{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture 15 - 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-2024-Applied-Data-Science-with-Python/blob/main/docs/Lectures/Theme_3-Model_Engineering/Lecture_15-ANNs/Lecture_15-ANNs.ipynb)\n", "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/avakanski/Fall-2024-Applied-Data-Science-with-Python/blob/main/docs/Lectures/Theme_3-Model_Engineering/Lecture_15-ANNs/Lecture_15-ANNs.ipynb) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- [15.1 Introduction to Artificial Neural Networks](#15.1-introduction-to-artificial-neural-networks)\n", " - [15.1.1 Elements of ANNs](#15.1.1-elements-of-anns)\n", " - [15.1.2 Common Activation Functions](#15.1.2-common-activation-functions)\n", " - [15.1.3 Training ANNs](#15.1.3-training-anns)\n", " - [15.1.4 Types of ANN Architectures](#15.1.4-types-of-ann-architectures)\n", " - [15.1.5 Fully-connected NNs](#15.1.5-fully-connected-nns)\n", "- [15.2 Classification with ANNs](#15.2-classification-with-anns)\n", " - [15.2.1 Binary Classification](#15.2.1-binary-classification)\n", " - [15.2.2 Multiclass Classification](#15.2.2-multiclass-classification)\n", "- [15.3 Regression with ANNs](#15.3-regression-with-anns)\n", "- [15.4 Saving and Loading Models in Keras](#15.4-saving-and-loading-models-in-keras)\n", "- [Appendix](#appendix)\n", "- [References](#references)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 15.1 Introduction to Artificial Neural Networks " ] }, { "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": [ "\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. " ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "### 15.1.1 Elements of ANNs " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The values of the connections between the neurons in ANNs are learned during a training phase. By using a training dataset 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", "\n", "\n", "*Figure: Artificial Neuron.*" ] }, { "cell_type": "markdown", "metadata": { "id": "a687f494" }, "source": [ "### 15.1.2 Common Activation Functions " ] }, { "cell_type": "markdown", "metadata": { "id": "d6dc2881" }, "source": [ "**Activation functions** are applied to each neuron in ANNs to introduce non-linearities into the outputs of the neurons. Without non-linear activation functions, ANNs will simply learn linear mappings between the inputs and outputs. Calculating non-linear mappings between inputs and outputs gives ANNs the 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", "- **Softmax** activation function: It is an extension of the sigmoid activation function for multiclass classification tasks, and it is used in the output layer for multiclass classification problems. \n", "- **ReLU (Rectified Linear Unit)** activation function: It outputs the input value if it is positive, and it outputs 0 if the input value is negative. In other words, this activation function just penalizes the negative inputs. ReLU is the most commonly used activation function in the hidden layers of modern ANNs. \n", "- **Leaky ReLU (Rectified Linear Unit)** activation function: It is a modification of ReLU, where instead of outputting 0 for negative values, Leaky ReLU has a small negative slope. Some networks use Leaky ReLU instead of ReLU.\n", "\n", "There are also several other modifications of ReLU activations such as GeLU (Gaussian error Linear Unit) and SeLU (Scaled exponential Linear Unit). Overall, ReLU is still the most common activation function. \n", "\n", "\n", "\n", "*Figure: Activation functions in ANNs.* \n", "*Figure source:* [link](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841357/)" ] }, { "cell_type": "markdown", "metadata": { "id": "GF5bogcC7mUa" }, "source": [ "### 15.1.3 Training ANNs " ] }, { "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-absolution error* (calculated as $\\sum{|y- \\hat{y}|}$) between the network predictions and target ground-truth values. \n", "\n", "**Error backpropagation** is also called **backward pass** or **backward propagation** (where the term backpropagation is short for backward propagation). In this step, the predicted outputs are traversed back through the network, from the last (output) layer backward toward the first (input) layer. During the backward step, the gradients of the loss with respect to the model parameters $\\nabla\\mathcal{L}(πœƒ)$ 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 $πœƒ$, the gradient is the vector $\\nabla\\mathcal{L} = [\\partial\\mathcal{L}/\\partialπœƒ_i]$. The gradient vector quantifies the impact of changing the network parameters to the predicted outputs. Automatic calculation of the gradients (automatic differentiation) is available in all current deep learning libraries, which significantly simplifies the implementation of deep learning algorithms. \n", "\n", "**Model parameters update** (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": [ "\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 $πœƒ$ 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", "\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, predicting on the test dataset (also known as **inference**) requires only a forward pass through the model, where for a given input instance the model outputs the prediction. For instance, in the above figure the output is a vector of class probabilities. " ] }, { "cell_type": "markdown", "metadata": { "id": "df16a232" }, "source": [ "### 15.1.4 Types of ANN Architectures " ] }, { "cell_type": "markdown", "metadata": { "id": "sKCDRk-PWEyK" }, "source": [ "The main types of ANN architectures are:\n", "\n", "- **Fully-connected neural networks (FCNNs)** (a.k.a. densely-connected neural networks, or multi-layer perceptrons)\n", "- **Convolutional neural networks (CNNs)**\n", "- **Recurrent neural networks (RNNs)**\n", "- **Transformer neural networks (TNNs)**\n", "- **Graph neural networks (GNNs)**\n", "\n", "In this lecture we will study Fully-connected NNs, and in the following lectures we will explore the other ANN architectures." ] }, { "cell_type": "markdown", "metadata": { "id": "XqAdxSlWWe9m" }, "source": [ "### 15.1.5 Fully-connected NNs " ] }, { "cell_type": "markdown", "metadata": { "id": "WziQjYJWWJcT" }, "source": [ "**Fully-connected NNs** are made of layers in which the neurons of any layer are connected to all other neurons of the preceding and succeeding layers. They are also called **Densely-connected NNs**, as well they are often referred to as **Multi-Layer Perceptrons (MLPs)**. In some contexts, these networks are also called **Feed-forward NNs** or **Linear NNs**.\n", "\n", "\n", "\n", "*Figure: Fully-connected NNs.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fully-connected NNs are mostly used in simple tasks, such as for classification or regression tasks with tabular data. Their performance for tasks with images, text data, or other data formats is often inferior in comparison to CNNs, RNNs, TNNs, and GNNs. 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": [ "## 15.2 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": [ "### 15.2.1 Binary Classification " ] }, { "cell_type": "markdown", "metadata": { "id": "5bcce621" }, "source": [ "We will first implement an ANN for binary classification.\n", "\n", "For this purpose, we will use again the Breast Cancer dataset which we also used in the previous lecture. Recall that the dataset has 569 samples, each having 30 features. The target labels are the benign or malignant class, hence it is a binary classification problem. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "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": [ { "data": { "text/html": [ "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
017.9910.38122.801001.00.118400.277600.30010.147100.24190.07871...17.33184.602019.00.16220.66560.71190.26540.46010.118900
120.5717.77132.901326.00.084740.078640.08690.070170.18120.05667...23.41158.801956.00.12380.18660.24160.18600.27500.089020
219.6921.25130.001203.00.109600.159900.19740.127900.20690.05999...25.53152.501709.00.14440.42450.45040.24300.36130.087580
311.4220.3877.58386.10.142500.283900.24140.105200.25970.09744...26.5098.87567.70.20980.86630.68690.25750.66380.173000
420.2914.34135.101297.00.100300.132800.19800.104300.18090.05883...16.67152.201575.00.13740.20500.40000.16250.23640.076780
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5 rows Γ— 31 columns

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" ], "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. Keras is part of TensorFlow, and notice in the next cell that Keras is imported from TensorFlow. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorFlow version:2.14.0\n" ] } ], "source": [ "import tensorflow as tf\n", "from tensorflow import keras\n", "\n", "# Print the version of tensorflow\n", "print(\"TensorFlow version:{}\".format(tf.__version__))" ] }, { "cell_type": "markdown", "metadata": {}, "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. Afterward we will create layers and model objects as instances of these classes. \n", "\n", "For the ANN for this case we will use `Input` and `Dense` layers, as explained below. `Model` is a general class in Keras that is used to create new ANN models. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "e_YS-JqfkyiK", "tags": [] }, "outputs": [], "source": [ "# Import the layers and the model\n", "from keras.layers import Input\n", "from keras.layers import Dense\n", "from keras.models import Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TensorFlow-Keras requires the first layer in an ANN to be an **Input** layer, which will provide the shape of the input data in the form of a tuple. For the Breast Cancer dataset, there are 30 input features for training the model, and therefore, we will set the shape of the input data to `(30,)`.\n", "\n", "Note also that to create and train an ANN, we don't need to provide information about the number of samples that are available for training or testing the model. \n", "\n", "Let's add two hidden layers. In Keras, `Dense` layers are fully-connected layers. Therefore, the layers `dense1` and `dense2` below are fully-connected layers, of which, the first layer has 30 units (or neurons), and the second layer has 15 neurons. The second argument in the Dense layers `activation` defines the activation function that is applied at each layer, and in this case we selected ReLU activations as the most common activation function. For each layer in Keras, we need to also define the previous layer to which it connects. In this case, for `dense1` the previous layer is the `inputs` layer, and `dense2` follows the `dense1` layer.\n", "\n", "Finally, we added an output layer which is also a Dense layer. As we mentioned, for binary classification problems, the output layer has 1 neuron and it uses a `sigmoid` activation function. Preceding layer to the `outputs` layer is `dense2` layer." ] }, { "cell_type": "code", "execution_count": 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": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " input_1 (InputLayer) [(None, 30)] 0 \n", " \n", " dense (Dense) (None, 30) 930 \n", " \n", " dense_1 (Dense) (None, 15) 465 \n", " \n", " dense_2 (Dense) (None, 1) 16 \n", " \n", "=================================================================\n", "Total params: 1411 (5.51 KB)\n", "Trainable params: 1411 (5.51 KB)\n", "Non-trainable params: 0 (0.00 Byte)\n", "_________________________________________________________________\n" ] } ], "source": [ "# Model summary\n", "model_1.summary()" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "The graph of the network is also shown in the following figure. We can see the layers with all 1,411 connections in the network.\n", "\n", "\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": [ "4/4 [==============================] - 0s 2ms/step - loss: 0.0709 - accuracy: 0.9737\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": [ "[0.07086548209190369, 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": [ "4/4 [==============================] - 0s 2ms/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([[1.3780599e-05],\n", " [9.9937958e-01],\n", " [5.5300228e-02],\n", " [5.8814484e-01],\n", " [3.9140336e-06],\n", " [9.6631348e-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", " [0.055],\n", " [0.588],\n", " [0. ],\n", " [0.966]], 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 0.055, the class label is 0. For the prediction of 0.588, 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 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": [ "4/4 [==============================] - 0s 2ms/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": [ "[[71 1]\n", " [ 2 40]]\n" ] } ], "source": [ "# Getting the confusion matrix\n", "from sklearn.metrics import confusion_matrix\n", "\n", "confmat = confusion_matrix(test_labels, np.round(predictions), labels=[1,0])\n", "print(confmat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As well as, we can use the function `classification_report` from scikit-learn, to display the precision, recall, F1-score, macro average accuracy, and weighted average accuracy per class. " ] }, { "cell_type": "code", "execution_count": 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 0.98 0.95 0.96 42\n", " 1 0.97 0.99 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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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot the accuracy and loss\n", "train_loss = history.history['loss']\n", "acc = history.history['accuracy'] \n", "\n", "epochsn = np.arange(1, len(train_loss)+1,1)\n", "plt.figure(figsize=(12, 4))\n", "\n", "plt.subplot(1,2,1)\n", "plt.plot(epochsn, acc, 'b')\n", "plt.grid(color='gray', linestyle='--')\n", "plt.title('TRAINING ACCURACY')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Accuracy')\n", "\n", "plt.subplot(1,2,2)\n", "plt.plot(epochsn,train_loss, 'b')\n", "plt.grid(color='gray', linestyle='--')\n", "plt.title('TRAINING LOSS')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Loss')\n", "plt.show() " ] }, { "cell_type": "markdown", "metadata": { "id": "c3xIjz-3jHP8" }, "source": [ "### 15.2.2 Multiclass Classification " ] }, { "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": [ "
" ] }, "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": [ "
" ] }, "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", "1875/1875 [==============================] - 5s 2ms/step - loss: 0.5282 - accuracy: 0.8148\n", "Epoch 2/10\n", "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3886 - accuracy: 0.8597\n", "Epoch 3/10\n", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3520 - accuracy: 0.8717\n", "Epoch 4/10\n", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3276 - accuracy: 0.8804\n", "Epoch 5/10\n", "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3119 - accuracy: 0.8868\n", "Epoch 6/10\n", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2982 - accuracy: 0.8909\n", "Epoch 7/10\n", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2878 - accuracy: 0.8939\n", "Epoch 8/10\n", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2780 - accuracy: 0.8988\n", "Epoch 9/10\n", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2706 - accuracy: 0.9001\n", "Epoch 10/10\n", "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2615 - accuracy: 0.9029\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": [ "313/313 [==============================] - 1s 1ms/step - loss: 0.3502 - accuracy: 0.8784\n", "Classification Accuracy: 0.8784000277519226\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": [ "313/313 [==============================] - 0s 1ms/step\n" ] } ], "source": [ "predictions = fashion_classifier.predict(fashion_test)" ] }, { "cell_type": "markdown", "metadata": { "id": "mYF9aQTj-GTa" }, "source": [ "In the next call, we checked the shape of the predicted outputs, and since there are 10,000 test images and 10 classes, the output is `(10000, 10)`. " ] }, { "cell_type": "code", "execution_count": 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. , 0. , 0.007, 0. ,\n", " 0.992],\n", " [0. , 0. , 0.97 , 0. , 0.005, 0. , 0.025, 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", " [0.049, 0. , 0.001, 0.001, 0.001, 0. , 0.948, 0. , 0. ,\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], dtype=int64)" ] }, "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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", 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" ] }, "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": [ "313/313 [==============================] - 1s 2ms/step - loss: 0.3710 - accuracy: 0.8771\n", "Classification Accuracy: 0.8770999908447266\n" ] } ], "source": [ "fashion_classifier_2 = Model(inputs, outputs)\n", "\n", "# Compile the model\n", "fashion_classifier_2.compile(optimizer='adam',\n", " loss='sparse_categorical_crossentropy',\n", " metrics=['accuracy'])\n", "\n", "# Train the model\n", "fashion_classifier_2.fit(fashion_train, fashion_train_label, epochs=10, batch_size=32, verbose=0)\n", "\n", "# Evaluate on test dataset\n", "evals_test = fashion_classifier_2.evaluate(fashion_test, fashion_test_label)\n", "print(\"Classification Accuracy: \", evals_test[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 15.3 Regression with ANNs " ] }, { "cell_type": "markdown", "metadata": { "id": "jG7bLsj03uxw" }, "source": [ "In regression tasks, we are interested in predicting single or multiple continuous values. The ANN architectures for regression tasks are similar to the architectures for classification tasks. The listed recommendations for the input and hidden layers are the same as for classification tasks. The number of neurons in the output layer depends on the problem. If we are predicting a single value it will be 1, and if we are predicting multiple values, the number of output neurons will be set to the number of predicted values. \n", "\n", "The choice of activation functions depends on the problem, but in most cases, `ReLU` will work well in the hidden layers. Unlike ANNs for classification, ANNs for regression don't need to have an activation function in the output layer, since the ANN outputs are continuous value(s).\n", "\n", "We stated earlier that the loss function used in regression is usually `Mean Squared Error(MSE)`, or if the dataset contains outliers, `Mean Absolute Error (MAE)` loss may be preferred.\n", "\n", "A suitable choice for an optimizer is Adam. Other optimizers to try include SGD, RMSProp, Nadam, and others. \n", "\n", "*Table: Typical values of hyperparameters in ANNs for regression*\n", "\n", "| **Hyperparameter** | **Typical value** |\n", "| --- | --- |\n", "| Neurons in input layer | Same as the number of input features |\n", "| Number of hidden layer(s) | Depends on the problem, typically from 1 to 10 |\n", "| Neurons per hidden layer | Depends on the problem, typically from 10 to 100 |\n", "| Activation in hidden layers| Mostly ReLU |\n", "| Activation in output layer |None in most cases|\n", "| Loss function | MSE or MAE |\n", "| Optimizer | Mostly Adam, SGD, RMSProp |" ] }, { "cell_type": "markdown", "metadata": { "id": "Ce1AurnvUWUr" }, "source": [ "Next, we will present an example of regression with ANNs, and for this purpose, we will use the California Housing Dataset that is available in scikit-learn. " ] }, { "cell_type": "markdown", "metadata": { "id": "_RplpeLNUYj0" }, "source": [ "#### Loading the Dataset" ] }, { "cell_type": "markdown", "metadata": { "id": "YFVMmxtm6R_a" }, "source": [ "We will load the California Housing Dataset from scikit-learn as a pandas DataFrame named `housing`. The objective of the regression task is to predict the median house value, shown in the column to the right. " ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MedIncHouseAgeAveRoomsAveBedrmsPopulationAveOccupLatitudeLongitudeMedHouseVal
08.325241.06.9841271.023810322.02.55555637.88-122.234.526
18.301421.06.2381370.9718802401.02.10984237.86-122.223.585
27.257452.08.2881361.073446496.02.80226037.85-122.243.521
35.643152.05.8173521.073059558.02.54794537.85-122.253.413
43.846252.06.2818531.081081565.02.18146737.85-122.253.422
\n", "
" ], "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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", 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" ] }, "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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B27loiZTYoSq0nWshJ/JqvnW0a9aD2MYWObevFTtEfXtL6FmZ4Wnof9fKsnLw/GwkzBo653udWf1qeHpC8X5Joddg1uC/ayw71Efq1X/hvnYi2t5cjWZH/GE7uE2B8WgbG0CQyZCbIYVxbUekhCq+75QTV1GuoUue15ar74yUE4r1k0MjYFSnCkTa4v/qvNtmqHKbVX4YgeQjl5F6Ku/h5xfnb6N8xwbQ/X9PlEmzmtCvUhGpJ67I64h0tGFYu4pSTCknIlCuQfU82zWq74yUExHvxBcBw7feQ2FEYi2ItMWQSbMUymWvsmDcqEaR2njDwO719yMpVPH/t2eFfD/K16+GpNB3vx9XYdbwv/9Hn5+PgkWLWjB0sgYAGLvaoXzj6kg4GlHk+PTsLSGxMkPyW3+mQlYOUs7egkk+3xMAMK7vjOR3/lyeh0bApEH+1+THtGlNNL35BxqF/QbnRaOgY2FcpOuyc3IR+eQZPKpVVChvUq0irsYm5XlNaORD1KxkgaCTN9De/y90+2U3FgdfRGb2f//gzMqRQfLOd0VPW4wrMQnFfGelRJCp5vhEfdRzzt7w8vKCn58fYmJiIBKJcObMGWzduhWhoaEAgPT0dCxevBjHjh2Dh4cHAMDJyQmnT5/G6tWr8dlnn2HlypVwcnLCr7/+CpFIBBcXF1y/fh0//vhjgffOzs7GqlWrUKXK627+sWPHYt68efLz8+fPxzfffIMJEybIyxo2fL9hq4+JnqUpACAzKVWhXPo0FQaV8x4qel915wxC0vnbSCtk7kp+RMYmEInFkKUqDj3JUpOhbWJW4LXGy/+CyNgEEIuRuWM9so4HK1bQN4TJiu2Atg4gk+FV4BLkXA8vdoySCqYAAOm7n2dSKvQL+DwllqbIeuearKRU6P7/zwcADOwtYTekHe6vDkb0b3tg4l4FrvN9IJNm4/H2U0ptakl0UP27L/Bk1xlo6epAS1uM7HfukZ2UCt0KpkrXAoCupSlS8qivpaMN7fLlkJ2YAh1LU2QlpbwTd4pCmxbdm8HQzRFXPfP/R9a/M9ah6qLRaBixBrLsHEAm4N43K/Hiwm15He3y5SDSFiP7qeL9spNSofPW5/Q2nQpmyE6KUKz/NOX/78EY2Yl5D2O+TZaeiReXbqPyxL64e/cRspNSYdGjOYzqVUPm/eLNn5RYvh4ue5/vR17XSN76nO8t3wdtYwO0Pr0IQq4MIrEWbvv/hSd7wooc35s/t7y+i3oFxKdbhO9vUTw7dgWJ+89C+igJenaWcJw2AHV3zsal9tMg5NND/0ZyhhS5MgHljfQVys2N9PD0xas8r3n8/AWuPEiArrYYiwe3Rkq6FAv3nkNqRhbm9nndk+nhXBEbT99EPUcr2JYvh/PRcQiNfIhcWfHm8ZUaLkJbIp9EcmZhYYEuXbpg/fr1EAQBXbp0gYXFf/9D37p1C5mZmWjfvr3CdVlZWfKhz8jISDRp0gQikUh+/k0iVxADAwN5YgYANjY2SExMBAAkJibiyZMnaNu2bZHfi1QqhVQqVSjTkkohkbz/fClNYNerKer/NFz++pTXz69/ePf3jEhU7EnEBXFf6AMTVzsc7z6v8MqFeicukXLRu17OHQ/o6UO7miv0BoyELOExssOO/VchMwMvpo+ASE8f2rXqQX/wV5AlPCmwhw0AKvZuhlo/j5S/vjTo//+IePezE+VR9u67KuQakZYWUq/+izsLtwIA0m7EoFz1yrDzaa+UnIm0xai7ejygpYWb09b9N18oj3sUFFaeMQGKn/c7dURvfXd0K5rDcf5Q3Oz/PQSp8pDhGzYjOqNcvWq45eUP6aOnMPaogSo/jERO0nOknXqnB7O4n61SfVHe5QW4N+43VFk8FvWvBEDIyUX69X/xdPcpGLoVPGRYqVcz1P55hPz1hcE/5R9TIeEohfvO/6MVu3ugcu/muDxmOV5EPYJJLXvUnOeNzIRkPPrrZJ5tWvZuDpefR8lfXxvkn098KDS+9/nOvytp73+JZPrth3gREY0m4Sth3q5eocOhb99WISz890f+LpkgQAQRFg5ogXJ6ugCAyTkNMXlzKPy6N4aejjamdm2EebvD0HPxHohEQOXy5dCtflXsC3+/h0FU7hPu9VKFTyI5A4Bhw4bJ53L9/vvvCudk/590eODAAVSqpPiky5ukR+kvgyLS0dFReC0SieRt6evr53VJgfz9/TF37lyFshlTxmPW1An5XFE2PDl4Gc8uR8tfi3VffzX1LE2QmZgiL5eYGyv1pr2vuvO9UbFDPRzv+T1exT1/73aEtFQIubnQMimPt/+tqGVsBiGt4B4QWVI8ACDr4X2ITMyg13uIYnImCJAlPAEA5D6IhlZFe0i6Dyo0OUsICUfKW7+ktSSvv4cSS1NI3/48LUyUej7eJk1MgeSdXgZdCxOF3ghpQjJe3lHsdXx55wms35qgD7xOzNzXToCBnSXO9/4eOS9fIVeaBVlOrlIPk46FiVJP1BtZiSlKPR86FiaQZecgJ/kFACA7MQW6lmZKdbKevo7bqLYTdCuYou6hnxTiM25SAzbDOiHM7gto6WrD3u8L3B72M5KPXAYAZEQ+gFFNB1Qc3V2enOU8fwEhJxc6FZTv926P4BvZScnK79lc8T0UhfRBAm71ngktfQnE5QyQnZiMaqu+gTQ2scDr4g+GI/lyUb4fxpA+Lfj7oWdpolD27jWuswbh3vK9eLL3LADgxe2H0K9cAdXGdcs3OXsWcgmX3vr+iiSvfx/oWpoi6634Xn8XU5CfvL4r735/30dWYgoyHyVB38mm0LpmBhKItUR49lKxl+z5y0yYG+X9d4BFOQNYGhvIEzMAcLQ0gSAACakZsLcwRnkjPSzxagNpdi5SMjJhaWyA30Iuo6KZUYneG2mGT2LOGQB4enoiKysLWVlZ6NhRcbKsq6srJBIJYmNjUbVqVYXD1tZWXufcuXMK1737urjKlSsHBwcHHD16tMjX+Pn5ITU1VeGYNmF0ieLQBDnpmUiPSZAfaXce41VCMqxa/jdBXqQjRgWP6nh26W6J7+e+YAgqd26IE30XIONh3vM+iiw3B7n370C7dgOFYm23+si5c6MYDYkg0tEtpIoIoncS/jxDSs9ERkyC/HgZ9QiZCcmw+Ezx8yzvUQPJF+/k205y+F1YvPVnAAAWn9VG8qX/rkm+eAeGVRTn0xhWscGrR0//u9f/EzNDJxtc6Dsf2f+f3ydk5yLt2n2YflZb4XrTz2rjxcW8J0u/CL+jXL9VHby8Gg0hJ1dexySPOm/aTD11HVdaTUJEu8ny40XEPSTtPIWIdpMBmQwibTG0dHUgvDNMJOTKAK3/ujyE7BykX4uGScs6CvVMWtbBi0u3kZeX4XeU6pt+Vgfpb72H4pC9kiI7MRliE0OYfFYXyQcL7s3J7/tR4Z3vh3kh34/n4XcVvlMAUKFVbSRf/O//UbG+bj6fYf5//eSmZ+JVTLz8yIh6BGlCMsze+jMV6WjD1MMVqfl8T4DXy12YtVT8Hph9Vgepl/K/pii0zYygV9E836do36ajLUaNiuY4e1dxqPn8vSeoY1chz2vqOlgi6UUGMt7q1X3wNA1aIhGsTAwU6kp0xLAyMUSOTMDRGw/QylVDViHg05ol8sn0nInFYkRGRsp/flu5cuUwefJkTJo0CTKZDM2bN0daWhrCwsJgZGSEIUOGYPTo0Vi0aBF8fX0xatQohIeHq2R9tDlz5mD06NGwtLREp06d8OLFC5w5cwbjxo3Ls75EIlEawszOeppn3feRkfEKsY+eyF8/fpKA23eiYWJcDjbW+SwLUUrurg1B9fHd8OJ+PF7+G48a47sj91UWYnf9N8TQcOlovIpPxo2F2wC8/gvF2LkyAEBLRxv61mYwqWkvT/4AwN3fB3Y9m+LM0MXIfpkJyf8fT89+kQFZZv5DXAWRHtgOg6/9kPtvFHLu3IRu267QsrCC9Mh+AIDegBHQMquAjJWvh2d02/eA7FkCZE9iAQDaLm7Q69oP0oO75W1Kug9E7r9Rr3vOtLWhU7cJdFt0wKt1v75XjDFr/kGVCT2Q/m880u/HoeqEnsh9JcWTXWfkdWov+wrS+OeIWrBVfk2TvbPhNLYbEkIuwcqzASxa1sLZbnPk19xffQAef89DlQk9ELf3LEzrVYWtVxvcmLwWwOuJ6/UCJsHYzRGXBv8IaGlB981nnvIS91cdQJ3lX+Hl1X/x4lIUrAe3h6SShXzdMvtvB0LXxhx3xy0DAMRvOASbYZ5wmDMECZuOoFwDF1h90QZ3xiyRx/RkbTDc9sxDpbE98DzkAsp7NoJJCzdc7zYTwP+Tk9sPFT4fWYYUOckv5OW5L18hNewmHGZ54d/MLEgfJcHEwxUV+n6G2LlBCtfGrdmPKkvHI/3aPby4FAWrwR0gqWSBhP+/B1u/QdC1Nkf0hKUAgIQNB2E1tBPsZ/sgYdNhlGvgggpftMW9r/77sxXpaEP//99lkY42dG3Kw6CmA3LTMyGNed3javJZXUAkQmb0Y+g52sBupjcyox8jadsxFNe/a/9BtfHdkf5vHNLvx6Pq+B7IfZWFR299P+ouG4PMuGTc/v8Q9v21/6DpntmoMvZzJISEw8qzPixa1MKZt74fCYcvo9qEHnj1+BleRD2ESS0HOI3ujIdbQosV36M1B2A/oRde/RuPV/fjYDehF3JfSZG467S8TvVlYyGNf477CzbLr3HfOw+2Y7vjWchFmHs2hFlLN1z5//cAAMQGetB3tJa/1rOzhFFNB2SnvIT08VOIDfTgMKUvkg6cR1ZCMvRsK8Dx24HIfv6iyEOaXi1c8d1fp1Gzsjlq21XAzgt3EJeSjj6NXz+YsDQkHIlpGZjfrwUAoHMdR6w9dhWzdpzBmHZ1kZKRiV+Dw9G9QVXo6bz+a/t6bBIS0zLgUrE8ElMzsOpoBGSCAJ+W7/c0t8pxWLNEPpnkDACMjfN/uub777+HpaUl/P398e+//8LU1BT16tXDt99+CwCws7PDzp07MWnSJKxYsQKNGjXCwoULlZ76LK4hQ4YgMzMTv/76KyZPngwLCwv06dOnRG2WxI3bdzFs3DT565+WrQEAdO/UDgtmfPNBY4n6/W+I9XRRz98HuiaGeH4lGicH/ICc9Ex5HYNK5sBb/yrXtzJDhyML5a9dvuoKl6+6IjHsFk70XgAAqOrzem5h613//YIGgAsTVuNBPsMshck+dxyvyhlDr5c3RKblkfswBi9/nA7h6euEUMvUHFoWbyW3WiLoDxgJrQrWgCwXuQlP8GrLWmQd3S+vIpLoQX/oRGiZV4CQJYXsSSwyfl+I7HPH3yvGf5fvg1hPFzV/HAYdE0OkXL6HC/0XIvetz1O/koXC55ly6Q4iRi2F8/R+cJ7WDxkxCbjy5W9IfWtILDXiX1weuhgu3w1AVd9eeBWbhMiZG/Bk5+u/1PUqmsPK83WvYovj/w0jAsC5nvMQt/csjMsbwNa3z+sFXG/H4taghZD+v+dNx8oMkrfWC5PGJuLWoIVwnOsDm6GeyEp4jvszAvHswHl5nReXohA1+lfYTfsCdlP7IzMmAVGjfsXLK8XrdY0a9SvsvxsI59/HQ9vUCNJHTxH7wxYkbDioUO/ZvjPQNiuHypP6QcfSDBlRsbg9eAGyHr/uldW1fOc9PEzE7cHz4TB3GKx8OiEr4TliZgbgefB/vfG6VmaofXix/HXFMT1QcUwPpIXdwK0+swAAYmMD2PkNhq6NOXJSXuJ58Fk8/GHze/W+RS/fD7GeLtx++P/340o0zg0o+PuRfOkuLo9eiurT+qH61H5Ij0lA+KilSLny3xSF698Gofq0fnD7YSgk5ibITEjGgw1HcWfxzmLF93D5Xoj1dFHtxxHQMTFE2uV7uNZ/vkJ8eu/El3bpDm6NWgLH6QPgOG0AXsXE49aXv+LFW9/fcnWdUHf3f1NFqs7zAQDEbw3F7Qm/Q5DJYFjDDlb9PoO2sSGyEpKRcuYmbn35q8K9C9KxtiNS0qVYffQqnr54hapWplju01Y+BJn04hXiUv5bysVAooNVwzrgh/3nMej3v2FiIEEHNwd83eG/5Z+kObn4/fAVPHr+Aga6OmjuUgnz+7WAsX4hve9UJoiE951MRRoj++m/6g6hWPa4zSy8kgZp3+pJ4ZU0SNix/Fcd11QmooKfeNM0Yq2y1SuQJCtbDwyVE8rW96HxSvfCK2kQ/V7flvo9pNcOFl6pCCS1i79m38fgk+o5IyIiotInCFxKoyQ+mQcCiIiIiMoC9pwRERGRavGBgBJhckZERESq9Qkvg6EKTM6IiIhItdhzViKcc0ZERESkQdhzRkRERKrFjc9LhMkZERERqRaHNUuEw5pEREREGoQ9Z0RERKRafFqzRJicERERkWpxWLNEOKxJREREpEHYc0ZERESqxWHNEmFyRkRERKrF5KxEOKxJREREpEHYc/YR2OM2U90hFEuP69+rO4Ri2VJnlrpDKBYzUdlb/PGoRKLuEIqlXVamukMoFnNRlrpDKJYnIj11h1AsD6afVncIxVK9V+nfQxDK3u8hTcLkjIiIiFSLw5olwuSMiIiIVItLaZQI55wRERHRR2PFihVwdHSEnp4e6tevj1OnTuVb18fHByKRSOmoWbOmvE5QUFCedTIzS296A5MzIiIiUi2ZTDVHMW3btg0TJ07Ed999hytXrqBFixbo1KkTYmNj86z/22+/IS4uTn48fPgQ5cuXR9++fRXqGRsbK9SLi4uDnl7pzY1kckZERESqJchUcxTT4sWLMXz4cIwYMQI1atTAkiVLYGtri5UrV+ZZ38TEBNbW1vLj0qVLSE5OxtChQxXqiUQihXrW1tbv9bEUFZMzIiIi0khSqRRpaWkKh1QqzbNuVlYWwsPD0aFDB4XyDh06ICwsrEj3CwgIQLt27WBvb69Q/vLlS9jb26Ny5cro2rUrrly58n5vqIiYnBEREZFqqWhY09/fHyYmJgqHv79/nrd8+vQpcnNzYWVlpVBuZWWF+Pj4QkOOi4vDP//8gxEjRiiUV69eHUFBQdi3bx+2bNkCPT09NGvWDHfv3n3/z6cQfFqTiIiIVEtFT2v6+fnB19dXoUxSyLqIIpFIMRRBUCrLS1BQEExNTdGjRw+F8iZNmqBJkyby182aNUO9evWwbNkyLF26tNB23weTMyIiItJIEomk0GTsDQsLC4jFYqVessTERKXetHcJgoB169bBy8sLurq6BdbV0tJCw4YNS7XnjMOaREREpFpqeFpTV1cX9evXx+HDhxXKDx8+jKZNmxZ47YkTJ3Dv3j0MHz680PsIgoCIiAjY2NgUK77iYM8ZERERqZaadgjw9fWFl5cXGjRoAA8PD6xZswaxsbEYPXo0gNfDpI8fP8aGDRsUrgsICEDjxo1Rq1YtpTbnzp2LJk2aoFq1akhLS8PSpUsRERGB33//vdTeB5MzIiIi+ij0798fz549w7x58xAXF4datWohODhY/vRlXFyc0ppnqamp2LlzJ3777bc820xJScGXX36J+Ph4mJiYwN3dHSdPnkSjRo1K7X2IBEEQSq11+iC22wxSdwjFwo3PS5dZbtnbcPiKpGzNsChrG5+XNU9QtjY+r1UuWd0hFEv1O8Glfo9Xfy9WSTv6XX0Lr/QRYs8ZERERqRY3Pi8Rjfrnqo+Pj9IjrAAQGhoKkUiElJSUDx5TQV69egUzMzOUL18er169Unc4REREmkFNOwR8LDQqOStrdu7ciVq1asHV1RW7du1SdzhERET0ESiTydnOnTtRs2ZNSCQSODg4YNGiRQrnRSIR9uzZo1BmamqKoKAgAK+3eBg7dixsbGygp6cHBwcHhRWHU1NT8eWXX8LS0hLGxsZo06YNrl69qhRHQEAABg8ejMGDByMgIEDp/O3bt9G8eXPo6enB1dUVR44cUYrt8ePH6N+/P8zMzGBubo7u3bsjJibmvT8bIiIitVPTxucfizKXnIWHh6Nfv34YMGAArl+/jjlz5mDmzJnyxKsoli5din379uGvv/5CVFQU/vzzTzg4OAB4vX5Jly5dEB8fj+DgYISHh6NevXpo27Ytnj9/Lm8jOjoaZ8+eRb9+/dCvXz+EhYXh33//lZ+XyWTo0aMHDAwMcP78eaxZswbfffedQhwZGRlo3bo1jIyMcPLkSZw+fRpGRkbw9PREVlZWiT4nIiIiteGwZolo3AMBf//9N4yMjBTKct96+mzx4sVo27YtZs6cCQBwdnbGrVu38PPPP8PHx6dI94iNjUW1atXQvHlziEQihQ1Ojx8/juvXryMxMVG+KvEvv/yCPXv2YMeOHfjyyy8BAOvWrUOnTp1gZmYGAPD09MS6deswf/58AMChQ4cQHR2N0NBQ+e71CxYsQPv27eX32rp1K7S0tPDHH3/It5YIDAyEqakpQkNDlTZvJSIioo+fxvWctW7dGhEREQrHH3/8IT8fGRmJZs2aKVzzZgPS3CIuIeDj44OIiAi4uLhg/PjxOHTokPxceHg4Xr58CXNzcxgZGcmP+/fvIzo6GsDrZHH9+vUYPHiw/LrBgwdj/fr18hiioqJga2srT8wAKK2JEh4ejnv37qFcuXLy+5QvXx6ZmZnye71LKpUiLS1N4cgWyt7SCURE9BHjsGaJaFzPmaGhIapWrapQ9ujRI/nPeW1g+u5SbSKRSKksOztb/nO9evVw//59/PPPPzhy5Aj69euHdu3aYceOHZDJZLCxsUFoaKhSbKampgCAgwcPyueKvS03NxeHDh1Cp06dirTRqkwmQ/369bFp0yalcxUqVMjzGn9/f8ydO1ehrI9hLfQrV7vAexEREX0wn3BipQoal5wVxtXVFadPn1YoCwsLg7OzM8RiMYDXiU1cXJz8/N27d5GRkaFwjbGxMfr374/+/fujT58+8PT0xPPnz1GvXj3Ex8dDW1tbPg/tXQEBARgwYIDSHLIffvgBAQEB6NSpE6pXr47Y2FgkJCTIN1y9ePGiQv169eph27Zt8gcPisLPzw++voqL8v3t/GWRriUiIiLNV+aSs2+++QYNGzbE999/j/79++Ps2bNYvnw5VqxYIa/Tpk0bLF++HE2aNIFMJsO0adOgo6MjP//rr7/CxsYGdevWhZaWFrZv3w5ra2uYmpqiXbt28PDwQI8ePfDjjz/CxcUFT548QXBwMHr06AF7e3vs378f+/btU9qDa8iQIejSpQuSkpLQvn17VKlSBUOGDMFPP/2EFy9eyJO5Nz1qgwYNws8//4zu3btj3rx5qFy5MmJjY7Fr1y5MmTIFlStXVnr/EolEPhfuDR2RWGWfLxERUYlx86ES0bg5Z4WpV68e/vrrL2zduhW1atXCrFmzMG/ePIWHARYtWgRbW1u0bNkSAwcOxOTJk2FgYCA/b2RkhB9//BENGjRAw4YNERMTg+DgYGhpaUEkEiE4OBgtW7bEsGHD4OzsjAEDBiAmJgZWVlbYsGEDDA0N0bZtW6XYWrdujXLlymHjxo0Qi8XYs2cPXr58iYYNG2LEiBGYMWMGAEBP7/XWJAYGBjh58iTs7OzQq1cv1KhRA8OGDcOrV6+K3JNGRESkcTjnrES4t+YHdObMGTRv3hz37t1DlSpVVNYu99YsXdxbs/Rxb016G/fWLF0fZG/NLbNV0o7+F3MLr/QRKnPDmmXJ7t27YWRkhGrVquHevXuYMGECmjVrptLEjIiISON8wr1eqsDkrBS9ePECU6dOxcOHD2FhYYF27dop7WZARET00fmEF5BVBSZnpcjb2xve3t7qDoOIiOjDYs9ZiZStiR5EREREHzn2nBEREZFq8VnDEmFyRkRERKrFYc0S4bAmERERkQZhzxkRERGpFnvOSoTJGREREakWl9IoEQ5rEhEREWkQ9pwRERGRSgkyPq1ZEkzOiIiISLU456xEOKxJREREpEHYc0ZERESqxQcCSoTJGREREakW55yVCJOzj0D7Vk/UHUKxbKkzS90hFMsXV+epO4RiyY29oe4Qik2r0xZ1h1AsuSKRukMolgrGGeoOoVgyU8XqDqFYJPrZ6g5B83DOWYlwzhkRERGRBmHPGREREakWe85KhMkZERERqZbAOWclwWFNIiIiIg3CnjMiIiJSLQ5rlgiTMyIiIlItLqVRIhzWJCIiItIg7DkjIiIi1eIOASXC5IyIiIhUi8OaJcJhTSIiIvporFixAo6OjtDT00P9+vVx6tSpfOuGhoZCJBIpHbdv31aot3PnTri6ukIikcDV1RW7d+8u1ffA5IyIiIhUSpDJVHIU17Zt2zBx4kR89913uHLlClq0aIFOnTohNja2wOuioqIQFxcnP6pVqyY/d/bsWfTv3x9eXl64evUqvLy80K9fP5w/f77Y8RUVkzMiIiJSLZmgmqOYFi9ejOHDh2PEiBGoUaMGlixZAltbW6xcubLA6ywtLWFtbS0/xOL/9nddsmQJ2rdvDz8/P1SvXh1+fn5o27YtlixZUuz4iorJGREREamWIFPJIZVKkZaWpnBIpdI8b5mVlYXw8HB06NBBobxDhw4ICwsrMFx3d3fY2Nigbdu2OH78uMK5s2fPKrXZsWPHQtssCSZnREREpJH8/f1hYmKicPj7++dZ9+nTp8jNzYWVlZVCuZWVFeLj4/O8xsbGBmvWrMHOnTuxa9cuuLi4oG3btjh58qS8Tnx8fLHaVAWNSc7CwsIgFovh6emp0nbfnexnbm6ONm3a4MyZMyq9DxEREf2fioY1/fz8kJqaqnD4+fkVeGuRSKTwWhAEpbI3XFxcMHLkSNSrVw8eHh5YsWIFunTpgl9++eW921QFjUnO1q1bh3HjxuH06dOFTtx7H28m+4WGhqJChQro0qULEhMTVX4fIiKiT55MppJDIpHA2NhY4ZBIJHne0sLCAmKxWKlHKzExUannqyBNmjTB3bt35a+tra1L3GZxaURylp6ejr/++gtjxoxB165dERQUBADw8PDA9OnTFeomJSVBR0dHPiaclZWFqVOnolKlSjA0NETjxo0RGhqqdI83k/3c3NwwY8YMpKamKjxpceLECTRq1AgSiQQ2NjaYPn06cnJy5OelUinGjx8PS0tL6OnpoXnz5rh48aL8/JseuoMHD8Ld3R36+vpo06YNEhMT8c8//6BGjRowNjbGF198gYyMDPl1O3bsgJubG/T19WFubo527dohPT1dFR8rERHRJ0NXVxf169fH4cOHFcoPHz6Mpk2bFrmdK1euwMbGRv7aw8NDqc1Dhw4Vq83i0ojkbNu2bXBxcYGLiwsGDx6MwMBACIKAQYMGYcuWLRAEQaGulZUVPvvsMwDA0KFDcebMGWzduhXXrl1D37594enpqZD1vi0jIwOBgYEAAB0dHQDA48eP0blzZzRs2BBXr17FypUrERAQgPnz58uvmzp1Knbu3In169fj8uXLqFq1Kjp27Ijnz58rtD9nzhwsX74cYWFhePjwIfr164clS5Zg8+bNOHDgAA4fPoxly5YBAOLi4vDFF19g2LBhiIyMRGhoKHr16qXwfomIiMocNT2t6evriz/++APr1q1DZGQkJk2ahNjYWIwePRoA4OfnB29vb3n9JUuWYM+ePbh79y5u3rwJPz8/7Ny5E2PHjpXXmTBhAg4dOoQff/wRt2/fxo8//ogjR45g4sSJJf6Y8qMROwQEBARg8ODBAABPT0+8fPkSR48eRf/+/TFp0iScPn0aLVq0AABs3rwZAwcOhJaWFqKjo7FlyxY8evQIFStWBABMnjwZISEhCAwMxMKFC+X3qFy5MoDXyZkgCKhfvz7atm0L4PWCdba2tli+fDlEIhGqV6+OJ0+eYNq0aZg1axZevXqFlStXIigoCJ06dQIArF27FocPH0ZAQACmTJkiv8/8+fPRrFkzAMDw4cPh5+eH6OhoODk5AQD69OmD48ePY9q0aYiLi0NOTg569eoFe3t7AICbm1upfc5EREQfhJq2b+rfvz+ePXuGefPmIS4uDrVq1UJwcLD879i4uDiFqVNZWVmYPHkyHj9+DH19fdSsWRMHDhxA586d5XWaNm2KrVu3YsaMGZg5cyaqVKmCbdu2oXHjxqX2PtSenEVFReHChQvYtWsXAEBbWxv9+/fHunXrsHnzZrRv3x6bNm1CixYtcP/+fZw9e1a+Xsnly5chCAKcnZ0V2pRKpTA3N1coO3XqFAwNDXHlyhVMmzYNQUFB8p6zyMhIeHh4KEzua9asGV6+fIlHjx4hJSUF2dnZ8qQLeN3r1qhRI0RGRircp3bt2vKfraysYGBgIE/M3pRduHABAFCnTh20bdsWbm5u6NixIzp06IA+ffrAzMws389LKpUqPUYszZVBItaITlAiIiK1+uqrr/DVV1/lee7NtKk3pk6diqlTpxbaZp8+fdCnTx9VhFckak/OAgICkJOTg0qVKsnLBEGAjo4OkpOTMWjQIEyYMAHLli3D5s2bUbNmTdSpUwcAIJPJIBaLER4errBgHAAYGRkpvHZ0dISpqSmcnZ2RmZmJnj174saNG5BIJHk+dfFmaFEkEin8/G6dd8veJHxv6r/9+k2Z7P+rHovFYhw+fBhhYWE4dOgQli1bhu+++w7nz5+Ho6Njnp+Xv78/5s6dq1A2raY9prvlXZ+IiOiD496aJaLW7pacnBxs2LABixYtQkREhPy4evUq7O3tsWnTJvTo0QOZmZkICQnB5s2b5cOfwOtF43Jzc5GYmIiqVasqHNbW1vne18vLCzKZDCtWrAAAuLq6IiwsTGGuV1hYGMqVK4dKlSqhatWq0NXVxenTp+Xns7OzcenSJdSoUaNEn4FIJEKzZs0wd+5cXLlyBbq6ugXu2ZXXY8WTXO1LFAMREZEqqWv7po+FWnvO/v77byQnJ2P48OEwMTFRONenTx8EBARg7Nix6N69O2bOnInIyEgMHDhQXsfZ2RmDBg2Ct7c3Fi1aBHd3dzx9+hTHjh2Dm5ubwpjx27S0tDBx4kTMnz8fo0aNwldffYUlS5Zg3LhxGDt2LKKiojB79mz4+vpCS0sLhoaGGDNmDKZMmYLy5cvDzs4OP/30EzIyMjB8+PD3fv/nz5/H0aNH0aFDB1haWuL8+fNISkoqMOGTSCRKjxHLOKRJRET00VDr3+oBAQFo166dUmIGAL1790ZERAQuX76MQYMG4erVq2jRogXs7OwU6gUGBsLb2xvffPMNXFxc0K1bN5w/fx62trYF3nvYsGHIzs7G8uXLUalSJQQHB+PChQuoU6cORo8ejeHDh2PGjBny+j/88AN69+4NLy8v1KtXD/fu3cPBgwcLnB9WGGNjY5w8eRKdO3eGs7MzZsyYgUWLFskfOiAiIiqT1PS05sdCJHDdhjIv5YvW6g6hWPadrFR4JQ3yxdV56g6hWHJjb6g7hGI72mmLukMoFmNRtrpDKJYKxhmFV9IgT1KNCq+kQRytk9UdQrE4Xj1ceKUSejmlp0raMfo5/2k+HzO1PxBAREREHxk1LaXxseBkJSIiIiINwp4zIiIiUq1PeL6YKjA5IyIiIpUSmJyVCIc1iYiIiDQIe86IiIhItdhzViJMzoiIiEi1PuHV/VWBw5pEREREGoQ9Z0RERKRaHNYsESZnREREpFpMzkqEw5pEREREGoQ9Z0RERKRS3La7ZJicERERkWpxWLNEmJwRERGRajE5KxHOOSMiIiLSIOw5+wiEHbNSdwjFYibKVXcIxZIbe0PdIRSL2K6WukMoNhH4r+zSlJUlVncIxaKnVbZ+R9yNN1d3CMXi+AHuwb01S4bJGREREakWk7MS4bAmERERkQZhzxkRERGpFrfWLBEmZ0RERKRSnHNWMhzWJCIiItIg7DkjIiIi1WLPWYkwOSMiIiLV4pyzEuGwJhEREZEGYc8ZERERqRQfCCgZJmdERESkWhzWLBEmZ0RERKRS7DkrGc45IyIiItIg7DkjIiIi1eKwZomw56wIQkNDIRKJkJKSou5QiIiINJ4gU83xqfrgyVlYWBjEYjE8PT1V2u6bBOrNoa+vj5o1a2LNmjUqvQ8RERFRafrgydm6deswbtw4nD59GrGxsSpvPyoqCnFxcbh16xZGjRqFMWPG4OjRoyq/z9tyc3Mhk33CKT4REdHbZCo6PlEfNDlLT0/HX3/9hTFjxqBr164ICgoCAHh4eGD69OkKdZOSkqCjo4Pjx48DALKysjB16lRUqlQJhoaGaNy4MUJDQ5XuYWlpCWtrazg6OmL8+PFwcHDA5cuX5ecFQcBPP/0EJycn6Ovro06dOtixY4dCG8HBwXB2doa+vj5at26NmJgYhfNBQUEwNTXF33//DVdXV0gkEjx48AAODg6YP38+vL29YWRkBHt7e+zduxdJSUno3r07jIyM4ObmhkuXLsnbevDgAT7//HOYmZnB0NAQNWvWRHBwcAk+ZSIiIvVS57DmihUr4OjoCD09PdSvXx+nTp3Kt+6uXbvQvn17VKhQAcbGxvDw8MDBgwcV6gQFBSmMzL05MjMz3y/AIvigydm2bdvg4uICFxcXDB48GIGBgRAEAYMGDcKWLVsgCIJCXSsrK3z22WcAgKFDh+LMmTPYunUrrl27hr59+8LT0xN3797N816CICAkJAQPHz5E48aN5eUzZsxAYGAgVq5ciZs3b2LSpEkYPHgwTpw4AQB4+PAhevXqhc6dOyMiIgIjRoxQShwBICMjA/7+/vjjjz9w8+ZNWFpaAgB+/fVXNGvWDFeuXEGXLl3g5eUFb29vDB48GJcvX0bVqlXh7e0tf69ff/01pFIpTp48ievXr+PHH3+EkZGRaj5wIiKiT8i2bdswceJEfPfdd7hy5QpatGiBTp065TtSd/LkSbRv3x7BwcEIDw9H69at8fnnn+PKlSsK9YyNjREXF6dw6Onpldr7+KBPawYEBGDw4MEAAE9PT7x8+RJHjx5F//79MWnSJJw+fRotWrQAAGzevBkDBw6ElpYWoqOjsWXLFjx69AgVK1YEAEyePBkhISEIDAzEwoUL5feoXLkyAEAqlUImk2HevHlo2bIlgNc9d4sXL8axY8fg4eEBAHBycsLp06exevVqfPbZZ1i5ciWcnJzw66+/QiQSwcXFRZ40vS07OxsrVqxAnTp1FMo7d+6MUaNGAQBmzZqFlStXomHDhujbty8AYNq0afDw8EBCQgKsra0RGxuL3r17w83NTR4PERFRmaamIcnFixdj+PDhGDFiBABgyZIlOHjwIFauXAl/f3+l+kuWLFF4vXDhQuzduxf79++Hu7u7vFwkEsHa2rpUY3/bB0vOoqKicOHCBezatev1jbW10b9/f6xbtw6bN29G+/btsWnTJrRo0QL379/H2bNnsXLlSgDA5cuXIQgCnJ2dFdqUSqUwNzdXKDt16hTKlSsHqVSKCxcuYOzYsShfvjzGjBmDW7duITMzE+3bt1e4JisrS/6HEBkZiSZNmkAkEsnPv0nk3qarq4vatWsrlb9dZmVlBQDyxOvtssTERFhbW2P8+PEYM2YMDh06hHbt2qF37955tvv2e5ZKpQpl2UIudETifK8hIiL6kFT1pGVef+dJJBJIJBKlullZWQgPD1ca7erQoQPCwsKKdD+ZTIYXL16gfPnyCuUvX76Evb09cnNzUbduXXz//fcKyZuqfbDkLCAgADk5OahUqZK8TBAE6OjoIDk5GYMGDcKECROwbNkybN68GTVr1pT3SslkMojFYoSHh0MsVkxC3h0CdHR0hKmpKQCgZs2aOH/+PBYsWIAxY8bIJ+0fOHBAIQ4A8j/ot4dWC6Kvr6+QwL2ho6Mj//nN+bzK3sQyYsQIdOzYEQcOHMChQ4fg7++PRYsWYdy4cXne19/fH3PnzlUoG2hQE4OMahUpbiIiorIir7/zZs+ejTlz5ijVffr0KXJzc+WdIG9YWVkhPj6+SPdbtGgR0tPT0a9fP3lZ9erVERQUBDc3N6SlpeG3335Ds2bNcPXqVVSrVq34b6oIPkhylpOTgw0bNmDRokXo0KGDwrnevXtj06ZNGDp0KEaNGoWQkBBs3rwZXl5e8jru7u7Izc1FYmKifNizqMRiMV69egUA8sn7sbGx8rls73J1dcWePXsUys6dO1esexaXra0tRo8ejdGjR8PPzw9r167NNznz8/ODr6+vQtnxqsNLNT4iIqLiUFXPWV5/5+XVa/a2dztOBEHIszPlXVu2bMGcOXOwd+9e+TxyAGjSpAmaNGkif92sWTPUq1cPy5Ytw9KlS4vyNortgyRnf//9N5KTkzF8+HCYmJgonOvTpw8CAgIwduxYdO/eHTNnzkRkZCQGDhwor+Ps7IxBgwbB29sbixYtgru7O54+fYpjx47Bzc0NnTt3ltdNTExEZmamfFhz48aN6NOnDwCgXLlymDx5MiZNmgSZTIbmzZsjLS0NYWFhMDIywpAhQzB69GgsWrQIvr6+GDVqFMLDw+VPlZaGiRMnolOnTnB2dkZycjKOHTuGGjVq5Fs/r+5cDmkSEZEmUVVylt8QZl4sLCwgFouVeskSExOVetPetW3bNgwfPhzbt29Hu3btCqyrpaWFhg0b5vtAoip8kKc1AwIC0K5dO6XEDHjdcxYREYHLly9j0KBBuHr1Klq0aAE7OzuFeoGBgfD29sY333wDFxcXdOvWDefPn4etra1CPRcXF9jY2KBq1aqYNm0aRo0ahWXLlsnPf//995g1axb8/f1Ro0YNdOzYEfv374ejoyMAwM7ODjt37sT+/ftRp04drFq1SuGBA1XLzc3F119/jRo1asDT0xMuLi5YsWJFqd2PiIio1Aki1RzFoKuri/r16+Pw4cMK5YcPH0bTpk3zvW7Lli3w8fHB5s2b0aVLl8LfmiAgIiICNjY2xYqvOERCUSdZkcYKthqg7hCKJbcI3cuapH3wF+oOoVjEdmVv/uHhmt+qO4RiKSfKUXcIxWKiJy28kgZ5KdVVdwjF8lKmU3glDdIhYWup3yOhVSuVtGOVx3qmBdm2bRu8vLywatUqeHh4YM2aNVi7di1u3rwJe3t7+Pn54fHjx9iwYQOA14mZt7c3fvvtN/Tq1Uvejr6+vrxDae7cuWjSpAmqVauGtLQ0LF26FBs3bsSZM2fQqFEjlbzPd3HjcyIiIlIpde2L2b9/fzx79gzz5s1DXFwcatWqheDgYNjb2wMA4uLiFNY8W716NXJycvD111/j66+/lpcPGTJEPqUpJSUFX375JeLj42FiYgJ3d3ecPHmy1BIzgD1nHwX2nJUu9pyVPvaclS72nJUu9pwpi2veWiXt2Jw+rpJ2ypoPvrcmEREREeWPw5pERESkUuoa1vxYMDkjIiIilRKK+aQlKeKwJhEREZEGYc8ZERERqRSHNUuGyRkRERGplCDjsGZJcFiTiIiISIOw54yIiIhUiiuolgyTMyIiIlIpDmuWDJMzIiIiUikmZyXDOWdEREREGoQ9Z0RERKRSnHNWMkzOiIiISKU4rFkyTM4+AiaiHHWHUCxHJRJ1h1AsWp22qDuEYhGh7P2Ttf3NheoOoVgiG0xQdwjFckdqrO4QiqWmUbK6QyiWzDSxukOgjwyTMyIiIlIp7q1ZMkzOiIiISKW4fVPJ8GlNIiIiIg3CnjMiIiJSKRmHNUuEyRkRERGpFOeclQyHNYmIiIg0CHvOiIiISKW4zlnJMDkjIiIileIOASXD5IyIiIhUij1nJcM5Z0REREQahD1nREREpFJcSqNkmJwRERGRSnEpjZLhsCYRERGRBmFyBsDHxwc9evTQmHaIiIjKMkFQzfGpUnty5uPjA5FIBJFIBB0dHTg5OWHy5MlIT09Xd2j5iomJgUgkQkREhEL5b7/9hqCgILXEREREpClkgkglx6dKI+aceXp6IjAwENnZ2Th16hRGjBiB9PR0rFy5Ut2hFYuJiYm6QyAiIqIyTu09ZwAgkUhgbW0NW1tbDBw4EIMGDcKePXsglUoxfvx4WFpaQk9PD82bN8fFixfl14WGhkIkEuHAgQOoU6cO9PT00LhxY1y/fl1eZ86cOahbt67C/ZYsWQIHB4d84wkJCUHz5s1hamoKc3NzdO3aFdHR0fLzjo6OAAB3d3eIRCK0atUKgPKwZlHjP3r0KBo0aAADAwM0bdoUUVFR7/EpEhERaQZBEKnk+FRpRHL2Ln19fWRnZ2Pq1KnYuXMn1q9fj8uXL6Nq1aro2LEjnj9/rlB/ypQp+OWXX3Dx4kVYWlqiW7duyM7Ofu/7p6enw9fXFxcvXsTRo0ehpaWFnj17QiaTAQAuXLgAADhy5Aji4uKwa9euPNspavzfffcdFi1ahEuXLkFbWxvDhg1779iJiIjUjXPOSkbjkrMLFy5g8+bNaN26NVauXImff/4ZnTp1gqurK9auXQt9fX0EBAQoXDN79my0b98ebm5uWL9+PRISErB79+73jqF3797o1asXqlWrhrp16yIgIADXr1/HrVu3AAAVKlQAAJibm8Pa2hrly5dXauPNsGxR4l+wYAE+++wzuLq6Yvr06QgLC0NmZuZ7x09ERERll0YkZ3///TeMjIygp6cHDw8PtGzZEuPGjUN2djaaNWsmr6ejo4NGjRohMjJS4XoPDw/5z+XLl4eLi4tSneKIjo7GwIED4eTkBGNjY/kwZmxsbLHaKGr8tWvXlv9sY2MDAEhMTMyzXalUirS0NIUjS8gtclxERESljQ8ElIxGJGetW7dGREQEoqKikJmZiV27dskn14tEin84giAoleXlTR0tLS0I7/SNFjbk+fnnn+PZs2dYu3Ytzp8/j/PnzwMAsrKyivye3tyzKPHr6Ogoxf1mCPVd/v7+MDExUTg2pnOOGhERaQ7OOSsZjUjODA0NUbVqVdjb28sTlapVq0JXVxenT5+W18vOzsalS5dQo0YNhevPnTsn/zk5ORl37txB9erVAbwegoyPj1dI0N5dAuNtz549Q2RkJGbMmIG2bduiRo0aSE5OVqijq6sLAMjNzb/HqjjxF4efnx9SU1MVDi9Dl/duj4iISNXYc1YyGrGURl4MDQ0xZswYTJkyBeXLl4ednR1++uknZGRkYPjw4Qp1582bB3Nzc1hZWeG7776DhYWF/KnJVq1aISkpCT/99BP69OmDkJAQ/PPPPzA2Ns7zvmZmZjA3N8eaNWtgY2OD2NhYTJ8+XaGOpaUl9PX1ERISgsqVK0NPT09pGY3ixF8cEokEEolEoUxXJH7v9oiIiEizaETPWX5++OEH9O7dG15eXqhXrx7u3buHgwcPwszMTKnehAkTUL9+fcTFxWHfvn3y3q0aNWpgxYoV+P3331GnTh1cuHABkydPzveeWlpa2Lp1K8LDw1GrVi1MmjQJP//8s0IdbW1tLF26FKtXr0bFihXRvXv3EsVPRET0MRFUdLyPFStWwNHREXp6eqhfvz5OnTpVYP0TJ06gfv360NPTg5OTE1atWqVUZ+fOnXB1dYVEIoGrq2uJHjosCpHw7oSsMiQ0NBStW7dGcnIyTE1N1R2O2pyx7qPuEIrl6Ds9f5qufmbe8/80lei9f6WpT/ubC9UdQrFENpig7hCK5Y4075ECTVXTKLnwShokLs1I3SEUS5uEv0r9HmE2vVXSTtO4ncWqv23bNnh5eWHFihVo1qwZVq9ejT/++AO3bt2CnZ2dUv379++jVq1aGDlyJEaNGoUzZ87gq6++wpYtW9C79+v3cPbsWbRo0QLff/89evbsid27d2PWrFk4ffo0GjdurJL3+S6N7jkjIiIiKqrFixdj+PDhGDFiBGrUqIElS5bA1tY23x2HVq1aBTs7OyxZsgQ1atTAiBEjMGzYMPzyyy/yOkuWLEH79u3h5+eH6tWrw8/PD23btsWSJUtK7X0wOSMiIiKVUtXTmnktHyWVSvO8Z1ZWFsLDw9GhQweF8g4dOiAsLCzPa86ePatUv2PHjrh06ZJ8ZYf86uTXpiqU6eSsVatWEAThkx7SJCIi0jQyFR15LR/l7++f5z2fPn2K3NxcWFlZKZRbWVkhPj4+z2vi4+PzrJ+Tk4OnT58WWCe/NlVBY5/WJCIiok+bn58ffH19FcreXbHgXcVdHzWv+u+Wv++aq++LyRkRERGplADVJC55LR+VHwsLC4jFYqUercTERKWerzesra3zrK+trQ1zc/MC6+TXpiqU6WFNIiIi0jwyQTVHcejq6qJ+/fo4fPiwQvnhw4fRtGnTPK/x8PBQqn/o0CE0aNBAvih+fnXya1MV2HNGREREHwVfX194eXmhQYMG8PDwwJo1axAbG4vRo0cDeD1M+vjxY2zYsAEAMHr0aCxfvhy+vr4YOXIkzp49i4CAAGzZskXe5oQJE9CyZUv8+OOP6N69O/bu3YsjR44o7ACkakzOiIiISKVkKhrWLK7+/fvj2bNnmDdvHuLi4lCrVi0EBwfD3t4eABAXF4fY2Fh5fUdHRwQHB2PSpEn4/fffUbFiRSxdulS+xhkANG3aFFu3bsWMGTMwc+ZMVKlSBdu2bSu1Nc6AMr4ILb3GRWhLFxehLX1chLZ0cRHa0sVFaJUdteqvknbaJmxTSTtlDXvOiIiISKXK1j9pNQ8fCCAiIiLSIOw5IyIiIpVS1VIanyomZ0RERKRSHNYsGQ5rEhEREWkQ9pwRERGRSrHnrGSYnBEREZFKcc5ZyTA5+wiItcrWv1HaZWWqO4RiyS3FzW3ptbK2bliNS7+pO4RieVFrqrpDKJbnLwzUHQKRWjE5IyIiIpWS8d+0JcLkjIiIiFRKXds3fSz4tCYRERGRBmHPGREREalU2dvhV7MwOSMiIiKVKluPqWkeJmdERESkUjI+5V4inHNGREREpEHYc0ZEREQqxTlnJcPkjIiIiFSKc85KhsOaRERERBqEPWdERESkUtwhoGSYnBEREZFKcYeAkuGwJhEREZEG0cjkLCwsDGKxGJ6enipvOzc3F7/++itq164NPT09mJqaolOnTjhz5ozK70VERPQpElR0fKo0Mjlbt24dxo0bh9OnTyM2NlZl7QqCgAEDBmDevHkYP348IiMjceLECdja2qJVq1bYs2ePyu5FRET0qZKJVHN8qjQuOUtPT8dff/2FMWPGoGvXrggKCgIAeHh4YPr06Qp1k5KSoKOjg+PHjwMAsrKyMHXqVFSqVAmGhoZo3LgxQkND5fX/+usv7NixAxs2bMCIESPg6OiIOnXqYM2aNejWrRtGjBiB9PR0ef19+/ahQYMG0NPTg4WFBXr16iU/J5VKMXXqVNja2kIikaBatWoICAgAAAQFBcHU1FQh1j179kD01orJc+bMQd26dbF69WrY2trCwMAAffv2RUpKigo+RSIiIiqrNC4527ZtG1xcXODi4oLBgwcjMDAQgiBg0KBB2LJlCwRBUKhrZWWFzz77DAAwdOhQnDlzBlu3bsW1a9fQt29feHp64u7duwCAzZs3w9nZGZ9//rnSfb/55hs8e/YMhw8fBgAcOHAAvXr1QpcuXXDlyhUcPXoUDRo0kNf39vbG1q1bsXTpUkRGRmLVqlUwMjIq1nu9d+8e/vrrL+zfvx8hISGIiIjA119/XezPjIiISJPIVHR8qjTuac2AgAAMHjwYAODp6YmXL1/i6NGj6N+/PyZNmoTTp0+jRYsWAF4nWwMHDoSWlhaio6OxZcsWPHr0CBUrVgQATJ48GSEhIQgMDMTChQtx584d1KhRI8/7vim/c+cOAGDBggUYMGAA5s6dK69Tp04deZ2//voLhw8fRrt27QAATk5OxX6vmZmZWL9+PSpXrgwAWLZsGbp06YJFixbB2tq62O0RERFpgk95vpgqaFTPWVRUFC5cuIABAwYAALS1tdG/f3+sW7cOFSpUQPv27bFp0yYAwP3793H27FkMGjQIAHD58mUIggBnZ2cYGRnJjxMnTiA6OrrIMbwZeoyIiEDbtm3zrBMREQGxWCzvsXtfdnZ28sQMeD10K5PJEBUVle81UqkUaWlpCkeWkFuiOIiIiFSJc85KRqN6zgICApCTk4NKlSrJywRBgI6ODpKTkzFo0CBMmDABy5Ytw+bNm1GzZk15b5ZMJoNYLEZ4eDjEYrFCu2+GG52dnXHr1q087x0ZGQkAqFatGgBAX18/3zgLOgcAWlpaCsOvAJCdnV3gNcB/ieHbc9Pe5e/vr9CbBwDDjapjRLm8ewSJiIiobNGYnrOcnBxs2LABixYtQkREhPy4evUq7O3tsWnTJvTo0QOZmZkICQnB5s2b5cOfAODu7o7c3FwkJiaiatWqCsebIcIBAwbg7t272L9/v9L9Fy1aBHNzc7Rv3x4AULt2bRw9ejTPWN3c3CCTyXDixIk8z1eoUAEvXrxQeLggIiJCqV5sbCyePHkif3327FloaWnB2dk538/Jz88PqampCoe3Uf71iYiIPjTOOSsZjek5+/vvv5GcnIzhw4fDxMRE4VyfPn0QEBCAsWPHonv37pg5cyYiIyMxcOBAeR1nZ2cMGjQI3t7eWLRoEdzd3fH06VMcO3YMbm5u6Ny5MwYMGIDt27djyJAh+Pnnn9G2bVukpaXh999/x759+7B9+3YYGhoCAGbPno22bduiSpUqGDBgAHJycvDPP/9g6tSpcHBwwJAhQzBs2DAsXboUderUwYMHD5CYmIh+/fqhcePGMDAwwLfffotx48bhwoUL8qdO36anp4chQ4bgl19+QVpaGsaPH49+/foVON9MIpFAIpEolOmKxPnUJiIi+vA+5cRKFTSm5ywgIADt2rVTSswAoHfv3oiIiMDly5cxaNAgXL16FS1atICdnZ1CvcDAQHh7e+Obb76Bi4sLunXrhvPnz8PW1hbA6+HCv/76C9999x1+/fVXVK9eHS1atMCDBw9w/Phx9OjRQ95Wq1atsH37duzbtw9169ZFmzZtcP78efn5lStXok+fPvjqq69QvXp1jBw5Ut5TVr58efz5558IDg6Gm5sbtmzZgjlz5ii9r6pVq6JXr17o3LkzOnTogFq1amHFihUq+DSJiIiorBIJ706Oog9izpw52LNnT57DncV1rmKvwitpEJlQtmZ55paxeMsiEz2pukMolhqXflN3CMVyodZUdYfwUZMKGtPPUSRtEv4q9Xussh1ceKUiGP3wT5W0U9ZozLAmERERfRw4rFkyZSvdJyIiIvrIMTlTkzlz5qhkSJOIiEjT8GnNkuGwJhEREakUJ7OXDHvOiIiIiDQIkzMiIiJSqbKwfVNycjK8vLxgYmICExMTeHl5ISUlJd/62dnZmDZtGtzc3GBoaIiKFSvC29tbYTF54PVSXCKRSOF4sy1lUTE5IyIiIpUqC3POBg4ciIiICISEhCAkJAQRERHw8vLKt35GRgYuX76MmTNn4vLly9i1axfu3LmDbt26KdUdOXIk4uLi5Mfq1auLFRvnnBEREZFKafpk/sjISISEhODcuXNo3LgxAGDt2rXw8PBAVFQUXFxclK4xMTHB4cOHFcqWLVuGRo0aITY2VmFhfAMDgwJ3+ykMe86IiIhII0mlUqSlpSkcUmnJF60+e/YsTExM5IkZADRp0gQmJiYICwsrcjupqakQiUQwNTVVKN+0aRMsLCxQs2ZNTJ48GS9evChWfEzOiIiISKUEFR3+/v7yOWFvDn9//xLHFx8fD0tLS6VyS0tLxMfHF6mNzMxMTJ8+HQMHDoSxsbG8fNCgQdiyZQtCQ0Mxc+ZM7Ny5E716FW8nHw5rEhERkUqpajK/n58ffH19FcokEkm+9efMmYO5c+cW2ObFixcBvN5v+12CIORZ/q7s7GwMGDAAMplMaU/skSNHyn+uVasWqlWrhgYNGuDy5cuoV69eoW0DTM6IiIhIQ0kkkgKTsXeNHTu20CcjHRwccO3aNSQkJCidS0pKgpWVVYHXZ2dno1+/frh//z6OHTum0GuWl3r16kFHRwd3795lckZERETqoa4HAiwsLGBhYVFoPQ8PD6SmpuLChQto1KgRAOD8+fNITU1F06ZN873uTWJ29+5dHD9+HObm5oXe6+bNm8jOzoaNjU2R3wfnnBEREZFKqWrOWWmpUaMGPD09MXLkSJw7dw7nzp3DyJEj0bVrV4UnNatXr47du3cDAHJyctCnTx9cunQJmzZtQm5uLuLj4xEfH4+srCwAQHR0NObNm4dLly4hJiYGwcHB6Nu3L9zd3dGsWbMix8fkjIiIiD45mzZtgpubGzp06IAOHTqgdu3a2Lhxo0KdqKgopKamAgAePXqEffv24dGjR6hbty5sbGzkx5snPHV1dXH06FF07NgRLi4uGD9+PDp06IAjR45ALBYXOTYOaxIREZFKycrA7prly5fHn3/+WWAdQfjvfTg4OCi8zoutrS1OnDhR4tiYnH0EkmRFnyypCcxFWeoOoVgqGGeoO4Riycoq+r/ONMUdacETajXNi1pT1R1CsTS68ZO6QygW/Yot1B1CsQRZtFZ3CBpH0xeh1XQc1iQiIiLSIOw5IyIiIpXS/EFNzcbkjIiIiFSKw5olw+SMiIiIVEpVOwR8qjjnjIiIiEiDsOeMiIiIVKosLKWhyZicERERkUoxNSsZDmsSERERaRD2nBEREZFK8WnNkmFyRkRERCrFOWclw2FNIiIiIg3CnjMiIiJSKfablQx7zvLg4OCAJUuWlErbrVq1wsSJE0ulbSIiIk0gU9HxqSrzyZmPjw969OjxXtcGBQXB1NRUqfzixYv48ssv5a9FIhH27NnzfgESERF9YmQQVHJ8qjismYcKFSqoOwQiIiL6RJX5nrOCLF68GG5ubjA0NIStrS2++uorvHz5EgAQGhqKoUOHIjU1FSKRCCKRCHPmzAGgOKzp4OAAAOjZsydEIpH8dV49dhMnTkSrVq3kr9PT0+Ht7Q0jIyPY2Nhg0aJFSjFmZWVh6tSpqFSpEgwNDdG4cWOEhoaq8FMgIiL6sAQVHZ+qjzo509LSwtKlS3Hjxg2sX78ex44dw9SpUwEATZs2xZIlS2BsbIy4uDjExcVh8uTJSm1cvHgRABAYGIi4uDj566KYMmUKjh8/jt27d+PQoUMIDQ1FeHi4Qp2hQ4fizJkz2Lp1K65du4a+ffvC09MTd+/eLcE7JyIiUh/OOSuZj3pY8+2J946Ojvj+++8xZswYrFixArq6ujAxMYFIJIK1tXW+bbwZ4jQ1NS2w3rtevnyJgIAAbNiwAe3btwcArF+/HpUrV5bXiY6OxpYtW/Do0SNUrFgRADB58mSEhIQgMDAQCxcuLM7bJSIioo/AR52cHT9+HAsXLsStW7eQlpaGnJwcZGZmIj09HYaGhqV67+joaGRlZcHDw0NeVr58ebi4uMhfX758GYIgwNnZWeFaqVQKc3PzPNuVSqWQSqUKZdlCLnREYhVGT0RE9P6ET3pQsuQ+2uTswYMH6Ny5M0aPHo3vv/8e5cuXx+nTpzF8+HBkZ2eXuH0tLS0IguKX7+123z2XF5lMBrFYjPDwcIjFismVkZFRntf4+/tj7ty5CmUDDGtioJFbUUMnIiIqVZ/ykKQqfLRzzi5duoScnBwsWrQITZo0gbOzM548eaJQR1dXF7m5uYW2paOjo1SvQoUKiIuLUyiLiIiQ/1y1alXo6Ojg3Llz8rLk5GTcuXNH/trd3R25ublITExE1apVFY78hlD9/PyQmpqqcPQ1dC30PRAREVHZ8FH0nKWmpiokRsDr5CknJwfLli3D559/jjNnzmDVqlUKdRwcHPDy5UscPXoUderUgYGBAQwMDJTad3BwwNGjR9GsWTNIJBKYmZmhTZs2+Pnnn7FhwwZ4eHjgzz//xI0bN+Du7g7gdc/X8OHDMWXKFJibm8PKygrfffcdtLT+y4ednZ0xaNAgeHt7Y9GiRXB3d8fTp09x7NgxuLm5oXPnzkqxSCQSSCQShTIOaRIRkSb5lNcoU4WPoucsNDQU7u7uCse6deuwePFi/Pjjj6hVqxY2bdoEf39/heuaNm2K0aNHo3///qhQoQJ++umnPNtftGgRDh8+DFtbW3ny1bFjR8ycORNTp05Fw4YN8eLFC3h7eytc9/PPP6Nly5bo1q0b2rVrh+bNm6N+/foKdQIDA+Ht7Y1vvvkGLi4u6NatG86fPw9bW1sVfkJEREQfDpfSKBmRUJTJUaTR9lt/oe4QisVclKXuEIqlfLlX6g6hWLKyyl5P6h2psbpDKBYbQVp4JQ3S6Ebe//DUVPoVW6g7hGIJsmit7hCKZfCTP0v9HmMc+qmknZUxf6mknbLmoxjWJCIiIs3BYc2SYXJGREREKsWnNUuGyRkRERGpFNc5K5mP4oEAIiIioo8Fe86IiIhIpTisWTJMzoiIiEilOKxZMhzWJCIiItIg7DkjIiIileKwZskwOSMiIiKVknF9+xLhsCYRERGRBmHPGREREakU+81KhskZERERqRS3byoZDmsSERHRJyc5ORleXl4wMTGBiYkJvLy8kJKSUuA1Pj4+EIlECkeTJk0U6kilUowbNw4WFhYwNDREt27d8OjRo2LFxuSMiIiIVEpQ0X+laeDAgYiIiEBISAhCQkIQEREBLy+vQq/z9PREXFyc/AgODlY4P3HiROzevRtbt27F6dOn8fLlS3Tt2hW5ublFjo3DmkRERKRSmr6URmRkJEJCQnDu3Dk0btwYALB27Vp4eHggKioKLi4u+V4rkUhgbW2d57nU1FQEBARg48aNaNeuHQDgzz//hK2tLY4cOYKOHTsWKT72nBEREZFKySCo5JBKpUhLS1M4pFJpieM7e/YsTExM5IkZADRp0gQmJiYICwsr8NrQ0FBYWlrC2dkZI0eORGJiovxceHg4srOz0aFDB3lZxYoVUatWrULbfRt7zj4C5YQcdYdQLE9EeuoOoVgyU8XqDqFY9LSK3nWuKWoaJas7hGJ5/sJA3SEUi37FFuoOoVhePTml7hCK5XjNb9UdwkfL398fc+fOVSibPXs25syZU6J24+PjYWlpqVRuaWmJ+Pj4fK/r1KkT+vbtC3t7e9y/fx8zZ85EmzZtEB4eDolEgvj4eOjq6sLMzEzhOisrqwLbfReTMyIiIlIpVc0X8/Pzg6+vr0KZRCLJt/6cOXOUkrl3Xbx4EQAgEomUzgmCkGf5G/3795f/XKtWLTRo0AD29vY4cOAAevXqle91hbX7LiZnREREpFKqmnMmkUgKTMbeNXbsWAwYMKDAOg4ODrh27RoSEhKUziUlJcHKyqrI97OxsYG9vT3u3r0LALC2tkZWVhaSk5MVes8SExPRtGnTIrfL5IyIiIg+ChYWFrCwsCi0noeHB1JTU3HhwgU0atQIAHD+/HmkpqYWK4l69uwZHj58CBsbGwBA/fr1oaOjg8OHD6Nfv34AgLi4ONy4cQM//fRTkdvlAwFERESkUoIgqOQoLTVq1ICnpydGjhyJc+fO4dy5cxg5ciS6du2q8KRm9erVsXv3bgDAy5cvMXnyZJw9exYxMTEIDQ3F559/DgsLC/Ts2RMAYGJiguHDh+Obb77B0aNHceXKFQwePBhubm7ypzeLgj1nREREpFJlYYeATZs2Yfz48fInK7t164bly5cr1ImKikJqaioAQCwW4/r169iwYQNSUlJgY2OD1q1bY9u2bShXrpz8ml9//RXa2tro168fXr16hbZt2yIoKAhicdEfLmNyRkRERJ+c8uXL488//yywztu9d/r6+jh48GCh7erp6WHZsmVYtmzZe8fG5IyIiIhUStMXodV0TM6IiIhIpUp766WPHR8IICIiItIg7DkjIiIilSoLDwRoMiZnREREpFKluQzGp4DJGREREakUHwgoGc45e0dMTAxEIhEiIiJKpX2RSIQ9e/aUSttERERU9mlccubj44MePXqo7f62traIi4tDrVq1AAChoaEQiURISUlRW0xERERliaCi/z5VHNZ8h1gshrW1tbrDICIiKrP4QEDJaFzPWUFOnDiBRo0aQSKRwMbGBtOnT0dOTo78fKtWrTB+/HhMnToV5cuXh7W1NebMmaPQxu3bt9G8eXPo6enB1dUVR44cURhqfHtYMyYmBq1btwYAmJmZQSQSwcfHB8DrXe2XLFmi0HbdunUV7nf37l20bNlSfq/Dhw8rvafHjx+jf//+MDMzg7m5Obp3746YmJiSflRERERURpWZ5Ozx48fo3LkzGjZsiKtXr2LlypUICAjA/PnzFeqtX78ehoaGOH/+PH766SfMmzdPnhTJZDL06NEDBgYGOH/+PNasWYPvvvsu33va2tpi586dAF7vrxUXF4fffvutSPHKZDL06tULYrEY586dw6pVqzBt2jSFOhkZGWjdujWMjIxw8uRJnD59GkZGRvD09ERWVlZxPh4iIiKNoekbn2u6MjOsuWLFCtja2mL58uUQiUSoXr06njx5gmnTpmHWrFnQ0nqdZ9auXRuzZ88GAFSrVg3Lly/H0aNH0b59exw6dAjR0dEIDQ2VD10uWLAA7du3z/OeYrEY5cuXBwBYWlrC1NS0yPEeOXIEkZGRiImJQeXKlQEACxcuRKdOneR1tm7dCi0tLfzxxx8QiUQAgMDAQJiamiI0NFS+GSsREVFZwmHNkikzyVlkZCQ8PDzkSQwANGvWDC9fvsSjR49gZ2cH4HVy9jYbGxskJiYCeN37ZWtrqzCnrFGjRqUWr52dnTwxAwAPDw+FOuHh4bh3757CbvYAkJmZiejo6DzblUqlkEqlCmVZQi50RUXf7Z6IiIg0V5lJzgRBUEjM3pQBUCjX0dFRqCMSiSCTyfJt431paWkpdblmZ2crxfZuLG+TyWSoX78+Nm3apFS3QoUKed7X398fc+fOVSgbYlADPkY1ixw7ERFRafqUn7RUhTIz58zV1RVhYWEKSU9YWBjKlSuHSpUqFamN6tWrIzY2FgkJCfKyixcvFniNrq4uACA3N1ehvEKFCoiLi5O/TktLw/379xXijY2NxZMnT+RlZ8+eVWijXr16uHv3LiwtLVG1alWFw8TEJM94/Pz8kJqaqnAMNKxeyDsnIiL6cGSCoJLjU6WRyVlqaioiIiIUji+//BIPHz7EuHHjcPv2bezduxezZ8+Gr6+vfL5ZYdq3b48qVapgyJAhuHbtGs6cOSN/ICC/HjV7e3uIRCL8/fffSEpKwsuXLwEAbdq0wcaNG3Hq1CncuHEDQ4YMgVj839Biu3bt4OLiAm9vb1y9ehWnTp1Sevhg0KBBsLCwQPfu3XHq1Cncv38fJ06cwIQJE/Do0aM845FIJDA2NlY4OKRJRET08dDI5Cw0NBTu7u4Kx+zZsxEcHIwLFy6gTp06GD16NIYPH44ZM2YUuV2xWIw9e/bg5cuXaNiwIUaMGCG/Xk9PL89rKlWqhLlz52L69OmwsrLC2LFjAbzuwWrZsiW6du2Kzp07o0ePHqhSpYr8Oi0tLezevRtSqRSNGjXCiBEjsGDBAoW2DQwMcPLkSdjZ2aFXr16oUaMGhg0bhlevXsHY2Li4HxsREZFGEFR0fKpEwqf8rCqAM2fOoHnz5rh3755CclWWhFr1VXcIxfJUS1fdIRRLeVl24ZU0iJ5WbuGVNIyZ0St1h1Asz18YqDuEYvns+dnCK2mQV09OqTuEYjle81t1h1AsHRK2lvo9mlVqo5J2zjw+ppJ2ypoy80CAquzevRtGRkaoVq0a7t27hwkTJqBZs2ZlNjEjIiLSNFxKo2Q+ueTsxYsXmDp1Kh4+fAgLCwu0a9cOixYtUndYRERERAA+weTM29sb3t7e6g6DiIjoo/WJz5gqsU8uOSMiIqLSxWHNktHIpzWJiIiIPlXsOSMiIiKV4g4BJcPkjIiIiFSKc85KhsOaRERERBqEPWdERESkUnwgoGSYnBEREZFKcVizZDisSURERKRB2HNGREREKsVhzZJhckZEREQqxaU0SobJGREREamUjHPOSoRzzoiIiIg0CHvOiIiISKU4rFkyIoHPu5Z5r3YtVHcIxfJg+ml1h1AsEv1sdYdQLHfjzdUdQrFpQ6buED5qT8S66g6hWCxzc9QdQrG0vlm2fgfrWDiV+j1qWDZSSTuRiRdU0k5Zw2FNIiIiIg3CYU0iIiJSKQ5rlgx7zoiIiEilZIKgkqM0JScnw8vLCyYmJjAxMYGXlxdSUlIKvEYkEuV5/Pzzz/I6rVq1Ujo/YMCAYsXGnjMiIiL65AwcOBCPHj1CSEgIAODLL7+El5cX9u/fn+81cXFxCq//+ecfDB8+HL1791YoHzlyJObNmyd/ra+vX6zYmJwRERGRSmn6sGZkZCRCQkJw7tw5NG7cGACwdu1aeHh4ICoqCi4uLnleZ21trfB67969aN26NZycFB+yMDAwUKpbHBzWJCIiIpVS1bCmVCpFWlqawiGVSksc39mzZ2FiYiJPzACgSZMmMDExQVhYWJHaSEhIwIEDBzB8+HClc5s2bYKFhQVq1qyJyZMn48WLF8WKj8kZERERaSR/f3/5nLA3h7+/f4nbjY+Ph6WlpVK5paUl4uPji9TG+vXrUa5cOfTq1UuhfNCgQdiyZQtCQ0Mxc+ZM7Ny5U6lOYTisSURERCqlqmFNPz8/+Pr6KpRJJJJ868+ZMwdz584tsM2LFy8CeD25/12CIORZnpd169Zh0KBB0NPTUygfOXKk/OdatWqhWrVqaNCgAS5fvox69eoVqW0mZ0RERKRSgqCahaUlEkmBydi7xo4dW+iTkQ4ODrh27RoSEhKUziUlJcHKyqrQ+5w6dQpRUVHYtm1boXXr1asHHR0d3L17l8kZERERqYdMTQ8EWFhYwMLCotB6Hh4eSE1NxYULF9Co0evdDM6fP4/U1FQ0bdq00OsDAgJQv3591KlTp9C6N2/eRHZ2NmxsbAp/A//HOWdERET0SalRowY8PT0xcuRInDt3DufOncPIkSPRtWtXhSc1q1evjt27dytcm5aWhu3bt2PEiBFK7UZHR2PevHm4dOkSYmJiEBwcjL59+8Ld3R3NmjUrcnxMzoiIiEilBEFQyVGaNm3aBDc3N3To0AEdOnRA7dq1sXHjRoU6UVFRSE1NVSjbunUrBEHAF198odSmrq4ujh49io4dO8LFxQXjx49Hhw4dcOTIEYjF4iLHxo3PPwLc+Lx0cePz0seNz0sXNz4vXdz4XFnl8rVU0s6j5zdU0k5Zo1E9Z6GhoRCJRIVun/CxiomJgUgkQkREhLpDISIiIjUpVnLm4+MDkUiE0aNHK5376quvIBKJ4OPjo6rYCkzWHBwcsGTJEpXdqyQSEhKgo6ODP//8M8/zo0aNQu3atT9wVEREROpRFoY1NVmxe85sbW2xdetWvHr1Sl6WmZmJLVu2wM7OTqXBlRVWVlbo0qULAgMDlc69evUKW7duzXMFYSIioo9RWdj4XJMVOzmrV68e7OzssGvXLnnZrl27YGtrC3d3d3mZIAj46aef4OTkBH19fdSpUwc7duxQaCs4OBjOzs7Q19dH69atERMT895vJDY2Ft27d4eRkRGMjY3Rr18/hTVMfHx80KNHD4VrJk6ciFatWslf79ixA25ubtDX14e5uTnatWuH9PR0+fnAwEDUqFEDenp6qF69OlasWCE/N3z4cBw/flzpPezYsQOZmZkYPHgwQkJC0Lx5c5iamsLc3Bxdu3ZFdHT0e79nIiIi+vi815yzoUOHKvQSrVu3DsOGDVOoM2PGDAQGBmLlypW4efMmJk2ahMGDB+PEiRMAgIcPH6JXr17o3LkzIiIiMGLECEyfPv293oQgCOjRoweeP3+OEydO4PDhw4iOjkb//v2L3EZcXBy++OILDBs2DJGRkQgNDUWvXr3k3apr167Fd999hwULFiAyMhILFy7EzJkzsX79egBA586dYW1tjaCgIIV2161bhx49esDc3Bzp6enw9fXFxYsXcfToUWhpaaFnz56QyTgZmoiIPh6Civ77VL3XIrReXl7w8/OTT2A/c+YMtm7ditDQUABAeno6Fi9ejGPHjsHDwwMA4OTkhNOnT2P16tX47LPPsHLlSjg5OeHXX3+FSCSCi4sLrl+/jh9//FHpfpUrV1Yqy8jIkP985MgRXLt2Dffv34etrS0AYOPGjahZsyYuXryIhg0bFvqe4uLikJOTg169esHe3h4A4ObmJj///fffY9GiRfL9sRwdHXHr1i2sXr0aQ4YMgVgshre3N4KCgjB79myIRCLcv38fJ06cQEhICACgd+/eCvcMCAiApaUlbt26hVq1ivZki1QqVdr0VZadA4kO1xMmIiLN8CnPF1OF9+o5s7CwQJcuXbB+/XoEBgaiS5cuCivy3rp1C5mZmWjfvj2MjIzkx4YNG+TDeJGRkWjSpInCHlZvErl3nTp1ChEREQpHxYoV5ecjIyNha2srT8wAwNXVFaampoiMjCzSe6pTpw7atm0LNzc39O3bF2vXrkVycjKA19s5PHz4EMOHD1d4P/Pnz1cYlhw+fDgePHiAY8eOAXjda1a5cmW0a9cOwOvF6QYOHAgnJycYGxvD0dERwOsh2aLKaxPYn3edKPL1REREpNneu7tl2LBhGDt2LADg999/Vzj3ZpjuwIEDqFSpksK5N3tkFSerdnR0hKmpqUKZtvZ/oee3Uenb5VpaWkr3zM7+b/0qsViMw4cPIywsDIcOHcKyZcvw3Xff4fz58zAwMADwemizcePGCm28vahctWrV0KJFCwQGBqJ169ZYv349hg4dCi2t1znw559/DltbW6xduxYVK1aETCZDrVq1kJWVVeTPIq9NYGX/LCny9URERKVNXds3fSzeOznz9PSUJxUdO3ZUOOfq6gqJRILY2Fh89tlneV7v6uqKPXv2KJSdO3fuvWJxdXVFbGwsHj58KO89u3XrFlJTU1GjRg0AQIUKFXDjhuJidhEREdDR0ZG/FolEaNasGZo1a4ZZs2bB3t4eu3fvhq+vLypVqoR///0XgwYNKjCW4cOHY8yYMejevTsePXqEoUOHAgCePXuGyMhIrF69Gi1atAAAnD5d/MVY89oE9hWHNImISINwWLNk3vtvdbFYLB8yfHdLgnLlymHy5MmYNGkSZDIZmjdvjrS0NISFhcHIyAhDhgzB6NGjsWjRIvj6+mLUqFEIDw9XmkxfVO3atUPt2rUxaNAgLFmyBDk5Ofjqq6/w2WefoUGDBgCANm3a4Oeff8aGDRvg4eGBP//8Ezdu3JA/YXr+/HkcPXoUHTp0gKWlJc6fP4+kpCR5cjdnzhyMHz8exsbG6NSpE6RSKS5duoTk5GSFnqy+ffti/PjxGDVqFNq2bQsHBwcAgJmZGczNzbFmzRrY2NggNjb2vR+AICIi0mSf8jIYqlCiHQKMjY1hbGyc57nvv/8es2bNgr+/P2rUqIGOHTti//798nlWdnZ22LlzJ/bv3486depg1apVWLjw/bbAEIlE2LNnD8zMzNCyZUu0a9cOTk5O2LZtm7xOx44dMXPmTEydOhUNGzbEixcv4O3trfBeTp48ic6dO8PZ2RkzZszAokWL0KlTJwDAiBEj8McffyAoKAhubm747LPPEBQUJH8/bxgYGGDAgAFITk5WeIJVS0sLW7duRXh4OGrVqoVJkybh559/fq/3S0RERB8v7q35EeDemqWLe2uWPu6tWbq4t2bp4t6aysyMqqqkneSX91TSTlnDyUpERESkUnwgoGQ0auNzIiIiok8de86IiIhIpThjqmSYnBEREZFK8WnNkuGwJhEREZEGYc8ZERERqdSnvGm5KjA5IyIiIpXisGbJcFiTiIiISIOw54yIiIhUik9rlgyTMyIiIlIpzjkrGSZnREREpFLsOSsZzjkjIiIi0iDsOSMiIiKVYs9ZyTA5IyIiIpVialYyHNYkIiIi0iQCUR4yMzOF2bNnC5mZmeoOpUgYb+krazEz3tLFeEtXWYuXVEskCBwYJmVpaWkwMTFBamoqjI2N1R1OoRhv6StrMTPe0sV4S1dZi5dUi8OaRERERBqEyRkRERGRBmFyRkRERKRBmJxRniQSCWbPng2JRKLuUIqE8Za+shYz4y1djLd0lbV4SbX4QAARERGRBmHPGREREZEGYXJGREREpEGYnBERERFpECZnRERERBqEyRkRERGRBmFyRvSBvHr1ChkZGfLXDx48wJIlS3Do0CE1RpW/kJAQnD59Wv76999/R926dTFw4EAkJyerMbKPS2JiIk6dOoXTp08jMTFR3eEQkQbgUhpU5uXk5CA0NBTR0dEYOHAgypUrhydPnsDY2BhGRkbqDk+uQ4cO6NWrF0aPHo2UlBRUr14dOjo6ePr0KRYvXowxY8aoO0QFbm5u+PHHH9G5c2dcv34dDRs2hK+vL44dO4YaNWogMDBQ3SHmKysrC4mJiZDJZArldnZ2aopIWVpaGr7++mts3boVubm5AACxWIz+/fvj999/h4mJiZojzFt0dDQCAwMRHR2N3377DZaWlggJCYGtrS1q1qyp7vCUaPrvh2vXrhW5bu3atUsxEtIo6tx1nTTPuHHjhN9++02pfNmyZcKECRM+fECFiImJEapXry4YGBgIYrFYiI6OFgRBECZMmCCMGjVKzdEpMjc3F27cuCEIgiCsXbtWqF27tpCbmyv89ddfQvXq1dUcnTJDQ0Ph/v37giAIwuzZs4XevXsLgiAI4eHhgpWVlRojy9+dO3eE5s2bC1paWgqHSCQStLS01B2egr59+wrVqlUTQkJChNTUVCEtLU0ICQkRXFxchL59+6o7vDyFhoYK+vr6Qrt27QRdXV35/28//vij/PuhScrC74c3302RSJTnoanfXypd2upODkmz7Ny5E/v27VMqb9q0KX744QcsWbLkwwdVgAkTJqBBgwa4evUqzM3N5eU9e/bEiBEj1BiZsoyMDJQrVw4AcOjQIfTq1QtaWlpo0qQJHjx4oObolOnq6sqHYY8cOQJvb28AQPny5ZGWlqbO0PLl4+MDbW1t/P3337CxsYFIJFJ3SPk6cOAADh48iObNm8vLOnbsiLVr18LT01ONkeVv+vTpmD9/Pnx9feXfZQBo3bo1fvvtNzVGlrey8Pvh/v376g6BNBCTM1Lw7NmzPIdTjI2N8fTpUzVEVLDTp0/jzJkz0NXVVSi3t7fH48eP1RRV3qpWrYo9e/agZ8+eOHjwICZNmgTg9ZwjY2NjNUenrHnz5vD19UWzZs1w4cIFbNu2DQBw584dVK5cWc3R5S0iIgLh4eGoXr26ukMplLm5eZ7/r5mYmMDMzEwNERXu+vXr2Lx5s1J5hQoV8OzZMzVEVLCy8PvB3t5e3SGQBuIDAaSgatWqCAkJUSr/559/4OTkpIaICiaTyeTzdd726NEjhX/Za4JZs2Zh8uTJcHBwQOPGjeHh4QHgdS+au7u7mqNTtnz5cmhra2PHjh1YuXIlKlWqBOD1d0FTe3ZcXV018h8ReZkxYwZ8fX0RFxcnL4uPj8eUKVMwc+ZMNUaWP1NTU4V437hy5Yr8+6FJytLvh7fdunULISEh2Ldvn8JBnw4+EEAK1q1bh7Fjx2LKlClo06YNAODo0aNYtGgRlixZgpEjR6o5QkX9+/eHiYkJ1qxZg3LlyuHatWuoUKECunfvDjs7O42btB4fH4+4uDjUqVMHWlqv/2104cIFGBsbl4neHk137NgxzJgxAwsXLoSbmxt0dHQUzmtSD6W7uzvu3bsHqVQqf1AhNjYWEokE1apVU6h7+fJldYSoZOrUqTh79iy2b98OZ2dnXL58GQkJCfD29oa3tzdmz56t7hAVlLXfD//++y969uyJ69evQyQS4c1fz2+G5/NKNOnjxOSMlKxcuRILFizAkydPAAAODg6YM2eOfM6RJnny5Alat24NsViMu3fvokGDBrh79y4sLCxw8uRJWFpaqjvEMi8xMTHPJx818cmxNwnvu3PNBEGASCTSqL/c5s6dW+S6mpL0ZGdnw8fHB1u3boUgCNDW1kZubi4GDhyIoKAgiMVidYeooKz9fvj8888hFouxdu1aODk54cKFC3j27Bm++eYb/PLLL2jRooW6Q6QPhMkZ5SspKQn6+voa8bh5QV69eoUtW7bg8uXLkMlkqFevHgYNGgR9fX11h6YgMzMTy5Ytw/Hjx/NMdjSld+SN8PBwDBkyBJGRkQr/gtfEROeNEydOFHj+s88++0CRfNyio6Nx5coVyGQyuLu7K/X0aZKy8vsBACwsLHDs2DHUrl0bJiYmuHDhAlxcXHDs2DF88803uHLlirpDpA+EyRnRBzJw4EAcPnwYffr0gZWVlVLvjqb0jrxRu3ZtVK1aFdOmTcszXk5kJlItMzMzhIeHw8nJCVWqVMEff/yB1q1bIzo6Gm5ubgqLWNPHjU9rkoKEhARMnjwZR48eRWJiIt7N3TWttyS/SbIikQh6enqoWrUqHB0dP3BUeTtw4ACCg4PRrFkzdYdSJPfv38euXbtQtWpVdYdSLCkpKQgICEBkZCREIhFcXV0xbNgwjVvUVUtLq8ClPjTl/zVfX98i1128eHEpRlI0xZk4361bt1KMpPhq1aqFa9euwcnJCY0bN8ZPP/0EXV1drFmzRiMfyKLSw+SMFPj4+CA2NhYzZ87U+HWiAKBHjx4KE2ffeHv4rXnz5tizZ4/alyeoVKmSRj8h9q62bdvi6tWrZSo5u3TpEjp27Ah9fX00atQIgiBg8eLFWLBgAQ4dOoR69eqpO0S53bt3K7zOzs7GlStXsH79+mLNRytt7w6lhYeHIzc3Fy4uLgBeL60iFotRv359dYSnpEePHgqv8/v9AGhOAvzGjBkzkJ6eDgCYP38+unbtihYtWsDc3Fy+lA19Ij70qrek2YyMjIQrV66oO4wiO3LkiNC4cWPhyJEjQlpampCWliYcOXJEaNKkiXDgwAHh9OnTQs2aNYVhw4apO1QhODhY8PT0FGJiYtQdSpEkJSUJnTt3FubMmSPs2LFD2Lt3r8KhiZo3by74+PgI2dnZ8rLs7GxhyJAhQosWLdQYWdFt2rRJ6Natm7rDyNOiRYuEzz//XHj+/Lm87Pnz50L37t2FX375RY2R5e3w4cNCvXr1lHZhaNCggXDo0CF1hydXp04dYdmyZQqf6xvPnj0TZDKZGqIidWJyRgpq1KghXL58Wd1hFFnNmjWFM2fOKJWfPn1acHV1FQTh9S9oW1vbDx2aksTERKFVq1aClpaWYGRkJJiZmSkcmmbv3r2CsbFxvlvKaCI9PT0hMjJSqfzmzZuCvr6+GiIqvnv37gkGBgbqDiNPFStWlG9B9rbr168LNjY2aoioYDVr1hROnTqlVH7y5EmN2jLtyy+/FExMTAQ9PT3hiy++EI4cOaLukEjNOKxJCpYsWYLp06dj9erVcHBwUHc4hYqOjs5z7SpjY2P8+++/AIBq1appxMKkX3zxBR4/foyFCxfmOcFe04wfPx5eXl6YOXMmrKys1B1OkRgbGyM2NlZpzbiHDx+WiSHlV69eYdmyZRq7A0NaWhoSEhKUNjhPTEzEixcv1BRV/qKjo/PdhSEmJubDB5SP1atX47fffsP27dsRGBiIDh06wNbWFsOGDYOPj498HTz6hKg7OyTNYmpqKujq6paZ3p1mzZoJnp6eQmJiorwsMTFR8PT0lA9jHT58WKhWrZq6QpTT19cXIiIi1B1GkRkZGQn37t1TdxjFMm7cOKFy5crC1q1bhdjYWOHhw4fCli1bhMqVKwsTJkxQd3gKTE1NFf7fMjU1FcRisVCuXDmNHTb28vIS7OzshO3btwsPHz4UHj58KGzfvl1wcHAQvL291R2ekhYtWght2rQRnjx5Ii+Li4sT2rVrJ7Rs2VKNkRXs33//FWbMmCHY2dkJYrFY6NChg7Bt2zZ1h0UfEJfSIAXr168v8PyQIUM+UCRFExUVhe7du+P+/fuwtbWFSCRCbGwsnJycsHfvXjg7O2PPnj148eIFvLy81BprvXr1sGLFCjRp0kStcRTVkCFD0KJFC43ZILoosrKyMGXKFKxatQo5OTkAAB0dHYwZMwY//PADJBKJmiP8T1BQkELvqZaWFipUqIDGjRur/eGV/GRkZGDy5MlYt24dsrOzAQDa2toYPnw4fv75ZxgaGqo5QkX37t1Dz549ERUVpbALw5vfC5r+sIsgCNi5cydGjRqFlJQUjXuAgUoPkzMq8wRBwMGDB3Hnzh0IgoDq1aujffv28tXiNcWhQ4cwd+5cLFiwQOO3FgKABQsWYMmSJejSpUue8Y4fP15NkRUuIyMD0dHREAQBVatWhYGBgbpD+qikp6crfL6alpS9TRAEHD58GLdv34YgCHB1dUW7du00flrB8ePHERgYiF27dkFbWxsDBgzAqlWr1B0WfSBMzgjA67kkRaFpCUR+nj17ho0bN2LixInqDkWuLG0tBKDA9eFEIpF8Th+9n8DAQBgZGaFv374K5du3b0dGRobG9VJT6YuNjUVQUBCCgoIQExODFi1aYPjw4ejbt69G7mhApYfJGQEofEFMTU0g3iYIAg4dOoSAgADs3bsXxsbGSEpKUndYcmVpayFBEPDgwQNYWlpqfK9Tr169EBQUBGNjY/Tq1avAurt27fpAURXOxcUFq1atQuvWrRXKT5w4gS+//BJRUVFqiix/rVu3LvD3xLFjxz5gNIWbN29egednzZr1gSIp2ObNmxEYGIjjx4/DysoK3t7eGD58uMYPu1Lp4dOaBOB1F3pZFRMTg3Xr1iEoKAiPHz/GwIEDceDAAaW/9NRNk5KvwgiCAGdnZ9y8eVOj900EXj959yZhMDY21vjhqjcePHiQZ++kvb09YmNj1RBR4erWravwOjs7GxEREbhx44ZG9vTltdDv/fv3oa2tjSpVqmhMcubj44MuXbpgz5496Ny5s8ZNyaAPj8kZAShbiQMASKVS7Nq1C3/88QfCwsLQqVMnLF68GF988QX8/Pzg6uqq7hDzVJa2FqpWrRqePXum8clZYGCg/OegoCD1BVJMlpaWuHbtmtKSNVevXoW5ubl6girEr7/+mmf5nDlz8PLlyw8cTeHy2ig8LS0NPj4+6NmzpxoiytujR49gaWmp7jBIk3zYh0NJE6Wmphb50BTm5uZCixYthNWrVyusqq2trS3cvHlTjZHl7+LFi0L58uWFSpUqCT179hR69OghVK5cWTA3NxfCw8PVHZ6Sv//+W2jevLlw/fp1dYdSZK1btxaSk5OVylNTU4XWrVt/+IAKMGXKFMHe3l44duyYkJOTI+Tk5AhHjx4V7O3thW+++Ubd4RXL3bt3NXKpnfxcv35dsLe3V3cYedqwYYPQtGlTwcbGRr6byK+//irs2bNHzZHRh8SeM4KpqWmRh4I0Zc5Zbm4uRCIRRCIRxGKxusMpkkmTJqFbt25Yu3YttLVf/6+Xk5ODESNGYOLEiTh58qSaI1Q0ePBgZGRkoE6dOtDV1VWakPz8+XM1RZa/0NBQZGVlKZVnZmbi1KlTaogof/Pnz8eDBw/Qtm1b+fdBJpPB29sbCxcuVHN0xXP27Fno6empO4wiS0lJQWpqqrrDULJy5UrMmjULEydOxIIFC+S/b01NTbFkyRJ0795dzRHSh8LkjBTmm8XExGD69Onw8fGBh4cHgNe/eNevXw9/f391hagkLi4OO3fuREBAACZMmIBOnTph8ODBGj3f6NKlSwqJGfB6jaipU6eiQYMGaowsb0uWLFF3CEV27do1+c+3bt1CfHy8/HVubi5CQkJQqVIldYSWL11dXWzbtg3z589HREQE9PX14ebmBnt7e3WHlq93H7gQBAFxcXG4dOkSZs6cqaao8rd06VKF12/i3bhxIzw9PdUUVf6WLVuGtWvXokePHvjhhx/k5Q0aNMDkyZPVGBl9aHxakxS0bdsWI0aMwBdffKFQvnnzZqxZswahoaHqCawA0dHRCAwMxPr16/H48WN88cUX8PHxQZs2bTSqV83KygobN25Ehw4dFMoPHjwIb29vJCQkqCmysu/tp43z+pWmr6+PZcuWYdiwYR86tI+Kj49PngvntmnTRul7rQnefeDi7Xj9/Pw0bksvfX193L59G/b29ihXrhyuXr0KJycn3L17F7Vr18arV6/UHSJ9IEzOSIGBgQGuXr2qNAn8zp07qFu3LjIyMtQUWeFkMhlCQkKwbt067N+/H0ZGRnj27Jm6w5IbP348du/ejV9++QVNmzaFSCTC6dOnMWXKFPTu3VsjeqqKut4doFlr3j148ACCIMDJyQkXLlxAhQoV5Od0dXVhaWmpUYk6APTp0wcNGjTA9OnTFcp//vlnXLhwAdu3b1dTZKQurq6u8Pf3R/fu3RWSs6VLl2L9+vUIDw9Xd4j0oahvuhtpImdnZ8HX11ep3NfXV3B2dlZDRO8nMTFRWLRokbrDUCCVSoXx48fL9y7V0tISJBKJMHHiRCEzM1Pd4QmCIAgikUgeW2EHlYyFhYVw7do1pfJr164JlpaWaoiocI6OjsLTp0+VypOTkwVHR0c1RFSwoUOHCmlpaUrlL1++FIYOHaqGiAq2bt06oVKlSsLWrVsFQ0NDYcuWLcL8+fPlP9Ongz1npCA4OBi9e/dGlSpV5HtAnjt3DtHR0di5cyc6d+6s5giVpaSkYMeOHYiOjsaUKVNQvnx5XL58GVZWVho3zwjQ7K2F3l4ot7D5h5q4rtUbt27dQmxsrNLDAd26dVNTRMr09fUREREBFxcXhfLbt2/D3d1dI4ewtLS0EB8fr7TsQ0JCAuzs7CCVStUUWd7EYjHi4uKU4n369Cmsra3l+69qkrVr12L+/Pl4+PAhAKBSpUqYM2cOhg8frubI6ENickZKHj58iJUrVyrsRTd69GjY2tqqOzQl165dQ7t27WBiYoKYmBhERUXByckJM2fOxIMHD7BhwwZ1h5inR48eQSQSaWTy+EZZnH/477//omfPnrh+/TpEIpF8/tmbeVKa8rQxADRs2BCff/650kKoc+bMwf79+zVqCGvfvn0AgB49emD9+vUK6/Ll5ubi6NGjOHz4sMbsapCWlgZBEGBmZoa7d+8qDHPn5uZi//79mD59Op48eaLGKAv29OlTyGQyrn/2qVJfpx1RybVt21aYMmWKIAiCYGRkJERHRwuCIAhnzpzRuHWMcnNzhblz5wrGxsbyoUETExNh3rx5Qm5urrrDU6Kvry/cuXNHqTwqKkrQ19dXQ0SF69q1q9C9e3chMTFRMDIyEm7duiWcOnVKaNSokXDy5El1h6dg7969gra2tuDt7S0EBQUJQUFBgpeXl6CtrS3s3r1b3eEpEIlE8iHvNz+/OXR1dQVnZ2dh//796g5TrrDhebFYLMyfP1/dYSrJyMgQ0tPT5a9jYmKEX3/9VTh48KAaoyJ1YHJGSk6ePCkMGjRI8PDwEB49eiQIwuuFEU+dOqXmyJQZGxsL9+7dEwRBMTmLiYkRJBKJOkNTMn36dKFChQrCihUrhKtXrwoRERHC77//r717j6o53f8A/v7upMsRmpQJJZ1GyTXLEWUQcstRLgdH6SROZFyWo1xKZ6qp0XQGZWTcSgo1oXGfSClSOE3pMkkoJNdxG00i9fz+aLXHbhfNbx37+X6bz2sta9V398d7zexVn/18ns/zRDB9fX3m4+PDO54SKe4/1NPTY3l5eYyx+vdGcXExY4yxlJQUNmDAAJ7RmnTs2DFmY2PDtLW1mZ6eHrOzs2NpaWm8YzXLxMSEPXr0iHeM90pLS2NnzpxhgiCwxMRElpaWJv+XmZnJKioqeEdskr29Pfv2228ZY/X7+AwMDFi3bt2YpqYm27JlC+d0RJWoOCMKDhw4wLS0tNj8+fOZhoaGvNiJiIhgEyZM4JxOmYGBAcvJyWGMKRZnJ0+eZN26deMZTYmhoSE7fPiw0vNDhw6xLl26cEj0bsePH2eampqsd+/ebN68eWzevHmsd+/eTFNTkx0/fpx3vCZ17NhR/h4wNTVlqampjDHGrl+/LtrVPvLh3Lx5k9XV1fGO0WJ6enqssLCQMcbYjh07WL9+/VhtbS1LSEhgFhYWnNMRVaJDaImCoKAgbN26Fa6uroiPj5c/t7GxQWBgIMdkTXN0dERgYCASEhIA1O8tun37NlavXo1p06ZxTqfoyZMnsLCwUHpuYWEhytP2J06ciJKSEoX9h46OjqLdfwgAffr0QX5+PkxNTWFtbY3Q0FC0bdsW27dvh6mpKe94Cl6+fInk5GSUlJRAEAT07NkTY8aMUbqJgbdNmzbBw8MDmpqaSoe6NrZ06VIVpWpefn4++vTpA5lMhufPn6OgoKDZn+3Xr58Kk71fVVWV/Oy1U6dOYerUqZDJZBgyZAhu3brFOR1RKd7VIREXLS0tVlZWxhhTXIm6ceOG6NqEjNXfmWhra8s6duzI1NTUmJGREVNXV2fDhw9nlZWVvOMpGDx4MFuyZInS88WLFzNra2sOiVqfpKQkdvDgQcZY/Xu2V69eTBAE1qlTJ5aSksI53W8OHz7M9PX1lfZv6evrsyNHjvCOp8DExER+fIaJiUmz/8RylIYgCOzBgwfyr5vaJ9fwXGz69u3LwsPD2e3bt1n79u1ZZmYmY4yx7Oxs1rlzZ87piCrRyhlRYGhoiOvXr8PExETheUZGhuhWHoD6g1AzMjKQmpqKnJwc1NXVYeDAgRgzZgzvaEpCQ0Ph4OCA06dPY+jQoRAEAZmZmSgvL8eJEyd4x2vSuXPnsG3bNpSWlmL//v3o2rUrYmNj0aNHDwwbNox3PCXjxo2Tf21qaoqioiI8efIEurq6ornaKzMzE9OnT8fkyZOxYsUK9OrVC0D98R/r16/H9OnTkZaWJj++hLeysrImvxarsrIy+XSmFPK+7d///jdmz56N5cuXY/To0fL3wKlTp2BlZcU5HVEp3tUhEZevvvqKWVpasgsXLjAdHR127tw5tmfPHqavr8+++eYb3vFa5OnTp7wjNKuiooL5+PiwqVOnsilTpjBfX1/Rbk6W2v5DqZgwYQLz8PBo9nUPDw/R/vcNCAhQmCZsUFVVxQICAjgkan3u3bvHcnJyFCa4L168yK5cucIxFVE1OueMKPH19cXGjRtRXV0NANDQ0ICXlxe++OILzsmUffXVVzAxMcHMmTMBADNmzMDBgwfx8ccf48SJE+jfvz/nhO9XXV2NzZs3i+5iYysrKyxfvhyurq4KV8lcvnwZ48ePV7hcnKfGl3G/S2Ji4gdM0jK6uro4e/Ys+vbt2+Tr+fn5GDFiBJ4+fariZO/X3KGujx8/hoGBgajOkQN+O5+tMUEQoKmpCTMzM6X7NwkRA2prEiXBwcHw9fVFUVER6urqYGlpiXbt2vGO1aRt27Zhz549AIDk5GQkJyfjhx9+QEJCAry9vXHq1CnOCev9/PPPuHjxItTV1TF69GioqamhpqYGW7Zswbp16/DmzRvRFWdXr17F8OHDlZ63b98ez549U32gZrx9IKoUVFdXv/Ne0g4dOojupP0GjLEm28N5eXn46KOPOCR6NycnJ4XDiBs0PBMEAcOGDcOhQ4egq6vLKeVv7Ozs3tl+T01NVWEawhMVZwQA4O7u3qKfi4qK+sBJfp979+7JJwePHTuGGTNmYOzYsTAxMYG1tTXndPUyMzPh4OCA58+fQxAEDBo0CLt27YKTkxPq6uqwdu3aFv/3VyWp7D/ctWsX7wi/S8+ePZGamoq5c+c2+XpKSgrMzMxUnOrdGvbsNUyVvl1A1NbWorKyEgsXLuSYsGnJycnw9fVFcHAwBg8eDAC4dOkS1q5dCz8/P3To0AELFiyAl5cXIiMjOacFBgwYoPB9TU0NLl++jMLCQlFfl0b+96g4IwCA6OhodO/eHVZWVkqfMsVMV1cX5eXlMDIyQlJSEoKCggDUf8IXS4vFz88P48aNw9q1axEVFYWwsDBMmjQJ/v7+mDNnjmg2qje2YMECLFu2DFFRURAEAXfv3kVWVha8vLyUrhwiLefm5gYvLy907txZ6a7a48ePY+XKlfD19eWUrmlhYWFgjMHd3R0BAQEKq5Vt27aFiYmJaAYY3rZs2TJs374dNjY28mejR4+GpqYmPDw88NNPPyEsLEw0H442btzY5HN/f39UVlaqOA3hit92NyImnp6eTFdXl/Xv35+Fh4ezx48f847UIp999hnr3r07GzNmDNPT02MvXrxgjDEWHx/PrKysOKer9/bBkr/++iuTyWQsISGBc6qW8fHxYVpaWvLjBzQ1NdnatWt5x2pWw5EOzf0Tg9raWjZ9+nQmCAKzsLBgU6ZMYVOmTGHm5uZMJpOxqVOnivI6L8bqT95//fo17xgtpqmpyQoKCpSe5+fnM01NTcZY/UG1Yj+g+Nq1a0xXV5d3DKJCNBBA5F69eoXExERERUXJW3Hz5s3D2LFjRbu6U1NTg/DwcJSXl8PNzU0+bh4WFoZ27dph/vz5nBMCMpkM9+/fl2+i1tHRQW5uruhaV82pqqqSxP5DAAgPD1f4vqamBrm5uUhKSoK3tzdWr17NKZmy7777DnFxcSgpKQFQ3+6cNWsWZs2axTlZy7x8+RI1NTUKz961l46HYcOGQUdHBzExMfLjNR49egRXV1f8+uuvOHv2LE6fPo1FixbJ/z+IUWxsLFatWiXqi9rJ/xYVZ6RJt27dQnR0NGJiYlBTU4OioiJR/1EWMzU1NZSUlEBfXx+MMRgZGSEjI0NpL5fY/rC1JhEREcjOzpbc/jSxqaqqwsqVK5GQkIDHjx8rvS6WrQQNrl69CkdHR5SVlcHIyEh+g4ipqSkOHz6Mnj174tChQ3jx4gXmzJnDO67S5DFjDPfu3UN2djb8/Pzw+eefc0pGVI2KM9Kk27dvIzo6GtHR0Xj9+jWKi4tFWZzFxMS883VXV1cVJWmeTCZTWHlkjSbeGr4X0x+2M2fOICcnB0OGDIGtrS22bduG4OBgvHz5Ek5OTti0aZPorhl6l9LSUgwYMAC//PIL7ygKbty4gV27dqG0tBRhYWEwMDBAUlISjIyM0Lt3b97xlHz22Wc4c+YMAgMD4erqioiICFRUVGDbtm0ICQmBs7Mz74hKGGM4efIkSkpKwBiDhYUF7O3tIZPJeEdT0nhIRCaTQV9fH6NGjcLYsWM5pSI8UHFG5N5ua2ZkZGDSpEmYO3cuxo8fL8pfZACUxt9rampQVVWFtm3bQltbWxR3Vqanp7fo50aMGPGBk7TMjh074OnpCRMTE9y5cweff/45goODMWfOHMhkMuzZsweenp4ICQnhHbXFQkNDsWXLFty8eZN3FLn09HRMmDABtra2OHv2LK5cuQJTU1OEhobi0qVLOHDgAO+ISoyNjRETE4ORI0eiffv2yMnJgZmZGWJjYxEXFyfamy4IkRqa1iQAgEWLFiE+Ph7GxsaYO3cu4uPjoaenxzvWezV1UOe1a9fg6ekJb29vDomUiaXoaqnw8HBs3LgRS5YsQVJSEv76179i586d8lH+kSNHYs2aNaIszqysrJRWJe/fv49Hjx5hy5YtHJMpW716NYKCgvCvf/1Lftk1UH/WVeO9c2Lx5MkT+aGt7du3l3/4GTZsGDw9PXlGa1ZKSgpSUlLw8OFD1NXVKbwmtqOBGvz444+4cuUKBEGApaUlXd30B0TFGQEAbN26FcbGxujRowfS09ObXe0Rwwnr7/PJJ58gJCQELi4uKC4u5h1HQUMb68aNGwgPDxdlG6u0tBSTJ08GAIwfPx6CIMjPiAIAa2trlJeX84r3Tk5OTgrfN7SFRo4cCQsLCz6hmlFQUIB9+/YpPdfX129yP5cYmJqa4ubNm+jevTssLS2RkJCAwYMH4+jRo6I8DDggIACBgYEYNGgQDA0NRTvY1ODhw4eYNWsW0tLS0LFjRzDG8Pz5c9jZ2SE+Pl4+1EBaPyrOCID6vVli/8X1e6ipqYlusqlxGys4OBgGBgbIz8/Hzp07RdPGqq6uVthPpqGhAQ0NDYXv37x5wyPae0lpw3THjh1x7949peuDcnNz0bVrV06p3m3u3LnIy8vDiBEjsGbNGjg4OOCbb77BmzdvsGHDBt7xlGzduhXR0dGi2OzfEkuWLMEvv/yCn376Cb169QIAFBUV4R//+AeWLl2KuLg4zgmJqlBxRgDUH0IrRY3vzmuYbtq8eTNsbW05pWqaVNpYgiDgxYsX0NTUlA8rVFZWyjfTi21TfWO1tbX4/vvv5W2hXr16wdHREW3aiOvX3ezZs7Fq1Srs378fgiCgrq4O58+fh5eXlygGWZqyfPly+dd2dnYoLi5GdnY29PX1RTkJ+/r1a4UDaMUuKSkJp0+flhdmAGBpaYmIiAgaCPiDoYEAImmNBxUEQZBPN61fvx6Ghoackilr164dCgoK0KNHD4WLxG/evAkLCwv5RfO8SXG6tEFhYSEcHR1x//59mJubA4D8GJMjR440e9k4DzU1NXBzc0N8fDwYY2jTpg1qa2sxe/ZsREdHQ01NjXfEFsvLy8PAgQNF955YtWoV2rVrBz8/P95RWkRHRwfnzp1TusYpNzcXI0aMEP0HI/K/I66PkoT8To03+IqZVNpYZ86c4R3h/23+/Pno3bs3srOz5ZO8T58+hZubGzw8PJCVlcU54W/U1dWxd+9eBAYGIjc3F3V1dbCyssInn3zCO1qrUV1dje3bt+P06dPo168f1NXVFV4XWyt21KhRWLZsGeLi4tClSxcAQEVFBZYvX47Ro0dzTkdUiVbOSKvR8FYW6965lStXIisrC/v370fPnj2Rk5ODBw8ewNXVFa6urpLaLyVWWlpayM7OVhquKCwsxF/+8he8fPmSUzJl6enpkpvkbY5YV87s7OyafU0QBKSmpqowzfuVl5fD0dERhYWFCofm9u3bF4cPH0a3bt14RyQqQitnRPJiYmLwn//8B9euXQNQfw2Ot7e36DYBBwcHw83NDV27dgVjDJaWlvI21tq1a3nHa5IUpkvfZm5ujgcPHihle/jwoeiuy7K3t8fHH3+M2bNnw8XFBX369OEdqdWR2iqwkZERcnJykJycjOLiYvnviTFjxvCORlSMVs6IpG3YsAF+fn5YvHgxbG1twRjD+fPnERERgaCgIIUNzGJx48YNSbSxpHhI6okTJ7By5Ur4+/tjyJAhAIALFy4gMDAQISEhGDZsmPxneV+X9fPPPyM+Ph5xcXHIyspCnz594OLigtmzZ4tuhaTxtUKNPXv2DOnp6aJbOXvbnTt3IAiCqLYQENIcKs6IpPXo0QMBAQFK0227d++Gv78/ysrKOCVTJrU21tChQ/G3v/1NPl3aMMDw3//+F05OTqioqOAdUcnbAyIN7e3G7W4xDjSUlZVh3759iIuLQ3FxMYYPHy6qllvja4WaI7aJzbq6OgQFBWH9+vWorKwEUL/pfsWKFfD19RXNzSebNm1q0c8tXbr0AychYkHFGZE0TU1NFBYWKrWsrl27hr59+4pmAhIA2rZtK6k2llSmS9/W0quyAPHd3FBbW4sffvgBfn5+yM/PF1XxKFVr1qxBZGQkAgICFFbW/f398c9//hPBwcG8IwKA0pBQeXk5DA0NFY5/EQQBpaWlqo5GOKE9Z0TSzMzMkJCQAB8fH4Xn3333nejahXfv3pW3sUJDQ0XdxgKkM136NrEVXC1x/vx57N27FwcOHEB1dTUmT56ML7/8knesVmH37t3YuXOn/MYLAOjfvz+6du2KRYsWiaY4a7zCr6Ojg/T0dJiamnJKRHij4oxIWkBAAGbOnImzZ8/C1tYWgiAgIyMDKSkpSEhI4B1PQadOnbB48WIsXrxY3saKiYmBj4+P6NpYgDQPSQXq9z9FRkYq3E3o7u4uuuuFfHx8EBcXh4qKCtjb2yMsLAxOTk7Q1tbG5cuXecdrFZ48edLktV0WFhbye0EJESNqaxLJ+/HHH7Fx40ZcuXJFPt20YsUK0V8WLPY2lhQPSc3Ozsa4ceOgpaWFwYMHgzGG7OxsvHz5EqdOncLAgQN5R5SzsbGBs7MzZs6ciU6dOuH58+fYu3cvIiMjcfnyZdG9H6TI2toa1tbWSnu6lixZgkuXLuHixYuckr3b29sIyB8TFWdEklp6UjbvibymNNXGcnZ2xoQJE3hHa5JUpksB4NNPP4WZmRl27Ngh36/z5s0bzJ8/H6WlpTh79iznhMpSU1MRFRWFxMREdO/eHdOmTcO0adNE/+FCCtLT0+Hg4ABjY2MMHToUgiAgMzMT5eXlOHHiBD799FPeEZtExRmh4oxIUuMrhpojptWHxm0sZ2dnhTZW4ytbeJPadClQfwhtbm6uUiurqKgIgwYNQlVVFadkiu7cuYPo6Gjs2rULlZWVmDFjBrZu3Yq8vDxYWlryjteq3L17FxEREQrnhnl4eMDf3x9RUVG84wFQ/rDZrVs3ZGRkwMTEROG5GD9skg+DijMiSW9P5THGMHHiROzcuVNpo7qYiguptbGkNl0KAJ07d0ZsbKzSJdEnT56Eq6srHjx4wCnZbyZOnIiMjAw4ODjAxcUF48ePh5qaGtTV1ak4UxGx3Wgg5ftsyYdBAwFEkhoXXWpqahgyZIio2wCZmZkAmm5j7dy5k3M6ZVKbLgWAmTNnYt68efj6669hY2MjHxDx9vbG3//+d97xAACnTp3C0qVL4enpKeoWMVEdqd1kQD48Ks4IUYGm2lg1NTU4ePCgaFdKpDZdCgBff/01ZDIZXF1d8ebNGwD1F4x7enoiJCSEc7p6586dQ1RUFAYNGgQLCwvMmTMHM2fO5B2LcCSmFX4iDtTWJK2CmDfQtpY2lpinS6uqquDt7Y1Dhw6hpqYGdnZ2WLx4MTp06AAzMzNoa2vzjqikqqoK8fHxiIqKwqVLl1BbW4sNGzbA3d0dOjo6vOO1amJqa7Z0uAmgPWd/JFSckVZBR0cH+fn5SgemikGbNm2abGNJpTiTwnSpt7c3tmzZAmdnZ2hpaWHfvn0YOXIk9u/fzztai1y9ehWRkZGIjY3Fs2fPYG9vjyNHjvCOJVlSugu0pcNNgLgGnMiHRW1NIkmNf/lWV1dj4cKF+NOf/qTwPDExUZWxmiTVNpaUDklNTExEZGQkZs2aBQBwdnaGra0tamtrRXkeW2Pm5uYIDQ3FunXrcPToUdFMEUrV+w4c7tChg2gOUn57v9nNmzexevVquLm5YejQoQCArKws7N69G+vWreMVkXBAK2dEkqR4EbPU2lhSmi5t27YtysrKFKZ1tbS0UFJSAiMjI47JCGm50aNHY/78+UrDK/v27cP27duRlpbGJxhROSrOCOFASm0sKRySqqamhvv370NfX1/+TMytbkKaoq2tjby8PKUp3pKSEgwYMEA05/SRD4+KM0I4qq2tlbexxFScSe2QVJlMhgkTJkBDQ0P+7OjRoxg1apRCq1sMbW5CmmNubo5JkyZh/fr1Cs9XrFiBY8eO4erVq5ySEVWj4owQokCK06VSbHMT0tiJEycwbdo0/PnPf8aQIUMAABcuXMCNGzdw8OBBTJw4kXNCoipUnBFCFEh9upQQKSsvL8e3336rcN3UwoULae/kHwwVZ4QQBVlZWYiKikJCQoLCdGmXLl2oOCOEEBWQ8Q5ACBGXoUOHYseOHbh37x4WLFiA+Ph4dO3aFXV1dUhOTsaLFy94RySk1Tp37hxcXFxgY2ODiooKAEBsbCwyMjI4JyOqRMUZIaRJ2tracHd3R0ZGBgoKCrBixQqEhITAwMAAkydP5h2PkFbn4MGDGDduHLS0tJCTk4NXr14BAF68eIEvv/ySczqiSlScEULeq+GQ1Dt37iAuLo53HEJapaCgIGzduhU7duyAurq6/LmNjQ1ycnI4JiOqRnvOCCGEEBHQ1tZGUVERTExMFO4LLi0thaWlJaqrq3lHJCpCK2eEEEKICBgaGuL69etKzzMyMmBqasohEeGFijNCCCFEBBYsWIBly5bh4sWLEAQBd+/exd69e+Hl5YVFixbxjkdUiNqahBBCiEj4+vpi48aN8hamhoYGvLy88MUXX3BORlSJijNCCCFERKqqqlBUVIS6ujpYWlqiXbt2vCMRFWvDOwAhhBDyR+bu7t6in4uKivrASYhY0MoZIYQQwpFMJkP37t1hZWWFd/1J/v7771WYivBExRkhhBDC0aJFixAfHw9jY2O4u7vDxcUFH330Ee9YhCMqzgghhBDOXr16hcTERERFRSEzMxMODg6YN28exo4dC0EQeMcjKkbFGSGEECIit27dQnR0NGJiYlBTU4OioiIaCviDoXPOCCGEEBERBAGCIIAxhrq6Ot5xCAdUnBFCCCGcvXr1CnFxcbC3t4e5uTkKCgqwefNm3L59m1bN/oDoKA1CCCGEo7cHAubOnYv4+Hjo6enxjkU4oj1nhBBCCEcymQzGxsawsrJ65+b/xMREFaYiPNHKGSGEEMKRq6srTWQSBbRyRgghhBAiIjQQQAghhBAiIlScEUIIIYSICBVnhBBCCCEiQsUZIYQQQoiIUHFGCCGEECIiVJwRQgghhIgIFWeEEEIIISJCxRkhhBBCiIj8Hwz1VvQKAkxAAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "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": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "housing.plot(kind=\"scatter\", x=\"Longitude\", y=\"Latitude\", c=\"MedHouseVal\", cmap=\"jet\", colorbar=True, legend=True, sharex=False, figsize=(6,6),)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And, if we check the median house value column, we can see that the values are in the range from 0 to 5." ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(housing['MedHouseVal'], 'bo')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get the features and the target values, and create training and testing datasets." ] }, { "cell_type": "code", "execution_count": 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": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model_3\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " input_3 (InputLayer) [(None, 8)] 0 \n", " \n", " dense_6 (Dense) (None, 32) 288 \n", " \n", " dense_7 (Dense) (None, 32) 1056 \n", " \n", " dense_8 (Dense) (None, 1) 33 \n", " \n", "=================================================================\n", "Total params: 1377 (5.38 KB)\n", "Trainable params: 1377 (5.38 KB)\n", "Non-trainable params: 0 (0.00 Byte)\n", "_________________________________________________________________\n" ] } ], "source": [ "model_2.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Compile the ANN\n", "\n", "In this case, we will use `mean_squared_error` loss function. During training, the loss function will calculate the squared difference between the target value $y$ and the predicted value $\\hat{y}$, calculated as $\\sum{(y - \\hat{y})^2}$. \n", "\n", "We can use the Adam optimizer for regression tasks as well. Note that we cannot measure accuracy as in classification tasks, therefore we don't need to specify a performance metric. Alternatively, we can use mean-squared error, mean-absolute error, or R2 score as metrics. " ] }, { "cell_type": "code", "execution_count": 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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", "text/plain": [ "
" ] }, "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": [ "1/1 [==============================] - 0s 63ms/step\n" ] }, { "data": { "text/plain": [ "array([[0.5601754],\n", " [1.0535405],\n", " [4.1935463],\n", " [2.8048782],\n", " [2.8342881],\n", " [1.7369726],\n", " [2.502098 ],\n", " [1.688604 ],\n", " [2.5543702],\n", " [4.6797976]], 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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", 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" ] }, "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": [ "129/129 [==============================] - 0s 1ms/step\n", "MSE: 0.2897\n", "MAE: 0.3699\n", "R2 score: 0.7789\n" ] } ], "source": [ "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", "\n", "all_preds = model_2.predict(X_test_scaled)\n", "\n", "print(f'MSE: {mean_squared_error(y_test, all_preds):7.4f}')\n", "print(f'MAE: {mean_absolute_error(y_test, all_preds):7.4f}')\n", "print(f'R2 score: {r2_score(y_test, all_preds):7.4f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 15.4 Saving and Loading Models in Keras " ] }, { "cell_type": "markdown", "metadata": { "id": "0pNOtvAgTboI" }, "source": [ "To save a model in Keras, we simply use the `save()` method, and provide the path to the directory. \n", "\n", "The saved model contains the architecture with the layers, the weights for all layers in the model, and other information related to the used optimizer, loss, and metrics. \n", "\n", "There are two possible formats to save the models into: **HDF5** and **Keras-TensorFlow SavedModel**. If we provide the `.h5` extension, then the saved model format is HDF5. If we provide the `.keras` extension, or if we don't provide any extension, the model will be saved into Keras-TensorFlow SavedModel format. \n", "\n", "Keras-TensorFlow SavedModel is the default, and it is recommended, since it is a newer format, and it saves additional information that is not included in the `h5` file, such as losses and metrics (e.g., they can be useful if we wish to resume training).\n", "\n", "Let's use the Keras-TensorFlow SavedModel format to save the first classification model `model_1` from Section 15.2.1 in the current working directory under the name `classification_model`." ] }, { "cell_type": "code", "execution_count": 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 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": [ "4/4 [==============================] - 0s 2ms/step - loss: 0.0709 - accuracy: 0.9737\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": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " input_1 (InputLayer) [(None, 30)] 0 \n", " \n", " dense (Dense) (None, 30) 930 \n", " \n", " dense_1 (Dense) (None, 15) 465 \n", " \n", " dense_2 (Dense) (None, 1) 16 \n", " \n", "=================================================================\n", "Total params: 1411 (5.51 KB)\n", "Trainable params: 1411 (5.51 KB)\n", "Non-trainable params: 0 (0.00 Byte)\n", "_________________________________________________________________\n" ] } ], "source": [ "model_3.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Appendix " ] }, { "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 15.2.1." ] }, { "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 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": [ "4/4 [==============================] - 0s 7ms/step - loss: 0.0661 - accuracy: 0.9912\n", "Classification Accuracy: 0.9912280440330505\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": [ "4/4 [==============================] - 0s 3ms/step - loss: 0.0737 - accuracy: 0.9825\n", "Classification Accuracy: 0.9824561476707458\n" ] } ], "source": [ "# Compile the model\n", "model_1_subclass.compile(optimizer='adam',\n", " loss='binary_crossentropy',\n", " metrics=['accuracy'])\n", "\n", "# Train the model\n", "model_1_subclass.fit(train_data, train_labels, epochs=100, batch_size=64, verbose=0)\n", "\n", "# Evaluate on test dataset\n", "evals_test_subclass = model_1_subclass.evaluate(test_data, test_labels)\n", "print(\"Classification Accuracy: \", evals_test_subclass[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Subclassing API in Keras is also similar to the model building in PyTorch. We will learn about PyTorch in an upcoming lecture." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References \n", "\n", "1. Complete Machine Learning Package, Jean de Dieu Nyandwi, available at: [https://github.com/Nyandwi/machine_learning_complete](https://github.com/Nyandwi/machine_learning_complete).\n", "2. Implementing Linear Regression on California Housing Dataset, Debarshi Raj Basumatary, available at: [https://medium.com/mlearning-ai/implementing-linear-regression-on-california-housing-dataset-378e14e421b7](https://medium.com/mlearning-ai/implementing-linear-regression-on-california-housing-dataset-378e14e421b7). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[BACK TO TOP](#top)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 4 }