{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 14 - Artificial Neural Networks with Keras-TensorFlow"
   ]
  },
  {
   "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-2025-Applied-Data-Science-with-Python/blob/main/docs/Lectures/Theme_3-Model_Engineering/Lecture_14-ANNs/Lecture_14-ANNs.ipynb)\n",
    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/avakanski/Fall-2025-Applied-Data-Science-with-Python/blob/main/docs/Lectures/Theme_3-Model_Engineering/Lecture_14-ANNs/Lecture_14-ANNs.ipynb) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='top'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [14.1 Introduction to Artificial Neural Networks](#14.1-introduction-to-artificial-neural-networks)\n",
    "    - [14.1.1 Elements of ANNs](#14.1.1-elements-of-anns)\n",
    "    - [14.1.2 Common Activation Functions](#14.1.2-common-activation-functions)\n",
    "    - [14.1.3 Training ANNs](#14.1.3-training-anns)\n",
    "    - [14.1.4 Types of ANN Architectures](#14.1.4-types-of-ann-architectures)\n",
    "    - [14.1.5 Fully-connected NNs](#14.1.5-fully-connected-nns)\n",
    "- [14.2 Classification with ANNs](#14.2-classification-with-anns)\n",
    "    - [14.2.1 Binary Classification](#14.2.1-binary-classification)\n",
    "    - [14.2.2 Multiclass Classification](#14.2.2-multiclass-classification)\n",
    "- [14.3 Regression with ANNs](#14.3-regression-with-anns)\n",
    "- [14.4 Saving and Loading Models in Keras](#14.4-saving-and-loading-models-in-keras)\n",
    "- [Appendix](#appendix)\n",
    "- [References](#references)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.1 Introduction to Artificial Neural Networks <a id=\"14.1-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 multiple interconnected nodes. 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 (i.e., computational units) in ANNs are commonly referred to as **neurons**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "710bd0e6"
   },
   "source": [
    "A simple ANN 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: Simple ANN with one hidden layer. 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 multiple hidden layers are commonly referred to as **Deep Neural Networks (DNNs)**. Applying DNNs for solving a learning 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. Modern LLMs typically have several hundreds of hidden layers, and can have over one trillion connections between the computational nodes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 14.1.1 Elements of ANNs <a id=\"14.1.1-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 consisting of input features and target/output values (e.g., class labels in classification tasks), 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 a set of inputs $x_1, x_2, ..., x_m$ and weights $w_1, w_2, ..., w_m$. Each neuron calculates the sum of the products of the inputs and the weights plus 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": [
    "### 14.1.2 Common Activation Functions <a id=\"14.1.2-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 capability to learn complex tasks. \n",
    "\n",
    "The most common activation functions in ANNs are:\n",
    "\n",
    "- **Sigmoid** activation function (also known as 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 the sigmoid activation function, but it outputs a value between -1 and 1. Tanh is rarely used in modern ANNs. \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 among the most commonly used activation functions 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",
    "- **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",
    "\n",
    "There are also several other modifications of ReLU activations, including GeLU (Gaussian error Linear Unit), SeLU (Scaled exponential Linear Unit), SiLU (Sigmoid Linear Unit), SwiGLU (Switch-Gated Linear Unit), etc. We will study those activation functions in subsequent lectures on Transformer Networks and LLMs. Overall, ReLU is one of the most used activation functions, although modern LLMs and multimodal models use GeLU, SiLU, and SwiGLU. \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": [
    "### 14.1.3 Training ANNs <a id=\"14.1.3-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 values.\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 is an image, the input data consist of the intensities of all image pixels; the data are fed to the network and are transformed through the layers of the network; for classification problems, the output is a vector of predicted class probabilities for that image. \n",
    "\n",
    "**Loss calculation** is the second step, i.e., the loss of the network is calculated as a difference between the network outputs (predictions of the image class) and the target labels (also referred to as ground-truth labels) for all images in the dataset. 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. The sum sign $\\sum$ indicates that the loss is calculated as a summation for all images in the dataset. For regression tasks commonly used loss functions include *mean-squared error* (calculated as $\\sum{(y- \\hat{y})^2}$) and *mean-absolute 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 gradient vector quantifies the impact of changing the network parameters on 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** (weight update in the figure below) is the last step in which new values for the model parameters are calculated and updated, using an optimization algorithm. Modern ANNs use variants of the **Gradient Descent** optimization algorithm for updating model parameters."
   ]
  },
  {
   "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. A small loss value means there is a small difference between the model predictions and the ground-truth values.\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. 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 which includes forward pass, loss calculation, backward pass, and parameters update, the **prediction phase** (also known as **inference**) requires only a forward pass through the model. In the figure, during inference an image instance is passed to the model, and the model outputs a prediction for the class of the instance. \n",
    "\n",
    "<img src=\"images/inference.png\" width=\"550\">\n",
    "\n",
    "*Figure: Training vs inference.* Source: [3]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "df16a232"
   },
   "source": [
    "### 14.1.4 Types of ANN Architectures <a id=\"14.1.4-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, dense 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 lectures we will explore the other ANN architectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XqAdxSlWWe9m"
   },
   "source": [
    "### 14.1.5 Fully-connected NNs <a id=\"14.1.5-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 layers. They are also called **Densely-connected NNs**, **Dense NNs**, as well as they are often referred to as **Multi-Layer Perceptrons (MLPs)**. In some contexts, these networks are also called **Feed-forward NNs** or **Linear NNs** (although these two terms are ambiguous when referring to fully-connected NNs).\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 simpler 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. On the other hand, layers of fully-connected neurons are often integrated in other architectures, such as CNNs, RNNs, TNNs, and GNNs (we will learn more in the next lectures). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AAqeb2v2jSWT"
   },
   "source": [
    "## 14.2 Classification with ANNs <a id=\"14.2-classification-with-anns\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hcDOlUdZxDGu"
   },
   "source": [
    "In classification 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": [
    "### 14.2.1 Binary Classification  <a id=\"14.2.1-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": {
    "tags": []
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "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": {
    "tags": []
   },
   "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": {
    "tags": []
   },
   "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": {
    "tags": []
   },
   "outputs": [
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       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <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",
    "tags": []
   },
   "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",
    "tags": []
   },
   "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",
    "tags": []
   },
   "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 use `fit` for the scaler on the test dataset, but we only use `transform` for the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "301cebe5",
    "tags": []
   },
   "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 lecture we will use the **TensorFlow-Keras** library. TensorFlow is a Machine Learning framework developed by Google for training, evaluating, and deploying Machine Learning models. Keras is part of TensorFlow, and it provides a high-level user-friendly interface for working with neural networks with minimal code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version:2.20.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# Print the version of tensorflow\n",
    "print(\"TensorFlow version:{}\".format(tf.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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 from the `tensorflow.keras` module. 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 `tensorflow.keras` that is used to create new ANN models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "e_YS-JqfkyiK",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import the layers and the model\n",
    "from tensorflow.keras.layers import Input, Dense\n",
    "from tensorflow.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": 11,
   "metadata": {
    "id": "gjqyblPbHmmc",
    "tags": []
   },
   "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": 12,
   "metadata": {
    "tags": []
   },
   "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": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">930</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">465</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m)                  │             \u001b[38;5;34m930\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m)                  │             \u001b[38;5;34m465\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m16\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,411</span> (5.51 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,411\u001b[0m (5.51 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,411</span> (5.51 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,411\u001b[0m (5.51 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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** (also known as 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": 14,
   "metadata": {
    "id": "OZuZ18XZ2zVV",
    "tags": []
   },
   "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 a batch of 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": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zmkoLSQJ39IZ",
    "outputId": "b6faa7cf-5d93-4381-c8a5-de9f5a2b4633",
    "tags": []
   },
   "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": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9737 - loss: 0.0904 \n",
      "Classification Accuracy:  0.9736841917037964\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": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.09040903300046921, 0.9736841917037964]"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/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": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 30)"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.7355642e-05],\n",
       "       [9.9898452e-01],\n",
       "       [4.1395552e-02],\n",
       "       [5.0613523e-01],\n",
       "       [8.5411630e-06],\n",
       "       [9.4184214e-01]], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.   ],\n",
       "       [0.999],\n",
       "       [0.041],\n",
       "       [0.506],\n",
       "       [0.   ],\n",
       "       [0.942]], dtype=float32)"
      ]
     },
     "execution_count": 22,
     "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 0.041, the class label is 0. For the prediction of 0.506, the class label is 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [1.]], dtype=float32)"
      ]
     },
     "execution_count": 23,
     "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 we 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": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K79im89prfzv",
    "outputId": "a06c7797-cfb5-46fd-b90e-0518f88897cb",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
      "The test accuracy is 97.3684 %\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": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "id": "pDiAYiI5MakN",
    "outputId": "78971f50-5aae-43bf-e422-5896420af450",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[70  2]\n",
      " [ 1 41]]\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": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q89i8ozyD_Ek",
    "outputId": "2b18d612-48c3-4b2b-9f31-dedd5f37de26",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.98      0.96        42\n",
      "           1       0.99      0.97      0.98        72\n",
      "\n",
      "    accuracy                           0.97       114\n",
      "   macro avg       0.97      0.97      0.97       114\n",
      "weighted avg       0.97      0.97      0.97       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": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WKFq0KC5fvoxff/0V+/fvz/OSKg4zZ85E0aJF8eabbyI8PDzX3/v06YPExETs378fdevWRWJiIk6cOIEXXngBGzZsQJcuXVCyZElcvHgRKSkpmD17Nr755hs88MADiIyMxMqVK/Hkk08iJiYGgwcPRpMmTWA0GvHXX39h/vz52L9/P7p27QpA7Knv1KkTXn/9dZw6dQotW7aEJEnYsmULkpKS0KlTJ7Rq1eqOuRkMBkyYMAHdunXDxx9/jM6dO2PXrl0YNGgQHn300VzLN2vWDB999BFmzpyJwYMHy643ERGFFn6WcC8jIwPffvttrvKKFSuiYcOGmDdvHnr16oUHH3wQw4YNQ/Xq1XHu3DnMmjUL69atw5tvvonu3bs7H/fyyy/j8uXLePrpp1G7dm3odDocPnwYSUlJ0Gq1eOuttwCIowIrVqyIZ599Fo8//jjKlSuHq1evYtOmTfj4449Ro0YNvmeTOlS6PjuRavr27SsVLlzY7d9OnjwpAZCmTJni9u/PPvuspNfrpalTp0oApO+++y7P7Xz++ecSAGnZsmWSJElSy5YtpVq1arksU6FCBalDhw65Hnvw4EFJp9NJAKSlS5c6y2fPni0BkHbv3u0se//99yUA0oULF3Ktp2nTphIAt9vIzMyUJkyYID344IPS3XffLen1eqlEiRJS69atpc8++0y6du1anm3bv3+/BEAaOnRonsscPnxYAiANGTLEWWa1WqW5c+dKjz76qFSsWDFJr9dL0dHRUrt27aSFCxdKNpvNZR0ZGRnSW2+9JdWqVUuKiIiQwsLCpKpVq0qvvPKKdODAAZdlzWazNGHCBKlWrVpSWFiYFBYWJtWqVUuaMGGCZDabXZb9+eefc2V7s8aNG0tFixaVhg4dKgGQ9u3bl2c74+PjJQDSnj17FNVbku78vCMiIv/CzxJCfj5L3Fx/AG5vffv2dS73559/Sn379pXKli0rGQwGqVixYtITTzwhff/997nWuWHDBql///5SzZo1pcjISEmv10ulSpWSunbtKu3cudO5nMlkkj788EOpXbt2Uvny5aWwsDApPDxcqlGjhjRixAjp0qVLd6w/kS9oJIlTFhIRERERERH5A56TTkREREREROQnOEgnIiIiIiIi8hMcpBMRERERERH5CQ7SiYiIiIiIiPwEB+lEREREREREfoKDdCIiIiIiIiI/oVe7AgXNbrcjLS0NRYoUgUajUbs6REREkCQJV65cQenSpaHV8vtzb+D7PRER+RNZ7/VqXqR98+bN0pNPPimVKlVKAiCtWLHijo/ZtGmTVL9+fSksLEyqVKmSlJycLGubZ86ckQDwxhtvvPHGm9/dzpw5o/AdlW7F93veeOONN9788ebJe72qe9KvXbuGunXrol+/fnj66afvuPzJkyfRvn17vPTSS5g/fz62b9+O2NhYREdHe/R4AChSpAgA4MyZM7j77rtl19lkMiExMRHDhg1DWFiY7MeHImamDHNThrnJx8yU8WZuWVlZKFeunPM9ivIvP+/3fE0ow9zkY2bKMDdlmJt8ar3XayRJkvK1NS/RaDRYsWIFOnfunOcyb731FlatWoVDhw45ywYOHIj9+/dj586dHm0nKysLkZGRyMzMVDRIt9vtOHHiBCpXrsxDEj3EzJRhbsowN/mYmTLezC2/702UW34y5WtCGeYmHzNThrkpw9zkU+u9PqDOSd+5cyfatGnjUta2bVvMnDkTFosFBoMh12NMJhNMJpPz96ysrFzlWq0WBoMBFosFdrvduaxOp4Ner4fZbMbN32VUqlQJWq02V7nBYIBWq3XZnqNco9HAbDa7lBuNRkiSBIvF4lIeFhYGu93uUq7RaGA0GmGz2WC1WnOVW61W2Gw2Z7ncNun1euh0Op+1qWrVqrDb7S7rCfQ2FUQ/lS9f3mX9wdCmguincuXKOdsQLG26mS/a5MgsmNrk4Ms2lStXDhqNJlfd5bbp1pxIXVqtFlWrVlW7GgGHucnHzJRhbsowN/nUyiygBukZGRkoWbKkS1nJkiVhtVpx8eJFlCpVKtdjJk6ciDFjxuQqT0xMRHh4OAAgJiYGnTp1wrp165CamupcpmXLlmjVqhWWLFmC48ePO8t1Oh2GDx+OmTNn4sKFC87yXr16oWrVqkhMTHT5wDVo0CBERkYiISHBpQ7x8fHIzMxEcnKys8xoNGLkyJE4ceIEFixY4CyPjo5GbGws9u/fj9WrVzvLq1Spgt69e2Pbtm3YvHmzs1xumzp27Ij69etjxowZPmkTAHTu3BlLliwJmjb5up+aNGmCSZMmuQweAr1NwdhPbBPbFBcXh+zs7Hy1qXTp0iD/wUNClWFu8jEzZZibMsxNPrUyC6jD3e+77z7069cPI0eOdJZt374dDz/8MNLT03Hvvffmeoy7PenlypXD+fPnnYcZyNn7YjKZkJSUhPj4eGg0Gu5R8qBNjsxGjBjhcphIILcJ8H0/2Ww2JCQkIC4uzvlPIdDbVBD9dPXqVSQlJTlzC4Y2+bqfbty44cwsIiIiKNpUEP3k+N/21ltvwWg05qtNV65cQXR0NA9396L8HO5uMpmQkJCA+Ph4fpCVgbnJx8yUYW7KMDf5vJlZ0B7ufu+99yIjI8Ol7Pz589Dr9ShevLjbxzg+pHtS7u5weSBnT7Cn5Xl1oLtyjUbjtlyr1bot1+l00Ol0ucr1ej30+tzdyTYFbpscgw93z9VAbRNQcP10c27B0qabebNNjgFtWFiYc5lAb5M7vmqTRqPJs+6etunWLwSIiIgodAXUjAFNmjRBSkqKS9nGjRvRsGHDPD+UEREREREREQUKVQ93v3r1Ko4dOwZAnEuYmJiIRx55BMWKFUP58uUxcuRInD17Fl9//TUAcQm22rVr45VXXsFLL72EnTt3YuDAgVi0aJHHl2DzxuzuFy9eRFRUFGdF9BAzU4a5KcPc5GNmyngzN87u7n35nd2drwn5mJt8zEwZ5qYMc5NPrfd6VQ93/+233/DII484fx82bBgAoG/fvpgzZw7S09Nx+vRp598rVaqEtWvXIi4uDp999hlKly6NTz75xOMBujdoNBpERkZCo9EU2DYDHTNThrkpw9zkY2bKMLfgxb5VhrnJx8yUYW7KMDf51MpM1a9QWrVqBUmSct3mzJkDAJgzZw42bdrk8piWLVti7969MJlMOHnyJAYOHFigdTabzUhISODlcmRgZsowN2WYm3zMTBnmFrzYt8owN/mYmTLMTRnmJp9amfE4ByIiIiIiIiI/wUE6ERGRG2lpwE2XW6cQZbEAmzYBS5aoXRMiIgoVHKQTERHdRJKAOXOA++4D6tcHYmOBGzfUrhWpZfNm4JFHgNdeA/5/tUIiIiKfUnV2dzXkdwZdSZJgNpthNBo56YKHmJkyzE0Z5iafNzNbvRp4+22gdWtgwgQgPFzZev79V+y5XLQIuHoVeOYZoFcvoHz5fFUPgNgzun49MG8e8PvvwGOPAc8/DzRuDGRlAYMGie3erFYt4JtvgNq1c8q8mRtnd/e+/GR6c99aLBpERQFXrgC7dgEPPuijCgcB/v+Vj5kpw9yUYW7yqfVer+rs7oFIkiRkZmYiKiqKT24PMTNlmJt8168DCxdKaNToCmrXLlagudlswFdfiT1u1asX2GYBAKdOAWvXAuXKAW3bAkajvMd747mWnQ0MHw5MmyZ+/+MP4McfxcC2Rg3HdoCdO4Hdu4HmzYGYGODmzZlMoh3z5gHffw/cPEfL3r1i8N+qFfDww4DSq6CcPw8sWwZcuJBTduQIMH06UK2aGMCfOgXodMCYMUDDhsALLwB//ikGZ4MGAUWK5LTHbLZgwgQDX6NB5ubXhNGoQdu2wLffii+hOEjPG9+35GNmyjA3ZZibfKplJoWYzMxMCYCUmZmp6PHZ2dnS6NGjpezsbC/XLHgxM2WYm3tmsyTZ7bnLTSZJatNGkgBJKl78gvTvv97PzWSSJJvN/d8+/9yxbUk6eND9MpcuSVJaWt63a9c8r8t//0nSl19KUvPmYruOW/HikvTqq5K0Y4frui9cyHtd169nS2+8MUU6eTI7z7rduJH34//8U5Lq1MmpwwsvSFJ0tLhfqJAkTZ0qSe+9J0mVK7vWtWZNSZo4UZI2bpSkgQMlqWhR178/8IAkTZkiSTNmSFKrVq5/y++tZElJGjpUkpYskaRevSQpIiLnbxUrivwczp2TpPbt3a/HYDB55TWa3/cmyi0/md76/3fOHNHfMTHermVw4fuWfMxMGeamDHOTz5uZyXlf4p50IgoYZ88CDz0E3H232Ntav74ot9mAPn2AjRvF75cuReGdd6z47DPvbfvwYXFY9L33ikNedTrXv3/xhWPbQJs2wPbtOYdmZ2aK85oXLrz9NgoVAqZMEcu6+7LWbAbWrRNtX706Z0+zRiP2Lv/1F5CRAXz2Gdy2vV49oHdvoGdPoFQp4NAhsa758404c+ZNfPRR3nWLiAC6dhWPf+wxwGoVdZg3T9TJagVKlADmzgWeeELUo08fICUFGDo0Zz133SUOK9+2DTh4EBg50nU7pUuL+j3/PPDAAznlAwYAp0+LPfOnT98+x9sxGkX/PP44oP//O+Czz4pDmVesAP7+GxgyBLjnnpzHlCgBrFkDfP21OArAwWazYd++PQDqK68QBYT27cXrLDUV+OcfoGxZtWtERETBjIN0Irqtq1eBhAQgKgp4/XX3g8ebpaUB778P1KkD9OghBjg3/23hQuDoUeCDD4CSJeXV5d13xQdkQAzWExLEAPC114DFiwGDAYiLs2LyZD2mT9ejc2dxbrTDhg3AjBmuh1HffTfQuTPw5JNAWJj77Z45I9aTliZuy5eLgZ3Dnj3iw7vRCFSqJA6fbtNGDESPHRODzpMnxbK3Du4dJElMTjZ4sPiyYeZMkbkkAb/8IgbDixeLc7UdatcWg9mePcWgwWoVh5jPmwesWiUO/3ew2YB9+8RtxAigcmVRN0EDQPp/3XJ3sCSJdc2fL2733ivqmpmZs0z79sCsWTl9eu+94rzvxETRTw8+KOr61FNA4cLisd9+K+p6+LAY2PfuLU4XyCuj8uVF3X2hSBHxpUJeNBqgb19xczCZrEhI2AgO0oNfdLT4n7NzpzgV45VX1K4REREFMw7SFTDKPeGTmJlCaue2dy/w3HNiUA2IgdX779/+MbGxwMqV4v6wYeIc6cceE3tbf/xRDPgAsTd03brcg/7//hPbqVjRtXz/fjHjNiAGcj//DLzxBvDll2JQrNGIPZ1dutiwefN+/PprA/TrBxw4IPYCx8cDU6e6r/P8+WLPabduYiDZrFlOvS5eFAPuf/4RXwJYLMCkSWIiM8cyX30lfj79tPhbs2aiTo0biz2zNptoz6JF4oO+O5IEfPqpOK971Sqgbl0xaF2+/ObBtNgD7tjTXLeu6zr0epF327a51++YiG3ePGDHDrFOvR5o1w7o0cOCY8eS8NZbryPMzTcVkiSOHpg3T+zJzsgQ5eXLi8ncnn8+57zzm2m1wJtvitutIiPF3vEBA9znESjUfo2S79zat08+KQbpa9ZwkH47fE3Ix8yUYW7KMDf51MiMs7sTUS52uxjQxseLQWnx4uIwbkAMJAcPdv+4rVuBFi3EntB69cQe5ls1aSL2Omdni8m6Bg3K+duRI+Kw7f/+E3tZO3fO+VvbtmIPc/fuYrD7xRdAXJxYD+C6rmvXxKRkf/0lPlj/84/YgwyID9cNG+as9+hRsXf/7NmcskqVxAC5a1fg5ZfFIc7lyokvH5o1E3uRf/pJfFlw9ao4RPvKFfElxKOPij3DDz+ck1mPHsDnn4uB6Z3s2ye+GDl8OKescGFRl+efF+vPa0+zp44fF196NG8u9hDKYTaLS1IVKgQ0bap8Ajdyxfcm7/N2pgcOiFMwwsPFazsiwguVJCKikCHrfSnfZ8AHmPxOzmOz2aS//vpLsuU1exTlwsyU8WVuycmS1LSpJP3wQ+6/ZWRI0hNP5EyM1aWLmPDs/ffF7xqNJC1c6K6+kvTgg2KZV14RZYcOSdKoUZL0+OOSNGaMJB07JsqnThXLRURI0tGjouzMGUkqVy5nu2FhkvTzz+Jv69c7JumSpOPHc7Z54IAkde4sSdOn31wPkduOHTZJq81ZX1SUJK1e7T4Pq1Vk0bevJN11V+7JwYoXF22RJDEpGyAykiRJmjlT/F6liuukcnv2SFKHDmLCKXcT3d3O1auS9NprktSxoyTNmydJV67Ie7xcfI0q483cOHGc9+UnU3d9a7dLUvny4vWe1/+SUMf/JfIxM2WYmzLMTT613us5SJeJsyLKx8yU8VVuO3dKzsGrRiNJ8fFixnRJkqQNG8Ss14AkhYeLwbxjgGm35wxQ9XpJWrPGdb2LFom/3XWXJKWn374ONpskPfqoWP6hh8Ts2TVqiN/vu0+SnnxS3C9SRJJ2786ZOTwu7s7tuzm3Dz4Qj3vsMUk6e9azfK5dE19CtGsnSTqdqMOvv+b8/fjxnPz27xf1B8Qs5YGKr1Fl1JrxNVB99tlnUsWKFaWwsDCpfv360pYtW/Jctm/fvhKAXLeaNWt6vD1vzu7u4Pgf+PLLslcZEvi/RD5mpgxzU4a5yafWez0PVCQKIdeuicmx7HYxcZgkiUm9Hn5YTArXti1w7pyYkGz3bmDgwJzzrjUa4JNPxKHbVivQqZM4P91qFde3dszSPWKEmDTsdrRaYPZsMWnbL78ANWuKmcbLlBGHtC9dCrRsKQ4hf/hhcZjpPfcA77wjr73vvCMOdU9JEYekeyIiQhxuvnatOPf6xAmgUaOcv1euLM5HB0Q+v/wizu1+4QV5dSMKJYsXL8bQoUMxatQopKamonnz5mjXrh1O5zFV/8cff4z09HTn7cyZMyhWrBievXnGRhU8+aT4uWZNzvwaRERE3saJ44hCyIgR4jztMmWA334T51W/+KKYFGzXLrFMbCzw4YfinONbabXiElvh4WISt7FjxXnYTZoAp06JSc2GDfOsLuXLA9OmiS8NLl0CihUTA/QKFcTfV64U53ynporf33lHLCNXmTLyH+MQFeW+fPhwMQnbzp3i944d7/zFBFEoS0xMxIABA/Diiy8CAKZOnYoNGzYgOTkZEydOzLV8ZGQkIm+axOG7777Df//9h379+uW5DZPJBJPJ5Pw9KysrV7lWq4XBYIDFYoHdbncuq9PpoNfrYTabIUmSc3mbzQYAzvImTYDChY1IS9MgNRWoVStnewBgMBig0WhgvvkSEhCTDkmSBIvF4lIeFhYGu93uUq7RaGA0GmGz2WC1WnOVW61WZ73ktMlBr9dDp9PlKjcYDNBqtS4Zym2T47F2u91lPYHcJsC3/eR47K11CeQ2FUQ/ObZ382MCvU0F0U+O+phMpqBpk6/76ebM8tumW7dzOxyky6TRaBAdHQ3Nna5DRU7MTJnb5Xb9OjBqFJCenlNmNIqJ05o0cb++DRvE5GqAGGAXLSpmI3/wQaB/f3HN6uRkcYms2zEaxV7w1q3FnuTt28UNEJdVK1zY8zb27g38+qu4pNGiRWKPukNkpLiEV9u2Ypt5TVZ3q4J4vjVsKCZw++kn8ftLL/lsUwWCr1FlmJtnzGYz9uzZg/j4eJfyNm3aYMeOHR6tY+bMmXj88cdRwfEtnhsTJ07EmDFjcpUnJiYiPDwcABATE4NOnTph3bp1SHV8AwigZcuWaNWqFZYsWYLjx487y//44w80atQIM2bMwIULFwAA5cp1w+HDNbBmDbBhQ6LLB9NBgwYhMjISCQkJLnWIj49HZmYmkpOTnWVGoxEjR47EiRMnsGDBAmd5dHQ0YmNjsX//fqxevdpZXqVKFfTu3Rvbtm3D5s2bneVy29SxY0fUr1/fpU0A0KtXL1StWhWJiflrU3R0NP7++28sXrw4aNrky356+OGHER0djRUrVuCk41qdAd6mguinf/9/PdKkpKSgaVNB9lNSUlLQtQnwbT8lJSXlu03ZjtmOPcDZ3YkC0JAhYi/0rSIixJ7tWy/z9e+/4rrlaWnisZ98kvuxknTna6Df6sQJcTmwX38V609NVTbzuJJtqy0lRVyarVIlcXRCfmdcp9AWzO9NaWlpKFOmDLZv346mTZs6yydMmIC5c+fiyJEjt318eno6ypUrh4ULF6Jbt255LuduT3q5cuVw/vx5Z6be2PsyZ44WAwcaUKcOsGuXyeV/V7DvUWKb2Ca2iW1im5S3KSsrCyVKlPDovZ6DdJlsNhv279+PunXrQsdP5R5hZsrklZtjcAiIc8KLFxf3v/tO7NktWlRcCq1WLVF+5Ig4j3zfPqB6dXHtc29eOshiEXvCmzYFSpTw3nqVKsjn2w8/iEF6lSo+3YzP8TWqjDdzC4VB+o4dO9DkpkN9xo8fj3nz5uHwzdcbdGPixIn46KOPkJaWJutatfnJ9HZ9+99/QNmy4ogmx6UYSeD/EvmYmTLMTRnmJp9a7/WcOE4mq9WK1atXu3zLQ7fHzJRxl9t//wGOUzIHDwZGjxZ7xocMAVatEnvQ//tPDOJPnQJmzQLq1xcD9OLFxfXAvX1tX4NBXM/cHwboQME+3x5/PPAH6ABfo0oxN89ERUVBp9MhIyPDpfz8+fMoWbLkbR8rSRJmzZqF559/XtYAPb9u17dFi+ZMFPn/I23p//iakI+ZKcPclGFu8qmVGQfpRAFk8GDg7FngvvuASZNc/1a4sNijXauWOKz9gQeAAQPE3p7HHgN+/10M2ImICpLRaESDBg2QkpLiUp6SkuJy+Ls7mzdvxrFjxzBgwABfVlG2118XP9esAY4eVbcuREQUfDhIJ/JTkgRYrTqYTOISZ998I/aE63TAvHnu94gXKyYmiKtQQVy+TK8Xl1jbuNHzS5AREXnbsGHDMGPGDMyaNQuHDh1CXFwcTp8+jYEDBwIARo4ciT59+uR63MyZM9G4cWPUrl27oKt8W/fdJy7HJknAxx+rXRsiIgo2HKTLpNFoUKVKFc7mKwMzy81qFdfiLlJEzHC+YYMokyQx+dobbwCVKxsxbtw7iIwMQ3i4WB4A3n7b9brdtypTRpwnGRcH7NgBvPWWuHRaqODzTT5mpgxz81z37t0xdepUjB07FvXq1cOWLVuwdu1a52zt6enpua6ZnpmZiWXLlqmyF92Tvo2LEz/nzBGTcxJfE0owM2WYmzLMTT61MuPEcUQFTJLEYeizZ7uW33uv2BN+8GDej334YTEANxh8W0ciKlh8b/I+X2cqSUC9euJUooQE8YUoERFRXjhxnA9ZrVZs2rSJEy7IwMxcvfWWGKBrteIwyVdfFZO6ZWSIAXpYGPDss8Dy5TasXr0Nly5ZkZkJZGYCW7ZwgH4nfL7Jx8yUYW7By5O+1Why9qZ/+qm40kWo42tCPmamDHNThrnJp1ZmHKTLZLPZsHnzZpdr9dHtMbMckycDU6aI+zNmAK+9Jq53np4uJiBasEAM1pcsAdq3t2LPnh9RuLANd98N3H134F1LXA18vsnHzJRhbsHL07597jmgZEkxoee33xZQ5fwYXxPyMTNlmJsyzE0+tTLjIJ2oAEgS8NFHOYdDTpmScyk1QOwd79AB6NkTuOceVapIREQyhYUBsbHi/uTJ4n89ERFRfnGQTqSQJIlLnqWn3365CxeAjh2BN98Uv48YkXOfiIgC26uviktg7tsHrFundm2IiCgYcJAuk1arRUxMDLShNF12PgVrZh99JC7B06qVuBa5Oz/+CNStKwbzYWHivMWEBM/WH6y5+Rpzk4+ZKcPcgpecvi1eHPj/leQwfnxo703na0I+ZqYMc1OGucmnVmac3Z1IgQMHgIYNAbNZ/D54sBiA32zaNHHOuSQBNWsCixYBDzxQ8HUlIv/H9ybvK8hM09OBSpUAkwnYtAlo2dKnmyMiogDE2d19yGKxYNWqVbBwGlePBVtmZjPw/PPip2PQPW0akJKSs8z8+cCQIWKA/tJLwO7d8gfowZZbQWFu8jEzZZhb8JLbt6VKAf37i/vjx/uwYn6Orwn5mJkyzE0Z5iafWplxkC6T3W5Hamoq7Ha72lUJGMGW2ejRwP794hDHDRvE+YiAmAjuv//Eoe2OSeFefx344gsgIkL+doItt4LC3ORjZsowt+ClpG+HDwd0OvGF7e7dPqycH+NrQj5mpgxzU4a5yadWZhykE8mwYwcwaZK4/8UXwL33it+rVROX4OnaVVzj3GoFevcGEhN52TQiolBQqRLQq5e4H8p704mIKP84SCfywLVr4hD2nj0Bu10c7v700+JvhQsD8+YBWq04F/HGDXE5tVmzRBkREYWGkSPFF7MrV4q5S4iIiJTQq12BQKPT6dCyZUvodDq1qxIw/Dmzv/8Gtm7NezZemw346Sdg+XIxUAeA8uWBTz5xXa5xY+Ddd4ExY4CmTYElS8S1z/PDn3PzZ8xNPmamDHMLXkr79v77xRe4334LjBsHLF7sowr6Kb4m5GNmyjA3ZZibfGplxtndKSRJktjT/dpreV8+7VZVqohD2F95RUwS5G6d+/cDtWrlf4BORKGF703ep1amv/8uLr2p0Yj3hDp1CmzTRETkxzi7uw+ZzWbMnz8fZse1t+iO1MrMYhGHHpYqJQ5P37hR7Bm/fBno3h148UUxQK9bF2jbNu/bq6+Kc9H/+ktMGudugA6ID2T16nlvgM7nmjLMTT5mpgxzC1756dsHHhBzk0iSOLoqlPA1IR8zU4a5KcPc5FMrMx7uLpMkSTh+/DhC7ACEfFEjs+PHgeeey5lhd/58cStVSsy++88/gF4vJvd5803/PHeczzVlmJt8zEwZ5ha88tu3778vDnlftgzYt098gRsK+JqQj5kpw9yUYW7yqZWZHw5NiPJn/nzxgWj3bqBoUSA5GYiNFZdMS08XA/QqVYDt24ERI/xzgE5ERIGrVi2gRw9x//331a0LEREFHg5PKGhcuQL06SMObb96FWjRQpwPOHAg8NlnQFqamHF3yhRg716gUSO1a0xERMHqvffEl8CrVgG//aZ2bYiIKJBwkC6TXq9Hx44dodfzTAFPeSuzGzeAb74R16EdPlwMwB127wZiYnIuhTZmjJiVvVy5nGWMRqBTJ3F4eyDMy8TnmjLMTT5mpgxzC17e6Nv778+5bnqo7E3na0I+ZqYMc1OGucmnVmac3Z383s6dwFdfifP7rlxx/VudOkCTJmKmdqtVXB5t4UKgWTN16kpEpATfm7zPHzL96y+gRg0xaenOncBDD6lSDSIi8gOc3d2HzGYzpk+fzlkRZVCamckEDB0qrjs+e7YYoFeoIM4j79pV7Bk/cAD48ksxQH/2WTFBT7AM0PlcU4a5ycfMlGFuwctbfVutmjgNCxCHvwc7vibkY2bKMDdlmJt8amXGYx1kkiQJFy5c4KyIMtwus/PnxaRuhw+LQXbv3mJSt0OHxOzsjkPa+/QRl0xr1ixnorf//gOWLgW+/x7o3Bl44QVxGbRgweeaMsxNPmamDHMLXt7s23ffFadipaQAW7cCzZt7oYJ+iq8J+ZiZMsxNGeYmn1qZcZBOqklJEYPvjAzx+59/iuuQN24s9pBfvw5ERQFz5gAdOuR+fNGiwMsvixsREZE/qlQJ6N9fHPX13nvAzz+rXSMiIvJ3PNydCpzZLA5Zb9NGDNBr1RKXSWvbVuwl//VXMUB//HHg99/dD9CJiIgCxahR4hStTZvEpKZERES3w4njZLLb7Thx4gQqV64MLS+w7ZGbM9NotHjqKWD1avG3gQOBjz4CIiLE7+np4hD2QoWAAQNC+xrmfK4pw9zkY2bKeDM3f5jkLNjkJ1NfvCYGDxaXA23WTBz2HkynZznwf4l8zEwZ5qYMc5NPrfd6DtKpQH3+OTBoEBAWBixaBHTponaNiIjUx/cm7/O3TNPSxJwr2dnA+vXi6DEiIgodnN3dh0wmEyZOnAiTyaR2VfyWzQb89htgsYjfHZkdPGjCG2+IsoQEDtDvhM81ZZibfMxMGeYWvHzRt6VLiy+pATGZXDDuIuFrQj5mpgxzU4a5yadWZhykK8DLFuTNagWefhp48EFxSN/x46L8xg0LBgww4Pp14JFHgNdeU7eegYLPNWWYm3zMTBnmFrx80bdvvSVO79q9W1yZJBjxNSEfM1OGuSnD3ORTIzMO0km2jAwxqduIEeKwPQdJEjOtr1wpft+9G6hXD1i0SIsdO5rhl1+0uPtuMVs7T4MhIqJQU7Ik8Oqr4v4HHwTn3nQiIso/DpVItqlTgR9/BKZMEZdLO3RIlL/1FjB7thiAf/65uBbs1atAv34G/PjjowCATz8FypdXr+5ERERqeuMNMTnqrl3Axo1q14aIiPwRJ46TyW634+LFi4iKigrJWREtFqBcOeDcOfEh48YN8fOpp4BvvhHLzJoF9Osnzk0fPx4YM0aC3a5B584Sli/XBOWMtr4Q6s81pZibfMxMGW/m5m+TnAWD/M7u7svXxLBhQFIS0LQpsG1b8Mz0zv8l8jEzZZibMsxNPrXe69k7Mmk0GkRGRkITLO+oMq1eLQboJUsCR4+Ka53fuJEzQJ8yRQzQAUCnA957T3wAee89K2bNCp4PIgUh1J9rSjE3+ZiZMswtePm6b4cPF1c52bED+Plnn2xCFXxNyMfMlGFuyjA3+dTKjIN0mcxmMxISEvx+0oUjR4DYWGDhQuD6de+t96uvxM9+/YCyZYF168TAPCoKGD0aePPN3I+pX98MrXY8IiL8OzN/EyjPNX/D3ORjZsowt+Dl674tVUrM4QIAY8f6ZBOq4GtCPmamDHNThrnJp1Zmqg/Sp0+fjkqVKiE8PBwNGjTA1q1bb7v8Z599hho1aqBQoUKoXr06vv766wKqaWAZOhRITgZ69RJ7vV94Adi8OX/r/PtvYMMGcf/FF8VPrVYMzM+fB95/P3/rJyKi4CX3/d5kMmHUqFGoUKECwsLCUKVKFcyaNauAaut7I0YARqN4b87v+zMREQUXVQfpixcvxtChQzFq1CikpqaiefPmaNeuHU6fPu12+eTkZIwcORKjR4/Gn3/+iTFjxuDVV1/F6tWrC7jm/u3iRSAlRdyvUEFM3jZ3LtCqFTBxovL1zpolZqJ99FGgShXXv/GoGSIiyovc93sA6NatG3788UfMnDkTR44cwaJFi3D//fcXYK19q2xZoH9/cf+DD9StCxER+Re9mhtPTEzEgAED8OL/d8tOnToVGzZsQHJyMia6GU3OmzcPr7zyCrp37w4AqFy5Mn755RdMmjQJHTt2LNC6+7Nly8SkbfXrA7/9Js55++orMVB/+21xaPpLL8lbp80mBumA/McSEVFok/t+v379emzevBknTpxAsWLFAAAVK1a87TZMJhNMJpPz96ysrFzlWq0WBoMBFosFdrvduaxOp4Ner4fZbIYkSc7lbTYbADjLHQwGA7Rarcv2HOUajSbXYZFGoxGSJMFisbiUx8eHYcYMCT/+qMG2bWY8+KAEjUYDo9EIm80Gq9XqXNZRbrVanfWS0yYHvV4PnU7nkzY5Hmu3213WE8htAoCwsDDY7XaXcm+1yfHYW+sSyG0qiH5ybO/mxwR6mwqinxz1MZlMQdMmX/fTzZnlt023bud2VBukm81m7NmzB/Hx8S7lbdq0wY4dO9w+xmQyITw83KWsUKFC2LVrFywWCwwGg9vHeOtNGwAkScLw4cNhNBr99sm7cKEBgBY9egBWqwUNG9rRsCFQsqQOkyfrMXAgcPfdFnTuLNpqNgM//6xHWJgOjzzivk1r1ljwzz8GFC8uoX17M+x2z9skSRLi4+Oh1+v5pi2jTUajEcOGDXP5wBjobSqIfpIkCXFxcc7cgqFNvu6nmzOzWq1B0aaC6CdHbgaDIVfd5bYpmM8PVPJ+v2rVKjRs2BCTJ0/GvHnzULhwYXTq1AkffPABChUq5PYxEydOxJgxY3KVJyYmOj87xMTEoFOnTli3bh1SU1Ody7Rs2RKtWrXCkiVLcPz4cWf5oUOH0KBBA8yYMQMXLlxwlvfq1QtVq1ZFYmKiS98NGjQIkZGRSEhIcKlDfHw8MjMzkZyc7CwzGo0YOXIknnzyCr777m4MHnwQXbqsRHR0NGJjY7F//36XIwWrVKmC3r17Y9u2bdh80/HxctvUsWNH1K9f32dtio+Px+nTp7Fw4UJneaC3aeTIkThx4gQWLFjg9Ta1aNEC8fHxWLJkCU6cOBEUbSqIfnJ8nk9KSgqaNhVkPyUlJQVdmwDf9lNSUlK+25SdnQ1PqXYJtrS0NJQpUwbbt29H06ZNneUTJkzA3LlzceTIkVyPefvttzF79mysWbMG9evXx549e9ChQwecP38eaWlpKFWqVK7HjB492u2bdnx8fK437VWrVrnt6Pnz57t0dMuWLdGiRQt8/vnnbjt64sSJ+X7yHjt2zG1H79271+2Td9OmTdi8eTOysoogMTEOgAanTgH79+e0SZKAPXtewpo1paHX29Cu3VpkZJTEH3/Uxo0bEQCA7t03o0aNTbnaVKvWERw8WB0PPbQTTzyxUXabBgwYgMzMTLdv2ndqk4PcfnK8IKdPn+53/eRJm1q0aIE5c+bgzJkzQdOmYOwntoltGjFiBK5cuZKvNpUuXRovv/xyUF6CTcn7/RNPPIFNmzbh8ccfx3vvvYeLFy8iNjYWjz76aJ7npbv7Ur5cuXI4f/68M1NPv8ix2+24dOkSSpYsCYPB4NMvp3butKNpUy3CwiQcP25GdLR/fzl1uzbZ7XZcuXIFxYoVc6mjv3/hdrs2Ab79ElGj0eDy5cu4++67XWaPDuQ2FUQ/2Ww2ZGRkoHjx4s7LYgV6mwqin6xWKy5duoTixYvDYDAERZt83U+O94PixYsjPDw8X23KyspCiRIlPHuvl1Ry9uxZCYC0Y8cOl/Jx48ZJ1atXd/uY69evS/369ZP0er2k0+mk0qVLSyNGjJAASOfOnXP7mOzsbCkzM9N5O3PmjARAOn/+vJSdnS1lZ2dLZrNZkiRJMpvNzrLs7GzJYrFIkiRJJpPJWZaZmSmNHj1ays7OdinPzs6WbDabc5u3ltvt9lzldrtdstlsucolScpVbjKZJEmSJKvV6rbcYrFI2dnZ0pQpFgmQpCZNbG7blJ1tkZ5+WpLEkD3nFhlplwBJ0mjs0vz5Zpe6r1ghSTqd+Htqqkl2mxyZXb9+XVGblPRTdna2ZLVa3Zb7Qz950qbs7Gxp9OjRUmZmZtC0qSD6yfF8c+QWDG3ydT/dnFmwtKkg+smR240bN/LdpgsXLkgApMzMTCnYKHm/b926tRQeHi5dvnzZWbZs2TJJo9FI169f92i7mZmZijPNzs52vtf7mt0uSQ0aiPfihASfb86nCjK3YMHMlGFuyjA3+byZmZz3JdUOd4+KioJOp0NGRoZL+fnz51GyZEm3jylUqBBmzZqFL774AufOnUOpUqXw5ZdfokiRIoiKinL7mLCwMISFhXlU7u5weUB8EySn3N328irXaDRuy7VardtynU4HnU6Xq1yv10Ov1+Pbb8XvPXuKbxXdtWnBAsBkAn76CejSBXj+eeCxxzR4/XVg+nQN+vUzIDoaaN4cePVV4PPPAUCDTp2AevVc21wQbbpVMPTTrdy1yfGNobvnaqC2CSi4fro5t2Bp08282SbHt9BhYWHOZQK9Te74qk0ajSbPunvaJjnnqQUaJe/3pUqVQpkyZRAZGeksq1GjBiRJwj///INq1ar5tM4FSaMR77X9+4ursrz5JuDm6U5ERCFEtdndjUYjGjRogBTHNOT/l5KS4nI4nDsGgwFly5aFTqfDN998gyeffNJ5qEsoO3kS+PVXcVm0Z57Je7mwMGDVKjHr+/z5QNu2gF4PfPIJ0L07YLEAXbsCDRo4BujA8OHA0qUF0w4iIgoeSt7vmzVrhrS0NFy9etVZdvToUWi1WpQtW9an9VVDjx5AsWLiUqdr16pdGyIiUpuqI9thw4ZhxowZmDVrFg4dOoS4uDicPn0aAwcOBACMHDkSffr0cS5/9OhRzJ8/H3/99Rd27dqFHj164I8//sCECRMKtN557XVR2+LF4ucjjwD33nv7ZTWa3JdN0+mAr78GWrcGrl0DDh0S11jfsAGYPFlcz1Upf83M3zE3ZZibfMxMGebmGbnv9z179kTx4sXRr18/HDx4EFu2bMHw4cPRv3//PCeO87aC7NtChXIux/bZZwW2WZ/ga0I+ZqYMc1OGucmnRmaqTRznMH36dEyePBnp6emoXbs2kpKS0KJFCwDACy+8gFOnTmHTpk0AxCyrPXv2xJEjR2AwGPDII49g0qRJqF69usfby8rKQmRkZFBOzlO3LvD77+Jya/+/yo0iV68CAwYABgOQmAiUKOG9OhIRUW7B/N7kIOf9HgAOHz6MIUOGYPv27ShevDi6deuGcePGeTxID7RMT5wAqlYVM8UcPQoE0RH9REQEee9Lqg/SC1p+37TtdjtOnDiBypUr+9Uh9gcPArVqiYF1RoY4bM5f+Gtm/o65KcPc5GNmyngzt0AbUAaC/GSq1muiQwdxuHtcnPiSPNDwf4l8zEwZ5qYMc5NPrfd69o5MFosFCxYsyDX1vi/knoPd/XJms3hDB4A2bfxrgA4UbGbBhLkpw9zkY2bKMLfgpVbfxsaKn7NnA9evF+imvYKvCfmYmTLMTRnmJp9amXGQ7qeuXQMeeEBMAnfz7ckngfT0nOVsNqBPH2DjRqBwYcDNJeGJiIgoADzxBFCxInD5MrB8udq1ISIitXCQ7qfWrwf++CN3+fffi8H72rViz/prr4kJ4wwG8YbeoEHB15WIiIjyT6fLmUBu5kx160JEROrhIF0mjUaD6OhoaG6dGt3LVq0SP199FTh/Xtz27hWTw128KM5ba9kSmD5dzNI+b5441N0fFVRmwYa5KcPc5GNmyjC34KVm3/btK97XN20Cjh8v8M3nC18T8jEzZZibMsxNPrUy48RxfshqFZc++/dfYPNm4P+T3wIAsrOBt94S1zR3mD4dGDSo4OtJRETeEQjvTYEmkDN94glx+dO33wbGj1e7NkRE5A2cOM6HbDYb9u7dC5vN5rNt7NghBujFigFNm7r+LTwc+PhjYM0acWh7UpL/D9ALIrNgxNyUYW7yMTNlmFvwUrtvBwwQP+fMEXPPBAq1cwtEzEwZ5qYMc5NPrcw4SJfJarVi9erVsFqtPtvGypXi55NPAnq9+2U6dAB++w0YOtRn1fCagsgsGDE3ZZibfMxMGeYWvNTu206dgOLFgbQ0sUc9UKidWyBiZsowN2WYm3xqZcZBup+RpJxBeqdO6taFiIiICl5YGNC7t7jPCeSIiEIPB+l+5tAhMVGM0ei/E8ERERGRbzkOeV+1SkweS0REoYODdJk0Gg2qVKnisxn+HLO6P/YYUKSITzZR4HydWbBibsowN/mYmTLMLXj5Q9/WqQM8+KCYTHb+fNWqIYs/5BZomJkyzE0Z5iafWplxdnc/06QJ8MsvQHIyMHCg2rUhIqKC4O/vTYEoGDL9/HMxOWzNmsAff4hLsxERUWDi7O4+ZLVasWnTJp9MHpCRAfz6q7jfsaPXV68aX2YWzJibMsxNPmamDHMLXv7St889J67qcvAgsHevqlXxiL/kFkiYmTLMTRnmJp9amXGQLpPNZsPmzZt9Mg3/mjVi4riGDYEyZby+etX4MrNgxtyUYW7yMTNlmFvw8pe+jYzMmUQ2EA5595fcAgkzU4a5KcPc5FMrMw7S/YjjfPSnnlK3HkREROQfHLO8L1okzk8nIqLgx0G6n7h2DUhJEfd56TUiIiICgCeeENdMP3cO+PFHtWtDREQFgYN0mbRaLWJiYqDVeje6H34AsrOBihXFjK7BxFeZBTvmpgxzk4+ZKcPcgpc/9a3BAPToIe77+yHv/pRboGBmyjA3ZZibfGplxtnd/UT//sDs2cBrrwEff6x2bYiIqCD563tTIAumTH/5RVz9JSJC7FG/6y61a0RERHJxdncfslgsWLVqFSwWi9fWabOJSeOA4Dwf3ReZhQLmpgxzk4+ZKcPcgpe/9W3jxkCVKsD168B336ldm7z5W26BgJkpw9yUYW7yqZUZB+ky2e12pKamwm63e22dv/wCXLgA3HMP0Ly511brN3yRWShgbsowN/mYmTLMLXj5W99qNDkTyPnzIe/+llsgYGbKMDdlmJt8amXGQbofcMzq3r69OPeMiIiI6GaOQXpKCpCRoW5diIjItzhI9wMrV4qfnNWdiIiI3KlaFXjoIcBuB775Ru3aEBGRL3GQLpNOp0PLli2h0+m8sr4jR8TNYADatfPKKv2OtzMLFcxNGeYmHzNThrkFL3/tW38/5N1fc/NnzEwZ5qYMc5NPrcw4u7vKpkwBRowA2rQBNmxQuzZERKQGf3tvCgbBmOmFC0CpUmLC2ePHgcqV1a4RERF5irO7+5DZbMb8+fNhNpu9sj7H+ejBfKi7tzMLFcxNGeYmHzNThrkFL3/t2+hooFUrcX/pUlWr4pa/5ubPmJkyzE0Z5iafWplxkC6TJEk4fvw4vHEAwoULwI4d4n4wD9K9mVkoYW7KMDf5mJkyzC14+XPfPvus+OmPg3R/zs1fMTNlmJsyzE0+tTLjIF1F338vJoCJiQHKlVO7NkREROTvunQBtFpgzx7gxAm1a0NERL7AQbqKHLO6P/WUuvUgIiKiwFCiRM4h799+q2pViIjIRzhIl0mv16Njx47Q6/X5Xte2beJnsM7q7uDNzEIJc1OGucnHzJRhbsHL3/vWXw959/fc/BEzU4a5KcPc5FMrM87urpIbN4CICHH/33+BokVVqwoREanMX96bgkkwZ3r+vJjl3W4Xh7xXqqR2jYiI6E44u7sPmc1mTJ8+Pd8z/J09K34WLgzcc0/+6+XPvJVZqGFuyjA3+ZiZMswtePl735YoAbRsKe770yHv/p6bP2JmyjA3ZZibfGplxkG6TJIk4cKFC/me4e+ff8TPsmUBjcYLFfNj3sos1DA3ZZibfMxMGeYWvAKhb/3xkPdAyM3fMDNlmJsyzE0+tTLjIF0lZ86In2XLqlsPIiKigjB9+nRUqlQJ4eHhaNCgAbZu3Zrnsps2bYJGo8l1O3z4cAHW2L917Spmed+9Gzh1Su3aEBGRN3GQrpKb96QTEREFs8WLF2Po0KEYNWoUUlNT0bx5c7Rr1w6nT5++7eOOHDmC9PR0561atWoFVGP/V7Ik0KKFuO9Pe9OJiCj/OLWfTAaDAb169YLBYMjXehyD9FC4Prq3Mgs1zE0Z5iYfM1OGuXkuMTERAwYMwIsvvggAmDp1KjZs2IDk5GRMnDgxz8eVKFEC93g4cYvJZILJZHL+npWVlatcq9XCYDDAYrHAbrc7l9XpdNDr9TCbzZAkCXa7Hd26dYNWK/ZlOModDAYDtFqty/Yc5RqNJte5i0ajEZIkwWKxuJSHhYXBbre7lGs0GhiNRthsNlit1lzlVqsVNpsNANClixabNhmwbBkwdOjt2+Sg1+uh0+l80ia73Y5evXpBp9O5rEdOmwDP+6kg2gTkv59u1yaNRoNevXpBkiSXegZymwqin3Q6Hbp16wa73e58XKC3qaD6yZGb1WoNmjY5+KKfbs5MkqR8tenW7dwOB+kyabVaVK1aNd/rCaU96d7KLNQwN2WYm3zMTBnm5hmz2Yw9e/YgPj7epbxNmzbYsWPHbR8bExOD7Oxs1KxZE++88w4eeeSRPJedOHEixowZk6s8MTER4eHhzvV16tQJ69atQ2pqqnOZli1bolWrVliyZAmOHz/uLO/YsSPq16+PGTNm4MKFC87yXr16oWrVqkhMTHT5YDpo0CBERkYiISHBpQ7x8fHIzMxEcnKys8xoNGLkyJE4ceIEFixY4CyPjo5GbGws9u/fj9WrVzvLq1Spgt69e2Pbtm3YvHkzACAr6y4Ab+DXX4EFC37C33//4hdtOnbsmOI2AcHXT560af78+UHXJl/305IlS4KuTcHYT2xTTpuys7PhKV6CTSaTyYTExEQMGzYMYWFhiutRvz6QmgqsWQN06KB4NQHBW5mFGuamDHOTj5kp483cgvlyYWlpaShTpgy2b9+Opk2bOssnTJiAuXPn4siRI7kec+TIEWzZsgUNGjSAyWTCvHnz8Pnnn2PTpk1o4TjG+xbu9qSXK1cO58+fd2bq6d4Xk8mEadOmYejQoYiIiPDrPUotWhiwa5cWn31mw4ABOcursefPZDLhs88+w9ChQ51HIShpUyjszXSwWq345JNPMHjwYBiNxqBoU0H0U3Z2NpKSkjB48GDn/99Ab1NB9NONGzcwbdo0DB48GBEREUHRJl/3k+P9YPDgwShSpEi+2pSVlYUSJUp49F7PPekKeGMK/lDakw54J7NQxNyUYW7yMTNlmJvnNLdcykSSpFxlDtWrV0f16tWdvzdp0gRnzpzBhx9+mOcgPSwszO2XJe7K8zpF4eZBksVigU6ny1V+67o9LddoNG7LtVqt23KdTufc/s30ej30+pyPb126ALt2AatX6xAbm3v5vOruqzaZzeZ8t8nBk37ypNwf+snBXZvMZjOMRqPb9QdqmwDf9pNWq4XFYsn1+g7kNhVEPzkGkmFhYc5lAr1N7ni7TY7MHJOYKm2TnC/0OXGcCrKzAceRFqFwTjoREYWuqKgo6HQ6ZGRkuJSfP38eJUuW9Hg9Dz30EP766y9vVy/gPfWU+PnTT8D/T8MnIqIAx0G6CtLSxM9ChYCiRdWtCxERkS8ZjUY0aNAAKSkpLuUpKSkuh7/fSWpqKkqVKuXt6gW8++8H7rsPMJuB9evVrg0REXkDz0mXyW634+LFi4iKinI530qOzZuBVq2AatWAo0cVrSKgeCOzUMTclGFu8jEzZbyZWzCfkw6IS7A9//zz+Pzzz9GkSRN8+eWX+Oqrr/Dnn3+iQoUKGDlyJM6ePYuvv/4agJj9vWLFiqhVqxbMZjPmz5+PhIQELFu2DF27dvVom/nJNNBeEyNGAFOmAM89ByxcqF49Ai03f8DMlGFuyjA3+dR6r+c56TJpNBpERkbmeR6dJ0LtfHRvZBaKmJsyzE0+ZqYMc/Nc9+7dcenSJYwdOxbp6emoXbs21q5diwoVKgAA0tPTXa6Zbjab8eabb+Ls2bMoVKgQatWqhe+//x7t27cvkPoGWt927iwG6WvXij3qeZx26XOBlps/YGbKMDdlmJt8amXGr1BkMpvNSEhIyNdkQaF0jXTAO5mFIuamDHOTj5kpw9zkiY2NxalTp2AymbBnzx6XCeDmzJmDTZs2OX8fMWIEjh07hhs3buDff//F1q1bC2yADgRe3zZuDJQoAWRmiqP11BJoufkDZqYMc1OGucmnVmYcpKvgzBnxM1T2pBMREZHv6HRAp07i/nffqVoVIiLyAg7SVRBqh7sTERGRb3XuLH6uXAmE1mxDRETBh4N0FXCQTkRERN702GNA4cLA2bPAnj1q14aIiPKDs7vLJEkSzGYzjEaj4gkE7r0XOHcOSE0F6tVTtIqA4o3MQhFzU4a5ycfMlPFmbsE+u7sa8pNpoL4mnnkGWLYMGDUKGDeu4LcfqLmpiZkpw9yUYW7yqfVezz3pMkmShMzMTCj9bsNkEgN0IHT2pOc3s1DF3JRhbvIxM2WYW/AK1L69+ZB3NQRqbmpiZsowN2WYm3xqZcZBukwWiwXJycmwWCyKHp+WJn6GhQHFi3uxYn4sv5mFKuamDHOTj5kpw9yCV6D2bfv2YhK5P/4Ajh0r+O0Ham5qYmbKMDdlmJt8amXGQXoBu/l8dB5lQkRERN5SrBjQsqW4r9bedCIiyj8O0gtYqF0jnYiIiAqO2oe8ExFR/skepFesWBFjx47F6dOnfVGfgGA0GhU/NlSvkZ6fzEIZc1OGucnHzJRhbsErUPv2qafEz+3bgfPnC377gZqbmpiZMsxNGeYmnxqZyZ7d/dNPP8WcOXOwf/9+PPLIIxgwYAC6dOmCsLAwX9XRq9SeQfe114BPPwXi44GJEwt880RE5IfUfm8KRqGcaYMGwN69wMyZQP/+ateGiIgAH8/uPmTIEOzZswd79uxBzZo18dprr6FUqVIYPHgw9u7dq7jSgcJut+PYsWOw2+2KHh+K10jPb2ahirkpw9zkY2bKMLfgFeh969ib/t13BbvdQM9NDcxMGeamDHOTT63MFJ+TXrduXXz88cc4e/Ys3n//fcyYMQMPPvgg6tati1mzZnk8Tf306dNRqVIlhIeHo0GDBti6dettl1+wYAHq1q2LiIgIlCpVCv369cOlS5eUNkM2i8WCBQsWKJ7hLxTPSc9vZqGKuSnD3ORjZsowt+AV6H3rOC89JQW4dq3gthvouamBmSnD3JRhbvKplZniQbrFYsGSJUvQqVMnvPHGG2jYsCFmzJiBbt26YdSoUejVq9cd17F48WIMHToUo0aNQmpqKpo3b4527drleb77tm3b0KdPHwwYMAB//vknli5dit27d+PFF19U2owCF6rnpBMREVHBqFMHqFQJyM4GNm5UuzZERCSX7EH63r17MWTIEJQqVQpDhgxBrVq18Mcff2Dbtm3o168fRo0ahVWrVmHFihV3XFdiYiIGDBiAF198ETVq1MDUqVNRrlw5JCcnu13+l19+QcWKFfHaa6+hUqVKePjhh/HKK6/gt99+k9sMVZjNwLlz4j4H6UREROQLGk3O3vSCPuSdiIjyTy/3AQ8++CBat26N5ORkdO7cGQaDIdcyNWvWRI8ePW67HrPZjD179iA+Pt6lvE2bNtixY4fbxzRt2hSjRo3C2rVr0a5dO5w/fx7ffvstOnTokOd2TCYTTCaT8/esrKxc5VqtFgaDARaLxeV8A51OB71eD7PZ7Dx832w2IyoqChqNxqUcAAwGA7Rarcv2HOUajQanTpkhSWEwGiUUKWKGJBkhSVKuwyfCwsJgt9tdyjUaDYxGI2w2G6xWa65yq9UKm83mLJfTJgDQ6/XQ6XSy22Q2m13KjcbcbTKbzYiOjoYkSS7rCeQ2Ab7vJ41Gg6ioKJf6BHqbCqKfzGYzihcv7vx7MLTJ1/10c2bB0qaC6CdHbgBy1V1um27NidSl0WgQHR0NjUajdlUU69wZSEoC1qwBrFZAL/sTn3zBkFtBY2bKMDdlmJt8amUme3b3v//+GxUqVMj3htPS0lCmTBls374dTZs2dZZPmDABc+fOxZEjR9w+7ttvv0W/fv2QnZ0Nq9WKTp064dtvv3X7ZQEAjB49GmPGjMlVHh8fj/DwcABATEwMOnXqhFWrViE1NdW5TMuWLdGqVSvMnz8fx48fd5Z37NgR9evXx/Tp03HhwgVnea9evVC1alVMnDjR5QPXoEGDEBkZidjYBZg1qz+KFv0Xr7/+KeLj45GZmely5IDRaMTIkSNx7NgxLFiwwFkeHR2N2NhY7N27F6tXr3aWV6lSBb1798amTZuwefNmZ3lBtSkhISFXrmwT28Q2sU1sk7w2lS5dGi+//HJIzkTuK6E8uzsgBualSgEXLwI//wy0aqV2jYiIQpuc9yXZg/Tdu3fDbrejcePGLuW//vordDodGjZs6NF6HIP0HTt2oEmTJs7y8ePHY968eTh8+HCuxxw8eBCPP/444uLi0LZtW6Snp2P48OF48MEHMXPmTLfbcbcnvVy5cjh//rwzHDl7X2w2Gw4ePIiYmBjYbDZZe1/mz7eiTx8DHn7Yjh9+sATVHiUHd22y2Ww4fPgw6tSp41KXQG4TUDB70vfs2YPatWtDp9MFRZsKop+ys7Nx4MAB1KlTBzqdLija5Ot+slgszsyMRmNQtKkg+slms+HAgQNo0KABtFptvtp05coVREdHh+yA0hfyM0i32WzYv38/6tat6/z/G4j69QPmzBGXf/34Y99vL1hyK0jMTBnmpgxzk8+bmcl5X5J98NOrr76KESNG5Bqknz17FpMmTcKvv/7q0XqioqKg0+mQkZHhUn7+/HmULFnS7WMmTpyIZs2aYfjw4QCABx54AIULF0bz5s0xbtw4lCpVKtdjwsLC3F7D3V15Xnvjb76Avclkwvfff486derkeW34vMrPnRPrL19e61xGo9G4XV6r1bot1+l0bp8ger0eejfHsnnSJk/K5bT11jaZTCasXr0atWrVCpo2Ofiyn0wmE9atW4e6devm2kagtgnwfT/pdDqsX78e9erVcy4T6G3ydT/Z7XZnZo5lAr1N7ni7TSaTySW3/LTp1i8ESF1Wq9X5vhXIH2Q7dxaD9JUrgalTxbnqvhQsuRUkZqYMc1OGucmnVmayJ447ePAg6tevn6s8JiYGBw8e9Hg9RqMRDRo0QEpKikt5SkqKy+HvN7t+/Tq0WtcqO8KSeUCAKkLxGulERESkjtatgUKFgL//Bn7/Xe3aEBGRp2QP0sPCwnDOMUX5TdLT093u0bidYcOGYcaMGZg1axYOHTqEuLg4nD59GgMHDgQAjBw5En369HEu37FjRyxfvhzJyck4ceIEtm/fjtdeew2NGjVC6dKl5TalwIXiNdKJiIhIHRERQNu24j5neSciChyyB+mtW7fGyJEjkZmZ6Sy7fPky3n77bbRu3VrWurp3746pU6di7NixqFevHrZs2YK1a9c6J6ZLT093uWb6Cy+8gMTEREybNg21a9fGs88+i+rVq2P58uVym6GYRqNBlSpVFM3wl5YmfgbA9wlelZ/MQhlzU4a5ycfMlGFuwSuY+vapp8TPghikB1NuBYWZKcPclGFu8qmVmeyJ486ePYsWLVrg0qVLiImJAQDs27cPJUuWREpKCsr5+W5iNWd7feAB4MABICUFePzxAt00ERH5sVCfidwXmKlw8SJQsiRgtwMnTwIVK6pdIyKi0CTnfUn2nvQyZcrg999/x+TJk1GzZk00aNAAH3/8MQ4cOOD3A3RvsFqt2LRpk8usvJ66dk38LFzYy5Xyc/nJLJQxN2WYm3zMTBnmFryCqW+jooDmzcX9Vat8u61gyq2gMDNlmJsyzE0+tTKTPUgHgMKFC+Pll1/GZ599hg8//BB9+vTJczbdYGOz2bB582aXy/546vp18TMiwsuV8nP5ySyUMTdlmJt8zEwZ5ha8gq1vC+qQ92DLrSAwM2WYmzLMTT61MpN9CTaHgwcP4vTp07mugdupU6d8VypYheqedCIiIlLPU08Bw4YBW7YAly4BxYurXSMiIrod2YP0EydOoEuXLjhw4AA0Go3z0meOk+n5zYx7khS6e9KJiCgwnTlzBhqNBmX/f+3QXbt2YeHChahZsyZefvlllWtHnqpcWcyL8/vvwPffAzddOIeIiPyQ7MPdX3/9dVSqVAnnzp1DREQE/vzzT2zZsgUNGzbEpk2bfFBF/6LVahETE5Preu13YrEAju8vQm2QrjSzUMfclGFu8jEzZUIht549e+Lnn38GAGRkZKB169bYtWsX3n77bYwdO1bl2vlOMPZt587i58qVvttGMObma8xMGeamDHOTT63MZM/uHhUVhZ9++gkPPPAAIiMjsWvXLlSvXh0//fQT3njjDaSmpvqqrl6h1myv//0HFCsm7pvNQIicwk9ERB7w15nIixYtil9++QXVq1fHJ598gsWLF2P79u3YuHEjBg4ciBMnTqhdxTz5a6Zq2bsXaNBA7Ci4eBEoVEjtGhERhRafzu5us9lw1113ARAD9rT/X/y7QoUKOHLkiILqBhaLxYJVq1bBYrHIepzjUHe9PvQG6EozC3XMTRnmJh8zUyYUcrNYLAgLCwMA/PDDD855Z+6//36kp6erWTWfCsa+jYkBypUTn0dSUnyzjWDMzdeYmTLMTRnmJp9amckepNeuXRu///47AKBx48aYPHkytm/fjrFjx6Jy5cper6C/sdvtSE1Nhd1ul/W4UD4fXWlmoY65KcPc5GNmyoRCbrVq1cLnn3+OrVu3IiUlBU888QQAIC0tDcWDePaxYOxbjQbo2lXcX7rUN9sIxtx8jZkpw9yUYW7yqZWZ7EH6O++846zkuHHj8Pfff6N58+ZYu3YtPvnkE69XMFhwZnciIgo0kyZNwhdffIFWrVrhueeeQ926dQEAq1atQqNGjVSuHcnVrZv4uXIlcOOGunUhIqK8yR6kt23bFl3//1Vs5cqVcfDgQVy8eBHnz5/Ho48+6vUKBotQ3pNORESBqVWrVrh48SIuXryIWbNmOctffvllfP7557LWNX36dFSqVAnh4eFo0KABtm7d6tHjtm/fDr1ej3r16snaHuX20EPikPcrV4B169SuDRER5UXWIN1qtUKv1+OPP/5wKS9WrJjzEmzBTqfToWXLltDpdLIeF8p70pVmFuqYmzLMTT5mpkwo5Hbjxg2YTCYULVoUAPD3339j6tSpOHLkCEqUKOHxehYvXoyhQ4di1KhRSE1NRfPmzdGuXTucPn36to/LzMxEnz598Nhjj+WrHXIFa99qtTl70xcv9v76gzU3X2JmyjA3ZZibfGplJnt29ypVqmD58uXOQ94CjVqzva5cKS5/8tBDwM6dBbZZIiIKAP46E3mbNm3QtWtXDBw4EJcvX8b9998Pg8GAixcvIjExEYMGDfJoPY0bN0b9+vWRnJzsLKtRowY6d+6MiRMn5vm4Hj16oFq1atDpdPjuu++wb9++PJc1mUwwmUzO37OyslCuXDmcP3/emalWq4XBYIDFYnE5v1Cn00Gv18NsNuPmj0V6vR46nS5XucFggFarddmeo1yj0cBsNruUG41GSJKUa+KhsLAw2O12l3KNRgOj0QibzQar1Zqr3Gq1wua4pquCNqWm6tGkiQ4RERLOnDE7dx4EcpuCsZ/YJraJbQq+NmVlZaFEiRIevdfrb/tXN9555x2MHDkS8+fPRzHHNcVCiNlsxpIlS9CtWzcYjUaPHxfKh7srzSzUMTdlmJt8zEyZUMht7969SEpKAgB8++23KFmyJFJTU7Fs2TK89957Hg3SzWYz9uzZg/j4eJfyNm3aYMeOHXk+bvbs2Th+/Djmz5+PcePG3XE7EydOxJgxY3KVJyYmIjw8HAAQExODTp06Yd26dS6XjG3ZsiVatWqFJUuW4Pjx487ydu3aoVGjRpgxYwYuXLjgLO/VqxeqVq2KxMRElw9xgwYNQmRkJBISElzqEB8fj8zMTJcvKYxGI0aOHIkTJ05gwYIFzvLo6GjExsZi//79WL16tbO8SpUq6N27N7Zt24bNmzc7y+W26cknO6Jy5fo4cUKDV15Zjdq1//Rqm8qVK4eGDRti8U276n3dpo4dO6J+/foB2U8PP/ww0tPTYbfbcfLkyaBoU0H008WLFzFjxoygalMw9hPb5Nqm7OxseEr2nvSYmBgcO3YMFosFFSpUQOFbjt/eu3evnNUVuPzurTCZTEhISEB8fLzzsjSemDEDeOkloGNHYNUq2ZsNaEozC3XMTRnmJh8zU8abufnrnvSIiAgcPnwY5cuXR7du3VCrVi28//77OHPmDKpXr47rjm+gbyMtLQ1lypTB9u3b0bRpU2f5hAkTMHfuXLeXb/3rr7/w8MMPY+vWrbjvvvswevToAt2TbjKZkJSUhOHDhyMiIkL1vS83l3tjj9I77+iQkAA89ZQNixdbvdYmR24jRoyAVptzRmWo7CVT0iar1YoPP/wQw4YNc/myL5DbVBD9lJ2djcmTJyMuLs75/zfQ21QQ/XTjxg0kJSUhLi4OERERQdEmX/eT4/9aXFwcihQp4r970jt37iz3IYTQ3pNORESBqWrVqvjuu+/QpUsXbNiwAXFxcQDgMvD11K1z10iS5HY+G5vNhp49e2LMmDG47777PF5/WFiY2y9L3JUbDAa367j1iAjHOYh5HSmR15cz7so1Go3bcq1W67Zcp9O5PQdSr9dDr8/98c3TNgFA9+5AQgKwfr0OJpMON3dloLbpduWB0iZ36w/0Nskpl9Mmx5dAt76+A7lNBdFPjgFtWFiYc5lAb5M7vmhTWFgYNBpNvtok5wt92YP0999/X+5DCDkTx3GQTkREgeK9995Dz549ERcXh0cffRRNmjQBAGzcuBExMTEerSMqKgo6nQ4ZGRku5efPn0fJkiVzLX/lyhX89ttvSE1NxeDBgwGI69RKkgS9Xo+NGzfyajL5VLcucN99wNGj4ui+3r3VrhEREd1M9iXYQp1er0fHjh3dfptzO4496aE4u7vSzEIdc1OGucnHzJQJhdyeeeYZnD59Gr/99hs2bNjgLH/sscec56rfidFoRIMGDZCSkuJSnpKS4nL4u8Pdd9+NAwcOYN++fc7bwIEDUb16dezbtw+NGzfOX6M8EOx9q9EAPXqI+96c5T3Yc/MFZqYMc1OGucmnVmayz0nXarW3vdzazecL+CO1zvt74w0gMREYMQKYNKnANktERAHAX89Jv9k///wDjUaDMmXKyH7s4sWL8fzzz+Pzzz9HkyZN8OWXX+Krr77Cn3/+iQoVKmDkyJE4e/Ysvv76a7eP9+Sc9FsFQqZqOngQqFULMBiAc+eA/19lj4iIfETO+5LsPekrVqzA8uXLnbfFixcjPj4epUqVwpdffqm40oHCbDZj+vTpuSZHuJNQvk660sxCHXNThrnJx8yUCYXc7HY7xo4di8jISFSoUAHly5fHPffcgw8++MBlsp476d69O6ZOnYqxY8eiXr162LJlC9auXYsKFSoAANLT0+94zfSCFAp9W7MmULs2YLEAS5d6Z52hkJu3MTNlmJsyzE0+tTKTvd/+qaeeylX2zDPPoFatWli8eDEGDBjglYr5K0mScOHCBcg8ACGkJ45TmlmoY27KMDf5mJkyoZDbqFGjMHPmTCQkJKBZs2aQJAnbt2/H6NGjkZ2djfHjx3u8rtjYWMTGxrr925w5c2772NGjR2P06NEyap4/odC3ANCnjzjCb+5c4OWX87++UMnNm5iZMsxNGeYmn1qZee2c9MaNG+OHH37w1uqCTigP0omIKDDNnTsXM2bMwKBBg/DAAw+gbt26iI2NxVdffXXHgTX5v969AZ0O2LEDcHMlPCIiUolXBuk3btzAp59+irJly3pjdUEplA93JyKiwPTvv//i/vvvz1V+//33499//1WhRuRNpUoBTzwh7s+dq25diIgoh+xBetGiRVGsWDHnrWjRoihSpAhmzZqFKVOm+KKOfsVgMKBXr155XpcvL6G8J11pZqGOuSnD3ORjZsqEQm5169bFtGnTcpVPmzYNDzzwgAo1Khih0LcOL7wgfn79NZDfuX9DKTdvYWbKMDdlmJt8amUme3b3OXPmuMzurtVqER0djcaNG6NoAEwNqtZsrw0bAnv2AN9/D7RvX2CbJSKiAOCvM5Fv3rwZHTp0QPny5dGkSRNoNBrs2LEDZ86cwdq1a9G8eXO1q5gnf83U35hMQOnSwL//AuvXA23bql0jIqLg5NPZ3V944QX07dvXeXv++efxxBNPBMQA3RtMJhMmTpwIk8kk63GhvCddaWahjrkpw9zkY2bKhEJuLVu2xNGjR9GlSxdcvnwZ//77L7p27Yo///wTs2fPVrt6PhMKfesQFgb07Cnu53eagVDKzVuYmTLMTRnmJp9amcme3X327Nm466678Oyzz7qUL126FNevX0ffvn29Vjl/pWQK/lAepAPKMiPmphRzk4+ZKRMKuZUuXTrXLO779+/H3LlzMWvWLJVq5Xuh0LcO/foB06YBK1YAly8D99yjfF2hlJu3MDNlmJsyzE0+NTKTvSc9ISEBUVFRucpLlCiBCRMmeKVSwYgTxxEREZE/iokB6tQRh75/843atSEiItmD9L///huVKlXKVV6hQgWcPn3aK5UKRqG+J52IiIj8k0Yj9qYD+T/knYiI8k/2xHHly5fHtGnT0KlTJ5fylStX4tVXX8U///zj1Qp6W34nkrHb7bh48SKioqKg1Xr2HYckAY5FMzKAkiVlbzagKcmMmJtSzE0+ZqaMN3MLtEnO9u/fj/r168OW3+nAfSg/mYbia+L8eaBMGcBqBQ4dAtxcee+OQjG3/GJmyjA3ZZibfGq918s+J71Hjx547bXXUKRIEbRo0QKAmP319ddfR48ePZTVOIBoNBpERka6zHB/Jzdu5NwPxcPdlWRGzE0p5iYfM1MmmHPr2rXrbf9++fLlgqmISoK5b/NSooS4ZvqaNcCCBcAHH8hfRyjmll/MTBnmpgxzk0+tzGR/HTBu3Dg0btwYjz32GAoVKoRChQqhTZs2ePTRR0PinHSz2YyEhARZEwg4DnUHgEKFfFApP6ckM2JuSjE3+ZiZMsGcW2Rk5G1vFSpUQJ8+fdSups8Ec9/eTu/e4ueCBeIoQLlCNbf8YGbKMDdlmJt8amUme0+60WjE4sWLMW7cOOzbtw+FChVCnTp1UKFCBV/ULyg4Jo0LDwd0OnXrQkREdCfBfHk1ylvHjsBddwEnTwI7dwJNm6pdIyKi0CR7kO5QrVo1VKtWzZt1CVqcNI6IiIj8XUQE0LUr8PXXwPz5HKQTEalF9uHuzzzzDBISEnKVT5kyJde100ngIJ2IiIgCgeOQ9yVLAB4RS0SkDtmzu0dHR+Onn35CnTp1XMoPHDiAxx9/HOfOnfNqBb0tvzPoSpIEs9kMo9Ho8QQCW7YALVsC1asDhw/L3mTAU5IZMTelmJt8zEwZb+YWaLO7B4L8ZBrKrwmbDShbVlyNZtUqcQi8p0I5N6WYmTLMTRnmJp9a7/Wy96RfvXoVRqMxV7nBYEBWVpbc1QUcSZKQmZkJOd9thPqedCWZEXNTirnJx8yUYW7BK5T7VqcDnntO3F+wQN5jQzk3pZiZMsxNGeYmn1qZyR6k165dG4sXL85V/s0336BmzZpeqZQ/s1gsSE5OhsVi8fgxoT5IV5IZMTelmJt8zEwZ5ha8Qr1ve/USP1euBOTsfwn13JRgZsowN2WYm3xqZSZ74rh3330XTz/9NI4fP45HH30UAPDjjz9i4cKF+Pbbb71ewWDgmN09FK+RTkRERIGlfn3g/vvFKXorVgB9+6pdIyKi0CJ7T3qnTp3w3Xff4dixY4iNjcUbb7yBs2fP4qeffkLFihV9UMXAF+p70omIiChwaDQ5e9Pnz1e3LkREoUj2IB0AOnTogO3bt+PatWs4duwYunbtiqFDh6JBgwberp9fcndO/u1wT7r8zEhgbsowN/mYmTLMLXiFet86Buk//ggcO+b540I9NyWYmTLMTRnmJp8amcme3d3hp59+wqxZs7B8+XJUqFABTz/9NJ5++mnExMR4u45epcYMuuPGAe++C7z0EvDllwWySSIiCiCc3d37mGn+tW8PrFsHDBwIJCerXRsiosDms9nd//nnH4wbNw6VK1fGc889h6JFi8JisWDZsmUYN26c3w/QvcFut+PYsWOw2+0ePybUD3dXkhkxN6WYm3zMTBnmFrzYt8Jbb4mfs2eLS7LdCXOTj5kpw9yUYW7yqZWZx4P09u3bo2bNmjh48CA+/fRTpKWl4dNPP/Vl3fySxWLBggULZM3wF+qHuyvJjJibUsxNPmamDHMLXuxboUUL4KGHAJMJ+OSTOy/P3ORjZsowN2WYm3xqZebxIH3jxo148cUXMWbMGHTo0AE6nc6X9Qoqob4nnYiIiAKPRpOzN336dHmXYyMiIuU8HqRv3boVV65cQcOGDdG4cWNMmzYNFy5c8GXdggYH6URERBSIOnUSl2PLzOS8OkREBcXjQXqTJk3w1VdfIT09Ha+88gq++eYblClTBna7HSkpKbhy5Yov6+k3NBoNoqOjodFoPH5MqB/uriQzYm5KMTf5mJkyzC14sW9zaLXA8OHiflKSOPQ9L8xNPmamDHNThrnJp1Zmimd3B4AjR45g5syZmDdvHi5fvozWrVtj1apV3qyf16kx22ubNkBKCjBvHtC7d4FskoiIAghnIvc+Zuo9JhNQpQpw9iwwcybQv7/aNSIiCjw+m939VtWrV8fkyZPxzz//YNGiRflZVcCw2WzYu3cvbDabx48J9T3pSjIj5qYUc5OPmSnD3IIX+9ZVWBgQFyfuT54M5DXJMXOTj5kpw9yUYW7yqZVZvgbpDjqdDp07d/b7vejeYLVasXr1alitVo8fE+rnpCvJjJibUsxNPmamDHMLXuzb3F5+GbjnHuDIEWD1avfLMDf5mJkyzE0Z5iafWpl5ZZBOtxfqg3QiIiIKbEWKALGx4v6kSYDykyWJiOhOOEgvAKF+uDsREREFviFDxKHvO3cC27apXRsiouDFQbpMGo0GVapUkTXDX6jvSVeSGTE3pZibfMxMGeYmz/Tp01GpUiWEh4ejQYMG2Lp1a57Lbtu2Dc2aNUPx4sVRqFAh3H///UhKSiqwurJv3bv3XqBvX3F/8uTcf2du8jEzZZibMsxNPrUyy9fs7oFIjdlew8IAsxn4+2+gfPkC2SQREQWQYJ+JfPHixXj++ecxffp0NGvWDF988QVmzJiBgwcPorybN8bU1FQcPnwYDzzwAAoXLoxt27bhlVdeQVJSEl5++WWPthnsmarlr7+A6tXF4e4HDgC1a6tdIyKiwCDnfUn1Qfr06dMxZcoUpKeno1atWpg6dSqaN2/udtkXXngBc+fOzVVes2ZN/Pnnnx5tL79v2larFdu2bcPDDz8MvV7vwfKAwSDuX7wIFC8ue5MBT25mJDA3ZZibfMxMGW/mFuwDysaNG6N+/fpITk52ltWoUQOdO3fGxIkTPVpH165dUbhwYcybN8/t300mE0w3XcQ7KysL5cqVw/nz552ZarVaGAwGWCwW2G+aolyn00Gv18NsNkOSJFitVuzcuRPNmzdHWFiYs9zBYDBAq9W6bM9RrtFoYDabXcqNRiMkSYLFYnEpDwsLg91udynXaDQwGo2w2WwuExU5yq1Wq8ssw562yUGv10On0+WrTc89p8eKFTr06SPhyy9zyq1WK3bv3o2mTZu6rDsQ2gSo00+SJOGXX35Bo0aNoNPpgqJNBdFPFosFW7ZsQZMmTZz/fwO9TQXRT2azGTt37kSTJk0QFhYWFG3ydT853g+aNGmCiIiIfLUpKysLJUqU8Oi9XtVPY4sXL8bQoUNdvllv165dnt+sf/zxx0hISHD+brVaUbduXTz77LMFVmebzYbNmze7/FO4nRs3cu6H6uHucjMjgbkpw9zkY2bKMDfPmM1m7NmzB/Hx8S7lbdq0wY4dOzxaR2pqKnbs2IFx48bluczEiRMxZsyYXOWJiYkIDw8HAMTExKBTp05Yt24dUlNTncu0bNkSrVq1wpIlS3D8+HFn+d13341GjRphxowZuHDhgrO8V69eqFq1KhITE10+mA4aNAiRkZEun1UAID4+HpmZmS5fUhiNRowcORInTpzAggULnOXR0dGIjY3F/v37sfqmadSrVKmC3r17Y9u2bdi8ebOzXG6bOnbsiPr16+erTVFRpQG8hIULgeLFpyMyMsvZJrPZjJIlS2LJkiUB1Sa1+qlZs2bYvn07Tp8+jZMnTwZFmwqiny5cuIDt27dj+/btQdOmguyn7du3B12bAN/20/bt2/PdpuzsbHhK1T3p+f1m/bvvvkPXrl1x8uRJVKhQwaNt5ndvhclkQkJCAuLj4xEWFnbH5TMygFKlAI0GsNnEz1AjNzMSmJsyzE0+ZqaMN3ML5j3paWlpKFOmDLZv346mTZs6yydMmIC5c+fiyJEjeT62bNmyuHDhAqxWK0aPHo133303z2W9uSfdZDIhKSkJw4cPR0REBPcouWnTE08YsWmTBkOGWDFlis3ZB0lJSRgxYgS02pxpjwKlTWr0k9VqxYcffohhw4bBaDQGRZsKop+ys7MxefJkxMXFOf//BnqbCqKfbty4gaSkJMTFxSEiIiIo2uTrfnL8X4uLi0ORIkWCf0+6N75ZnzlzJh5//PHbDtDdvWnfWi6no29elycd/d9/ABCGiAgpaJ+8d2qT47F2u91lPYHcJsD3/XRrfsHQpoLoJ8fyjp/B0CZf99PNmQVLmwqinxx/lyQpV93ltunWnILRrZPuSJJ0x4l4tm7diqtXr+KXX35BfHw8qlatiueee87tsmFhYW6/LHFXbnCch3aLmwdJQM7/4lvLb163p+UajcZtuVardVuu0+lc3gsc9Hq92yM3PG3Tnco9bdNbbwGbNgFffaXH8OF6lCuX87dAbROgbj+5W3+gt0lOuZw2Ob4EuvX1HchtKoh+crw3Og51v13dA6VN7viiTWFhYdBoNPlqk5wv9FUbpF+8eBE2mw0lS5Z0KS9ZsiQyMjLu+Pj09HSsW7cOCxcuvO1yvjj8rVy5ctBqtfjqq6/ueMjEuXPRAGIREYGgPwzkdm2KiYnB6dOn8c033wRNm3zdT82aNUORIkVcZjQO9DYVZD8lJSUFXZsA3/ZTUlJS0LUJ8H0/Wa1WZGVl5atNpUuXRrCKioqCTqfL9d5+/vz5XJ8BblWpUiUAQJ06dXDu3DmMHj06z0G6N2m1WsTExLjsDSZXbdsCzZsDW7cC778PzJrF3JRgZsowN2WYm3xqZaba4e6Ow9927NiBJk2aOMvHjx+PefPm4fDhw7d9/MSJE/HRRx8hLS0tz29FAO8e/uYgZ+/Lrl0atGhhRMWKEg4fDt49SmwT28Q2sU1sk/I2XblyBdHR0UF5uDsgTm9r0KABpk+f7iyrWbMmnnrqKY8njvvggw8wc+ZMnDp1yqPlg/kUAn/x66/AQw+JU/n27wfq1FG7RkRE/kvW+5KkEpPJJOl0Omn58uUu5a+99prUokWL2z7WbrdLVatWlYYOHSp7u5mZmRIAKTMzU/ZjJUmSzGaztHLlSslsNnu0/E8/SRIgSTVrKtpcUJCbGQnMTRnmJh8zU8abueX3vcnfffPNN5LBYJBmzpwpHTx4UBo6dKhUuHBh6dSpU5IkSVJ8fLz0/PPPO5efNm2atGrVKuno0aPS0aNHpVmzZkl33323NGrUKI+3mZ9M+Zrw3DPPiM857dszNyWYmTLMTRnmJp9a7/WqHetgNBrRoEEDpKSkuJSnpKS4TCzjzubNm3Hs2DEMGDDAl1V0y263IzU11WUvze1cuyZ+hurM7oD8zEhgbsowN/mYmTLMzXPdu3fH1KlTMXbsWNSrVw9btmzB2rVrnXPKpKen4/Tp087l7XY7Ro4ciXr16qFhw4b49NNPkZCQgLFjxxZIfdm3npswAdDrgbVrgZ9+kpibTHyuKcPclGFu8qmVmarXjBk2bBief/55NGzYEE2aNMGXX36J06dPY+DAgQCAkSNH4uzZs/j6669dHjdz5kw0btwYtWvXVqPasly/Ln4WLqxuPYiIiNQUGxuL2NhYt3+bM2eOy+9DhgzBkCFDCqBWlF/VqgGvvAJ89hkwapQeHTqoXSMiosCn6iC9e/fuuHTpEsaOHYv09HTUrl37tt+sA0BmZiaWLVuGjz/+WI0qy+YYpIfynnQiIiIKXu+9B8ydC+zZo0XFirXUrg4RUcBTdZAOyPtmHQAiIyNx3THyVYFOp0PLli3dXh7AHcfh7qG8J11uZiQwN2WYm3zMTBnmFrzYt/KUKCEuyfbuu8D27R1gsegg40pDIY3PNWWYmzLMTT61MlNtdne1FPRsr1OmACNGAH36iG+ZiYiIbsWZyL2PmRasa9eA6tWBs2eBiROB+Hi1a0RE5F/kvC/xInkymc1mzJ8/P9clePLCw93lZ0YCc1OGucnHzJRhbsGLfStf4cLABx+IywpOmCDh3DmVKxQg+FxThrkpw9zkUyszDtJlkiQJx48fh6cHIPBwd/mZkcDclGFu8jEzZZhb8GLfKtOjhw2lS5/FlSsavPuu2rUJDHyuKcPclGFu8qmVGQfpPsY96URERBQKtFqgbdsNAICZM4Hff1e5QkREAYqDdB/jnnQiIiIKFRUqnMEzz9hgtwPDhgHcYUdEJB8H6TLp9Xp07NgRer1nE+NzT7r8zEhgbsowN/mYmTLMLXixb5Vx5JaQAISFAT/+CKxapXat/Bufa8owN2WYm3xqZcbZ3X2sY0dgzRpgxgxgwACfb46IiAIQZyL3PmaqrrffFrO8lywpDnsvUULtGhERqYuzu/uQ2WzG9OnTPZ7hj4e7y8+MBOamDHOTj5kpw9yCF/tWmZtze/ddoHZt4Nw5sZMitHYJeY7PNWWYmzLMTT61MuMgXSZJknDhwgWPZ/jj4e7yMyOBuSnD3ORjZsowt+DFvlXm5twKFQIWLhSHva9ZAyQnq107/8TnmjLMTRnmJp9amXGQ7mMcpBMREVEoqlMHmDRJ3H/jDeDPP9WtDxFRoOAg3cd4uDsRERGFqtdeA554AsjOBnr2BEwmtWtEROT/OHGcTHa7HSdOnEDlypWh1d75O45SpYCMDGDfPqBuXQUVDgJyMyOBuSnD3ORjZsp4MzdOcuZ9+cmUrwll8sotIwN44AHgwgXg1VeBadNUrKSf4XNNGeamDHOTT633eg7Sfezuu4ErV4C//gKqVvX55oiIKABxkO59zNS/rFsHtG8v7i9cCDz3nLr1ISIqaJzd3YdMJhMmTpwIkwfHa0kSz0kH5GVGOZibMsxNPmamDHMLXuxbZW6XW7t2wKhR4v5LLwGHDhVw5fwUn2vKMDdlmJt8amXGQboCnk7Bb7EANpu4H8qDdMDzzMgVc1OGucnHzJRhbsGLfavM7XIbMwZ49FExX8/TTwNXrxZgxfwYn2vKMDdlmJt8amTGQboPOSaNAzhxHBEREYU2nQ5YtAgoXVrsSX/lFV4/nYjIHQ7SfchxqLteDxgM6taFiIiISG0lSgBLlogB+8KFwMsvA9yxR0TkihPHyWS323Hx4kVERUXdcYa/o0eB6tXF5HGZmUprHPjkZEY5mJsyzE0+ZqaMN3PjJGfel9/Z3fmakE9Obl99BQwcCNjtwMMPA8uWiQF8qOFzTRnmpgxzk0+t93r2jkwajQaRkZHQaDR3XNaxJz3UD3WXkxnlYG7KMDf5mJkyzC14sW+VkZPbSy8Ba9aIHRnbtgEPPgjs318AlfQzfK4pw9yUYW7yqZUZB+kymc1mJCQkeDSBAGd2F+RkRjmYmzLMTT5mpgxzC17sW2Xk5tauHfDrr0C1asDp00CzZsBvv/m4kn6GzzVlmJsyzE0+tTLjIN2HHBPHhfqedCIiIiJ37r9fDNRbtRKfm556Cjh7Vu1aERGpi4N0H+KedCIiIqLbK1oUWLkSqFULSEsDOnZ0vUIOEVGo4SDdhzhIJyIiIrqzu+8W56hHRwOpqUDv3mJSOSKiUMTZ3WWSJAlmsxlGo/GOEwjMmCEmRunYEVi1SmmNA5+czCgHc1OGucnHzJTxZm6c3d378pMpXxPKeCO3HTuARx4Rl2V76y0gIcHLlfQzfK4pw9yUYW7yqfVezz3pMkmShMzMTHjy3Qb3pAtyMqMczE0Z5iYfM1OGuQUv9q0y3sitaVNg1ixxf9Ik4P33gWDuBj7XlGFuyjA3+dTKjIN0mSwWC5KTk2GxWO64rON8qlAfpMvJjHIwN2WYm3zMTBnmFrzYt8p4K7devYDJk8X9sWOB4cODd6DO55oyzE0Z5iafWplxkO5DvE46ERERkXzDhwOffCLuf/QR8OqrPEediEIHB+k+xMPdiYiIiJQZMkTM76PRAMnJQL9+AHcAElEo4CBdAaPR6NFyvE56Dk8zI1fMTRnmJh8zU4a5BS/2rTLezm3AAGD+fECnA77+GmjfHsjM9OomVMfnmjLMTRnmJp8amXF2dx/q0weYNw+YMgV4802fboqIiAJYKMzuPn36dEyZMgXp6emoVasWpk6diubNm7tddvny5UhOTsa+fftgMplQq1YtjB49Gm3btvV4e6GQaShZuxbo1k3sAKlVC/j+e6BCBbVrRUTkOc7u7kN2ux3Hjh2D3YMTo/79V/yMjPRxpfycnMwoB3NThrnJx8yUYW6eW7x4MYYOHYpRo0YhNTUVzZs3R7t27XD69Gm3y2/ZsgWtW7fG2rVrsWfPHjzyyCPo2LEjUlNTC6S+7FtlfJlb+/bA1q1A6dLAn38CDz0E7Nnj9c0UOD7XlGFuyjA3+dTKjIN0mSwWCxYsWODRDH9//y1+li/v40r5OTmZUQ7mpgxzk4+ZKcPcPJeYmIgBAwbgxRdfRI0aNTB16lSUK1cOycnJbpefOnUqRowYgQcffBDVqlXDhAkTUK1aNaxevTrPbZhMJmRlZbncHOWOm6OvLBaLS7nVagUAmM1mmEwmXL16FQsWLEB2drZLuePm+LB2c5mjXJKkXOWSJMFut+cqB5Cr3Gw2AwBsNpvbcqvVqqhNjpvNZvNZmxy53bqst9pUp44Vv/wC1K5tR0YG0KqVhLVrzT5tk6/76caNG1iwYAGuXbtWYP0UDM89k8mEBQsW4OrVq0HTpoLoJ8dr9OrVq0HTJl/3082ZeaNNntJ7vCTJ5thBwMOxiIgoVJnNZuzZswfx8fEu5W3atMGOHTs8WofdbseVK1dQrFixPJeZOHEixowZk6s8MTER4eHhAICYmBh06tQJ69atc9kr37JlS7Rq1QpLlizB8ePHneV//PEHGjVqhBkzZuDChQvO8l69eqFq1apITEx0fhADgEGDBiEyMhIJCQkudYiPj0dmZqbLlxJGoxEjR47EiRMnsGDBAmd5dHQ0YmNjsX//fpcvJapUqYLevXtj27Zt2Lx5s7Ncbps6duyI+vXr+6xNAHDq1CksWbLEZ2167bWlSEhoiBMnqqBjRx3Gjz+N+PhKAdlPzZo1AwCsWLECJ0+edJb7up8C/bl36dIlAEBSUlLQtKkg+ykpKSno2gT4tp+SkpLy3SbHF7+e4DnpMplMJiQkJCA+Ph5hYWF5Lnf5MlC0qLh/9WpoTx7naWbkirkpw9zkY2bKeDO3YD5/Oi0tDWXKlMH27dvRtGlTZ/mECRMwd+5cHDly5I7rmDJlChISEnDo0CGUKFHC7TK37qXIyspCuXLlcP78eWemWq0WBoMBFovF5dBFnU4HvV4Ps9ns3HuSlJSE4cOHIyIiwlnuYDAYoNVqc+0VMRgM0Gg0Lh/2APEhTpKkXEddhIWFwW63u5RrNBoYjUbYbDbnXqGby61Wq3OvkJw2Oej1euh0Op+0yZHbiBEjoNXmHKzpizZlZ0sYMECPb7/VQaOR8OmnGrz0UuD1k9VqxYcffohhw4a5TE7ly37ydZsK4rmXnZ2NyZMnIy4uzvn/N9DbVBD9dOPGDSQlJSEuLg4RERFB0SZf95Pj/1pcXByKFCmSrzZlZWWhRIkSHr3Xc0+6TBqNBtHR0dBoNLddznGoe1RUaA/QAc8zI1fMTRnmJh8zU4a5yXNrTpIkeZTdokWLMHr0aKxcuTLPATogPiS5+7LEXbnBYHC7DscgydG3er3epdzduj0t12g0bsu1Wq3bcp1OB51Ol6tcr9c763WzO7XJ0/L8tMmRm06nc7t+b7bJaAS++QZ4/XXgs880GDwYOH3aiPHjgVtX5c/95MgsLCzMbWZ87rkv1+l0iI6ORnh4uMv6ArlNBdFPkiQ5c7vT/7dAaZM73myT4zUaHh4OjUaTrzbJ+UKfe9J9ZNUq4KmngAYNgN9+89lmiIgoCATznnSz2YyIiAgsXboUXbp0cZa//vrr2Ldvn8shjLdavHgx+vXrh6VLl6JDhw6ythvMmVIOSQI++AB4/33xe6tWwKJFwL33qlotIqJcOLu7D9lsNuzdu9flkAt3eD56Dk8zI1fMTRnmJh8zU4a5ecZoNKJBgwZISUlxKU9JSXE5/P1WixYtwgsvvICFCxfKHqDnF/tWGTVy02iA994Te9XvugvYtAmIiQFu892PX+FzTRnmpgxzk0+tzDhIl8lqtWL16tUu50u4w5ndc3iaGblibsowN/mYmTLMzXPDhg3DjBkzMGvWLBw6dAhxcXE4ffo0Bg4cCAAYOXIk+vTp41x+0aJF6NOnDz766CM89NBDyMjIQEZGBjIzMwukvuxbZdTMrXt3ceRirVpARgbw6KNAQgLg71ea4nNNGeamDHOTT63MOEj3EccgnXvSiYgo1HXv3h1Tp07F2LFjUa9ePWzZsgVr165Fhf+/Saanp7tcM/2LL76A1WrFq6++ilKlSjlvr7/+ulpNoABQvTrw66/A88+LwfnIkUCHDsBNkzkTEQUEThznIxykExER5YiNjUVsbKzbv82ZM8fl902bNvm+QhSUChcG5s4V56YPHgysXw/UqyfOU2/RQu3aERF5hnvSZdJoNKhSpcodZ6R17BDg4e6eZ0aumJsyzE0+ZqYMcwte7Ftl/CU3jQbo3x/YtQuoUQNISwMeeQR44w0gK0vVquXiL5kFGuamDHOTT63MOLu7D2RnA4UKifsXLojLsBEREeWFM5F7HzMlALh2TexRdxysUaoU8NFHQI8eYjBPRFRQOLu7D1mtVmzatOm2kwecOSN+RkQAxYsXUMX8mCeZUW7MTRnmJh8zU4a5BS/2rTL+mFvhwsDs2cC6dUCVKkB6OtCzJ/DYY8Aff6hdO//MLBAwN2WYm3xqZcZBukw2mw2bN2++7TT8N5+Pzm9pPcuMcmNuyjA3+ZiZMswteLFvlfHn3J54QgzKx44FwsOBn38G6tYVe9kvXVKvXv6cmT9jbsowN/nUyoyDdB/g+ehERERE/iU8HHj3XeDgQaBrVzED/GefAdWqAdOmAdy5SET+goN0H+DM7kRERET+qVIlYNky4KefgDp1gP/+A4YMAR58EPjlF7VrR0TEQbpsWq0WMTEx0Grzjo6DdFeeZEa5MTdlmJt8zEwZ5ha82LfKBFpujzwC7N0LTJ8OFC0K7NsHNG0KvPIK8O+/BVOHQMvMXzA3ZZibfGplxtndfeDRR8W5TvPnA716+WQTREQURDgTufcxU5Lj/HlgxAhxjXUAKFYMGDkSePXVnCv2EBHlB2d39yGLxYJVq1bBYrHkuYxjTzrPSRc8yYxyY27KMDf5mJkyzC14sW+VCeTcSpQQl2nbvBmoVUvsSR8+HKhaFUhOBsxm32w3kDNTE3NThrnJp1ZmHKTLZLfbkZqaCrvdnsffcy7BxsPdhTtlRu4xN2WYm3zMTBnmFrzYt8oEQ24tWojD3mfNEjtb0tKA2FhxHnt8PHDokHe3FwyZqYG5KcPc5FMrMw7SvSw9HbBYAJ0OKF1a7doQERERkRx6PdCvH3D0KPDpp0DJkmKwPmkSULOmmGBuzhyAV7EiIl/hIN3LHJdfK1NG/JMnIiIiosATFiauo/7338C33wIdO4rPdr/9JgbxDz4IbN2qdi2JKBhxkC6TTqdDy5YtodPp3P6dM7vndqfMyD3mpgxzk4+ZKcPcghf7VplgzS0sDHj6aWDVKuDsWWDiRODuu4HUVHF4fLduwMmTytYdrJn5GnNThrnJp1Zmqs/uPn36dEyZMgXp6emoVasWpk6diubNm+e5vMlkwtixYzF//nxkZGSgbNmyGDVqFPr37+/R9nw92+ukSeKcpd69gXnzvL56IiIKQpyJ3PuYKfnS+fPAu+8CM2aI+Yj0emDAAGDUKKBcObVrR0T+KGBmd1+8eDGGDh2KUaNGITU1Fc2bN0e7du1w2nHMuBvdunXDjz/+iJkzZ+LIkSNYtGgR7r///gKrs9lsxvz582HOY5pP7knP7U6ZkXvMTRnmJh8zU4a5BS/2rTKhlFuJEsAXX4jrrLduDVit4veqVYEhQ8Qed0+EUmbexNyUYW7yqZWZqoP0xMREDBgwAC+++CJq1KiBqVOnoly5ckhOTna7/Pr167F582asXbsWjz/+OCpWrIhGjRqhadOmBVZnSZJw/Phx5HUAguP7BV5+LcedMiP3mJsyzE0+ZqYMcwte7FtlQjG3unWBjRuBLVuAVq3EpdqmTROzwb/0EvDXX7d/fChm5g3MTRnmJp9amak2tZnZbMaePXsQHx/vUt6mTRvs2LHD7WNWrVqFhg0bYvLkyZg3bx4KFy6MTp064YMPPkChQoXcPsZkMsFkMjl/z8rKylWu1WphMBhgsVhcptfX6XTQ6/Uwm83Ojrl5XTeXA4DBYMDff4vvPUqXNsNkkpzlGo0m1zcwRqMRkiTluu5eWFgY7Ha7S7lGo4HRaITNZoPVas1VbrVaYbtpmlE5bQIAvV4PnU7ntk1ardal3XLb5His3W53WU8gtwnwfT/dml8wtKkg+smxvONnMLTJ1/10c2bB0qaC6CfH3yVJylV3uW3iXg2iwNa8OfDzz+I2erQYtM+YIS7l9swzwCuvAC1biqv/EBHdiWqD9IsXL8Jms6FkyZIu5SVLlkRGRobbx5w4cQLbtm1DeHg4VqxYgYsXLyI2Nhb//vsvZs2a5fYxEydOxJgxY3KVJyYmIjw8HAAQExODTp06Yd26dUhNTXUu07JlS7Rq1QpLlizB8ePHc61jxowZuHDhgvP3nj174e+/qwIA1q//Env2XAIADBo0CJGRkUhISHB5fHx8PDIzM12OHDAajRg5ciROnDiBBQsWOMujo6MRGxuL/fv3Y/Xq1c7yKlWqoHfv3ti2bRs2b97sLJfbpo4dO6J+/fq52tSrVy9UrVoViYmJLh8i5bYJAE6dOoUlS5YETZt83U9NmjQBACQlJQVNmwqyn5KSkoKuTYBv+ykpKSno2gT4vp/MZjOysrLy1abSvGYnUVB45BFx27FDTDC3Zg2wZIm4lS4N9OgB9OwJ1K8PaDRq15aI/JVqE8elpaWhTJky2LFjh3MwAgDjx4/HvHnzcPjw4VyPadOmDbZu3YqMjAxERkYCAJYvX45nnnkG165dc7s33d2e9HLlyuH8+fPOE/bl7H2x2Ww4ePAgYmJiYLPZXPa+XL1qQFSU2JP+778mRESI8lDYo3S7NtlsNhw+fBh16tRxqUsgtwnwfT9pNBrs2bMHtWvXdu5ZD/Q2FUQ/ZWdn48CBA6hTpw50Ol1QtMnX/WSxWJyZGY3GoGhTQfSTzWbDgQMH0KBBA2i12ny16cqVK4iOjuYkZ16Un4njbDYb9u/fj7p163IWZBmYW26//y4Of1+6FLh8Oae8Xj1g4ECge3cbTpxgZnLxuaYMc5PPm5nJeV9SbZBuNpsRERGBpUuXokuXLs7y119/Hfv27XPZO+LQt29fbN++HceOHXOWHTp0CDVr1sTRo0dRrVq1O27Xl7O97t8v/ulGRQE37ZQhIiK6Lc5E7n3MlPyJyQRs2AAsWACsXCl+B4C77gI6dBCHwd+4IW5lygDjxgH33qtunYnIuwJidnej0YgGDRogJSXFpTwlJSXPieCaNWuGtLQ0XL161Vl29OhRaLValC1b1qf1dTCbzZg+fbrb8wc5s7t7t8uM8sbclGFu8jEzZZhb8GLfKsPc8hYWBnTqBCxeLGZ+T0wE7rsPuHpVlC1cCKxYAaxfD8ycKXb6/PCD2rX2X3yuKcPc5FMrM1Vndx82bBhmzJiBWbNm4dChQ4iLi8Pp06cxcOBAAMDIkSPRp08f5/I9e/ZE8eLF0a9fPxw8eBBbtmzB8OHD0b9//zwnjvM2SZJw4cIFtzP8cZDu3u0yo7wxN2WYm3zMTBnmFrzYt8owN88ULw7ExQGHDwMbNpjx2GM/YPJkK5KTxQC9Th3g3DmgTRtxLfabzoyh/+NzTRnmJp9amak2cRwAdO/eHZcuXcLYsWORnp6O2rVrY+3atajw/1Fuenq6yzXT77rrLqSkpGDIkCFo2LAhihcvjm7dumHcuHFqNcEFB+lERERE5AmNBmjZUsLOndvx2mvNERYmPpY/9xwwdCjw5ZfisPd168T56888A9xzj6pVJqICouogHQBiY2MRGxvr9m9z5szJVXb//ffnOkTeXzgmDa5cWd16EBEREVFgKlQI+OILMUv8Sy8Be/aIn4MHAx07ArVrA5cuidt//wEPPST2zBcponbNichbVJs4Ti35nUjGbrfjxIkTqFy5MrRa17MFHngAOHAAWLsWaNfOWzUOfLfLjPLG3JRhbvIxM2W8mRsnOfO+/GTK14QyzE2+O2WWng58/TUwbx7w5595r6dECXF99hdfBAwG39XXX/C5pgxzk0+t93oO0r1EksQMndevA0eOiMlAiIiIPMFBuvcxUwomkiSuIrR4MfDvv+K89qgoQK8HPv0UcFz4qHp1YNgwoGtX8Xci8h8BMbt7oDKZTJg4cWKu6+dmZIgBulYLVKyoTt38VV6Z0e0xN2WYm3zMTBnmFrzYt8owN/k8zUyjETO+T5woDoWfMEEMxl97DTh4UAzUo6LEjqJXXhGXb2vbFpgxQ0xQZ7MVTHsKCp9ryjA3+dTKjIN0BdxNwe84H718ecBoLOAKBQBe6kEZ5qYMc5OPmSnD3IIX+1YZ5iZffjMzGMT56sePAwkJQEyMGJRv3CjOZa9RQ5yv3qiRGMCvXx8cM8bzuaYMc5NPjcw4SPcSxyC9ShV160FEREREoefuu4G33gL27gWOHgXGjwcaNxYT0d24AezeLWaMb9dO7FQaPhzYtw+w29WuORHdioN0L3GcC1S1qrr1ICIiIqLQVq0a8PbbwC+/AFeuiEPelywBXn1VnM+eng58+KHY6160KPDoo0B8PLB0qZigjjtbidTFieNkstvtuHjxIqKiolxm+OvZE1i0CJg8WXwzSTnyyoxuj7kpw9zkY2bKeDM3TnLmffmd3Z2vCfmYm3xqZGY2i2uvz50rDn2/cSP3MjqdODq0alVxfnvJkuL2wANAixbi73Js3AgkJgIjRwItW+a/DXyuKcPc5FPrvZ69I5NGo0FkZCQ0Go1LOQ93z1temdHtMTdlmJt8zEwZ5ibP9OnTUalSJYSHh6NBgwbYunVrnsump6ejZ8+eqF69OrRaLYYOHVpwFQX7VinmJp8amRmNwFNPAcuXA1lZ4pD3r74S5683aiTOX7fZxCHza9cCs2aJCeuGDhV73MuVE5PW7dkjZp2/k5UrgSefBDZsENs9ejT/beBzTRnmJp9amXGQLpPZbEZCQkKuCQQcg3Qe7p5bXpnR7TE3ZZibfMxMGebmucWLF2Po0KEYNWoUUlNT0bx5c7Rr1w6nT592u7zJZEJ0dDRGjRqFunXrFnBt2bdKMTf51M5Mrwfq1hXXV//yS+DXX4HMTOCff4CUFDE7/LhxwJAhwNNPi0Pj09OBpCSgYUMxYH/6aXEk6ZYtQHa26/qXLgWeeQawWMTgPzMT6NRJ/MwPtXMLVMxNPrUy0xfo1oLU5cvApUvifuXKqlaFiIjI7yQmJmLAgAF48cUXAQBTp07Fhg0bkJycjIkTJ+ZavmLFivj4448BALNmzfJoGyaTyeUSOVlZWbnKtVotDAYDLBYL7DfNlqXT6aDX62E2myFJknN52/+vW+UodzAYDNBqtbkuyWMwGKDRaHJ9mDMajZAkCRaLxaU8LCwMdrvdpVyj0cBoNMJms8F60xTcjnKr1eqsl5w2Oej1euh0Op+0yfFYu93usp5AbhPg235yPPbWuqjZJrvdhqgoK5o3B5o3d23T9es2bNyoxeLFWqxZo8XZsxosXy72ygNAoUISWrWS0L69Fna7FXFxOtjtGjz3nA0JCUCzZjocOQL06GHDsmVW6HTK2uTI8ObHBNvryRdtctTHZDIFTZt83U83Z5bfNsm5jBsH6V7g2ItesiRw113q1oWIiMifmM1m7NmzB/Hx8S7lbdq0wY4dO7y2nYkTJ2LMmDG5yhMTExEeHg4AiImJQadOnbBu3TqkpqY6l2nZsiVatWqFJUuW4LjjTR3AH3/8gUaNGmHGjBm4cOGCs7xXr16oWrUqEhMTXT6YDho0CJGRkUhISHCpQ3x8PDIzM5GcnOwsMxqNGDlyJE6cOIEFCxY4y6OjoxEbG4v9+/dj9erVzvIqVaqgd+/e2LZtGzZv3uwsl9umjh07on79+j5rEwCcOnUKS5YsCZo2+bKfmjVrBgBYsWIFTp48GTBtql0bePbZhrj33g6YNetP7NqlwZkz5XD1ahGsW6fBunWAY5gRE5OKatVW4+LFJ/Hdd/XRpIkV69fr8eiju1GhwmkUK/Y4jh8vhmPHMtCs2RZUrXrijm269P+9Y0lJSQXST8H23EtKSgq6NgG+7aekpKR8tyn71kNNboMTx8lkMpmQkJCA+Ph4hIWFARCzZXbvDjRtCmzf7u0aBz53mdGdMTdlmJt8zEwZb+YWzBPHpaWloUyZMti+fTuaNm3qLJ8wYQLmzp2LI0eO3PbxrVq1Qr169TB16tTbLuduT3q5cuVw/vx5Z6Zy9qQnJSVh+PDhiIiI4B4lGXvSk5KSMGLECJcJlgK5TYDv96R/+OGHGDZsmPNLjkBtkyQBf/6pQUqKDhs26LBjh4SXXrJhyhQbtNqcNs2fb8Xzz+e9n7B7dxumTLGiTBnRpuxsM/76S4PsbKBSJQlRUUZkZ2dj8uTJiIuLc/7/DbbXky/adOPGDSQlJSEuLg4RERFB0aaC2JPuyKxIkSL5alNWVhZKlCjh0Xs9B+kySZIEs9kMo9HonEBgwgRg1CigTx8xUya5cpcZ3RlzU4a5ycfMlPFmbqEwSN+xYweaNGniLB8/fjzmzZuHw4cP3/bxng7Sb5WfTPmaUIa5yReqmY0bJz4/33+/OLe9QQPg4EFg2jRx3faiRcXn6oMHgV27XM9hj44GqlSRULWqHbVra1Hrf+3df1RVdb7/8dcB5AiIXhEVUUOcNEPSMaiWZr+0ZZrZsrTM5Q+cuet28dfoeMtsdEbzTukf/VqtWzR2y/nO0qLrVVtYWWGZ/ZhVekEUf2Q6+QNNLlkmEorA+Xz/+FyPHkE9e4OeAzwfa+0V7L2BD69z8N1nfz77s/t6lJYm9eghsWD5pbXU91tDhKrWM93dIWOMTpw4ocTERP8Lxcrul1ZfZrg8cnOH3JwjM3fILTiJiYmKjIxUaWlpwP6ysjJ17tw5RK26NF5bd8jNuZaa2YIFdrvQpEl2lfmiIun/lqWQJLVubRee++GHs5tHX30V+By47t3tzNbx4+3z3z0e+7i5f/xDKimx/5/es6fdfyk+n7R/v7Rjx7ktIUF67jnbjqaspb7fGiJUmXG9yaHq6mrl5OQETGegk35p9WWGyyM3d8jNOTJzh9yCEx0drYyMDOXn5wfsz8/PD5j+Hk54bd0hN+fILFBmprRli/Tyy9K//quUkyMVFtpHxZWV2RH1wkJp5cpq3XXXRj30UK369bOPlSspkZ591o7K9+5tn7gUEyOlpUn33GM/T0iQhg6VHnvMrlC/YoV9hvtHH0n//u/SiBFShw723NGj7YWE3FzplVekBx6QHKz7FZZ4vzkXqswYSW8EPH4NAICLmzNnjiZNmqTMzEwNHDhQy5Yt06FDh5SdnS1JevLJJ3XkyBH97W9/839NUVGRJKmiokI//PCDioqKFB0drbS0tFD8CgCukqgoadq0+o+1bWtHydPSfNq79zPNmzdIXm+kTp2S1q+X3npLevddad++c1/Tpo3UrZv03Xf2iUyffGK3S/F6bec+PV1KTbWd/w8+sI+TW73aXhQAriQ66Q106pR9lqTESDoAAPUZN26cfvzxRy1evFhHjx5Venq63n//faWkpEiSjh49WueZ6QMGDPB/XFBQoDfffFMpKSk6cODA1Ww6gCYgJkZ68EG7nTwpbdxoO/S9e0tdupyb+r5zp1RQIBUX25H5sjI7fb6qyo7ADxpkt3797MWCs+64Qxo50l4AeOQR6e23pVat7LHqansus8fRmOiku3D+6ptnn5bRtq2dHoP6RXPJ0RVyc4fcnCMzd8gteNOmTdO0iwyP/fWvf62zL9Tr2vLaukNuzpGZOxfLLT5euv/++s63o/DnXf8L2pAhUl6eNGqUtHatnT3r80k//SRVVtrR+j597Oj79dfbfkFEhP5vVXs7kNevn10QL9R4vzkXisxY3b2B1q2z/xDceKO9MgcAgFPNeXX3UCFTAI1t/Xp7r/oFTxsLWvfuthPv8djR+6oqyRgpKUnq2tVuCQm24//LL3aLjpauu85+Xe/eUlxco/5KfpWV0oED59qHxsfq7leQz+fTd999p549eyoiIsJ/zwtT3S/uwswQHHJzh9ycIzN3yK354rV1h9ycIzN3QpXbiBF2Lardu+2oeEKC1K6dnTa/e7fd9uyxHV6fz26nT9t9Bw7Yxe1KShrWhuTkcx36rl1tHyQ93W5JSfacEyfszyktlW644dz++nIzxk7ff+wx6cgRadgwu6gey39YoXqv0Ul3qLq6WitXrtS8efPk9XpZ2T0IF2aG4JCbO+TmHJm5Q27NF6+tO+TmHJm5E8rcunWz2/k6dLAj0Jdy4oS9F/7bb6XISLs43dmmHz1qO8hHjtjF7WJj7Yh5mzZ2NP2bb+wFgGPHpO+/t9uWLXV/Rvv29h75iopz+yIi7D3148ZJ990XmNv27dLMmdJnn507/6OP7NT86dPtsb17pf/5Hztj2Bi7Ov6wYXZ0vyEj7sY0jRH7UL3X6KQ3EJ10AAAAAJfSrp00eLDd3Dp2zI7In+3Ql5TYTv+OHXZF++PHz52bkGC3ffvsQnobN0qSVxERC/TMMxGKiLCj/JJdeO/JJ+3CewsWSO+8I730kt0ulJdn/3vNNVKPHvbiw4kTdsG+a6+1K+CPHWuPXWjPHrs6/urV0vbt9pbhRYvsaH84MEZ6+ml7O/OyZVL//qFrC530BuLxawAAAACutMREu2Vm1j126pTtsLdube99j421+/fvl1atslPaCwslny8y4J76sWOl556znW7JLoy3YYM0Z47t/F93nV35PiPDjtLn50uffy4dOmS38/34o/T119Ljj9s2JifbdlVW2lsC9u4NPH/NGrs9/LA0d65dcO/s/fiSbVNysp0NcKUZY9v93HP28/vukzZvthc6QoFOukMej0cdO3aUx+NRTc251d0ZSb+48zND8MjNHXJzjszcIbfmi9fWHXJzjszcIbe6YmLqH/lNTbUd4LlzpaNHz2j58rc0btx4RUVFKyZG6tSp7tfcfbcd6a6uPveoubPmzrUd6S++sCP3//RPdpZAbKz05Zf2gsCmTXaK/IWioux0+TFj7D30L7xgz/+v/7JbfaKjbWc9JeXcrQZdu9qp/WdX0I+IsG1ISrKP3Gvfvu5U+r177Qj5unXS1q3SPfdICxfae++Nkf7t32x7JPt9Dh+2I/35+aF5r7G6ewPs3y/17GnvJ6msvDpXeQAAzQ8rkTc+MgWA0Pjf/5U+/NCuXh8TYzvwbdpIN99sO/Xn275deuop6f337QWBuDh7fm2t7SjX1jr/+V6v/Xler+3k19TY73Uhj8c+975tW+kvf7H7Xn3VXqS45RY7M2DsWDsLoTH6eazufgXV1tZq27Zt6t+/v/7xj0hJtqNOB/3izs8sMjIy1M1pMsjNHXJzjszcIbfmi9fWHXJzjszcITd3rlZunTtLkycHd26/fvYe9frU1NhF8g4ckA4etPfhHz5s78UvL7cj4D6f7cj//LNdgO/48XOPtztfVJRdQG/UKHsP/H/8h53a/9Zb5875y1+kRx+1H69da0f9//u/pX/5l1ItW9bxqr7X6KQ7VFNTo3Xr1qlv377at8++UEx1v7TzM+Mf0uCRmzvk5hyZuUNuzRevrTvk5hyZuUNu7jS13KKi7FT3s/fLB+P0aTuS/8sv9nn2Z87Yafvp6XZK/FlDhthp7089JX3yiZ3q/s//fO74bbdJ//mfUlaW9MYbSbrzzmpNmkQnvUlISLAv4E03hbolAAAAANCytW5t718PxoABdiX7iz0ObvJkaefOGq1cWarbb+/YqO28HDrpDfDww3YDAAAAADQ9l1oTbtGiWrVq9f+UlPTY1WuQJO6kdsjj8ehXv/oVq0k6QGbukJs75OYcmblDbs0Xr6075OYcmblDbu6Qm3ORkR716ZPC6u5XGqu9AgDCDbWp8ZEpACCcOKlLjKQ7VFNTo08//VQ1NTWhbkqTQWbukJs75OYcmblDbs0Xr6075OYcmblDbu6Qm3OhyoxOukO1tbXatGmTat08tK+FIjN3yM0dcnOOzNwht+aL19YdcnOOzNwhN3fIzblQZUYnHQAAAACAMEEnHQAAAACAMEEn3aGIiAgNGDBAERFEFywyc4fc3CE358jMHXJrvnht3SE358jMHXJzh9ycC1VmrO4OAECIUZsaH5kCAMIJq7tfQdXV1crLy1N1dXWom9JkkJk75OYOuTlHZu6QW/PFa+sOuTlHZu6Qmzvk5lyoMqOT7pDP59PWrVvl8/lC3ZQmg8zcITd3yM05MnOH3JovXlt3yM05MnOH3NwhN+dClRmddAAAAAAAwkRUqBtwtZ29Bb+8vNzV11dVVen06dMqLy+X1+ttzKY1W2TmDrm5Q27OkZk7jZnb2ZrUwpaJuaIaUu/5m3CH3JwjM3fIzR1ycy5Utb7FLRx3+PBhde/ePdTNAACgjpKSEnXr1i3UzWgWqPcAgHAUTK1vcZ10n8+n77//XvHx8fJ4PI6/vry8XN27d1dJSQmrxQaJzNwhN3fIzTkyc6cxczPG6OTJk0pOTubROI2kIfWevwl3yM05MnOH3NwhN+dCVetb3HT3iIiIRhmlaNu2LW9uh8jMHXJzh9ycIzN3Giu3du3aNUJrcFZj1Hv+JtwhN+fIzB1yc4fcnLvatZ7L9QAAAAAAhAk66QAAAAAAhAk66Q55vV4tXLiQFREdIDN3yM0dcnOOzNwht+aL19YdcnOOzNwhN3fIzblQZdbiFo4DAAAAACBcMZIOAAAAAECYoJMOAAAAAECYoJMOAAAAAECYoJMOAAAAAECYoJPuwCuvvKLU1FS1bt1aGRkZ+vzzz0PdpLCxZMkS3XTTTYqPj1enTp00evRo7dmzJ+AcY4wWLVqk5ORkxcTE6M4779TOnTtD1OLwtGTJEnk8Hs2ePdu/j9zqd+TIEU2cOFEdOnRQbGysfv3rX6ugoMB/nNwC1dTUaMGCBUpNTVVMTIx69uypxYsXy+fz+c8hM+mzzz7TqFGjlJycLI/Ho3feeSfgeDAZVVVVaebMmUpMTFRcXJzuv/9+HT58+Cr+FmgIav2lUe8bjlofPGq9c9T7y2sStd4gKLm5uaZVq1bmtddeM7t27TKzZs0ycXFx5uDBg6FuWli45557zPLly82OHTtMUVGRGTlypLnmmmtMRUWF/5ylS5ea+Ph4s3r1alNcXGzGjRtnunTpYsrLy0PY8vCxefNm06NHD9OvXz8za9Ys/35yq+unn34yKSkpZsqUKebrr782+/fvNxs2bDD79u3zn0Nugf785z+bDh06mHfffdfs37/frFq1yrRp08a8+OKL/nPIzJj333/fzJ8/36xevdpIMmvXrg04HkxG2dnZpmvXriY/P98UFhaau+66y/Tv39/U1NRc5d8GTlHrL4963zDU+uBR692h3l9eU6j1dNKDdPPNN5vs7OyAfX369DHz5s0LUYvCW1lZmZFkNm3aZIwxxufzmaSkJLN06VL/OadPnzbt2rUzr776aqiaGTZOnjxpevXqZfLz880dd9zhL9zkVr8nnnjCDB48+KLHya2ukSNHmt/+9rcB+x588EEzceJEYwyZ1efCwh1MRj///LNp1aqVyc3N9Z9z5MgRExERYT744IOr1na4Q613jnofPGq9M9R6d6j3zoRrrWe6exDOnDmjgoICDRs2LGD/sGHD9Pe//z1ErQpvJ06ckCQlJCRIkvbv36/S0tKADL1er+644w4ylDR9+nSNHDlSd999d8B+cqtfXl6eMjMz9dBDD6lTp04aMGCAXnvtNf9xcqtr8ODB+vjjj/Xtt99KkrZt26YvvvhC9957ryQyC0YwGRUUFKi6ujrgnOTkZKWnp5NjmKPWu0O9Dx613hlqvTvU+4YJl1of1SjfpZk7duyYamtr1blz54D9nTt3VmlpaYhaFb6MMZozZ44GDx6s9PR0SfLnVF+GBw8evOptDCe5ubkqLCzUli1b6hwjt/p99913ysnJ0Zw5c/SHP/xBmzdv1u9+9zt5vV5NnjyZ3OrxxBNP6MSJE+rTp48iIyNVW1urp59+WuPHj5fEey0YwWRUWlqq6OhotW/fvs451IvwRq13jnofPGq9c9R6d6j3DRMutZ5OugMejyfgc2NMnX2QZsyYoe3bt+uLL76oc4wMA5WUlGjWrFn66KOP1Lp164ueR26BfD6fMjMz9cwzz0iSBgwYoJ07dyonJ0eTJ0/2n0du57z99ttasWKF3nzzTfXt21dFRUWaPXu2kpOTlZWV5T+PzC7PTUbk2HTwNxA86n1wqPXuUOvdod43jlDXeqa7ByExMVGRkZF1royUlZXVucrS0s2cOVN5eXnauHGjunXr5t+flJQkSWR4gYKCApWVlSkjI0NRUVGKiorSpk2b9NJLLykqKsqfDbkF6tKli9LS0gL2XX/99Tp06JAk3m/1efzxxzVv3jw98sgjuuGGGzRp0iT9/ve/15IlSySRWTCCySgpKUlnzpzR8ePHL3oOwhO13hnqffCo9e5Q692h3jdMuNR6OulBiI6OVkZGhvLz8wP25+fna9CgQSFqVXgxxmjGjBlas2aNPvnkE6WmpgYcT01NVVJSUkCGZ86c0aZNm1p0hkOHDlVxcbGKior8W2ZmpiZMmKCioiL17NmT3Opx66231nnkz7fffquUlBRJvN/qU1lZqYiIwH/yIyMj/Y9kIbPLCyajjIwMtWrVKuCco0ePaseOHeQY5qj1waHeO0etd4da7w71vmHCptY3yvJzLcDZx7K8/vrrZteuXWb27NkmLi7OHDhwINRNCwtTp0417dq1M59++qk5evSof6usrPSfs3TpUtOuXTuzZs0aU1xcbMaPH9+iHvcQrPNXfDWG3OqzefNmExUVZZ5++mmzd+9es3LlShMbG2tWrFjhP4fcAmVlZZmuXbv6H8myZs0ak5iYaObOnes/h8zs6stbt241W7duNZLM888/b7Zu3ep/BFcwGWVnZ5tu3bqZDRs2mMLCQjNkyBAewdZEUOsvj3rfOKj1l0etd4d6f3lNodbTSXfg5ZdfNikpKSY6OtrceOON/seNwD6+oL5t+fLl/nN8Pp9ZuHChSUpKMl6v19x+++2muLg4dI0OUxcWbnKr37p160x6errxer2mT58+ZtmyZQHHyS1QeXm5mTVrlrnmmmtM69atTc+ePc38+fNNVVWV/xwyM2bjxo31/luWlZVljAkuo1OnTpkZM2aYhIQEExMTY+677z5z6NChEPw2cINaf2nU+8ZBrQ8Otd456v3lNYVa7zHGmMYZkwcAAAAAAA3BPekAAAAAAIQJOukAAAAAAIQJOukAAAAAAIQJOukAAAAAAIQJOukAAAAAAIQJOukAAAAAAIQJOukAAAAAAIQJOukAAAAAAIQJOukArjiPx6N33nkn1M0AAABXEPUeaBx00oFmbsqUKfJ4PHW24cOHh7ppAACgkVDvgeYjKtQNAHDlDR8+XMuXLw/Y5/V6Q9QaAABwJVDvgeaBkXSgBfB6vUpKSgrY2rdvL8lOTcvJydGIESMUExOj1NRUrVq1KuDri4uLNWTIEMXExKhDhw569NFHVVFREXDOG2+8ob59+8rr9apLly6aMWNGwPFjx47pgQceUGxsrHr16qW8vDz/sePHj2vChAnq2LGjYmJi1KtXrzr/kwEAAC6Neg80D3TSAeiPf/yjxowZo23btmnixIkaP368du/eLUmqrKzU8OHD1b59e23ZskWrVq3Shg0bAopyTk6Opk+frkcffVTFxcXKy8vTtddeG/AznnrqKT388MPavn277r33Xk2YMEE//fST/+fv2rVL69ev1+7du5WTk6PExMSrFwAAAC0A9R5oIgyAZi0rK8tERkaauLi4gG3x4sXGGGMkmezs7ICvueWWW8zUqVONMcYsW7bMtG/f3lRUVPiPv/feeyYiIsKUlpYaY4xJTk428+fPv2gbJJkFCxb4P6+oqDAej8esX7/eGGPMqFGjzG9+85vG+YUBAGiBqPdA88E96UALcNdddyknJydgX0JCgv/jgQMHBhwbOHCgioqKJEm7d+9W//79FRcX5z9+6623yufzac+ePfJ4PPr+++81dOjQS7ahX79+/o/j4uIUHx+vsrIySdLUqVM1ZswYFRYWatiwYRo9erQGDRrk6ncFAKClot4DzQOddKAFiIuLqzMd7XI8Ho8kyRjj/7i+c2JiYoL6fq1atarztT6fT5I0YsQIHTx4UO+99542bNigoUOHavr06Xr22WcdtRkAgJaMeg80D9yTDkBfffVVnc/79OkjSUpLS1NRUZF++eUX//Evv/xSERER6t27t+Lj49WjRw99/PHHDWpDx44dNWXKFK1YsUIvvviili1b1qDvBwAAAlHvgaaBkXSgBaiqqlJpaWnAvqioKP9iLatWrVJmZqYGDx6slStXavPmzXr99dclSRMmTNDChQuVlZWlRYsW6YcfftDMmTM1adIkde7cWZK0aNEiZWdnq1OnThoxYoROnjypL7/8UjNnzgyqfX/605+UkZGhvn37qqqqSu+++66uv/76RkwAAIDmj3oPNA900oEW4IMPPlCXLl0C9l133XX65ptvJNmVWHNzczVt2jQlJSVp5cqVSktLkyTFxsbqww8/1KxZs3TTTTcpNjZWY8aM0fPPP+//XllZWTp9+rReeOEFPfbYY0pMTNTYsWODbl90dLSefPJJHThwQDExMbrtttuUm5vbCL85AAAtB/UeaB48xhgT6kYACB2Px6O1a9dq9OjRoW4KAAC4Qqj3QNPBPekAAAAAAIQJOukAAAAAAIQJprsDAAAAABAmGEkHAAAAACBM0EkHAAAAACBM0EkHAAAAACBM0EkHAAAAACBM0EkHAAAAACBM0EkHAAAAACBM0EkHAAAAACBM0EkHAAAAACBM/H9wN3xc9dE6TAAAAABJRU5ErkJggg==",
      "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": [
    "### 14.2.2 Multiclass Classification <a id=\"14.2.2-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 image 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": 28,
   "metadata": {
    "id": "YONwWTmbI4Kq",
    "tags": []
   },
   "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": 29,
   "metadata": {
    "tags": []
   },
   "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": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of each sample\n",
    "fashion_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pixels in the first train image\n",
    "fashion_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "a93MnJ5LSeFN",
    "tags": []
   },
   "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": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "nQBbX87MLfQ0",
    "outputId": "8aee400e-950a-4324-e1a0-8be8148a5d57",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This type of image is: Ankle boot(9)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "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": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "DMd5B7SiUQOg",
    "outputId": "56d1ac6d-1939-48df-e9bc-6605da6b0611",
    "tags": []
   },
   "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": 35,
   "metadata": {
    "id": "YsagpEZYWxp9",
    "tags": []
   },
   "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": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fashion_train = fashion_train.reshape(-1, 784)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fashion_test = fashion_test.reshape(-1, 784)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 38,
     "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 the 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": 39,
   "metadata": {
    "tags": []
   },
   "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": 40,
   "metadata": {
    "tags": []
   },
   "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": 41,
   "metadata": {
    "tags": []
   },
   "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": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nbg2muwlmzaz",
    "outputId": "290c28dc-c430-4400-aa7b-bd5d6b19580f",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.8172 - loss: 0.5174\n",
      "Epoch 2/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8616 - loss: 0.3883\n",
      "Epoch 3/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8711 - loss: 0.3526\n",
      "Epoch 4/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.8831 - loss: 0.3253\n",
      "Epoch 5/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8870 - loss: 0.3096\n",
      "Epoch 6/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.8910 - loss: 0.2953\n",
      "Epoch 7/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8944 - loss: 0.2843\n",
      "Epoch 8/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8988 - loss: 0.2749\n",
      "Epoch 9/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.9020 - loss: 0.2640\n",
      "Epoch 10/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.9043 - loss: 0.2581\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": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O2XST14vufRB",
    "outputId": "d6fe60f0-3f36-48dc-a554-b837d33777ed",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8695 - loss: 0.3645   \n",
      "Classification Accuracy:  0.8694999814033508\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": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K79im89prfzv",
    "outputId": "a06c7797-cfb5-46fd-b90e-0518f88897cb",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 736us/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": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ai46Kj1d7p1m",
    "outputId": "f85e905b-6a1a-4ec6-fc9b-c7a8226e34cb",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10)"
      ]
     },
     "execution_count": 45,
     "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": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ph2r53Kt77Hg",
    "outputId": "83732785-130e-4c8b-dcde-850cc89b013c",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.   , 0.   , 0.   , 0.   , 0.   , 0.001, 0.   , 0.063, 0.   ,\n",
       "        0.936],\n",
       "       [0.   , 0.   , 0.999, 0.   , 0.   , 0.   , 0.   , 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",
       "       [0.269, 0.   , 0.038, 0.017, 0.001, 0.   , 0.673, 0.   , 0.002,\n",
       "        0.   ]], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "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": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C7bmDQ2k8191",
    "outputId": "3c97e0df-778d-452e-8f49-027e49feda69",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 2, 1, 1, 6])"
      ]
     },
     "execution_count": 47,
     "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": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5r3luKAi9IJp",
    "outputId": "670aabec-a36f-4dd7-bf97-30bd21c94aa8",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 2, 1, 1, 6], dtype=uint8)"
      ]
     },
     "execution_count": 48,
     "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": 49,
   "metadata": {
    "tags": []
   },
   "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": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 968us/step - accuracy: 0.8731 - loss: 0.3826\n",
      "Classification Accuracy:  0.8730999827384949\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": [
    "## 14.3 Regression with ANNs <a id=\"14.3-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** | 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",
    "| **Neurons in output layer** | Neurons equivalent to number of target variables (1, if one target variable) |\n",
    "| **Activation in hidden layers**|  Mostly ReLU |\n",
    "| **Activation in output layer** |None in most cases|\n",
    "| **Loss functio**n | 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": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    }\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": 51,
     "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": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 9)"
      ]
     },
     "execution_count": 52,
     "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": 53,
   "metadata": {
    "tags": []
   },
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "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": 55,
   "metadata": {
    "tags": []
   },
   "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": 56,
   "metadata": {
    "tags": []
   },
   "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),);"
   ]
  },
  {
   "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": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(housing['MedHouseVal'], 'bo');"
   ]
  },
  {
   "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": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = housing.drop('MedHouseVal', axis=1)\n",
    "y = housing['MedHouseVal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": 60,
   "metadata": {
    "tags": []
   },
   "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": 61,
   "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": 62,
   "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": 63,
   "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": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_3\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">288</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,056</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_2 (\u001b[38;5;33mInputLayer\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │             \u001b[38;5;34m288\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m1,056\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m33\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,377</span> (5.38 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,377\u001b[0m (5.38 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,377</span> (5.38 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,377\u001b[0m (5.38 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 65,
   "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": 66,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "history = model_2.fit(X_train_scaled, y_train, epochs=200, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Us+8KhaJO61RVVYXLly+jqqoKjLFGsU6Oep2uz8b0OO6+To56na7PRi6XN4p1cuX/U83Hq41bFf3CwkIEBgZatAUGBkKv16O4uBjBwcFW91m4cCHmzp1r1Z6SkgIvLy8AQEREBOLj47Fjxw5kZ2ebl4mJiUFsbCw2b94MQWgCAPjzzwvIyfkHvXv3xvLly3H58mXz8gkJCejSpQtSUlIs/iCnTJkCX19fLFq0yKIPycnJKC0tRVpamrlNpVJh5syZyM3Nxbp168zt/v7+mDp1KnJycrBt2zZze+fOnTFmzBhkZWUhMzPT3H6jddqwYQPOnTtnbh8+fHid1in1fxcNNn1vDOvk6NcpNTW10a0T4JjXKTU1tdGtE+D8/yfThoUtBNZABsoKgoDNmzdj5MiRkst069YN48aNw8yZM81tBw4cwMCBA1FQUICgoCCr+4ht6bdr1w5FRUXmq8bf6F28vLwcEybswMaNj2LAACMyM2lL37RO5eXlSE1NxfTp06FWqxvFOjnqdaqoqDBn4+Xl1SjWyZFb+qZsfHx8GsU6ufL/qaysDAEBASgtLTXXNSlutaUfFBSEwsJCi7aioiIoFAq0bt1a9D5qtRpqtdqmdrHdQwDg4+OD2NhB2LgRYEwG0zEWlUol+Zy2tguCINouk8lE200fhWtSKBSiR/yl1kmq7/auU9OmTZGQkICmTZuar/bj7uvkqNdJLBt3XydHvU4KhcIqG3dfJ3vaHf3/JPV4YtxqnH50dDQyMjIs2r777jtERUVJvhiOIJPJ0LYt33VEB3ItyWQydOnShS57J4KykUbZiHNGLi5N/Nq1azh27BiOHTsGgA/JPHbsGPLy8gAAM2fOxNixY83LT548GRcuXMCMGTNw6tQprFixAunp6XjhhRfqtZ8ajQZffbUBABX9mjQaDRYuXGjXgSRPQdlIo2zEOSMXl+7e+eWXX3DXXXeZf58xYwYAIDExEatWrUJBQYH5DQAAOnXqhO3bt2P69On44IMPEBISgnfffRcPPfRQvffVYOD7zmgaBms07E4aZSONshFX37m4tOjHxsbWOrHQqlWrrNpiYmJw9OjReuyVONOVs2hLnxDizmiHmo1o7h1CSGPQYIZsOktZWRl8fX1tGtpkYjQa8fXXpRg1qiVCQ4GTJ+u5k27EaDSiuLgYfn5+dFCuBspGGmUjrq652FPXKG0bCIKA5s19ANCWfk2CIMDX1xeCILi6Kw0OZSONshHnjFyo6NtAq9Xis8/WAKADuTVptVosWrSIDsqJoGykUTbinJELFX0bmS6iQlv6hBB3RkXfRnQglxDSGFDRtxEN2SSENAY0escGjDH88osOffuqEBQEFBTUcyfdCGMMWq0WKpWKDsrVQNlIo2zE1TUXGr3jYIwxVFaWA6At/ZoYYygtLa31JDtPRdlIo2zEOSMXKvo20Ol02LBhPQAavVOTTqdDWlqa1dSvhLKpDWUjzhm5UNG3ER3IJYQ0BlT0bUQHcgkhjQEVfRup1XxuOir61qQuFEEom9pQNuLqOxcavWOjvDygQwdArQbsuBwlIYTUOxq942BGoxEXLvz5v59d3JkGxmg04uzZsxbXDSUcZSONshHnjFyo6NtAp9Ph6683AaDdOzXpdDqsW7eORmGIoGykUTbinJELFX0bmQ7kGo2AZ+0QI4Q0JlT0bWQasgnQLh5CiPuiom8DQRDg79/K/Dvt4qnGs/GnU+lFUDbSKBtxzsiFRu/YqLwcMC1eWQl4e9dTBwkhxE40esfBDAYDfv31mPl32r1TzWAw4OjRozDQxx8rlI00ykacM3Khom8DvV6PHTu+Mf9Of6fV9Ho9tm3bBr1e7+quNDiUjTTKRpwzcqGib6PrD+RS0SeEuCsq+jYyDdkEqOgTQtwXFX0bCIKArl07m3+nol9NEAR07tyZRmGIoGykUTbinJELjd6xg1zOD+Lm5wPBwfXUQUIIsRON3nEwvV6PvXv3Qi6nOfVrMmVDB+SsUTbSKBtxzsiFir4NDAYDMjMzIZebfndtfxoSUzY09M4aZSONshHnjFyo6NuBij4hxN1R0bcDFX1CiLujom8DmUyGiIgIKvoiTNnIZPSnVBNlI42yEeeMXGj0jh38/ICSEuDECaBHj3rqICGE2IlG7ziYTqfD1q1bafSOCFM2dDEMa5SNNMpGnDNyoaJvA6PRiOzsbNq9I8KUDV32zhplI42yEeeMXKjo28G0m42KPiHEXVHRtwNt6RNC3B0VfRvI5XLExMRQ0RdRnY3c1V1pcCgbaZSNOGfkQqN37NC1K3D2LJCVBQwYUE8dJIQQO9HoHQfTarVYu3ateU592tKvZspGq9W6uisNDmUjjbIR54xcqOjbgDGGc+fO0ZBNEaZsPOwDo00oG2mUjThn5EJF3w60T58Q4u5cXvSXLVuGTp06wcvLC5GRkdi/f3+ty69btw7h4eFo0qQJgoODMW7cOJSUlDilr1T0CSHuzqVF/4svvkBSUhJmzZqF7OxsDBo0CEOHDkVeXp7o8llZWRg7diwmTJiAEydOYOPGjTh8+DAmTpxYr/1UKBQYPnw45HJ+NRs6n6SaKRuFQuHqrjQ4lI00ykacU3JhLtS3b182efJki7bu3buz5ORk0eXfeustdsstt1i0vfvuu6xt27Y2P2dpaSkDwEpLS+3ub58+jAGMbdtm910JIaTe2FPXXPY2q9VqceTIESQnJ1u0x8XF4eDBg6L36d+/P2bNmoXt27dj6NChKCoqwpdffolhw4ZJPo9Go4FGozH/XlZWZtUuk8mgVCqh0+ksTn+Wy+VQKBS4du0aVq1aBWAKADl0OgMAObRarcUBF6VSCZlMZvF8pnZBEKyOyKtUKjDGrObZUKvVMBqNFu2CIEClUsFgMFhcVcfUrtfrLS68cKN1qtl3hUIBudz+dbp27Ro+/fRTJCYmQqVSNYp1ctTrVFlZac5GrVY3inVy1Ouk0WjM2TRp0qRRrJMr/59qPl5tXFb0i4uLYTAYEBgYaNEeGBiIwsJC0fv0798f69atw+jRo1FVVQW9Xo/4+Hi89957ks+zcOFCzJ0716o9JSUFXl5eAICIiAjEx8djx44dyM7ONi8TExOD2NhYbNq0CSUlJSgs/AtAe5w7dwHALVi+fDkuX75sXj4hIQFdunRBSkqKxR/klClT4Ovri0WLFln0ITk5GaWlpUhLSzO3qVQqzJw5E7m5uVi3bp253d/fH1OnTkVOTg62bdtmbu/cuTPGjBmDrKwsZGZmmttvtE4bNmzAuXPnzO3Dhw9H79697V6nlJQUc56NZZ0c/TqlpKQ0unUCHPM6paSkNLp1Apz//1RVVQVbuezkrPz8fLRp0wYHDx5EdHS0uX3BggVYs2YNTp8+bXWfkydP4p577sH06dNx3333oaCgAC+++CL69OmD9PR00ecR29Jv164dioqKzCcx3OhdvLy8HCkpKcjImIUDBxRYv96A0aNpy0QQBJSXlyM1NRXTp083b826+zo56nWqqKgwZ+Pl5dUo1slRr1NVVZU5Gx8fn0axTq78fyorK0NAQIBNJ2e5bEvfz88Pcrncaqu+qKjIauvfZOHChRgwYABefPFFAECvXr3g4+ODQYMGYf78+QgODra6j1qthlqttqldqVSKPq9KpQIAyOX8uDdjcot2sce2tV0QBNF2mUwm2i6Xy0VP0VYoFKIHf260Tra232idrs+zsazT9eqyTtdnY+qDu6+To14nUyFUq9Xmx3T3dbKn3dH/T1KPJ8Zlo3dUKhUiIyORkZFh0Z6RkYH+/fuL3qeystLqijKmF6w+P7AolUokJCRAqaTROzVVZyP+z+DJKBtplI04Z+Ti0vFSM2bMwJNPPomoqChER0fj448/Rl5eHiZPngwAmDlzJv766y+sXr0aAN9PNmnSJKSlpZl37yQlJaFv374ICQmpt37KZDJ06dKFxumLMGVDrFE20igbcc7IxaXj9EePHo2lS5di3rx5uP3227Fv3z5s374dHTp0AAAUFBRYjNl/6qmnkJKSgvfffx9hYWF45JFHcOutt2LTpk312k+NRoOFCxdCEHi1p6JfzZSNPaMHPAVlI42yEeeMXFx+ZsTUqVMxdepU0dv4MElL06ZNw7Rp0+q5V9a0Wi1t6UugSbOkUTbSKBtx9Z2Ly6dhcCdU9Akh7o6Kvh1Mx5DpQC4hxF1R0beBUqnElClToFDwuGhLv5opGxqFYY2ykUbZiHNGLlT0bSAIAnx9fWEatktFv5opG0EQXN2VBoeykUbZiHNGLlT0baDVav93yjffr0NFv5opGzooZ42ykUbZiHNGLlT07UAHcgkh7o6Kvh1MB3Kp6BNC3BUVfTuYtvRp9A4hxF25bJZNVykrK4Ovr69Ns9GZMMag1Wrx7LMqLF8u4I03gFdfreeOuglTNiqVig7K1UDZSKNsxNU1F3vqGm3p24AxhtLSUtqnL8KUjYdtO9iEspFG2YhzRi5U9G2g0+mQlpYGQaDROzWZsqk53zehbGpD2YhzRi5U9O0gl/N3Xyr6hBB3RUXfDjQNAyHE3VHRt5FKpaJ9+hKkrg5EKJvaUDbi6jsXGr1jh5dfBpYsAWbMAN55p546SAghdqLROw5mNBpx9uxZyGS0T78mUzZG2udlhbKRRtmIc0YudSr6Fy9exKVLl8y///zzz0hKSsLHH3/ssI41JDqdDuvWrQNAV86qyZQNjcKwRtlIo2zEOSOXOhX9J554Anv27AEAFBYW4t5778XPP/+MV155BfPmzXNoBxsSmoaBEOLu6lT0f/vtN/Tt2xcAsGHDBoSFheHgwYP47LPPRC9x2FjQNAyEEHdXp6Kv0+mgVqsBALt370Z8fDwAoHv37igoKHBc7xoIQRDg7+9P8+mLMGVDp9Jbo2ykUTbinJFLnUbv9OvXD3fddReGDRuGuLg4HDp0COHh4Th06BAefvhhi/39Dc3NjN5ZtAiYORMYNw5YsaKeOkgIIXaq99E7ixcvxkcffYTY2Fg8/vjjCA8PBwBs3brVvNunMTEYDDh69ChNwyDClI2BQrFC2UijbMQ5I5c6Ff3Y2FgUFxejuLgYK67b5H366afx4YcfOqxzDYVer8e2bduo6IswZaPX613dlQaHspFG2YhzRi51Kvr//vsvNBoNWrZsCQC4cOECli5dit9//x0BAQEO7WBDQqN3CCHurk5Ff8SIEVi9ejUA4OrVq+jXrx/eeecdjBw5EmlpaQ7tYENimnCNRu8QQtxVnYr+0aNHMWjQIADAl19+icDAQFy4cAGrV6/Gu+++69AONgSCIKBz586Qy/kRddrSr2bKhkZhWKNspFE24pyRS51G7zRp0gSnT59G+/bt8eijj6Jnz56YPXs2Ll68iFtvvRWVlZX10VeHuJnROx99BEyeDIwcCWzeXD/9I4QQe9X76J0uXbpgy5YtuHjxInbt2oW4uDgAQFFRkd2F1B3o9Xrs3bsXNA2DNVM2dEDOGmUjjbIR54xc6lT0X3/9dbzwwgvo2LEj+vbti+joaADAd999h4iICId2sCEwGAzIzMwEQKN3ajJlQ0PvrFE20igbcc7IRVGXOz388MMYOHAgCgoKzGP0AWDw4MF48MEHHda5hoamYSCEuLs6FX0ACAoKQlBQEC5dugRBENCmTZtGeWLW9egiKoQQd1en3TtGoxHz5s2Dr68vOnTogPbt26NFixZ44403GuX82DKZDBEREVAoaPROTaZsZDK6NENNlI00ykacM3Kp0+idmTNnIj09HXPnzsWAAQPAGMOBAwcwZ84cTJo0CQsWLKiPvjrEzYze2bABGD0aiIkB9u6tn/4RQoi96n30zqefforly5djypQp6NWrF8LDwzF16lR88sknjXJqZZ1Oh61bt4IxfkSdtvSrmbKhi2FYo2ykUTbinJFLnYr+lStX0L17d6v27t2748qVKzfdqYbGaDQiOzsbgkCXS6zJlE1j3K13sygbaZSNOGfkUqeiHx4ejvfff9+q/f3330evXr1uulMNFY3eIYS4uzqN3lmyZAmGDRuG3bt3Izo6GoIg4ODBg7h48SK2b9/u6D42GDR6hxDi7uq0pR8TE4M//vgDDz74IK5evYorV65g1KhROHHiBFauXOnoPrqcXC5HTEwMlEoeFxX9aqZs5KZ3RGJG2UijbMQ5I5c6jd6RkpOTg969ezfos+xuZvRORgYQFwf06gXk5NRTBwkhxE71PnrH02i1WqxduxYGAz+i3oDf05zOlI1Wq3V1VxocykYaZSPOGbm4vOgvW7YMnTp1gpeXFyIjI7F///5al9doNJg1axY6dOgAtVqNzp07W1y9qz4wxnDu3DnIZDSffk2mbBz4gbHRoGykUTbinJFLnadhcIQvvvgCSUlJWLZsGQYMGICPPvoIQ4cOxcmTJ9G+fXvR+zz66KP4+++/kZ6eji5duqCoqMhpM/XRgVxCiLuzq+iPGjWq1tuvXr1q15OnpKRgwoQJmDhxIgBg6dKl2LVrF9LS0rBw4UKr5Xfu3InMzEzk5uaiVatWAICOHTva9Zw3g4o+IcTd2VX0fX19b3j72LFjbXosrVaLI0eOIDk52aI9Li4OBw8eFL3P1q1bERUVhSVLlmDNmjXw8fFBfHw83njjDXh7e4veR6PRQKPRmH8vKyuzapfJZFAqldDpdBYnRcjlcigUChiNRgwZMgRVVaaplRkAAVqt1uJjmFKphEwms3g+U7sgCFb76VQqFRhjVmffqdVqGI1Gi3ZBEKBSqWAwGCw+2Zja9Xq9xQH0G61Tzb4rFArI5XK718lgMGDIkCEwGAzQaDSNYp0c9Tpdn41Wq20U6+So1+n6bAwGQ6NYJ1f+P9V8vNrYVfQdORyzuLgYBoMBgYGBFu2BgYEoLCwUvU9ubi6ysrLg5eWFzZs3o7i4GFOnTsWVK1ck9+svXLgQc+fOtWpPSUmBl5cXACAiIgLx8fHYsWMHsrOzzcvExMQgNjYWX331Fc6dO4e//soB8DT+/VcHQIXly5fj8uXL5uUTEhLQpUsXpKSkWPxBTpkyBb6+vli0aJFFH5KTk1FaWmpxXWGVSoWZM2ciNzcX69atM7f7+/tj6tSpyMnJwbZt28ztnTt3xpgxY5CVlfW/Of9h0zpt2LAB586dM7cPHz4cvXv3tnud3nrrLQD8U1hjWSdHv047d+5sdOsEOOZ12rlzZ6NbJ8D5/09VVVWwlUOHbNojPz8fbdq0wcGDB80XYQGABQsWYM2aNTh9+rTVfeLi4rB//34UFhaaP3Vs2rQJDz/8MCoqKkS39sW29Nu1a2dxla8bvYtfu3YNq1atQnj4ONx5pw9CQhj++ou29AVBwLVr1/Dpp58iMTERKpWqUayTo16nyspKczZqtbpRrJOjXieNRmPOpkmTJo1inVz5/1RWVoaAgACbhmy67ECun58f5HK51VZ9UVGR1da/SXBwMNq0aWOxmyk0NBSMMVy6dAldu3a1uo9arYZarbapXalUij6vUqlESUkJvLz47UYjn2JZpVKJLi/2fFLtgiCItstkMtF2uVwueuKGQqGAQmH9ckqtk1Tf7V0nlUqFkpISqFQq8zLuvk6Oep2uz8bUB3dfJ0e9Towxczamx3T3dbKn3dH/T1KPJ8ZlQzZVKhUiIyORkZFh0Z6RkYH+/fuL3mfAgAHIz8/HtWvXzG1//PEHZDIZ2rZtW6/9BehALiHE/bl0nP6MGTOwfPlyrFixAqdOncL06dORl5eHyZMnA+Dz9l9/YPiJJ55A69atMW7cOJw8eRL79u3Diy++iPHjx0seyHUkKvqEEHfn0nH6o0ePRklJCebNm4eCggKEhYVh+/bt6NChAwCgoKAAeXl55uWbNm2KjIwMTJs2DVFRUWjdujUeffRRzJ8/v177qVQqkZCQAL2ex0VFv5opG6mPvZ6MspFG2YhzRi4uO5DrKjcz905uLtC5M+DtDVRUAIJQT50khBA70Nw7DqbRaLBw4UL4+WmgUgH//gucP+/qXjUMpmzsGSfsKSgbaZSNOGfkQkXfRlqtFmo1cNtt/PdffnFtfxoSmjRLGmUjjbIRV9+5UNG3U1QU/37kiGv7QQghdUFF306Rkfw7bekTQtwRHci1gdFoRHFxMfz8/JCTI0Pv3kCLFsCVK3Qw9/psZDLahrgeZSONshFX11zoQK6DCYIAX19fCIKAnj0BlQq4epWP5vF012dDLFE20igbcc7IhYq+DbRaLRYtWvS/mRKB8HDefviwa/vVEFyfDbFE2UijbMQ5Ixcq+nVwxx38+4EDru0HIYTYi4p+Hdx5J/++b59r+0EIIfaiol8Hgwbx78eP84O5hBDiLmj0jg0YY+YrH5kOsHTvDvz+O7B1KzB8eH32uGETy4ZwlI00ykZcXXOh0TsOxhhDaWmpxcUQTFv7nr6LRywbwlE20igbcc7IhYq+DXQ6HdLS0iyuXmPar5+V5aJONRBi2RCOspFG2YhzRi5U9OuoTx/+/ddfaaplQoj7oKJfR127Ak2aAJWVwJkzru4NIYTYhoq+jWpe61IuB3r14j9nZ7ugQw2I1HVACWVTG8pGXH3nQqN3bsLUqUBaGvDSS8DixQ7qICGE2IlG7ziY0WjE2bNnYTQaLdojIvh3T97Sl8qGUDa1oWzEOSMXKvo20Ol0WLdundUR9dtv59+PHQM86/NSNalsCGVTG8pGnDNyoaJ/E8LC+L79y5eB/HxX94YQQm6Miv5N8Pau3trftculXSGEEJtQ0beBIAjw9/cXPS165Ej+fdMm5/apoagtG09H2UijbMQ5IxcavXOTTp6E+cIqly8DDnhIQgixC43ecTCDwYCjR4/CIHLqbY8efPI1rRb49lsXdM7FasvG01E20igbcc7IhYq+DfR6PbZt2wa9Xi96+6hR/PvKlU7sVANxo2w8GWUjjbIR54xcqOg7wKRJfBRPRgYfvkkIIQ0VFX0H6NgReOQR/vPbb7u0K4QQUisq+jYQBAGdO3eu9Yj6iy/y7+vXA3/95aSONQC2ZOOpKBtplI04Z+RCo3ccaNAgPr/+nDnA7NkOfWhCCJFEo3ccTK/XY+/evTc8uDJ1Kv/+ySeApxyfsjUbT0TZSKNsxDkjFyr6NjAYDMjMzLzhMKpRowB/f757Z8UKJ3XOxWzNxhNRNtIoG3HOyIWKvgOp1UBSEv/5mWeA7dtd2h1CCLFCRd/BkpOBhAS+e2f0aOD3313dI0IIqUZF3wYymQwRERGQyW4cl0zGT9KKiQGuXQMefhgoLnZCJ13Enmw8DWUjjbIR54xcaPROPSks5BdZKSwEgoOBzz/nbwSEEOJoNHrHwXQ6HbZu3WrXhQ2CgvgZut27AwUFwODBwLvvNr6LrdQlG09B2UijbMQ5Ixcq+jYwGo3Izs62+xJmYWHAkSPAmDGAwQA89xzw1FNAaWn99NMV6pqNJ6BspFE24pyRCxX9etakCbB6NZCayufnWb0aCAnho3z++cfVvSOEeBoq+k4gCLzIZ2TwufcrK4H//hfo1g346itX944Q4kmo6NtALpcjJiYGcrn8ph7nrruA48eBnTv5PPzFxXx0T3Q0MHeue+72cVQ2jRFlI42yEeeMXGj0jototXyOnkWLqg/uBgQA06dXvyE8/ji/Di8hhNTGrUbvLFu2DJ06dYKXlxciIyOxf/9+m+534MABKBQK3G66Mnk90mq1WLt2LbRarcMeU6UC3nwTOH8eSE/nu3qKioCZM4ERI4AJE4D4eCAvjw/7bKjqI5vGgrKRRtmIc0YuLi36X3zxBZKSkjBr1ixkZ2dj0KBBGDp0KPLy8mq9X2lpKcaOHYvBgwc7pZ+MMZw7dw718aGofXtg/Hi+22fFCj5TZ69egI8PsHs30KED0K4d8N13Dn9qh6jPbNwdZSONshHnjFxcWvRTUlIwYcIETJw4EaGhoVi6dCnatWuHtLS0Wu/3f//3f3jiiScQHR3tpJ7WP5UKGDcO2LcPyMnh8/YEBPDb9HrgiSf4Qd/cXNf2kxDi3hSuemKtVosjR44gOTnZoj0uLg4HDx6UvN/KlStx7tw5rF27FvPnz7/h82g0Gmg0GvPvZWVlVu0ymQxKpRI6nc5ifKxcLodCoTB/1DItr1AoIJfLodVqLd6RlUolZDKZxfOZ2gVBsPrIplKpwBizOhFDrVZj4EAjcnN1+PdfIC5OiexsGR5+GJDLGRYsMGDSJAN8fPhFF1QqFfR6vcXMfLas0/V9r+s6mdpN32tbJ6PRaNFu6rvBYLCYStbV62TP61TbOl2fTWNZJ0e9Ttdn01jWyZX/TzUfrzYuK/rFxcUwGAwIDAy0aA8MDEShxE7sM2fOIDk5Gfv374dCYVvXFy5ciLlz51q1p6SkwMvLCwAQERGB+Ph47NixA9nZ2eZlYmJiEBsbiy1btgAAUlNTAQDDhw9H7969sXz5cly+fNm8fEJCArp06YKUlBSLP8gpU6bA19cXixYtsuhDcnIySktLLT7ZqFQqzJw5E7m5uVi3bh0AIDa2OWSyB6DVdsXx4wKSkxVITlagS5czSEo6hmeeeQRZWVnIzMw0P86N1mnDhg04d+6cub2u62TKxPTd1nUCAH9/f0ydOhU5OTnYtm2bub1z584YM2aMy9aprq+T1DqlpqY2unUCHPM6paamNrp1Apz//1RVVQVbuWz0Tn5+Ptq0aYODBw9a7KZZsGAB1qxZg9OnT1ssbzAYcMcdd2DChAmYPHkyAGDOnDnYsmULjtVyNXKxLf127dqhqKjIfJTbXba2lEoVli0zYsECAQUF/HJqbdsypKYKGD5cD4C2IGmdaJ08cZ3KysoQEBBg26hE5iIajYbJ5XK2adMmi/b//Oc/7M4777Ra/p9//mEAmFwuN38JgmBu+/7772163tLSUgaAlZaW2tXXDz74gGk0GpvvU99+/ZWxbt0Y4wM+GZPJGAsMZKx3b8aWL2fMaHROPxpiNg0FZSONshFX11zsqWsuO5CrUqkQGRmJjIwMi/aMjAz079/favnmzZvj+PHjOHbsmPlr8uTJuPXWW3Hs2DH069ev3vrKGMPly5cb1EiD227j8/rMng00awYYjcDffwNHjwITJwJDhwIXL9Z/PxpiNg0FZSONshHnjFxcOnpnxowZWL58OVasWIFTp05h+vTpyMvLM+++mTlzJsaOHcs7KpMhLCzM4isgIABeXl4ICwuDj4+PK1fFJZo25Sd4XbkC5OcD2dnA4sWAlxewaxef8G3ZMj7ZGyGEAC48kAsAo0ePRklJCebNm4eCggKEhYVh+/bt6NChAwCgoKDghmP2CaBQ8Dn7g4OB22/nJ3c99RRw6BC/bOO77wKxsfxEsJEjgf/7Pz4fECHE89A0DDYwGo3Izc3FLbfc4jZX+jEYgA8/BF55BfjfKFWzO+/kF3F/8kmgVaubex53zMZZKBtplI24uuZiT12jot/IlZXxq3adOcPn8VmyhM/7AwC+vsBjjwFRUUBiIqBUuravhJC6cau5d9yBRqPBwoUL7ToBoqFo3pzvznn7beCNN4ATJ3jhv+02PqvnRx8Bkybxi7hfuQLYu4runE19o2ykUTbinJELFX0bNZaJobp0AV58ETh2DNi8mf+sUvGfW7cGWrTgxwTmzgV+/NG2x2ws2dQHykYaZSOuvnOhou+hZDJ+UHfJkuqCDwBVVcDWrXxU0IAB/PaiosZ3bV9CPBUVfYL77+fTN//7L/8EsGgR39pnDHj5ZSAwEOjUiY8Aat0aSEgArjuznBDiRuhArg2MRiOKi4vh5+fnMSMNGAM++IBv6V+6ZL2l37Qp8OCDQGioEa1bl6N//2a49VYZHQy+jif+3diKshFX11xo9E4t6lL0GWPQarVQqVQQPHCAe0UF8P33QEEBn///lVf4J4KaVCrggQeAl14C6vEEabfh6X83taFsxNU1Fxq942BarRaLFi3y2ANPPj78Kl7/9398eoejR4GsLH6Vr4QEA9q2vYimTRm0WmDTJuCOO/iuoGXLgAsXXN171/H0v5vaUDbinJELFX1iN0HgB3nffBNIT9dj4sQVKCrSIieHnwmsUACZmfxs4C5dgEceAe6+m48UqnmiGCHEuajoE4eQyfhlHleuBP78E5g/Hxg4kF/168svgT17+LkCXbvy6/+OGsUvE3nihKt7TohncencO6RxatsWmDWLf+3bx6/1GxAALF0KnDvHrwVssnIl0LcvHxH09NN8sjhCSP2hA7k2oINO0uzJRqvlB4T37uXDQA8c4McATIKDgbg4Phy0qAjw9wfGjAEGD+bTRoeGutdUEfR3I42yEeeMA7lU9G1Aw8uk3Ww2BQX8gu+LF/OhobXx8QEef5wfTN6yhX86uO++uvXbGejvRhplI84ZQzYpbRvodDqkpaVZXbKM3Hw2wcHAs88CZ88C27cDr77Kp4L++mt+VnDz5vzAcbNmfOjo8uXAQw8Ba9bwk8pGjOAHldes4QeJ//ij+voBmZn89u+/d9z62oP+bqRRNuKckQvt0ycNglrNt+CHDq1ui4/n5wRotXyG0AMH+AigkyeB3r15Ud+6lS978CB/c2CMzx7apw+/XacDvvsOWLeOjyBq0cIlq0dIg0FFnzRoSmX1fvxBg/iFYYxGXuA/+qj6kpCLF/MtfJWKzx66ezdvDwzkxwMeeojf5447+C6ip57inx5MDAb+5tCxIz92QEhjRUXfRiqVytVdaLCcnY1pV+f/rqoJAJg+nX8iCAgAfv2VfypQKoGxY4H//AfYuRP46y8+c+iPP/K2li35XEJ+fkBxMd/F5OMDZGTwTwitWwPduwNyed37Sn830igbcfWdCx3IJR7j0iU+WmjZMuD3361vN+0eul5gIJCUBNx6K//d25vvWgoIqPfuEmIzGr1TC0+5XKKzuGM2jPEte9NXSQm/eMyAAfw4QnY2L+oVFfxLzJAh/PjCwYPAF1/wXUVbtvBPDcuXA7/8ArzxhhETJlSgsLApvv1WQGCgU1ezQXPHvxtncMblEsE8TGlpKQPASktLbb5PVVUVmzNnDquqqqrHnrmnxpZNZSVjx44xZjAwptUytmoVY4MHMzZgAP8KDWWMv21Yf/Xowdgdd1T/3qaN0fxzbCxjOh1jf/zB2PjxjIWEMDZhAn+emnQ6xoxG56+7MzW2vxtHqWsu9tQ12qdPyHW8vYHwcP6zTMavHZyYaLnMH38A06bxief69ePnCsyfz0cVmR6DMeCvv4T/PQ7D3r0Chg/nnwCKi/ly6en8oPSoUfxEtG7dgNxc/mmje3dg2zagSZPq5z17lk9616YNsGoV759ez4eq3uwF7onnoKJPiJ26dQN27bJsu/dePpqoSxc+vfT33/M5hjp1ysWiRe0wZowSO3fyZaOi+GiimTP5NBQrV/J2lYoPXS0vB/LzgeHDgchIPmOpVssf78oVvmxcHBATw99wcnOB9ev5ldAY49NeZ2byvgwbxo9VEGJCRd8GgiDA39+fThcXQdlw3bsDqanVv48fD/Tpo8X332dg5Mhx+OknPtqodWvg88/5uQS33MK32IuK+LDSS5d4cQ8L458mfviBf731VvXjmkYazZjBRyfl5/P2Rx8Fpk7lQ1p/+ql6+b59+SeKsDD+O2P8DevoUT5MNT6++pPNzdJq+ZTb/fvfeA4l+rsR54xc6EAuIQ3EkSN8OOmTT/Li/cUXfBfO55/zA80vvcS/IiKA8+f5fbp2BXr25AeRTby8+DkNBw/yA9FqNd995O3Nz0PIyLB8Xl9fPkPqc8/xcxT0ev58588D778P/PYbcM89/I3s7rurdyft2sWvrxwTwz9NvPkmXzYuDtixo3poLal/NHqnFnUp+gaDATk5OQgPD4f8ZgZtN0KUjTRHZXPlClBZyWcvBfhW+urV/Kzj+HhezDdt4rt0Wrbk01oEBfHrHo8fzwvw9eRy4LHH+DWRv/mGb6HbyjSs9ZZb+G4lKW+9BTz/PP85M5Mfy+jRg79BnD8PrFljhEaTjwceCMbAgXKo1Zb3P32aP4/YiXJGI//eGN9U6vo3Y09do907NtDr9di2bRt69uxJha0Gykaao7Jp1cryQG3v3vzreo8+yr+uFxTEi/qhQ7xAnj/Pr2swYQLf7QPwwn/uHD8msGoVUFXFL4KjUvFdSTEx/I1lyxZ+7KG8nN/PVPBHjeI/e3vzg9r+/nxK7Rdf5MszBpw6Vd2nZs34c+h0MgBtsXQpv97yuHH8/oIA5OTway8wxg+iN2/OP82MGcN3Z40axa/I9thj1fczGPi6CgL/1NKxo+WxjF9+4W+InTtb58sYcPw4f8xhw1z7ZuKM/ycq+oQ0YjIZ38cO8CkoHnvM8nZvb76/f/58/iXlrruAhQv5p44mTfjkeG3b8oPM12MM+Ocf4L33qkczqdV8l8/x49W7pe65x4j8/FMoKemBv/8W8N57/D41rVpV/fN//sO/azT8+8cf86/u3Xmfjh6tXtbXF5g4kc/d9Omn/BgIwOd2mjqVf1rYswfYv5+fqV1YyG9//XW+a+zUKf4mo1bzdVIogMOHgatX+bEUPz+ebWAgEBJS/bw//wwsWMDfVJ9/vvrYxoUL/GJCKhXPomvX6k8/jPG+3H23c6YOp6JPCLFJkybVQ0jHjBFfRhD4rp2XX+YX0FGr+QikoCBeaPfv57uTBg3SYfHiL/Hyy8nIylJj+XJ+khxjvPBNmMA/3WzYwJ9z8+bqN4x+/Xgx37iRT8t9+jRvNx0cP3GCz7/0zjv863o7dljv7gL4c+p0wBtvVJ+V/cIL1bu+FAp+rENMRAS//Z9/+KcmxvhEgB9/DDzxBD/R7403+O0AzyQ8nL9BtGvHnzs3F2jfHnjrLZl591W9qcsJBO6sLidnaTQatmbNGqbRaOqxZ+6JspFG2UizNxu9nrFz5xg7edLyhLarVxn7+GPGkpIYy8vjbVotY998w1hYGD8xThAYe+01xs6c4cuFhDB222385LgNGxg7fJixsjLGEhOrT6zr2NH65LuAAMb69GGsVy/+GMHB/LFrLjdqFL+tZnt4OD/BT+zEvusfJyTkH7Z5s9auPO2pa3QglxDSaF29yrfg/f1vvGxFBbBkCT9APmwYHzbbujXfir96FejQwfqch4ICPkzV25t/0mjbFujUiT/Wtm18or/iYv5p4NVX+e6dZcv4Vv6MGfzTSnk5/2T0wQd8tFRZGf+E88gjtq8njd6pRV2Kvl6vR1ZWFgYOHAiFgvaIXY+ykUbZSKNsxJWU6DFvXi6WLLkFarXtudCVsxzMYDAgMzMTBtMlmYgZZSONspFG2Yhr2tSAVq0+B1B/uVDRJ4QQD0JFnxBCPAgVfRvIZDJERETQvN8iKBtplI00ykacM3KhA7mEEOLm6ECug+l0OmzduhU6nc7VXWlwKBtplI00ykacM3Khom8Do9GI7OxsGOv9VDn3Q9lIo2ykUTbinJELFX1CCPEgHndWhOkQRllZmc330Wg0qKqqQllZGdQ154D1cJSNNMpGGmUjrq65mOqZLYdoPe5A7qVLl9CuXTtXd4MQQhzu4sWLaGu68IIEjyv6RqMR+fn5aNasmc2XJCsrK0O7du1w8eJFGvFTA2UjjbKRRtmIq2sujDGUl5cjJCTkhsM9PW73jkwmu+E7oZTmzZvTH6gEykYaZSONshFXl1x8fX1tWo4O5BJCiAehok8IIR6Eir4N1Go1Zs+eTaMMRFA20igbaZSNOGfk4nEHcgkhxJPRlj4hhHgQKvqEEOJBqOgTQogHoaJPCCEehIq+DZYtW4ZOnTrBy8sLkZGR2L9/v6u75FRz5syBIAgWX0FBQebbGWOYM2cOQkJC4O3tjdjYWJw4ccKFPa4/+/btw/DhwxESEgJBELBlyxaL223JQqPRYNq0afDz84OPjw/i4+Nx6dIlJ65F/bhRNk899ZTV39Edd9xhsUxjzGbhwoXo06cPmjVrhoCAAIwcORK///67xTLO/Luhon8DX3zxBZKSkjBr1ixkZ2dj0KBBGDp0KPLy8lzdNafq2bMnCgoKzF/Hjx8337ZkyRKkpKTg/fffx+HDhxEUFIR7770X5eXlLuxx/aioqEB4eDjef/990dttySIpKQmbN2/G+vXrkZWVhWvXruGBBx5w+4uE3ygbABgyZIjF39H27dstbm+M2WRmZuKZZ57BoUOHkJGRAb1ej7i4OFRUVJiXcerfDSO16tu3L5s8ebJFW/fu3VlycrKLeuR8s2fPZuHh4aK3GY1GFhQUxBYtWmRuq6qqYr6+vuzDDz90Ug9dAwDbvHmz+Xdbsrh69SpTKpVs/fr15mX++usvJpPJ2M6dO53W9/pWMxvGGEtMTGQjRoyQvI+nZFNUVMQAsMzMTMaY8/9uaEu/FlqtFkeOHEFcXJxFe1xcHA4ePOiiXrnGmTNnEBISgk6dOuGxxx5Dbm4uAODPP/9EYWGhRUZqtRoxMTEel5EtWRw5cgQ6nc5imZCQEISFhXlEXnv37kVAQAC6deuGSZMmoaioyHybp2RTWloKAGjVqhUA5//dUNGvRXFxMQwGAwIDAy3aAwMDUVhY6KJeOV+/fv2wevVq7Nq1C5988gkKCwvRv39/lJSUmHPw9IwA2JRFYWEhVCoVWrZsKblMYzV06FCsW7cOP/zwA9555x0cPnwYd999NzQaDQDPyIYxhhkzZmDgwIEICwsD4Py/G4+bZbMuak7BzBizeVrmxmDo0KHmn2+77TZER0ejc+fO+PTTT80H4jw9o+vVJQtPyGv06NHmn8PCwhAVFYUOHTrg22+/xahRoyTv15iyefbZZ/Hrr78iKyvL6jZn/d3Qln4t/Pz8IJfLrd5Ji4qKrN6VPYmPjw9uu+02nDlzxjyKhzKCTVkEBQVBq9Xin3/+kVzGUwQHB6NDhw44c+YMgMafzbRp07B161bs2bPHYnp3Z//dUNGvhUqlQmRkJDIyMizaMzIy0L9/fxf1yvU0Gg1OnTqF4OBgdOrUCUFBQRYZabVaZGZmelxGtmQRGRkJpVJpsUxBQQF+++03j8urpKQEFy9eRHBwMIDGmw1jDM8++yw2bdqEH374AZ06dbK43el/N3U/Bu0Z1q9fz5RKJUtPT2cnT55kSUlJzMfHh50/f97VXXOa559/nu3du5fl5uayQ4cOsQceeIA1a9bMnMGiRYuYr68v27RpEzt+/Dh7/PHHWXBwMCsrK3Nxzx2vvLycZWdns+zsbAaApaSksOzsbHbhwgXGmG1ZTJ48mbVt25bt3r2bHT16lN19990sPDyc6fV6V62WQ9SWTXl5OXv++efZwYMH2Z9//sn27NnDoqOjWZs2bRp9NlOmTGG+vr5s7969rKCgwPxVWVlpXsaZfzdU9G3wwQcfsA4dOjCVSsV69+5tHmrlKUaPHs2Cg4OZUqlkISEhbNSoUezEiRPm241GI5s9ezYLCgpiarWa3Xnnnez48eMu7HH92bNnDwNg9ZWYmMgYsy2Lf//9lz377LOsVatWzNvbmz3wwAMsLy/PBWvjWLVlU1lZyeLi4pi/vz9TKpWsffv2LDEx0Wq9G2M2YpkAYCtXrjQv48y/G5pamRBCPAjt0yeEEA9CRZ8QQjwIFX1CCPEgVPQJIcSDUNEnhBAPQkWfEEI8CBV9QgjxIFT0CSHEg1DRJ6QBELu8ICH1gYo+8Xhi124VBAFDhgxxddcIcTiaT58Q8Gu3rly50qJNrVa7qDeE1B/a0icEvMAHBQVZfJmuUiQIAtLS0jB06FB4e3ujU6dO2Lhxo8X9jx8/jrvvvhve3t5o3bo1nn76aVy7ds1imRUrVqBnz55Qq9UIDg7Gs88+a3F7cXExHnzwQTRp0gRdu3bF1q1bzbf9888/SEhIgL+/P7y9vdG1a1erNylCbEFFnxAbvPbaa3jooYeQk5ODMWPG4PHHH8epU6cAAJWVlRgyZAhatmyJw4cPY+PGjdi9e7dFUU9LS8MzzzyDp59+GsePH8fWrVvRpUsXi+eYO3cuHn30Ufz666+4//77kZCQgCtXrpif/+TJk9ixYwdOnTqFtLQ0+Pn5OS8A0njc3KShhLi/xMREJpfLmY+Pj8XXvHnzGGN8atzJkydb3Kdfv35sypQpjDHGPv74Y9ayZUt27do18+3ffvstk8lkrLCwkDHGWEhICJs1a5ZkHwCwV1991fz7tWvXmCAIbMeOHYwxxoYPH87GjRvnmBUmHo326RMC4K677kJaWppFW6tWrcw/R0dHW9wWHR2NY8eOAQBOnTqF8PBw+Pj4mG8fMGAAjEYjfv/9dwiCgPz8fAwePLjWPvTq1cv8s4+PD5o1a4aioiIAwJQpU/DQQw/h6NGjiIuLw8iRI936alLEdajoEwJeZGvubrkR0wWpWS0XpxYEAd7e3jY9nlKptLqv0WgEwC9Of+HCBXz77bfYvXs3Bg8ejGeeeQZvv/22XX0mhPbpE2KDQ4cOWf3evXt3AECPHj1w7NgxVFRUmG8/cOAAZDIZunXrhmbNmqFjx474/vvvb6oP/v7+eOqpp7B27VosXboUH3/88U09HvFMtKVPCPjF3gsLCy3aFAqF+WDpxo0bERUVhYEDB2LdunX4+eefkZ6eDgBISEjA7NmzkZiYiDlz5uDy5cuYNm0annzySQQGBgIA5syZg8mTJyMgIABDhw5FeXk5Dhw4gGnTptnUv9dffx2RkZHo2bMnNBoNvvnmG4SGhjowAeIpqOgTAmDnzp0IDg62aLv11ltx+vRpAHxkzfr16zF16lQEBQVh3bp16NGjBwCgSZMm2LVrF5577jn06dMHTZo0wUMPPYSUlBTzYyUmJqKqqgqpqal44YUX4Ofnh4cfftjm/qlUKsycORPnz5+Ht7c3Bg0ahPXr1ztgzYmnoWvkEnIDgiBg8+bNGDlypKu7QshNo336hBDiQajoE0KIB6F9+oTcAO0BJY0JbekTQogHoaJPCCEehIo+IYR4ECr6hBDiQajoE0KIB6GiTwghHoSKPiGEeBAq+oQQ4kH+HxNGPl0xfDWbAAAAAElFTkSuQmCC",
      "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": 68,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.50592124],\n",
       "       [0.7766473 ],\n",
       "       [5.044109  ],\n",
       "       [2.6232712 ],\n",
       "       [2.767562  ],\n",
       "       [1.7727039 ],\n",
       "       [2.3566236 ],\n",
       "       [1.7581401 ],\n",
       "       [2.9044712 ],\n",
       "       [4.960486  ]], dtype=float32)"
      ]
     },
     "execution_count": 68,
     "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": 69,
   "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": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ground-truth values\n",
    "np.array(y_test[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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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 R-squared score. R-squared score (or $R^2$) 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": 71,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m129/129\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 558us/step\n",
      "MSE:  0.2939\n",
      "MAE:  0.3815\n",
      "R2 score:  0.7757\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": [
    "## 14.4 Saving and Loading Models in Keras <a id=\"14.4-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 14.2.1 in the current working directory under the name `classification_model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "-I1ZmeLCSJXW",
    "tags": []
   },
   "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": 73,
   "metadata": {
    "id": "HV_-3wAcSn-Y",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Loading a saved model\n",
    "from tensorflow.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": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w8bW12rbX9Wk",
    "outputId": "85177afb-6e02-4372-878e-25db0580ade2",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9737 - loss: 0.0904  \n",
      "Classification Accuracy:  0.9736841917037964\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": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fbTIrz5ES8hG",
    "outputId": "f40ce904-77f2-47aa-bf11-e954b0c3b40e",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">930</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">465</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m)                  │             \u001b[38;5;34m930\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m)                  │             \u001b[38;5;34m465\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m16\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,235</span> (16.55 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,235\u001b[0m (16.55 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,411</span> (5.51 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,411\u001b[0m (5.51 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,824</span> (11.04 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m2,824\u001b[0m (11.04 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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 14.2.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "tags": []
   },
   "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": 77,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.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": 78,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9737 - loss: 0.0931 \n",
      "Classification Accuracy:  0.9736841917037964\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": 79,
   "metadata": {
    "tags": []
   },
   "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",
    "        return x\n",
    "\n",
    "# Instantiate the model\n",
    "model_1_subclass = Model1Subclass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9737 - loss: 0.0865 \n",
      "Classification Accuracy:  0.9736841917037964\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). \n",
    "3. The Difference Between Deep Learning Training and Inference, Mark Robins, available at: [https://www.linkedin.com/pulse/difference-between-deep-learning-training-inference-mark-robins-mdq8c/](https://www.linkedin.com/pulse/difference-between-deep-learning-training-inference-mark-robins-mdq8c/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[BACK TO TOP](#top)"
   ]
  }
 ],
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