884 [==============================] - 5s 3ms/step\n",
      "confusion matrix for (3, 3) features (dot):\n",
      "[[1724    0    0]\n",
      " [ 134    0    0]\n",
      " [  26    0    0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfe12769b0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Kfd+/8kJ5aCAusr2PLZnUTd6z0ZzzDuAGd99sZj8DZpjZGGAVMCo69kWq5qVXANuAqxtT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\n4SwiEkMKZxGRGFI4i4jEkMJZRCSG/h8TJaRkuVxi2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfe0bd4e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "y_pred = dot_model.predict_classes(x_train)\n",
    "y_original = np.argmax(y_train_dot, axis=1)\n",
    "\n",
    "cm = confusion_matrix(y_original, y_pred)\n",
    "print('confusion matrix for {} features (dot):'.format(cm.shape))\n",
    "print(cm)\n",
    "\n",
    "#fig, ax = plt.subplots(figsize=(20,20))\n",
    "sns.heatmap(cm, cmap=sns.light_palette('purple', n_colors=500, as_cmap=True), annot=True, linewidths = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MATRA CLASS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1884, 16)\n"
     ]
    }
   ],
   "source": [
    "le_matra = LabelEncoder()\n",
    "le_matra.fit(matra_class)\n",
    "y_labeled_matra = le_matra.transform(matra_class)\n",
    "y_train_matra = np_utils.to_categorical(y_labeled_matra)\n",
    "\n",
    "print(y_train_matra.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1318 samples, validate on 566 samples\n",
      "Epoch 1/45\n",
      "1318/1318 [==============================] - 14s 11ms/step - loss: 0.1774 - categorical_accuracy: 0.4469 - val_loss: 0.1653 - val_categorical_accuracy: 0.4682\n",
      "Epoch 2/45\n",
      "1318/1318 [==============================] - 14s 11ms/step - loss: 0.1514 - categorical_accuracy: 0.5038 - val_loss: 0.1283 - val_categorical_accuracy: 0.5795\n",
      "Epoch 3/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.1248 - categorical_accuracy: 0.5827 - val_loss: 0.1070 - val_categorical_accuracy: 0.6820\n",
      "Epoch 4/45\n",
      "1318/1318 [==============================] - 18s 13ms/step - loss: 0.1138 - categorical_accuracy: 0.6252 - val_loss: 0.0937 - val_categorical_accuracy: 0.7049\n",
      "Epoch 5/45\n",
      "1318/1318 [==============================] - 14s 11ms/step - loss: 0.1024 - categorical_accuracy: 0.6753 - val_loss: 0.1027 - val_categorical_accuracy: 0.6784\n",
      "Epoch 6/45\n",
      "1318/1318 [==============================] - 14s 11ms/step - loss: 0.0936 - categorical_accuracy: 0.7223 - val_loss: 0.0804 - val_categorical_accuracy: 0.7420\n",
      "Epoch 7/45\n",
      "1318/1318 [==============================] - 14s 10ms/step - loss: 0.0821 - categorical_accuracy: 0.7405 - val_loss: 0.0760 - val_categorical_accuracy: 0.7650\n",
      "Epoch 8/45\n",
      "1318/1318 [==============================] - 14s 11ms/step - loss: 0.0780 - categorical_accuracy: 0.7511 - val_loss: 0.0712 - val_categorical_accuracy: 0.7809\n",
      "Epoch 9/45\n",
      "1318/1318 [==============================] - 14s 11ms/step - loss: 0.0746 - categorical_accuracy: 0.7602 - val_loss: 0.0702 - val_categorical_accuracy: 0.7827\n",
      "Epoch 10/45\n",
      "1318/1318 [==============================] - 17s 13ms/step - loss: 0.0698 - categorical_accuracy: 0.7785 - val_loss: 0.0663 - val_categorical_accuracy: 0.7951\n",
      "Epoch 11/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0667 - categorical_accuracy: 0.7967 - val_loss: 0.0638 - val_categorical_accuracy: 0.8127\n",
      "Epoch 12/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0611 - categorical_accuracy: 0.8020 - val_loss: 0.0670 - val_categorical_accuracy: 0.8163\n",
      "Epoch 13/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0600 - categorical_accuracy: 0.8035 - val_loss: 0.0578 - val_categorical_accuracy: 0.8339\n",
      "Epoch 14/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0566 - categorical_accuracy: 0.8171 - val_loss: 0.0593 - val_categorical_accuracy: 0.8269\n",
      "Epoch 15/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0547 - categorical_accuracy: 0.8293 - val_loss: 0.0555 - val_categorical_accuracy: 0.8163\n",
      "Epoch 16/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0551 - categorical_accuracy: 0.8247 - val_loss: 0.0591 - val_categorical_accuracy: 0.8375\n",
      "Epoch 17/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0532 - categorical_accuracy: 0.8331 - val_loss: 0.0529 - val_categorical_accuracy: 0.8498\n",
      "Epoch 18/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0456 - categorical_accuracy: 0.8581 - val_loss: 0.0512 - val_categorical_accuracy: 0.8498\n",
      "Epoch 19/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0481 - categorical_accuracy: 0.8543 - val_loss: 0.0498 - val_categorical_accuracy: 0.8604\n",
      "Epoch 20/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0442 - categorical_accuracy: 0.8604 - val_loss: 0.0497 - val_categorical_accuracy: 0.8604\n",
      "Epoch 21/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0430 - categorical_accuracy: 0.8627 - val_loss: 0.0464 - val_categorical_accuracy: 0.8799\n",
      "Epoch 22/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0418 - categorical_accuracy: 0.8748 - val_loss: 0.0501 - val_categorical_accuracy: 0.8710\n",
      "Epoch 23/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0389 - categorical_accuracy: 0.8877 - val_loss: 0.0481 - val_categorical_accuracy: 0.8746\n",
      "Epoch 24/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0379 - categorical_accuracy: 0.8869 - val_loss: 0.0458 - val_categorical_accuracy: 0.8746\n",
      "Epoch 25/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0354 - categorical_accuracy: 0.8953 - val_loss: 0.0459 - val_categorical_accuracy: 0.8728\n",
      "Epoch 26/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0342 - categorical_accuracy: 0.8968 - val_loss: 0.0461 - val_categorical_accuracy: 0.8905\n",
      "Epoch 27/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0357 - categorical_accuracy: 0.8998 - val_loss: 0.0463 - val_categorical_accuracy: 0.8958\n",
      "Epoch 28/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0305 - categorical_accuracy: 0.9127 - val_loss: 0.0408 - val_categorical_accuracy: 0.8993\n",
      "Epoch 29/45\n",
      "1318/1318 [==============================] - 15s 12ms/step - loss: 0.0305 - categorical_accuracy: 0.9158 - val_loss: 0.0439 - val_categorical_accuracy: 0.8993\n",
      "Epoch 30/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0318 - categorical_accuracy: 0.8998 - val_loss: 0.0395 - val_categorical_accuracy: 0.9205\n",
      "Epoch 31/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0284 - categorical_accuracy: 0.9173 - val_loss: 0.0409 - val_categorical_accuracy: 0.9081\n",
      "Epoch 32/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0275 - categorical_accuracy: 0.9181 - val_loss: 0.0437 - val_categorical_accuracy: 0.8975\n",
      "Epoch 33/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0261 - categorical_accuracy: 0.9272 - val_loss: 0.0409 - val_categorical_accuracy: 0.8993\n",
      "Epoch 34/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0230 - categorical_accuracy: 0.9393 - val_loss: 0.0366 - val_categorical_accuracy: 0.9170\n",
      "Epoch 35/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0242 - categorical_accuracy: 0.9355 - val_loss: 0.0396 - val_categorical_accuracy: 0.8905\n",
      "Epoch 36/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0236 - categorical_accuracy: 0.9340 - val_loss: 0.0370 - val_categorical_accuracy: 0.9134\n",
      "Epoch 37/45\n",
      "1318/1318 [==============================] - 15s 12ms/step - loss: 0.0222 - categorical_accuracy: 0.9363 - val_loss: 0.0368 - val_categorical_accuracy: 0.9152\n",
      "Epoch 38/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0216 - categorical_accuracy: 0.9408 - val_loss: 0.0384 - val_categorical_accuracy: 0.9117\n",
      "Epoch 39/45\n",
      "1318/1318 [==============================] - 15s 12ms/step - loss: 0.0207 - categorical_accuracy: 0.9454 - val_loss: 0.0383 - val_categorical_accuracy: 0.9187\n",
      "Epoch 40/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0233 - categorical_accuracy: 0.9340 - val_loss: 0.0385 - val_categorical_accuracy: 0.9205\n",
      "Epoch 41/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0210 - categorical_accuracy: 0.9461 - val_loss: 0.0377 - val_categorical_accuracy: 0.9170\n",
      "Epoch 42/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0215 - categorical_accuracy: 0.9446 - val_loss: 0.0382 - val_categorical_accuracy: 0.9134\n",
      "Epoch 43/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0179 - categorical_accuracy: 0.9613 - val_loss: 0.0383 - val_categorical_accuracy: 0.9117\n",
      "Epoch 44/45\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.0168 - categorical_accuracy: 0.9628 - val_loss: 0.0367 - val_categorical_accuracy: 0.9187\n",
      "Epoch 45/45\n",
      "1318/1318 [==============================] - 15s 12ms/step - loss: 0.0181 - categorical_accuracy: 0.9545 - val_loss: 0.0373 - val_categorical_accuracy: 0.9170\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fbfe06c7e80>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matra_model = Sequential()\n",
    "\n",
    "matra_model.add(Conv2D(16,(3,3),input_shape = (img_shape[0],img_shape[1],1),activation = 'relu'))\n",
    "matra_model.add(Conv2D(16,(3,3),activation = 'relu'))\n",
    "matra_model.add(MaxPooling2D())\n",
    "matra_model.add(Conv2D(32,(3,3),activation = 'relu'))\n",
    "matra_model.add(Conv2D(32,(3,3),activation = 'relu'))\n",
    "matra_model.add(MaxPooling2D())\n",
    "matra_model.add(Conv2D(64,(3,3),activation = 'relu'))\n",
    "matra_model.add(Conv2D(64,(3,3),activation = 'relu'))\n",
    "matra_model.add(MaxPooling2D())\n",
    "matra_model.add(MaxPooling2D())\n",
    "matra_model.add(Dropout(0.5))\n",
    "matra_model.add(Flatten())\n",
    "matra_model.add(Dense(128,activation = 'sigmoid'))\n",
    "matra_model.add(Dropout(0.25))\n",
    "matra_model.add(Dense(len(set(matra_class)),activation = 'softmax'))\n",
    "\n",
    "matra_model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['categorical_accuracy'])\n",
    "\n",
    "matra_model.fit(x_train,y_train_matra,epochs=45,batch_size=32,validation_split=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1318 samples, validate on 566 samples\n",
      "Epoch 1/2\n",
      "1318/1318 [==============================] - 15s 12ms/step - loss: 0.1543 - categorical_accuracy: 0.9598 - val_loss: 0.3516 - val_categorical_accuracy: 0.9170\n",
      "Epoch 2/2\n",
      "1318/1318 [==============================] - 15s 11ms/step - loss: 0.1228 - categorical_accuracy: 0.9659 - val_loss: 0.3665 - val_categorical_accuracy: 0.9205\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fbfd8334710>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_adam = keras.optimizers.Adam(lr=0.00005, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
    "matra_model.compile(optimizer=new_adam,\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['categorical_accuracy'])\n",
    "matra_model.fit(x_train,y_train_matra,epochs=2,batch_size=32,validation_split=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1884/1884 [==============================] - 6s 3ms/step\n",
      "confusion matrix for (16, 16) features (matra):\n",
      "[[895   0   5   1   0   2   1   0   0   0   0   1   0   0   0   0]\n",
      " [  0   4   3   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  3   0 286   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0 180   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  1   0   2   1 136   0   0   0   0   0   2   0   0   0   0   0]\n",
      " [  5   0   0   0   0  37   0   0   0   0   0   0   0   0   0   0]\n",
      " [  7   0   1   0   0   0  20   0   0   0   0   0   0   0   0   0]\n",
      " [  1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  1   0   0   2   0   0   0   0  81   0   0   0   0   0   0   0]\n",
      " [  0   0   0   3   0   0   0   0   0  79   0   4   0   0   0   0]\n",
      " [  0   1   0   0   5   0   0   0   2   0  47   1   0   0   0   0]\n",
      " [  0   0   1   0   0   0   0   0   0   3   1  54   0   0   0   0]\n",
      " [  1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]\n",
      " [  2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbfe121df98>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JB0PLchsSpZSdSik7Lv78ZaWUE0op+/a+tJUz85aZ2WLMFhk7ZmzWWH2NTBg/\nIdOvnd7vsrqizdmSoZtv+622z7prr9vx+ZKSRx5/JFVV5dEnHs26a6+bYasNS5J8Y/o3ctSnj8oR\nZxyRr1z2lRU+57W/ujZ777R3kmTvnfbOT371kyTJK8a9YkktL3vxy3LPA/c811hdN1TXr1vanK/N\n2RL5mk6+5mpztkS+ppMPhpZlNiRKKacmOSfJF0spn0zy70nWSXJSKeXkGupbYQNzBzJ65Oglj0dt\nMioDcwf6WFH3tDlb0tx879jtHbnt7tsy8SMTc/gnD8+xE4/Naqutll/c/Ivcfs/t+dL7v5SpH5ia\n383+Xf531v+u0Ne876H7MmL9EUmSEeuPyP0P3f+s10z76bTs9NKduprl+Wjq+q2oNudrc7ZEvqaT\nr7nanC2Rr+nkg6Fl+HKePyjJq5KsmeTuJJtVVTWvlPKvSX6e5ONLe1MpZXKSyUly7rnnZvLkyd2r\nuIOqqpZWR8/PW4c2Z0uam2/GzTOy1WZb5axjz8odc+/IiZ8/Ma8c98r84ne/yC9u+UUmfWpSkuTR\nxx/N7ffcnu232j5Hf+boPLHwiTz6+KN56JGHcuSZRyZJjnrbUSvUZLjx9zfmsp9dln87/t96mm1l\nNHX9VlSb87U5WyJf08nXXG3OlsjXdPLB0LK8hsTCqqoGkzxSSvm/qqrmJUlVVY+WUp7s9KaqqqYk\neWqCyrO/K3pg9Cajc/ecu5c8HrhnICNHjKzj1D3X5mxJc/Nd8fMr8nd7/l1KKdlsk82y6YhN8+c5\nf06q5OA9D87bXv+2Z73ni+//YpJFMySu+PkV+eDff/AZz2+07ka598F7M2L9Ebn3wXuz4bobLnnu\n/+74v/zr1/81Zx59ZtZ/4fq9DbcSmrp+K6rN+dqcLZGv6eRrrjZnS+RrOvl4LjR1emd5MySeKKWs\nvfjz1z51sJSyfpKODYl+2G7b7XLrHbdm9l2z88SCJzLtqmkZv+v4fpfVFW3OljQ338gNR+b6312f\nJLlv3n2ZPWd2Nh2xaXbcdsdc/rPL88jjjyRJ7nngnqXeerE0u75i11wx44okyRUzrsjrt3t9kmTg\nvoF85MsfyYcO+VDGjhzbgzTPXVPXb0W1OV+bsyXyNZ18zdXmbIl8TScfDC3L2yGxW1VVjydJVVVP\nb0CsnuTdPavqORg+bHhOOe6UTPrApAw+OZiJ+0zM1ltu3e+yuqLN2ZKhm+/0r56em2bdlAfnP5iD\nPnJQDt/38CwcXJgkOeANB+TQvQ/NGf95Rg7/5OGpUmXy2yZng3U2yI4v3TG3DdyWYz57TJJkrTXX\nysmHnPyM3Q6d/N2ef5fTvnJdhAbAAAAgAElEQVRaLvvZZRm14ah89PCPJkkuuOKCzHt4Xs769llJ\nkmGrDcuUfxoav8ZpqK5ft7Q5X5uzJfI1nXzN1eZsiXxNJx8MLWVp9xl12V9OcGevT1WTMR2Oy9cM\nHfLd9cO76q2jBzbda9OlP9HytZOvIeRrrjZnS+RrOvmaq83ZklUtX6vvafjya79cyxiCuh15/ZF9\nX7fl7ZAAAACAVVfff2xvr+XNkAAAAADoOg0JAAAAoHYaEgAAAEDtzJAAAACADkoxRKJX7JAAAAAA\naqchAQAAANROQwIAAAConYYEAAAAUDtDLQEAAKATMy17xg4JAAAAoHYaEgAAAEDtNCQAAACA2pkh\nAQAAAB2UYohEr9ghAQAAANROQwIAAAConYYEAAAAUDszJAAAAKATIyR6xg4JAAAAoHYaEgAAAEDt\nNCQAAACA2pkhAQAAAB2UYohEr9ghAQAAANROQwIAAAConYYEAAAAUDsNCQAAAKB2paqqXp+j5ycA\nAACgb1o99fGru361lT/THnbdYX1fNzskAAAAgNppSAAAAAC105AAAAAAaje83wUAAADAUFVK30ct\ntJYdEgAAAEDt6t0hcWetZ+udMR2Oy9cMbc7XIdu9P7m33jp6ZMQbRiz9iTasXdLuazORr8nanC2R\nr+nka642Z0tW3XywEuyQAAAAAGqnIQEAAADUTkMCAAAAqJ2GBAAAAFA7DQkAAACgdvX+lg0AAABo\nkFJKv0toLTskAAAAgNppSAAAAAC105AAAAAAaqchAQAAANTOUEsAAADoxEzLnrFDAgAAAKidhgQA\nAABQOw0JAAAAoHZmSAAAAEAHpRgi0St2SAAAAAC105AAAAAAaqchAQAAANTODAkAAADoxAiJnrFD\nAgAAAKidhgQAAABQOw0JAAAAoHZmSAAAAEAHpRgi0St2SAAAAAC105AAAAAAateahsQHz/xgdnnH\nLtnv8P36XUpPXD3j6ux16F7Z8+A9M+WiKf0up6vumnNXDvnHQ7LPu/fJhMMm5ILvXNDvkrpqqF6b\nHz//49n3+H1z8EcOXubrfvun3+YNk96Qq3551fM+57z58/K+z7wv7/rgu/K+z7wv8x6elyT54c9+\nmENOPSSHnHpIJn9icv4w+w/P+1zd1ObvvzZnS+Rrujbna/vffW1eu0S+ppMPho7WNCQO3PvATD1z\nar/L6InBwcGcfvbpmXrG1Ez76rRcOv3SzLp1Vr/L6pphw4blpKNPyuUXXJ5vfuGbueiSi1qVb6he\nm/u+ft+c9Y9nLfM1g08O5gvf+UJ2fsXOK/W1b7jlhvzLl//lWcf/4/L/yGtf+tp865Pfymtf+tr8\nx2X/kSQZs/GYfP4Dn89/nPYfOXz/w3PmBWeu1Pl6qc3ff23OlsjXdG3P1+a/+9q+dvI1m3wwtLSm\nIbHj9jtm/fXW73cZPTHzlpnZYswWGTtmbNZYfY1MGD8h06+d3u+yumbkiJF5+TYvT5Kss/Y6Gbf5\nuAzMHehzVd0zVK/NV//Nq7PeC9db5mu+M/072eO1e2TDdTd8xvGvXfG1HPGxI3LIqYdk6sUr3my5\n5sZrsu+u+yZJ9t1131xz4zVJku222m5JLS8f9/LMuX/OykTpqTZ//7U5WyJf07U9X5v/7mv72snX\nbPLxnJSWfgwBK92QKKVc2ItC6Gxg7kBGjxy95PGoTUa15v+0/LXb7749N8+6Odu/dPt+l7LKu+f+\ne/LjG36ct7/p7c84/vNf/zyzB2bnyx/+ci449YLcctstufF3N67Q17xv3n3ZeIONkyQbb7Bx7n/o\n/me95tJrLs0u2+3y/AN0SZu//9qcLZGv6dqe7+na9ndf29dOvmaTD4aWZf7az1LK9//6UJI9Sikb\nJElVVW/rVWH8RVVVzzrWxl898/CjD+e4U47Lh475UNZ54Tr9LmeV97mvfy7/30H/X4atNuwZx2f8\nZkZm/GZGDjvtsCTJI48/ktvn3J5X/82rM+lfJmXBwgV55PFHMm/+vLz7o+9Okhx90NF53Stet9xz\nXn/L9fnBT36QL530pa7nea7a/P3X5myJfE3X9nxPaePffW1fO/maTT4YWpbZkEiyWZLfJpmapMqi\nhsQOST6zrDeVUiYnmZwk5557biZPnvz8K12Fjd5kdO6ec/eSxwP3DGTkiJF9rKj7FixckONOOS77\nv2X/vHW3t/a7HJLcctstOeXcU5IkD85/MNf96roMW21YqlQ5dN9Dn7VzIkmmfnjR7Rs33HJDLrv2\nsnz4yA8/4/mN1tsocx+Ym4032DhzH5j7jFtBZs2elU9+9ZP57PGfzfrrDJ1bXNr8/dfmbIl8Tdf2\nfEl7/+5r+9rJ12zywdCyvFs2dkhyfZKTkzxYVdWPkjxaVdWPq6r6cac3VVU1paqqHaqq2kEz4vnb\nbtvtcusdt2b2XbPzxIInMu2qaRm/6/h+l9U1VVXl5E+dnHFbjMvh7zq83+Ww2HfP/G6+96nv5Xuf\n+l72eO0eOfHvT8zur9k9O79851z6k0vzyGOPJFl0a8d98+5boa/5hle9IZddd1mS5LLrLssbX/3G\nJMnd996dD37hgzl10qnZfPTmvQn0HLX5+6/N2RL5mq7t+dr8d1/b106+ZpOP56KU0sqPoWCZOySq\nqnoyyVmllG8v/t+B5b2nX0742AmZcdOM3P/g/dntnbvl2MOOzTsnvLPfZXXF8GHDc8pxp2TSByZl\n8MnBTNxnYrbecut+l9U11//6+lxy5SXZZtw2OWDSAUmSEyadkN1ft3ufK+uOoXptnnLuKbnxdzfm\ngfkP5IATD8ikAyZl4eDCJMk73vSOju/b+RU759a7bs3kTyxqNq615lo59T2nZqP1NlruOQ/Z95B8\n+IsfzqXXXJpRG43Kx4/+eJLkKz/4SubNn5dP/+enkyTDVhuW8085/3km7I42f/+1OVsiX9O1PV+b\n/+5r+9rJ12zywdBSlnafUccXlzIhyeurqvrQSpzjLye4cyXeNZSN6XBcvmZoc74O2e79yb311tEj\nI94wYulPtGHtknZfm4l8TdbmbIl8TSdfc7U5W7Kq5Rsa/9zeI18b/7UV/6G5QQ6+6uC+r9tK7Xao\nqmpakmk9qgUAAABYRQzJ2y8AAABgSOj7PoL2Wt5QSwAAAICu05AAAAAAaqchAQAAANTODAkAAADo\noBRDJHrFDgkAAACgdhoSAAAAQO00JAAAAIDaaUgAAAAAtTPUEgAAADox07Jn7JAAAAAAaqchAQAA\nANROQwIAAAConRkSAAAA0EExRKJn7JAAAAAAaqchAQAAANROQwIAAAConRkSAAAA0IkREj1jhwQA\nAABQOw0JAAAAoHYaEgAAAEDtzJAAAACADkoxRKJX7JAAAAAAaqchAQAAANROQwIAAAConYYEAAAA\nULt6h1qOqfVs9ZOv2Vqcb8QbRvS7hN5q8dolka/p2pyvzdkS+ZpOvuZqc7ak/fnayEzLnrFDAgAA\nAKidhgQAAABQOw0JAAAAoHb1zpAAAACABinFEIlesUMCAAAAqF29OyTurPVsvdNpMq58zdDmfG3O\nlnTMt+D/FtRbR4+s/pLVl/5Ey9dPvgZoc7ZEvqaTr7nanC1ZdfPBSrBDAgAAAKidGRIAAADQiRES\nPWOHBAAAAFA7DQkAAACgdhoSAAAAQO3MkAAAAIAOSjFEolfskAAAAABqpyEBAAAA1E5DAgAAAKid\nhgQAAABQO0MtAQAAoBMzLXvGDgkAAADgWUopG5RSvlNKuaWUcnMpZZdSykallCtLKX9Y/L8bLn5t\nKaWcU0qZVUqZWUp5zfK+voYEAAAAsDRnJ7miqqptk2yf5OYkJyWZXlXV1kmmL36cJPsk2Xrxx+Qk\nX1zeF9eQAAAAAJ6hlLJekt2SfDlJqqp6oqqqB5IckOSCxS+7IMnbF39+QJILq0V+lmSDUsqmyzqH\nGRIAAADQQSmr7BCJcUnuSfKVUsr2Sa5P8r4ko6qquitJqqq6q5QycvHrX5Rk9tPef/viY3d1OoEd\nEgAAALCKKaVMLqX88mkfk//qJcOTvCbJF6uqenWSh/OX2zOW+iWXcqxaVg12SAAAAMAqpqqqKUmm\nLOMltye5vaqqny9+/J0sakgMlFI2Xbw7YtMkc572+rFPe/9mSe5cVg12SAAAAADPUFXV3Ulml1L+\nZvGhNyf5bZLvJ3n34mPvTnLJ4s+/n+TQxb9t43VJHnzq1o5O7JAAAACATlbZERJJkmOTfK2UskaS\nPyY5PIs2NnyrlHJkkj8neefi116WZN8ks5I8svi1y6QhAQAAADxLVVU3JdlhKU+9eSmvrZIcszJf\n3y0bAAAAQO00JAAAAIDauWUDAAAAOiir+BCJXrJDAgAAAKidhgQAAABQu1Y1JMb/7fjsf8T+OWDS\nATnwqAP7XU5XXT3j6ux16F7Z8+A9M+WiKf0up+vka7a25Xv8icfzt8f/bQ485sAc8A8H5N//89+T\nJIf+06GZ+N6Jmfjeidnj7/fIcacf1+dKn7+2rd1fk6/Z5GuuNmdL5Gs6+WDoaN0MiQvOuiAbrb9R\nv8voqsHBwZx+9un5yr9+JaM2GZWD/uGgjN91fLZ68Vb9Lq0r5Gu2NuZbY/U1cv4nz8/aa62dBQsX\n5NATD80bd3hjLvzXC5e85vh/OT577LJHH6t8/tq4dk8nX7PJ11xtzpbI13TywdDSqh0SbTXzlpnZ\nYswWGTtmbNZYfY1MGD8h06+d3u+yuka+ZmtjvlJK1l5r7STJwoULs3Bw4TOGGT38yMOZMXNG3rzL\ns379cqO0ce2eTr5mk6+52pwtka/p5OM5KS39GAJWqiFRSnlDKeWEUspbe1XQ81KSI//pyBw4+cB8\n8wff7Hc1XTMwdyCjR45e8njUJqMyMHegjxV1l3zN1tZ8g4ODmfjeidnt73bLLq/eJa/c9pVLnvvv\nn/53dt5+56yz9jp9rPD5a+vaPUW+ZpOvudqcLZGv6eSDoWWZDYlSyoynff6eJP+eZN0kp5ZSTupx\nbSvt6//29fzXlP/KeWeel69d/LX84n9/0e+SuqKqqmcdK2WItLS6QL5ma2u+YcOG5bv//t1Mv3B6\nfvX7X+UPt/5hyXOX/+jy7Lv7vn2srjvaunZPka/Z5GuuNmdL5Gs6+WBoWd4OidWf9vnkJHtWVXVa\nkrcmObjTm0opk0spvyyl/HLKlPoGqYzaeFSSZMSGI7LnG/fMzFtm1nbuXhq9yejcPefuJY8H7hnI\nyBEj+1hRd8nXbG3Pt94662XH7XbMT67/SZLkgXkP5Fe//1V222m3Plf2/LV97eRrNvmaq83ZEvma\nTj4YWpbXkFitlLJhKWVEklJV1T1JUlXVw0kWdnpTVVVTqqraoaqqHSZPntzFcjt75NFHMv+R+Us+\nv/aX12brLbeu5dy9tt222+XWO27N7Ltm54kFT2TaVdMyftfx/S6ra+Rrtjbmu+/B+zJv/rwkyWOP\nP5af3fSzbLnZlkmSH17zw+y+0+5Zc401+1liV7Rx7Z5OvmaTr7nanC2Rr+nk47kopbTyYyhY3m/Z\nWD/J9Vk08qIqpYyuquruUso6GTJjMBa59/57c8xHjkmy6N7v/d6yXyv+BTNJhg8bnlOOOyWTPjAp\ng08OZuI+E1vTbEnka7o25rvnvnty8mdOzuCTg6mqKnu9ca+8aec3JUkuv/ryTHrnpP4W2CVtXLun\nk6/Z5GuuNmdL5Gs6+WBoKUu7z2i5bypl7SSjqqr60wq8/C8nuHOlTzU0jelwXL5maHO+NmdLOuZb\n8H8L6q2jR1Z/yepLf6Ll6ydfA7Q5WyJf08nXXG3Olqxq+YbUP1Z323ff/t2V/6G5ASZePLHv67a8\nHRJLVVXVI0lWpBkBAAAA8CzPqSEBAAAAq4S+7yNor+UNtQQAAADoOg0JAAAAoHYaEgAAAEDtzJAA\nAACADkoxRKJX7JAAAAAAaqchAQAAANROQwIAAAConYYEAAAAUDtDLQEAAKATMy17xg4JAAAAoHYa\nEgAAAEDtNCQAAACA2pkhAQAAAB2UYohEr9ghAQAAANROQwIAAAConYYEAAAAUDszJAAAAKATIyR6\nxg4JAAAAoHYaEgAAAEDtNCQAAACA2pkhAQAAAB2UYohEr9ghAQAAANROQwIAAAConYYEAAAAUDsN\nCQAAAKB29Q61HFPr2eonX7O1OV+bsyVZ/SWr97uE3mr5+snXYG3OlsjXdPI1V5uzJe3P10ZmWvaM\nHRIAAABA7TQkAAAAgNppSAAAAAC1q3eGBAAAADRIMUSiZ+yQAAAAAGpX6w6J6o6qztP1THlRhw7Z\nnfXW0TOdJv/KN/S1OVuyyuZ78s9P1ltHj6y2eYceeMvXrxX52pwtka/p5GuuNmdLVt18sBLskAAA\nAABqZ4YEAAAAdGKERM/YIQEAAADUTkMCAAAAqJ2GBAAAAFA7MyQAAACgg1IMkegVOyQAAACA2mlI\nAAAAALXTkAAAAABqpyEBAAAA1M5QSwAAAOjETMuesUMCAAAAqJ2GBAAAAFA7DQkAAACgdmZIAAAA\nQAelGCLRK3ZIAAAAALXTkAAAAABqpyEBAAAA1M4MCQAAAOjECImesUMCAAAAqJ2GBAAAAFA7DQkA\nAACgdmZIAAAAQAelGCLRK3ZIAAAAALXTkAAAAABq15qGxB///Me8/T1vX/Lx2v1emwu+c0G/y+qa\nq2dcnb0O3St7Hrxnplw0pd/ldN0Hz/xgdnnHLtnv8P36XUpPtH395GuOu+bclXef+O5MOGJC9pu0\nXy783oVJkgfmPZAj/vmI7PXuvXLEPx+RBx96sM+Vdkeb1m5p5Gu2Nudrc7ZEvqaTD4aO1jQkxm0+\nLhefd3EuPu/ifPdL381aa66Vt7zhLf0uqysGBwdz+tmnZ+oZUzPtq9Ny6fRLM+vWWf0uq6sO3PvA\nTD1zar/L6Im2r598zTJs2LB84KgPZNr50/LNc76Zi75/UWbdNivnffO87PLqXfLDC36YXV69S877\nxnn9LvV5a9va/TX5mq3N+dqcLZGv6eSDoaU1DYmn++kNP83YMWPzotEv6ncpXTHzlpnZYswWGTtm\nbNZYfY1MGD8h06+d3u+yumrH7XfM+uut3+8yeqLt6ydfs4wcMTIv3/rlSZIXrv3CvGTzl2Rg7kCu\nuu6qHLDnAUmSA/Y8INOva27Gp7Rt7f6afM3W5nxtzpbI13Ty8ZyUln4MActsSJRSdi6lrLf487VK\nKaeVUn5QSjmzlDJkf3q87H8uy4TxE/pdRtcMzB3I6JGjlzwetcmoDMwd6GNFrIy2r598zXXH3Xfk\n5lk3Z/ttt8+999+bkSNGJlnUtLjvgfv6XN3z1+a1S+Rrujbna3O2RL6mkw+GluXtkDg/ySOLPz87\nyfpJzlx87Cs9rOs5e2LBE7nququy9+5797uUrqmq6lnH/OqZ5mj7+snXTA8/+nCOO/24nHT0SVnn\nhev0u5yeaOvaPUW+ZmtzvjZnS+RrOvlgaFleQ2K1qqoWLv58h6qqjq+q6idVVZ2WZFynN5VSJpdS\nfllK+eWUKfUOUrlmxjV52dYvy8YbbVzreXtp9Cajc/ecu5c8HrhnYMm/ZDL0tX395GueBQsX5H2n\nvS/7j98/b33jW5MkIzYckTn3zkmSzLl3TjbaYKN+ltgVbVy7p5Ov2dqcr83ZEvmaTj4YWpbXkPh1\nKeXwxZ//byllhyQppWyTZEGnN1VVNaWqqh2qqtph8uTJXSp1xUy7alqrbtdIku223S633nFrZt81\nO08seCLTrpqW8buO73dZrKC2r598zVJVVT78mQ9n3ObjcthBhy05Pn6X8bnkykuSJJdceUmjMz6l\nbWv31+Rrtjbna3O2RL6mk4/nopTSyo+hYPhynp+U5OxSyoeTzE3y01LK7CSzFz83pDz62KO59vpr\nc9o/ntbvUrpq+LDhOeW4UzLpA5My+ORgJu4zMVtvuXW/y+qqEz52QmbcNCP3P3h/dnvnbjn2sGPz\nzgnv7HdZXdH29ZOvWW74zQ35/n9/P9tsuU3ecdQ7kiTHH3F8Jv3tpJzwsRPyncu/kzEjx+Ssj5zV\n50qfv7at3V+Tr9nanK/N2RL5mk4+GFrK0u4zetaLSlk3i27RGJ7k9qqqVmYyypITVHcs/1xNUF7U\noZt0Z7119MyYDsflG/ranC1ZZfM9+ecn662jR1bbvMOmvJavXyvytTlbIl/Tyddcbc6WrGr5hsY/\nt/fItHdPa8cPsn9lwgUT+r5uy9shkSSpquqhJP/b41oAAACAVcTyZkgAAAAAdJ2GBAAAAFA7DQkA\nAACgdhoSAAAAQO1WaKglAAAArIpK6fsvo2gtOyQAAACA2mlIAAAAALXTkAAAAABqpyEBAAAA1M5Q\nSwAAAOjETMuesUMCAAAAqJ2GBPD/s3f34XaV5Z34v48JoRUKaMyLwYhgo7QMhioo0BY1FQMNSnlJ\ndXRUKGms44BKlUr9TVqxdsDpaK2tradxBGsRlc4Umyi2DWIoWiK0aXyjNm3T4S0HgaCXiAkc1u8P\nDjFA9jkE9l77rOd8Pl776tlr7XPW/eVJmuw7z7o3AABA6zQkAAAAgNaZIQEAAAA9lGKIxKDYIQEA\nAAC0TkMCAAAAaJ2GBAAAANA6MyQAAACgFyMkBsYOCQAAAKB1GhIAAABA6zQkAAAAgNaZIQEAAAA9\nlGKIxKDYIQEAAAC0TkMCAAAAaJ2GBAAAANA6DQkAAACgda0OtSwHVj4MZMGwCxgw+bqr5mxJ9fme\n9MzKe8eVr1/V+WrOlsjXdfJ1V83Zkvrz1ajyt7HDVPnfcgEAAICpSEMCAAAAaJ2GBAAAANC6VmdI\nAAAAQJeUYojEoNghAQAAALSu3R0St7Z6tcHpNRlXvm6oOV/N2RL5uk6+7qo5WyJf18nXXTVnS6Zv\nPtgDdkgAAAAArTNDAgAAAHoxQmJg7JAAAAAAWqchAQAAALROQwIAAABonRkSAAAA0EMphkgMih0S\nAAAAQOs0JAAAAIDWaUgAAAAArdOQAAAAAFqnIQEAAAC0TkMCAAAAaJ2GBAAAANA6DQkAAACgdTOH\nXQAAAABMVaWUYZdQLTskAAAAgNZpSAAAAACt05AAAAAAWmeGBAAAAPRihMTA2CEBAAAAtE5DAgAA\nAGidhgQAAADQOjMkAAAAoIdSDJEYFDskAAAAgNZpSAAAAACtq6Yhcf5F5+eYU47JSWeeNOxSBmL9\nhvVZ+vqlOf61x2fk0pFhl9N38nWbfN1Vc7ZEvq6Tr7tqzpbI13XywdRRTUPi1BNOzeqLVg+7jIEY\nGxvLBR+8IKsvXJ21F6/NmnVrsnnL5mGX1TfydZt83VVztkS+rpOvu2rOlsjXdfLxuJRKH1NANQ2J\noxYflf3323/YZQzEphs35aAFB2XhgoWZtdesLFuyLOuuXTfssvpGvm6Tr7tqzpbI13XydVfN2RL5\nuk4+mFombEiUUs4ppSxsqxh2b/SO0cyfO3/n83lz5mX0jtEhVtRf8nWbfN1Vc7ZEvq6Tr7tqzpbI\n13XywdQy2Q6J9yS5rpRyTSnlv5ZS5rRRFA/XNM2jjtX00TPydZt83VVztkS+rpOvu2rOlsjXdfLB\n1DJZQ+LfkjwjDzYmXpDkm6WUK0spbyil/ESvbyqlrCylXF9KuX5kxCCVJ2r+nPnZevvWnc9HvzOa\nubPnDrGi/pKv2+TrrpqzJfJ1nXzdVXO2RL6ukw+mlskaEk3TNA80TfPXTdOclWRBkg8nOSEPNit6\nfdNI0zRHNk1z5MqVK/tY7vR0+KGHZ8stW3LTbTdlx307svaqtVly7JJhl9U38nWbfN1Vc7ZEvq6T\nr7tqzpbI13Xy8XiUUqp8TAUzJzn/sCqbprkvyWeTfLaU8uMDq+pxOPc952bDxg3Z9t1tOW75cTn7\njLOzfNnyYZfVFzNnzMyqc1ZlxXkrMvbAWE478bQsOnjRsMvqG/m6Tb7uqjlbIl/XydddNWdL5Os6\n+WBqKbu7z2jnyVKe0zTNt5/gNX50gVuf4E+aKhb0OC5fN9Scr+ZsiXxdJ1931Zwtka/r5OuumrMl\n0y3f1Pjn9gFZd/a63m+aO+wXPvQLQ1+3CW/Z6EMzAgAAAOBRJrtlAwAAAKavoe8jqNdkQy0BAAAA\n+k5DAgAAAGidhgQAAADQOjMkAAAAoIdSDJEYFDskAAAAgNZpSAAAAACt05AAAAAAWmeGBAAAAPRi\nhMTA2CEBAAAAtE5DAgAAAGidhgQAAADQOg0JAAAAoHWGWgIAAEAPpZhqOSh2SAAAAACt05AAAAAA\nWqchAQAAALTODAkAAADoxQiJgbFDAgAAAGidhgQAAADQOg0JAAAAoHVmSAAAAEAPxRCJgbFDAgAA\nANitUsqMUso/llLWjD+/uJTy76WUjeOPI8aPl1LKH5RSNpdSNpVSnj/Zz7ZDAgAAAOjlLUm+lWS/\nXY69o2mayx/xuhOTLBp/vCjJH4//357skAAAAAAepZTyjCTLkqx+DC8/OcnHmwf9fZIDSilPn+gb\nNCQAAACgl1Lp47H5/STnJXngEcffO35bxgdKKXuPHzswyU27vObm8WM9tXvLxoJWr9Y++bqt5nw1\nZ0vk6zr5uqvmbIl8XSdfd9WcLak/H51RSlmZZOUuh0aaphnZ5fxJSW5vmuaGUspLdnnd+Um2JpmV\nZCTJbyS5ILtvczQT1WCGBAAAAEwz482HkQle8rNJXllK+cUkP5Zkv1LKJ5qm+S/j57eXUj6W5O3j\nz29OsnCX739GklsnqsEtGwAAAMDDNE1zftM0z2ia5llJXp3kqqZp/stDcyFKKSXJLyX5+vi3fDbJ\n68c/bePoJN9tmua2ia5hhwQAAADwWP15KWVOHrxFY2OSXxs//rkkv5hkc5IfJDlzsh+kIQEAAAA9\nPLgRYHprmubqJFePf72kx2uaJG/ek5/rlg0AAACgde3ukJhwnEWH9JqMK1831Jyv5myJfF03TfPd\ns+medusYgH2et8/uTxJTIPoAACAASURBVFS+dvJ1hHzdVXO2ZPrmgz1ghwQAAADQOjMkAAAAoBcj\nJAbGDgkAAACgdRoSAAAAQOs0JAAAAIDWmSEBAAAAPZRiiMSg2CEBAAAAtE5DAgAAAGidhgQAAADQ\nOjMkAAAAoBcjJAbGDgkAAACgdRoSAAAAQOs0JAAAAIDWaUgAAAAArTPUEgAAAHooxVTLQbFDAgAA\nAGidhgQAAADQOg0JAAAAoHVmSAAAAEAvRkgMjB0SAAAAQOs0JAAAAIDWaUgAAAAArTNDAgAAAHoo\nxRCJQbFDAgAAAGidhgQAAADQumoaEudfdH6OOeWYnHTmScMuZSDWb1ifpa9fmuNfe3xGLh0Zdjl9\nV3O+226/La972+ty4htOzLIzluWSyy8Zdkl9V/P6JXXnqzlbUme+T6z5RE5/2+lZfu7ynP/752f7\nju257POX5ZX/7ZV5/vLnZ9v3tg27xL6pcf12VXO+mrMl8nWdfDB1VNOQOPWEU7P6otXDLmMgxsbG\ncsEHL8jqC1dn7cVrs2bdmmzesnnYZfVN7flmzJiRd77pnfn8JZ/Ppz78qVx6xaVV5at9/WrOV3O2\npM58t995ey773GX5xIWfyGfe/5k88MAD+cK1X8gRhx6RP1n1J3n6nKcPu8S+qXH9dlVzvpqzJfJ1\nnXw8LqXSxxRQTUPiqMVHZf/99h92GQOx6cZNOWjBQVm4YGFm7TUry5Ysy7pr1w27rL6pPd/c2XNz\n2HMOS5Ls++R9c8gzD8noHaNDrqp/al+/mvPVnC2pN9/YA2PZvmN77h+7P/duvzdznjonhx58aBbM\nXTDs0vqq1vV7SM35as6WyNd18sHUMmFDopQyq5Ty+lLKy8afv6aU8oellDeXUvZqp0RG7xjN/Lnz\ndz6fN2deVW9oa8+3q5u33pxvbf5WFv/U4mGX0je1r1/N+WrOltSZb+7suXndK16XX3zTL+blv/ry\n/MSTfyLHLD5m2GUNRI3rt6ua89WcLZGv6+SDqWWyHRIfS7IsyVtKKX+WZHmS65IclaTO+yOmoKZp\nHnWspo+eqT3fQ+65956cs+qc/OabfzP77rPvsMvpm9rXr+Z8NWdL6sz3ve9/L1d/9eqs+aM1+cLI\nF3Lv9nuzdv3aYZc1EDWu365qzldztkS+rpMPppbJGhKHN03zqiSnJHl5ktObpvmzJGcm+Zle31RK\nWVlKub6Ucv3IiEEqT9T8OfOz9fatO5+Pfmc0c2fPHWJF/VV7viS57/77cs6qc/KKl70iLz/u5cMu\np69qX7+a89WcLakz33Vfuy4Hzj0wT9n/Kdlr5l5Z8qIl2fTPm4Zd1kDUuH67qjlfzdkS+bpOPpha\nJmtIPKmUMivJTyR5cpKHhjTsnaTnLRtN04w0TXNk0zRHrly5sj+VTmOHH3p4ttyyJTfddlN23Lcj\na69amyXHLhl2WX1Te76mafKu970rhxx0SM785TOHXU7f1b5+NeerOVtSZ775T5ufr/3L13Lv9nvT\nNE02fG1DDn7GwcMuayBqXL9d1Zyv5myJfF0nH49HqfR/U8HMSc5/NMmNSWYkeVeSz5RS/i3J0Uku\nG3Bte+Tc95ybDRs3ZNt3t+W45cfl7DPOzvJly4ddVl/MnDEzq85ZlRXnrcjYA2M57cTTsujgRcMu\nq29qz3fD12/IFX9zRZ5zyHNy8oqTkyTnrjg3Lz76xUOurD9qX7+a89WcLakz3+GLDs8vHP0Lee15\nr82MGTPy3Gc9N6e+7NR88nOfzCVXXJI7774zr3r7q/JzP/NzWfWmVcMu9wmpcf12VXO+mrMl8nWd\nfDC1lN3dZ/SwF5SyIEmaprm1lHJAkpcl+X9N02x4jNf40QVufZxVTjW9BpnL1w0156s5WyJf103T\nfPdsuqfdOgZgn+fts/sTla+dfB0hX3fVnC2Zbvmmxj+3D8i177p24jfNHfWz7/3Zoa/bZDsk0jTN\nrbt8fXeSywdaEQAAAFC9SRsSAAAAMG0NfR9BvSYbagkAAADQdxoSAAAAQOs0JAAAAIDWmSEBAAAA\nPZRiiMSg2CEBAAAAtE5DAgAAAGidhgQAAADQOjMkAAAAoBcjJAbGDgkAAACgdRoSAAAAQOs0JAAA\nAIDWaUgAAAAArTPUEgAAAHooxVTLQbFDAgAAAGidhgQAAADQOg0JAAAAoHVmSAAAAEAvRkgMjB0S\nAAAAQOs0JAAAAIDWaUgAAAAArTNDAgAAAHooxRCJQbFDAgAAAGidhgQAAADQOg0JAAAAoHVmSAAA\nAEAvRkgMTLsNiQWtXq198nVbzflqzpbI13WV59v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MVaWUYZdQLTskAAAAgNZp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\nAwlJkiRJktS7/wXGujgyDlF0ZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfe11b5320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "y_pred = matra_model.predict_classes(x_train)\n",
    "y_original = np.argmax(y_train_matra, axis=1)\n",
    "\n",
    "cm = confusion_matrix(y_original, y_pred)\n",
    "print('confusion matrix for {} features (matra):'.format(cm.shape))\n",
    "print(cm)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,20))\n",
    "sns.heatmap(cm, cmap=sns.light_palette('purple', n_colors=500, as_cmap=True), annot=True, linewidths = 5, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dumping Models to Disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dot_model.save('models/dot_model4w.model')\n",
    "matra_model.save('models/matra_model4w.model')\n",
    "base_model.save('models/base_model4w.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "dot_model2 = load_model('models/dot_model4k.model')\n",
    "matra_model2 = load_model('models/matra_model4k.model')\n",
    "base_model2 = load_model('models/base_model4k.model')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Half Characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}