docs/Lectures/Theme_3-Model_Engineering/Lecture_15-ANNs/Lecture_15-ANNs.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Lecture 15 - Artificial Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![View notebook on Github](https://img.shields.io/static/v1.svg?logo=github&label=Repo&message=View%20On%20Github&color=lightgrey)](https://github.com/avakanski/Fall-2023-Python-Programming-for-Data-Science/blob/main/docs/Lectures/Theme_3-Model_Engineering/Lecture_15-ANNs/Lecture_15-ANNs.ipynb)\n",
    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/avakanski/Fall-2023-Python-Programming-for-Data-Science/blob/main/docs/Lectures/Theme_3-Model_Engineering/Lecture_15-ANNs/Lecture_15-ANNs.ipynb) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='top'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [15.1 Introduction to Artificial Neural Networks](#15.1-introduction-to-artificial-neural-networks)\n",
    "    - [15.1.1 Elements of ANNs](#15.1.1-elements-of-anns)\n",
    "    - [15.1.2 Common Activation Functions](#15.1.2-common-activation-functions)\n",
    "    - [15.1.3 Training ANNs](#15.1.3-training-anns)\n",
    "    - [15.1.4 Types of ANN Architectures](#15.1.4-types-of-ann-architectures)\n",
    "    - [15.1.5 Fully-connected NNs](#15.1.5-fully-connected-nns)\n",
    "- [15.2 Classification with ANNs](#15.2-classification-with-anns)\n",
    "    - [15.2.1 Binary Classification](#15.2.1-binary-classification)\n",
    "    - [15.2.2 Multiclass Classification](#15.2.2-multiclass-classification)\n",
    "- [15.3 Regression with ANNs](#15.3-regression-with-anns)\n",
    "- [15.4 Saving and Loading Models in Keras](#15.4-saving-and-loading-models-in-keras)\n",
    "- [Appendix](#appendix)\n",
    "- [References](#references)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.1 Introduction to Artificial Neural Networks <a id=\"15.1-introduction-to-artificial-neural-networks\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Artificial Neural Networks (ANNs)** are a class of machine learning algorithms that employ a network of connected computational units for information processing. ANNs are organized in layers, where each layer is made up of interconnected nodes (or computational units). The concept of ANNs is motivated by biological neural networks, and the development of ANNs is the result of an attempt to replicate the workings of the human brain. Hence, the nodes in ANNs are commonly referred to as **neurons**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "710bd0e6"
   },
   "source": [
    "A simple ANN with one single layer is depicted in the following figure. The main layers in the ANN are:\n",
    "\n",
    "* **Input layer**, takes the input data in numerical format, where inputs to ANNs can be tabular data, images, texts, audio, etc. \n",
    "* **Hidden layer**, connects the input and the output layers. The neurons in the hidden layer process the input data and extract relevant patterns in the data.\n",
    "* **Output layer**, is the output of the network, which can be a continuous numeric value as in regression problems, or discrete class values as in classification problems. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a21b0198"
   },
   "source": [
    "<img src=\"images/Neuralnetwork.png\" width=\"350\">\n",
    "\n",
    "*Figure: One-layer ANN. Figure source: Wikipedia.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, ANNs can have multiple input layers, multiple hidden layers, and multiple output layers. \n",
    "\n",
    "ANNs having more than one layer are commonly referred to as a **Deep Neural Network (DNNs)**. Similarly, applying DNNs for solving a task is referred to as **Deep Learning (DL)**.\n",
    "\n",
    "Most current ANNs that are implemented for solving real-world tasks have multiple hidden layers. The number of hidden layers depends on the specific task, and typically ranges from 10 to 100. For solving complex tasks, ANNs with hundreds of hidden layers are used. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 15.1.1 Elements of ANNs <a id=\"15.1.1-elements-of-anns\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values of the connections between the neurons in ANNs are learned during a training phase. By using a training dataset, the values of the connections are updated with a goal of correctly predicting the outputs for given inputs. \n",
    "\n",
    "The following figure depicts the operation of a single neuron in an ANN. The neuron is connected to other neurons in the previous layer via a set of inputs $x_1, x_2, ..., x_m$ and weights $w_1, w_2, ..., w_m$. Each neuron calculates the sum of products of the inputs and weights and a bias value, i.e., $v = x_1 \\cdot w_1 + x_2 \\cdot w_2 + ... + x_m \\cdot w_m + b$. The obtained value $v$ is then passed through an activation function and the output $y = \\varphi(v)$ is transferred to the next layer in the network. \n",
    "\n",
    "The **weights** and **biases** in an ANN are called **network parameters**. And very often the term *weights* is used to refer to both weights and biases as network parameters.\n",
    "\n",
    "<img src=\"images/neuron.png\" width=\"450\">\n",
    "\n",
    "*Figure: Artificial Neuron.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a687f494"
   },
   "source": [
    "### 15.1.2 Common Activation Functions <a id=\"15.1.2-common-activation-functions\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d6dc2881"
   },
   "source": [
    "Activation functions are applied to each neuron in ANNs to introduce non-linearities into the outputs of the neurons. Without non-linear activation functions, ANNs will simply learn linear mappings between the inputs and outputs. Calculating non-linear mappings between inputs and outputs gives ANNs the capabilities to learn complex tasks. \n",
    "\n",
    "The most common activation functions in ANNs are:\n",
    "\n",
    "- **Sigmoid** activation function (logistic activation function): For a real-valued input, it outputs a value between 0 and 1. Sigmoid activation is used in the output layer for binary classification problems. \n",
    "- **Tanh** activation function (tangent hyperbolic activation function): It is similar to sigmoid activation function, but it outputs a value between -1 and 1. Tanh is rarely used in modern ANNs. \n",
    "- **Softmax** activation function: It is an extension of the sigmoid activation function for multiclass classification tasks, and it is used in the output layer for multiclass classification problems. \n",
    "- **ReLU (Rectified Linear Unit)** activation function: It outputs the input value if it is positive, and it outputs 0 if the input value is negative. In other words, this activation function just penalizes the negative inputs. ReLU is the most used activation function in the hidden layers of modern ANNs. \n",
    "- **Leaky ReLU (Rectified Linear Unit)** activation function: It is a modification of ReLU, where instead of outputting 0 for negative values, Leaky ReLU has a small negative slope. Some networks use Leaky ReLU instead of ReLU.\n",
    "\n",
    "There are also several other modifications of ReLU activations such as GeLU (Gaussian error Linear Unit) and SeLU (Scaled exponential Linear Unit). Overall, ReLU is still the most common activation function. \n",
    "\n",
    "<img src=\"images/activation_functions.jpg\" width=\"750\">\n",
    "\n",
    "*Figure: Activation functions in ANNs.* \n",
    "*Figure source:* [link](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841357/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GF5bogcC7mUa"
   },
   "source": [
    "### 15.1.3 Training ANNs <a id=\"15.1.3-training-anns\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DtN0q_bh7mUa"
   },
   "source": [
    "ANNs are trained by iteratively updating the network parameters (weights and biases) to minimize the difference between the network predictions and target labels.\n",
    "\n",
    "Each iteration in the training phase includes the following 4 steps:\n",
    "\n",
    "1. Forward pass (forward propagation)\n",
    "2. Loss calculation \n",
    "3. Error backpropagation (backward pass)\n",
    "4. Model parameters update\n",
    "\n",
    "**Forward propagation** is also known as **forward pass**, because the input data is passed through all hidden layers of the neural network toward the output layer to obtain the network predictions. For instance, if the input data is an image, the image is transformed through the layers of the network, and for classification problems, the output is a vector of predicted class probabilities. \n",
    "\n",
    "**Loss calculation** is the second step, in which the loss of the network is calculated as a difference between the network output (predictions for each class) and the target  ground-truth label for an image image. Common loss function for classification tasks is *crossentropy loss*, calculated as $-\\sum{y\\cdot log(\\hat{y})}$, where $y$ is the ground-truth label and $\\hat{y}$ is the network prediction. For regression tasks commonly used loss functions include *mean-squared error* (calculated as $\\sum{(y- \\hat{y})^2}$) and *mean-absolution error* (calculated as $\\sum{|y- \\hat{y}|}$) between the network predictions and target ground-truth values. \n",
    "\n",
    "**Error backpropagation** is also called **backward pass** or **backward propagation** (where the term backpropagation is short for backward propagation). In this step, the predicted outputs are traversed back through the network, from the last (output) layer backward toward the first (input) layer. During the backward step, the gradients of the loss with respect to the model parameters $\\nabla\\mathcal{L}(𝜃)$ are calculated. If you recall from calculus, **gradient** is the vector of partial derivatives of a function, or for a loss function $\\mathcal{L}$ and network parameters $𝜃$, the gradient is the vector $\\nabla\\mathcal{L} = [\\partial\\mathcal{L}/\\partial𝜃_i]$. The gradients quantify the impact of changing the parameters in the network to the predicted outputs. Automatic calculation of the gradients (automatic differentiation) is available in all current deep learning libraries, which significantly simplifies the implementation of deep learning algorithms. \n",
    "\n",
    "**Model parameters update** is the last step in which new values for the model parameters are calculated and updated, typically using the **Gradient Descent** algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PJCris5Cb1_g"
   },
   "source": [
    "<img src=\"images/training_ANN.png\" width=\"650\">\n",
    "\n",
    "*Figure: Steps in training ANNs.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zkylzalo7mUe"
   },
   "source": [
    "A simple depiction of the ***Gradient Descent*** algorithm is shown in the next figure. The blue curve is a loss function $\\mathcal{L}$, and the gradient of the loss function $\\nabla\\mathcal{L}$ gives the slope of the function. By updating the parameters in the opposite direction of the gradient of the loss $\\nabla\\mathcal{L}$, the algorithm finds parameters $𝜃$ for which the loss $\\mathcal{L}$ has a minimal value. \n",
    "\n",
    "<img src=\"images/gradient_descent.png\" width=\"850\">\n",
    "\n",
    "*Figure: Steps in training ANNs.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_F3QAjRj7mUe"
   },
   "source": [
    "Almost all modern neural networks are trained by applying a modified version of the Gradient Descent algorithm. Examples of such advanced Gradient Descent algorithms include Adam, SGD (Stochastic Gradient Descent), RMSprop, Adagrad, Nadam, and others. The deep learning libraries typically refer to the used algorithms for minimizing the loss of the model as **optimizers**.\n",
    "\n",
    "Unlike the model training phase, predicting on test data (also known as **inference**) requires only a forward pass through the model, where for a given input instance the model outputs the prediction. For instance, in the above figure the output is a vector of class probabilities. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "df16a232"
   },
   "source": [
    "### 15.1.4 Types of ANN Architectures <a id=\"15.1.4-types-of-ann-architectures\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sKCDRk-PWEyK"
   },
   "source": [
    "The main types of ANN architectures are:\n",
    "\n",
    "- **Fully-connected neural networks (FCNNs)** (a.k.a. densely-connected neural networks, or multi-layer perceptrons)\n",
    "- **Convolutional neural networks (CNNs)**\n",
    "- **Recurrent neural networks (RNNs)**\n",
    "- **Transformer neural networks (TNNs)**\n",
    "- **Graph neural networks (GNNs)**\n",
    "\n",
    "In this lecture, we will study Fully-connected NNs, and in the following lecture we will explore the other ANN architectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XqAdxSlWWe9m"
   },
   "source": [
    "### 15.1.5 Fully-connected NNs <a id=\"15.1.5-fully-connected-nns\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WziQjYJWWJcT"
   },
   "source": [
    "**Fully-connected NNs** are made of layers in which the neurons of any layer are connected to all other neurons of the preceding and succeeding layer. They are also called **Densely-connected NNs**, as well they are often referred to as **Multi-Layer Perceptrons (MLPs)**.\n",
    "\n",
    "\n",
    "<img src=\"images/fully_connected_NN.png\" width=\"550\">\n",
    "\n",
    "*Figure: Fully-connected NNs.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fully-connected NNs are mostly used in simple tasks, such as for classification or regression tasks with tabular data. Their performance for tasks with images, text data, or other data formats is often inferior in comparison to CNNs, RNNs, TNNs, and GNNs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AAqeb2v2jSWT"
   },
   "source": [
    "## 15.2 Classification with ANNs <a id=\"15.2-classification-with-anns\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hcDOlUdZxDGu"
   },
   "source": [
    "In calssification tasks, the goal is to predict the category, i.e., class label of the data samples.  \n",
    "\n",
    "Classification tasks can be divided into two main types: \n",
    "\n",
    "- **Binary classification** is classification with two classes. An example is to classify a tweet as positive or negative depending on its content. In the output layer, we need to use a single output neuron with a `sigmoid` activation function to output a value between 0 and 1. A threshold value (by default set to 0.5) can be used to differentiate between positive and negative classes. For example, if the value of the output neuron is 0.7, the tweet can be assigned as positive (since the value is greater than 0.5). The common loss function in binary classification is `binary crossentropy`. \n",
    "\n",
    "- **Multiclass classification**: is classification with three or more classes. An example is to classify a tweet into several classes, such as news, tech, sports, or weather. In the output layer, we need to use a number of output neurons equal to the number of classes, with a `softmax` activation function. E.g., for the above tweet example, we will use 4 output neurons. Consequently, the output of the network will be a vector of class probabilities whose dimension is equivalent to the number of classes, such as [0.2 0.5 0.1 0.2] for the tweet example. The category with the maximum value is assigned as a class label to the tweet. In this case, if the four categories were news, tech, sports, or weather, the tweet will be assigned the class tech, since the network output of 0.5 for this class is the greatest. The commonly used loss functions in multiclass classification are `categorical crossentropy` (when the target labels are in one-hot matrix encoding format) and `sparse categorical crossentropy` (when the target labels are in ordinal encoding format).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jG7bLsj03uxw"
   },
   "source": [
    "In both binary and multiclass classification problems, the hyperparameters of the ANN related to the number of input neurons, activation functions in the hidden layers, number of hidden layers, etc. depend on the specific problem we are solving. \n",
    "\n",
    "*Table: Typical values of hyperparameters in ANNs for classification*\n",
    "\n",
    "| **Hyperparameter** | **Binary classifier** | **Multiclass classifier**  |\n",
    "| --- | --- | --- |\n",
    "| **Neurons in input layer** | Number of input features | Number of input features |\n",
    "| **Number of hidden layer(s)** | Depends on the problem, typically from 1-10 | Depends on the problem, typically from 1-10 |\n",
    "| **Neurons per hidden layer** | Depends on the problem, typically 10-100| Depends on the problem, typically 10-100 |\n",
    "| **Neurons in output layer** | 1 | Neurons equivalent to number of classes |\n",
    "| **Activation in hidden layers**| Mostly ReLU| Mostly ReLU |\n",
    "| **Activation in output layer** | sigmoid | softmax |\n",
    "| **Loss function** | binary cross entropy| categorical crossentropy / sparse categorical crossentropy |\n",
    "| **Optimizer** | Mostly Adam, SGD, RMSProp| Mostly: Adam, SGD, RMSProp |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8H7r9hKeHAeM"
   },
   "source": [
    "ANNs have many hyperparameters, and selecting the best values of the hyperparameters can be challenging. In the next lectures we will explore best practices for hyperparameter finetuning in ANNs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9pEQ8VbkIRsO"
   },
   "source": [
    "### 15.2.1 Binary Classification  <a id=\"15.2.1-binary-classification\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5bcce621"
   },
   "source": [
    "We will first implement an ANN for binary classification.\n",
    "\n",
    "For this purpose, we will use again the Breast Cancer dataset which we also used in the previous lecture. Recall that the dataset has 569 samples, each having 30 features. The target labels are the benign or malignant class, hence it is a binary classification problem. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version:2.14.0\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# Print the version of tensorflow\n",
    "print(\"TensorFlow version:{}\".format(tf.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "637e0e99"
   },
   "source": [
    "#### Loading the Dataset\n",
    "\n",
    "The Breast Cancer dataset can be directly loaded from scikit-learn datasets. Let's create a DataFrame `df` having the features and the target column as the last column to the right. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "bc = load_breast_cancer()\n",
    "df = pd.DataFrame(data=bc.data, columns=bc.feature_names)\n",
    "df[\"target\"] = bc.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the shape of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
       "0                 0.07871  ...          17.33           184.60      2019.0   \n",
       "1                 0.05667  ...          23.41           158.80      1956.0   \n",
       "2                 0.05999  ...          25.53           152.50      1709.0   \n",
       "3                 0.09744  ...          26.50            98.87       567.7   \n",
       "4                 0.05883  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  target  \n",
       "0          0.4601                  0.11890       0  \n",
       "1          0.2750                  0.08902       0  \n",
       "2          0.3613                  0.08758       0  \n",
       "3          0.6638                  0.17300       0  \n",
       "4          0.2364                  0.07678       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first few rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will extract the features and the target labels, and we will split them into training and testing sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "84be13ea"
   },
   "outputs": [],
   "source": [
    "# Extract the data and the labels\n",
    "X = df.drop('target', axis=1)\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "7680f98f"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data, test_data, train_labels, test_labels = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e291944b",
    "outputId": "47818bad-6dfb-4d94-a436-720a1beaf95b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data inputs (455, 30)\n",
      "Training labels (455,)\n",
      "Testing data inputs (114, 30)\n",
      "Testing labels (114,)\n"
     ]
    }
   ],
   "source": [
    "print('Training data inputs', train_data.shape)\n",
    "print('Training labels', train_labels.shape)\n",
    "print('Testing data inputs', test_data.shape)\n",
    "print('Testing labels', test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oWg6eykR6QOD"
   },
   "source": [
    "We will also scale the data with the `MinMaxScaler`. Recall again that we do not fit the scaler on the test dataset, but we only transform it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "301cebe5"
   },
   "outputs": [],
   "source": [
    "# Scaling the features to be between 0 and 1.\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "train_data = scaler.fit_transform(train_data)\n",
    "test_data = scaler.transform(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "giLJg2i4GkCU"
   },
   "source": [
    "#### Create the ANN "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i4EJITkbGzdZ"
   },
   "source": [
    "To create ANNs in this lectue we will use the Tensorflow-Keras library. Keras is part of TensorFlow, and if you notice in the above cell Keras is imported from TensorFlow. \n",
    "\n",
    "To define the network architecture in Keras, we will use the data structures **layers** and **models**.\n",
    "\n",
    "In the cell below we imported the needed layers and model classes and afterward we will create layers and model objects as instances of these classes. \n",
    "\n",
    "For the ANN for this case we will use `Input` and `Dense` layers, as explained below. `Model` is a general class in Keras that is used to create new ANN models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "e_YS-JqfkyiK"
   },
   "outputs": [],
   "source": [
    "# Import the layers and the model\n",
    "from keras.layers import Input\n",
    "from keras.layers import Dense\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow-Keras requires the first layer in an ANN to be an **Input** layer, which will provide the shape of the input data in the form of a tuple. For the Breast Cancer dataset, there are 30 input features for training the model, and therefore, we will set the shape of the input data to `(30,)`.\n",
    "\n",
    "Note also that to create and train an ANN, we don't need to provide information about the number of samples that are available for training or testing the model. \n",
    "\n",
    "Let's add two hidden layers. In Keras, `Dense` layers are fully-connected layers. Therefore, the layers `dense1` and `dense2` below are fully-connected layers, of which, the first layer has 30 units (or neurons), and the second layer has 15 neurons. The second argument in the Dense layers `activation` defines the activation function that is applied at each layer, and in this case we selected ReLU activations as the most common activation function. For each layer in Keras, we need to also define the previous layer to which it connects. In this case, for `dense1` the previous layer is the `inputs` layer, and `dense2` follows the `dense1` layer.\n",
    "\n",
    "Finally, we added an output layer which is also a Dense layer. As we mentioned, for binary classification problems, the output layer has 1 neuron and it uses a `sigmoid` activation function. Preceding layer to the `outputs` layer is `dense2` layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "gjqyblPbHmmc"
   },
   "outputs": [],
   "source": [
    "# Define the layers in the network\n",
    "inputs = Input(shape=(30,))\n",
    "dense1 = Dense(units=30, activation='relu')(inputs)     \n",
    "dense2 = Dense(units=15, activation='relu')(dense1)\n",
    "outputs = Dense(units=1, activation='sigmoid')(dense2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we define the layers, we will create a new model as an instance of the `Model` class. Let's name the new instance `model_1`. The arguments in `Model` are the input and output layers, which we named in the above cell `inputs` and `outputs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model by providing the inputs and outputs\n",
    "model_1 = Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2nL-Znyr1WKt"
   },
   "source": [
    "Now that we have created a model, we can inspect its architecture with the `.summary()` method. It provides a table with all layers in the network, the shape of the tensors that are output by each layer, and the number of parameters in each layer. \n",
    "\n",
    "We can see that the first layer in the model expects the passed data to be of shape `(None, 30)`. Here `None` is a placeholder, and specifies that the model can accept any number of samples that have 30 elements.\n",
    "\n",
    "The output of the model is of shape `(None, 1)`, meaning that it will output one single value for each data sample.\n",
    "\n",
    "Also, we can see in the information under the table that this model has 1,411 trainable parameters, of which 930 are in the first hidden layer, 465 are in the second hidden layer, and 16 are in the output layer. During the model training, we will try to find optimal values of the network parameters that minimize the misclassification of the samples in the training dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 30)]              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 30)                930       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 15)                465       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 16        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1411 (5.51 KB)\n",
      "Trainable params: 1411 (5.51 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Model summary\n",
    "model_1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "The graph of the network is also shown in the following figure. We can see the layers with all 1,411 connections in the network.\n",
    "\n",
    "<img src=\"images/model_1.png\" width=\"550\">\n",
    "\n",
    "*Figure: Graphical depiction of the network.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XDYdR1SG1zh4"
   },
   "source": [
    "#### Compile the ANN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GrJHF5Qe2wwr"
   },
   "source": [
    "To be able to train the ANN model, first we need to compile it. During the compiling step, we will specify the loss function, optimizer, and metric. \n",
    "\n",
    "The **loss function** (cost function) calculates the difference between the predicted class label by the model and the ground-truth target class label for each sample. If the difference is high, this means a higher loss.  \n",
    "\n",
    "In Keras and TensorFlow, there are three main crossentropy losses: \n",
    "\n",
    "- `binary_crosssentropy` used for binary classification with two classes.\n",
    "- `categorical_crossentropy` used for multiclass classification with three and more classes, and the target labels are encoded in one-hot matrix format.\n",
    "- `sparse_categorical_crossentropy` used for multiclass classification with three and more classes, and the target labels are encoded in ordinal format.\n",
    "\n",
    "The **optimizer** is the optimization algorithm that is used to reduce the loss function. As we explained earlier, several different optimization algorithms are used with ANNs. In this case we will use `Adam` optimizer (which stands for Adaptive Moment Estimation). It is one of the fastest and most commonly used optimizers at present.\n",
    "\n",
    "We will use accuracy as a **metric**, but we can also use AUC-ROC, or F1 score, or other metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "OZuZ18XZ2zVV"
   },
   "outputs": [],
   "source": [
    "model_1.compile(optimizer='adam',\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f7Bqx-N03K1D"
   },
   "source": [
    "#### Train the ANN\n",
    "\n",
    "To train the model, Keras uses the `fit` method as in scikit-learn. Required arguments in `fit` include the training dataset and training labels, and additional arguments can include the number of epochs, batch size, verbose, and others.\n",
    "\n",
    "The **batch size** below means that the `fit` method will repeatedly randomly select 64 samples from the training dataset, and use the batch of samples to train the model and update the model parameters. Therefore, for the training dataset of 455 samples, the updates will be repeated 455/64 = 7.1 times (i.e., the model will be updated 8 times to process the entire dataset). When all 455 samples in the training dataset are used, that is considered one **epoch**. In this case, the training will continue for 100 epochs. Therefore, the parameters in the model will be updated 800 times in total (100 epoch x 8 steps per epoch = 800). Or, we can say that there are 800 training steps. \n",
    "\n",
    "The `verbose` argument is set to 0, and the results from the training will not be displayed after the cell. Setting `verbose` to 1 will display a progress bar, and the default value of 2 will display the training loss and accuracy for each epoch.\n",
    "\n",
    "Also, in the next cell we assign the output of the `fit` method to `history`, that is, we will save the training results in this variable, and later we will use it to plot the learning curves and analyze the model training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zmkoLSQJ39IZ",
    "outputId": "b6faa7cf-5d93-4381-c8a5-de9f5a2b4633"
   },
   "outputs": [],
   "source": [
    "history = model_1.fit(train_data, train_labels, epochs=100, batch_size=64, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dkO2ycHHrbgT"
   },
   "source": [
    "#### Evaluate the ANN on Test Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keras has a method `evaluate` that we can use for calculating the accuracy of the model. The arguments are `test_data` and `test_labels`. Note that `evaluate()` returns two values: the loss and accuracy of the model. Therefore, to print the accuracy we used the element with index `1` in `evals_test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 3ms/step - loss: 0.0708 - accuracy: 0.9649\n",
      "Classification Accuracy:  0.9649122953414917\n"
     ]
    }
   ],
   "source": [
    "# Evaluate on test dataset\n",
    "evals_test = model_1.evaluate(test_data, test_labels)\n",
    "print(\"Classification Accuracy: \", evals_test[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0707874745130539, 0.9649122953414917]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evals_test contains the loss and accuracy of the model\n",
    "evals_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To obtain the predictions by the model on data samples, we will use the `predict` method in Keras, which works similarly to the `predict` method in scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 0s/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model_1.predict(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the shape of `predictions` to understand the output of the model, and compare it to the shape of `test_data` that we used in `predict()`. The test dataset has 114 samples, each with 30 features. Therefore, for each sample the model made a prediction and outputted one single value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 30)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare to the shape of test_data\n",
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's inspect the output for the first 6 samples in `test_data` in the next cell. The shown values are difficult to interpret because they are displayed in scientific notation, therefore, in the following cell the values are shown rounded to 3 decimal places."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.8629966e-04],\n",
       "       [9.9987435e-01],\n",
       "       [7.2749399e-02],\n",
       "       [7.1462798e-01],\n",
       "       [1.4644288e-04],\n",
       "       [9.5147675e-01]], dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.   ],\n",
       "       [1.   ],\n",
       "       [0.073],\n",
       "       [0.715],\n",
       "       [0.   ],\n",
       "       [0.951]], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(predictions[:6], 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can notice that the predictions by the model have values between 0 and 1, and they can be considered as the probability of each sample to be either benign (0) or malignant (1). \n",
    "\n",
    "This form of the outputs is expected, because we used a `sigmoid` activation function in the output layer, which restricts the outputs to be in the range between 0 and 1.\n",
    "\n",
    "To assign the class label 0 or 1 to the predicted values, we can round the predictions with `np.round()` or `tf.math.round()` to return the closest integer. For example, for the prediction of 0.016, the class label is 0. For the prediction of 0.64, the class label is 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.]], dtype=float32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Class labels for the first  predicted values\n",
    "np.round(predictions[:6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also calculate the accuracy in the same way as did with scikit-learn models, by using the `accuracy_score` function. As expected, the calculated value for the accuracy is the same as the one calculated in the Keras `evaluate` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K79im89prfzv",
    "outputId": "a06c7797-cfb5-46fd-b90e-0518f88897cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 1ms/step\n",
      "The test accuracy is 96.4912 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "predictions = model_1.predict(test_data)\n",
    "\n",
    "accuracy = accuracy_score(test_labels, np.round(predictions))\n",
    "print('The test accuracy is {0:6.4f} %'.format(accuracy*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VEA49XD3MIbv"
   },
   "source": [
    "To better understand the model performance, we can also display the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "id": "pDiAYiI5MakN",
    "outputId": "78971f50-5aae-43bf-e422-5896420af450"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[70  2]\n",
      " [ 2 40]]\n"
     ]
    }
   ],
   "source": [
    "# Getting the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confmat = confusion_matrix(test_labels, np.round(predictions), labels=[1,0])\n",
    "print(confmat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As well as, we can use the function `classification_report` from scikit-learn, to display the precision, recall, F1-score, macro average accuracy, and weighted average accuracy per class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q89i8ozyD_Ek",
    "outputId": "2b18d612-48c3-4b2b-9f31-dedd5f37de26"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.95      0.95        42\n",
      "           1       0.97      0.97      0.97        72\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.96      0.96      0.96       114\n",
      "weighted avg       0.96      0.96      0.96       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report: F1 score, Recall, Precision\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(test_labels, np.round(predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0fG35BZQyTDe"
   },
   "source": [
    "#### Visualize the Training Loss and Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xk4Xkr-77loE"
   },
   "source": [
    "The following figure visualizes the history of model training, and shows the training accuracy and loss of the model for each of the 100 training epochs. For this purpose, we used the `history` variable that we assigned to `fit` when training the model. It contains an attribute `history.history` which is a dictionary that stores the loss and accuracy of the model for each epoch. \n",
    "\n",
    "These plots are called **learning curves** and are important for understanding the model performance. It is a good practice to always create a plot after training a model and analyze the learning curves.\n",
    "\n",
    "For instance, we can notice that at the end of the 100th epoch, the training loss is still decreasing. Thus, we could have trained the model for longer than 100 epochs, and this could have resulted in higher accuracy. In the next lectures we will describe best practices for determining the number of epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the accuracy and loss\n",
    "train_loss = history.history['loss']\n",
    "acc = history.history['accuracy'] \n",
    "\n",
    "epochsn = np.arange(1, len(train_loss)+1,1)\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(epochsn, acc, 'b')\n",
    "plt.grid(color='gray', linestyle='--')\n",
    "plt.title('TRAINING ACCURACY')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(epochsn,train_loss, 'b')\n",
    "plt.grid(color='gray', linestyle='--')\n",
    "plt.title('TRAINING LOSS')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c3xIjz-3jHP8"
   },
   "source": [
    "### 15.2.2 Multiclass Classification <a id=\"15.2.2-multiclass-classification\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-JVxm-UWCF1W"
   },
   "source": [
    "In this section, we will consider a multiclass classification problem. Our goal will be to train an ANN model for classifying the images in the Fashion MNIST dataset into 10 classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ohFiSjasDQ8P"
   },
   "source": [
    "#### Loading the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vWho02IHEx2x"
   },
   "source": [
    "Fashion MNIST dataset has 70,000 images, of which 60,000 are allocated for the training set and 10,000 for the test set. Examples of images in the dataset are shown in the next figure. Each is a grayscale images with 28 x 28 pixels. \n",
    "\n",
    "!['Fashion Dataset'](https://github.com/zalandoresearch/fashion-mnist/raw/master/doc/img/fashion-mnist-sprite.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The images are classified into the following 10 categories: \n",
    "\n",
    "| Label | Description |\n",
    "| --- | --- |\n",
    "| 0 | T-shirt/top |\n",
    "| 1 | Trouser |\n",
    "| 2 | Pullover |\n",
    "| 3 | Dress |\n",
    "| 4 | Coat |\n",
    "| 5 | Sandal |\n",
    "| 6 | Shirt |\n",
    "| 7 | Sneaker |\n",
    "| 8 | Bag |\n",
    "| 9 | Ankle boot |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TIq4NpVzIz2o"
   },
   "source": [
    "The dataset can be imported directly from Keras datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "YONwWTmbI4Kq"
   },
   "outputs": [],
   "source": [
    "# Load the Fashion MNIST dataset\n",
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "\n",
    "(fashion_train, fashion_train_label), (fashion_test, fashion_test_label) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data inputs (60000, 28, 28)\n",
      "Training labels (60000,)\n",
      "Testing data inputs (10000, 28, 28)\n",
      "Testing labels (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('Training data inputs', fashion_train.shape)\n",
    "print('Training labels', fashion_train_label.shape)\n",
    "print('Testing data inputs', fashion_test.shape)\n",
    "print('Testing labels', fashion_test_label.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the shape of the training data and the values in the first image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of each sample\n",
    "fashion_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   1,\n",
       "          0,   0,  13,  73,   0,   0,   1,   4,   0,   0,   0,   0,   1,\n",
       "          1,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   3,\n",
       "          0,  36, 136, 127,  62,  54,   0,   0,   0,   1,   3,   4,   0,\n",
       "          0,   3],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   6,\n",
       "          0, 102, 204, 176, 134, 144, 123,  23,   0,   0,   0,   0,  12,\n",
       "         10,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0, 155, 236, 207, 178, 107, 156, 161, 109,  64,  23,  77, 130,\n",
       "         72,  15],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   0,\n",
       "         69, 207, 223, 218, 216, 216, 163, 127, 121, 122, 146, 141,  88,\n",
       "        172,  66],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   1,   1,   0,\n",
       "        200, 232, 232, 233, 229, 223, 223, 215, 213, 164, 127, 123, 196,\n",
       "        229,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "        183, 225, 216, 223, 228, 235, 227, 224, 222, 224, 221, 223, 245,\n",
       "        173,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "        193, 228, 218, 213, 198, 180, 212, 210, 211, 213, 223, 220, 243,\n",
       "        202,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   3,   0,  12,\n",
       "        219, 220, 212, 218, 192, 169, 227, 208, 218, 224, 212, 226, 197,\n",
       "        209,  52],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   6,   0,  99,\n",
       "        244, 222, 220, 218, 203, 198, 221, 215, 213, 222, 220, 245, 119,\n",
       "        167,  56],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   4,   0,   0,  55,\n",
       "        236, 228, 230, 228, 240, 232, 213, 218, 223, 234, 217, 217, 209,\n",
       "         92,   0],\n",
       "       [  0,   0,   1,   4,   6,   7,   2,   0,   0,   0,   0,   0, 237,\n",
       "        226, 217, 223, 222, 219, 222, 221, 216, 223, 229, 215, 218, 255,\n",
       "         77,   0],\n",
       "       [  0,   3,   0,   0,   0,   0,   0,   0,   0,  62, 145, 204, 228,\n",
       "        207, 213, 221, 218, 208, 211, 218, 224, 223, 219, 215, 224, 244,\n",
       "        159,   0],\n",
       "       [  0,   0,   0,   0,  18,  44,  82, 107, 189, 228, 220, 222, 217,\n",
       "        226, 200, 205, 211, 230, 224, 234, 176, 188, 250, 248, 233, 238,\n",
       "        215,   0],\n",
       "       [  0,  57, 187, 208, 224, 221, 224, 208, 204, 214, 208, 209, 200,\n",
       "        159, 245, 193, 206, 223, 255, 255, 221, 234, 221, 211, 220, 232,\n",
       "        246,   0],\n",
       "       [  3, 202, 228, 224, 221, 211, 211, 214, 205, 205, 205, 220, 240,\n",
       "         80, 150, 255, 229, 221, 188, 154, 191, 210, 204, 209, 222, 228,\n",
       "        225,   0],\n",
       "       [ 98, 233, 198, 210, 222, 229, 229, 234, 249, 220, 194, 215, 217,\n",
       "        241,  65,  73, 106, 117, 168, 219, 221, 215, 217, 223, 223, 224,\n",
       "        229,  29],\n",
       "       [ 75, 204, 212, 204, 193, 205, 211, 225, 216, 185, 197, 206, 198,\n",
       "        213, 240, 195, 227, 245, 239, 223, 218, 212, 209, 222, 220, 221,\n",
       "        230,  67],\n",
       "       [ 48, 203, 183, 194, 213, 197, 185, 190, 194, 192, 202, 214, 219,\n",
       "        221, 220, 236, 225, 216, 199, 206, 186, 181, 177, 172, 181, 205,\n",
       "        206, 115],\n",
       "       [  0, 122, 219, 193, 179, 171, 183, 196, 204, 210, 213, 207, 211,\n",
       "        210, 200, 196, 194, 191, 195, 191, 198, 192, 176, 156, 167, 177,\n",
       "        210,  92],\n",
       "       [  0,   0,  74, 189, 212, 191, 175, 172, 175, 181, 185, 188, 189,\n",
       "        188, 193, 198, 204, 209, 210, 210, 211, 188, 188, 194, 192, 216,\n",
       "        170,   0],\n",
       "       [  2,   0,   0,   0,  66, 200, 222, 237, 239, 242, 246, 243, 244,\n",
       "        221, 220, 193, 191, 179, 182, 182, 181, 176, 166, 168,  99,  58,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,  40,  61,  44,  72,  41,  35,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0],\n",
       "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "          0,   0]], dtype=uint8)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pixels in the first train image\n",
    "fashion_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "a93MnJ5LSeFN"
   },
   "outputs": [],
   "source": [
    "# A list of label names\n",
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "nQBbX87MLfQ0",
    "outputId": "8aee400e-950a-4324-e1a0-8be8148a5d57"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This type of image is: Ankle boot(9)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWMAAAFfCAYAAACbeq03AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAcxElEQVR4nO3df0yV5/3/8fdB8Yj0eBqKcM6pSNkmsSnObv5matFU4klmZnGprcuCWWpqiyaGmqbOPyRrIsZF0yy0XdYtzmY6+486F52WTUEbyoaORueMwYoVFUSZnoOAhwLX549+Pd8eRbguek7PxeH5SO6knPPynOs+9/Hl3cN9ncuhlFICAIirpHgPAABAGQOAFShjALAAZQwAFqCMAcAClDEAWIAyBgALjI73AB7U19cn169fF5fLJQ6HI97DAYAhU0pJe3u7+Hw+SUoa+NzXujK+fv26ZGVlxXsYABA1TU1NMnHixAEz1n1M4XK54j0EAIgqnV6LWRm/9957kpOTI2PHjpXp06fLyZMntf4cH00ASDQ6vRaTMv7oo49k/fr1smnTJqmvr5f58+eL3++XK1euxOLpAGD4UzEwa9YstWbNmojbpkyZot56661B/2wgEFAiwsbGxpYwWyAQGLT7on5m3N3dLadPn5bCwsKI2wsLC6WmpuahfCgUkmAwGLEBwEgT9TK+deuW9Pb2SmZmZsTtmZmZ0tLS8lC+vLxc3G53eONKCgAjUcx+gffgB9ZKqX4/xN64caMEAoHw1tTUFKshAYC1on6dcXp6uowaNeqhs+DW1taHzpZFRJxOpzidzmgPAwCGlaifGY8ZM0amT58ulZWVEbdXVlZKfn5+tJ8OABJCTGbglZaWys9//nOZMWOGzJ07V373u9/JlStXZM2aNbF4OgAY9mJSxitWrJC2tjb51a9+Jc3NzZKXlyeHDx+W7OzsWDwdAAx7DqXsWpA0GAyK2+2O9zAAIGoCgYCMHz9+wIx1300BACMRZQwAFqCMAcAClDEAWIAyBgALUMYAYAHKGAAsQBkDgAUoYwCwAGUMABagjAHAApQxAFiAMgYAC1DGAGAByhgALEAZA4AFKGMAsABlDAAWoIwBwAKUMQBYICarQwPR5HA4tLOxWl/X5XJpZ+fNm6ed/dvf/jaU4QzK5DUbNWqUdranp2cow4kbk9fBRCzeZ5wZA4AFKGMAsABlDAAWoIwBwAKUMQBYgDIGAAtQxgBgAcoYACxAGQOABShjALAA06FhvaQk/XOG3t5e7ez3vvc97ewrr7yine3q6tLOdnR0aGfv3bunnf3Xv/6lnY3VFGeTqcgmx9jkcWO1b7pTyJVS0tfXp5XlzBgALEAZA4AFKGMAsABlDAAWoIwBwAKUMQBYgDIGAAtQxgBgAcoYACxAGQOABZgODeuZrF5sMh160aJF2tnnn39eO3v16lXtrNPp1M6OGzdOO7t48WLt7O9//3vt7I0bN7SzJisomxw3E4899ph2VnfasohIZ2fnUIYzIM6MAcACUS/jsrIycTgcEZvH44n20wBAQonJxxTPPPOM/P3vfw//bPK/mQAwEsWkjEePHs3ZMAAYiMlnxg0NDeLz+SQnJ0deeukluXTp0iOzoVBIgsFgxAYAI03Uy3j27Nny4YcfytGjR+WDDz6QlpYWyc/Pl7a2tn7z5eXl4na7w1tWVla0hwQA1ot6Gfv9flm+fLlMnTpVnn/+eTl06JCIiOzatavf/MaNGyUQCIS3pqamaA8JAKwX8+uMU1NTZerUqdLQ0NDv/U6n0+haSwBIRDG/zjgUCsn58+fF6/XG+qkAYNiKehlv2LBBqqurpbGxUf75z3/KT3/6UwkGg1JcXBztpwKAhBH1jymuXr0qL7/8sty6dUsmTJggc+bMkdraWsnOzo72U2GE6O7ujsnjzpw5Uzv71FNPaWdNrqs3WRX56NGj2tkf/OAH2tlt27ZpZ0+dOqWdPXv2rHb2/Pnz2tlZs2ZpZ02OcU1NjXb2008/1coppbSvEIt6Ge/duzfaDwkACY/vpgAAC1DGAGAByhgALEAZA4AFKGMAsABlDAAWoIwBwAKUMQBYgDIGAAuwOjTiwuFwaGdNVhk2WRV5xowZ2tn29nbtbGpqqnY2Nzc3Jtm6ujrt7MWLF7WzJqstz507VztbVFSknf3yyy+1syavwyuvvKKdDYVCWrmenh45efKkVpYzYwCwAGUMABagjAHAApQxAFiAMgYAC1DGAGAByhgALEAZA4AFKGMAsABlDAAWcCiTuabfgmAwKG63O97DwP9jMm05VkzeorW1tdpZkxWfTZi8Zj09PdrZWK2Sfe/ePe1sX1+fdvbf//63dtZkSrbJa7ZkyRLt7He+8x3t7JNPPqmdFREJBAIyfvz4ATOcGQOABShjALAAZQwAFqCMAcAClDEAWIAyBgALUMYAYAHKGAAsQBkDgAUoYwCwAKtDY0CWzZYf1O3bt7WzXq9XO9vV1aWddTqd2tnRo/X/CpqszGwyxTklJUU7azIdev78+drZ/Px87WxSkv45ZEZGhnb2yJEj2tlY4MwYACxAGQOABShjALAAZQwAFqCMAcAClDEAWIAyBgALUMYAYAHKGAAsQBkDgAWYDo2EMm7cOO2sybRak2xnZ6d2NhAIaGfb2tq0syYrX5tMeTdZ+drkNTM5br29vdpZk+nbWVlZ2tlY4MwYACxgXMYnTpyQpUuXis/nE4fDIQcOHIi4XyklZWVl4vP5JCUlRQoKCuTcuXPRGi8AJCTjMu7o6JBp06ZJRUVFv/dv27ZNduzYIRUVFVJXVycej0cWL14s7e3t33iwAJCojD8z9vv94vf7+71PKSXvvPOObNq0SYqKikREZNeuXZKZmSl79uyRV1999ZuNFgASVFQ/M25sbJSWlhYpLCwM3+Z0OuW5556Tmpqafv9MKBSSYDAYsQHASBPVMm5paRERkczMzIjbMzMzw/c9qLy8XNxud3iL9280ASAeYnI1xYOXvyilHnlJzMaNGyUQCIS3pqamWAwJAKwW1euMPR6PiHx1hvz1JW1aW1sfOlu+z+l0Gi1TAwCJKKpnxjk5OeLxeKSysjJ8W3d3t1RXVxutcQUAI43xmfHdu3fl4sWL4Z8bGxvls88+k7S0NJk0aZKsX79etmzZIpMnT5bJkyfLli1bZNy4cbJy5cqoDhwAEolxGZ86dUoWLlwY/rm0tFRERIqLi+WPf/yjvPnmm9LV1SWvv/663L59W2bPni0ff/yxuFyu6I0a35pYTX81mdJqsiqyz+fTzoZCoZhkTT526+7u1s6aTLN+/PHHtbMm06xNpi2PGTNGO2syD8Htdmtnz5w5o501eZ/NmDFDK9fb2yv19fVaWeMyLigoGHAuu8PhkLKyMikrKzN9aAAYsfhuCgCwAGUMABagjAHAApQxAFiAMgYAC1DGAGAByhgALEAZA4AFKGMAsACrQ2NAJisHjxo1SjtrMh16xYoV2tn73xyo4+bNm9rZlJQU7azJisSpqanaWZPv+jaZZm0yffvLL7/Uzo4erV8vJq/vE088oZ199913tbPPPvusdtZk33RxZgwAFqCMAcAClDEAWIAyBgALUMYAYAHKGAAsQBkDgAUoYwCwAGUMABagjAHAAkyHxoBMpn2aTME18Z///Ec7a7KKc3JysnY2VlO9MzIytLP37t3Tzpqs+GzyOowdO1Y7azLV+/bt29rZq1evamdXrlypnf31r3+tna2trdXO6uLMGAAsQBkDgAUoYwCwAGUMABagjAHAApQxAFiAMgYAC1DGAGAByhgALEAZA4AFRtR0aIfDoZ01mf6alKT/b5rJGExW4jVZkdhET09PTB7XxOHDh7WzHR0d2tmuri7t7JgxY7SzJitqm6xQbfKeNJm2bPI+MxGr96/J6/D9739fOxsIBLSzscCZMQBYgDIGAAtQxgBgAcoYACxAGQOABShjALAAZQwAFqCMAcAClDEAWIAyBgALDPvp0LFatdeGacA2WLBggXZ2+fLl2tkf/ehH2tnOzk7trMmqyCZTnE1WyTZ5n5nsm8l73el0amdNpk6bTPU22TcTJsft7t272tmioiLt7F//+lftrC7OjAHAAsZlfOLECVm6dKn4fD5xOBxy4MCBiPtXrVolDocjYpszZ060xgsACcm4jDs6OmTatGlSUVHxyMySJUukubk5vJl86xYAjETGnxn7/X7x+/0DZpxOp3g8niEPCgBGmph8ZlxVVSUZGRmSm5srq1evltbW1kdmQ6GQBIPBiA0ARpqol7Hf75fdu3fLsWPHZPv27VJXVyeLFi2SUCjUb768vFzcbnd4y8rKivaQAMB6Ub+0bcWKFeH/zsvLkxkzZkh2drYcOnSo30tHNm7cKKWlpeGfg8EghQxgxIn5dcZer1eys7OloaGh3/udTqfRNZEAkIhifp1xW1ubNDU1idfrjfVTAcCwZXxmfPfuXbl48WL458bGRvnss88kLS1N0tLSpKysTJYvXy5er1cuX74sv/zlLyU9PV1eeOGFqA4cABKJQ5nMb5SvrpRYuHDhQ7cXFxfL+++/L8uWLZP6+nq5c+eOeL1eWbhwobz99tvanwMHg0Fxu90mQ4q7tLQ07azP59POTp48OSaPazLtMzc3Vzv7qF/S9sdkRW2TVYZTUlK0s9evX9fOJicna2dNpus+8cQT2tnu7m7t7Lhx47SzNTU12tnHHntMO2syld5kdWiTVZxNjtuNGze0s08//bR2VuSrMY8fP37AjPGZcUFBwYDz048ePWr6kAAw4vHdFABgAcoYACxAGQOABShjALAAZQwAFqCMAcAClDEAWIAyBgALUMYAYIFhvzq0yfp6b7/9tnZ2woQJ2tnHH39cO2uycrDJasB37tzRzpqsfN3e3q6dNZmu63A4tLNdXV3aWZOpvS+++KJ29tSpU9pZl8ulnTWZQv7UU09pZ01MnTpVO2uyb01NTdpZk5WkTaa8m0zfzs7O1s7GAmfGAGAByhgALEAZA4AFKGMAsABlDAAWoIwBwAKUMQBYgDIGAAtQxgBgAcoYACxg7XTopKQkrSmzv/nNb7Qf0+v1amdNpi2bZE2mfZowWZHYZLwmU5FNmKwAbjJNdevWrdpZk3177bXXtLMmq07fu3dPO/uPf/xDO3vp0iXtrMkq5LFazdpkFedYrSx+8+ZN7WwscGYMABagjAHAApQxAFiAMgYAC1DGAGAByhgALEAZA4AFKGMAsABlDAAWoIwBwAIOpZSK9yC+LhgMitvtlp/97GdaU3xNpr9+/vnn2lmTVWVNsk6nUztrwmQ6qclUZJMVfk2mAZusvm0y/dXj8Whnly1bpp0dO3asdtZkFWeT98706dNjkjV5fU2mOJs8rsl0fhMmq5Cb/B3SXZW+r69Prl27JoFAQMaPHz9gljNjALAAZQwAFqCMAcAClDEAWIAyBgALUMYAYAHKGAAsQBkDgAUoYwCwAGUMABawdnXomzdvak1PNJmu63K5tLOhUEg7azIGk+mvJlNEB5tq+XX/+9//tLNffPGFdtZk30xWZjZZQbmnp0c7u3//fu3s2bNntbMm06HT0tK0syZTke/cuaOdNVlB2eT17evr086aTEU2eVyT6dAmf99yc3O1cj09PXLt2jWtLGfGAGABozIuLy+XmTNnisvlkoyMDFm2bJlcuHAhIqOUkrKyMvH5fJKSkiIFBQVy7ty5qA4aABKNURlXV1dLSUmJ1NbWSmVlpfT09EhhYaF0dHSEM9u2bZMdO3ZIRUWF1NXVicfjkcWLF0t7e3vUBw8AicLoM+MjR45E/Lxz507JyMiQ06dPy4IFC0QpJe+8845s2rRJioqKRERk165dkpmZKXv27JFXX301eiMHgATyjT4zDgQCIvL/fwnR2NgoLS0tUlhYGM44nU557rnnpKampt/HCIVCEgwGIzYAGGmGXMZKKSktLZV58+ZJXl6eiIi0tLSIiEhmZmZENjMzM3zfg8rLy8Xtdoe3rKysoQ4JAIatIZfx2rVr5cyZM/LnP//5ofsevJxEKfXIS0w2btwogUAgvJlcJgYAiWJI1xmvW7dODh48KCdOnJCJEyeGb7+/5E1LS4t4vd7w7a2trQ+dLd/ndDpjthQRAAwXRmfGSilZu3at7Nu3T44dOyY5OTkR9+fk5IjH45HKysrwbd3d3VJdXS35+fnRGTEAJCCjM+OSkhLZs2eP/OUvfxGXyxX+HNjtdktKSoo4HA5Zv369bNmyRSZPniyTJ0+WLVu2yLhx42TlypUx2QEASARGZfz++++LiEhBQUHE7Tt37pRVq1aJiMibb74pXV1d8vrrr8vt27dl9uzZ8vHHHxtNRRYRaW5ullGjRg2aM1nc+urVq9rZ1NRU7Wx6erp21mSa6q1bt7SzN2/e1M6OHq1/2E0+QjKZ0mqy2rLJe8dkRWKT1/fpp5/Wzn79uvvBmPyO5Pbt29pZk+Nm8jrEauq0yeOmpKRoZ01WC79/dZiOZ599VisXCoWkurpaK2tUxjrF53A4pKysTMrKykweGgBGNL6bAgAsQBkDgAUoYwCwAGUMABagjAHAApQxAFiAMgYAC1DGAGAByhgALGDt6tC6q/Hu27dP+zF/8YtfaGevX7+unb106ZJ21mSlY5PVlk2mIptMJzVZMVdn+vp9Jqtv9/b2amdNpsd3dnZqZ5ubm2MyBpN9M5nGHqv3WaxWqLZhNesHv/hsIDdu3NDKmbxenBkDgAUoYwCwAGUMABagjAHAApQxAFiAMgYAC1DGAGAByhgALEAZA4AFKGMAsIBDmczd/BYEg0Fxu90xeWy/36+d3bBhg3Y2IyNDO2uyEq/JFFGTabUm05ZNpkObTNc1GYPD4dDOmrydTaaQm2RNXjOTxzV5HUyYPK7uNGBTJq9ZX1+fdtZkdegzZ85oZ1988UXtrMhXK0+PHz9+wAxnxgBgAcoYACxAGQOABShjALAAZQwAFqCMAcAClDEAWIAyBgALUMYAYAHKGAAsYO10aIfDoTVN02RqZKwsXLhQO1teXq6dNZlmbTKFPClJ/99gk2nLJtOhTaZvm2htbdXOmrz1r127pp01eU/evXtXO2tyLEyYvA4mKzObrL5t8p6srKzUzp4/f147W1NTo501xXRoABgmKGMAsABlDAAWoIwBwAKUMQBYgDIGAAtQxgBgAcoYACxAGQOABShjALCAtdOhYWbKlCna2fT0dO2syQrVEydO1M5evnxZO2syBffzzz/XzgLfFqZDA8AwYVTG5eXlMnPmTHG5XJKRkSHLli2TCxcuRGRWrVoV/pKf+9ucOXOiOmgASDRGZVxdXS0lJSVSW1srlZWV0tPTI4WFhdLR0RGRW7JkiTQ3N4e3w4cPR3XQAJBo9L/zUESOHDkS8fPOnTslIyNDTp8+LQsWLAjf7nQ6xePxRGeEADACfKPPjAOBgIiIpKWlRdxeVVUlGRkZkpubK6tXrx7wO2ZDoZAEg8GIDQBGmiGXsVJKSktLZd68eZKXlxe+3e/3y+7du+XYsWOyfft2qaurk0WLFkkoFOr3ccrLy8Xtdoe3rKysoQ4JAIatIV/aVlJSIocOHZJPPvlkwEuampubJTs7W/bu3StFRUUP3R8KhSKKOhgMUshDwKVtX+HSNthI59I2o8+M71u3bp0cPHhQTpw4MehfQK/XK9nZ2dLQ0NDv/U6nU5xO51CGAQAJw6iMlVKybt062b9/v1RVVUlOTs6gf6atrU2amprE6/UOeZAAkOiMPjMuKSmRP/3pT7Jnzx5xuVzS0tIiLS0t0tXVJSJfLa64YcMG+fTTT+Xy5ctSVVUlS5culfT0dHnhhRdisgMAkBCUARHpd9u5c6dSSqnOzk5VWFioJkyYoJKTk9WkSZNUcXGxunLlivZzBAKBRz4PGxsb23DcAoHAoN3Hd1MAQIzx3RQAMExQxgBgAcoYACxAGQOABShjALAAZQwAFqCMAcAClDEAWIAyBgALUMYAYAHKGAAsQBkDgAUoYwCwAGUMABagjAHAApQxAFiAMgYAC1DGAGAByhgALGBdGVu2JB8AfGM6vWZdGbe3t8d7CAAQVTq9Zt3q0H19fXL9+nVxuVzicDjCtweDQcnKypKmpqZBV1kdbti34Yl9G56+zX1TSkl7e7v4fD5JShr43Hd0TEcyBElJSTJx4sRH3j9+/PiEe3Pcx74NT+zb8PRt7Zvb7dbKWfcxBQCMRJQxAFhg2JSx0+mUzZs3i9PpjPdQoo59G57Yt+HJ1n2z7hd4ADASDZszYwBIZJQxAFiAMgYAC1DGAGAByhgALDAsyvi9996TnJwcGTt2rEyfPl1OnjwZ7yFFRVlZmTgcjojN4/HEe1hDcuLECVm6dKn4fD5xOBxy4MCBiPuVUlJWViY+n09SUlKkoKBAzp07F5/BGhps31atWvXQcZwzZ058BmugvLxcZs6cKS6XSzIyMmTZsmVy4cKFiMxwPW46+2bbcbO+jD/66CNZv369bNq0Serr62X+/Pni9/vlypUr8R5aVDzzzDPS3Nwc3s6ePRvvIQ1JR0eHTJs2TSoqKvq9f9u2bbJjxw6pqKiQuro68Xg8snjx4mHxxVCD7ZuIyJIlSyKO4+HDh7/FEQ5NdXW1lJSUSG1trVRWVkpPT48UFhZKR0dHODNcj5vOvolYdtyU5WbNmqXWrFkTcduUKVPUW2+9FacRRc/mzZvVtGnT4j2MqBMRtX///vDPfX19yuPxqK1bt4Zvu3fvnnK73eq3v/1tHEY4dA/um1JKFRcXq5/85CdxGU80tba2KhFR1dXVSqnEOm4P7ptS9h03q8+Mu7u75fTp01JYWBhxe2FhodTU1MRpVNHV0NAgPp9PcnJy5KWXXpJLly7Fe0hR19jYKC0tLRHH0el0ynPPPZcwx7GqqkoyMjIkNzdXVq9eLa2trfEekrFAICAiImlpaSKSWMftwX27z6bjZnUZ37p1S3p7eyUzMzPi9szMTGlpaYnTqKJn9uzZ8uGHH8rRo0flgw8+kJaWFsnPz5e2trZ4Dy2q7h+rRD2Ofr9fdu/eLceOHZPt27dLXV2dLFq0SEKhULyHpk0pJaWlpTJv3jzJy8sTkcQ5bv3tm4h9x826r9Dsz9e/11jkqxf3wduGI7/fH/7vqVOnyty5c+W73/2u7Nq1S0pLS+M4sthI1OO4YsWK8H/n5eXJjBkzJDs7Ww4dOiRFRUVxHJm+tWvXypkzZ+STTz556L7hftwetW+2HTerz4zT09Nl1KhRD/0r3Nra+tC/1okgNTVVpk6dKg0NDfEeSlTdv0JkpBxHr9cr2dnZw+Y4rlu3Tg4ePCjHjx+P+C7xRDhuj9q3/sT7uFldxmPGjJHp06dLZWVlxO2VlZWSn58fp1HFTigUkvPnz4vX6433UKIqJydHPB5PxHHs7u6W6urqhDyObW1t0tTUZP1xVErJ2rVrZd++fXLs2DHJycmJuH84H7fB9q0/cT9ucfzloZa9e/eq5ORk9Yc//EH997//VevXr1epqanq8uXL8R7aN/bGG2+oqqoqdenSJVVbW6t+/OMfK5fLNSz3rb29XdXX16v6+nolImrHjh2qvr5effHFF0oppbZu3arcbrfat2+fOnv2rHr55ZeV1+tVwWAwziMf3ED71t7ert544w1VU1OjGhsb1fHjx9XcuXPVk08+af2+vfbaa8rtdquqqirV3Nwc3jo7O8OZ4XrcBts3G4+b9WWslFLvvvuuys7OVmPGjFE//OEPIy5PGc5WrFihvF6vSk5OVj6fTxUVFalz587Fe1hDcvz4cSUiD23FxcVKqa8uk9q8ebPyeDzK6XSqBQsWqLNnz8Z30JoG2rfOzk5VWFioJkyYoJKTk9WkSZNUcXGxunLlSryHPaj+9klE1M6dO8OZ4XrcBts3G48b32cMABaw+jNjABgpKGMAsABlDAAWoIwBwAKUMQBYgDIGAAtQxgBgAcoYACxAGQOABShjALAAZQwAFvg/W5KMplAnATAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the first image\n",
    "index = 0\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(fashion_train[index], cmap='gray')\n",
    "# Display the label\n",
    "image_label = fashion_train_label[index]\n",
    "print('This type of image is: {}({})'.format(class_names[image_label], image_label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AXLTvIzIR7f3"
   },
   "source": [
    "We can also visualize several random images, to ensure that the labels match the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "DMd5B7SiUQOg",
    "outputId": "56d1ac6d-1939-48df-e9bc-6605da6b0611"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "for index in range(6):\n",
    "    ax = plt.subplot(2, 3, index+1)\n",
    "    random_index = random.choice(range(len(fashion_train)))\n",
    "    plt.imshow(fashion_train[random_index], cmap='gray')\n",
    "    plt.title(class_names[fashion_train_label[random_index]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aGBUWgukW3kx"
   },
   "source": [
    "As expected, the pixel values in the images range from 0 to 255. To scale the values to the range from 0 to 1, we can just divide the training and test sets by `255.0`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "YsagpEZYWxp9"
   },
   "outputs": [],
   "source": [
    "# Scaling the image pixels to be between 0 and 1\n",
    "fashion_train = fashion_train/255.0\n",
    "fashion_test = fashion_test/255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, to create an ANN with fully-connected layers, we will need to reshape the images into one-dimensional arrays. Since each image has 28 x 28 = 784 pixels, let's use `reshape` to flatten the images. \n",
    "\n",
    "In the next lecture we will study Convolutional Neural Networks which are particularly suitable for processing image data, and they can work with 2-dimensional images without the need to flatten the arrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_train = fashion_train.reshape(-1, 784)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_test = fashion_test.reshape(-1, 784)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the shape\n",
    "fashion_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xj9ygbuUZQjz"
   },
   "source": [
    "#### Create the ANN "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xfUxY7d8Zl-t"
   },
   "source": [
    "In the next cell, we define the layers in the ANN. The architecture of the model is similar to the model we used in the previous example. Note that in the arguments for `Dense` layers we don't need to specify the `units` keyword, as the first positional argument is the number of units (neurons). \n",
    "\n",
    "Since this is a multiclass classification problem, notice that the number of neurons in output layer is 10, as it needs to match the number of class labels in the dataset. Also, we need to use `softmax` activation in the output layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the layers in the network\n",
    "inputs = Input(shape=(784,))\n",
    "dense1 = Dense(64, activation='relu')(inputs)\n",
    "dense2 = Dense(32, activation='relu')(dense1)\n",
    "outputs = Dense(10, activation='softmax')(dense2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_classifier = Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compile the ANN\n",
    "\n",
    "To compile the model, we used the loss `sparse_categorical_crossentropy` because the labels are integers in ordinal encoding format. If the labels were in one-hot encoding format, we would have used `categorical_crossentropy` loss. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "fashion_classifier.compile(optimizer='adam',\n",
    "                           loss='sparse_categorical_crossentropy',\n",
    "                           metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train the ANN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CPzwj8KNj-7T"
   },
   "source": [
    "To train the model, we pass the training data and labels. In this case, let's train the model for 10 epochs, and use a `batch_size` of 32 images. Also, we used the default verbose, and the training results are displayed at the end of each epoch. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nbg2muwlmzaz",
    "outputId": "290c28dc-c430-4400-aa7b-bd5d6b19580f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5234 - accuracy: 0.8152\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3838 - accuracy: 0.8618\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3481 - accuracy: 0.8727\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3262 - accuracy: 0.8802\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3093 - accuracy: 0.8869\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2945 - accuracy: 0.8914\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2857 - accuracy: 0.8942\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2746 - accuracy: 0.8977\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2669 - accuracy: 0.9016\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2586 - accuracy: 0.9028\n"
     ]
    }
   ],
   "source": [
    "history = fashion_classifier.fit(fashion_train, fashion_train_label, epochs=10, batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "71JCDJfZubbv"
   },
   "source": [
    "#### Evaluate the ANN on Test Dataset\n",
    "\n",
    "By using the `evaluate` method in Keras, we can see that the classification accuracy on test data is 88.4%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O2XST14vufRB",
    "outputId": "d6fe60f0-3f36-48dc-a554-b837d33777ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.3391 - accuracy: 0.8843\n",
      "Classification Accuracy:  0.8842999935150146\n"
     ]
    }
   ],
   "source": [
    "# Evaluate on test dataset\n",
    "evals_test = fashion_classifier.evaluate(fashion_test, fashion_test_label)\n",
    "print(\"Classification Accuracy: \", evals_test[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use `predict()` to obtain the model's predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K79im89prfzv",
    "outputId": "a06c7797-cfb5-46fd-b90e-0518f88897cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = fashion_classifier.predict(fashion_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mYF9aQTj-GTa"
   },
   "source": [
    "In the next call, we checked the shape of the predicted outputs, and since there are 10,000 test images and 10 classes, the output is `(10000, 10)`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ai46Kj1d7p1m",
    "outputId": "f85e905b-6a1a-4ec6-fc9b-c7a8226e34cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the shape of the predictions\n",
    "predictions.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, we displayed the predictions for the first 5 test images in the next cell. For each image, the model outputs probabilities for each of the 10 classes. The probabilities sum to 1. For instance, for the first test image, the model assigned the highest probability to the class with index 9. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ph2r53Kt77Hg",
    "outputId": "83732785-130e-4c8b-dcde-850cc89b013c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.   , 0.   , 0.   , 0.   , 0.   , 0.015, 0.   , 0.031, 0.   ,\n",
       "        0.954],\n",
       "       [0.   , 0.   , 0.996, 0.   , 0.002, 0.   , 0.002, 0.   , 0.   ,\n",
       "        0.   ],\n",
       "       [0.   , 1.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   ,\n",
       "        0.   ],\n",
       "       [0.   , 1.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   ,\n",
       "        0.   ],\n",
       "       [0.087, 0.   , 0.002, 0.   , 0.   , 0.   , 0.91 , 0.   , 0.   ,\n",
       "        0.   ]], dtype=float32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the predictions for the first 5 test images\n",
    "np.around(predictions[:5],3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bOG64FhY-zdH"
   },
   "source": [
    "To output the indices with the highest probability for each image we can use `np.argmax`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C7bmDQ2k8191",
    "outputId": "3c97e0df-778d-452e-8f49-027e49feda69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 2, 1, 1, 6], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the index with the highest probability for the first 5 test images\n",
    "np.argmax(predictions[:5], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ground-truth labels are also shown in the next cell, and they match correctly the predictions for the first 5 test images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5r3luKAi9IJp",
    "outputId": "670aabec-a36f-4dd7-bf97-30bd21c94aa8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 2, 1, 1, 6], dtype=uint8)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the ground-truth label for the first 5 test images\n",
    "fashion_test_label[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TY-5ODXpvMjN"
   },
   "source": [
    "#### Visualize the Training Loss and Accuracy\n",
    "\n",
    "The learning curves are shown in the figure below. It becomes obvious that if we train the model for longer than 10 epochs, we can improve the classification performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the accuracy and loss\n",
    "train_loss = history.history['loss']\n",
    "acc = history.history['accuracy'] \n",
    "\n",
    "epochsn = np.arange(1, len(train_loss)+1,1)\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(epochsn, acc, 'b')\n",
    "plt.grid(color='gray', linestyle='--')\n",
    "plt.title('TRAINING ACCURACY')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(epochsn,train_loss, 'b')\n",
    "plt.grid(color='gray', linestyle='--')\n",
    "plt.title('TRAINING LOSS')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Re-train and Evaluate the ANN\n",
    "\n",
    "Note that if we re-train the same model on the same training data and evaluate it on the same testing data, in most cases we will obtain similar, but different values for the accuracy!!!\n",
    "\n",
    "The reason for this is because every time a model is trained, the values of the network parameters are randomly initialized to small values close to 0 (but not 0 though). Because of the random initialization, the model will converge to a different solution every time we train it.\n",
    "\n",
    "Consequently, ANNs have a stochastic nature, meaning that every time we train a model we obtain a different output. \n",
    "\n",
    "If the repeatability of the results is important, we can use a random seed to initialize the model parameters, and in that case, the accuracy will be the same if we re-train the model.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.3802 - accuracy: 0.8761\n",
      "Classification Accuracy:  0.8761000037193298\n"
     ]
    }
   ],
   "source": [
    "fashion_classifier_2 = Model(inputs, outputs)\n",
    "\n",
    "# Compile the model\n",
    "fashion_classifier_2.compile(optimizer='adam',\n",
    "                  loss='sparse_categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "fashion_classifier_2.fit(fashion_train, fashion_train_label, epochs=10, batch_size=32, verbose=0)\n",
    "\n",
    "# Evaluate on test dataset\n",
    "evals_test = fashion_classifier_2.evaluate(fashion_test, fashion_test_label)\n",
    "print(\"Classification Accuracy: \", evals_test[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.3 Regression with ANNs <a id=\"15.3-regression-with-anns\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jG7bLsj03uxw"
   },
   "source": [
    "In regression tasks, we are interested in predicting single or multiple continuous values. The ANN architectures for regression tasks are similar to the architectures for classification tasks. The listed recommendations for the input and hidden layers are the same as for classification tasks. The number of neurons in the output layer depends on the problem. If we are predicting a single value it will be 1, and if we are predicting multiple values, the number of output neurons will be set to the number of predicted values. \n",
    "\n",
    "The choice of activation functions depends on the problem, but in most cases, `ReLU` will work well in the hidden layers. Unlike ANNs for classification, ANNs for regression don't need to have an activation function in the output layer, since the ANN outputs are continuous value(s).\n",
    "\n",
    "We stated earlier that the loss function used in regression is usually `Mean Squared Error(MSE)`, or if the dataset contains outliers, `Mean Absolute Error (MAE)` loss may be preferred.\n",
    "\n",
    "A suitable choice for an optimizer is Adam. Other optimizers to try include SGD, RMSProp, Nadam, and others. \n",
    "\n",
    "*Table: Typical values of hyperparameters in ANNs for regression*\n",
    "\n",
    "| **Hyperparameter** | **Typical value** |\n",
    "| --- | --- |\n",
    "| Neurons in input layer | Same as the number of input features |\n",
    "| Number of hidden layer(s) | Depends on the problem, typically from 1 to 10 |\n",
    "| Neurons per hidden layer | Depends on the problem, typically from 10 to 100 |\n",
    "| Activation in hidden layers|  Mostly ReLU |\n",
    "| Activation in output layer |None in most cases|\n",
    "| Loss function | MSE or MAE |\n",
    "| Optimizer | Mostly Adam, SGD, RMSProp |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ce1AurnvUWUr"
   },
   "source": [
    "Next, we will present an example of regression with ANNs, and for this purpose, we will use the California Housing Dataset that is available in scikit-learn. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_RplpeLNUYj0"
   },
   "source": [
    "#### Loading the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YFVMmxtm6R_a"
   },
   "source": [
    "We will load the California Housing Dataset from scikit-learn as a pandas DataFrame named `housing`. The objective of the regression task is to predict the median house value, shown in the column to the right. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  MedHouseVal  \n",
       "0    -122.23        4.526  \n",
       "1    -122.22        3.585  \n",
       "2    -122.24        3.521  \n",
       "3    -122.25        3.413  \n",
       "4    -122.25        3.422  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing(as_frame=True).frame\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 9)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the shape of the dataset\n",
    "housing.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check if there are missing values, and visualize the histograms of the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc         0\n",
       "HouseAge       0\n",
       "AveRooms       0\n",
       "AveBedrms      0\n",
       "Population     0\n",
       "AveOccup       0\n",
       "Latitude       0\n",
       "Longitude      0\n",
       "MedHouseVal    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.hist(bins=20, figsize=(8,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next cell we will check if there are correlated features. We can notice high correlation between the house value and the number of bedrooms, which is expected. We can also notice high negative correlation between the price and longitude and latitude. In the following cell we plotted the house value versus longitude and latitude. The plot looks like the map of California, and we can see that houses that are closer to the coast are more expensive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = housing.corr()\n",
    "sns.heatmap(correlation, annot=True);\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"Longitude\", y=\"Latitude\", c=\"MedHouseVal\", cmap=\"jet\", colorbar=True, legend=True, sharex=False, figsize=(6,6),)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, if we check the median house value column, we can see that the values are in the range from 0 to 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N8eNxSPndu3FYeBpKS9nmqMOH6TPLWmGcb+w2a7R92+sYqK7GXnleCMq8HTp8Opo5gkwbCxE2FLSGjXafdTqnC4KR2OrV7ueJ8TgOyEJrSGm0eaA9z5w6Nb1drOesXs7KeW0UaG07aJDhtUJTFi6kP1OmfcayIzq++KLcM/gwwRvxtL6e3jtm7Vp37y67Yp5znbzQZAcw4/VYCsK8HRSyyngTIXp3MqfJktfyPOiCBcsAa2ujd9OtrU3dgyWYkxfX1rY2OldZK0QsgKKMuFS5kE6ZQncdq+u1LHdTAISWLvX33QQVEca7jY103jE879M857pt1hYvpq+bZpwb4TVC7dcvPAa+fpB1ggVCdPlCnBYkHstz2dE9eWEdYETwYn0eLB4ClZWp58Hip+9VoyRCSyAyCh+t10oshlBxMX/bBwxgc+dl1QqtXSsnf8icOf6/m6DR0yPm2ZKx6rbBqqigr9Ocn8M459LMi5WV9HMTa9wREWO+vl7Yaww9WSlYIGQ/YGiOOLwmOkIo+O6mrAOMNjBTcXF6LhVW10PyPFhcTZ0mHSfhTURobNHvzy0pE+mfrMmoeEoiYR0ynWa32NQkvj0NDcEeW35A+wy8jg0jrHNFa6u9kEJb1+LF9No7lu8iYswXFmqtBSFrBQsraLUJtIui1QTmR3RPHlgGWDyeGZjJbdCTRYd1IJsDIDnZybD44VvBu3sxalhEQoTh2trM71hVhTUBLDtI3jJhAp1WyEmIN483L0c+5pgqMuPQBB1arY3d82HZ5bOM4eJicQkaFy9m08zRCJKibJmiLLSyoAULA7Sdq7XV2wTGarugAtYBZkwqRGMw5uW81Pw83ISY00+nq9NOeOO1a5CZjtjYRvNi7behp9txiV0SOKNGw/w9vv99tjbYndPzhuQOa4AsWjsTu/fFYpfA0t9+8AMxddFuGmjGuRGWaL5OxWhHls1owcIAyxEH6wTGmpUzLDYWZKGoq2NTUVZWevMmIbgdDdAUJ+GNVgNjN0GreH9ByW5K8/6txkhzM3vaaqvFkDckt1ejX9U0NyM0cCDb80sk8GLoRXjq6aG/z8qVznXJNFKm3aTRpiZwK0HvJ36gBQsDrPYPLBOY7HS/IpFpuW8stJ4HdruA3l52zQdAuvDmtDPlEV781DiR70DrIhqEYuVmyLKozJjh7tpqfq80WggeN3KVeBmzxhwzXmCZ02jGA48wb1fcjmDM8HrUqN4YBgUtWBigsag22hUgRK8yZfFmCAKyLPeNhSVWghWsVunG0txMtzP1umj7ZSMjQnOjshCDXpbvYB6DdhjH5uLFdO86yB5bdvCMAx4BmDauBMvibtWfvRx/kJKXxy4MNjXR58KR8VyjgBYsDMj02Ai6N4gVMiz3zYuKm/rTaSL3oq0AwG5vTU1sO1PWxc+P9+iXZklmKSjAbnosn6GxYaERuMzvOixj1LyZ4UnuxiMA03qguNlXuH0/ntTs5D2zChc9PXw2F1F2aaZBCxYGeNxI3aAJukRSEQcJGTti487Pq7Ed76JaWEjXPtZ7+rWrlRnFsrbWeyRFmSUWoxcqvLgkyhz/orAajzyxS/zQWLjZV7ghwijZy5j0unHhfa5RIKuym7qhExhlUlOD86Y0NIipz5ygyi5hUkWFdb4JAJw74Jpr+NqxZ4/93xCyzrhYUwPQ3GyfT8DP5Fvt7Xy5GZx47DGc+yFoFBcDnHaac9K/ZBJg7lz8Dmkwvuugj/+WFpzB1pxkbvdu9rpE5LYwj1ma61iTNgLgNvIkOAOgz6Bq5Ljj2O+jc4awkRWCBUkQ5oTXTtPR4b4QdHWxd34/iMcBbrjBOXkaLXYJqswLgdPCIHNRNdLZmTkJ1tQAfPopwOLFmZOd3XeTgcwU5N3ddNe59QXevmKmqwvgssuck/7RZoM1s21bsBMEsgpMTogSgD/9lO5eZ5yB/+0laSMAbuMFF3hvJ8EqoZ+doJNM0n0/K/zM6ht6fNKgHEHFUQhC7lbBXmMUhEHNaocxMBNRH7OqBu1c2rxY4fvl/WA2GistTf8exrPg1lZc/Ip7ECYPEJnF3E+8utwaPb1ExMEQjcgYJazxKqxgiftQWGjvjk7zXHt7xYSqNx9P2Blv19V5O/6Nx7MjwR0NWWVj4ebBIdMOgiVkbZCwGnxmbxGa83iraJRerfCDsKgaPQpExT1gCcrEY7AnstTWZnokVFTQR5gVUYz9hPW5WPUx3jgYMqAVmMz5OABSBrLmfsUTBEykoONmlySir5tj4bC6N9OWbLetIGSNYEEz+dN24NZWfD3LwKR1CQuSAafb4KutZctNYB50Xq3wg7Cokp0Wz07M/KytFmi7z8s03mSZcO3cOHkM37wWVg8Jp3cUtMibLFGB6+uxMGF+J+aspDzCsIxgbHaLsoiNxNy5qfp4M5k6lSBpnEkfXrkSz9MrV/rXl7NCsKBVt9N24IUL7a2zFy+2f3G0bnVEcFEJrTaBxRVs4cL0Z+P1eEjmosrqM+8U64PWQ8TN20SWZwxvKSx0FkJKSvxN997YyLbgqdZCsECTI4eEUHd65iR+C0+OF4TkhI+3W5RFCBYNDXLbTooojQWvYOvkzedHFNnICxYs6nbaDjxlivuEymMbsHAh11cWAu2OkzWborFT8whaMhbVqVP5feZZJxve47eJE8W3l7a4nXvn54uPpOj2nGkXDWOOG1780m642X+sXeu+Ey8uppsPaXK8iN71240TERpKo8urDG2LSDdzXm0SrVu8TOEi8oIFi7qdtgPTGC1ZvbiwCBYsi/bKlWyGVcYjBNrP2Glwmputz5S9lngcofXrxU86TupR1uM3I7zhh/0oU6f6ExmUROP0O3qm33lFnOw/ZCehM2s0TjtNXL1O70REDg+j0CLjOYlaqHlDytNqc2XH24m8YMGibqd5KU6BlczF/OJoF6316zkfHgesO5HWVm/HEiy7WLsAOzyhjO3KsmXikyE5aSy8Cps04eeDUIqLcVtZNVten3Nzs31/FO3Z4WSDJHNHaKch8SMJHVmQ9u4VW6+TLVF5OV/dVmkYRI9xEUb3IoRiVu2OLGPTyAfIYgl6E48DPPKI83U//CH9vc1BWXIonyLtdTJgiQOQSAC89JK3mBII0V+7c6f17zs6cKwJkWzeDHDffWzts0N03AOjz/28eXTBhVSzezfAxo2pOCgyWbcOB5Cy64/FxeLijLjFlUAIoLZWzjuKxwHGjAGYNg3/JDET/AjchRCe12bMkH8vADzGP/mEr45kEvdBQjyOxziAuFgrXoJpmXGbe8mzd4p1xBrfxiq2h5+EVrAQHfRm4EC2+xtf3I4ddJ+hvU4GLB1t506AX/xCXlsIiYT170ULFQAAw4eLWQxogxCNGUNXXzyeHlzowQc5G8hJfj79tdu24fbfc4+89gAAPPmks0CYlwcwcaKYe9EI4F6iPfIgIkIlLa+/Lq6uWMxeCBO18JnrsYv465VBg/jroP2uIoUB1VGkQytYOEmn5smf7ELsiMUAVqxg23kZX9ymTeyf8RvVHc2KIUOsf2+nyfBKTg7AtdcCXH89f11OIcmNjBljHyKckJ8PcPvt3iJKyuLmm+mvJX3KTkDkJRbDdbv1h61bxS30tEKtDOHXLlokiZBLi918SMOWLfTXuuG0Exc1H1nVQ9IVtLYCLFgAUFAg5l5eERFSnnajAhCM0OOhFSwA7KVTcwhmGlXU1q0AZ5/tfk+zJiSZdD9mIW1S+bKrqwFKS9Xd3wo7DYLohWrePIDXXhMTLpz2KIXm+K1vXzFHM6Lo1w/g61+n2+0Z+7MMtStZDKdPp7teVBtohVrRwq/XsNhm6uut58O1a91D98sKV231bqqrAcrL+eu2C8+9bh3AVVdhzevevd7r/+QT9hwoZkRo12k2KqSuQIQel2PiYY+KyJu0xk80XhBm4y1aS+SpU4V9Xc/4GTWRpth5yYi07p44Edcp0gCONUBWUAJN0RYarxQvY4ClEA8MHu8aL/iV2dOIm8cAi5cQMVa3mg9lRaV0K3aGhCJcy63CbYv8nkVF1v3S6zvmCSnv9rzswiGIJPJeISyInPjMVsIsIXl7etRG/ZPttsZabrnFup0i3NAAEPrJT+R8d1aXLquJ3ktcjVgMoX795L+Xyko8WVt5YVhNXjKs8VUJFl6jxnqFxluLxUvIrV1+uzLn5KR7bphpbkaob1/++8iMw2EsPB5IIkLKW0XydQvgKBItWBigiW5HG7PBHLuAZcEyu7T6ESmN5Tn4Xa67zr6NvHWbc5jIcGH1urg0N9MnelJViADU2oo1SwsX4n/bTV6iA5uRCZxWyyYq5DJN/xMZJ0CkwJtIOId3lr3oeh0nIgJlkXfix+aJJ1aEiKBrKsPSa8HChJsqilY1bR4kPBn6VGRWtHsOKsqsWZntEzEx2D1T0dEsvSxmqtTRsr+bjIipsRh9KHaRSf78zIQqK0aF1aZFlcbSrS/19IgZE2Sx9et70W4seAWBIOW30YKFBU6qKNqY/VYvlee8XHakNNrnQNTefi56xjj/BNqJwa6ddqpFGYGnWDUWqnaMsr+b7O9Fo92xyrLLg1+ZUGUv9kYVuZ+LLktfmjpVzH2Itsav70UjfIsI4+1nBFg3tGBhg1n6M9o9LF7sbaciOjStH8/ASsXtlOBGdLE7e2WdGG68kS7Dn8gIkV6FwaDZuNgV1kVa9veaPFnNGPJjp0hzPBmP8wn8ZCGifU+sCfucits4EWnzkUik8qr4sUFy62+8Ybx5Py8DLViYsJok7HbuZqM1J+MYURk5/UrL6yYBE6FDZK4Oq3LxxdbtY7UDoV3g58wR13avg1rVjpG1/OAHwfpetOHRg5TamgW3o5e6Ov7jS5pkZkRgXrNG3LtzGieitYgin5fbfdzmHd4w3n7nxqFFCxYGnFT/Vi8MAKvnzLYTViooEYZHAP5oLGhzIMhMX06K1TEITTvt6nIb5LNni2k3j0tXWDQWc+YE53uVlNCPMZoxFKTzaiNuRy9Wf2dNEmi1aTL+nTaTKsu7c3q+MvLMOGVxFXkPtzmA17uIJZuvn31ZCxb/h0hjOSsVFO1uym0gyO4ULNbufiyAbnEAmprYtCZ2546ij3d43BqD5pVjV2bODM73KinBO1snQZd2DMk6rxYlrLjVY/67qE0Nec4yMqk6CXsitYhW47S3F6GlS8XWS9tfWJJk8nxedF92QwsWSI5RmXkS4xEs/DwrY5Gg/VDZO004TpksWZ6laA8MEQJgkLxy7Eoigb8ny4Ip09uFxjha1Xm1SuO6nh5x9hDErkb02Hc6npKZGbe4GGtfli0T2w9px79fGgur/iyz72nBAsndeZMOwbJrME9sMqzM7WCJKChbY0EWLit43BaNQp9ooVJ0Wu6ge4csXsy+YFoF7xFR3FT+bip3WefVKo3rZPShtjZ/NRYyPLVkFKvonm7weBmSz3s9jpapAdeCBZK78yaSeG8vQgMG0H2muBgLIirOd2l3B8ReQebCZzdIRQWxEj1BygiV29sb7LDeVoVmwVT1vZwWMBnRNFUa1zU1yXmGJBy4qLFP8/39jgTqpaxe7e2oiyceCq+dmyybPdr1O9RJyNyQmdHTWHffvnSf2b0bJ4eZNg0nlfEzUQxtYq9EQm4q7Lo6gClTrP/W0SEma+S2bWKSUsViAIsW4URHbtlMvbBihfg6ZYIQ/mmXChsA952f/hSgqYk/6V0sRp8ufN06+7/JSFtNk9hQRnr1piaAyy8XWyehrEzs2L/8cvc57q67xKW8l0F+Ps746yU5HG2STCs6OviSJsrIvstCpAULt6xyXjBnouvowAIDLTIyQdJAk7HSeB3thG5HjqlnFRUB3HgjwHe+Y78oiXo2ZWVihMo1a3C2SBkCoNvCZKayEmD+fPHtYIVmwWxpAbjpJoBdu7zfh4zZuXPprr/3XvvJXkTaajMyhBU3WloALrvMW4ZNNxIJvBi1t/OPfcLdd7svwC0tAM8+K+Z+Mti3L3Ocbt2KN0e0wsWHHwK0tQE0NuKfmze7b1R4+43o7LvMyFGY2KPKK8RKHQWQqW6qqkr5QdupOI0qLNbjFlHJklihUXHm5KTUfpMm8asQr7oK53owR060O6sXcXxBztt5PBVKS7E6X+aRFW2/GT8eH0/t3x8suww7ozyvRpzms3ZzRFy3zzsdPfCed1vhd7IyUYaaNO/Ga4oCq+Jk/xKmSLRWRaYtA+9c+MQTctqlbSwMOFlu21m+19VlTnbxOP69EdYOoEqwQMj/PBVu7qJm4aKnBws3vPc0p4lm+c6FhZl2HjKs/Fn7TdCSllktmF4Xv4YG58y/LAa9Tim6Reb/oBFciU2VCE8iUe9fZFRN2mI354l0lw3SOBABrwu3U5wgHrRgYaC5GaHy8vQHX15uP5mwWHuzGhyqjg64di3/4i2qmHczogwujROZCOt5GVb+fgQhk1WsdmpNTQgVFbE/V9pdn4gsp6Lzf9AKrjyCqejNAPH6WrnSPyFj4ULr7zVwoPq+LOJ5yoLn3ctqlzbe/D9aWgAmTwb45JP033/yCf69+ZwsmcTnughl1kV+ZzRei8cB7r+fvj0yDUppKC4GOHxYbRsIXV34TJcg6jzaWKf5jHPxYvb6rN57lMnPd/672Sjvxz8GuPRSgO5utvsghMdgR4f7c6U18HMaX17Pu53qszLOM9PZSX8mb8RpLvJKRQU2HK+oUHcO39KCn8dnn6m5v0hkPkPSv7wYQdPa1ElDjlxjj98Bstx2hfn56epKL2entKnTRWdg9AJvpFCZuxlRGgurHRJCYrQEolSfQQ7v7faMjFoGrwn4zMeMbrt6GXYSoiD5dZzmAJl2HDTFfH8/89YYNYhht6swF5kaCwLrEaPMdSZrNRbJJN6xrloFcN997i47+/YBnH9+yoXIi7U3rWfI1Vf762IaNqqrxVikjxlj/fuf/5zPhQtAnFZFlXcQDW7PiHiFHDqE+zQLRBti1lC47erjcTyeATK9vMj/771XzfiKx3FxmgMQcvemMc5d7e3iXAatns+gQWLqdiMvL308snpDBR0/NAO5uQAPP0zv3XjffQFYZ+TINfbI1FjwnKeTs1La4D7GnStvXHg/CZrBlNmwa+pUvvrsrNBptUqs7fWKCo2FSNsaK28fmlJYaP83ml29aDsJUfDOAVbfy6vBptl2wawNkhUh1aoYjalZnlMYit8asqYmZ81FXl5wcoUcpViuEQY5t0PI2+cRwhLhihU4ZkBnp3VdsRj+O4ljASDHT14WY8YAlJTw79xFUFKSvptJJgH+9Ce+Oh95xFpaZ403IhsSY8Wun8lApG3Nvfd6+9yePfZ/Qyi1q7fSOiWTWKN15534bDuRwDvG6mr1OzSeOcBu7vIaB8TpWfDOk6zs25f+PoMwB/KiQkNGYsM42XQcOADw6qtygvkxI1e+yUSGxkL0ud3ixWyuaUE+/7WCJx+HyGIO7S1iF28nsdN6FbgVkVqnMCQksyqy8ztYPWOVyb5ocDsHt5sDWOJ0eH2eMlKie32fsjLhzpmDtYl+ZA/2W0PG4h0Sj+O+KIuscjcVrVZubGRXubot1kGZAAlWcTr8LmZDyCee4K/T6iikt1dcHADRfutBeA9BK+Zn7FeyL6/pz92OYJ3aSTt38fbfWExNDAuW9yniHrKF9YUL/d0getk0y4phgVCWCRaiz+1IJ2VNG+1UZ5AEC78DZdkV88509mwx9S5enF6vKMEzkRC7G/D6HpzsFMJezLt62cm+yBivrc1ceGk0IjTv0GlDQpt1+NZb1b8bEe/T+NwKCvjrt3r/MrMHywqIZYeXuWvOHHntySrBQqTGwsskRePG6Jba2S+C5O5lHqSzZompt6QkPZKjSBdbUep3nveQl6f+3ckqXo/HvEz4PJoG2nfoJozSZh2ePt2/dyBSs+GUbnz9ejH3qK3N3PgZN4Vnny3mPoWFco8ZrPCyaf7BD+S1J6sEC5GqbqeBYAetJ8mUKf6nSzcTlPgJOTmZg5R2kqUpXifHKVP4FpuwvYegFbOAIMvjilZb5KQRESH00Gospk0T83zdtF0lJXgOFKXRNGsPjfT08NVt9nKyEvp7esQeNZaU+Kt99jpPyGpjVsWxiMcBli8XU5cxypnZr9wqOmAymfKvd+Opp9jS7sogKPETDh8G2Lgx/XezZmVmRfWK14h4s2cDvP++fZp5MnRZonBa9aOgvIegYX4utF4EmzbR34MloiVC9vEnRGQ4pY2DIGJc5OQAHEXhB/i97+G5qryc/553320/TszjnxWzl5NVLJTly8VGy+3q8hZF1SujRnnzPLnmGrVRgrm665IlSyAWi0Ftba2g5njn0ksB6ur46yGTQEsLFgDGjgW44gp7gcCLG6PXEL8ioJ2o7RZWkZgn3NxcnFZdFZWVeDD+7GfugolbsCOCXT9iWQiDyBlnyHG1M/dP4pbrFhyovh6gqYnuHl6CNFkJByLczMn3c6KqCuDgQbp7OXH4sPtc1dWFn8+rrwJs385/z3370kPsGxEtXBNB0Sj0v/++2HuQ+/gV3n/jRm/3MadL8B2vKpG//OUvaOjQoejkk09Gc+fOFa5K8YpVEJGqKoTq6+nVlixW6F4NR1W5oNK6xopUhzo9azMqjwhYw327hfN16kde7hfl4jQeaN2j43G6o0wvY9aqr4pyM3c6lonFsOeQn+9iwgSx9dmF2Jc51kkgO5HHqzR9QjQ8jgl2z50HqUch+/btg+nTp8OKFStg4MCBYiUdTqZMwZKwMdHQPfcAPPCA+2erqrDqiSUJmdfQuAjR73pFQhsaecoUugRLPO0YNSrz915VfywUFKT/v6QE/2QNGuak1XBLZkcbnjcbcAs4xHLkdOml7ppAliBNsRieF4wB8QiiwoyTZFNmzUVVFcCaNfgIzU+ef96f+9Boa7xy2WW4H8yaJW8+8eM4M7QBxbxILd///vdRbW0tQgihc88911FjcfDgQdTd3X2kbNmyhUriEQVLMKimJnaDLF7LZlVhvmnjdMgMAa5KY2E29PUa6tpJY0H7Pa66Sv73DVoxP28nd0wvhtmlpc7W+7RBmmgNda1ikcTj+PcsWLm3s4wHESHbBwwQ/75ffNH+O8sM1kfe3cSJcuoXFd7frU941WzKaJ+0kN6rV6+GN954A/76179SXb9kyRJY7CVXtUeSSawF2LYNaxPmzqX/bGkpu0HWyy+zt9GIKom0pganoibPqqzMOjSyiHNWO6yetR+7AHOoZK+hrp20ObTf4/zzAVpbo5WYyY3DhwGmTwcYNgyHeh4zxn5X2dHBHtp61y5sI/TYY5nhjcn8MGUK1ibEYngatqKyEl/jFCK5pQVg6dLMOpJJ/Pszz6QPsRyPZ4Yyp01EduaZAPPn4506QHp7nL6jmdJSgC++oLuWFift3MSJOEy7rHD7otPOGyFG2U7zpwh6etg/Y06X4Dss0srHH3+MBg0ahN58880jvwuSxoI3MArZKdBcS3bbXmMkBC3Mtx1jx8rbUVhJ1LSuu6qL27tj6UdBCVimorjFBeENfmdOvmWeH8yahkTCOi6CEaJJWLnS3a2Zd4yz2AisXYvHjznZXlUV/bg66ijx79jprD/Mbtfm5ywjxLxXjTGrtowWKXEsnn76aQQAKB6PHykAgGKxGIrH46iXYgTJMt4UoVJra6PzezbGY/fy4kWHIpbF6tVyB6ZZsOjt5c+6GIshdPPNmQuIyKA/NO+O1bCvuTl7jTmdnifvwlNZiZ+xm/DmJkwQvGxeeIz8aONcAGQehRQXY4Git5ctu69oIXfBAvvvF6VspzLmdZ6Nq4z1RYpgsWfPHvTWW2+llVNPPRVdeeWV6K233hLaMBZ4zqFIIZM8q8aitxehfv3Y7xV0oWLtWvm7aLN9iYjdy8yZuC7zeTXLBC3q3dnlLbCbgF58Ue7z9qv07ct2vZP2rrcXoT59+NpDklN5ub/V+2S9v5v3kBM8Y8Lcz0aPpvvc178utj8sXSrn+wWxiNZEB00jLsUrpKCgAEaMGJFWBgwYACUlJTBixAjRpzTUtLfzpwEn1tvr1tFdbzxDpwk6AwBw4YXYS2Xz5oCktrUgmQS4/XZ8VouQ3HuZ7UtE2FdccIH177167xCKi7EtBMu7I9b+ZluMykr8e3M9QUhlLwLWM2GE7D2kkkmAL7/ka097u7MNi9X9zUHNDh3yfl7/6afsnyHweE6QthIvNtpYGMcfj2MCiQpWN2SI/d+qqwFkORbGYvjZyfJss8KpL3vBq52E6Hawwmy8GUReeonv87W1eJJPJgFWrqT7DFkUOzpwEBgaXn9dnoGPCJ56CuD669mN5VghA97swiciaFRFBTaomzs3fTEhLqVeiMUAVqwAGDeO/bO0RrIA4XMtq6wEuPpqgL/+FeC55/jrsxIsRUXUZbm/Vf8pLfU+LngExngcYNo0HMHSC2SB+fnPAf7yF7rPnHMONgS94w6Aiy/GAjUPTgt7PA7w9a8D/PnPfPewAqGUO/DkyeLrd6K5Gf/kne/HjMFzl9c+pCzCr1hFiTsyjkKuvJJPfUWONWjVcolESsXEekbod3Y8WkQH4cnPd/67+RhAhH2FiMBe/ftn1unXsVWQEsS5lfXrcXtF5mKwGhtz5vDVWVlJbwcly5CWJ1CRqD5Ba18BgND+/an719Tw3ddNHS8z+Nf/RURACMm3F3Pqf7zzB4/9oOj1JqtyhRxzDN/nyU6EVrqbPj0lhbKq2IOYI6KpyfuOyI7rr2e7vqOD3rXOTCyGy7JlADfdhIeUV2pr04Or+XlsRQIuObnn9enjT1vcyMlJ5egREdo4kbAOQjV8OF+9992Hd31uYcETCbyzv+46vv5jhVmdTZODiOAl/LgVLO6cr72GfyaTABs2eLsfGZNOAcIOHcLBC2UxcWLq34MHy7uPEyLSN7z6KvtnnAK7+YJYecYdGRoL3iBOXg03vdw7aBoLkZlhAbAR7dq17MZyPNbhBQXY+l1EMK/WVutARX5SV5dp4R+LiQ+1zFMWLsTPZdIkMfUZd5dGenq8aRAKCzNdTa0MaWWXkpL0/mPlVeK0q+X1mojF2LQVACmjah7DShpNn8xw2yLnFxntocWrRjBUXiEikOUVwjp4zIUsIG4R+Xg6rHmSUQlZPL1aHduVnByEpk6lf+YEEdbhvH2ApIw2T/ylpfTuiLzIjEQosrill2ctdum1vXqFWHli8Ma58VKshBvzNU5uijzCMqmXNTYMGZdePakWLXIeJ2TuGT9e3nM354yR4X3COt942VR6Eb5kHt9mlWCBEP+ETKR0VhdBloFfXy/0K3uit9c6iI6KYnQ3DYJ9QV2d+45WRhAc4zPI1lgWALhfmrVEXhdWu0mcJbgVbSkpyXxv5n7i1r/t3AN5BAuywKxdS7/rJTF6mpu9azKddud+CXcLF6b3o7Vrxd9j2TJ8D1o7IDutnBO0dZeX4z4te/OTdYIFQrjTel0wjRMRbR4NhNgGvh+x5Z0IWhAm8+Svcrf+k5/QLTQyg5vJzMtCW0pL3eOy+HGcQBZmLxo1o3G1HaJ2sORIyO34zMsxK0LsKvxFi9Lb4MUYtb6e/x1bzXUqIsxWVmKhgtcw3KoQrZgXo39aaDUWRx/tjzY8KwULhPDDbW3FA37hQhx0yO14w+qIwlwPOXs3wzLwvUisogiaip1ERLRqpwrhh0XdLiv4jOhjKS/lxhvdPXr8KESAmzyZ/bNEDe602Is6c6dVb9PejzdonLE9XrWAIhb/gQPZNDYy+5GsusnxHYudGutxSE8PfXK5hgb5wkVWCxbmyYQmu515B0prZMUy8L1IrCIIwjGD3aC0a29QjmtEThJuLFig/jsFKdtqLMaujp84ET9Lt/ErQmNRVYUnfhpDX68aC9axaxRMghDVUuTzDloxbo5qa9nfDw3NzWybHplHtQhlqWBhNZnQ7r7MeRtojaxY4y+o8AoJ4qB20940N2P1nup2ipwk7CDC8PTp6r9TELQV5pKXR3+tUywT4/ilTZ3uVOrq6D08WHPHmMcCbZuMRxCqPSFIf+rtDUZbZBTWGEgs878XLbPsPFRZJ1iIOL8jxx2sRlYsHUDUYsRCEAe1k/YmaMc2dkWEMa4KT4WwlTPPZO9bdn8zjl8eF9SpU9k9PFgNw43U19O1yyhYBGVD8eKLwWmL6ELmcx7B0QqegIEyM2dnlWAhStWfn0/vmmWWPL24WPpFUAe11bPo7UVowAD+uv0yEuPZGWRzunTVhfQ9mlTq5lJR4T2pGYthuBHazUFtbabni+o+duWVYjREQSxmo3+vgqMZEXO2jLUmqwQLFQun2VWSxh5AVRyL3t7MUNVBKFbaG1EZPs1n84WFcr6D151BEO1esqmYx6/RToIcpdgtEF43H3b3o+k/LHNckDy/AHAQNYTUBSmTVay0rl4FRzMitMwytONZFdJbRZhsY8Ko6dPpQubu3y+vPU7E4wA//KGaezthlXTrf/5HTN3mTI4DBojL1mjEawZBUaGaNd5wSvj2ve85Z6U97ji6e9jNS/E4DvM9bRr+SZOkaudO+mRWQcuSe845+Kddtt+wcu65me+kpgbgww/50wKISEioNKmheJnGmSCG9GYpZjUna+AVVbEsgnQc4qQqFhUi2uqesr6Pl51BEO1eZD+nIBRz33PyHrHTLMgw1HNC1JFZaSlCubn+P3NjUjOEUs/1u99V3x94ypQp7u/Oa3oAbWPBSJgEC7ezMi95NhYsEPa1mfBb9Z6f7+28celS9RMGa/GygARJ0MuWYu57XkJsG8cSS+h/r0ThyMxufIgc625B3WQUt/ABrHlhrD7vtZ9rrxABPP64+E7zgx+4n5V5WRwuv1zY12bGT2NBknfDLHW7Daz9+/2fILwWnp1BVI3ZRBeR7q/G8es1xDbtWJo6VUwSO1UCaN++4uqyytuCkPdcJEEqdkKTV6HVqh4Wm5l4PDNPikiyysbimWfE1zlunPtZmRfbjh07hDWRGXLGWVkp/15dXQBvvw3Q08P2OZKyOeiQFNxOaaGdICnSjXVpMpkyhe/zCxdaj183GxeEnO1namoAbr7Z/vNr1gBccQXA2LEAQ4d6T5utwn4MIDPVOw87d1r/Pgq2FlbvJ5kEmDsX9yEz5He1tfg6N2pqAD79FKC1FeDKK92vTyYBSkvdr5NNJASLL74QX2dFhbuRlRfjGNaFVjRm46ILLpB3r8WLAXbtSv/d1q14sbCbaFVNpKwQQz5WoywjQTVmC5Kgc955fEa348ZZj1/afmZ3XTIJsGoVXR2dnc593olNm9g/Y0UsBlBcTH/9KaeIuS8AQCJh/fvq6uD1fVas1gBeodVMPI778YUX0l2/bh3ddTKJhGAxbJjY+qqqcKd3o7qaffefl0cnqcqECEyXXabGUwUhe4k9CIPCiZNO8m7pbYVR0FuwgL8+EVjttFTR1QVw+LC3z5aU2I9j2k2B3XUsXj2su1RCMgnwyCP019sRi+E2sAiMXrRwdpiFh2QSoL0dYO1agGuuEXcfv0kkrPsXr9BqJpkEuP12gOuvp7v+ySfVrzGhFyxaWvCuTxSxGMCyZXjiWLUKDwC7lxSP490QC62tfKpREZCOOmgQwJ//rKYNVhL7oUMATU1q2kPL6NH0LoK0EEFPhjts2PnwQzn1kk2B3WIbizlvMFg1a6y7VAB8bWcn232sqKzE2kMWN9SjjuK/LwDu06NGpf7f0oLnv7Fj8VFRfb2Y+6hg+nTreYBXaDXS0gIweDB+Tnv30tW7c6c3F3ihyDPzsEak8aZoY8SqKvb4/15cgmTHc3eiqUlesCjWYnbTpE0RrLLs3Svv3QQhu2nQCk0qe6fi5LHDEynRq1Glk2uy2TWR17hxzpyU8Sire3NlpbgkgMTFPmqRZp0CoIkI782T2kBW6ojIe4WIdMOaNg13frvkRaSsXZveBh6LbZm+xnbU1akfjOaBaZxMZcWwEN1mWfgZjyVMhScstdsE29SUKbzQREqkjbZL23+sXBNZXdmd7uVlrho9Wsz7W7hQrtvs177mf590i6LMG96b93nJipcUecFCtBtWRYW7W4/ZlUdEkCO/coewBvKSXfLy8LMMm4++zCRyvb3B0SYFqdTWehcs3DQWVou5eQNhB21obwD3/CEid/JW91IZD2PBgujFbSFZW53gCe/N+7xUCxaCTtL8R7T3AM1ZZjIJcOmlAM3N2OhORMhUP7wgkkmAWbPk34eFAwfwswwbgwal/z+ZxOeZ27bh/lBd7W5/QYzX2tvx/8eMSdlt/OY32KhWk+L997FF/O9/z/Y5JxuJlhbsqYFQ+u+7ugCmTsXvwso41/i+zz4bID8fYN8+53Y4uSY7uSZ6we5exL158mQx92GhuDg83l607NuHx++4cZl/I32kpwfg8cfx73bsoJ8fAPifl8qwBgAAIEeusSeoGguWQnYDvb0IHXUUX11+aCyitltQWdavTz1XL5H17ALelJSkPqfyyGrsWPXPWFRxi5xp9zk77YLV+87JcW+HU58QPTbddsS06ddFlpUrozkHnXGG9fjmibYpql+o1liAnNvbI0qw6OlxT28ssxCBYPhwb5/308YiqHkp/Cw33ihG3XzrrfiZeomsR2OMVVuL+9aaNfyGi15LSUn4jezs8jj09tIbCVulxPbSFqdJXuTYbGhwn09U2PG0tuJ2BS3rKm+JxfA65NZHvBjrh93GIrQObhs3qvXV7ezE96d1ATJiVlcS1bibe6tXlGa5CwiVlXjI8fLaa94i6yWTADfe6F7/vfdiV7z58wEefDAVyOzcc/nbnu0QV8ebbqK7nqijeY8rnNTStGPTLQZFPI6PO93U7CpU5KpjKsgCIYDly/G/RUbbBEgdXXkNVqf6KCS0goXqMzviK+zlBRqjNpr9unlDAFvhJZBX1HjjDTH1fPqpt8h6rDEJOjvxWf/u3ThWyrXXem8zK11d4bfzeOqp9DFEbCpYUtWTRZ83xb2T8EATTyORcBdqkkm82XJDVCRPFtrb8TMMWjp3EZDnKTraJgBeH9as8RYzx2wL5jehFSxU78ITCXbhJh7HAbJI1Ea7yY4nBLDdfUleimzl7bfF1HP00d4i63kJqIQQwNVXA/zhD3Ly4TixYYO/9xNNLJbaIbJqHEhwrFGj8KLY3Oy9HYlEeoAoM045Y8j/jzmG7l5ufUxUJE9W/vd/1W8EZUHekehom4REwpvGR7WWKLSChepdeEUFu3Bz5ZXYipgcf4hUnblRU4Oj72UrhYVi6pk0yVtkPa+C8O7dAOefj8Mf+8nu3f7eTzTGHaIXjcPllwMMH441iL/6lfd27NyJ63HaJNjljKmsxMejtNo2tz4mKpInK3l56jeCsjjjDPyTVhPEqjHyKpCtWOHtc6IIrWChchdeWYkX/C1b2D5nTPglQ3XmxmefiasrbEyahHNH8HLjjd7CQVdXi7m/ho1t29gm55wcbN+ydCnf8YcRGg2kOTkgyUezdSudpuXoo93zGz39NEurxVFRgdsWhKyboqmqwmsB7Vq0YgXbZtGrQGY+CvSb0AoWAACvvurv/chCcuAA3kV+//tsnzfuSGSpzuxIJgEefVRMXWEjFgOYM4dfDZyfD/Dcc3Tqa6s4AmPH8t1fw05ZGdt58+HDuJ+IMPQl0GogrbIp/+lPdPcoLXU+i29pAbj/fsoGC+ass3DbaNJ+hwmyeWhvp9fwbd3Ktll028Q4IVLjzUpoBYtDhwDuucffew4YgH96MULKz0/fUYhMVENDezvAnj1i6pKFrBTKCAEcfzz+d3Nz5n1od1L79qV2nk7qa7t06l/9KnvbVWGX6jpMlJQA7NoFMHMm2+dkjBOvGsj8fLrrPvoIz4lWkGNXVZSX458TJ6prgwzI5oEEuqOFZbMYjwM0NHgTdEVrvFkIrWCxfLn/0phbhD0nvvrV9B0Fb3ZFVlg7vx8sW4aNWYnq96OP8Hm0DIg6GgDfx6hyvvdetrrITsBOfW2XTn3MGO/t9wvS7848U207SkoATj6Zr47zz8feLTLsCubMwRM+K6wayBkz6K778kss5Fqpv3m9WkQRpePAkhLvghLLZrGlhd492gpVRrOhDen9/vuqW8DGwIHp/yfq9ClT8GRulEidQgBHiWOOSQ+J29Ii772S53vddfgoq6ICLzqsOw7jzpOoq2kFhl27GButiClTvC2aIpg0CcfsmDULa5euuMJbPbm5AH/+s9gjDSOTJ+OF8o472DSYrBrIceMA+vTBgoMbu3bhd7dmTcprraxMjcGmEeKSv25ddFxOu7pSc8CYMbgf0FBSQr9ZtAs7z4Iyo1k58bnsERV5Mwwpto3liitS6ZCN0fF4EtWwEMTMmcbIo15T0POUigr8nN3SHFuVlSvZnn9vL3+2Sj/KvHnq2wCA3wdP+Ol+/eS1LZHA75MlrTVPpN3Jk9naZ45IrLrfRTXyZm1tamzTJg9cvJh+vuBNGicjsrMO6R3gYs6gaEwdbhY8RBHUgd3air/zwoXq2tDczB6yOZFgE/7CkiuhqEh9GwCCHVL8xhvZJn7WkM7m+eCnP1X/nXnKiy8Gc2PDW4wC5oAB7te7pVoXPV/U1dHPT7REXrBASG2yJhFFxot3gmWH5VcpLlbfhgED8IBvaqLf3bEuFjpfS3RKIsGWMp1FA+k10VmQy4IFajcOMgtLP2hqop+rRcwXKjUWoTXeBAC46y6ACRNUt8I7d9+NPQj8oqaGzxBIBkEIxPTFFwCjR+NnQ2sHgRD+6ebSRfLAvPsubys1siktxWfgbq59u3YB1NfT1blwobNBrxG7SLyHD9PdiwYVsSQ++sj/e/rFkiX017I8exG2EdorhIP58/2/Z0GBuLpmzfLPuyWZBPjv//bnXmFj40Z2y3mEnAevMQ8MrXGXRg2JBDZypIl1QoRKGkikXTd4E53RMm2a98RWPIwe7f89/eDgQfpr162jv5YnfoURVV4hoRcsqquxBbif/OQn2LVw5Ur+HQBJZuYHHR3Bj2URRqwGr5ekV2YSCdzH2tqwt4RGPLEYLg8/jOeRmhqAm2/GETh562VxF/fLJfSBB+QLL2aOPVbdzjlIPPkke3ZTAD7hQlUystALFvE4wMUX+3vPzk7sYlRRIcaF0C+pUrXbWVQxqy15d59kIpkzJ7XAXX219/Zp7DEHNGtpweG8WY4frCZ+hNjcxf2YA3iFJZ77/uxnau4dJFg3kSQIHwkwFiZCL1gAAFx/vb/3a2zEi4eoycAvX+OdO/25T7Zgtytl2X2edVam1qu4GJ/119fjOA5jx+K4CX4QiwH89Kf+3EsltbWZAc28CoQkIq8R1kBQfswBIm01WHjgATX3DSJe1gwejcVzz3n/LA+RECz8Vu0RyVPEZCAyuqYbmzf7c58g0b+/nHqdgpixTB6vvIK1XoWFABdeCPCDH2CDVnMgIZazXB4Qio6h6YQJmRmQq6pw4K2GBjzuOjpwBtH2dly8HEdYReTdvds98ZgRUWfqTvWrortb3b2DBmvUTd7jVJbjF6GIdUZxR6S7KUGFK1Njo5igTiwuSDwENUCT7HgFt94qph6zy5+TC2FYYlaEofC4WpKYMFYxYqzcOkW7PrMGxSKxVESPiYEDscun6neZ7YXEvaCdr3kDZJHS1kZ3Txqywt2UoELFV1aGd6rnnMNXj1/uXx0dwQsp/ZOfyEs8BoATOInKTXD4MN7lNjbi/CaPPQbQ04N3ueYdAdl9avjhGdu7dllnDLXbCYp2fUaIzeXPLrEdLzk5OCy4Ri3nnktvcyPSmFeFZ0gkBIviYv46Kivp01onEnjxSCYB1q/nu69fBpWq3I6cqK5OT+K1cKHY+uvqcP2i+OgjgL59Aa66Cie4IvYPQ4emq7yNFt0afkaO9Pa5efMyhT6/3DqNWI09Et+EHMOQdhoT282ZI+b+u3cDLF6M+64KVLi3BhGW7MYi52sV+UIiIVgMGeL9s7W1eAf6//4fHsw0TJ+OF4+ODoDPPvN+bwD/DCo3bfLnPiy8/HL6jtKYkEwEZ50F8Pnn4up79FHrnS7JnGoULmpqAG68Udy9s5nXX/f2OSttgYpMn+aJ3RjfxEo4JWNClMEuEaJUeYX4KcQFGZbsxqKEAbIJ9pusFSwqK7ERV3U1wMyZWKKnhaTLFSFVJhL8dbjR0kIfKVAlooWs8eNxHAhR7NljPUmS35mjcH7ve+LurfHGffcB/OEPqffit+YukQAYNSr1f7tjGCvhtLpajDaWcOCAuLo0bJSUsAkWoo5Tly9XkyE7EoKFFx5/HP+cMoXtOMKY9laEVCk7/TtR/QaRwkI86a9ahX/Om6e6Rd6xOk/XthbqeeYZfGw1eDBetEWqhauq8HEbCbJlxc6dAMOH43s7HcNYCafxeHDHroaNhx9mW+DJcSrPMdJZZ+H1TQni7EXpkOEV4iVhy8qV3qxu8/PFpvqurJSTzZSgPRT8LY2N6c9flqW/Lt7K2rVivD8aGpw9TMwlFqNPWGW04mdJyR3UIjOFfVgKbbp0M83N3rNSt7Z6u6cTWeUV8t577J955BHvPus//zn+dzwOcP/97HUY2bpVbrhbHW3TX8w7Yt7oef368beJlW9/2/97+sW8eXy2LyQo2g03pHagNTUA//43QFGR/ecQojfoNR7XxOMAP/yh9/YGgalTVbdAPfX19DFNzHjxViooYDt6EU3oBYtkEuDBB9k/9/LL3u95//0pdeWECfxWzzIXfx1t0x+cckPU1AD89rfe6iWBsUQmvnPjK1/x715+s3UrdhGncUM2j2u7oGgtLQDHHOMeCIp2gTALp8Smi5YgeWHEYngTp4UL90zIZng8mBBiS3ommtALFiriM3R1pbQMy5d7e/FGZC7+fhiHanAfcMoNsWMHX/179/J9noXTTvPvXm5ccon4OtvbAWbMsP4bsZeoq8uMJ2HOKwKQMsaknYOKi+0XfjvhtLqaTuOVk4PDsQ8cSNcWP+jXD4+JJ5/EcWWyGdY05jweTPv2sUV+FU3oBQtV8RnIfUW4ccpc/GUGoNLQo8KX3CsvvaS6BSnmzWPfsbtxxx1YCATIFASJ8HDXXekxVsx5RQC87SjJd6HVhpA2Xnute92HDwPcfntKMzJwIDaQVsmBA3iBjMexsJbtsKxXItY2Vi2JKEIvWKiasMl9N27kr0vm4q89E/whFnMexLJzQYikqUl1C1Js2wbw/e+Ldbs0Qt6XVVIyq6idRrzsKMeNs46uaaUNIe1rb/em1fz8c+wirRqyQN52W7C0KSpgWa941zaE2LUkogi9YFFd7V9YbAIJOnLoEMCbb4qpSxYi3JY07rgNYmM0zqC/iyDFO9i0CQeKEh1u20gsloppw+IS6MU2qqIiPbrmypU4VPySJVh4MgqmxkBav/oV+714j2hFQRbIeBzg179W2xaV5OSwHduL2ozokN4eiMexnYOfkKAjIu7rRwAT4pmQ7bsFP7AbxMkkXjjmzvVfEPaCLA0BLbEYnlR5va5oIEJhezvb51i1CEb7iXgcC0u33AJw000AV16JBYhjj8XHGTfdhAUqv6OEiuboo9M3Ttls83X4MMCll9LbPYhKDaBEqy/e09UZGXEsEEKors4ff+SpU1P3nDWL37+7p0foY3CktVW9P3fUi1UmQas4BwUF6tvqVGhjLsgs9fX8dRQUIFRTQ3dtcbF9xlornniCvh2xWHrdJL6J6mcsu1x0Ufoz8xJzKGqFNXbRxIne78WSXZcGKXEsHnroITj55JOhsLAQCgsL4ayzzoLf//73ciQeBpJJ/yyOjdIfr4rq4EG8K/PLclemOpmVb35TdQvEYmfR39JivfP008uDlaoqgK9/XW0bJk8G+PvfvX2WZKFta8O5fG64ge5zu3ezWdJ3ddFdd/TR6fYTKhKhqWL8+PT/BzFnkd+wxC46dAjguee838vJU00qLNLKs88+i55//nn03nvvoffeew8tWLAA9enTB7399tvCJR5ampvpol8WFYmTOMnO4/HHxdRn3s3IoLfXW6RRWSWRUN8GUYVE1jS/w95e71HzVJbVq4PVV1iLOfop6fs0GoJYDO/yenqw9qmxEf+02vWtXEnXnieeSP9ctkTDzclJ18g2N6tvU1CKuY/a0dDg/R4lJeKjOtOu38B7o4EDB6Lf/OY3whtGA6s6MZEQo36srESoqQmh0lIxnYxMZkEI7e1H+GBRz82tFBQgNGWK/PtUVVkLhmE9euKZzIJQzMdRvb3sRztmwbeyMvMd044pc3uy5TjAnP4gzMKq6GJ1ZGrFt7/tz31okS5Y9Pb2olWrVqHc3Fz0zjvv2F538OBB1N3dfaRs2bKFqmHu92fvqLW1wT7XFN0JjNBOZgMHyv+efgkWMsvkyc67WYT8EWpkFF7bIZXFLKDT5PGgKVZaKZo5yGrDkC0aC4DUnJZN39mtGAUuJ3p7Eerfn+9etJoRWqQJFv/4xz/QgAEDUDweR0VFRej55593vL6+vh4BQEbhFSy8dNTWVv+MPIPQCXifly72JZFwnhx6e/EEorqdXorIY0O/i0wDSaNmkWhBnN6x3REny9FM2As5BsoWLQ1taWryZ85WpbFgdjc94YQT4M0334RXX30Vrr/+epg5cya8++67ttffeuut0N3dfaRs2bKF2Q7ECi++uVOnAjzwgJDbS0GmW9DOne5GPH7HVwh6PAcndu50NsDq6MBhdcOIW86LMCDDQBIh7Jb685/jNOz19fbvuKDAOuAVQLhimvBCAghqo810Zs1yj4jJG3/CLneRL/BKMOPGjUPXXHONcInHjSjtwGXbWATZtS2o7aIpkyfbvzOeHVpRUbifi8pCXPmCMD+47UpFHdMEuZx9Nn4f5eXq2xK04qZN4OnDshwCfEubjhCCnp4efgmHkTCFSHbCKUeACILs2vatb2WGNqbJOhkUmpvxztXKPZFH+9TdHcz3FQa2bgX40Y+8RcUUHbzJbVdqjMBZWwuQlyf2/kGhowPgk09UtyJ4uGUf9ZqOIS/PXlvmGyzSyq233opefvlltHnzZvSPf/wDLViwAOXk5KD169cLl3hoCPJOnLbYeRSIwu+dW3Ex/bVHH23t1jd/vvr3wlKsdgc9Perblc2F1rupoSHV93p6xNs+0JxxR2Eesyu//KW2r3AqbnO/l75RUCBP+y1FY/Hpp5/CjBkz4IQTToBx48bBa6+9Bi+88AJccMEFcqQeF0io6qOPVnJ7bhYvzsyYKBq/48SzBOH6/HN8BmtM9AQAsGaNhIZJBKHMBGQiktNpvOOWfIuEDD/ppNTvZNg+uJmUBVmjKIJvfStcmX39xC1xIUBqjWPRXOzdyx6eXjhy5Bp7ZIT0Dmu8AFq3Ix6CcNbsVMyeMEFvr1Mx7k71Li04xbzjI/83By8jsSpE2j7Mnh3u8clbGhvxHEcTxDCMRYR7Po1Wq7cXoWnT6OtkDU9Pi282FkFgzBi5qcfNiNrN7NsH8Ic/iKnLjqDbogwalP5/FZn4RGFsu96lBQdz0jeSYM0ckruzE4f0BsC2D62tODkYD//5j/XvSTr05ma++oNOWRnWBPmRTE4Fubn8ddDOeUVF9HWyhqcXTSQECwAAP09jEBJX1//8j7i6rCDqXZFtlkmYF2SjkFRd7a+wq7GnoQEbSDY2YmHBzkiSjJHaWoCnnwa46iqc1pyH117LVHXzpkMPCzk5AKNGqW6FXI49lr8OtznvqafwNQ8/zF6321GLLEIvWJBB+vjjqlviDT9iHdTUYHuOILJjR/r/WVNRB5V4HHsnaNRTUZGy44nHnVORI4TtIi69VEzK8s8/T4930tKCd5JhT4dOw+HD+LsTO5Io8vbbfJ93izXx4x/jvuhlXiR9mTbhmUiO8v+W4iCDNCy7cSvOOcef+xx3nD/3YcUorSeTAPPmqWsLL2YhSYEXtsZEZWX6xK3iqI3cM+qGmla89JK7MBdm9u/n+/w999iHGWhqArj7br76AdT0+dBqLKIwSHNy6FM68xLEIwbzpN/REe4JyBxd8PXX1bTDK/36qW6BeK6+On3iVjEOyD3D3r+98PHH4babko3Z/oeQTOI4KCJQ0edDK1hEYZDOny/G+McMMQxbtQr/TCbpQnr7jVlaD/sEtGJF+nlm//7q2uKFgwdVt0A8n32W/n+RxswDBwIUFjpfk0ik7AzC3r+9cMwxmQbamhRNTak52khHB8CuXfz1qwrrHVrBQvUgra/3HiUyFgOoqwO46y6xbQJINwy74gr8c/BggMsuU2PE48S8eelWy0HUqrCwdWv6eWZY46tEiUcfTe/3ImNVfPaZe7yMnTsBhg/H/Tzs/dsL554L8OWXqlsRXJYvx3P00KHpc6Go9e3yy9VsKEMrWKgepNXVqQmKFVnHN3aGYWa3uqCwdWu6S9SoUcF1i6WFhJJOJgE2bOCvb/p0gIULAUaP5q8rG9mzJzNYEAk6RNxOZUPcWHfuDLbrtwziceyNo3HGPBeKWt+WLlXkcio+hIYzogJkqU493NiIUH09Xx00qXNZn4fqgDGsxZiALayBzoyloQG/j6gHPgpTmTQpFS5e1Xgh/bypCf87qiG8zeWJJxA67TT17QhDMc6Fvb30YendisgEl5EPkKU6PsOmTfwunDSpc2kJq80JQimXKOVhaAVAElmpPqrTpHjmmUx1s9/jhfTz0lKsLSkv9+/eKnn5ZYB//Ut1K+QhUvtknAvjcYBHHhFTrwqX09AKFgBYpTl1qv/3razEhnq87Nwp7oWHfSELe/sJJCiW6qM6TSZbtwJMnowN5lT1N3LfbDkO+fOfce6KKDJlipzjNNJHpk4FGDlSbJ1+EWrBoqVFTcKqH/1I3G5H1AsP+0L27rvhN3Y0WmDryJvBZdo0gH//W829N23KngBZAAAffaS6BfLIzZWT6t44ly9dKr5OPwitYKEymtuhQ+LqEvXCg54TxI077gC4+WbVrfBOLAZw770pC2wdeTO4JJMAixZhry6n8ZIjcHYk2VRXrFB3fKuCw4dVt0AeH30kVkCMxTLdQ3kjEVvV6QehFSxU2hR8/LGYekTG0peR8llDRzyOhaKamvTfv/OOmvZo2LAbL6IWRVL/1Vdnj6aCUFDgn/eN38gIKGfcnCSTANdf770u0u+MdfpFaAULlWfylZVi6jl8GGDjRjF1AaTc6Mwq+KoqcWd1mkwOH85060omAZ57Tl2bNO50dWHNhXm8iNRUAOBAWosW4XgW2UZ5uX/Rhf3m9NPF1VVVhedu4+akvZ0vVEBFRWadfhHaXCEqbQpEZiQULSDV1ABMnIg1Otu24ee0axdOZKORA0J4d1Bbi599PI4nhShGsowaxx2HU6ST8fLJJ2KO5AoLAY46Cqev3r0bB9SzC98cZY49Vk3kRz/4z3+wRobHOHXWLDw3V1dnahV4veQefxxg3Di+OrwSWsGC2BSoUC2KzEgqQ0CKx3E2RwC8cx48WPw9NOkYXcXGjImG62w28OmnAGvX4nF42WVYsyACq4icIkI0h43RowG2b1fdCjk89RS/vcyll6bmajO8brqffML3eR5CexQSj2Pr7rBz6qly6+dVp2nYiIrbbDYQjwPcdFMq9P2QIdg7yQ+ywQ4qFsPHILwGiEGFV6ggeWTMeZ0AUvmeeHjtNb7P8xBajUUyiV9G2LnlFmx0aTy6sFKLeUXvnP2FaKDOOUdtO3goKcEp30Vq5oKIOTjdrl3+hT8uLY3ugksYMADPYyRonCadsWOx3Y1R615ZideD4mJ+DZdK76PQChZhjTRp5k9/whEBrTqXCqMb1cRi4XXHM2ayDLNHSFcXPjvWyKOhARvX/eEP2NU6iuzbh+dpHc/FmrVrM39H8sqIMHg97jj+OrwS2qOQqKic//73TAGJdC4RuydR7qx+UVmJB1wYJyNjJsvNm1W3ho+oRksMChUV+Gz9619X3RK5bNuGNbBaUKWDbKp++1v+uq69lr8Or4RWsAh7pEknSOeqreXPJeLXmTEvCxcCtLXhBTkeD69HBREKo36MkA3I8OIwByzatEn8PYJElOdpWSAE0N3NX49KG4vQChbEKySqGL0MePjwQyHNkc7Xv453cOvW4YU5rAanRChcv15tOzT8XHMNQGsrwJw5YuozByxKJlNB7aJIIoHn6Y4OrQFTgUqtfmgFC2OkySjD2znCEpSnrCwVpj2sNhYEhNS6emnE8ItfAFx1lTjjw9JS3L+Li1NW/7t3i6k7iCxfjufpqBxb+w2vxmzQIDHt8EJoBQsAbNy4dm20Xbd4VYmzZvkfzpUVohqOikGuJjp0duLgVvn53usoK8PCyc6dWFtBUrg//LCoVgaPCRNwxNE//CE8x7F+4rRmkeOyhx7iuwfvMToPoRYsAHCAkfp61a0Qj6jkMbm5APPmiWmTLGpqsFDR2am6JRpNOkR79sUX3uvYti3TtbSzEwdYiirPPQdw/vm4RNXrhQc3rey99+Ij4dpa7/dYscL7Z3mJIeSv4nnPnj1QVFQE3d3dUFhYKKTOZBKrfcKqVjS7WBJpliXOezJpHwujpQVg8mSxbZZBaWk0ohPGYtiHXxtwajQaFuJxgNWrsVABgI/Lxo71VldeHrZtEamxpl2/Q6+xAMAPTlUKdR5KSgCamjJdKysr2YSKlhasWh07NhVFcOhQ/HuV6eVZiYJQQZg/X3ULNBpN2Egm020rqqu9Z4c9cEBdgMRICBbJJMCXX6puhTe+9z3sudHWBtDYmHK5ZBEqpkyxj4Xx85+H024hrHYzJSVYKFywQHyWTI1GE32Mxq7r1vFp4lUJFqGNvEloacE78jAunl1dqaRVdolonHDyoiAZN8PqOVNQYJ3IKeisWYMzCra343TqGo1GwwIx2E8mAa6+Wm1bvBJqwSIstgNO8LhiuXlRIBReu5OjQtYzYzF8hEUERO1iFw2I/VNuLsChQ+LrLi4Ob8wWjXgSCaxtbm/H/Y13/h49WkizmAmtsjaZxAFswg5P5D1aL4ri4vAdLYRRICKBjwDU+pBrxEFCzIs2cSfj0YumUhNddu4EuPJKbCdHDDh5UHUcG1rBIirpwBct8pYTpKWF3hUpTEGnyC4uTCQSbMa2muBTU4Ojbm7eDPDPf4q34aqowGNfZawBTbAREa10xw7+OrwQasEiKrDmBCEGm25eFCQWxi23YKPCoEN2cePGqW0HC/374+Mos1Dx3HNq2qMRQ0sLwNSp+KcoO6WcHIAnngBYvBj/v74e4JlnxNStCR/TpwOsXCk3rbyqXC2hFSyiAmtOENqw18a8BBs3hkO7U1mJjR/b2lS3hJ68vEw/8WQSTxiacNPVBXDZZeKO5ebPx/FNFi0Kp7G5RizDhmHNlTl4mkhU2XqFVrCI2tkkbQegDXtdWppSzwfdkLC2NuVmm0iEK54F8ewx0tERru+gkc/FFwMsWRKuY0mNOzw2DGPGyJ+bZ81Sc9wWasEiDOp9WmhVVrQdsaEhpZ4PcuriWAyguTkVKTToQpAV69al/z+M30Ejl9NP17lwokifPt4+l5+P1zDZc/Pnn/NnyPZCaAWLeBzgkUdUt0IMJSUAZ5yBjy1uuAH/tHNto+2Ixmie1dUA/frxtlIO5qOgIAtBdtx7b7oBbhi/g0YuixZlCqCa8NPT4+1z3/kOXsOqq/ERsEyUbHSQz3R3dyMAQN3d3ULqW7sWIbw8hbvk5KT/Px5HqK4u8/v29iJUWYlQLGZdTyyGUFUVvo5QV6f++7mVxkbc1p4e++8W1GJ+5uQdqW6XLsEqiYT6NugSjJJIpOYL2fNzW5uQpRYhRL9+h1ZjQbj0UoDTTvP3nnl54us0R2lMJgHuvhvgxz9O/308nrJSN8emMBpsEoPCQ4cAli0T3lzhkF1+RwceDmECoXSti/EdacKPqCROO3fqMO8azM6deL5IJgFWrZJ3n0SCP0O2F0LfzVtaAN5+2997Hjjg373uuSfzWKSmBhtm0iQvu+++4IeWLilJdf4wuxEbVY41NQA33qiuLRpxiDR+C/pY1PhHZyfAAw/ItbuZPl1sdlNaQi1YkHgOfi70fpNMAixfnvn7mpr05GWtrQCPPYbP/Nrb8edaWgB++lO/W8zO/v2qWyAGs23FsGFq2qHRaILPrFkAN90k9x4PPaS9QpigjecQBd5/3/r38Ti2LO7bF+CqqwDOPz+VNn3wYJxH5eBBP1vqjQMHcBZWADXStSjMUe4GDlTTDk0wCXPf1ojHjySLPT0AP/uZ/PuYCa1gkU2uW8OH2//NLm16GAJiGbn/fnzk85vfqG6Jd+bMSd8d/PWv6tqiCR46fLdGBQ0N/ve90AoW2RIrIB7HKjMroqS16erCRz60idWCCDHIIujzdI1Go5o9e/yPZRFawSJbYgXMm4dTNlsRNa2N3ZFPmDAKRh98oK4dGo1GQ/B7Ix5awYIEFglbOnBa4nGAujqAu+6yvyZqWhunI5+wQOL+t7QAvPii2rZoNJpw0tAgNsuz3xvx0AoWKmMF9O8vr+4BAwCWLsWeEk5CBUC0tDZVVfjIJ2wp082UlOAjqunTVbdEo9GEDZKRurJSXPI7FbEsQitYAKTiORQW+nvfo4+WV/fVVwOMHElnQR4VrU0shoN65eZim5Ew09UFsH59OLxxNBpNcCDz+LJlYt1QZ83y3yMp1IIFobfX3/t98om8uu+9F7uLDh2ann/CCqK1Cbvx5s03p4J63XZbuJPLlZSEI9KpRqMJFiTAYSIh1nbuhBPE1UVLqAUL4moZlQBLRjo78XdzEy5qagAWL/anTbJYvTrlDkWSy4VVC9PVBfDee2rufffd4df4aDTZQmUlDmzY2IgDHW7ejOdz0bZzKo7MQytYRMnV0gryvWpr3X2QjztOenOkYsyzAZA64iotVdcmr5SU+H80R/jWt7DGK+yCpkaTDVx+OcC4cQDTpuFAh+S4QqQgoHOFMBI1V0srzMmt7IiCEecf/pAuQE2cCHDNNera45UdOwBGjVJzb5JnJezHSVGjb1/VLdA4cfnlau77299abxqrq8UZsS9frnOFMBE1V0sn3L5rFIw477gjZVfS0oL//YtfqG4VO3/5i/pFPR4HmDFDbRs0KXp6VLdA44SqeDPmgHqEeFzMkeaJJ+LjdBWEVrCIwi6dFrfvGpU03Z2dOL/J5Mnh1UatXQvw4INq7j1mTOrfEyeqaYNGEzbeekvdvc2aWsItt/BvFHfsUBdGPrSCRRR26W4Qn2aaM7KaGuxdYVZ75eQA9Osnp32iiYq9zL59/t8zJyddsKiuVq850WjCgMrs2EZNrZGNG/nnQzuNiB+EVrAw7tKjKFyQ73TvvXRnZC0tOLCWWUJFSMdUyAby89P7ybp14UtEp9H4TX6+6hZYewCKOupft05MPayEVrAASHkPVFSobol4iE8zie9gRTKJDfaefBLguuusJdyoaAE0zuzfj+Nn3HADwD33ANx4o+oWaTTBp65OdQvwHI1QugegqKP+J59UdByCGPjFL36BTj31VJSfn48SiQSaOHEi+te//sVSBeru7kYAgLq7u5k+50RvL0JtbQitXIlQ//7kNYWvXHcdQo2N+Lv09jp/5+ZmhCor1bdZF1100SWMpb4ez7NBmkfb2lJrWnGx2DpFQLt+M2ks/vjHP8Ls2bPh1VdfhQ0bNkBvby+MHz8evvjiCzlSDyXxOD5ffv/9cAfL+t73Mn2arSCBwcJq4KjRaDQq6dsXYMECbIOgynPCimeeSf1bVERpY8ZlvziK5eIXXngh7f+PPfYYDBo0CF5//XUYPXq00Iax0tICUF+vtAncdHTgc/GyMmx8ZyVcRD0wmEaj0cimpwegvDx4dkj33QcwejSOY7Fnj5g6P/1UTD0scNlYdHd3AwBAsUM0j56eHtizZ09aEQ1ZbMPOHXcAXHGFc66QKAcGi8cBpk5V3QqNRuOFoiLVLWAjaEIFobYWB0YUhYrv6VmwQAjBvHnz4JxzzoERI0bYXrdkyRIoKio6Uqqqqrze0pYoLrZbt1rnColyYLBkEmDNGuwmGWZPn7C492o0Irn+eoCGBr46YrFwj30RbNmC3U1FkaPARcPzLefMmQP/+Mc/YNWqVY7X3XrrrdDd3X2kbBEpiv0fKs6Q/AChzFwh2RQYLIwTTFUVwPnnq26FRuM/Rx3F76FHTA7DOPZFInIDaYxv4xeeBIsbbrgBnn32WWhra4PKykrHa/v27QuFhYVpRSQtLQA/+pHQKgOFOVdIdTXd4C0uBvjd7+S1SyZdXQCLFoXTjXjLFoDnnlPdCo3Gf84+G2DePP565s4N59gXyYAB4uoK/FEIQgjmzJkDLS0t8NJLL8GwYcNktYuKlhYc/jnqAaCMGpl4nC451+7dAHl5AGeeKa9dMhk+HODDD3E64QkTVLdGo9G48eSTYo6khw7FY7+1FWDhwvDOYV6pqgL45jfF1Tdnjv+xLJi8QmbPng2NjY2wbt06KCgogO3btwMAQFFREeTl5UlpoB3JZPYEAdq5M/3/tGnSt23DO/8LLxTeJOns3JlyIx41SmeI1GiCzsqVYupJJHDEyLlzo2c7R8O99wLs3SuuPhLa288jESaNxUMPPQTd3d0wZswYKCsrO1LWrFkjq322dHRE17bCTCKR/n9aO4uyMoB33hHfHj9IJFKRRS+4QHVrgs8vf8lnlX/CCeLaotHwsGlT9sbpmTsXR1sWabwJ4L/RP5PGAgUoeEKUvSPMDBmS/n+SgK2z0zqeRSyG/15dDdDU5E8bRfPsszjDXzZOLqzEYtjI9ytfwROyl2H63nvCm6XReOLBB7M3Tg+J3CB6Q+i30X9oc4Vkk3eE+XzMKQGbOXlZWAfo2rVaqKClpgYgNxf/XLOGLmmdRhNUdu1S3QJ1rFiB53uRzyAex8fJfhJawaK6OhiZ6fzAKvWtXQI2c/IywU44mgBy9dWpf5MjJE0wqajQrpQae7Zuxce/H30krs5kUvzRihtMRyFBIh4HGD/eOjplNpBMYrXZnXdi45xEAk9a5lDgetcfff7nfwC+/W38b1VpkjV0HH989tiGabzR3i7e0zHQNhZBY9So7BAsjj4aCxJEYGhpybSYrqzExyNaDZ59vP46/plMirPM18ihrU11CzRB51//El+ntrFgYNAg1S3wh5tvTuUOscts2tlpHQJcQgR1TcDYvBkLFR0dfGezWkWv0ajnqafE1pdIYE22n4RaYxHUJDIy6OzEwcBKSqwNMsnvrr4aux2S1OtmV1VN9OjpwUIFr7rzO98BeOklgAMHxLRLo9GoZ/p0/zXZodZYZNOiSQQHN2Fq926cq4JoOAYPlt40TQDYto1fg/f881qo0KinXz+tPROJisjFoRYsohZPfvRocXVt3Yo1HJs2iatTE1yy5VhQE32OPx4fB7ikodIEmFALFqNGRUuyffll8XUuWya+Tk0w2bFDdQs0Gn6GDsU/zUe+UZrr/UTFvBBqG4uNG8MbAMov9u1T3QJvxGL63bKwY0d2BY3TRJf9+60jyOr5wBsq5oVQayyyKaw3obAQL7pRlt7r6qJ3zCWbkpJUqHeNJsy8/LIWIkRB5gW/CbVgkY3nyj/8oXXEzahBUqZffrnqloSDv/89PdS7RuMnIlObHzokri6NGkItWGQj+fk44ua//w1w7bWqWyOHZctwXIYxY7AgpXGHhOytqcFqZI3GD6qqAJqbsSeaJnh0dVmnhJBNqAWLbDRWu+MOgLFjAQoKAH79a9WtkcPhwwDXXYf/nc0JiVgYMCD1769+VV07NNnB3LlYo7h5MxZmx4xR3SJ6jGMlG1BhMhBqwSKbjdWinmiqqQl/x2x+xyx84xupf/udyVCTfZgX5+pqgJyQrCZBb+fpp4utT4XJQMAfsTPV1QClpapboZHBvn1YhVddjVOCa5wpL0/9+9131bVDkx384hdYc0oC8W3ciDWNYWDvXtUtcOa998TWp49CGInHAWbOVN0KjSy2bcPvuG9f1S0JPkOGpP7d2qquHZrsYutWbNOjs+qKo7sbRx8VxeLF/ifrDLVgkUwCPPmk6lZoZFFWhi3Eg77DCBLJZMqQU6PxCz0Pi0V02vTaWn+Pz0MtWHR0AGzfrroVGhmQjHwPPKC6JeGAGDJ3dADs2aO2LZrsAiGAnTtxzIQwkJenugX+s2WLv0cioRYsnn5adQs0sli+HB+DqDgfDCPEQKuzU207NNnLiSeqbgEdQU+0Jyv4oZ/eIaEVLJJJgMcfV90KjQzy8wG+9z387y++UNuWsLFzp+oWaLKVgQNVt4Ce/HzVLbCnf3859frpYRdawUKrfKML8QgBADj1VLVtCQvkKCSRUNsOTfYRi+FAWaLtAmSybx9AfX0wx4vozRR5P36G9g6tYJGNeUKyCaLSHztWbTvCAjkKiXqod00wWbYM4NVXVbeCjRNOwOvIwoWqWyIXhADuvRcfLftFaAULHTgp2hCVvp+DIQpUV4dLJa0JN1VVOHdRIoHdJMNEWRmeX8aNU90SudxwA46O6iehFSx0dMFo8+GH+Gc2hm33AnlO8TjAd7+rti2aaDN9OsDKlekhvcNkNGw+Goh6VuDeXv/veZT/txSD9tWPNvfdBzB6dHZmsPWCUYOnbY80MvnBDzJ3+WEzGjYeDZCswFOmRDNduwqhL7QaC21jEX1qa8MTJlglhYWp3VcyCfDCC2rbo4k2V12VGckxiEaQVuTkAKxdm3k0UFODj3QKC9W0Syb79/t/z9AKFtrGIvps2QLw8suqWxF8broptfvq6Ai+n74m3HR24t29UbgIi9Hw4cP2+aVqagCuvNLf9vjByJH+3zO0goU2UssOiK2Fxp4FC1L/DtNZtyackOMCY5jo6mqx+S1k4qTtPu44/9rhFyrWydAKFvE4wMSJqluhkc3KlapbEHyM0UnDdtatCScIpYeJjsfV7Iy9sGmT/d+uvVZe5EtV/P3v/t8ztIIFAMD556tugUajnvb21L/Dkq9BEw2MGrKwGA2vWGGdkKulBeD446NnwPnxx/7fM7ReIQDpqaI1Gg1AV5fqFmiyiZtuSiX1eusttW2hZetWrGkZMyb1u5aW6HqFDB3q/z1DLVjoBFUaTfoEqTUWGj/ZtQtg8uTw9TujnUUyCTB3bjSFCgCAnh7/7xnao5BkUqfU1mj69k0XLHRAMY2fkMU4bJoyo1dhRwfWYkSVlhaAQ4f8vWdoBYuODoDdu1W3QqNRyyWXpIc937VLXVs0mrDQ0JD6d9RjIh0+DLB8ub/3DK1gEfXOoNHQ8Oqr6YZoKgy1NBoZ5EhcnZ59NhXvJRtiIjl5wsggtIJFNnQGjcYNYohGiJqrnCZ7OXwYaxZWrsRHfqKpq8M/q6vtg2ZFBb/nhdAKFtXVAMXFqluh0ajHqL079lh17dBkJ7GYPOPNwYNx0rOTThJfN9nFx+PRjLhp5LTT/L1faAWLeBxg9mzVrYg+RNItKVGzGx4yBODGG/2/b5gwau/OO09dOzTZB5kTHnkEYMQI8fWTvi3Ds8EYZTPqwRY/+8zf+4VWsAAAOPpo1S2IPpWVAM3NeOJQwfbtAE8+qebeYcCY/hkg/d8ajWwqK3HyrpoasYu/MbV5Mgnwr3+Jq5tw992pf48aJdemQzV+J4kLdRyLzZtVtyC61NZiKb66OuV18NRT2N/bb9cs1a5ssVhwfdzvuSfdK0THdtHIpqEBH1GUlaXmh2QS4P33xdRPtCAktXl7O8CXX4qpmzBxYiqwFwDAxo3RzqTsd5K4UMtow4erbkH0qKrCGoqGBhwfwbho1dTgpGBtbVjw6NNHUSN9ok8fbOAV5MyNZqOzl15S0w5awpKoSmNNYSHADTcATJuWPj90dIhbmI1aEADxHoATJwI880z676LsZZib678mM9SCxaxZqlsQLZYuxVogMqCtiMfxhNLQALBvn29NU8KXX+Jncs89WJg64wzVLcrEPCEGXYsXdWE0W+FdmM8/H6CxEY8z8xwk0gPwd7/LFCpE3yNofPmldW4UmYRasMjNBRg9Wlx9CxYALFuWvkvPFvLzsRaC5bvH4+nqxCiCEMD8+VjiP+YY1a3JxDwhBj3y5t69dNctXYr7o/lsuLISYOpU4c3SULJnj/VxG+/CnEhkakEI1dXiBNLPP7f+fZSzAiOkA2Qxc+654uoaMQLglFP8l+6CgBc/8Y6OVJCZKEPSQ/sdZMaNRCJTxbl/v5q2iIQIuQ0NeCfc1pbazX74IcDq1QA/+YnqVmYvVtqJTz/lq9MpsFs8DvDjH/PVT7ASgJJJgHnzxNQfVHSALAaSSYAHHxRXX1lZtM/anOjqYjf8y6ZntW1b8LQBy5dn7u7691fTFpEkk1h4IOngx4zJ3M0WFSlqnCZjcU4mAebM4avTLQPnT3/KVz+AtSAOEP1cIQA6QBYTIvOFlJTgThflszY3WAWFbHpWZWUA3d2qW5Him9/EaZ7N+B0IRwYHDuCARWPH4gWnpSXzmg8/9LtVGgDskmnOR9PRwZ+j5qSTnDXFd9zBVz8ADrRlddSbDRskv+3DQi1YyOgQ1dXZGxaZVVCorvbPY2LuXHy+biQ/X/59jf70QTI8nDnT+vcibY6CQGcnFqDMwoX2CFPD4cMAl16a/j7WreOv95Zb7IXIZBLb3PBiFwQrGzZIVVX+3i/UgsWgQeLqMh4FBDVmgUzMgZZoiMcB7r9fTnvMDB2acnVtbARobeV3Xayqwu6kduGIiYC5bBnuGzLyFXghHrf3iHr7bX/bIhsyFmtr03e0116rpDma/+Oaa/D7SCZxLg8R2AmRImy5nOa36mq8aYnyhtLvrMehFixEs21b9gYYIsFoWEgm/VvIEgncPnJc1d7ON1jOOQdrQe64AxueLV6cmXumshLg5puxYdfYsfwGaqKYMAF7RFnxpz/52xY/QChlQEt47TV17dHgjdjPfy7mGIRgJ0SK0EyXlgI88ADAoUOZf4vHAe67j/8eQea663x2SkA+093djQAAdXd3c9e1ciVCuDuKKW1tCDU2iq0zDGXKFPZn39yMUEWFf21sa8P3rKwUW288jlBdHf5Ovb2pPtDWhlBTE0KxmPr3Yy5VVbitVpxyivr2ySqNjanvmY3jNGglPx+huXPl1N3WlnrXra3i6jWOd6s5raRE/XOVVVpb2ed5M7Trd6hDem/fLq6uykq8G85GjcWPfsR2fUsLVlkiJKc9Zqqq8K7ossvE3zOZTOUMuOsu7HlAfj90qH/fkQWyeydtNRKU4xoZGM/Cs+FcPOjs2ydvpy/LoNI83o3U1AC8/HJ0tRft7QDjxvlzr1AfhYg8N7rvvpSqPdsCZP397/TXJpP4CMHPBXfZMoCbbpJ7z3vuSVeTBt0FzW7iHTDA33b4gdGAlpAN5+LZjFFwlOHmbR7vAHhui3LCQz9zoYRasNiyhb+OkhKcG4OEkI3Hs8/i/Nln6a9VseC++678eyaT6dHpgu6CZme4fMop/rZDNuaEVARyLh5EjZLGO1ZCpAztlHm8A4i1FwkidkbqMgi1YCHLGMXPFxA2VCy4v/ylP/cxZmcMuqrdbvfhd3pk2ZgTUhmpqbF3u9WEF7MQWV0NcJSEQ3tzNtagbyZ4GTzYv3uFWrAQEd+9qyvTxcmcMTLqnHQS/bUqFly/woYbo//5rWpntY0gUSnNqE4xL5KFC92T4tnlftCEk5tvznzf8bicAE9mzXTQNxO8DBni371CLViIWnAQSndx8vMFBAGWwE+qzrbz8uTf0yhgGV3QZN83FgN47DG2z9jlVhBxPBgUxo1zt3f64gt/2qLxh9WrrTXRohMAWsWCibrdjp/upqEWLMyRGHkw+slHOdOdFXaD2Qo/F1wj5CzdfE+RbTC/95oarIaXGV20qgrfg3W3ZDfR+mmgJRPagG2nniq/LRr/MMcrIeQIXqnmzcuMBaNqbvMLPz0emV/Xyy+/DJdccgmUl5dDLBaDZ6yS2/uE6N0KCU0bdZWYmZ072TqdHwuumYMHARYtyrxnZaW4TJdWAmVNTSri58qV4mwYBg3CdRJVf2cn2+fPO09MO4LK5ZfTeWd98IH8tmgyIUbvM2aIr9vK1sFLSOpYLFNAiMdxtF2zqylBxdwWRZgFiy+++AK+8Y1vwK9+9SsZ7WFCdCrYRx/FO/dhw8TWGwa8GC75bZF/3HHpYb3JwnzuuWLqtxMa4nEcM2L6dICHHxZzry+/TM/W2dpK/9mCAusYFgDR0VjQaNGamgDWrvWnPRqA44/Hdi+trTgKbU2NnBwUVhs7Fru3SZMAGhrwZuTgQfzvOXPwz/377YUKAtlM+BXzwS/s5gwZMNvaXnTRRXDRRRfJaAszdmGNvbJnDw5Tm5cntt4wwKKl8TtAFqGsLLXIGxHl506zS6mpwYG6eBe0np7Uv1taAB5/nP6z+/fb/01k0DgvFBcDfPYZf99wCgIGgIUO1sBuGj7+/W/cb40LrugjCrsjMBa7t8suA5g2LfX/2lr2dsTj/hmN+0Fenr+CRahtLK68Unyd99/PtnuMAokEfQIymgBZ/fvj2PRPPIGfpYjzyuJi+zaKOLqiPdNvaRGzSybuc+R5spBMArzwgvXfVKcTJ99FxDt30qK1t+ONgMZfzIGlRC9WdjmLWI4mRB1lR0mwOHgwYsabPT09sGfPnrQiipNPFlbVEbq6sIo9m7juutRgTibxpL1qFf5p7ow0AbL27wc44QSAK67Ai7UIzYZTt6mu5ncRpknC5kUIsINMfl4DjtXXW//+yy+9t4mXqiqA224Td0bttEC89BJ//Rp2zIGlWDMi2xGP46MtO9diWndTkppBBFEKj48QgJ/WC9IFiyVLlkBRUdGRUiXwUE5WJkeVk7MKyPdtacGxHMaOxULB2LH4/8YYH7S2GDfdhD8rKr11b6+9ganV8YgMREYdJbshr0F5du+2/n3//t7qEwExuDQavBJbmKYm+sBzVtEXzdi522rkYwwstXGjmDpXrcLHq3b8+td09VxwgbiUDPn5YuoJCoH2CmHl1ltvhe7u7iNli0BH+yj57Kvkf/83ZTdhXjg7O9MDiLGoGTs72WwH3HBahL/6Ve/1xmKZqZpZ788K8Wjyqra1+5zKqLFGg0si7E2bhn/m5NgLQ0bsQnibER3XQEOPMbAUqzeTFRdcAHDppc7XmKNk2iEyV07UXJkLCvy7l3TBom/fvlBYWJhWRFFeLqwqAMCTWtRCItPQt6+93QT5HVl4WYLIiDbudFqEee6FkL3/PO39WSHPjzxPVn74Q+vfq9RY2D1DlsR1TiG8jfhpiKZJYQ4sJSLmz2efuV9Dm79JZJ6nqLl0T5/u372YBYt9+/bBm2++CW+++SYAAGzevBnefPNN+FiBblKk8RaZ6B98MPtyhWza5KziNy68KoPI2J2zJpMADzzAX7+bRsKrEGAFcWkmz5P1We7da/170Vb6rJifIXk3NEdIDQ3WIbzNdj+HDgG89ZaoFmtYqK1N98YbOJC/zv/9X3x0arTpMr/za6+l69snnphuH+ZmM+aE6rHkBuvG2ldXdMRIW1sbAoCMMnPmTKrPd3d3IwBA3d3drLfOYNYshPCyx1+qqhBqbsb1rl0rrt4wlD596K5rbEw9++ZmhCor/W1nQ4N1P2hrE1N/W5t7n2tuFnOvU07JrJflea5cad2+iRPV9iXjM2T9Tsb+5VRHPK72O2ZzqaxMzZMIITR7tvj66+oy33lFBUL9+rHVVVKCi1P7nVi5Uv3zFlmuvJLueztBu34D/63YEClYNDTwP+ziYjwZ9vam6u3pUd8JgljMC29vr5h3QFtmzbLuB42NfPXGYliwNPYBJ2pr+b/LJZdk1tvU5P1dEJYtU9c/EonUM2xuxs+Vp395qUMXuSUWw4UsziI3dyra74Sfc5sfZeJEuvnNCdr1O+DKHmdEBMgh8QTWrk2pykSo1cNGcbG9Ot7OSj8eB7jhBrE5W5ywax+P7QOtsaCRiRO9349gdsdMJrE6mAYnj4k5c9TlOTjjDPwMWWwqCObv5KUOjXzI+yA2V2EzojW334moHYn7maoi1ILFI4/w17F7d6Z75Zo1/PWGDbvARm4Lr9HmQjannWb9++pqurgJ/fplCkG0xoLm+/FmQRw1Kv3/LK6sTkJQPK7O//7ll/Fk7cUt95570r+TSNdejVgQStlcbd6sujXsGNvvxCef+NMevzj6aP/uFWrB4uWX+evo7U3/f2cnwF//yl9v2Kirsw5sRLPw1tQAXHih3PYB2FuPx+M4YqobTz5pnWuERagg9+MVpszhXGhdWWtrndvb3o6j7Klgzx58fy9uueYAZyJdezVy2LYtnIIFwa2PrV/vTzv8wk9j1FALFvv2ia8zW1Wvv/61dWAj2oX329+W3kRHV+CaGpxt0Up9OXAg/ltNTWZ8Ba/BdEgWRC/HQEVFmUcZtGpKt2OY9nb29ojkpZe8qVzNk3y2ZRgOI2VlODFgWHHrY1HTmPl5tBNqwWLQINUtiA4kAI3XhXfWLHER7+xwO+6oqcFZF1tbcRZGkolx5052rQQNRkGMpf6rrsp8Vm7HKzTRKIPAxx97OyoyT/Iijps08iB98e67VbfEGyUl7mPJL9sxvxg82L97hVqwOPZY1S2IDryBZXJzAebNc77GzkaChoICukU1HsfZF3/2M1zGjZMr8BBBzCkcsZlJk6zrsYsPwmJgqjpw1DHHeDsq+ta30v+vMl6Kxh3SF3NzUwbwUWPoUNUtEIuI/D20hFqw4E08pcHEYunR9Lxy113YVsO8+OXkAMyfD/CXv1j/nQZyjBFUaFX3TplkyfGKFzsXwpgxaq3ZiWBDvgutIelVV2X+zu55BLkfhAFeQW3x4lRf7OjItFPjoaoKzxFmbYFo+4CuLnfjzX/+U+w9VZKb67O2k9+zlQ2RcSyiFsBEVbnoIgEv1sCaNQgVFaXfwxiYpqcH+4jPmYPQOefQtdEuIFRQ6O2lCwbV1ERXV1sbjs9hjrFCg6ggXl5Ka2t6W048ke5zI0bQP4+eHvxz5UqE+vdXP35UF5oAdyRWS1MTX2C74uL0/sg7B69aZd3Xze989Wq6+kpL6WNrWAVkMzJihPp3K7Ls3882j1iRFQGyWlvFP/xsDMgze7aAF/t/2AU1sgtMQxs1kyYqpmrcAjrV1fnblvx8//uSebI++2y6z519Nv13My469fXqx4/q8tWvul9jHHvG59faisvChXT3uuqqdMFi6VLv7bYKEudEXZ17nc3N4uaUMWPUv1uRRcQ8nxWCxYsv8j3oWCxTeq+qEhNZMUxl/HgBLxa579qtIlzS7PRZomKqxioEdSKBw8T7zd13+9+XzJP1I4/Qfe6RR+i+k9XzPeoo9WMoyMWYrsAOMg5pNlZG7eMtt/C1jTa8NuHmm93rc/sutJF2oyZYiJjnsyLyJm9++dmzrd0rRURWDBMksBEvbkGNEMoMTOOWhCsWY4uKqRorl91t29zTQsvAT5dNO6+Vv/2N7vM017W0YCNZcx8TecYfNRIJgP/8x90+h8VYtrMTv4eWFn6XzGuvxfFlaBKEJZMAq1fb/z0Ww3FeAMQYQovI3BokfHUN5pdh2BCpsaBV39mVpUut6+3tVaNGVlm8HDWYz0Fpz1tpk03R7LR42xwWTYgXRCVncytO+RcmTKCrw00tTmvDoktmGTcOofXr6fo6beI4suvn1VgYi1uCMNYjjuZmnLyM5R5GzjpL/bsTWfbupfveTmSFxoLXtW7XLuvfx+MAI0bw1R02tmxhu76lBbtjGcOhk7DgbljFH+EJzsXT5qFD8e+jSHU1QH6+/PvYea20tAC88AJdHW7hk3WIb+/84Q8A48fjkM5ufZ2Mw4YG5+sQwnOGU9A6VoyaECtoo7Ear+PxgIlarpDXXvPvXqEXLAoKvH/ebqJKJgE2bfJebxhh6XR2KumuLr42iIqKaYVdm90mszATjwNMniy+3ksvdRcAyfOmPaZw87Hv7PTWVk2Kfftwf3Dr6/E4fTAlkUGXEMI/7RKE0c7JZWVixnvUAmT5GZU31IJFPA6wYoX3z9tl5uvo4F8kwwatjYWIrJM7dnj/rBec2uw2mYWdCy4QW19+PsCqVc4CoJc+Mnq089+jlhBKJXPnuvd1WvucIUPExpggmhCz/VwySZd0srISJ/gTMd7DYtdFy+HD/t0r1IIFAJ/EfN551r/PxgRItINIhEra7zwQXoxKo8KQIWLr++1v3fsKax/JyQG44Qbna/7+d/r6VBKGKKFbt7r39VGjnI85iLHu4cNyFizzHNzRQae1uvpqgI0bxYx3nkjBQWTgQP/uFXrBwqsQUFJib6ORjQmQzjiD7jpeoYsmRr9ovJzNRoWeHnF1jR5NZ/PC+hznz8eRAZ344gu2Ov3CLEhUVgIMGKCmLSw4vaOWFhzi384rwuhZISLDtBXmOXjdOrrPHXecuPEeNa21XXZoGYResPAqBDzyiP3Oyy+jtyBhTuNtB6/QdeON/qsYadscRYFy2TJxdd12G911tM8xJweHb77rLvdrzzmHrk6/SSQy7U3CkGPC7h3Z2SYYMRrrij4+tHJbbmnBQgwNZWXixnvUBAtf4XdAYUOkuylCOPCQSJcmgteIfpWV4XRV7emhe94sgXTMpaREjXunqIA5YaS8PJh9pKiILcTw3r18bS8slDNuxozJbOudd6ofz06lstK6r9O49CYS6f3g6qvFts3stkzrZmwcw6LGO284g6AVu/AKLGSFu2ky6Z5REwBg0aLUjuLDD+nUuQsWeGvTfffhc+iwQWtfwJN10klLJBNRmUPDSHe3uLo2bqS7zu15x2IAjz4KkJdHf+/f/Ib+Wit++EO+z9tx8smZv7vpJjn3MhKLeXeHvO8+675OYxuzc2d6P3jnHW9tsKKkJNNtmdZeB6HUGA5SpuAg2dyItrdyItSCBW2nO/dcdhdG2knUSE1NqjQ3+5umlpeXXqK/1i7rpF1mwqoq/DxYY1Ikk9hFatUqush8XtpsjsEg8p5BQGRKa6szabvnRZ53eXn69RUV9JlajfC6f8syXGtuzuwnsjNJksXqkUfw/VmObadOtX/2tC69Gzakvq9IwXXNmsy20dpL1Namf1ZUpmBWzOtLRQVAnz7s9cjA1/WIXznChsijkMZGOhWQWxY7K7xk7Vu2LL0OEuWxthahnBz1qjCnMmMG+zOyi2IpIrqlVQRAlqh5rG2WeU+ViDwKMWcudXteIp/n7Nl8bR84UO74KSjAx6ekP61fL+9e5oi0vb30ETCdjiMbGujbQN7jt74l5jvF49aZf3kTivHMRT097N9j8uT0+8lIlAmAUG4uvldZGf1ndHZTSmRmxmQZZKQ4pfY+/XQ5HUxUWbDA40uQAGuG1LDe0w/OPFNcH3nxxVS9bs+rrk7s83ziCfVjhKbk5+PvRrvpoS2XXOK8OLKEb7ebD1k2U+Q9jhwp7jta9QuV9lFe1oAf/Si9DtH9gKc0NPA/k6ywsaiuxqotpwRWVomRaPByfumkavLT1ccLdjE9/EZFMKsoB9AS6aHwxz/in27PCyGAe+4R+zzNRypBhUS3FB25NxZzPs6trgYoLqary+54gUVVTt6jSBsLgMx+odI+6v332T/z7rvp/w+Sp5mf0aRDLVjI7HReXI2cBJhDh9jr84vCQjGGSiJQEcwqygG0ROa8+egj/JPGtslJaAjz86TlkUfEhoTev9/57/E4fa4eu8WuuhqgtJS+TQgBHDxIfz1NfVb9QoS9hBeGD2f/jNnmpLo6ODlH/DQkDbVgASCv07Em1ykpcRZgghyS+Ic/DI5HhIpgVlEOoNXYKL5OUc+BpR6/w8Dz0tmJo0CKmsxp5qPbbnNexNw0uPE4wJVXemufSKz6hdckhTzG2LNmsYcrN0eCjscBZsxgq0MWtEEQRRB6wQJATmZMVgtat90JbTImFUycqLoFKVQEs4pqAK1kMlM1y8Oxx+Kfop4DSz289ywocI/uKZrjjsObGxGai2HD3K+Jx93dat00uEGYC+zeNWuSQt5sxrm57K7Dp5+e+bsgPFMA+iCIQuA352BDdIAsWfT2IlRaSm8Y861vOdcXZK+QNWv8eaY0qDDWimoALRaDPpqyfj2ulyYAVjzO9zzN1vw9PdijwWvbm5r8D3hEjCTJd1m5EhvQXXwxe11mjxwr7AxqSamrc6+DJQBeLIbfs6jnJXKciTTGZvG8sHpPPEEFRZVEQsxzzQqvENmwRPW8/HLnuvr2Vdep/Op0oiCTgnkg+uEV4uc9ZSPaIv13v0vV7fa8iFeIl+dp5aZaUYFQv37e2k0WVD8t9O2iWxKqqujroolYSxOhknbRtnu3Vu9x0iS259K/v3N9IsaZ27NgEWB6e+kF2rw8+zrdhD7ZxUs4ASu0YCEIWnc9N3dNkZK9jOLFJVcmVouL2X8/CveUiWgf+iuvTK/f7Xl5eZ4iJ+BEAm8OCIsX+zee3PrMiBH0ddFoGkS73lu9O6v3yNrHbrjBeqEuKRE3zkQ+Cxatn5sA6PZMZZaJE8U8W9r1W2Bcvmhy++0A48e7X+fmVZGTE2x3xaAZJtbU4LPJjg7ctrIybHQm08hUxT3DxN696f93e16sz9PJjZWWK68E+M53Mu/V0gJQX++9Xivy8gAOHEj/XUkJ9ghxs+8qLKS/z6OPAixZ4twPRRsgm9/doEH49zt2pD9b8/d344EHrH8vMuEX7Xfs7MQGnU59k2Ve7OrCz8tuLTA+06efBrj/fvq6zeTksKWrd/MqEo0WLFygneTcrsvNBfjyS/72yMIvw8Rkkn6hIcZafqLinrIQ7Ulh1UfcnhfL86QN0e/E0KHYuM8IEVhEUleHF/v2dlwA8PekNSr8xz/o79XVhe8xbpz9NTIMkGne3a9+xVaf3eYqFsMxLCZO5Bfkab/jTTelp4avrMThC4xCIeu86CaIGJ8pj2Bx6qkAv/wlQEMDwLPPul8/cqT3e3lCjIKEnrAdhSxYQKdqsjsKIYZbQc546nYeLIoohswOMqKNN594Qm57RdhAWBnP8TyHAQPS/28+XmHF61HPwoXO9aoyQD77bLF9jOVI1imlgBdjSSs7j95eHK5ddPt5+/pFF+F6aI+iaIx/aciKyJt+8PHH3q8zujvt2ye0WUJpaJCv7m9pAZgyJXNH2tmJf0/rAqahh0SmdWPUKLr6ZLur8WrNSkqsd9g8x3y3357uxr5tG8Cll3qrS8RRjx1Bz+BLGzmV9l05uZJ6zcBM3osx+mc8TncUDoCPt2ijPPP2dTKPjhnjHoDLblxIRYwcQ0/YNBa0yX1uuSX9c6qtgGXtErwg0kpbw4ZbP/zud8V6FPDA65Znp/nyqrGIx7Gbqyh4NCe0O06/DZDvuouu/ddeK24uonUltXoWiQR7O2i1AvX19M+ttzdTG8ZSJk1Kfx5exoUXtFeIIJYupXvRS5emPkMzUQepeMn+yoLMZHEad6wm2KIihFavTr/Gr8nJra1ubqysx2leBRY3bwzWzJle1d807qY87eKps6fH/bnGYjizpoijGtZNirndtInWjHMijctpv37sz3nmTO9z9t696e1bvDgzg6+MY2YtWAiCtiMaM5vy7ExEZqOkLbIXdJnp7TV0uC02QREsSFucdt1eFk6a2AykxOPuQoUXeyGv84JqGyS371pX59x+8ixFxIrh3aR4/bzb+PDynl580Vt/OOEE53dTXIwFDRkaRi1YCMJLR/S6M4nFcHRDL5/1ej8/VNxaYxFsgnhUJWPXbSewrF6NI2LOmYN/uh1/eI3q2NPDFs8mFsMRQ1VC+13r6jKjC+fkZApovEc1vJsUr0aubloLL2PEyzoRj6fuITK6KC1asBCEl/Nn3rPUigp/hAq/IkpGNWR2VMgmwY9XYOERwljnBR7vExGwfteeHjoBjecdiOirXjQnMsaIl3XCqLVTsRnQgoVAnAzgrDoijxFaYyOd2o23+B1RMoohs6NCth9VsSx0PAsM7XMuLg7GeAiiwClKc8CqOZExRljWCfMxm6p3o91NBUJSs5td96qqrFOze3V3AsBuSDU1AM3N7m5ELBB3OVHZX1mRld5ew09Us7vSwJoBkyfCJe3zW7s2GONBdDRPEaxb5xylEyE6t1rWjNiyApC5rRO1tbhtH36Y3rYgvps0xMoz7oRRY0FgVeGxxIa3krR7e/HRyJQpbEFaVO8qnJBxdq7hI1uPqrycUfPsFMP2nIOmsaA5lmb1oGG9t4x358XuJOgaCy1YSIYIBwsXYgGBdELaicxYD21MDasiO2qiJtxk21GV1zNq3gXGzTvFzppfhUDO+l1lt1G1oCNzjLA+O1VCqhYsAoKVNGq2DKe1d+AxCp09W/pX1YScqGV3dYJnkeJdYNw0mebzdJWh8Gm/qx9tDIItUJDGiIrNgBYsAoBb1MPaWjbJntVdzVhmzZL6VTURIVuOqngXKd4FhgQ1stttkoVBhUuhGbfv6lcbVWssCEEaI34LOrTrdwwhhPy06dizZw8UFRVBd3c3FLLkDg4ZySQ2ArPL1hiLYcPFzZvp4/e3t2PjMi80NGBDII1GQz+W2trs8yywZOq1+qzb/EAMnUXOIV6x+64y5jmnNgwdivMLWa1afj6PIMHTD1mhXb+1YCEJEROXmVWrsOU6K/E4wP79OHV7VPFzcGnCj+pFimeTYIZlDhGNjHnOCZLMECD9vRGvCu1hJhfa9Vu7m0pClDtQMokH76pVAJ9+6q0t8+ZFW6hgdRnMRoz9qL09lb0xW1GdDVSkG6Ayl0KGe4tqY1Td1iM3PuWcxNiTLTYWoiLEuRl+OhWanAdhJwhn0EFHpfFf0FFljMdjiO23TYGI7yG6jUGyc+AlTONT21gohlfVSlR+rG9n7FiAE08EGD4cYNasaGsq/DzfDSt2/UirjlOoOEajmR/IrjzINgWqj5TCTtjGp7axCABezwPdFkwnqqqyZxD7fb4bNrTgFWxo5geA4NsUaLsHb4RxfGobiwDg9Tywo8ObUAEAsGUL/nw2EPiwtopx60cIZVd/CRo080MYbArC0MYgEuXxeZTqBkSdmhqAiRPZVK28C2G2LKTZnOOCBi14BR+a+cHLHOI3YWhj0Ijy+NSChQ/E42yqeN6FMFsW0upqvCtyO9+trva/bUFAC17hgGZ+YJ1DVBCGNgaJKI9PfRQSQMiCyZoZNRbDNhbZspCqdhkMOm79KNv6i0YTJKI8PrVgEUC8pF3P1oVUn+/aowUvjSa4RHl8asEioNgtmFVVAHV1eOE0ks0LaU0NwIcfYu+Pxkb8c/Pm7HwWZrTgpdEEl6iOT+1uGnCcYvRrQykNLbq/aDTBJSzjU8ex0Gg0Go1GIwypcSyWL18Ow4YNg379+sHIkSOhI4yOthqNRqPRaITDLFisWbMGamtr4bbbboO//e1vUF1dDRdddBF8/PHHMtqn0Wg0Go0mRDAfhZxxxhlwyimnwEMPPXTkd1/72tdg0qRJsGTJEtfP66MQjUaj0WjCh5SjkEOHDsHrr78O48ePT/v9+PHjYePGjZaf6enpgT179qQVjUaj0Wg00YRJsNi1axckk0kYPHhw2u8HDx4M27dvt/zMkiVLoKio6Eipqqry3lqNRqPRaDSBxpPxZswUzQMhlPE7wq233grd3d1HypYtW7zcUqPRaDQaTQhgyhVSWloK8Xg8QzuxY8eODC0GoW/fvtC3b1/vLdRoNBqNRhMamDQWubm5MHLkSNiwYUPa7zds2ACjRo0S2jCNRqPRaDThgzm76bx582DGjBlw6qmnwllnnQWPPPIIfPzxx3DdddfJaJ9Go9FoNJoQwSxYTJ06Fbq6uuD222+Hbdu2wYgRI+B3v/sdHHvssVSfJ96t2jtEo9FoNJrwQNZttygVvof03rp1q/YM0Wg0Go0mpGzZsgUqzZkwDfguWBw+fBg++eQTKCgosPUk8cKePXugqqoKtmzZogNvhQT9zsKJfm/hQ7+z8BHEd4YQgr1790J5eTnk5NibaDIfhfCSk5PjKOnwUlhYGJiXoKFDv7Nwot9b+NDvLHwE7Z0VFRW5XuMpjoVGo9FoNBqNFVqw0Gg0Go1GI4zICBZ9+/aF+vp6HYwrROh3Fk70ewsf+p2FjzC/M9+NNzUajUaj0USXyGgsNBqNRqPRqEcLFhqNRqPRaIShBQuNRqPRaDTC0IKFRqPRaDQaYURGsFi+fDkMGzYM+vXrByNHjoSOjg7VTcoKFi1aBLFYLK0MGTLkyN8RQrBo0SIoLy+HvLw8GDNmDLzzzjtpdfT09MANN9wApaWlMGDAAPjud78LW7duTbvms88+gxkzZkBRUREUFRXBjBkz4PPPP/fjK4ael19+GS655BIoLy+HWCwGzzzzTNrf/XxHH3/8MVxyySUwYMAAKC0thRtvvBEOHTok42uHGrd3dtVVV2WMuzPPPDPtGv3O/GXJkiVw2mmnQUFBAQwaNAgmTZoE7733Xto1WTPWUARYvXo16tOnD1qxYgV699130dy5c9GAAQPQRx99pLppkae+vh6deOKJaNu2bUfKjh07jvz9zjvvRAUFBai5uRm99dZbaOrUqaisrAzt2bPnyDXXXXcdqqioQBs2bEBvvPEGGjt2LPrGN76Bent7j1xz4YUXohEjRqCNGzeijRs3ohEjRqAJEyb4+l3Dyu9+9zt02223oebmZgQA6Omnn077u1/vqLe3F40YMQKNHTsWvfHGG2jDhg2ovLwczZkzR/ozCBtu72zmzJnowgsvTBt3XV1dadfod+Yv3/72t9Fjjz2G3n77bfTmm2+iiy++GB1zzDFo3759R67JlrEWCcHi9NNPR9ddd13a77761a+iW265RVGLsof6+nr0jW98w/Jvhw8fRkOGDEF33nnnkd8dPHgQFRUVoYcffhghhNDnn3+O+vTpg1avXn3kms7OTpSTk4NeeOEFhBBC7777LgIA9Oqrrx655pVXXkEAgP71r39J+FbRxbxI+fmOfve736GcnBzU2dl55JpVq1ahvn37ou7ubinfNwrYCRYTJ060/Yx+Z+rZsWMHAgD0xz/+ESGUXWMt9Echhw4dgtdffx3Gjx+f9vvx48fDxo0bFbUqu9i0aROUl5fDsGHD4PLLL4cPPvgAAAA2b94M27dvT3s3ffv2hXPPPffIu3n99dfhyy+/TLumvLwcRowYceSaV155BYqKiuCMM844cs2ZZ54JRUVF+h1z4uc7euWVV2DEiBFQXl5+5Jpvf/vb0NPTA6+//rrU7xlF2tvbYdCgQXD88cfD1VdfDTt27DjyN/3O1NPd3Q0AAMXFxQCQXWMt9ILFrl27IJlMwuDBg9N+P3jwYNi+fbuiVmUPZ5xxBjzxxBPw4osvwooVK2D79u0watQo6OrqOvL8nd7N9u3bITc3FwYOHOh4zaBBgzLuPWjQIP2OOfHzHW3fvj3jPgMHDoTc3Fz9Hhm56KKL4Mknn4SXXnoJli1bBn/961/hvPPOg56eHgDQ70w1CCGYN28enHPOOTBixAgAyK6x5nt2U1mYU7AjhISmZddYc9FFFx3590knnQRnnXUWDB8+HH77298eMSbz8m7M11hdr9+xOPx6R/o9imHq1KlH/j1ixAg49dRT4dhjj4Xnn38eampqbD+n35k/zJkzB/7xj3/An/70p4y/ZcNYC73GorS0FOLxeIYUtmPHjgyJTSOfAQMGwEknnQSbNm064h3i9G6GDBkChw4dgs8++8zxmk8//TTjXjt37tTvmBM/39GQIUMy7vPZZ5/Bl19+qd8jJ2VlZXDsscfCpk2bAEC/M5XccMMN8Oyzz0JbWxtUVlYe+X02jbXQCxa5ubkwcuRI2LBhQ9rvN2zYAKNGjVLUquylp6cH/vnPf0JZWRkMGzYMhgwZkvZuDh06BH/84x+PvJuRI0dCnz590q7Ztm0bvP3220euOeuss6C7uxv+8pe/HLnmtddeg+7ubv2OOfHzHZ111lnw9ttvw7Zt245cs379eujbty+MHDlS6veMOl1dXbBlyxYoKysDAP3OVIAQgjlz5kBLSwu89NJLMGzYsLS/Z9VYk24e6gPE3fS///u/0bvvvotqa2vRgAED0Icffqi6aZFn/vz5qL29HX3wwQfo1VdfRRMmTEAFBQVHnv2dd96JioqKUEtLC3rrrbfQtGnTLN2rKisrUWtrK3rjjTfQeeedZ+ledfLJJ6NXXnkFvfLKK+ikk07S7qaU7N27F/3tb39Df/vb3xAAoHvuuQf97W9/O+KO7dc7Ii5w48aNQ2+88QZqbW1FlZWV2nXRAqd3tnfvXjR//ny0ceNGtHnzZtTW1obOOussVFFRod+ZQq6//npUVFSE2tvb09yA9+/ff+SabBlrkRAsEELowQcfRMceeyzKzc1Fp5xyyhEXH41ciB92nz59UHl5OaqpqUHvvPPOkb8fPnwY1dfXoyFDhqC+ffui0aNHo7feeiutjgMHDqA5c+ag4uJilJeXhyZMmIA+/vjjtGu6urrQ9OnTUUFBASooKEDTp09Hn332mR9fMfS0tbUhAMgoM2fORAj5+44++ugjdPHFF6O8vDxUXFyM5syZgw4ePCjz64cSp3e2f/9+NH78eJRIJFCfPn3QMcccg2bOnJnxPvQ78xer9wUA6LHHHjtyTbaMNZ02XaPRaDQajTBCb2Oh0Wg0Go0mOGjBQqPRaDQajTC0YKHRaDQajUYYWrDQaDQajUYjDC1YaDQajUajEYYWLDQajUaj0QhDCxYajUaj0WiEoQULjUaj0Wg0wtCChUaj0Wg0GmFowUKj0Wg0Go0wtGCh0Wg0Go1GGFqw0Gg0Go1GI4z/DzGyA8MtOwMeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(housing['MedHouseVal'], 'bo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the features and the target values, and create training and testing datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = housing.drop('MedHouseVal', axis=1)\n",
    "y = housing['MedHouseVal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data inputs (16512, 8)\n",
      "Training labels (16512,)\n",
      "Testing data inputs (4128, 8)\n",
      "Testing labels (4128,)\n"
     ]
    }
   ],
   "source": [
    "print('Training data inputs', X_train.shape)\n",
    "print('Training labels', y_train.shape)\n",
    "print('Testing data inputs', X_test.shape)\n",
    "print('Testing labels', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Scaling the features to be between 0 and 1\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the ANN\n",
    "\n",
    "Let's use a similar ANN architecture as in the classification tasks, with two Dense layers with ReLU activations, having 32 neurons each. \n",
    "\n",
    "Since we would like to predict a single value, we will use 1 neuron in the output layer, without activation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the layers in the network\n",
    "inputs = Input(shape=(8,))\n",
    "dense1 = Dense(32, activation='relu')(inputs)\n",
    "dense2 = Dense(32, activation='relu')(dense1)\n",
    "outputs = Dense(1)(dense2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the model by providing the inputs and outputs\n",
    "model_2 = Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "We can inspect the architecture of the ANN with the `summary()` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_3 (InputLayer)        [(None, 8)]               0         \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 32)                288       \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 32)                1056      \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1377 (5.38 KB)\n",
      "Trainable params: 1377 (5.38 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compile the ANN\n",
    "\n",
    "In this case, we will use `mean_squared_error` loss function. During training, the loss function will calculate the squared difference between the target value $y$ and the predicted value $\\hat{y}$, calculated as $\\sum{(y - \\hat{y})^2}$.  \n",
    "\n",
    "We can use the Adam optimizer for regression tasks as well. Note that we cannot measure accuracy as in classification tasks, therefore we don't need to specify a performance metric. Alternatively, we can use mean-squared error, mean-absolute error, or R2 score as metrics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model_2.compile(loss='mean_squared_error', optimizer='adam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train the ANN\n",
    "\n",
    "Similarly to classification tasks, to fit the model we will provide the training data and the target values. Let's fit the model for 200 epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "history = model_2.fit(X_train_scaled, y_train, epochs=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the loss\n",
    "train_loss = history.history['loss']\n",
    "\n",
    "epochsn = np.arange(1, len(train_loss)+1,1)\n",
    "plt.figure(figsize=(4, 3))\n",
    "\n",
    "plt.plot(epochsn,train_loss, 'b')\n",
    "plt.grid(color='gray', linestyle='--')\n",
    "plt.title('TRAINING LOSS')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate the ANN\n",
    "\n",
    "We will again use `predict` to obtain the outputs of the model. Let's check the predicted values for the first 10 test samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 52ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.5398234],\n",
       "       [0.9367044],\n",
       "       [4.2926064],\n",
       "       [2.5250819],\n",
       "       [2.730356 ],\n",
       "       [1.5289242],\n",
       "       [2.1832035],\n",
       "       [1.4888892],\n",
       "       [2.338513 ],\n",
       "       [4.607725 ]], dtype=float32)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model_2.predict(X_test_scaled[:10])\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can compare the predicted values by the model to the target ground-truth values. It would be easier to create a plot of the predicted and target values, as in the following cell. We can see that for most values, the model predictions are close to the target values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.477  , 0.458  , 5.00001, 2.186  , 2.78   , 1.587  , 1.982  ,\n",
       "       1.575  , 3.4    , 4.466  ])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ground-truth values\n",
    "np.array(y_test[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt. figure(figsize=(8, 4))\n",
    "plt.plot(predictions, 'rx', label='Predicted values')\n",
    "plt.plot(np.array(y_test[:10]), 'go', label='Target values')\n",
    "plt.legend()\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Median House Value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we mentioned, metrics that we can use to evaluate the performance of regression models include mean-squared error (MSE), mean-absolute error (MAE),  or R2 score. R2 score (or $R^2$ pronounced 'R squared') is also called coefficient of determination, and evaluates the goodness of fit of regression models, by measuring the proportion of the variation in the target variable that is contained in the predicted variable.\n",
    "\n",
    "In the next cell we imported these metrics from scikit-learn and calculated their values. Note that for MSE and MAE lower values indicate better performance, and for R2 score greater values indicate better performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129/129 [==============================] - 0s 1ms/step\n",
      "MSE:  0.3119\n",
      "MAE:  0.3678\n",
      "R2 score:  0.7620\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "\n",
    "all_preds = model_2.predict(X_test_scaled)\n",
    "\n",
    "print(f'MSE: {mean_squared_error(y_test, all_preds):7.4f}')\n",
    "print(f'MAE: {mean_absolute_error(y_test, all_preds):7.4f}')\n",
    "print(f'R2 score: {r2_score(y_test, all_preds):7.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15.4 Saving and Loading Models in Keras <a id=\"15.4-saving-and-loading-models-in-keras\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0pNOtvAgTboI"
   },
   "source": [
    "To save a model in Keras, we simply use the `save()` method, and provide the path to the directory. \n",
    "\n",
    "The saved model contains the architecture with the layers, the weights for all layers in the model, and other information related to the used optimizer, loss, and metrics. \n",
    "\n",
    "There are two possible formats to save the models into: HDF5 and Keras-TensorFlow SavedModel. If we provide the `.h5` extension, then the saved model format is HDF5. If we provide the `.keras` extension, or if we don't provide any extension, the model will be saved into Keras-TensorFlow SavedModel format. \n",
    "\n",
    "Keras-TensorFlow SavedModel is the default, and it is recommended, since it is a newer format, and it saves additional information that is not included in the `h5` file, such as losses and metrics (e.g., they can be useful if we wish to resume training).\n",
    "\n",
    "Let's use the Keras-TensorFlow SavedModel format to save the first classification model `model_1` from Section 15.2.1 in the current working directory under the name `classification_model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "-I1ZmeLCSJXW"
   },
   "outputs": [],
   "source": [
    "# Saving a model\n",
    "model_1.save('classification_model.keras')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZBx6J5qVXd5Q"
   },
   "source": [
    "To load a saved model we will use the `load_model` method. It returns the architecture and weights of the saved model. \n",
    "\n",
    "Let's load the save model as `model_3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "HV_-3wAcSn-Y"
   },
   "outputs": [],
   "source": [
    "# Loading a saved model\n",
    "from keras.models import load_model\n",
    "\n",
    "model_3 = load_model('classification_model.keras')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Afterward, we can use the loaded model as we would use any other model.\n",
    "\n",
    "Let's evaluate the model on the test dataset, just to check whether the returned value would be as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w8bW12rbX9Wk",
    "outputId": "85177afb-6e02-4372-878e-25db0580ade2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 0s/step - loss: 0.0708 - accuracy: 0.9649\n",
      "Classification Accuracy:  0.9649122953414917\n"
     ]
    }
   ],
   "source": [
    "# Evaluate on test dataset\n",
    "evals_test = model_3.evaluate(test_data, test_labels)\n",
    "print(\"Classification Accuracy: \", evals_test[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary of the model is displayed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fbTIrz5ES8hG",
    "outputId": "f40ce904-77f2-47aa-bf11-e954b0c3b40e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 30)]              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 30)                930       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 15)                465       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 16        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1411 (5.51 KB)\n",
      "Trainable params: 1411 (5.51 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_3.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix <a id=\"appendix\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The material in the Appendix is not required for quizzes and assignments.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keras-TensorFlow Model APIs\n",
    "\n",
    "In Keras-TensorFlow, there are three ways to build ANNs:\n",
    "\n",
    "- Functional API\n",
    "- Sequential API\n",
    "- Subclassing API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Functional API in Keras\n",
    "\n",
    "In the above examples, we used Functional API to build our models. Repeated again in the next cell is the first model example for classification which we used in Section 5.2.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the layers in the network\n",
    "inputs = Input(shape=(30,))\n",
    "dense1 = Dense(units=30, activation='relu')(inputs)     \n",
    "dense2 = Dense(units=15, activation='relu')(dense1)\n",
    "outputs = Dense(units=1, activation='sigmoid')(dense2)\n",
    "\n",
    "# Define the model by providing the inputs and outputs\n",
    "model_1 = Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functional API is the recommended API, because it is a more recent and advanced API in comparison to Sequential API, but it is simpler than the Subclassing API.\n",
    "\n",
    "Functional API allows to build models with multiple inputs and outputs, or with custom connections between the hidden layers (examples are residual or skip connections, which we will study in the following lectures). Because of these characteristics, Functional API is well suited for creating networks for advanced tasks, such as object detection, segmentation, and others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sequential API in Keras\n",
    "\n",
    "Sequential API is the initial option in Keras for model building, as well as it is the simplest. \n",
    "\n",
    "The corresponding sequential model to the above `model_1` is shown in the next cell. To create the model, we just add the layers in a sequence, one after another. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "\n",
    "# Define the model and the layers in the network\n",
    "model_1_seq = Sequential([\n",
    "        Dense(30, activation='relu'),\n",
    "        Dense(15, activation='relu'),\n",
    "        Dense(1, activation='sigmoid')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After the model is created, the steps of compiling, training, and evaluation are the same, as shown in the next cell. As we explained above, the reason that the accuracy of `model_1_seq` is different than the accuracy of `model_1` is not because of the API used, but because of the stochastic nature of ANNs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 0s/step - loss: 0.0672 - accuracy: 0.9825\n",
      "Classification Accuracy:  0.9824561476707458\n"
     ]
    }
   ],
   "source": [
    "# Compile the model\n",
    "model_1_seq.compile(optimizer='adam',\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model_1_seq.fit(train_data, train_labels, epochs=100, batch_size=64, verbose=0)\n",
    "\n",
    "# Evaluate on test dataset\n",
    "evals_test_seq = model_1_seq.evaluate(test_data, test_labels)\n",
    "print(\"Classification Accuracy: \", evals_test_seq[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sequential API is suitable for simple classification or regression tasks. However, this API is not suited for tasks with multiple inputs or outputs, or for networks with advanced architectures. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subclassing API in Keras\n",
    "\n",
    "Subclassing API is designed for building custom models and having full control of every step in the model building and training. It is the most suitable for building models with complex or custom architectures. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Define the layers in the network\n",
    "class Model1Subclass(Model):\n",
    "\n",
    "    def __init__(self, **kwargs):\n",
    "        super().__init__()\n",
    "        self.dense_1 = Dense(30, activation='relu')\n",
    "        self.dense_2 = Dense(15, activation='relu')\n",
    "        self.dense_3 = Dense(1, activation='sigmoid')\n",
    "\n",
    "    def call(self, inputs):\n",
    "        x = self.dense_1(inputs)\n",
    "        x = self.dense_2(x)\n",
    "        x = self.dense_3(x)\n",
    "    \n",
    "    return x\n",
    "\n",
    "# Instantiate the model\n",
    "model_1_subclass = Model1Subclass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0725 - accuracy: 0.9825\n",
      "Classification Accuracy:  0.9824561476707458\n"
     ]
    }
   ],
   "source": [
    "# Compile the model\n",
    "model_1_subclass.compile(optimizer='adam',\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model_1_subclass.fit(train_data, train_labels, epochs=100, batch_size=64, verbose=0)\n",
    "\n",
    "# Evaluate on test dataset\n",
    "evals_test_subclass = model_1_subclass.evaluate(test_data, test_labels)\n",
    "print(\"Classification Accuracy: \", evals_test_subclass[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Subclassing API in Keras is also similar to the model building in PyTorch. We will learn about PyTorch in an upcoming lecture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References <a id=\"references\"/>\n",
    "\n",
    "1. Complete Machine Learning Package, Jean de Dieu Nyandwi, available at: [https://github.com/Nyandwi/machine_learning_complete](https://github.com/Nyandwi/machine_learning_complete).\n",
    "2. Implementing Linear Regression on California Housing Dataset, Debarshi Raj Basumatary, available at: [https://medium.com/mlearning-ai/implementing-linear-regression-on-california-housing-dataset-378e14e421b7](https://medium.com/mlearning-ai/implementing-linear-regression-on-california-housing-dataset-378e14e421b7). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[BACK TO TOP](#top)"
   ]
  }
 ],
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