Assignment_2_3_CIFAR10.ipynb

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    "# Feedforward Neural Network Implementation with Keras and TensorFlow: CIFAR-10 Dataset\n",
    "This Jupyter notebook demonstrates the implementation of a feedforward neural network using Keras and TensorFlow. The goal of this assignment is to walk through the process of building, training, and evaluating a neural network for image classification. We will use the MNIST or CIFAR-10 dataset as per your choice.<br>\n",
    "Dataset: https://www.cs.toronto.edu/%7Ekriz/cifar.html <br>\n",
    "Info: The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.\n",
    "\n",
    "#### **Train and Test Dataset: [Drive link](https://drive.google.com/drive/folders/1DdsOhkvzbAnenlP8iEUZlqMZbLp17b0y?usp=sharing)**\n",
    "I hope the dataset above is compatible with this notebook\n",
    "\n",
    "## Assignment Steps:\n",
    "\n",
    "**a. Import the necessary packages**: We will start by importing the required Python libraries, including Keras, TensorFlow, and other essential packages.\n",
    "\n",
    "**b. Load the training and testing data**: In this step, we will load the dataset (either MNIST or CIFAR-10) for training and testing the neural network.\n",
    "\n",
    "**c. Define the network architecture using Keras**: Here, we will define the architecture of our feedforward neural network, specifying the number of layers, neurons, and activation functions.\n",
    "\n",
    "**d. Train the model using SGD (Stochastic Gradient Descent)**: We will use the Stochastic Gradient Descent optimizer to train the neural network on the training data.\n",
    "\n",
    "**e. Evaluate the network**: After training, we will evaluate the performance of the model on the testing data to assess its accuracy and other relevant metrics.\n",
    "\n",
    "**f. Plot the training loss and accuracy**: We will create plots to visualize the training loss and accuracy over epochs, providing insights into the model's learning progress.\n",
    "\n",
    "Feel free to follow along with the code and explanations provided in this notebook to gain a better understanding of building and training neural networks for image classification.\n",
    "\n",
    "# Build the Image classification model by dividing the model into following 4 stages:\n",
    "a. Loading and preprocessing the image data<br>\n",
    "b. Defining the model’s architecture<br>\n",
    "c. Training the model<br>\n",
    "d. Estimating the model’s performance<br>\n",
    "\n",
    "Note: Both assignment can use this code accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VWZq69pHyS9G"
   },
   "outputs": [],
   "source": [
    "# !pip install tensorflow matplotlib\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1406,
     "status": "ok",
     "timestamp": 1698342092957,
     "user": {
      "displayName": "450-41 Ranjeet Kumbhar",
      "userId": "04055103669866965072"
     },
     "user_tz": -330
    },
    "id": "J3xl8LiWyieB",
    "outputId": "b8e26d4d-3a12-455e-8946-a37eef14d79f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3)\n",
      "(50000, 1)\n",
      "(10000, 32, 32, 3)\n",
      "(10000, 1)\n"
     ]
    }
   ],
   "source": [
    "cifar10 = tf.keras.datasets.cifar10 # Importing cifar10 dataset\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data() # Splitting it into training and testing data\n",
    "\n",
    "for i in (x_train, y_train, x_test, y_test):\n",
    "  print(i.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "executionInfo": {
     "elapsed": 755,
     "status": "ok",
     "timestamp": 1698342093710,
     "user": {
      "displayName": "450-41 Ranjeet Kumbhar",
      "userId": "04055103669866965072"
     },
     "user_tz": -330
    },
    "id": "5kgTxDehyrVw",
    "outputId": "cb13f92f-25f2-4310-f37c-1edca2221988"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6]\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[25])\n",
    "print(y_train[25])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9zI-dfTd1GTw"
   },
   "outputs": [],
   "source": [
    "#scaling the values\n",
    "x_train = x_train/255\n",
    "x_test = x_test/255\n",
    "# 0/255 = 255\n",
    "#255/255 = 1 maximum value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1698342093710,
     "user": {
      "displayName": "450-41 Ranjeet Kumbhar",
      "userId": "04055103669866965072"
     },
     "user_tz": -330
    },
    "id": "6cMzBeJd2HLd",
    "outputId": "bb0b096c-4a44-47d3-eeed-611d789962ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3) (10000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mk6-FAe37M_0"
   },
   "outputs": [],
   "source": [
    "num_classes = 10\n",
    "class_names = ['airplane','automobile','bird','cat','deer',\n",
    "               'dog','frog','horse','ship','truck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Vt3kCa6V6PGT"
   },
   "outputs": [],
   "source": [
    "# Convert class vectors to binary class matrices.\n",
    "y_train = tf.keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = tf.keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1698342093711,
     "user": {
      "displayName": "450-41 Ranjeet Kumbhar",
      "userId": "04055103669866965072"
     },
     "user_tz": -330
    },
    "id": "BQCktrR_7jp7",
    "outputId": "99f80109-e564-4eaf-8c97-d89525b77db2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 10) (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape, y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3527,
     "status": "ok",
     "timestamp": 1698342097222,
     "user": {
      "displayName": "450-41 Ranjeet Kumbhar",
      "userId": "04055103669866965072"
     },
     "user_tz": -330
    },
    "id": "IO5Lo5iS-Lvc",
    "outputId": "3ba6065a-373c-4c75-aa45-70b905d249ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 15, 15, 32)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 6, 6, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 4, 4, 64)          36928     \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1024)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                65600     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 122570 (478.79 KB)\n",
      "Trainable params: 122570 (478.79 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "# Create a sequential model\n",
    "model = keras.Sequential()\n",
    "\n",
    "# Convolutional layers\n",
    "model.add(keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
    "model.add(keras.layers.MaxPooling2D((2, 2)))\n",
    "model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(keras.layers.MaxPooling2D((2, 2)))\n",
    "model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "\n",
    "# Fully connected layers\n",
    "model.add(keras.layers.Flatten())\n",
    "model.add(keras.layers.Dense(64, activation='relu'))\n",
    "model.add(keras.layers.Dense(10, activation='softmax'))  # 10 classes for classification\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Print the model summary to check the architecture\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 147544,
     "status": "ok",
     "timestamp": 1698342244759,
     "user": {
      "displayName": "450-41 Ranjeet Kumbhar",
      "userId": "04055103669866965072"
     },
     "user_tz": -330
    },
    "id": "cjRoCt2-4Wrm",
    "outputId": "79b5cefe-568e-4e91-9378-ba72d5c7bfe3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1667/1667 [==============================] - 21s 9ms/step - loss: 2.0769 - accuracy: 0.2370 - val_loss: 1.7587 - val_accuracy: 0.3696\n",
      "Epoch 2/10\n",
      "1667/1667 [==============================] - 10s 6ms/step - loss: 1.6585 - accuracy: 0.4016 - val_loss: 1.8162 - val_accuracy: 0.3632\n",
      "Epoch 3/10\n",
      "1667/1667 [==============================] - 10s 6ms/step - loss: 1.4876 - accuracy: 0.4663 - val_loss: 1.4178 - val_accuracy: 0.4869\n",
      "Epoch 4/10\n",
      "1667/1667 [==============================] - 9s 6ms/step - loss: 1.3671 - accuracy: 0.5106 - val_loss: 1.3243 - val_accuracy: 0.5263\n",
      "Epoch 5/10\n",
      "1667/1667 [==============================] - 9s 5ms/step - loss: 1.2771 - accuracy: 0.5460 - val_loss: 1.2605 - val_accuracy: 0.5553\n",
      "Epoch 6/10\n",
      "1667/1667 [==============================] - 10s 6ms/step - loss: 1.1983 - accuracy: 0.5791 - val_loss: 1.1841 - val_accuracy: 0.5851\n",
      "Epoch 7/10\n",
      "1667/1667 [==============================] - 11s 7ms/step - loss: 1.1324 - accuracy: 0.6015 - val_loss: 1.1422 - val_accuracy: 0.5981\n",
      "Epoch 8/10\n",
      "1667/1667 [==============================] - 11s 7ms/step - loss: 1.0736 - accuracy: 0.6219 - val_loss: 1.1310 - val_accuracy: 0.6059\n",
      "Epoch 9/10\n",
      "1667/1667 [==============================] - 9s 5ms/step - loss: 1.0227 - accuracy: 0.6426 - val_loss: 1.0342 - val_accuracy: 0.6357\n",
      "Epoch 10/10\n",
      "1667/1667 [==============================] - 10s 6ms/step - loss: 0.9766 - accuracy: 0.6567 - val_loss: 1.0536 - val_accuracy: 0.6301\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "history = model.fit(x_train, y_train,\n",
    "              batch_size=30,\n",
    "              epochs=10,\n",
    "              validation_data=(x_test, y_test),\n",
    "              shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "executionInfo": {
     "elapsed": 1731,
     "status": "ok",
     "timestamp": 1698342246475,
     "user": {
      "displayName": "450-41 Ranjeet Kumbhar",
      "userId": "04055103669866965072"
     },
     "user_tz": -330
    },
    "id": "2iDBMnmx4ynZ",
    "outputId": "b3cdac4e-8506-414b-c3ae-850a5dcc9004"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.lineplot(model.history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternative Plot below for more details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 757
    },
    "executionInfo": {
     "elapsed": 1429,
     "status": "ok",
     "timestamp": 1698342961380,
     "user": {
      "displayName": "450-41 Ranjeet Kumbhar",
      "userId": "04055103669866965072"
     },
     "user_tz": -330
    },
    "id": "gaPf2uKqCfBH",
    "outputId": "fc1783fd-4ac6-4afa-fda7-cdb601d05f6a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Model Accuracy & Model Loss vs Epochs\n",
    "plt.figure(figsize=[10,8])\n",
    "\n",
    "# summarize history for accuracy\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy', size=25, pad=20)\n",
    "plt.ylabel('Accuracy', size=15)\n",
    "plt.xlabel('Epoch', size=15)\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "\n",
    "\n",
    "# summarize history for loss\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss', size=25, pad=20)\n",
    "plt.ylabel('Loss', size=15)\n",
    "plt.xlabel('Epoch', size=15)\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "APJSf4J8F4Nw"
   },
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 868,
     "status": "ok",
     "timestamp": 1698342620708,
     "user": {
      "displayName": "450-41 Ranjeet Kumbhar",
      "userId": "04055103669866965072"
     },
     "user_tz": -330
    },
    "id": "WKzzjd0sFzKT",
    "outputId": "b118e25a-5ec1-41f9-c178-a31d49ef40b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TA8Ycs8pGwc0"
   },
   "outputs": [],
   "source": [
    "class_names = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 861
    },
    "executionInfo": {
     "elapsed": 756,
     "status": "ok",
     "timestamp": 1698342821417,
     "user": {
      "displayName": "450-41 Ranjeet Kumbhar",
      "userId": "04055103669866965072"
     },
     "user_tz": -330
    },
    "id": "70kMtq3NHLR9",
    "outputId": "3411b342-0457-4bd6-d364-f52c64ad133b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,10])\n",
    "plt.imshow(x_test[0].reshape(32, 32, -1))\n",
    "plt.title(\"Predicted value: \" + str(class_names[np.argmax(predictions[0], axis=0)]), size=20)\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 861
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1698342855309,
     "user": {
      "displayName": "450-41 Ranjeet Kumbhar",
      "userId": "04055103669866965072"
     },
     "user_tz": -330
    },
    "id": "wi6xNjngHUYL",
    "outputId": "149eeb19-aa37-4ae7-89d0-bcaa4e6a9ed1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2Z3y3z4Bf//rXERFx9NFHD/udNU9/T8pItbe3D/7/ySefHHK63/72t0NeN7BcInZ+XR0to7XOD/xNlUplp+fvaGQAezYlBsjO5MmT47DDDouIpz7Z6rHHHhtxRldXV+y3334REXHppZcO+Z6J7u7uwZc27YwTTjghIp56Lf8//uM/jvj2A29WrlQqw043cLBfFEV88YtfHPH9REQsWLBgMOM73/nOkNNdcsklO/0JWX/qta997eAZgu9+97vR09MTl112WUREvOENb4iWlpbt3m7gU9WGOwty++23xy233LLTY3v6S5eGO9Ae+BS17VmwYMHgBzp86UtfGrX5tjNGa50/4YQTBsv1BRdcsFNjGY0MYM+mxABZOvfccyPiqY9bftOb3hTr168fctpKpRIXXXRR9PT0bPP7M844IyKe+sSjv/qrv9rubc8666ydfhNyRMRf/MVfDL7v5P/8n/8Ty5YtG3Lahx9++Bm/G/hAgEcffTQ2bdo05G2PPfbYwU+U+tznPvesxeuuu+6KK6+8cpvfnXTSSYP398lPfjJWrlz5jNvdc8898elPf3rY7JFobGwc/OjhK6+8MpYuXTr4dw73qWQveMELIiLiZz/7Wdx3333PuP6xxx6Ld77znbs0tv333z8mTJgQEREXXnjhdovkD3/4w7j00kuHzBg/fvzgp8H94he/iLPOOmvYl6atXbs2vvGNb2z3ur333nvwE7t21mis83Pnzh38sIXvf//78YUvfGHY+1y1atUzit5oZAB7uDH8jhpgNzPcl12O9PbDfbP9gA9+8IOD00+bNq34xCc+Ufz0pz8tbr/99uJnP/tZcckllxTvfve7i7322quIiGLTpk3b3L6vr6942cteNphx/PHHF5dffnmxYsWK4vLLLy+OPfbYIiKKV7ziFTv9ZZdFURTXX399UV9fX0REUV9fX5x++unFFVdcUaxYsaL4xS9+UXzrW98q3vSmNxWNjY3PuO211147eN/veMc7iuXLlxf33nvv4OXp7rvvvmLChAmD059wwgnFP//zPxe33HJLceuttxb//u//Xnz6058uDjvssCIiir/6q796xv396Ec/Grz9+PHjiyVLlhTLly8vfvGLXxSf+cxnis7OzqKzs7PYd999d+nLLp/uxhtv3OY+449fxjjcl51eeuml23xT+5e+9KXi5z//efHzn/+8+NznPldMnz69KJVKxfz584f9gsjhvuyyKIpi8eLFg7c//PDDi8svv7y47bbbiquvvrp417veVdTV1RWHH374kF92WRRF0dPTUxx66KGD08ybN6+48MILi5/97GfF7bffXlx//fXFl7/85eLEE08sGhsbi4MOOmjYse7KQ/dorfPr1q0r9tlnn8FpjjjiiOIb3/hGsXz58uK2224rrr322uLzn/98sWDBgqKurm67X0o7GhkDX3Y5GushkBclBhg1z3WJqdVqxfnnnz9YEIa7tLW1FVu2bHlGxh/+8Idi7ty5Q97u2GOPLa655ppdKjFFURQ//vGPB8vUcJc/Va1WB0vHjky/cuXKYv/993/W+4mI4vzzz9/uWD/3uc8VpVJpu7dpbW0trrrqqlE9eKzVasXMmTO3uZ8PfvCDz3q7008/fci/rVwuFxdccEFx3nnn7VKJ6e7uHnb+H3XUUcXdd989bIkpiqLYuHFj8cY3vnGHlsvRRx897Fh3pcQUxeis80VRFKtXry5e/epX79DfdPrppyfJUGJgz+XlZEC2SqVSfPzjH4/f/va3cc4558QrXvGKmDBhQpTL5Whvb48Xv/jFccopp8S3v/3tWL169XbfX9HV1RW33357fOpTn4r9998/WlpaYvz48XHYYYfFV77ylbj66qujsbFxl8d63HHHxe9+97v4zGc+E4cffnhMnDgxyuVydHR0xMtf/vL40Ic+FL/85S+fcbu6urr4yU9+Eueee27Mmzcvxo0bN+zLiV74whfGHXfcEUuXLo2TTz45Zs2aFS0tLdHY2BjTp0+Po446Ks4999xYsWJFfPzjH99uxoc//OH42c9+Fm984xtjypQp0dTUFLNnz453vetdceutt8b//J//c5fnx9OVSqV4+9vfvs3vhnsp2YBvfetb8X//7/+NV7/61dHe3j44zne+853xi1/8Ij74wQ/u8thaW1vj+uuvj09/+tNxwAEHREtLS3R0dMTBBx8cF154Yfz0pz+Ntra2Z81pb2+P//f//l/8x3/8R/zlX/5lzJ07N9rb26O+vj4mTJgQBx98cCxatCj+/d//Pa699tpdHvdwRmudnzZtWtx0001x1VVXxSmnnBL77LNPtLa2RkNDQ0yePDkOP/zw+Ku/+qtYtmxZfOtb30qWAeyZSkUxhu8yBAAAGCFnYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZKV+rAfwp2q1WjzyyCPR3t4+7HchAAAAu4+iKGLTpk3R1dUVdXXDn2v5sysxjzzySMycOXOshwEAAIyBhx56KGbMmDHsNH92Jaa9vT0iIt53zoXR1PTMb9ceHWm/3/PZmuOfe37qM2Dyh1er1ZLmR0Sk/orbaiReBom34XLmJ4FrRdp1qHvLhqT5j6xelTT/0UcfTJq/9tHfJc2vdG9Omr910xNJ85ubO5PmTxg/JWl+d/f6pPntE6cmzY+IKDc3J83v3pJ2HaqvT7uTrq9Pdfz5R3VpH8M2PPGHpPnV/t5k2bVqLVbf88hgHxjOn12JGThAbGpqiabm1kT3osQMJ/cSkHu+EvPslJjhpS4x/bV0D2AREQ2NTUnz6xsakubXlct556d+DCunzS/Xpz20ST3/U4//qftIuw2k/hvKiUtM8mWQuMSkXkeLIv1b6nfkWMsb+wEAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyEqyEnPRRRfF3nvvHc3NzXHooYfGL3/5y1R3BQAA7EGSlJgf/OAHcfbZZ8d5550Xt912W8ybNy+OO+64ePTRR1PcHQAAsAdJUmK+8IUvxHve8544/fTT48UvfnF87Wtfi9bW1vjWt76V4u4AAIA9yKiXmN7e3lixYkUsWLDgv++kri4WLFgQy5cvf8b0lUolNm7cuM0FAABgKKNeYh5//PGoVqsxderUbX4/derUWLNmzTOmX7JkSXR2dg5eZs6cOdpDAgAAdiNj/ulkixcvjg0bNgxeHnroobEeEgAA8GesfrQDJ02aFOVyOdauXbvN79euXRvTpk17xvRNTU3R1NQ02sMAAAB2U6N+JqaxsTEOOuiguO666wZ/V6vV4rrrrov58+eP9t0BAAB7mFE/ExMRcfbZZ8epp54ar3jFK+KQQw6JCy64ILq7u+P0009PcXcAAMAeJEmJeetb3xqPPfZYfPzjH481a9bES1/60vjxj3/8jDf7AwAAjFSSEhMRceaZZ8aZZ56ZKh4AANhDjfmnkwEAAIyEEgMAAGRFiQEAALKixAAAAFlRYgAAgKwk+3SyXVWr1aJWq6UJLxVpcgckjq9VE82XPyqV0nbbUtL0iIi086dIvHzryuk3y1LilbTUV0ma399fTZpfam5Nml8knv9F4uenmpvakuZ3tk9Imr9x45NJ88d3TEua3x2PJc1vrJaT5jeN60ia39vfmzh/S9L8TVtWJ82PiGippV0GDXWNSfNTH0kURdrHmL7ezUnzKxvT5lcTHgiN5BjXmRgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICs1I/1AIbS2liOpsZykuwiiiS5A0rlNOMezE88/lqtlDQ/Iu38iVI1bX6Rdv5HKXF+RNTXpb2P3s0bkuZv7e5Jml+qm5Q0v69nS9L8cZ1TkuYXdWm34WlTZyTNb21tTZp/X33a7WtLZV3S/PHTu5Lmb926KWn+pvVPJM1vbk+7/tTq+5PmR0Rs2px4HjWOS5rfV+1Lmt/b0500v1btTZpfbm5Im9+Srj7U+ms7PK0zMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFmpH+sBDKWprjea68pJsmu1WpLcAaVqKWl+uT7NfPnvO0gbXyqlnT9FVNPmF4nzk6Y/pdbbnTS/1P1k0vzW+sak+Y1FT9L8usR73qZyX9L8UintPrQaacdfbUo7/ub6tFtxc0NL0vwN61cnze+rpN2+6hsbkuY3NKV9kOyN3qT5EREN7WnnUbWvkja/N21+UU27DEqJj+PqmtM+RlY2pzuGqFV3fP/sTAwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBW6sd6AEOpayxFXWMpTXhfX5rcP6qmzq8kjY+6ulrS/KbGctL8uqJIml+rVZPm19en3yw39q5Pmr+10p00v74v7TJIuwZFjJ84KWl+tf/JpPl1dWnnf/T3Jo1vqaXdib5gxtSk+a3lRI+Nf/Tb+9PO/yd6H0uaX5/46dlqkfYxvq4l7WNkRET/1sQHErW062hDR9p51NDRmjS/vz/t+Hue2JQ0v7Yl3TZQq+34MagzMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFmpH+sBDKUualFXqiXJLtcXSXIHNNQ1JM2vrysnze/v70maX1T70+Yn7ualctr5X65Pmx8R0dbanjS/PLU5af6Gx55Imt/bvSFpfv2EtMu4ob4xaX6tqCbNLzWl3UfXJX767slN3Unzu3ueTJrfX/QlzW9uSbt/aGlJ+xi8oZZ2+daKtOt/RERfb+JtuFZKml9L/DBZ15z2Dvo2bEmaX+tPu3yb9mpJll2r1iIe2rFpnYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMjKqJeYT3ziE1Eqlba5vOhFLxrtuwEAAPZQSb7s8iUveUn89Kc//e87qf+z/U5NAAAgM0naRX19fUybNi1FNAAAsIdL8p6Ye++9N7q6umKfffaJU045JR588MEUdwMAAOyBRv1MzKGHHhqXXHJJzJ07N1avXh3nn39+vPrVr46777472tvbnzF9pVKJSqUy+PPGjRtHe0gAAMBuZNRLzMKFCwf/f+CBB8ahhx4as2fPjh/+8Ifx7ne/+xnTL1myJM4///zRHgYAALCbSv4Ry+PHj48XvvCFcd999233+sWLF8eGDRsGLw899FDqIQEAABlLXmI2b94c999/f0yfPn271zc1NUVHR8c2FwAAgKGMeon58Ic/HMuWLYsHHnggfvGLX8Qb3vCGKJfL8fa3v3207woAANgDjfp7Yh5++OF4+9vfHuvWrYvJkyfHq171qrj55ptj8uTJo31XAADAHmjUS8z3v//90Y4EAAAYlPw9MQAAAKNJiQEAALKixAAAAFlRYgAAgKwoMQAAQFZG/dPJRktDRDQUibIbm9IE/1Fvby1pfl0paXyUopo0v6+/J2l+uVxOmt9Yn3b9qUVv0vyIiObWhqT5be2Jv7S2lnYb692cND7WP9GdNL+lPdHO84/a2tOuP/21tPug9RufSJrfW9mSNL+5Me0+blLiL52uNDUnzV+99pGk+ZX6vqT5LS2tSfMjIorEjwHVStp9ULUv7T4iirSPMbWetONvbk+7jUXKw6D+HT/IdSYGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK/VjPYCh9EcR/VEkyS5Va0lyB9SX0+bXqr1J86vVStL8cinNch1QVPuT5vf1ps3vr6VdvhERDaVy2vxiS9L8ut5NSfPbW1uT5lfr2pLm9/WXkuZv3JR2He2rbkia31CXdh/d2pB2/j9R9CXN75o+PWn+r//rv5Lm9xZp99EtHWn3D3VF+ueX+9dXk+ZXEx9nVUtpl3Et8fjrioak+X1b0+4j+rvT5Y9k3jsTAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkJX6sR7AUIqiP4qiP0l2b29PktwBTfVpxj2gsaFIml/p2ZI0v1atJs0v1aXt5hs3bkia3xu9SfMjIlrLLUnzG7eUkuaX6tOOv9yUdhtrH5d21/vEk91J8+/97e+T5reW0m5jXR1NSfMbamkfA6aU0+7jtvRUkub39qbNb2ltS5tfbkyav3Xz1qT5ERHVrWnX0f5q2vxaX9rjiN5K2nW03NCQND/1/C/Xl5Nl12q1HZ7WmRgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBW6sd6AENpb41oaUmTXVTT5A7qS3sHtVri/KI/aX5ftZI0f+vWtPmb+zYmze8p9STNj4ioVBuS5k+NiUnzG9o6k+avvO+epPlFJW1+KfHzU+W+tPuIcS21pPnlUmvS/JamxqT5U8dPTppf1zAuaf6Etvak+Tf9Nu32tb57U9L8okj//HJdY+LDv74iaXxdqZR1fi1tfDTUl5PmF73pjkNLI5g5zsQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkpX6sBzCUttb6aG1NM7y+vnKS3AFbq0njo3tzJWl+X19f0vxKpTtpfk9P2vlTLZXS5hdp18+IiFI0ps2vT7truf/XtyXNv+s/b0+a31SqJc1/Xtf0pPlzZs9Kmj+upTlpfmNzW9L8SLuLiLqm9qT5zYnn/9yutOtPpSiS5v/i/ruT5m/ekvYxMiKiqTXtMo5a4sexxMu41pd2H11LO/yoVnqT5vf3pjvQHcmscSYGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWRlxibrrppjjhhBOiq6srSqVSXH755dtcXxRFfPzjH4/p06dHS0tLLFiwIO69997RGi8AALCHG3GJ6e7ujnnz5sVFF1203es/+9nPxpe+9KX42te+Frfccku0tbXFcccdFz09Pbs8WAAAgBF/I93ChQtj4cKF272uKIq44IIL4txzz40TTzwxIiK+853vxNSpU+Pyyy+Pt73tbbs2WgAAYI83qu+JWbVqVaxZsyYWLFgw+LvOzs449NBDY/ny5aN5VwAAwB5qxGdihrNmzZqIiJg6deo2v586dergdX+qUqlEpVIZ/Hnjxo2jOSQAAGA3M+afTrZkyZLo7OwcvMycOXOshwQAAPwZG9USM23atIiIWLt27Ta/X7t27eB1f2rx4sWxYcOGwctDDz00mkMCAAB2M6NaYubMmRPTpk2L6667bvB3GzdujFtuuSXmz5+/3ds0NTVFR0fHNhcAAIChjPg9MZs3b4777rtv8OdVq1bFHXfcERMmTIhZs2bFhz70ofjUpz4VL3jBC2LOnDnxsY99LLq6uuKkk04azXEDAAB7qBGXmFtvvTWOPvrowZ/PPvvsiIg49dRT45JLLolzzjknuru7473vfW+sX78+XvWqV8WPf/zjaG5uHr1RAwAAe6wRl5ijjjoqiqIY8vpSqRR/8zd/E3/zN3+zSwMDAADYnjH/dDIAAICRUGIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMjKiD9i+TlT7Y+ivz9JdG+lJ0nugC2VLUnza7W+tPn9WxPn96bNj7Tzp6dIs14OKD0Hm2Vz4vuo9qddBk+s/X3S/I7mUtL8qROmJM1vaUo7/sZyLWl+Q2NT0vzmprT5PT1pH2M2bdqcNL+7O23+uIkzkuZP6nhe0vyuCY8nzX+glnb/FhFRaqgmza/1p82vq0/7GLa1O+1xSn3icwgNdWnzm8oNybJr1VpsWPvEDk3rTAwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBW6sd6AEPZ2FOOvlI5SXZPT9o/u7/WljS/Gv1J82ulhrT5Dc1J84u67qT5ta2bk+b31HqT5kdENCV+/qKupydpfktDmn3DgMYJnUnzx7U0Jc2PUpE0vlKpJM1vbU07/q2J18++vrT5lZ6+pPmlSLt9FS1bk+avWr02af6mzWmXb2tne9L8iIhKT9ptuKm/MWl+2j1ERH0t8XFiOe1fUF9LGh9FpZosu9q/49nOxAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGSlfqwHMJQtfaWo1ZeSZNdK7UlyB/MbiqT51agkzW9qbEya31CXZrkOaOrvT5o/vi9pfPRW044/IiL6e5LG9z5yT9L8cc1NSfP7auWk+XWJt4GGhrTzJ4q049+6ZVPS/GqR9vm7okj7GNDb25s0v6Eh7WNAX+J93GOPP5Y0f9OmtOvn5BkTk+ZHRBTltNtwOfE20N+f9jjoyfLmpPl9pWrS/KZS4sew9nT1ob+vP+7f0XEkGwUAAEACSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICs1I/1AIZSrq6Pcn9Pkuz6xN2todyYNL8o96fNLypJ82tF0vgoyuWk+aXmtMu3vr45aX5ERH+1IWn+5tq0pPmlLU8kzS/3p90GolpLmx+pN7K049+6ZXPS/P4i7WNAXTnt9lXrrybNbxrXkjS/rrUzaX5z67ik+XUbmpLmz5z60qT5T0m7DVSrfUnz+3q7k+aP79mYNL9WpN2GGxIv355Kuvnf17fj644zMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFmpH+sBDKUU5SglGt7WnkqS3AHVhlLS/NQLra6uljS/2rs5aX5LU1PS/Lq6vqT5tb608z8iotrbmzS/cVxr0vzW9vFJ80vdTybN793akzS/Wq0mze/u3pI0v1xfTprf2NSWNL9USvv8YK2UdvnWt41Pmt9XakyaP+N5s5Pmr127Nml+S8eUpPkREW2Jl3G1mvZxrD/xPq7Wn3Yf3R/9SfNrvWnzK5s2JMvu7a1ExBU7NK0zMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWRlxibnpppvihBNOiK6uriiVSnH55Zdvc/1pp50WpVJpm8vxxx8/WuMFAAD2cCMuMd3d3TFv3ry46KKLhpzm+OOPj9WrVw9evve97+3SIAEAAAaM+MvfFy5cGAsXLhx2mqamppg2bdpODwoAAGAoSd4Tc+ONN8aUKVNi7ty5ccYZZ8S6deuGnLZSqcTGjRu3uQAAAAxl1EvM8ccfH9/5znfiuuuui7/7u7+LZcuWxcKFC6NarW53+iVLlkRnZ+fgZebMmaM9JAAAYDcy4peTPZu3ve1tg/8/4IAD4sADD4znP//5ceONN8YxxxzzjOkXL14cZ5999uDPGzduVGQAAIAhJf+I5X322ScmTZoU991333avb2pqio6Ojm0uAAAAQ0leYh5++OFYt25dTJ8+PfVdAQAAe4ARv5xs8+bN25xVWbVqVdxxxx0xYcKEmDBhQpx//vlx8sknx7Rp0+L++++Pc845J/bdd9847rjjRnXgAADAnmnEJebWW2+No48+evDngfeznHrqqfHVr3417rzzzvj2t78d69evj66urjj22GPjk5/8ZDQ1NY3eqAEAgD3WiEvMUUcdFUVRDHn9Nddcs0sDAgAAGE7y98QAAACMJiUGAADIihIDAABkRYkBAACyosQAAABZGfGnkz1X2jomR0tLa5rw5mqa3D/q60+bH+W08XUx9KfPjc4dtCWN7+7vS5rfUEs7f2q1xOtPRGytbE2a31if9iPVmzv2Spq/ZdP6pPm1Wi1pfk/i5VtXTvv8V0dT2uXb2JD2oa8opc2vFmnXn95q2n1cKfH6OWmvzqT5L9pv36T5jbEhaX5ERMMwnzI7KopS0vhS4seYUkPa45S+Wn/S/FpT2n10Y2u6fXSlZ8f3D87EAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZKV+rAcwlPHNtWhtqSXJbqgrkuQOKDfulTS/pX1q0vyimma+D3j88dVJ83sTj7+hPu1mU6qlXT8jIkqVrUnz+1L/CUVb2vjVv0+aX26dkDS/unVN0vy2tsa0+Z0Tk+ZXe/uS5vf39yfNLzeknf89/b1J8xu6NyTNL5fSPj/7/Bkzkub3ldM/BtTV9aS9g75K2vxa2mVcKqXdxqKvmjS+knj+9BfpjoOqvTu+bjoTAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkJX6sR7AUMp1dVGuS9OxWhtLSXIHFC3jkuaXGtLmR93WpPHjxjUmza+vpR1/U2M1aX5vX5E0PyJiSzntpr+x2pQ0v6enJ2l+uZT2+Z1xM16QNL8Sabex1qldSfPbZ78oaf6mB+5Mmt+77pGk+S3tnUnzGybOTppf60m7j65seDxpfqk/7f5hS7khaX5ERF8tbX5R9Ka9g1Li47haf9L8Wi3t43y1lPYxvreabv739lZ2eFpnYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALJSP9YDGEqpviFK9Q1JspsT5Q7ojsak+bWiOWl+U1Pa/FrvpqT5/Zs2ps2v9SfNr9WKpPkREY2Jn76YUJd2Hj1RfTRpfn192m24sTXtNtY2aVrS/KbxafOrzZ1J85smzUiaX+1elzQ/9S6iPfHyLdU3Jc3vqf990vzex9Lmj5s8OWl+RESlcXzS/FpRS5rf39+TNr+aNr9WSfsY2ZD4OLe5Ll19qFR2fN47EwMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJCV+rEewFDGt7dHW1tbkuyiqCXJHbB1Q3fS/FL/lqT5lWpf0vzoTzt/Otqbkub3VdN2/2qlN2l+RESt1p80f9y4cUnzo70lafzm8XslzW9uTrvrHdfWmjS/sy3tNtDbvSppfn+pkjS/uXNC0vzN6x5Pmt+/6ZGk+UX71KT5jZ1pt99q97qk+Q21tI+RERFtk5+fNL+nlPYxIPVjWClpekR/b9rH+b7etPu4SDj/63p2/BjXmRgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKyMqMQsWbIkDj744Ghvb48pU6bESSedFCtXrtxmmp6enli0aFFMnDgxxo0bFyeffHKsXbt2VAcNAADsuUZUYpYtWxaLFi2Km2++Oa699tro6+uLY489Nrq7//uLmc4666y48sor49JLL41ly5bFI488Em984xtHfeAAAMCeaURfG/3jH/94m58vueSSmDJlSqxYsSKOOOKI2LBhQ3zzm9+MpUuXxmte85qIiLj44otjv/32i5tvvjkOO+yw0Rs5AACwR9ql98Rs2LAhIiImTJgQERErVqyIvr6+WLBgweA0L3rRi2LWrFmxfPnyXbkrAACAiBjhmZinq9Vq8aEPfShe+cpXxv777x8REWvWrInGxsYYP378NtNOnTo11qxZs92cSqUSlUpl8OeNGzfu7JAAAIA9wE6fiVm0aFHcfffd8f3vf3+XBrBkyZLo7OwcvMycOXOX8gAAgN3bTpWYM888M6666qq44YYbYsaMGYO/nzZtWvT29sb69eu3mX7t2rUxbdq07WYtXrw4NmzYMHh56KGHdmZIAADAHmJEJaYoijjzzDPjsssui+uvvz7mzJmzzfUHHXRQNDQ0xHXXXTf4u5UrV8aDDz4Y8+fP325mU1NTdHR0bHMBAAAYyojeE7No0aJYunRpXHHFFdHe3j74PpfOzs5oaWmJzs7OePe73x1nn312TJgwITo6OuIDH/hAzJ8/3yeTAQAAo2JEJearX/1qREQcddRR2/z+4osvjtNOOy0iIv7hH/4h6urq4uSTT45KpRLHHXdcfOUrXxmVwQIAAIyoxBRF8azTNDc3x0UXXRQXXXTRTg8KAABgKLv0PTEAAADPNSUGAADIihIDAABkRYkBAACyosQAAABZGdGnkz2XSkVflIreJNl1pSSxgzav/0PS/P+8+/qk+bVq2m77khd0Jc3fa5+0+Y3NLUnzm1vS5kdEbN2yJWl+Y0PajazSvTlp/vjJ05PmN3e0Jc2v9fUlze8cl3YdrdVVk+b3tjQmzU+7dUVsWLcuaf7mR+5Nml9MeDJpfn3juKT5Te1pv5S7b/3DSfMjInoevidp/uaGtMuguW180vzW1glJ85sbm5LmNzam3UdXawn30eUd3z87EwMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJCV+rEewFAqvZWory+nCS9qaXL/qGN8e9L8NWv/kDQ/amm77QH7TUuaXyuqafP70ub39hVJ8yMiSonvYvOmDUnzu3u2Js0fN35q0vzx7Wn3EVFJu46WSknjo7GxMWl+fUNT0vxSNe1jTFNzc9L8/kra7aujsS1pfq1hXNL8SjXt/Nm6eUvS/IiIWmVN0vyivTNpfk9lfdL8/s2PJc1vqO9Iml9qTpvf0NSSLLuo9e3wtM7EAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZKV+rAcwlCc3bIxKX1+S7ObmliS5A8qltN1wwvjOpPmbuytJ81vbEs//cilpfn8l7fwp+qpJ8yMiimradbTW25s0v619QtL8SVPGJ80v1xdJ8xtbmpLmt3aMS5qfeBcaPZW062dzc+L535J2H/pE4vkzrr01aX6triFpflR7ksb3NSUef0REOe1G1pT4cb6/v5Y4f0vS/PUbNyTNj7q0h/d1Den2cZXKjm9fzsQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkpX6sBzCU/lot+mu1JNmrH1+XJHdAqa6UNH/q1ClJ82trnkiaX65rSJq//snNSfMjqknT6+vTb5ZNTeWk+Y3ltqT5tVradWhrb0/S/FJf2nWo3JB2/tSV064/pcRPr7U0NyXNryYef1EUSfPH7bVX0vy99upIml/p7U2a3942Pmn+hiLtY3xExPp1TybNr/Wn3ceVirQbWVN9c9L8+nGJ8xMfh/b1pjvOqi9VdnhaZ2IAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZqR/rAQyloaEhGhoakmRX+jYmyR1QLqedrZMnTUyav2nT1qT5zU1NSfOj2p80vlRXSppf6etLmh8RURRF0vwtG7qT5lfr0q5D6x5/PGn+xL0mJM0vp128USReRyu1tPmp1/9aJe0+tL9aTZo/sWt60vz6hrT70CLxoU19fXPS/N5x7UnzIyL6t1aS5o+fuXfS/C1btyTNX7PmkaT59Y1p16G+/rTnKHoqvcmyKyPIdiYGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWRlRilixZEgcffHC0t7fHlClT4qSTToqVK1duM81RRx0VpVJpm8v73ve+UR00AACw5xpRiVm2bFksWrQobr755rj22mujr68vjj322Oju3vY7Id7znvfE6tWrBy+f/exnR3XQAADAnmtE3wj14x//eJufL7nkkpgyZUqsWLEijjjiiMHft7a2xrRp00ZnhAAAAE+zS++J2bBhQ0RETJiw7bdPf/e7341JkybF/vvvH4sXL44tW9J+syoAALDnGNGZmKer1WrxoQ99KF75ylfG/vvvP/j7d7zjHTF79uzo6uqKO++8Mz7ykY/EypUr41/+5V+2m1OpVKJSqQz+vHHjxp0dEgAAsAfY6RKzaNGiuPvuu+NnP/vZNr9/73vfO/j/Aw44IKZPnx7HHHNM3H///fH85z//GTlLliyJ888/f2eHAQAA7GF26uVkZ555Zlx11VVxww03xIwZM4ad9tBDD42IiPvuu2+71y9evDg2bNgweHnooYd2ZkgAAMAeYkRnYoqiiA984ANx2WWXxY033hhz5sx51tvccccdERExffr07V7f1NQUTU1NIxkGAACwBxtRiVm0aFEsXbo0rrjiimhvb481a9ZERERnZ2e0tLTE/fffH0uXLo3Xvva1MXHixLjzzjvjrLPOiiOOOCIOPPDAJH8AAACwZxlRifnqV78aEU99oeXTXXzxxXHaaadFY2Nj/PSnP40LLrgguru7Y+bMmXHyySfHueeeO2oDBgAA9mwjfjnZcGbOnBnLli3bpQEBAAAMZ5e+JwYAAOC5psQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJCVEX3E8nOpvihFfVFKkj1lfGeS3AFNjU1J8/v6+pLmb1yfdv60NrcmzW9sSLPeDNi8aUPS/Kbyc7BZ1tLGb16/Pml+2157Jc2fPH5C0vyWpuak+esfeyJp/hNPPJY0v7mzI2l+U+L5v+GJtPO/sakxaf74xPM/+itJ45sa0s6favQnzX8uFFFNmt/ckvY58vqGtqT5vX1pj4PqEh9+F0U5aX7/uHTbWE9Pzw5P60wMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVurHegBDKUcR5SiSZPdWepPkDmhva0ua31BOGh/TJo1Pml9XSrNcB2zatDlp/pYtPUnzW+tLSfMjIjZufjJp/qqHHkqav3dj2udf9powMWl+qaglzd+wfl3S/C1btyTNn5h4H1TrrSTNf2L1o0nz95o4IWl+fWND0vzUz54WtWra/Ei7/UaiY5+n6+vvS5q/eWPax5i6+sak+R3j0h7HtbS0Js0vldIe3m/tSXcctHXr1h2e1pkYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArNSP9QCGUvvjvyTq0na3x598Iml+fSlpfJRSzfc/2rp1c9L8LVu2Js2vFeWk+ZvLlaT5ERGP9P0uaX65uSVpfrVIGh+9vb1J8yuVtPlNrY1J8+sb0+4j6ou+pPlPPrkhaX5jQ9p9xLSZ05Lml+rTHhqUIu2DWK2oJs0vJc6vK6XNj4hobEy7j9irozNpfl3idTQSr6OtbW1J8/v6+5PmN9anW38a6nZ82ToTAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkJX6sR7AUKrlUlTLpSTZjfUNSXIH9FWLpPm9lZ6k+X2VWtL83t7epPkNDWmXb0+lP2n+77fcmzQ/IqJpfNpN/8WtL06aX62mXQaVStp19A+PP5o0v60h7fIdN64taf76jRuT5j+6Zm3S/NbxnUnzi7q0zz/W+tNuX6VS2vHXl8tJ85tSP8bUdSfNj4ioVqtJ8+vrEy/jzJ+Cr6ulPU6sTxsfUZ9u/ekfQXbmqwEAALCnUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyIoSAwAAZEWJAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkpX6sBzCUnt5KlOrTdKyWUkOS3AF9ff1J8+tKpaT5RalImt9f6Uua31vUkuZvjZ6k+Wt6HkiaHxHx/NgnaX6pqCbNj0i7jRWRdhsYP649aX65vjFpfm+kXb6rV69Omr9h45NJ82dOmJA0/5HH1iXNr/an3Yc+b/r0pPn19c1J8xtSb189a5PmR0Rs7t6cNL+xsZw0v6Eu7eFrpTftccrWSiVpfl1d2uPE/v504+/v793haZ2JAQAAsqLEAAAAWVFiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIyohKzFe/+tU48MADo6OjIzo6OmL+/Plx9dVXD17f09MTixYtiokTJ8a4cePi5JNPjrVr03/eOQAAsOcYUYmZMWNG/O3f/m2sWLEibr311njNa14TJ554Yvz617+OiIizzjorrrzyyrj00ktj2bJl8cgjj8Qb3/jGJAMHAAD2TCP6ytMTTjhhm58//elPx1e/+tW4+eabY8aMGfHNb34zli5dGq95zWsiIuLiiy+O/fbbL26++eY47LDDRm/UAADAHmun3xNTrVbj+9//fnR3d8f8+fNjxYoV0dfXFwsWLBic5kUvelHMmjUrli9fPmROpVKJjRs3bnMBAAAYyohLzF133RXjxo2LpqameN/73heXXXZZvPjFL441a9ZEY2NjjB8/fpvpp06dGmvWrBkyb8mSJdHZ2Tl4mTlz5oj/CAAAYM8x4hIzd+7cuOOOO+KWW26JM844I0499dS45557dnoAixcvjg0bNgxeHnrooZ3OAgAAdn8jek9MRERjY2Psu+++ERFx0EEHxa9+9av44he/GG9961ujt7c31q9fv83ZmLVr18a0adOGzGtqaoqmpqaRjxwAANgj7fL3xNRqtahUKnHQQQdFQ0NDXHfddYPXrVy5Mh588MGYP3/+rt4NAABARIzwTMzixYtj4cKFMWvWrNi0aVMsXbo0brzxxrjmmmuis7Mz3v3ud8fZZ58dEyZMiI6OjvjABz4Q8+fP98lkAADAqBlRiXn00Ufjf/2v/xWrV6+Ozs7OOPDAA+Oaa66J//E//kdERPzDP/xD1NXVxcknnxyVSiWOO+64+MpXvpJk4AAAwJ5pRCXmm9/85rDXNzc3x0UXXRQXXXTRLg0KAABgKLv8nhgAAIDnkhIDAABkRYkBAACyosQAAABZUWIAAICsjOjTyZ5Lj294LJp7m5NkT2rcK0nuoFqRNL6hpSlp/uae3qT5Wytp84uiljT/D8WjSfO39G5Jmh8RUUTidaivO2l+1KXdxvoTb8Ptbe1J86ul/qT53Zu3Js0vN6ZdP7tmz0ya39mZePnWlZPmr9+Sdh+0dv2TSfPHNVeS5re1tiXNb2xPmx8RMWP2nKT5pVLa58jryg1J8xsa046/PvH4S6VS0vyWpnT76Lq6xh2fNtkoAAAAElBiAACArCgxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJAVJQYAAMiKEgMAAGRFiQEAALKixAAAAFlRYgAAgKwoMQAAQFaUGAAAICtKDAAAkBUlBgAAyEr9WA/gTxVFERERPT2VZPextbo1WXZERNSKpPF10Z80f+vWnqT5pfpa0vyBdSiVSpFu3YyI6O1Lu3wjInqqaf+Guv6061CU0i7j/sTrUFGUkuZXS9Wk+T09aZdvTyXt+hm1tPM/9T60WldOmp96+ZbSPgREOfH2VUr8/G9la+JjlIioJF7GW7ZsSZrfV592H1etps0vyg1J80ultNtAXcLHyIF1Z0eO5UpF6iO+EXr44Ydj5syZYz0MAABgDDz00EMxY8aMYaf5sysxtVotHnnkkWhvb9+hJrlx48aYOXNmPPTQQ9HR0fEcjJDnmmW8e7N8d2+W7+7PMt69Wb67tz+35VsURWzatCm6urqirm74s55/di8nq6ure9bmtT0dHR1/FjOfdCzj3Zvlu3uzfHd/lvHuzfLdvf05Ld/Ozs4dms4b+wEAgKwoMQAAQFayLzFNTU1x3nnnRVNT01gPhUQs492b5bt7s3x3f5bx7s3y3b3lvHz/7N7YDwAAMJzsz8QAAAB7FiUGAADIihIDAABkRYkBAACykn2Jueiii2LvvfeO5ubmOPTQQ+OXv/zlWA+JUfCJT3wiSqXSNpcXvehFYz0sdsFNN90UJ5xwQnR1dUWpVIrLL798m+uLooiPf/zjMX369GhpaYkFCxbEvffeOzaDZcSebfmedtppz9imjz/++LEZLCO2ZMmSOPjgg6O9vT2mTJkSJ510UqxcuXKbaXp6emLRokUxceLEGDduXJx88smxdu3aMRoxI7Ejy/eoo456xjb8vve9b4xGzEh99atfjQMPPHDwSy3nz58fV1999eD1OW6/WZeYH/zgB3H22WfHeeedF7fddlvMmzcvjjvuuHj00UfHemiMgpe85CWxevXqwcvPfvazsR4Su6C7uzvmzZsXF1100Xav/+xnPxtf+tKX4mtf+1rccsst0dbWFscdd1z09PQ8xyNlZzzb8o2IOP7447fZpr/3ve89hyNkVyxbtiwWLVoUN998c1x77bXR19cXxx57bHR3dw9Oc9ZZZ8WVV14Zl156aSxbtiweeeSReOMb3ziGo2ZH7cjyjYh4z3ves802/NnPfnaMRsxIzZgxI/72b/82VqxYEbfeemu85jWviRNPPDF+/etfR0Sm22+RsUMOOaRYtGjR4M/VarXo6uoqlixZMoajYjScd955xbx588Z6GCQSEcVll102+HOtViumTZtWfO5znxv83fr164umpqbie9/73hiMkF3xp8u3KIri1FNPLU488cQxGQ+j79FHHy0ioli2bFlRFE9trw0NDcWll146OM1vfvObIiKK5cuXj9Uw2Ul/unyLoiiOPPLI4oMf/ODYDYpRt9deexXf+MY3st1+sz0T09vbGytWrIgFCxYM/q6uri4WLFgQy5cvH8ORMVruvffe6Orqin322SdOOeWUePDBB8d6SCSyatWqWLNmzTbbc2dnZxx66KG2593IjTfeGFOmTIm5c+fGGWecEevWrRvrIbGTNmzYEBEREyZMiIiIFStWRF9f3zbb8Ite9KKYNWuWbThDf7p8B3z3u9+NSZMmxf777x+LFy+OLVu2jMXw2EXVajW+//3vR3d3d8yfPz/b7bd+rAewsx5//PGoVqsxderUbX4/derU+K//+q8xGhWj5dBDD41LLrkk5s6dG6tXr47zzz8/Xv3qV8fdd98d7e3tYz08RtmaNWsiIra7PQ9cR96OP/74eOMb3xhz5syJ+++/Pz760Y/GwoULY/ny5VEul8d6eIxArVaLD33oQ/HKV74y9t9//4h4ahtubGyM8ePHbzOtbTg/21u+ERHveMc7Yvbs2dHV1RV33nlnfOQjH4mVK1fGv/zLv4zhaBmJu+66K+bPnx89PT0xbty4uOyyy+LFL35x3HHHHVluv9mWGHZvCxcuHPz/gQceGIceemjMnj07fvjDH8a73/3uMRwZsDPe9ra3Df7/gAMOiAMPPDCe//znx4033hjHHHPMGI6MkVq0aFHcfffd3qe4mxpq+b73ve8d/P8BBxwQ06dPj2OOOSbuv//+eP7zn/9cD5OdMHfu3Ljjjjtiw4YN8aMf/ShOPfXUWLZs2VgPa6dl+3KySZMmRblcfsYnJ6xduzamTZs2RqMilfHjx8cLX/jCuO+++8Z6KCQwsM3anvcc++yzT0yaNMk2nZkzzzwzrrrqqrjhhhtixowZg7+fNm1a9Pb2xvr167eZ3jacl6GW7/YceuihERG24Yw0NjbGvvvuGwcddFAsWbIk5s2bF1/84hez3X6zLTGNjY1x0EEHxXXXXTf4u1qtFtddd13Mnz9/DEdGCps3b477778/pk+fPtZDIYE5c+bEtGnTttmeN27cGLfccovteTf18MMPx7p162zTmSiKIs4888y47LLL4vrrr485c+Zsc/1BBx0UDQ0N22zDK1eujAcffNA2nIFnW77bc8cdd0RE2IYzVqvVolKpZLv9Zv1ysrPPPjtOPfXUeMUrXhGHHHJIXHDBBdHd3R2nn376WA+NXfThD384TjjhhJg9e3Y88sgjcd5550W5XI63v/3tYz00dtLmzZu3ecZu1apVcccdd8SECRNi1qxZ8aEPfSg+9alPxQte8IKYM2dOfOxjH4uurq446aSTxm7Q7LDhlu+ECRPi/PPPj5NPPjmmTZsW999/f5xzzjmx7777xnHHHTeGo2ZHLVq0KJYuXRpXXHFFtLe3D75OvrOzM1paWqKzszPe/e53x9lnnx0TJkyIjo6O+MAHPhDz58+Pww47bIxHz7N5tuV7//33x9KlS+O1r31tTJw4Me68884466yz4ogjjogDDzxwjEfPjli8eHEsXLgwZs2aFZs2bYqlS5fGjTfeGNdcc02+2+9Yfzzarvryl79czJo1q2hsbCwOOeSQ4uabbx7rITEK3vrWtxbTp08vGhsbi+c973nFW9/61uK+++4b62GxC2644YYiIp5xOfXUU4uieOpjlj/2sY8VU6dOLZqamopjjjmmWLly5dgOmh023PLdsmVLceyxxxaTJ08uGhoaitmzZxfvec97ijVr1oz1sNlB21u2EVFcfPHFg9Ns3bq1eP/731/stddeRWtra/GGN7yhWL169dgNmh32bMv3wQcfLI444ohiwoQJRVNTU7HvvvsWf/3Xf11s2LBhbAfODnvXu95VzJ49u2hsbCwmT55cHHPMMcVPfvKTwetz3H5LRVEUz2VpAgAA2BXZvicGAADYMykxAABAVpQYAAAgK0oMAACQFSUGAADIihIDAABkRYkBAACyosQAAABZUWIAAICsKDEAAEBWlBgAACArSgwAAJCV/w/ost/JC59+ywAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,10])\n",
    "plt.imshow(x_test[100].reshape(32, 32, -1))\n",
    "plt.title(\"Predicted value: \" + str(class_names[np.argmax(predictions[100], axis=0)]), size=20)\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ezH-xOwIH1mM"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "authorship_tag": "ABX9TyMj3Pq9EmwscXz1+79sNmsM",
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}