Traffic-signals-cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Traffic signals classification with CNN\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make sure we are using GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 13199735684303098837\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 4945621811\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 3790509256298792513\n",
      "physical_device_desc: \"device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1\"\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "# It should show at least a device of type \"GPU\"\n",
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from skimage import color, exposure, transform\n",
    "\n",
    "NUM_CLASSES = 43\n",
    "IMG_SIZE = 48\n",
    "\n",
    "def preprocess_img(img):\n",
    "    # Histogram normalization in v channel\n",
    "    hsv = color.rgb2hsv(img)\n",
    "    hsv[:, :, 2] = exposure.equalize_hist(hsv[:, :, 2])\n",
    "    img = color.hsv2rgb(hsv)\n",
    "\n",
    "    # central square crop\n",
    "    min_side = min(img.shape[:-1])\n",
    "    centre = img.shape[0] // 2, img.shape[1] // 2\n",
    "    img = img[centre[0] - min_side // 2:centre[0] + min_side // 2,\n",
    "              centre[1] - min_side // 2:centre[1] + min_side // 2,\n",
    "              :]\n",
    "\n",
    "    # rescale to standard size\n",
    "    img = transform.resize(img, (IMG_SIZE, IMG_SIZE))\n",
    "    \n",
    "    # roll color axis to axis 0\n",
    "    img = np.rollaxis(img, -1)\n",
    "    \n",
    "    return img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading whole data from file or creating a file which contains whole images and labels for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded images from training_data.h5\n",
      "\n",
      "Example image after processing\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from skimage import io\n",
    "import os, glob, h5py, random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Each training image folder contains a number which indicates the image's class/label.\n",
    "def get_class(img_path):\n",
    "    return int(img_path.split('\\\\')[-2])\n",
    "\n",
    "try:\n",
    "    # Loading pre-compiled data images and labels\n",
    "    with  h5py.File('Data\\\\Saves\\\\training_data.h5') as hf: \n",
    "        X, Y = hf['imgs'][:], hf['labels'][:]\n",
    "    print('Loaded images from training_data.h5\\n')\n",
    "    try:\n",
    "        print('Example image after processing')\n",
    "        r = random.randint(0, len(X))\n",
    "        img = X[r]\n",
    "        img = np.rollaxis(img, 0, start=3)\n",
    "        plt.figure()\n",
    "        imgplot = plt.imshow(img)\n",
    "        plt.show()\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        print('It was not possible loading the image')\n",
    "    \n",
    "except:\n",
    "    print('It was not possible to load training saved data. Generating new training_data.h5 file')\n",
    "    root_dir = 'Data\\\\Dataset\\\\Training\\\\Images\\\\'\n",
    "    imgs = []\n",
    "    labels = []\n",
    "\n",
    "    all_img_paths = glob.glob(os.path.join(root_dir, '*/*.ppm'))\n",
    "    np.random.shuffle(all_img_paths)\n",
    "    num_images = len(all_img_paths)\n",
    "    print('Number of images to process:', num_images)\n",
    "    for img_path in all_img_paths:\n",
    "        img = preprocess_img(io.imread(img_path))\n",
    "        label = get_class(img_path)\n",
    "        imgs.append(img)\n",
    "        labels.append(label)\n",
    "        # Processing progress\n",
    "        if len(imgs)%4000 == 0:\n",
    "            print('Processed => {:.2%}'.format(len(imgs)/num_images))\n",
    "\n",
    "    # Images array\n",
    "    X = np.array(imgs, dtype='float32')\n",
    "    # Make one hot targets\n",
    "    Y = np.eye(NUM_CLASSES, dtype='uint8')[labels]\n",
    "\n",
    "    with h5py.File('Data\\\\Saves\\\\training_data.h5','w') as hf:\n",
    "        hf.create_dataset('imgs', data=X)\n",
    "        hf.create_dataset('labels', data=Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.optimizers import SGD\n",
    "from keras import backend as K\n",
    "K.set_image_data_format('channels_first')\n",
    "\n",
    "\n",
    "def cnn_model():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv2D(32, (3, 3), padding='same',\n",
    "                     input_shape=(3, IMG_SIZE, IMG_SIZE),\n",
    "                     activation='relu'))\n",
    "    model.add(Conv2D(32, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(Conv2D(64, (3, 3), padding='same',\n",
    "                     activation='relu'))\n",
    "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(Conv2D(128, (3, 3), padding='same',\n",
    "                     activation='relu'))\n",
    "    model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(512, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(NUM_CLASSES, activation='softmax'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model compilation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\ivan\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From c:\\users\\ivan\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 48, 48)        896       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 32, 46, 46)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 32, 23, 23)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 32, 23, 23)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 64, 23, 23)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 64, 21, 21)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 64, 10, 10)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64, 10, 10)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 128, 10, 10)       73856     \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 128, 8, 8)         147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 128, 4, 4)         0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 128, 4, 4)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               1049088   \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                32832     \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 43)                2795      \n",
      "=================================================================\n",
      "Total params: 1,371,723\n",
      "Trainable params: 1,371,723\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.optimizers import SGD\n",
    "\n",
    "model = cnn_model()\n",
    "model.summary()\n",
    "\n",
    "# Model is using SGD + momentum\n",
    "lr = 0.01\n",
    "sgd = SGD(lr=lr, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=sgd,\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model and saving it for overfitting check purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model weights\n",
      "Successfully loaded weigths, no training needed.\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint\n",
    "\n",
    "# Reduce the learning rate value after each epoch\n",
    "def lr_schedule(epoch):\n",
    "    return lr * (0.1 ** int(epoch / 10))\n",
    "\n",
    "batch_size = 32\n",
    "epochs = 18\n",
    "\n",
    "\n",
    "try:\n",
    "    print('Loading model weights')\n",
    "    model.load_weights('Data\\\\Saves\\\\model-cnn.h5')\n",
    "    print('Successfully loaded weigths, no training needed.')\n",
    "except:\n",
    "    print(\"It was not possible loading the weights, proceding to train\")\n",
    "    history = model.fit(X, Y,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\n",
    "          validation_split=0.2,\n",
    "          callbacks=[LearningRateScheduler(lr_schedule),\n",
    "                     ModelCheckpoint('Data\\\\Saves\\\\model-cnn.h5', save_best_only=True)]\n",
    "          )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and validation accuracy and loss (It won't work without training it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'history' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-13e974cb03f4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Printing history of accuracy and loss\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0macc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'acc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mval_acc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'val_acc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'loss'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mval_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'val_loss'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'history' is not defined"
     ]
    }
   ],
   "source": [
    "# Printing history of accuracy and loss\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded images from test_data.h5\n",
      "\n",
      "Example of test image after processing\n"
     ]
    },
    {
     "data": {
      "image/png": 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v5Pw9mdsOMcPjci549Cwua7Vlm5wLp0pJiJnJywqt2QxWdaWC/L7cHMEljwN12MfFeZkE8nYa9x2flnO4aAkrAEV9shrukWOYlGLScp470YPXtWBVdfWTY7ltfwi22SYRjCDKF6Xxp28EdYgfHpJVaX7745+CmEe2yPvxksNA02olZP3oAibb+IysBDwyi0aghXfQZOSzKv+2tmN1nUxeVlZa07wbYlJpmSTU13sKYhasirkDfWioyixKs9JNW6TJp5i7TgYeRVH+9aEDX1E8iA58RfEgOvAVxYNUVNwr5g2N90kBaXNSSkPb6tDAk4hvE+3v9aNB4+bNMvvq1OQoxIzPy22ZIlZh8c/KbKfNDsPIqLWM0sQoVg1qakGRJUXSjJPJozmoNDwgjx9HMWvGyDLU0SLuZ3xyTLZH8DyoKKvAVMfx2jd34LJaWSPv2dhFNDCZohRxU6kMxHS9Iav79D+Gy3xF190v2oM5zGqrXSEz+Pz1eD1qa2TZ8ngA157f985rsK2+VhrBbrv3YYgpjlrVn2axvHd7h3StRkNbIWZmUd7r7U1o3mrbIJeYG7BKiWOBcjf6xlcUD6IDX1E8iA58RfEgOvAVxYNUVNyLMNHmgBR95n1SdOnKrIbPtfilCyy8AtcdS4/KrLq1jVhqKlaQomDc9EBM03opwuRnWiGmJilFl4sXRyCmey+66XwsBch1mxIQs2qdFKZO9mIpsvFLUgCcn0chcXRQxhRyKBLGgjKDLlGD69JVt62FbQ010k03M42uPDMnrwk7VKeLQ1KAPPH9ZyHmoT+QYlbRcskREV1akOe6Io7C3WRxn2hXt+J9TbmuUUY+r4Mj6AidssqMJaJ4spGgfMc2t2GZsWa/vP5nu9Ddt+jrE+2ZlHWf85hx6kLf+IriQXTgK4oH0YGvKB6konP8PPmoj2WWVHlRlh0uTqLRYzAvTRSxCM7z6tNyOaq2FizBXayVc7/zOawYVleSc69jRayCQsdkn+9xzNeyuzbBtm/sOyLaiT4sXb0mIudok4511BfGpDYxM4YGovS8vEYxP16PaFQamGrasAJNtBG3BeNS42hbj2aU/qPSVFMo4pJRmZR8/F4+hiajWwdeEO1PrXwUYr55YFC089OY9fjAPVIrMfVjEFN/GA0zmYzUhQ69jnP86Wn5HG3YiOXO4wm576mxAYhpa5KVe0qO9/K5njdFe+1q+Zz5XSWTHOgbX1E8iA58RfEgOvAVxYPowFcUD1JRcS8cLlNnpxTvBt9qE+3VK6VQQ0RUIGmiWMFoWDkYlOWXdkZx/TTeJctrR7MowJ0YlaLLhvxNEDNcJ88hTyjUNK/CEk2BWZllGDrzHYhJxT8m2guOSkrpOVk2anK0D2J8VnmsmL1AOxE1NMtrX93WCTHBOK6FEAxJsbVpDa4vONR9QrQz05ixlrPKdI9dxHJYR1+TmX/3/A6W54rXS+Hs1AvfhZj1d0oBNhH+FYhZuRqfme4z0og1M4N9LBWkcHjh3HmIaW2RImlNEhefSufk8efnpyDGNmINjsg1APMFFFFd6BtfUTyIDnxF8SA68BXFg1R0jh/wB6ihWs7HJvdIo8eGDJZGPuqTc8iWUgvExKJyTh2b+nuIqfNtEe2LWKiFomVZcnswi0aPuoCc50W3Y5Wac/2wiW5fLU006xxz49PTUs9YuICJGgPdXaKdS6PJp1SW+2mpxso1DR3S0BSsxevKAXw3GEtziVVjmfKWddLUc3EOr6PPqiQ0v4g35LWXfyzad+7B0tUP3nKPaL/yvQ6IWZySxzdJNOKMzeJNe/yBXaL9zjhqPkdeflq0Q35MlKmOyoSsZALn+IMj0njU2owxLU1Sl7k0Lo1aPj9WnnKhb3xF8SA68BXFg+jAVxQPogNfUTxIRcW9cslH8zNS5NgYk0aPqYQUU4iI2sJSdJoYxUox4xmZRXcsiJlWG7MyM7A6GoSYjE/G+DNYgvvtw1I4HBhdDTEL1Vi5JhCW/Z5rx4y5iR5pasm+8ybEFOetctKONe+iMdnvunYUvGKNUigKhVEAND58RAzL4/kcGWEta6SQOtZ7GmIWp6RZy+/HYw33ywzGc69+C2I2b5Wmp1t33AExY4ekAHfvBlzD/u7bsXT2rXMyO2+y9WaI6a2T1XwGL+IafPPzcj/VQ2hoqm+QZqnZecwMbVsrheTyiDT0GIPPggt94yuKB9GBrygeRAe+oniQis7xDRsqhqRJozizQ7Rng7g81vCgNCXUxjFRotwn51lDzashZuAVadqI1mB1mfYmmYBStwGr66wrPiba9QuYkJNv7oVth98+INqZXpyPDfXLhI/hPkz4KOTkNayJoFZR1yR1keoWNBkFrKowPh8m8pgyVvk1vtLPbRMRRZNSL2hai9pN95SsxGsclXinF6QZ5o0DWG1o+6PS5HT/Q2iyeeGU1DjeOnIQYsbS+B78yZTUGKYSr0JMlXxkqKERjVAL1nzdNi8REc1n5LG4iM/H3HhKxhh5f/BuudE3vqJ4EB34iuJBdOArige56sBn5hXM/CozdzHzaWb+8tL2OmZ+iZkvLP2NFRsURVmWXIu4VySiPzTGvMPMCSI6wswvEdFvEtFeY8xXmPkpInqKiP745+2oXM5TOi2XulqckxlIZUelGH+NFKEOTwxBzNSwFO7a87jWuy+4U7QHJrBMd9cFKR7NN+OySpkZKcJsy5yBmMUwLuOUykqhKjeBBo3FS3KN+OziNMQYK/MuFEVDU12bFPOCyTaIIb80+ZQdRiAKoMnIR3Kbz6EocVA+Wi2OTMSxbml0Kaew4kzAWvG9awir5Jz66TdF++O/9T8gZv/Ou+WGS1hafX7qBGwbmJRGm8ACPp/ZrMyOTFZhtuLMjLyPEbys5IvIfWeyEYiZnZL7mbGq9BSLWLLdxVXf+MaYEWPMO0v/XiCiLiJqJ6IniOiZpbBniOgz13RERVFuOL/QHJ+ZVxPRLiI6SETNxpgRostfDkSEycOXP/MlZj7MzIcXZnFxRUVRKs81D3xmriKi54no940xWPnhPTDGPG2M2W2M2Z1IqgygKMuBazLwMHOQLg/6fzDGvFsadoyZW40xI8zcSkQ4abIIc4k2hKT55tW8nGeuasGkmMlxaaJpNjj3WVwrv8PiKUw4CURkUkikhPOhOWspriaHqWMhJE1Gx49jUsZ9GzApZm693HauG7WKyQmpMZTLWGY3GZOJRHVtaCAqW3P6hSKabGry0ghUcphKUgW8RiGW1yQRw/sRtJaFjjmW4LYTeWbOvgUxeev8xxawus3x1+SSYhsfweo6D9z6SdH+fyf/EmJmhlDzWVyUmkJpDifncwvyechlUxBTk5DP4+7O9RATXy91mYNvHIaYccv0lMvJ62FcLigH16LqMxF9nYi6jDF/ccV//YCInlz695NE9P1rOqKiKDeca3nj30lEv05EJ5n53a/S/0hEXyGibzLzF4logIg+9+F0UVGU681VB74xZj+9twX4gevbHUVRKoE69xTFg1Q0O89HYQqVpKhRskSnS4NYhrna8myU02jOWRWX4tHdO9CM0r0gl4gaW+iCGB6XZaHv24LCWTAol9D6WhAz+AbO4VJgqXGZZTg3juvBF/LSMBR0VKWpb2gU7aqWVRAznJLC3dl9P4KYDWvkvejcdjfE9A7h/Ri0lpXa2onrwa/bJqvgBEIoALaukVl0C0OYiWgWZbWhfAlFyjcGpSB7xxtvQ8yuX5ZluV/cg5mZc+NoDlqYlNl4bSvxXMtlKbCFQ2iWilmGJmrH5+pcV7doj0/htU9n5PNRtJYh0wo8iqK8JzrwFcWD6MBXFA9S0Tn+Qj5P+4bkvNZX2yzaxaJVzoSI1pSkIeNQaQvE1Mbk/DAQwIozkYJ0DiY33AkxPZPSxPHW6FGIaU7K4ycDH4OY4fSLsK37uFzyeWoalzvyWYkyjQlM+KhtXynaQavKKxFRYUTao6cnUHOYT8qKx4USLrGcy2JFpKF+qY2018YhJp+z7pHjFRNJSl2mfuVWiBnv2ifa5RIamqYmpebywmtoBPq9Bz8t2p/ejff+v//TK7Dt7h23i/ZYBpO2tm+R2lE2hc9welH62/yOJJ3FnDTfuKw4tkEnl5PnXi7rHF9RlPdAB76ieBAd+IriQXTgK4oHqai4F41FaPMuadp47Yhck/z2JjRfpH23iHaHY832Sz6ZEdUX7IaYjTfJZa3yQUwTTj4mSyO/uRcFp/a0zMbLDuK67qMnD8C2yREpbIYdRuhgyCrv3YJmkKoWeR6lGC4HVShKkdKu2kNEFApKhckQZuKVHZ+zl8wKBLG8dzAgsyyZ8VHzheXx61dhxtrsgCydnZtDU0vOMrF0H++BmIsvfk+0133+dyBm251ohKo+Ja9J/zxmpOcWpMCWzmH590hE3ux0PgMx7R2ypEVdNVb76euXIu2lS7KMe7mk4p6iKO+BDnxF8SA68BXFg+jAVxQPUlFxr5wvUPqSLFectUoq9xxDZ9S69bLc0PgsrsOWL28T7d6co8xwjXTh7Vq7E0L8aelmq09iCa/BERlTxehcW5jEsth+yxkXDKJ9q75BCjzJlVimO1Qjs/MWHBlrhZx1HR1ZW+GwFOAM43uAHaW//JbtLBrDbEnDlguNsY9MUryK19ZBTKJVOjAXprFcmZ+lcDY6h8/QK/t/Ktr/9uFPQ8z6W5+Ebf3j+0U7NIcFY9lIcXV8Akt/Fax18aod9ScLKZkdWJXA5yNpXaNoUGY9dvdiqXcX+sZXFA+iA19RPIgOfEXxIBWd4wfJRx1Gzod3rl4t2jUpXFs8biWNDdRXQcxt1rw7Gse52L4zck6ZH/8exFzMyXl/PoXlvof6LV3iLczEuzSM1X2qrK/ZeBWeR2OHLMEdb0ADjz8kz9XnkDPKjnm/Tcia45ccpRXLjiWZ7KhQGOf4+bL8XMSgGcVYmofxY0zDalnxZswxh81npJ5SzmOfj3dJ48/Jd/4ZYv7NfV+Cbd84LTMfm6qaIebgETlfT6Uwy2/GKrnd2twIMa0t8t73X7oAMZGgvNYmKrUb9l3bu1zf+IriQXTgK4oH0YGvKB5EB76ieJCKinuZYppOTsrMtmj846K9mECBJz0vv58+04amkk6rHFVmJZbeSqdlxlp4EdenX71hQLR73sA18Kb75H4uDZyEGJ/BPkZDUpipb8QFhu3MO18US3cHwvIalRyZXuWyNNAEHGW6w1GZCehady2bxczDbFYeb2hwAGJWbZAlq9iRNFbyy+OViljmK1wnS48lOzohZvTcQdGucmQ9Tk1Lce3ot1GA23ALrg/TfIc0UL30D1iKbSYjhVyfD6+1n6QZp+TIeky0SGNaqh9Ls5VJfi6WkAYe17Fd6BtfUTyIDnxF8SA68BXFg1R0jp8r+Kh7TJpG7m6Tc6ZUI86pG+LSNHHuEs5FV4blvHvddpzj5++R+x47Mg4xz719XG44/G2IGeqS65ZnrEQjIqIg40SzKmEl97SuhphQtZz3B4K4H7sCDjmSa0pFaY7x+zEmYhlvqmOYbNTUgjpEt1Vx5/gpXMe9rkFe63Xbb4MYf0g+C0E/VvIJBOTcuG3ddoiZ6JemnlIeq+SkLVPP/gsjEHPXvh/Dto998ndF+8WODRCT2v+aaGczuBTX+iZZ3ac6jM/n2NA50Y6tWAsxJau0/ELP1+X/m6sbt4j0ja8onkQHvqJ4EB34iuJBdOArigepqLhXE4nSI5tkpZzQnCyFXMrjOnD9wzL7qnM7mkp603KN8vlxzKIa65emmqZqFK5ubpQmijfn9kHM9JQ0rIQchpFoFDPW6i2TUVUzrpFOESv7KoCGppJPumFKBrPRcjlpWHGZc9gSBYMOkXDT+pthW2ZBGm0Ov44C6L79L4h2taO6Ttta+SyAaElEZUusqm7Ge9a6Su5n4Ox+iKmyKgClp7FKz6tvHIJtv/UJWTHq1z9+O8R89ZD8XHYRxcVws6zSE21ohxi2TFfBQSwTnqiWZeNDzXK8BM6iQOpC3/iK4kF04CuKB7nqwGfmCDO/zczHmfk0M//p0vY1zHyQmS8w83PMjD+TKoqyLLmWOX6OiO43xiwyc5CI9jPzPxPRHxDRV40xzzLz/yGiLxLRX/1m0Wl2AAAU8ElEQVS8HU3MTdPT//Sc2HbfKvndc1cJvz9qd0vTwugEVrDdl5BmnNsSOKct1cqklGPDaFg5d0Eu4WWvBU9EVCxIPaE2EoGYhhbUKmrapSEjXI1VWMhaaqpUxPMI2DF51DyKeTnHDwawYmvQSvZhx9rqATw16twq5/0T41j5tu/066I9O9oLMY3t0tQSCOFSYD4w9aCe0bhuk2gP92HSVNky9WQcVXouvIN9HNj7I9He+PhvQMyOj0kjUn4YdYhke41ob/gEagUvvC5NRaXcQYiJ56RO1RiXz0LQ7xCcHFz1jW8u866aE1z6Y4jofiJ6V9V5hog+c01HVBTlhnNNc3xm9jPzMSIaJ6KXiKiHiGaN+ZmcPEhEKFMqirIsuaaBb4wpGWN2ElEHEe0hos2uMNdnmflLzHyYmQ/nMilXiKIoFeYXUvWNMbNE9BoR3U5ESf6XtY87iGj4PT7ztDFmtzFmdziKc2pFUSrPVcU9Zm4kooIxZpaZo0T0IBH9ORG9SkSfJaJniehJIvr+1fYVrU7QTQ/dJ7aNvvmMaMfW3QKfKwTkTwrxdpxV+El+qYy+iBljp8NSXDvahd9V51/7jmjPzaBw5bPKycQTWCa7pmUlbAtaYl4gjMtz+QPyluQKWF2nUJJiXiaXg5h81jKoOLIFjV+KUmVG80epiEaXolU9proehUx/QO7LoS2Sz64KVHIs4WWVCS8HMCbRIK9r44qNEDN4XgplgTL+gDo6m4dt+/b/RLR/4+NPQMzqbb8s2kMOkbAhIvtd9IchJmllK4bW7YGYvtN7ZUxJipT5HJ6Di2tR9VuJ6Blm9tPlnxC+aYx5gZnPENGzzPzfiOgoEX395+1EUZTlw1UHvjHmBBHBKpXGmF66PN9XFOUjhjr3FMWDVDZJpypGj90pK6j8zUU5p//hcVw26IHV0hCRTuASw7vj0vxxth5NFENTclmtqVPPQsxE95uiHXBUQ41H5PyszmXWaV0F28JxWYGHHRNfYy1nHXI4aAolOT/1Ec7N7Sq7oYCjuo01x7aXtiYicmwCTAErCrO90JZjTgv7cSzhVbATiYzjXRWW59G0fgeEzAxZ8+6Co0pPEe/1yTNSX7pw/g2I+aVdj4j2yIbHIKbngtQYzBlMwCkOzor2+PBxiElNSV1q/Xr5nJ1yaCAu9I2vKB5EB76ieBAd+IriQXTgK4oHqai45zN5ihWlIaZz6x2i3fXtv4HP3XNamlj44S9CTD4kjQxnmtB4cuEVWZnl4rE3ISablqWRq4IoijU2SsNIsg3NOiFH5l0gLE1GpRJmiNmCW76A5hxjZUCXUFujQlaeR9Lhmgxa1X3YoeS5lmTyGWkSyeexj7aDOxDA/dhFgUwZrwcHpbhpHM7wMskLUFOPWX517XIprJmLxyDGlQk5PyWrDZ14/p8hZvWme0X7lifQQHT8q6+K9tDe70BMJifPPxxC8betsUW017Y/bn3mBHzGhb7xFcWD6MBXFA+iA19RPEhF5/j5Qpn6huXcc7Eg5169gXvhc6/u/6Zo74gcgJiXV8h5dvACLt08tl8ukTQzdgliwlal11g8CjE1LTJJKFSLSUPGsRxUsSwNKq7Kt4WiTMDxOxJnitbncgXUM/JpaVDxJTGRyEDiDlY/Mo45frEsNZe8owKQTSCI+/Zb+olxrKXNbOk7Bue9Aetal+N4zZrXbxXt6eFzEFMs4nnMpuS2t86ch5jbj/6jaO+8/XchJrpGGm14CA1EgbKVfFVEQ1O8QT7nvQvyeuRK16kCj6Io//rQga8oHkQHvqJ4EB34iuJBKiruFcsBmk01iG3nJi2hKIACz8t9MkMqcwKXSCrnPifax17GzKZLvXL5Ib/DMGIXgalvwqW44i2r5X4iWEmHfJhVV7TKV5cLWC3F75dijfGhAFgkuR97uSwiorLl6rGPTUTks0w1Pr8jFc9RqaZUtA08eHy/X17IUBiXFCta588BFKYCPinU+RyVhHxWH9mxFFeyRRpfalo7IWax32F+yUpz0tQwDpnXD0rB7/N3jkLMbz8kM/b+bgBNT5meU6IdmVuAmNoqy4gVlTHswwxDF/rGVxQPogNfUTyIDnxF8SCVNfBkMnSpS85j9hg5x89Uo4ki3iaXQT7ePw4xuaNPi/ZINyZhpBZlBZ6YH7/3WuqkBlHfuhpiQgkZU3Qk2/gd1XHLVoKJzzHvLlvVdKCSDREZq/Js1FHJJxqT1X6SNQ0QE7LmwsahORjGOaPPMhAFHHNqOynH7+jj5fqt/0I46Fivy4oxhPspWnN8n8GspUBQfq5tHZSRpNNDWP0pUJLmqPks7rvrdakNjD6A+tKGWx4S7bW34v0YOSuv2SMPP4B97JN9LExYxyric+dC3/iK4kF04CuKB9GBrygeRAe+oniQiop7VfEY3b77VrEtkZMVTtbdh1lc3/murGiSOfMtiJkfPyPaw6NYvrhkiWKBMB6rvlGWyo66Mu8sAc44KreUjKNyjvU1axyX3xbuyGW8sYxHK5pRKGp4+FdEO+AqnR2wTDXGkdnlMIQkrSXD9tx6N8RkNq4X7Vh1EncNmX94fC5LwbHsKK9dsvod8DmMQJYxqqYZKyQlHaae9CUp3OWKKO4Nj0lB+sCPn4aYz26+WbQ7Nn0CYhJrjor2+UnMHk3l5Llls9I8VXI8Ly70ja8oHkQHvqJ4EB34iuJBdOArigepqLjHzBQOS3FiNC3FiIkT2CVOyOy3hRSWip6blGW77QwyIqKQJfCkHGuJ9/RfFO2xORTpgrYoaK/zTkR+x3rwQbvUlOPy+ywXnF1u+3IQWTGOclRWyS72o5CZmpgUbX/Q0WnHvv1WqSufQ0yrCsh7VFjAa13MyHXg2JF5Zx/eF8A1+OzyYD5HnzOWcBj0YbZgc/sG2NY/Jp+HcnERYlI5KYB2v4Exs598WbQf3/lLEHN2u3QTFsexpBoHZDbekf4p0S5cm7anb3xF8SI68BXFg+jAVxQPUtE5fjTso62r5NyvJy6/e144L9cIJyJaGDwi2otDmP2UXpRznYhjbsxWNZuMIxttem5atGfmZiCmbK1hT465qWveXR2S89OcYz32omXOCTjKW/utNeP9juPbS02VHaW87aO7rlnJUZTHnovHHKWzc5YRqWRfMyLyg56BZbGrQnLfrnLfmaK8ZvZ+L/dHxsTCWDbdcYnIby0PFiI8j7x1Hy/NYIbc+edfFO09Wx6HmLsefFS03/ruEYiZHJVZp2w9Cw4PlBN94yuKB9GBryge5JoHPjP7mfkoM7+w1F7DzAeZ+QIzP8fM+POeoijLkl/kjf9lIuq6ov3nRPRVY0wnEc0QEa5drSjKsuSaxD1m7iCiR4noz4joD/iyunM/EX1+KeQZIvovRPRXP28/qTzRwQEpjhQD8runqRZ/cBgYkxlIOUfWUswvVY1wCMs4pS0xr+DIZAr6/FYMCnB2CS2H7YWKRSzHtWhlkZUdx7czy3IuI5IlcOUcwplN0CF42aJgxCEM5R37XsxbGWqu0mPWvkAQJaKAda0jDiNQk1W6PJash5hJS4Cdmp2GmLC5etkzu89ERAWrrFfaoQD6SF6PS46y2Ie6pFlqY+8hiLm180HRPrkO1+mLzslOpqw1EsuO59XFtb7xv0ZEf0T0M0mznohmjTHv3vFBIsL8VUVRliVXHfjM/BgRjRtjrvzdguuXBo5fhhAx85eY+TAzH56dnnif3VQU5XpyLT/q30lEn2bmR4goQkTVdPkngCQzB5be+h1ENOz6sDHmaSJ6moho07ZbnF8OiqJUlqsOfGPMnxDRnxARMfO9RPQfjDFfYOZvEdFniehZInqSiL5/tX0VS0TT8/KHjOGsnJPMTeD3R21cVjhZrMFqLiWfnGfl85hck0/L5IlaR8lnu0qPq+KKrRW4zDF2QhARUd6afwUd5b2z1tzTtWRUFpb+cvwAZm0qOeZ+YASyq/8QUciH5xGzk41cX+fW8cOO/dhB0RAaeKob5dJX/hAab5I+qQvNLuKSXqWCfB5cpieXDmHY1nNcjia5reRwPZ2akqaeE2/iHH9LraxOtTWUgJhh67mOxWSykZ3k9V58kN/j/zFdFvq66fKc/+sfYF+KolSQX8iya4x5jYheW/p3LxHtuf5dUhTlw0ade4riQXTgK4oHqWwFnnKG/BlZBjtuVX25dw1W17mQkKJHXUsdxJSmpChoMvirwxlL9ClkUMwpFqy1+xyCl238YYe6FXOsOVf0y+y8skMEylkiVJnQHGMlGUK5bSLM2ko7DEU+6/R9DnGr7DDnhGxdzDiukZWx5nOcR8E6f5fgRjU1ohmM1UKIf0EaZqoymOHJlhHK53j0iyU8/4Il7pYc1zoUkvsKBlCkTEdkSfIDZzHr82j+JdH+4duHIebM3n8UbXtNxGIe1550oW98RfEgOvAVxYPowFcUD1LROX45V6RMj5x7f/JTsrJptKYNPteZkvOs6Zl1ENN/Ue533dwBiHmxSiZ4PP+/vgEx6Z5zoh2LYVXXfFHOBRfmcE7Z1oQmo0/ee69oxxvvhJgjR/aLdnMS552z1vFX1KJWUBuTRpejUzjvnBy0zFIFrOq6uhGX51qzXl7/0TSapbILcq7pmnvac+qyQ0/xWSlQpQA+suGk1HxWtHZATMAyS/kIr4dhNBkZ2/jEeK4h62PlkjNtS7SmGLWswaNvinbvwZ9ATC4jjUATOXkPiw7DmQt94yuKB9GBrygeRAe+ongQHfiK4kEqKu7lmanfMjeMnnlLtBtvxqWF+uakCFRTwuo6w9wk2ndsvBliIo1yzXZfbS/EzM5KgWV2GsWcSEQef2RkEGKiPhQpz8zLWiULo1ipZbL3lGjfdt9WiEm0rBBtU49iUnpGGl8iqVGIyaSk4DZ08R2IqS6vh21bd20U7eksvj8GeuS+imksOT0xKq+/K4MwZGXjBYN470uWSFjIY3YeW1WLOlpRIJ6cRdNX2CqJ7g/ikAlYZi3jwypSxnd109fivCwRXx3D7Dx/vdyPvcRaNosCrQt94yuKB9GBrygeRAe+oniQis7xyR8gSkpDyHMjcj7W8JPn4WMttdtF+1gOu10dlPOjRYfRYua4NNokm6ogZtgyerCjhu6GtatFu6kJTS5jY1hJKLMozUGpRZzjz4x0iXZd6G6I6Vh3v2if8ePc+OKYPNdiHivPNjdIk1FL7R0QEwnhtZ6alfP1chbNMBNDcnnpeAw1j3RKnn+iBpOvFlMymSW1gEtQ+6yEpFAQ+xO0qi5nsmh0mZlBHcReAtyVtBQNyfl6VR2ex8yUrIabqML5ezQi9Yx4VTXEhK2YWFhqEKOjqDe50De+ongQHfiK4kF04CuKB9GBrygepLLiHhExSwNCU0BmzEUzKLowWRVWDGbMJcLyVI7NxCBmokeaG1YF5iBmzKruw1m8ROd7ekR7ZcdKiKmva4Rtp/a9INrNbasgJm1V9/n7nx6EmNuMzOzi5m0Qk/dJUazKh9lg4bg0+RRQNyN2iGAXz3aLdiyK13r3zbfL/jiWlSoZmUU3NoyCaLFgHd9hfAlbAlfOUVo9GpdCbrdjCatoBM1BVVZG59wsVs7JZGV1n3AGBcBgUPYxHHWYcyxzUDzmWIfWJ4VLn1XamytQXltRlI8oOvAVxYPowFcUD1LZOX5+kcqXZBJMXcMW0Z4wuAzy8IBcFjsUxphMk0w4WZfDCjjBdjl/D47cDzHbbpX76TmxH2KqYtLUMzqByR0hRzLHnFUNdvzkEYixl46eXbgIMed6Toh2YgIn58aaG6eHuiFm3lqurGQtuUzkXlarYOQcNuZY+ipcLa9/Uwcuprxn112iPZTE5c+PnHxNtBddVWSt6rx2ZVwiovkFaWgql1EryGQwkagqLjWXRAKfKy5LTeHmTZ+AmJ6pPvkZg9c6l5FaweI86iJJSzvKFuT9YVelYgf6xlcUD6IDX1E8iA58RfEgOvAVxYOwcS5u/iEdjHmCiPqJqIGIJit24OvDR7HPRB/Nfmuf3z+rjDHoHrOo6MD/2UGZDxtjdlf8wB+Aj2KfiT6a/dY+f/joj/qK4kF04CuKB7lRA//pG3TcD8JHsc9EH81+a58/ZG7IHF9RlBuL/qivKB6k4gOfmR9m5nPM3M3MT1X6+NcCM/81M48z86krttUx80vMfGHp79ob2UcbZl7BzK8ycxczn2bmLy9tX7b9ZuYIM7/NzMeX+vynS9vXMPPBpT4/x8yOxPQbCzP7mfkoM7+w1F72fb6Sig58ZvYT0f8mok8R0U1E9GvMfFMl+3CN/C0RPWxte4qI9hpjOolo71J7OVEkoj80xmwmotuJ6PeWru1y7neOiO43xuwgop1E9DAz305Ef05EX13q8wwRffEG9vG9+DIRXVkS+aPQ559R6Tf+HiLqNsb0GmPyRPQsET1R4T5cFWPM60Rk16N+goieWfr3M0T0mYp26ioYY0aMMe8s/XuBLj+U7bSM+20u825qYXDpjyGi+4no20vbl1WfiYiYuYOIHiWi/7vUZlrmfbap9MBvJ6Ircy8Hl7Z9FGg2xowQXR5kRNR0lfgbBjOvJqJdRHSQlnm/l35kPkZE40T0EhH1ENGsMT/L/V2Oz8jXiOiPiOjdnN16Wv59FlR64LuShfXXCtcRZq4ioueJ6PeNcSR9LzOMMSVjzE4i6qDLPxFudoVVtlfvDTM/RkTjxpgriyl85J7rShfbHCSiK5d67SAirLC4PBlj5lZjzAgzt9LlN9SygpmDdHnQ/4Mx5jtLm5d9v4mIjDGzzPwaXdYnkswcWHqDLrdn5E4i+jQzP0JEESKqpss/ASznPgOVfuMfIqLOJQU0RES/SkQ/qHAf3i8/IKInl/79JBF9/wb2BViaZ36diLqMMX9xxX8t234zcyMzJ5f+HSWiB+myNvEqEX12KWxZ9dkY8yfGmA5jzGq6/Py+Yoz5Ai3jPjsxxlT0DxE9QkTn6fJc7j9V+vjX2MdvENEIERXo8k8pX6TL87i9RHRh6e+6G91Pq8930eUfL08Q0bGlP48s534T0XYiOrrU51NE9J+Xtq8loreJqJuIvkVE4Rvd1/fo/71E9MJHqc/v/lHnnqJ4EHXuKYoH0YGvKB5EB76ieBAd+IriQXTgK4oH0YGvKB5EB76ieBAd+IriQf4/MGp0iA21rJ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "try:\n",
    "    # Loading pre-compiled TEST data images and labels\n",
    "    with  h5py.File('Data\\\\Saves\\\\test_data.h5') as hf: \n",
    "        X_test, Y_test = hf['imgs'][:], hf['labels'][:]\n",
    "    print('Loaded images from test_data.h5\\n')\n",
    "    try:\n",
    "        print('Example of test image after processing')\n",
    "        r = random.randint(0, len(X_test))\n",
    "        img = X_test[r]\n",
    "        img = np.rollaxis(img, 0, start=3)\n",
    "        plt.figure()\n",
    "        imgplot = plt.imshow(img)\n",
    "        plt.show()\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        print('It was not possible loading the test image')\n",
    "except:\n",
    "    test = pd.read_csv('Data\\\\Dataset\\\\GT-final_test.csv',sep=';')\n",
    "\n",
    "    X_test = []\n",
    "    Y_test = []\n",
    "    print('Now starting to process test images')\n",
    "    for file_name, class_id  in zip(list(test['Filename']), list(test['ClassId'])):\n",
    "        img_path = os.path.join('Data\\\\Dataset\\\\Test\\\\Images\\\\',file_name)\n",
    "        X_test.append(preprocess_img(io.imread(img_path)))\n",
    "        y_test.append(class_id)\n",
    "        # Processing progress\n",
    "        if len(X_test)%1000 == 0:\n",
    "            print('Processed => {:.2%}'.format(len(X_test)/len(list(test['Filename']))))\n",
    "    print('Finished processing test images')    \n",
    "\n",
    "    X_test = np.array(X_test)\n",
    "    Y_test = np.array(y_test)\n",
    "\n",
    "    with h5py.File('Data\\\\Saves\\\\test_data.h5','w') as hf:\n",
    "        hf.create_dataset('imgs', data=X_test)\n",
    "        hf.create_dataset('labels', data=Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final model accuracy against test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy = 97.57%\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict_classes(X_test)\n",
    "acc = np.sum(y_pred==Y_test)/np.size(y_pred)\n",
    "print(\"Test accuracy = {:.2%}\".format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One pixel attack\n",
    "#### Big part of this implementation was taken from Dan Kondratyuk ([Hyperparticle](https://github.com/Hyperparticle)) original work [One pixel attack Cifar-10](https://github.com/Hyperparticle/one-pixel-attack-keras/blob/master/1_one-pixel-attack-cifar10.ipynb) and adapted parts of it which was necessary for my dataset and own implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.io.parsers import read_csv\n",
    "\n",
    "signnames = read_csv('signnames.csv').values[:, 1]\n",
    "\n",
    "def plot_image(image):\n",
    "    plt.figure()\n",
    "    plt.imshow(np.rollaxis(image, 0, start=3))\n",
    "    plt.show() # Show the plot\n",
    "\n",
    "    \n",
    "def perturb_image(xs, img):\n",
    "    if xs.ndim < 2:\n",
    "        xs = np.array([xs])\n",
    "    \n",
    "    # Copy the image n == len(xs) times so that we can \n",
    "    # create n new perturbed images\n",
    "    tile = [len(xs)] + [1]*(xs.ndim+1)\n",
    "    imgs = np.tile(img, tile)\n",
    "    \n",
    "    # Make sure to floor the members of xs as int types\n",
    "    #xs = xs.astype(int)\n",
    "    \n",
    "    for x,img in zip(xs, imgs):\n",
    "        # Split x into an array of 5-tuples (perturbation pixels)\n",
    "        # i.e., [[x,y,r,g,b], ...]\n",
    "        pixels = np.split(x, len(x) // 5)\n",
    "        for idx, pixel in enumerate(pixels):\n",
    "            # At each pixel's x,y position, assign its rgb value\n",
    "            x_pos, y_pos, *rgb = pixel\n",
    "            # Modify axis (48, 3, 48) -> (48, 48, 3)\n",
    "            if idx == 0:\n",
    "                img = np.rollaxis(img, 0, start=3)\n",
    "            img[int(x_pos), int(y_pos)] = rgb\n",
    "\n",
    "    return imgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = X_test[0]\n",
    "plot_image(img)\n",
    "\n",
    "pixel = np.array([16, 16, 1, 1, 0]) # Pixel with X, Y, R, G, B (RGB 0 to 1) coordinates\n",
    "image_perturbed = perturb_image(pixel, img)[0]\n",
    "plot_image(image_perturbed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el siguiente cuadro podemos comprobar que efectivamente se han modificado 3 valores de la imagen original, indicando que se han modificado los colores RGB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result=> [[[[ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]\n",
      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
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      "   [ 0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.          0.          0.\n",
      "     0.          0.          0.        ]]]]\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import numpy\n",
    "numpy.set_printoptions(threshold=sys.maxsize)\n",
    "\n",
    "perturbed_arr = np.array(image_perturbed)\n",
    "normal_arr = np.array(np.expand_dims(X_test[0], axis=0))\n",
    "print('Result=>', normal_arr - perturbed_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_classes(xs, img, target_class, minimize = True):\n",
    "    # Perturb the image with the given pixel(s) x and get the prediction of the model\n",
    "    imgs_perturbed = perturb_image(xs, np.expand_dims(X_test[img], axis=0))\n",
    "    #print('This is predict classes',model.predict(imgs_perturbed))\n",
    "    predictions = model.predict(imgs_perturbed)[:,target_class]\n",
    "    # This function should always be minimized, so return its complement if needed\n",
    "    return predictions if minimize else 1 - predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def attack_success(xs, image, target_class,verbose=False):\n",
    "    attack_image = perturb_image(xs, X_test[image])\n",
    "    \n",
    "    confidence = model.predict(attack_image)[0]\n",
    "    predicted_class = np.argmax(confidence)\n",
    "    #print('Attack-succ func target_class', target_class)\n",
    "    #print('Attack-succ func predicted_class', predicted_class)\n",
    "    \n",
    "    if verbose:\n",
    "        print('Confidence:', confidence[target_class])\n",
    "    if (predicted_class != target_class):\n",
    "        return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model predicted => Speed limit (80km/h)\n",
      "Prior confidence 0.9422471\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After applying a random perturbation model predicted =>  Speed limit (20km/h)\n",
      "Confidence of being Speed limit (80km/h) after perturbation is => 0.23182726\n",
      "\n",
      "\n",
      "Attack success: True\n"
     ]
    }
   ],
   "source": [
    "image = 931\n",
    "pixel = np.array([30, 23, 1, 1, 0])\n",
    "true_class = Y_test[image]\n",
    "\n",
    "# Original image\n",
    "y_pred_org = model.predict(np.expand_dims(X_test[image], axis=0))[0]\n",
    "plot_image(X_test[image])\n",
    "\n",
    "print('Model predicted =>', signnames[np.argmax(y_pred_org)])\n",
    "print('Prior confidence', y_pred_org[true_class])\n",
    "\n",
    "#Modifying image\n",
    "image_perturbed = perturb_image(pixel, X_test[image])[0]\n",
    "plot_image(image_perturbed)\n",
    "confidence = predict_classes(pixel, image, true_class)\n",
    "\n",
    "print('After applying a random perturbation model predicted => ', signnames[np.argmax(confidence)])\n",
    "print('Confidence of being', signnames[np.argmax(y_pred_org)],'after perturbation is =>', confidence[0])\n",
    "print('')\n",
    "print('')\n",
    "success = attack_success(pixel, image, true_class, verbose=False)\n",
    "print('Attack success:', success == True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Scipy's implementation of differential evolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from differential_evolution import differential_evolution\n",
    "\n",
    "def attack(img, target=None, pixel_count=1, \n",
    "           maxiter=75, popsize=400, verbose=False):\n",
    "    # Change the target class based on whether this is a targeted attack or not\n",
    "    targeted_attack = target is not None\n",
    "    target_class = target if targeted_attack else Y_test[img]\n",
    "    \n",
    "    # Define bounds for a flat vector of x,y,r,g,b values\n",
    "    # For more pixels, repeat this layout\n",
    "    bounds = [(0,48), (0,48), (0,1), (0,1), (0,1)] * pixel_count\n",
    "    \n",
    "    # Population multiplier, in terms of the size of the perturbation vector x\n",
    "    popmul = max(1, popsize // len(bounds))\n",
    "    \n",
    "    # Format the predict/callback functions for the differential evolution algorithm\n",
    "    predict_fn = lambda xs: predict_classes(xs, img, target_class)\n",
    "\n",
    "    callback_fn = lambda x, convergence: attack_success(x, img, target_class, verbose)\n",
    "    if verbose:\n",
    "        print('Working on the attack please wait... :)')\n",
    "    \n",
    "    # Call Scipy's Implementation of Differential Evolution\n",
    "    attack_result = differential_evolution(\n",
    "        predict_fn, bounds, maxiter=maxiter, popsize=popmul,\n",
    "        recombination=1, atol=-1, callback=callback_fn, polish=False)\n",
    "\n",
    "    # Calculate useful data\n",
    "    \n",
    "    ## Attack result, perturbation to make, and number of pixels for the modification\n",
    "    if verbose:\n",
    "        print('----------------------------')\n",
    "    start_1 = 0\n",
    "    end_1 = 2\n",
    "    end_2 = 5\n",
    "    perturbation_result = np.array([])\n",
    "    for _ in range(pixel_count):\n",
    "        perturbation = np.append(np.append(np.array([]), attack_result.x[start_1:end_1]), attack_result.x[end_1:end_2])\n",
    "        start_1 = end_2\n",
    "        end_1 = start_1 + 2\n",
    "        end_2 = end_1 + 3\n",
    "        perturbation_result = np.append(perturbation_result, perturbation)\n",
    "    if verbose:\n",
    "        print('Attack result',perturbation_result) # [x_pos, y_pos, r, g, b]\n",
    "\n",
    "    ## Statistics from original image\n",
    "    if verbose:\n",
    "        plot_image(X_test[img])\n",
    "    y_pred_org = model.predict(np.expand_dims(X_test[image], axis=0))[0]\n",
    "    if verbose:\n",
    "        print('Model predicted =>', signnames[np.argmax(y_pred_org)])\n",
    "        print('Prior confidence', y_pred_org[target_class])\n",
    "        print('----------------------------')\n",
    "    ## Statistics from perturbed image\n",
    "    attack_image = perturb_image(perturbation_result, X_test[img])[0]\n",
    "    if verbose:\n",
    "        plot_image(attack_image)\n",
    "        \n",
    "    prediction_perturbed = model.predict(np.expand_dims(attack_image, axis=0))[0]\n",
    "    predicted_class = np.argmax(prediction_perturbed)\n",
    "    predicted_class_name = signnames[predicted_class]\n",
    "    if verbose:\n",
    "        print('After applying a random perturbation model predicted => ', predicted_class_name)\n",
    "        print('Confidence of being', signnames[np.argmax(y_pred_org)],'after perturbation is =>', prediction_perturbed[target_class])\n",
    "        print('----------------------------')\n",
    "    ## Attack's success\n",
    "    success = predicted_class_name != signnames[target_class]\n",
    "    if verbose:\n",
    "        print('Attack success:', success)\n",
    "\n",
    "\n",
    "    return [pixel_count, img, target_class, predicted_class, success, attack_result.x]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples of attack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on the attack please wait... :)\n",
      "Confidence: 1.0\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.99999726\n",
      "Confidence: 0.999997\n",
      "Confidence: 0.99999535\n",
      "Confidence: 0.99999535\n",
      "Confidence: 0.99999535\n",
      "Confidence: 0.99999535\n",
      "Confidence: 0.99999535\n",
      "Confidence: 0.99999535\n",
      "Confidence: 0.99999535\n",
      "Confidence: 0.99999535\n",
      "Confidence: 0.99999535\n",
      "Confidence: 0.99999535\n",
      "Confidence: 0.99999464\n",
      "Confidence: 0.99999464\n",
      "Confidence: 0.99999464\n",
      "Confidence: 0.99999464\n",
      "Confidence: 0.99999464\n",
      "Confidence: 0.99999464\n",
      "Confidence: 0.99999464\n",
      "Confidence: 0.99999404\n",
      "Confidence: 0.99999404\n",
      "Confidence: 0.9999881\n",
      "Confidence: 0.9999881\n",
      "Confidence: 0.9999881\n",
      "Confidence: 0.9999881\n",
      "Confidence: 0.9999881\n",
      "Confidence: 0.9999881\n",
      "Confidence: 0.9999881\n",
      "Confidence: 0.9999881\n",
      "Confidence: 0.9999881\n",
      "Confidence: 0.9999881\n",
      "Confidence: 0.99998605\n",
      "Confidence: 0.99998605\n",
      "Confidence: 0.99998605\n",
      "Confidence: 0.99998605\n",
      "Confidence: 0.99998605\n",
      "Confidence: 0.99998605\n",
      "Confidence: 0.99998486\n",
      "Confidence: 0.99998486\n",
      "Confidence: 0.99998486\n",
      "Confidence: 0.99998415\n",
      "----------------------------\n",
      "Attack result [25.82867132 24.32586847  0.99616478  0.9994214   0.99612964]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model predicted => Right-of-way at the next intersection\n",
      "Prior confidence 1.0\n",
      "----------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After applying a random perturbation model predicted =>  Right-of-way at the next intersection\n",
      "Confidence of being Right-of-way at the next intersection after perturbation is => 0.99998415\n",
      "----------------------------\n",
      "Attack success: False\n"
     ]
    }
   ],
   "source": [
    "image = 7920\n",
    "pixels = 1 #umber of pixels to attack\n",
    "\n",
    "_ = attack(image, pixel_count=pixels, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on the attack please wait... :)\n",
      "Confidence: 0.9999981\n",
      "Confidence: 0.999992\n",
      "Confidence: 0.999992\n",
      "Confidence: 0.9999783\n",
      "Confidence: 0.9999783\n",
      "Confidence: 0.9999783\n",
      "Confidence: 0.9999783\n",
      "Confidence: 0.9999783\n",
      "Confidence: 0.99994516\n",
      "Confidence: 0.99994516\n",
      "Confidence: 0.99994516\n",
      "Confidence: 0.99990094\n",
      "Confidence: 0.99990094\n",
      "Confidence: 0.99990094\n",
      "Confidence: 0.99990094\n",
      "Confidence: 0.99990094\n",
      "Confidence: 0.99990094\n",
      "Confidence: 0.99990094\n",
      "Confidence: 0.99990094\n",
      "Confidence: 0.99990094\n",
      "Confidence: 0.99990094\n",
      "Confidence: 0.99425274\n",
      "Confidence: 0.99425274\n",
      "Confidence: 0.99425274\n",
      "Confidence: 0.99425274\n",
      "Confidence: 0.99425274\n",
      "Confidence: 0.99425274\n",
      "Confidence: 0.99425274\n",
      "Confidence: 0.99425274\n",
      "Confidence: 0.99425274\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.96456915\n",
      "Confidence: 0.91416615\n",
      "Confidence: 0.91416615\n",
      "Confidence: 0.7720716\n",
      "Confidence: 0.06599495\n",
      "----------------------------\n",
      "Attack result [ 1.2040326   3.05031972  0.53710248  0.83227669  0.18316248 22.24478394\n",
      " 24.92355355  0.25778937  0.17179703  0.17828779 27.76766172 18.11541165\n",
      "  0.12157127  0.17201998  0.26824693]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model predicted => Speed limit (50km/h)\n",
      "Prior confidence 1.0\n",
      "----------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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HY4EqzDGvStDGhm7j3Q1UlQphT3zDiCC28Q0jgtjGN4wIYhvfMCLIQMW9br+H3aoOPikUdNCItDkbbG5eB2TkiyycPfn4WTXe3lgmm+UNLd4MFzhjLCFa3JsYZZtiUQt+Eso8a95Ic7mKFhzzdRazOuvfUuPSRbbpl3XASDbG58xlvEtb4Ky2jNeiKZ7nwJduh6Mt03EtIPUCJcDjXf1ZO2UOamnu6GvUb3GQUcILRGqWWCTslHUwztgdHPhydO5mNa5Pc9uxeo2vo595V6/7+WqAiA6yisf5nLW8+zoR4zS6tleRqFzlDELxgoWSSS32ibAgGMKe+IYRQWzjG0YEsY1vGBHENr5hRJCBint99FHv6wykjpchVg6IN1te37XZE3eTzfFTOpquUQ0cZ0Nn8K2ssXCW9kpFpwucHZcd0aLYeCqQwdfj17X3tDDkAhF3rWVdlru1yyWaElktQiXmFsgGR3TvvPbYDJnInLZxw1xPpd3mmL9+Rj8vpM/RYv22F4G5w5nbncuLemJzjWzaW1oAdBUund1d1r0Vd3t8XvNeCbUj428im+oMC6DVto7K3N4N9LPzymE1mhw16pe9DvVE9MW9jXWOJMzl9X2ey+nx3h6LqCHsiW8YEcQ2vmFEENv4hhFBBuvj93qoVHR2k8R1wMGZp3SlFACo+EE/kwtkk8/qEsvpgN/d7mg9YbvEPuXW43ou1EJr0ssYm05yMEaisUhz1e1vq/HelW+TjfO0ifQQawVDJ7XGULzzXrJpzdymxqV0oFSz1yO+HQj+CBTgQVe88tqBx0fMK+aTneAS4LkZnVWHNQ5YyWxo/719+UGy6Szrc1Zd5vLrjc6TajxyB5+PU0e4ug+SWhspbXHm35VLi2rc6wcCmhJaG2jUuAJPz8usa9RZpxJ4AW/+9Qm0cwthT3zDiCC28Q0jgtjGN4wIYhvfMCLIQMW9TruNzctX1NzIuBbKmpUqve62G3XZpMmQcOcVl0r72WkAUmmdyTQ3x0EtpV0tPhYC4lYxqbPzkg3OjmusPMVzl76uxp0Si1luTAeM5E+fJJux179FjVvjryKbtYZeY0P4O77rtaOPs46IQHVx9Lxy2oGq3PBbF7YCFaFiKb3GxBRntSUn9Fx6MlDmyyt11T2/SDa9dV2yq4wHyWYozYE3N0/dpcabc3w9njmnhcNugwVh5/W6r1dZgMwk9QWYmZ4jm7h3Qbw2htja4lJtIeyJbxgRxDa+YUQQ2/iGEUEG6uNnUhnceExXPknnx9T46CQnSvjt12OBdkw1L1FkZKRANrm8TsApl7niS3FE6wB33cjVfo4Mad+0vc4ll/curNCc29Y+/VAhRTaJ0zep8cQbfoxsKqMLarzT5fNR97SJWpd9454X7CGB4I9sIDon1tdzHY5XQa+rj9Vt8/s7r93TcCAQqtXRfm8qew/ZDJ3S1yjf+nOy6Z//ql7PGmtJe9/kpJihO3TlnoU5rtxz+vSdavytJ/6abOp17dMP5fn+vOmoV/1piluKlatamKl7yUeJuA54uhb2xDeMCGIb3zAiiG18w4ggtvENI4IMVNxzADpe/7hEV3/3JBwvqdvRIpDEWQRq9LR4UhgbIxt4ZZDTMRac3vo6Xd3n1a+6iWz6Jf1eu8+cJ5vmCvd4y8Z0MMrQseNkk7njB9V4PcFZbZtXdGZXBxx40uk5b8xRNqWOnouNsEg4NcE95oaz+nNU23w9tr2gpr1lzoSMeZmI+QyvMZfXnz/dGSWbmYwOxJo4fivZSElX7qkH+uS11jmgqpPXdrkTLO4Nj2hB2gWClfJeZaeJ0XGyGRkaVuOdPRaN80O6SlLTr+QTCNQKYU98w4ggtvENI4LYxjeMCDJYH98BfvvuuNM+Stov3QKg7/Uf366y79P2tIP8GFdKmZrWARKFLkeeFPPah4t1uK3S7pL26XvrT5PNUJ+rp6QndEDG8I1vJpudMR0w8kSg8uzKed0yqr7DCR/S1354z8/mALAb05rH3B3sv2Zy7IsOZfX1aAWCg56+ote49tRjZDPn6Q6tXuB2HNYJOHfdxIkryeK8Gsdi7OPHx7QOklz/K7apcyXgVFVXSerFOOhqclKfo1PHX002/ZausLy2wRWedyv6OvYTfD5qO5tq7LzktL7jaxHCnviGEUFs4xtGBLGNbxgR5LobX0SOisgXReSMiDwlIh/anx8Xkc+JyNn9fwf+cG4YxmHkIOJeF8DPOeceE5EhAI+KyOcA/D0An3fOfUxEPgrgowA+ct2jOS3otNs6GCWV5pI39brOSNrc3CSbcV84S3M1l5kRLQyNCttMD2lxLVnjlkk9vx1Ui9eDBIsssTktQtWmbyablb7XCizNglOloQUnV+EKQMX5O9S472UUAkBxyBMuc1w5Jpnh61Hr6fcrb7KQWf6azlB7610stt56Sq9xp8Zr/O3/+m/VuFvi/vRH3vkP1DiWei3Z9ApauOwNLZKNbHF7rnxNt+PKJebJ5vab36DG9W0OhNra0BWZylVudVXzMvjSgSxU8UqbF/I66Cce4/s1xHWf+M65VefcY/v/XQFwBsARAO8D8MC+2QMA3n+gdzQM42Xn+/LxRWQBwN0Avgpg2jm3Clz9cgDAHRevvuaDIvKIiDxSb/CfnQzDGDwH3vgiUgDwJwB+1jnHQc3XwDl3v3PuHufcPbks/5QzDGPwHCiAR0SSuLrpf9c596f70+siMuucWxWRWQDsjPq4PtDTgRSJhK4q22xxwknfq/46P3uUbDJZnQSxtsbL6bd0xZ35BW6ZVByZ1ROB9s5uV/9ykUBbZAxzoEd/Rgd6tKdYD632tV93aZf97vUVrZP8w3fdRzatgn6veoZM4BUdhgTK7LaFb5GdlvbxNy9zQNXtCV0V59apO8kmHtPXvhA4Z295v/bf/+fvfpxspsZ1sNA73/B6ssmd1ok01QpXeurs8vvvbevPVnuW9ZwRL7lpItCSvLy3qMbDI3ztkwkviSzHD8otr9V6rabX0w+0LA9xEFVfAHwCwBnn3H94zv/6DIAP7P/3BwB8+kDvaBjGy85BnvhvBPB3ATwhIo/vz/0rAB8D8Icich+AywD+1kuzRMMwXmyuu/Gdcw8DCLSVAAD8yIu7HMMwBoFF7hlGBBlodl48JhjJeAKS91uiHSjx3OnroARfBAGAnV0dRLJ44RzZJHs6qGZ0hDPvIFok3C1zIE65rufSff5BVBgOtIOa0mJWI82fo1PT38XiCWAA4DK6r/wSJ/ChGteZh5kiB3aM5fS5zgfW8+v//Ado7sd/w2sF1uVrlkjov+5eWedbreiVO28l+DmUHtVC7sTRO9gmqc9RTFjgio3qY3dHWO1sJ3gu3vEEtgbbiFfLPJXk0tkurj9rMpB5lx3TguzSOpdor9X0H9RKFS1YdzoczBXCnviGEUFs4xtGBLGNbxgRZKA+PpyD8/otZb3Kqpc32GH1ujmj2gv8kaHtVXXd5UCLaa+tVibNvmA6pY/T4zwJxGLap+2DfdzMCFeuwbj2e/cCH6MT91oiJXiNzZY+R089/WVeY0EH0JQucLj0kNOf9YfvvpFsfv53vkBzl5wXwBTjVmT70dzfZfbILNl0hrRW0gzEnlTj+iQ1HJ808Yo2+YksANBIao2jlmF9R1LDNJds6VZbyYZ/NwK5tg6yGsmFEmX0fS/+ogE0vdbZtQoHyPo+/V5ZBxj1elaBxzCMa2Ab3zAiiG18w4ggtvENI4IMVNxrd7u4tK1FtxGvIs+VLRblGi0tjNRq3Ns8k/DKSXdYhBkf0+2YcgX+3kvEtODlqlzxxdV11aB0gttMJVOcWdWL69OdkEDgjzd1LNDWaj2vRaA//KNfJJu3vuW9anz6Rs6OW17Sbb7+yb/4MNl85Lf+I80VT+o2YzffyG2+1le+qca5PF+zdlaLaeUaZ8clPHEvHSj3HUvoc+1CAmBCC26S5GuGJAdLIa7FtH4gWKnT0PcDAvdDs6fvx2cuniWbRtW7rwICZC4/+j3HpTJnSoawJ75hRBDb+IYRQWzjG0YEsY1vGBFkoOJe3zk0ezo8q7WnxbzCKJdhLia8iKomR6HFvZ57O9tcKnm4oMsdpdIswMW8U5JhLQfD3tdlEhyFFQtkGfbEj+him5TXTy7Z4Iy5U6d+SI1/6N+/l2xuHtciWCJQ53TlJv1em4HP8Wef5sJKf+c+3ZtuZ4M/R62p53qxQFieF2EXEuX8fu/pQN3GRFyf11jgc3S943TjvOaYcNSb80qJJwLXNe2lmLa7/P7bdV2ereoLggAmR7W4uLDA5deTniDq3x2Xr7BoGMKe+IYRQWzjG0YEsY1vGBFkoD6+CJD0fK31Hd1KaO5YoMJKTPur/Tz7R5W9JW0TCwRxxHSQTzIW8PHjusJKi/xyoO5lVmWlQzadQACRa+m5dFAH0OdnboI1j5miLg0dj3PgS8Prm+77ygBQbenzOH2Eg3y++rW/4TV6p7/X4CCjXlef60CsEnyNo9/nNab8Z1OLz2uvpbPjXC+Qdem53elApZp2M3Dsvn6hxPl+cDG9xl6Tffy4F/gTyqIre35/v8/HGS94epf3UV+0FlqGYfz/h218w4ggtvENI4LYxjeMCDJQca/VauG8V/Z6dl73rG9WOBsOBS1eVaq7ZFKuaJEwl+MyyHmvtLcE+vTFnFZLMuOjZBMv6BJenfoy2bTrXDYp6ZVGTgXEm4bzglFiHMDz4X/042r87377M2TT9EqSt+P8Hd/O6nPUTrACt7OyRHMZL9BlKMOC0lZXC1W9gODWaGvBq9vi92/W9Tmq7XDJ6X5Bi4vJwHlNNLQAm6xyFlu3xT3r0df3SCzFa4yl9Od3NRZ7Y14gUCAuDG3vHO3u8XoKXgnwobyXwef4s4ewJ75hRBDb+IYRQWzjG0YEGaiPn4zFMTWsExHOXTyvxm2ne50DwNFjt+jjJNmn9H3I1DBXL2l50Q6lZoVsan0dDNIInKGGVxWGvXCgEdAhYjvrajx6gv2xljdV6rJv/Asf/z01Xt/mgKbRpNZFcknWPLZLOmDFBaJsTt12A80Vsl7J6yKfgae8vu1T5/m6njyuy42X1/g4VS+oJhkP+NhpfT8E5ASk6jpLKV7aIhtpc5lweOXOXZ7PYyOj56qBJLKE59UnYvzMzXpJY602f5A973NUvICmTuB+CWFPfMOIILbxDSOC2MY3jAhiG98wIshgxb1EArNjOrOsm9Yi3G6bgx82NnQ1nXaTSzXPz82ocShjrdnV4tFOnQN4jogWS/KTLDilRrVQ095okU2jwtlXscta3Btf4GCldGpajXc3eY0Xt7SYJzkWCXNDWgQbTbEolfP68vV7HHR06tY30pw471g5zs4bfqOuEvQ//vd/J5sfOqkFttToq8hmDzrwZWpygWzGp06qcczPTAQg21psjW1zGfeUC1wz77O1snw/VGL6GpWqHPRUr+hrP5Tm+1O899/Y4sCwxSUthvuBQNUG740Q9sQ3jAhiG98wIsh1N76IZETkayLyTRF5SkR+eX/+hIh8VUTOisgfiAj/djEM41ByEB+/BeBtzrmqiCQBPCwi/wfAhwH8qnPuUyLyWwDuA/Cb3+tAzU4HZ9d0ksXY1G1qPDKpE2AAYHVVB3886fk5AJBKap+2MMxtndpOBw/t1TnYoeT5YsNj7NMdOakTd3Z3uWVSZZOruTRXdWJI7dwVssmd1hV3JvJcJejihvbxH//2Y2TzxOpfq3F7Y41sJhZ0cM7w0VvI5pZb3kRzfehrFAtUpbn91E1qXOy9nWyefPghNR6tByojz+v745YTt5LNsUmd6BWrcQpMc0X7+N09Dt4Kdpb3kmDi42NkUnb6WJc3vk02Tz37qBq3Oxx01e7o+zGe4POaTmvNQQKVlQ7CdV/lrvIdxSC5/48D8DYAf7w//wCA9z+vFRiGMXAO9HUhInEReRzABoDPATgPYM+57+awLgE48tIs0TCMF5sDbXznXM85dxeAeQCvAcC/CcMpxhCRD4rIIyLySDtQ4NAwjMHzfTkIzrk9AA8CeB2AURH5jkYwD4ArJFx9zf3OuXucc/ekkqb/GcZh4LrinohMAug45/ZEJAvg7QA+DuCLAH4CwKcAfAAA91ryqDd4BRL3AAATg0lEQVQbeOTpJ9RccV0LXr54AQC5rC7VPFLgqjjtlpZm1jc2Ajb6e25rneWcbFyvZyzFbYxSiRNqHM9w0EQiztloKOuKKq2lp8gkN6XFvfGxBbK5+bgOgpLCjWRTmdViZy8Q9DTjVRc6Ms2ZeEMT7MF14lrwTKc5Y+5IRouSc/e8mWyOTWtRLhMQCbNpLdKOx7jc+GhH/9hsBdpI9Zae1BNNzs5L+NVsAAwdu1uNt4ZOkc1XnvymGj/8lc+Rzfauvh/jCd56HS+tcDLPn3U4r4XVelsHj8XCdcyJg6j6swAeEJE4rv5C+EPn3GdF5NsAPiUi/wbANwB84kDvaBjGy851N75z7lsA7g7MX8BVf98wjFcYFrlnGBFkoEk68UQSxSntM25tak2wWuEqKPmc9mvGRjk4J53RrYVige+01VUd+LO7ywEjvY6eGx87RjbF+O1qXAvoEt0UB2gkmotqXF95lGxSM1rPGMqw3zmd1L5fYYF988bxo2rsEqxnDHsudVb4czRjLMi2vSSYdJL/oJPxjxUrkk3eC4TK9Pg4ybZXwTbQkru5rAOhupe+STbdPa2nOOEAnsw0/7GqP65buq3tcEDVyhWd3FQqcfJVt6/XnQ3cM8cmjuvX9Dj5ares90fSE8xdoC1bCHviG0YEsY1vGBHENr5hRBDb+IYRQQYq7jnn0Ol871ZC/UD8wa4nlrS7XKWn7wlO9QaLhNWaztCq1bjizN/U9XsVJlnweetrdYbY8I1zZFPtztBcd1FXZulsc4ukXS/TbqQ3TjZDR7S4mMtwoEc9o0WfWjwgnImec91A7/dARybxRKdEjC9a37+1Qp2d+vo6ph1nS2YbOkClusyCbPe8Fu66688G3strhTUyyzZeJR8AaOR05mV9ncump7taOB0e4evRaNS8MVdWymW8aj8dFmSbaS3+9rv+iT1YAI898Q0jgtjGN4wIYhvfMCLIQH18AOTrTXrVU4oTuq0SAOztetVYA1l+jYb211dL7D93u9pfTCa5us7OnvarHvobTriYHNN+1Dtf/YNkM4wFmtvydIfyKlcSii3rz9ptP0g22bKuvpqu3U42Y0d14k5+mKsEub7+3g/ld8QCLac7XqCNdEM+vj521+8NBiAP7ePnm9y6unn+GTVunzvDa9xYVONegxO0kNbVl2T0NjJpjXMCTiunA7EqjSfIprKrE7JGAtWfSnt6TdVAO/iNLW2TK4yQzfyRBTVeWdGahxwwScee+IYRQWzjG0YEsY1vGBHENr5hRJCBinsxEaSTOkghm9PZZ90et6NyI7qkcT/QA7zc1tlW7Q4fp+PN9QPCVSajRbClS+fI5ssPawHw1BifxlcdC5QquOMufWzHATPtNR3k013jdkzNmiderbGY1b+iBcD8/DzZxMe0kBobGiabthcwAgB+6Ek8EJzTb+pz1N0OVCkq6VLmvS2uWtS+pCvn9LfZJuFlvkmKP0fcy3xLLSyQzdYYC6BPLOmAqm88+UWy2djQgqwEMhr9GKdYjJ+5W7takM60+B72E/Z6XoTVwXLz7IlvGJHENr5hRBDb+IYRQQaepNNu6dZSfc8rKZU5sMF57YNbAd+n4yXuuEB2iV+cxH8NAIi3vlicvxvXN7S/+pVHuGVSvMfBFzee0G2gZ+PcnmrrGzpApbvJASPdqk4UadY5EKi3on384QscVBIb14kqsRH2jXs5rgDUTmptIt4PnMeGN1fipBTs6rZenRIn4HS8argxx8dJD+mqTlK8g2wSCzoBpzvFt/6ZCw/R3J9/4S/UeGORE4DE0xj6fU6uqdW1BpUMVNmt1PR9VW1wFaeyV4Enk9XXp9dj/SuEPfENI4LYxjeMCGIb3zAiiG18w4ggg8/O8xBPccunOYgi77XM6gkLGM4L/PFFEADY3tEBEs0mC0U9T5gZyvF6RoZ0VZydKouNT1zkNk6FCS2mzS9wdZ/ZZEaNN85wSEbFC3xJxllca63qktOJVQ6g6a5d8WY4s6sV5wxGpDxxD/z+KacrLfV6LHilEjrQpRUoJx3z2loNTyyQTTavz2t3nCvpbA3rEu2PP/1lsnnwi9wFrrSqxcWZQGn3jtfGqhJoVxaPa+E0keDgrVJFC4CJOG/P8aLeC6Wqfi8XCEoLYU98w4ggtvENI4LYxjeMCGIb3zAiyEDFvUQigempaTXXaGph5KaTXBIpP6xfs73ni1JAIq6Ps+JFrgFA34uwqtS4f1rNi56KB7Ko4l4f91yB+6khyeLNxRWd6ZdIs+A1f0wLVcX0q8mmt66Fs6E0i0m7eFiN3S73gxc/yssrfQ4AiR7PxVr6nMQkVINbn9v8GGesuaQ+by7DJckLR25W4+wYZxm2mzracrPPwu6Xz+g+hX/58F+QTXVjjeZOL+j3nylyKfWeFwG6U+P3rzX1+ei0+by2vL3QbLH4fOPJ02p8eVWvuRq4p0PYE98wIohtfMOIILbxDSOCDNTHT6fSOD6/oOaeeNbLbAv0Dd/c1hVmdrdWyCaW0P7q9i5n+fnVdUT4e6/tBWPsVrjN1oUlrTGMjI2RzdDIKM1d8vq4t1rcjmnviA4+Sac406xX1L5wusgBRLPjr9PHvXyZbLpei6Z+IOsx1mUdIuFlLHYDPdldQvuauRN8jkoNHawUG+by1rXho2p8eZv1hHJFn8e1rafI5uuPPajG65e5stLIMK+x5mVrbuywVuJn2jnHgVAx0Ta5FGtAJ+dO6DUGdJnJaa1xDBe1JrSyzhWbQtgT3zAiiG18w4ggB974IhIXkW+IyGf3xydE5KsiclZE/kBE+O81hmEcSr6fJ/6HADy3PMzHAfyqc+40gF0A972YCzMM46XjQOKeiMwD+FEAvwLgw3K1QdfbAPz0vskDAH4JwG9+r+N0uz3s7On+aM2W7ht+eZlLG3W9wIa5sSIf3M9kimXIpOdlLjVbXNoIXsBOucz93PyMqOVVLm+dSXMZq1RKC5d+DzoAWNr1Mu9SnPmGvhaPjh9hMenuO3WgiUsdJZuhvFdyOhCs1Guy4Jf0GiB2A6+TpA5Qacc4YKVR04LsWomFxCWvd97aEgfZ9MpaBGvVuIRXt6fXODrM91DTE/IA4OwFLT4nAp91KK+v9VCBr302o8uUJxP8A7no9dzLFTgTsNbQ52huTgu9oX6QIQ76xP81AL+A/9fysghgzzn3nSu3BOBI6IWGYRw+rrvxReQ9ADacc8+NeQy15AzW8heRD4rIIyLySKjJhWEYg+cgP/XfCOC9IvJuABkAw7j6C2BURBL7T/15APzHdQDOufsB3A8Ao8PjB230YRjGS4i4QPDFNY1F3gLg551z7xGRPwLwJ865T4nIbwH4lnPuP3+v1xfyQ+6u2+5Rcz2nfdiRYfaPEl7P7+LIBNmMjGlPo9zmBIdSRVfgSSb5e89P5Dkf6Md+8aLWIUaHOVjn5MJNNDcxqdc4NMQ+HLy2Wo06B3EsXn5ajcsVTlp6+4/o5J5SjYNTbrrtXWpcnOLzmgz8tst6sSf9DvvmrYYO4NnYXiebK6vaF9/Y5QSTXS/hpdNgzaWQ0n5tKslBYC3Pf+8GSquvb3Bi14XzOhjID/AC2K8eHx0nm+NeX/vpIicbZTP6PpIkJ389c0G39MrntHbwtUcfQrmyF/pFrnghf8f/CK4Kfedw1ef/xAs4lmEYA+T7Ctl1zj0I4MH9/74AINAZ0jCMw45F7hlGBLGNbxgRZKDZeSKCmNcovDiqAxBijkWXrlc6O9TPLp3XATvjeQ6QyOb0XL/PQSX5jLap7HAwyJXL+v397EEAqAf6no2s6gy5iYDAMzd9TE/0WaRMeOdwa4tFsQd+T/dx/4E3vJVs4su6596zG4tkUxxnoSrrZTnmA/3gxSsxvVvjKkEbm/oPQVs7nK0YT+tjjxYC5c6zWuCC8G3tB0/5YwAQYaF7bWVRjduByjl+YFi9ztfMOb2m0TGu5NPwAspSKRZNt7a0kLtY09mjjWYgKC2APfENI4LYxjeMCGIb3zAiyEB9/GargafP64CYbEa3Nrr9phvpdQ2/+muVfcHyoldNps9ttuKeb9zvcjBGKaFtXJePMzWhdYmtQKWUUiC5p+75X8kE+5kT4zqIRgIVbGlFAR876b3usS/9FdlsXtb6xfQkB/CceNd7aW7kqK760qvxOUpAX9ftOlcyWt/U77+ydJFsjh/TVWkyGdYchr0EmHjgvFZqNZrzmRjhxJ0Tx3VV2yvLi2Tjt2LL5obIJpXU56NW44SgXl/fH5XGJr+XpwN0unpvOGcttAzDuAa28Q0jgtjGN4wIYhvfMCLIQMW9fr9PAQbVqhZ9HtrjLK6Yl52XTHN1HV9wQyD7aszL/JspTpFNq62/C9OBDKmTx3UJ7OLkNNksr3LgT6mkS37X69xqaXPrkhrn8yNk0/fKIUioAo7Xaz4eyNdyLR1UMzfEAUUnj3NWX3NMi2fVeJZs/PZkV7ZYkG15WX2tQNBToqs/Ry7Gwl3Zq+oUCsTxM+hSKb6uqYAot3D0Bv3+Gf6sm17J7Vab771lLzjKF/IAYDin7+uOY9F0YkLfszs7OuO0UrEWWoZhXAPb+IYRQWzjG0YEGaiPn0qmccyrRNL3/LGZGU5e2N3WgQxra1wpZX1Ntw5qBtpBLa/r0jE7ZfaHRoa0vz4+ykEdMdG+V7HIPv7M3C009/TTT6jxmTNfIpuKV3Hmhhtu5ff3/Nx0mv3VuhcwkwwkpTjP8a8GkpZ6wudxJK+P1eVYFDSb2qbX42dMKqkTbmJ+pWQAQ3ntd6cCmsfqttZT6nVun1b0Kt9OZblqUjYbqP7k+fSZgA6QLeigosUrHIi0tKSrNjUarHm84W5dnaraYmHm1ptvV+NLl3Ti1/au9vmvhT3xDSOC2MY3jAhiG98wIohtfMOIIAMV95KJOKaLWgjZ9SqI1FtcqWV4SItXrz79w2QTT2sx6dlLi2Rzyetrv11iEWi3rNfz7IUnyKZW16Lg0BCLQkdmT9Jcs6EzxEKlzfe8YJTFxfNkMz+vg0rqNc58c15/k5af4QggN6SFsle/js9ry3HASqanRdJkMtB6ywu6CrVgiXs2oSCjRlOfs3KNsx6R1sE58T6vudPjajY+rTarlCtelaBSld9/dXVR2wTuqzFPAK6VWNw7c+6CGs/Pc9uzZFJ/tqPzC2r87We+Ra8JYU98w4ggtvENI4LYxjeMCDJQH38oX8Bb73mdmqs77Xt+/anH6XWphF7mzFEO8ol57bBGZ2fJ5q7u69U4GWilvbmufa9nzz1FNucv6LmdPa7Asxfw4XxCPr4/tx1oPbXnVfxJBlpGZbK64ku7w4kjFy7oFtRnznCL8swMJ+7kOtrH73T4c6T7+pmSCSTy5Id0AlAswS2eO14lpVag8WrKa6GVzrPm0vMSgs5euUQ2G1vcgrvuaQy5HH+OuNdSbHiYg3xOnLhDjZeXOIlrY83TcwLn44QX0LXd0QFfIgd7ltsT3zAiiG18w4ggtvENI4LYxjeMCCIhgeklezORTQCXAEwAYEXscPNKXDPwyly3rfn5c9w5N3k9o4Fu/O++qcgjzrl7rm95eHglrhl4Za7b1vzSYz/1DSOC2MY3jAjycm38+1+m930hvBLXDLwy121rfol5WXx8wzBeXuynvmFEkIFvfBG5V0SeEZFzIvLRQb//QRCRT4rIhog8+Zy5cRH5nIic3f83d5p4GRGRoyLyRRE5IyJPiciH9ucP7bpFJCMiXxORb+6v+Zf350+IyFf31/wHIsLtgF9mRCQuIt8Qkc/ujw/9mp/LQDe+iMQB/AaAdwG4FcBPiQiXkX35+R0A93pzHwXweefcaQCf3x8fJroAfs45dwuA1wH4p/vn9jCvuwXgbc65VwG4C8C9IvI6AB8H8Kv7a94FcN/LuMZr8SEAz+35/kpY83cZ9BP/NQDOOecuOOfaAD4F4H0DXsN1cc49BMAvo/I+AA/s//cDAN4/0EVdB+fcqnPusf3/ruDqTXkEh3jd7irfKbmU3P/HAXgbgD/enz9UawYAEZkH8KMA/sv+WHDI1+wz6I1/BMBz618t7c+9Eph2zq0CVzcZAG68d0gQkQUAdwP4Kg75uvd/Mj8OYAPA5wCcB7Dn3Hcbxx3Ge+TXAPwCgO809ivi8K9ZMeiNH6iqBvuzwouIiBQA/AmAn3XOcTG+Q4ZzruecuwvAPK7+IuROJIfoHhGR9wDYcM49+tzpgOmhWXOIgRbiwNVvwudWEJwHsHIN28PGuojMOudWRWQWV59QhwoRSeLqpv9d59yf7k8f+nUDgHNuT0QexFV9YlREEvtP0MN2j7wRwHtF5N0AMgCGcfUXwGFeMzHoJ/7XAZzeV0BTAH4SwGcGvIbny2cAfGD/vz8A4NMv41qIfT/zEwDOOOf+w3P+16Fdt4hMisjo/n9nAbwdV7WJLwL4iX2zQ7Vm59y/dM7NO+cWcPX+/YJz7mdwiNccxDk30H8AvBvAs7jqy/3rQb//Adf4+wBWAXRw9VfKfbjqx30ewNn9f4+/3Ov01vwmXP15+S0Aj+//8+7DvG4AdwL4xv6anwTwi/vzJwF8DcA5AH8EIP1yr/Ua638LgM++ktb8nX8scs8wIohF7hlGBLGNbxgRxDa+YUQQ2/iGEUFs4xtGBLGNbxgRxDa+YUQQ2/iGEUH+L9wtPrVQZQoXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After applying a random perturbation model predicted =>  Speed limit (80km/h)\n",
      "Confidence of being Speed limit (50km/h) after perturbation is => 0.06599495\n",
      "----------------------------\n",
      "Attack success: True\n"
     ]
    }
   ],
   "source": [
    "image = 1012\n",
    "pixels = 3 #umber of pixels to attack\n",
    "\n",
    "success = attack(image, pixel_count=pixels, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics about the attack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def attack_all(samples=10, pixels=(1, 3, 5), maxiter=75, popsize=400, verbose=False):\n",
    "    samples_list = [random.randint(0, len(X_test)) for x in range(samples)]\n",
    "    \n",
    "    total_count = 0\n",
    "    success_count = 0\n",
    "    results = []\n",
    "    for pixel in pixels:\n",
    "        total_count = 0\n",
    "        success_count = 0\n",
    "        for sample in samples_list:\n",
    "            if total_count%(samples//2) == 0:\n",
    "                print('Still working on the attack :)')\n",
    "            print('.')\n",
    "            result = attack(sample, pixel_count=pixel, verbose=verbose)\n",
    "            if result[4] == True:\n",
    "                success_count += 1\n",
    "            total_count += 1\n",
    "        results.append(total_count)\n",
    "        results.append(success_count)\n",
    "        print('Finished', pixel, 'pixel attack')\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Still working on the attack :)\n",
      ".\n",
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      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      "Still working on the attack :)\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
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      ".\n",
      ".\n",
      ".\n",
      ".\n",
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      ".\n",
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      ".\n",
      ".\n",
      ".\n",
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      ".\n",
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      ".\n",
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      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
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      ".\n",
      ".\n",
      ".\n",
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      ".\n",
      ".\n",
      ".\n",
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      ".\n",
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      ".\n",
      ".\n",
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      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
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      ".\n",
      ".\n",
      ".\n",
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      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      "Finished 1 pixel attack\n",
      "Still working on the attack :)\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      "Still working on the attack :)\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      "Finished 3 pixel attack\n",
      "Still working on the attack :)\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      "Still working on the attack :)\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
      ".\n",
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      ".\n",
      "Finished 5 pixel attack\n"
     ]
    }
   ],
   "source": [
    "result = attack_all(samples=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[500, 51, 500, 132, 500, 134]\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('stats_result.pkl', 'wb') as f:\n",
    "    pickle.dump(result, f)"
   ]
  }
 ],
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