{ "cells": [ { "cell_type": "markdown", "id": "1587aa08", "metadata": {}, "source": [ "# Lesson 28: TensorFlow/Keras neural network demonstration - part 1" ] }, { "cell_type": "markdown", "id": "1e712197", "metadata": {}, "source": [ "## Introduction to TensorFlow and Keras\n", "\n", "**TensorFlow** is an open-source numerical computation library developed by Google. It represents computations as a directed graph of operations on tensors (multi-dimensional arrays), and handles the low-level work of executing those operations efficiently on available hardware. It also provides automatic differentiation, which makes computing gradients for backpropagation automatic rather than something you have to derive by hand.\n", "\n", "**Keras** is a high-level neural network API that ships as part of TensorFlow (`tensorflow.keras`). It sits on top of TensorFlow and provides an 'easy' interface for defining model architectures, training loops, and evaluation. In practice, you write Keras code and TensorFlow does the underlying computation.\n", "\n", "### How the layers fit together\n", "\n", "```text\n", " ┌─────────────────────────────────────┐\n", " │ Your Python code │\n", " │ (define model, call .fit()) │\n", " └──────────────────┬──────────────────┘\n", " │\n", " ▼\n", " ┌─────────────────────────────────────┐\n", " │ Keras │\n", " │ (layers, losses, optimizers, │\n", " │ training loop) │\n", " └──────────────────┬──────────────────┘\n", " │\n", " ▼\n", " ┌─────────────────────────────────────┐\n", " │ TensorFlow Python API │\n", " │ (computation graph, autodiff, │\n", " │ op dispatch) │\n", " └──────────────────┬──────────────────┘\n", " │\n", " ▼\n", " ┌─────────────────────────────────────┐\n", " │ TensorFlow C++ runtime │\n", " │ (compiled ops: matmul, conv, │\n", " │ activations, autodiff) │\n", " └──────────────────┬──────────────────┘\n", " │\n", " ▼\n", " ┌─────────────────────────────────────┐\n", " │ Hardware backend │\n", " └──────────────────┬──────────────────┘\n", " ┌────────┴────────┐\n", " │ │\n", " ▼ ▼\n", " ┌─────────────┐ ┌─────────────┐\n", " │ CUDA GPU │ │ CPU │\n", " │ (parallel │ │ (fallback / │\n", " │ matrix ops) │ │small models)│\n", " └─────────────┘ └─────────────┘\n", "```\n", "\n", "When you call `model.fit()`, Keras translates your high-level instructions into TensorFlow Python API calls. Those calls are dispatched to TensorFlow's C++ runtime, where the actual numerical operations (matrix multiplications, convolutions, activation functions, and so on) are executed as compiled C++ code. The C++ runtime then hands off the work to whichever hardware backend is available: CUDA for a compatible NVIDIA GPU, or the CPU otherwise. The gradient computation for backpropagation is handled automatically by TensorFlow's autodiff engine at the C++ level, so neither you nor Keras needs to implement it explicitly." ] }, { "cell_type": "markdown", "id": "f55bc9f2", "metadata": {}, "source": [ "## Notebook set up\n", "\n", "### Vocareum compatibility" ] }, { "cell_type": "code", "execution_count": 1, "id": "235d8c63-1d5e-4a49-9481-b02ffc2667ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running in Vocareum environment\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "from pathlib import Path\n", "\n", "# Get the parts of the current directory path as a tuple\n", "path_parts = Path.cwd().parts\n", "\n", "# Check if 'voc' is the first directory level\n", "is_voc_root = len(path_parts) > 1 and path_parts[1] == \"voc\"\n", "\n", "# If we are in a Vocareum instance, upgrade TensorFlow and Keras\n", "if is_voc_root:\n", " print(\"Running in Vocareum environment\")\n", "\n", " get_ipython().run_line_magic(\n", " \"pip\", \"install -qq --no-warn-script-location tensorflow==2.17.0 keras==3.2.0\"\n", " )" ] }, { "cell_type": "markdown", "id": "cb78fd87-ab77-4980-9090-d158ca602ad1", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": null, "id": "39b9bf45", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-08-26 16:51:13.952201: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", "2026-08-26 16:51:13.966297: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "2026-08-26 16:51:14.002408: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "2026-08-26 16:51:14.006989: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "2026-08-26 16:51:14.018982: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2026-08-26 16:51:18.215136: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" ] } ], "source": [ "# Suppress noisy TensorFlow logs before importing\n", "import os\n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", "\n", "# Third party imports\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import tensorflow as tf\n", "\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from tensorflow import keras\n", "from tensorflow.keras import layers, Model\n", "\n", "tf.random.set_seed(315)" ] }, { "cell_type": "markdown", "id": "51df7cb5-2594-4c2c-8127-43f4900c58ce", "metadata": {}, "source": [ "### GPU configuration (if present)" ] }, { "cell_type": "code", "execution_count": null, "id": "a3e026f4-87f9-435b-84be-e6f8187228f8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2026-08-26 16:51:24.023398: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n" ] } ], "source": [ "# Configure GPU settings\n", "gpus = tf.config.list_physical_devices('GPU')\n", "\n", "if gpus:\n", " try:\n", " # Use only GPU 0\n", " tf.config.set_visible_devices(gpus[0], 'GPU')\n", "\n", " # Enable memory growth\n", " tf.config.experimental.set_memory_growth(gpus[0], True)\n", "\n", " # Get GPU\n", " details = tf.config.experimental.get_device_details(gpus[0])\n", " cc = details.get('compute_capability', (0, 0))\n", " device = details.get('device_name', '')\n", "\n", " print(f'Using GPU: {device}, compute capability {cc[0]}.{cc[1]}')\n", " print('Memory growth enabled')\n", "\n", " except RuntimeError as e:\n", " print(e)" ] }, { "cell_type": "markdown", "id": "98f5eab8", "metadata": {}, "source": [ "## 1. Data preparation\n", "\n", "### 1.1. Load California housing data" ] }, { "cell_type": "code", "execution_count": 4, "id": "e94bf1e5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "housing_df = pd.read_csv('https://media.githubusercontent.com/media/gperdrizet/fullstack-2605/refs/heads/main/data/california_housing.csv')\n", "housing_df.head()" ] }, { "cell_type": "code", "execution_count": 5, "id": "0561763f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 20640 entries, 0 to 20639\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 MedInc 20640 non-null float64\n", " 1 HouseAge 20640 non-null float64\n", " 2 AveRooms 20640 non-null float64\n", " 3 AveBedrms 20640 non-null float64\n", " 4 Population 20640 non-null float64\n", " 5 AveOccup 20640 non-null float64\n", " 6 Latitude 20640 non-null float64\n", " 7 Longitude 20640 non-null float64\n", " 8 MedHouseVal 20640 non-null float64\n", "dtypes: float64(9)\n", "memory usage: 1.4 MB\n" ] } ], "source": [ "housing_df.info()" ] }, { "cell_type": "code", "execution_count": 6, "id": "d5716bd3", "metadata": {}, "outputs": [], "source": [ "label = 'MedHouseVal'\n", "features = ['MedInc','HouseAge','AveRooms','AveBedrms','Population','AveOccup','Latitude','Longitude']" ] }, { "cell_type": "markdown", "id": "bdce7333", "metadata": {}, "source": [ "### 1.2. Train test split" ] }, { "cell_type": "code", "execution_count": 7, "id": "43267e58", "metadata": {}, "outputs": [], "source": [ "training_df, testing_df = train_test_split(housing_df, random_state=42)" ] }, { "cell_type": "markdown", "id": "cea0e509", "metadata": {}, "source": [ "### 1.3. Standard scale\n", "\n", "#### Features" ] }, { "cell_type": "code", "execution_count": 8, "id": "63115680", "metadata": {}, "outputs": [], "source": [ "feature_scaler = StandardScaler()\n", "feature_scaler.fit(training_df[features])\n", "\n", "training_df[features] = feature_scaler.transform(training_df[features])\n", "testing_df[features] = feature_scaler.transform(testing_df[features])" ] }, { "cell_type": "markdown", "id": "9e5cc0ca", "metadata": {}, "source": [ "#### Label" ] }, { "cell_type": "code", "execution_count": 9, "id": "83959886", "metadata": {}, "outputs": [], "source": [ "label_scaler = StandardScaler()\n", "label_scaler.fit(training_df[label].to_frame())\n", "\n", "training_df[label] = label_scaler.transform(training_df[label].to_frame())\n", "testing_df[label] = label_scaler.transform(testing_df[label].to_frame())" ] }, { "cell_type": "markdown", "id": "a9b8caf6", "metadata": {}, "source": [ "### 1.4. Handle outliers" ] }, { "cell_type": "code", "execution_count": 10, "id": "8d570725", "metadata": {}, "outputs": [], "source": [ "for feature in features:\n", "\n", " q1 = training_df[feature].quantile(0.25)\n", " q3 = training_df[feature].quantile(0.75)\n", " iqr = q3 - q1\n", "\n", " lower_bound = q1 - 1.5 * iqr\n", " upper_bound = q3 + 1.5 * iqr\n", "\n", " training_df[feature] = training_df[feature].clip(lower=lower_bound, upper=upper_bound)\n", " testing_df[feature] = testing_df[feature].clip(lower=lower_bound, upper=upper_bound)" ] }, { "cell_type": "markdown", "id": "9bccff99", "metadata": {}, "source": [ "## 2. Linear regression baseline\n", "\n", "### 2.1. Fit" ] }, { "cell_type": "code", "execution_count": 11, "id": "d8e85966", "metadata": {}, "outputs": [], "source": [ "linear_model = LinearRegression(n_jobs=-1)\n", "fit_result = linear_model.fit(training_df[features], training_df[label])" ] }, { "cell_type": "markdown", "id": "31d5c9c2", "metadata": {}, "source": [ "### 2.2. Test set evaluation" ] }, { "cell_type": "code", "execution_count": 12, "id": "b9eea129", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Linear regression R² on test set: 0.6586\n" ] } ], "source": [ "linear_predictions = linear_model.predict(testing_df[features])\n", "linear_rsquared = linear_model.score(testing_df[features], testing_df[label])\n", "print(f'Linear regression R² on test set: {linear_rsquared:.4f}')" ] }, { "cell_type": "markdown", "id": "01f45893", "metadata": {}, "source": [ "## 3. Keras Sequential API model\n", "\n", "The Sequential API is the simplest way to build a neural network in Keras. It allows you to create models layer-by-layer in a linear stack.\n", "\n", "### 3.1. Build model" ] }, { "cell_type": "code", "execution_count": 13, "id": "d285821c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
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\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sequential_model = keras.Sequential([\n", " layers.Input(shape=(8,)),\n", " layers.Dense(64, activation='relu'),\n", " layers.Dropout(0.2),\n", " layers.Dense(32, activation='relu'),\n", " layers.Dropout(0.2),\n", " layers.Dense(1)\n", "])\n", "\n", "sequential_model.compile(\n", " optimizer=keras.optimizers.Adam(learning_rate=5e-4),\n", " loss='mse',\n", " metrics=['mae']\n", ")\n", "\n", "sequential_model.summary()" ] }, { "cell_type": "markdown", "id": "04aa4af3", "metadata": {}, "source": [ "### 3.2. Train model" ] }, { "cell_type": "code", "execution_count": 14, "id": "5178fc7f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - loss: 0.6672 - mae: 0.6230 - val_loss: 0.3666 - val_mae: 0.4339\n", "Epoch 2/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3991 - mae: 0.4688 - val_loss: 0.3225 - val_mae: 0.4081\n", "Epoch 3/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3548 - mae: 0.4382 - val_loss: 0.3072 - val_mae: 0.3968\n", "Epoch 4/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3254 - mae: 0.4122 - val_loss: 0.2977 - val_mae: 0.3885\n", "Epoch 5/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3170 - mae: 0.4092 - val_loss: 0.2920 - val_mae: 0.3833\n", "Epoch 6/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3092 - mae: 0.4026 - val_loss: 0.2872 - val_mae: 0.3829\n", "Epoch 7/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2938 - mae: 0.3919 - val_loss: 0.2850 - val_mae: 0.3780\n", "Epoch 8/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2969 - mae: 0.3922 - val_loss: 0.2796 - val_mae: 0.3734\n", "Epoch 9/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2861 - mae: 0.3826 - val_loss: 0.2763 - val_mae: 0.3708\n", "Epoch 10/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2782 - mae: 0.3785 - val_loss: 0.2752 - val_mae: 0.3699\n", "Epoch 11/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2735 - mae: 0.3768 - val_loss: 0.2702 - val_mae: 0.3678\n", "Epoch 12/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2737 - mae: 0.3753 - val_loss: 0.2683 - val_mae: 0.3648\n", "Epoch 13/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2694 - mae: 0.3707 - val_loss: 0.2674 - val_mae: 0.3667\n", "Epoch 14/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2634 - mae: 0.3686 - val_loss: 0.2653 - val_mae: 0.3623\n", "Epoch 15/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2648 - mae: 0.3674 - val_loss: 0.2645 - val_mae: 0.3603\n", "Epoch 16/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2651 - mae: 0.3683 - val_loss: 0.2614 - val_mae: 0.3617\n", "Epoch 17/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2591 - mae: 0.3625 - val_loss: 0.2601 - val_mae: 0.3581\n", "Epoch 18/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2606 - mae: 0.3654 - val_loss: 0.2575 - val_mae: 0.3550\n", "Epoch 19/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2527 - mae: 0.3580 - val_loss: 0.2571 - val_mae: 0.3544\n", "Epoch 20/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2574 - mae: 0.3617 - val_loss: 0.2563 - val_mae: 0.3517\n", "Epoch 21/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2561 - mae: 0.3621 - val_loss: 0.2523 - val_mae: 0.3496\n", "Epoch 22/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2528 - mae: 0.3564 - val_loss: 0.2526 - val_mae: 0.3506\n", "Epoch 23/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2486 - mae: 0.3553 - val_loss: 0.2527 - val_mae: 0.3539\n", "Epoch 24/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2511 - mae: 0.3542 - val_loss: 0.2527 - val_mae: 0.3506\n", "Epoch 25/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2479 - mae: 0.3550 - val_loss: 0.2508 - val_mae: 0.3491\n", "Epoch 26/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2528 - mae: 0.3547 - val_loss: 0.2490 - val_mae: 0.3498\n", "Epoch 27/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 884us/step - loss: 0.2521 - mae: 0.3570 - val_loss: 0.2483 - val_mae: 0.3484\n", "Epoch 28/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2490 - mae: 0.3573 - val_loss: 0.2484 - val_mae: 0.3480\n", "Epoch 29/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2508 - mae: 0.3549 - val_loss: 0.2458 - val_mae: 0.3441\n", "Epoch 30/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2430 - mae: 0.3492 - val_loss: 0.2448 - val_mae: 0.3459\n", "Epoch 31/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2473 - mae: 0.3538 - val_loss: 0.2437 - val_mae: 0.3421\n", "Epoch 32/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2426 - mae: 0.3491 - val_loss: 0.2425 - val_mae: 0.3438\n", "Epoch 33/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2405 - mae: 0.3492 - val_loss: 0.2424 - val_mae: 0.3400\n", "Epoch 34/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2410 - mae: 0.3474 - val_loss: 0.2436 - val_mae: 0.3434\n", "Epoch 35/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2436 - mae: 0.3497 - val_loss: 0.2428 - val_mae: 0.3420\n", "Epoch 36/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2402 - mae: 0.3461 - val_loss: 0.2416 - val_mae: 0.3426\n", "Epoch 37/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2380 - mae: 0.3468 - val_loss: 0.2408 - val_mae: 0.3406\n", "Epoch 38/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2392 - mae: 0.3465 - val_loss: 0.2407 - val_mae: 0.3386\n", "Epoch 39/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2389 - mae: 0.3455 - val_loss: 0.2400 - val_mae: 0.3374\n", "Epoch 40/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2387 - mae: 0.3470 - val_loss: 0.2378 - val_mae: 0.3362\n", "Epoch 41/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2351 - mae: 0.3445 - val_loss: 0.2365 - val_mae: 0.3358\n", "Epoch 42/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2333 - mae: 0.3416 - val_loss: 0.2375 - val_mae: 0.3373\n", "Epoch 43/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2337 - mae: 0.3417 - val_loss: 0.2365 - val_mae: 0.3330\n", "Epoch 44/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2370 - mae: 0.3451 - val_loss: 0.2359 - val_mae: 0.3369\n", "Epoch 45/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2352 - mae: 0.3435 - val_loss: 0.2352 - val_mae: 0.3339\n", "Epoch 46/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2364 - mae: 0.3422 - val_loss: 0.2362 - val_mae: 0.3350\n", "Epoch 47/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2379 - mae: 0.3449 - val_loss: 0.2353 - val_mae: 0.3376\n", "Epoch 48/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2356 - mae: 0.3427 - val_loss: 0.2342 - val_mae: 0.3326\n", "Epoch 49/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2297 - mae: 0.3401 - val_loss: 0.2340 - val_mae: 0.3352\n", "Epoch 50/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2368 - mae: 0.3433 - val_loss: 0.2361 - val_mae: 0.3316\n", "Epoch 51/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2328 - mae: 0.3411 - val_loss: 0.2334 - val_mae: 0.3329\n", "Epoch 52/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2308 - mae: 0.3398 - val_loss: 0.2342 - val_mae: 0.3346\n", "Epoch 53/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.2297 - mae: 0.3403 - val_loss: 0.2337 - val_mae: 0.3376\n", "Epoch 54/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 879us/step - loss: 0.2314 - mae: 0.3403 - val_loss: 0.2308 - val_mae: 0.3310\n", "Epoch 55/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 943us/step - loss: 0.2312 - mae: 0.3390 - val_loss: 0.2316 - val_mae: 0.3316\n", "Epoch 56/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 862us/step - loss: 0.2353 - mae: 0.3413 - val_loss: 0.2311 - val_mae: 0.3309\n", "Epoch 57/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 873us/step - loss: 0.2320 - mae: 0.3382 - val_loss: 0.2310 - val_mae: 0.3320\n", "Epoch 58/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 938us/step - loss: 0.2311 - mae: 0.3390 - val_loss: 0.2307 - val_mae: 0.3309\n", "Epoch 59/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 869us/step - loss: 0.2288 - mae: 0.3367 - val_loss: 0.2325 - val_mae: 0.3310\n", "Epoch 60/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 936us/step - loss: 0.2274 - mae: 0.3360 - val_loss: 0.2299 - val_mae: 0.3268\n", "Epoch 61/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 876us/step - loss: 0.2296 - mae: 0.3369 - val_loss: 0.2306 - val_mae: 0.3268\n", "Epoch 62/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 868us/step - loss: 0.2304 - mae: 0.3365 - val_loss: 0.2292 - val_mae: 0.3309\n", "Epoch 63/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 860us/step - loss: 0.2308 - mae: 0.3392 - val_loss: 0.2284 - val_mae: 0.3296\n", "Epoch 64/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 856us/step - loss: 0.2292 - mae: 0.3371 - val_loss: 0.2280 - val_mae: 0.3275\n", "Epoch 65/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 881us/step - loss: 0.2296 - mae: 0.3366 - val_loss: 0.2279 - val_mae: 0.3297\n", "Epoch 66/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 890us/step - loss: 0.2280 - mae: 0.3371 - val_loss: 0.2263 - val_mae: 0.3255\n", "Epoch 67/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 885us/step - loss: 0.2294 - mae: 0.3387 - val_loss: 0.2296 - val_mae: 0.3309\n", "Epoch 68/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 888us/step - loss: 0.2279 - mae: 0.3356 - val_loss: 0.2277 - val_mae: 0.3275\n", "Epoch 69/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 876us/step - loss: 0.2264 - mae: 0.3359 - val_loss: 0.2270 - val_mae: 0.3274\n", "Epoch 70/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 888us/step - loss: 0.2261 - mae: 0.3357 - val_loss: 0.2272 - val_mae: 0.3274\n", "Epoch 71/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 913us/step - loss: 0.2264 - mae: 0.3332 - val_loss: 0.2267 - val_mae: 0.3258\n", "Epoch 72/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 912us/step - loss: 0.2237 - mae: 0.3357 - val_loss: 0.2257 - val_mae: 0.3277\n", "Epoch 73/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 869us/step - loss: 0.2275 - mae: 0.3368 - val_loss: 0.2274 - val_mae: 0.3255\n", "Epoch 74/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 867us/step - loss: 0.2278 - mae: 0.3345 - val_loss: 0.2270 - val_mae: 0.3252\n", "Epoch 75/75\n", "\u001b[1m387/387\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 872us/step - loss: 0.2265 - mae: 0.3361 - val_loss: 0.2251 - val_mae: 0.3234\n", "\n", "Training complete.\n", "Final training loss: 0.2301\n", "Final validation loss: 0.2251\n", "\n", "CPU times: user 44.4 s, sys: 4.55 s, total: 48.9 s\n", "Wall time: 45.6 s\n" ] } ], "source": [ "%%time\n", "\n", "sequential_history = sequential_model.fit(\n", " training_df[features],\n", " training_df[label],\n", " epochs=75,\n", " batch_size=32,\n", " validation_split=0.2,\n", " verbose=1\n", ")\n", "\n", "print('\\nTraining complete.')\n", "print(f'Final training loss: {sequential_history.history[\"loss\"][-1]:.4f}')\n", "print(f'Final validation loss: {sequential_history.history[\"val_loss\"][-1]:.4f}\\n')" ] }, { "cell_type": "markdown", "id": "d74f3b7e", "metadata": {}, "source": [ "### 3.3. Learning curves" ] }, { "cell_type": "code", "execution_count": 15, "id": "e0f48c27", "metadata": {}, "outputs": [ { "data": { "image/png": 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b3MkT5LoqFjZJz5jwMGPGDGbNmrXftlmzZjFjxowmz33zzTdJTU1t1X3rN8q33347J5xwQquuZVouwZswXVppbbEx4cDa4k6eIEf5IojyiQ2xMCZMnHfeebzxxhtUVVUBsHHjRrZt28azzz5LdnY2I0eO5Ne//nWD5/br14/du3cD8Ic//IEhQ4ZwzDHHsGrVqr3H/Pvf/+aII45g7NixnHvuuZSVlfH5558ze/ZsfvrTnzJu3DjWrVvHzJkzefHFFwF4//33GT9+PKNHj+aKK66gsrJy7/1+/etfM2HCBEaPHs3KlSuD+aPp0OrGINty08aEB2uLO3kdZHC9yJYgG9OAt26BHUva9prdR8Mpf250d3p6OpMmTeKtt97irLPOYtasWVxwwQX8/Oc/Jz09ndraWr75zW+yePFixowZ0+A15s2bx6xZs1i4cCE1NTVMmDCBiRMnAnDOOefwve99D4Bf/vKXPPLII/zwhz/kzDPP5PTTT+e8887b71oVFRXMnDmT999/nyFDhnDZZZfxj3/8gx/96EcAZGRkMH/+fB588EHuuusuHn744Tb4IXU+e8cgWw+yMQeytjgkbXGn7kEGNw7ZhlgYEz4CH+3VPdJ7/vnnmTBhAuPHj2fZsmX7PYKr75NPPuFb3/oW8fHxJCcnc+aZZ+7dt3TpUo499lhGjx7N008/zbJlyw4ay6pVq+jfvz9DhgwB4PLLL+fjjz/eu/+cc84BYOLEiWzcuLG1X7nTsx5kY8JPZ2+LO30Pcly0z5aaNqYhB+ldCKazzjqLG2+8kfnz51NWVkZ6ejp33XUXc+bMIS0tjZkzZ1JRUdGqa8+cOZNXX32VsWPH8vjjj/Phhx8eUqwxMTEA+Hw+amosuWstq2JhzEFYW9ykYLTF1oNsQyyMCSuJiYlMmzaNK664ghkzZlBUVERCQgIpKSns3LmTt95666DnH3fccbz66quUl5dTXFzMa6+9tndfcXExPXr0oLq6mqeffnrv9qSkJIqLiw+41tChQ9m4cSNr164F4Mknn+Qb3/hGG31TU2dvD3Kl/SPDmHDR2dvioCbIIjJdRFaJyFoRuaWB/TNFJFdEFnqv7wbsu1xE1nivy4MVoxuDbKs3GRNOZsyYwaJFi5gxYwZjx45l/PjxDBs2jIsvvpijjz76oOdOmDCBCy+8kLFjx3LKKadwxBFH7N33u9/9jsmTJ3P00UczbNiwvdsvuugi/vKXvzB+/HjWrVu3d3tsbCyPPfYY559/PqNHjyYiIoKrr7667b9wJxcfXbeqqXVWGBNOOnNbLKoanAuL+IDVwIlADjAHmKGqywOOmQlkq+p19c5NB+YC2YAC84CJqprf2P2ys7O1rgZfS1z00Bf4/fD81Ue1+FxjOpoVK1YwfPjwUIfR4TT0cxWReaqaHaKQgqa1bfGQX7zFFcf055ZThjV9sDEdnLXFwdGStjiYPciTgLWqul5Vq4BZwFnNPPdk4F1V3eMlxe8C04MRpA2xMMaY0IuP8dlCIcaYsBHMBLkXsCXgc463rb5zRWSxiLwoIr1bcq6IXCUic0Vkbm5ubquCjIu2BNkYY0ItITrSFgoxxoSNUE/Sew3op6pjcL3ET7TkZFV9SFWzVTU7MzOzVQHERlkVC2MCBWvYVWdlP8/miY+2HmRjAlnb0bZa+vMMZoK8Fegd8DnL27aXquapaqX38WFgYnPPbStWB9mYfWJjY8nLy7OGuY2oKnl5ecTGxoY6lLAXHxNJqXVWGANYW9zWWtMWB7MO8hxgsIj0xyW3FwEXBx4gIj1Udbv38Uxghff+HeCPIpLmfT4JuDUYQdoYZGP2ycrKIicnh9YOWTIHio2NJSsrK9RhhL2EaB9lVubNGMDa4mBoaVsctARZVWtE5DpcsusDHlXVZSJyOzBXVWcD14vImUANsAeY6Z27R0R+h0uyAW5X1T3BiLNuDLKqIiLBuIUxh42oqCj69+8f6jBMJxQfHUlBWXmowzAmLFhbHHpBXUlPVd8E3qy37baA97fSSM+wqj4KPBrM+MCNQVaFyho/sVG+YN/OGGNMAxKsioUxJoyEepJeyMV5SXGlLRZijDEhEx/tszHIxpiwYQly3QpONg7ZGGNCJj460sYgG2PChiXIUZYgG2NMqCVE+yirrsXvt1n7xpjQ6/QJct24Y6uFbIwxoRMfE4kqVNRYW2yMCb1OnyDbEAtjjAm9BK8tttX0jDHhwBJkrwfZFgsxxpjQiY92RZXsaZ4xJhxYgmxDLIwxJuQSYrweZCv1ZowJA5YgR7sfgQ2xMMaY0InzepCtFrIxJhx0+gQ5JtLGIBtjTKjZGGRjTDjp9Aly3SQ9G4NsjDGhE289yMaYMGIJso1BNsaYkNs7Btl6kI0xYaDTJ8ixtlCIMcaEnPUgG2PCSadPkH0RQnRkhCXIxpgOS0Smi8gqEVkrIrc0sH+miOSKyELv9d2AfZeLyBrvdXmwYtxXxcLaYmNM6EWGOoBwEBflo8IaZWNMByQiPuAB4EQgB5gjIrNVdXm9Q59T1evqnZsO/BrIBhSY552b39Zxxkb6EIEya4uNMWGg0/cgg5cgV/tDHYYxxgTDJGCtqq5X1SpgFnBWM889GXhXVfd4SfG7wPRgBBkRIcRH+SirtCEWxpjQswQZV8nChlgYYzqoXsCWgM853rb6zhWRxSLyooj0buG5bSIuOtKGWBhjwoIlyLiJepYgG2M6sdeAfqo6BtdL/ERLThaRq0RkrojMzc3NbXUQCTE+m6RnjAkLliADcVERVgfZGNNRbQV6B3zO8rbtpap5qlrpfXwYmNjcc73zH1LVbFXNzszMbHWg8dGRVubNGBMWLEHGG2Jhj/WMMR3THGCwiPQXkWjgImB24AEi0iPg45nACu/9O8BJIpImImnASd62oEiIth5kY0x4sCoWuEl6BWXVoQ7DGGPanKrWiMh1uMTWBzyqqstE5HZgrqrOBq4XkTOBGmAPMNM7d4+I/A6XZAPcrqp7ghVrfEwkheXWFhtjQs8SZGwMsjEmvHml2q5X1b+15nxVfRN4s9622wLe3wrc2si5jwKPtua+LZUQ7WNHYXl73MoYYw7KhlhgdZCNMeFNVWuBGaGOI9hsDLIxJlxYDzJW5s0Yc1j4TETuB54DSus2qur80IXUtqyKhTEmXAQ1QRaR6cC9uHFvD6vqnxs57lzgReAIVZ0rIv1wk0RWeYd8qapXByvOOBtiYYwJf+O8P28P2KbA8e0fSnDERfusDrIxJiwELUFu7vKmIpIE3AB8Ve8S61R1XLDiCxTrraTn9ysREdIetzTGmBZR1WmhjiHYEqIjqarxU13rJ8pnIwCNMaETzBaoucub/g64A6gIYiwHFRvlA6CyxpabNsaEJxFJEZG76xbkEJG/ikhKqONqS/HRri0us15kY0yIBTNBbnKJUhGZAPRW1TcaOL+/iCwQkY9E5NiGbtBWqzfFRbkfgw2zMMaEsUeBYuAC71UEPBbSiNpYQox7qGnjkI0xoRaySXoiEgHcjVdvs57tQB9VzRORicCrIjJSVYsCD1LVh4CHALKzs7W1scR5vRaWIBtjwthAVT034PNvRWRhqIIJhroeZKtkYYwJtWD2IDe1RGkSMAr4UEQ2AkcCs0UkW1UrVTUPQFXnAeuAIcEKtG6Iha2mZ4wJY+UickzdBxE5GuhQRYMTol2fjbXFxphQC2YP8t7lTXGJ8UXAxXU7VbUQyKj7LCIfAjd5VSwygT2qWisiA4DBwPpgBRrnJcgV1oNsjAlfVwP/CRh3nA9cHsJ42lx8jNeDbEMsjDEhFrQEuZnLmzbmOOB2EakG/MDVwVzetG6IhSXIxphw5FUF+raqjhWRZID6Q846groeZBuDbIwJtaCOQW5qedN626cGvH8JeCmYsQWq60G2McjGmHDkPU07xnvf4RLjOjYG2RgTLmwlPWwMsjHmsLBARGYDL7D/Snovhy6kthVvVSyMMWHCEmSsioUx5rAQC+Sx/8p5CnSYBDnBepCNMWHCEmRskp4xJrx5Y5DzVPWmUMcSTPE2BtkYEyZsLU8CxiDbEAtjTBhS1Vrg6FDHEWzRkRFE+cRW0jPGhJz1IBM4xMKWmjbGhK2FHX0MMrheZEuQjTGhZgkyEBNpS00bY8Jehx+DDG4ccmmlDbEwxoSWJciAiBAX5bMxyMaYsKWq3wl1DO0hLtpnPcjGmJCzMcieuGifjUE2xoQtERkiIu+LyFLv8xgR+WWo42prCTGRtpKeMSbkLEH2xEX5bIiFMSac/Ru4FagGUNXFwEUhjSgI4qN9lFmZN2NMiFmC7ImJirAE2RgTzuJV9et62zpcV2tCtPUgG2NCzxJkT1yUjwobYmGMCV+7RWQgbmIeInIesD20IbW9+JhIG+5mjAk5m6TnsSEWxpgwdy3wEDBMRLYCG4BLQhtS20uI9lkPsjEm5FqUIItIAlDhFa3vUOKstJAxJoyp6nrgBK8djlDV4lDHFAzx0ZE2BtkYE3IHHWIhIhEicrGIvCEiu4CVwHYRWS4ifxGRQe0TZvDFRvlsoRBjTNhT1dKOmhwDJMS4HmRVDXUoxphOrKkxyB8AA3Ezp7uram9V7QocA3wJ3CEilwY5xnZhdZCNMSb04qJ9+BUqa6zDwhgTOk0NsThBVavrb1TVPcBLwEsiEhWUyNpZXJTVQTbGmFBLiHa/lkora4iN8oU4GmNMZ9VUD/KxdW9EpH/gDhE5B6ChBPpwFBdtk/SMMeFLROJF5Fci8m/v82AROT3UcbW1+GiXFJfaOGRjTAg1lSDfFfD+pXr7OtQKTrFWxcIYE94eAyqBo7zPW4Hfhy6c4MhIjAFgV3FFiCMxxnRmTSXI0sj7hj4f1uKifFTV+Kn128QQY0xYGqiqd7JvJb0yOlg7DDAwMxGAtbtKQhyJMaYzaypB1kbeN/T5sBYX7X4UNlHPGBOmqkQkjn0LhQzE9Sh3KL3S4oiJjGBdriXIxpjQaWqS3gARmY3rpah7j/e5f+OnHX7ivMkg5dW1JMTY+inGmLDzG+BtoLeIPA0cDXwnpBEFgS9C6J+RwLrc0lCHYozpxJrKBM8KeH9XvX31Px/W6mZLWyULY0w4UtX/isg84EhcJ8UNqro7xGEFxaCuiSzOKQx1GMaYTuygCbKqfhT42SvpNgrYqqq7ghlYe4vzZk7bEAtjTDgSkfdV9ZvAGw1s61AGZibyxpLtVFTXWqk3Y0xINLWS3j9FZKT3PgVYBPwHWCAiM5q6uIhMF5FVIrJWRG45yHHnioiKSHbAtlu981aJyMnN/katFDjEwhhjwoWIxIpIOpAhImkiku69+gG9QhxeUAzsmogqbNhtwyyMMaHRZB1kVV3mvf8OsFpVRwMTgZsPdqKI+IAHgFOAEcAMERnRwHFJwA3AVwHbRgAXASOB6cCD3vWCJs6GWBhjwtP3gXnAMGC+934e8H/A/c25QGs7K0Skn4iUi8hC7/XPQ/42zTDIq2RhE/WMMaHS1BjkqoD3JwIvAKjqDpEmqwtNAtaq6noAEZmFG9O8vN5xvwPuAH4asO0sYJaqVgIbRGStd70vmrppa8VYD7IxJgyp6r3AvSLyQ1X9e0vPD+isOBHIAeaIyGxVXV7vuAM6KzzrVHVcq4JvpQGZCYhYqTdjTOg01YNcICKni8h43IzptwFEJBKIa+LcXsCWgM851HscKCITgN6q+gb7a/Jc7/yrRGSuiMzNzc1tIpyDq+tBtjHIxpgwVSgil9V/NeO8vZ0VqloF1HVW1FfXWRHyFTpio3xkpcVZJQtjTMg0lSB/H7gOt4LTj1R1h7d9v4kirSEiEcDdwE9aew1VfUhVs1U1OzMz81DCCZik5z+k6xhjTJAcEfA6Flf27cxmnHconRUA/UVkgYh8JCLHNnSDtuysqDMwM5F11oNsjAmRpqpYrMaNAa6//R3gnSauvRXoHfA5y9tWJwlXEeNDb7hGd2C2iJzZjHPbnE3SM8aEM1X9YeBnEUnF9QYfkoDOipkN7N4O9FHVPBGZCLwqIiNVtahebA8BDwFkZ2e3ySJSgzIT+XJ9Hn6/EhHR4RYMNMaEuYMmyCJy38H2q+r1B9k9BxgsIv1xye1FwMUB5xYCGQH3+hC4SVXnikg58IyI3A30BAYDXx/8qxwam6RnjDnMlNK8BZta3VmhqnPxVutT1Xkisg4YAsw99PAPbmDXRCqq/WwtKKd3enywb2eMMftpapLe1cBS4HlgG644fbOoao2IXIfrafYBj6rqMhG5HZirqrMPcu4yEXkeN6GvBrhWVYOaucZ6S01bD7IxJhyJyGt4y0zjhseNwLXNTTmUzopMYI+q1orIAFxnxfo2+DpNGuhVslibW2IJsjGm3TWVIPcAzgcuxCWqzwEvqmpBcy6uqm8Cb9bbdlsjx06t9/kPwB+ac5+2EO2LIEJskp4xJmwFrl5aA2xS1ZymTjqUzgrgOOB2EakG/MDVqrqn9V+h+QZ19Uq97Sph2tCu7XFLY4zZq6kxyHnAP4F/ikgWrudhuYj8TFWfbI8A24uIkBAdSVF5dahDMcaYA9Rf2bSF57aqs0JVXwJeau19D0V6QjRp8VFWC9kYExJN9SADe2c4z8DV0XwLV6S+w+nTJZ6NeWWhDsMYY/YSkWL2Da3YbxegqprcziG1m0FdE1m3y0q9GWPaX1OT9G4HTgNW4GZL36qqNe0RWCgMyExk4Zb8UIdhjDF7qWpSqGMIlYGZiby7fGeowzDGdEJN1UH+JZAKjAX+BMwXkcUiskREFgc7uPY2MDOBnPxyG4dsjAlLIjJWRK7zXmNCHU+wDeqaSF5pFfmlVU0fbIwxbaipIRbNKSHUYQzMTEQVNuaVMqx7h31qaYw5DInIDcD3gJe9TU+LyEOtWX76cFFXyWJdbgnZCekhjsYY05k0lSBvVtWDFn0XEWnqmMPFgMwEANbtsgTZGBN2rgQmq2opgIjcAXwBdPgEee2uErL7WYJsjGk/TQ2x+EBEfigifQI3iki0iBwvIk8AlwcvvPY1IGNfb4UxxoQZAQLHf9XSgtr0h6NeaXHEREZYm2yMaXdN9SBPB64AnvWKzBcAsbhamv8F7lHVBUGNsB3FRfvolRrHemuMjTHh5zHgKxF5BZcYnwU8EtqQgssXIfTPSGBdrlWyMMa0r6bqIFcADwIPikgUbrWl8uYuFHI4GpBpjbExJvyo6t3eKnfHeJu+0yE6KMrz4aXvwfhLYeTZB+we3C2JeRvbZW0SY4zZq6khFnuparWqbu/IyTG4MW/rc0voIMOqjTEdhIgMBJap6n3AEuBYEUkNbVRtICYZNn4COXMa3D2hTyrbCivYVlDezoEZYzqzZifIncXAzARKq2rZWVQZ6lCMMSbQS0CtiAzCrXDaG3gmtCG1gQgfZAyG3JUN7j7Cm5w3d5PVqDfGtB9LkOsJLCtkjDFhxO8t1HQOcL+q/hToEeKY2kbmcMhd1eCuYd2TSIj2MdeGWRhj2lGzEmQRSRCRCO/9EBE50xuT3OEM8BJkm6hnjAkz1SIyA7gMeN3b1jHa4cyhULgFKosP2BXpi2B8nzTmbLQeZGNM+2luD/LHQKyI9MJVr/g28HiwggqlbskxJET7bKKeMSbcfAc4CviDqm7wKgs9GeKY2kbmMPdn7uoGd2f3S2PljiKKKqrbMShjTGfW3ARZVLUM92jvQVU9HxgZvLDayWf3wf9dt98mEWFg10QbYmGMCSuquhy4CVgmIqOBrap6R4jDahtdh7s/DzIOWRXm2zhkY0w7aXaCLCJHAZcAb3jbfMEJqR3lb4CVrx+weUBGAuutB9kYE0ZE5DRgHXAfcD+wVkROCW1UbSS1L/hiIHdFg7vH9U7FFyHMswTZGNNOmpsg/wi4FXhFVZeJyADgg6BF1V7S+rkanOUF+20emJnI1oJyyqpqQhKWMcY04K/ANFWdqqrfAKYBfwtxTG3DF+lVsmh4ol5CTCQjeyYzxybqGWPaSbMSZFX9SFXPVNU7vMl6u1X1+iDHFnxp/dyfBZv227xvop71Ihtjwkaxqq4N+LweOHBW2+Eqc1ijQywAJvZNY+GWAqpq/O0YlDGms2puFYtnRCRZRBKApcByEflpcENrB3UJcv7G/TYP7JoAwPrdliAbY0JLRM4RkXOAuSLypojMFJHLgdeAhlfXOBxlDoOCzVDZ8PyPI/qlU1HtZ9m2wnYOzBjTGTV3iMUIVS0CzgbeAvrjKlkc3hpJkPt1SUAE1u2yiXrGmJA7w3vFAjuBbwBTgVwgLnRhtbHMoe7P3Y1UsuibBsBcK/dmjGkHkc08Lsqre3w2rkB9tYgc/msxx6ZAbCrk7z/EIjbKR1ZanPUgG2NCTlW/E+oY2sXeSharoNeEA3cnx9K3SzxzN+3hewxo5+CMMZ1NcxPkfwEbgUXAxyLSFygKVlDtKq3fAT3I4CbqWQ+yMSZciEgscCWuxGZs3XZVvSJkQbWltP4QEdVoJQuA7L7pfLhqF6qKiLRjcMaYzqa5k/TuU9VeqnqqOptwM6gPSkSmi8gqEVkrIrc0sP9qEVkiIgtF5FMRGeFt7yci5d72hSLyzxZ/s+ZqJEEekJHI+t0l+P2Hf0e5MaZDeBLoDpwMfARk0ZEm6TVRyQLcgiF5pVVssKd7xpgga+4kvRQRuVtE5nqvvwIJTZzjAx4ATgFGADPqEuAAz6jqaFUdB9wJ3B2wb52qjvNeVzf3C7VYWj83McRfu9/mgV0TqKj2s72oImi3NsaYFhikqr8CSlX1CeA0YHKIY2pbmcNgV+M9yEf0c+OQrdybMSbYmjtJ71FcT8UF3qsIeKyJcyYBa1V1vapWAbOAswIP8Cb+1UkA2r+7Nq0f+KuhePt+m4d0SwJgSY7NmDbGhIW6dZYLRGQUkAJ0DWE8ba+ukkVVwz3EAzMT6ZUaxyOfbqCyprbBY4wxpi00N0EeqKq/9pLd9ar6W2hylkQvYEvA5xxv235E5FoRWYfrQQ6srdxfRBaIyEcicmwz42y5tL7uz3rDLMZmpZIQ7eOj1blBu7UxxrTAQyKSBvwSmA0sBzrGUtN1ug4DFHavaXC3iPD7b41i9c4S7n2v4WOMMaYtNDdBLheRY+o+iMjRQHlbBKCqD6jqQOBnuIYfYDvQR1XHAz8GnhGR5PrnishVdcM+cnNbmcg2UuotOjKCYwZn8JE3IcQYY0JJVR9W1XxV/VhVB6hqV1X9V6jjalOZw9yfB1kwZNrQrpw/MYt/frSORVsK2icuY0yn09wE+WrgARHZKCIbgfuB7zdxzlagd8DnLG9bY2bhysihqpWqmue9nwesA4bUP0FVH1LVbFXNzszMbOZXqSelN0hEgxP1pg3tyrbCClbvtGoWxhgTdOkDvEoWjSfIAL88fQRdk2K56YVFNtTCGBMUza1isUhVxwJjgDFez+7xTZw2BxgsIv1FJBq4CPdYcC8RGRzw8TRgjbc905vkh4gMAAbjllVte74oSMlqMEH+xlCXdH+4aldQbm2MMSaALwq6DDpoJQuAlLgo/nTuaNbssqEWxpjgaG4PMuAm1QVMrPtxE8fWANcB7wArgOdVdZmI3C4iZ3qHXSciy0RkoXe9y73txwGLve0vAleravCmLaf1O2CxEIAeKXEM657EB5YgG2NM+8gcetBKFnWmDe3KBdk21MIYExzNXSikIU1WaVfVN4E36227LeD9DY2c9xLw0iHE1jKpfWH1Ow3umjq0Kw9/sp7iimqSYqPaLSRjjKlPRKYA/Qhou1X1PyELKBgyh8Hy/4Pqcog6+Eravzx9BB+tzuVnLy1m9nXHEB3Zoj4fY4xp1KG0Jh1n5lpaPyjd1WBpoWlDM6nxK5+t3d3+cRljjEdEngTuAo4BjvBe2SENKhh6jAEUNn/Z5KHJsVH88VujWbmjmAc/XBv82IwxncZBe5BFpJiGE2EBDv5P+8PJ3koWm6Db/muZTOibRlJMJB+uymX6qB7tH5sxxjjZwAjt6GV1Bn4TYlJg0bMwsMkFW/nm8G6cNa4nD3ywllNG9WBo96R2CNIY09EdtAdZVZNUNbmBV5KqHsrwjPCS1t/92cBEvShfBMcOyeDDVblW7s0YE0pLcUtNd2xRsTD6XFg+GyqKmj4e+PUZI0mOjeLmFxdRU+sPcoDGmM7ABmzBvsVCCg6cqAcwdUhXdhRVsHJHcTsGZYwx+8kAlovIOyIyu+4V6qCCYtwlUFMOy19t1uHpCdH85syRLMop5JFPNwQ3NmNMp2AJMkB8F4hObLAHGfaVe7NqFsaYEPoNrlb8H4G/BryaJCLTRWSViKwVkVsOcty5IqIikh2w7VbvvFUicvIhfYPm6jURMobAwmeafcrpY3pw0ohu3P3uatbnWu16Y8yhsQQZQMQr9baxwd3dkmMZ0SOZD1fastPGmNBQ1Y8aejV1nldT/gHgFGAEMENERjRwXBJwA/BVwLYRuBr2I4HpwIN1NeqDSgTGXQybv4C8dc08Rfj92aOIjfJx84uLqfXbkDhjTOtZglznIAkywIkjujFn0x427D6w0oUxxgSbiBwpInNEpEREqkSkVkSaM0h3ErBWVderahVu1dKzGjjud8AdQEXAtrOAWd7qphuAtd71gm/MhW6V00Wzmn1K1+RYbjt9BHM35fP45xuDF5sxpsOzBLlO3WIhjUzEu+TIPkRFRPDwJ8FZ0M8YY5pwPzADt+JoHPBdXM9wU3oBWwI+53jb9hKRCUBvVX2jped6518lInNFZG5ubhs9aUvuCQOPd9Us/M2feHfOhF4cP6wrf3lnJRutQ8MY00qWINdJ7esmhZQ0PM64a1Is507sxQvzcthdUtnOwRljDKjqWsCnqrWq+hhu2MMhEZEI4G7gJ4cQ10Oqmq2q2ZmZmYca0j7jLobCLbDxk2afIiL88VujifJFcPNLi/HbUAtjTCtYglxnby3kjY0e8t1jB1Bd6+c/9ujOGNP+ykQkGlgoIneKyI00rw3fCvQO+JzlbauTBIwCPhSRjcCRwGxvol5T5wbX0NNcTeSFT7fotO4psfzq9BF8vWEPj1l7bYxpBUuQ6zQjQR6YmcgJw7vxny83UVZV0y5hGWOM59u4Nvs6oBSXuJ7bjPPmAINFpL+XYF8E7C0Pp6qFqpqhqv1UtR/wJXCmqs71jrtIRGJEpD8wGPi6Lb/UQUXFwtgLYenLULC5RaeePzGLE4Z3409vruDL9XlBCtAY01FZglwntY/7M//gNTSv/sYACsqqeX7OloMeZ4wxbUlVN+FWMe2hqr9V1R97Qy6aOq8Gl1S/A6wAnlfVZSJyu4ic2cS5y4DngeXA28C1qlp7qN+lRY7+kZus99GdLTpNRLj7wrH07RLPNU/NY8uesuDEZ4zpkCxBrhMVC91Gw+q3D3rYxL7pTOybxsOfbrAVm4wx7UZEzgAW4hJVRGRccxcKUdU3VXWIqg5U1T94225T1QPOV9WpXu9x3ec/eOcNVdW32uTLtERKL8j+jquJ3MySb3WSY6N4+PIjqPUr3/vPXEor7cmfMaZ5LEEONO5i2LYAdi4/6GFXHTeAnPxy3liyvZ0CM8YYfoMrsVYAoKoLgf6hC6cdHfNj8EXDR3e0+NT+GQncf/EEVu8s5ifPL7JJe8aYZrEEOdCYCyAisskJIScO78aQbon86c2V7CmtaqfgjDGdXLWqFtbb1jmyvaRuMPkqWPw87FrZ4tOPG5LJz08dztvLdvCLV5dakmyMaZIlyIESMmDIdFj8HNRWN3pYRIRw9wXj2FNaxU9fWIQ2UjvZGGPa0DIRuRjwichgEfk78Hmog2o3U26A6AT48E+tOv3KY/rzg6kDefbrzdz0wiIbImeMOShLkOsb/20ozYU1/z3oYaN6pXDrqcN4f+UuHv1sY/vEZozpzH6IW/K5EngWKAJ+FMqA2lVCFzjyB7D8Vdi+uMWniwg3Tx/GTScN4eUFW7l+1gKqaixJNsY0zBLk+gadAIndYMFTTR46c0o/ThjejT+/tYIlOfWffBpjTNtR1TJV/YWqHuEtyvELVa1o+swO5KhrIS4NXr0GKktadYnrjh/ML08bzptLdnDNU/OoqG7fohzGmMODJcj1+SJhzIWw+p1GV9WrIyL85bwxZCTGcN2z8ymuaHxYhjHGtIaIzD7YK9Txtau4VDj3Edi1HF75fouWoA703WMH8PuzR/H+yl1cbUmyMaYBliA3ZPyloLVuLHIT0hKiuW/GeDbvKeOBD1pWgsgYY5rhKNwKdp8AdwF/rffqXAZ9E07+I6x8HT74Q6svc+mRffnTOaP5cFUuVz1pSbIxZn+WIDckcyj0yoYFT0MzJuAd0S+ds8f14rHPNrCtoLwdAjTGdCLdgZ/jloO+FzgR2K2qH6nqRyGNLFQmX+3mi3xyFyx5sdWXmTGpD3eeO4ZP1uTyvf/MtSTZGLOXJciNGX8p5K6AjZ806/AfnzgEVfjbu6uDHJgxpjNR1VpVfVtVLweOBNYCH4rIdSEOLXRE4LS7oc8UeOVq+Ndx8MxF8PqPW5wwX3BEb+44dwyfrt3N5Y9+TWGZDZUzxliC3LgxF0BKb3jzZqhputZx7/R4Lp/Slxfn57ByR1E7BGiM6SxEJEZEzgGeAq4F7gNeCW1UIRYZDRc+BUdc6SZWF26BpS/BS1fC3MdadKkLsntzz4XjWLC5gHP+8Rmb82xZamM6u6AmyCIyXURWichaEbmlgf1Xi8gSEVkoIp+KyIiAfbd6560SkZODGWeDohPg1L+4XuQvH2jWKddOG0RSTCR3vNXyQvbGGNMQEfkP8AUwAfitV8Xid6q6NcShhV5CFzjlDrjkBbjmM/jpOhh8ErzxYzfRugXOGteLJ6+cxO6SKr714GfM35wfpKCNMYeDoCXIIuIDHgBOAUYAMwITYM8zqjpaVccBdwJ3e+eOAC7C1fycDjzoXa99DT0Fhp0OH94B+ZuaPDw1Ppprpw3ig1W5fL5udzsEaIzpBC4FBgM3AJ+LSJH3KhYRe1wVyBcJ5z0G3cfACzNh6/wWnT55QBde/sEUEmIimfHQl9z0wiJeWZDDrqLOVU3PGBPcHuRJwFpVXa+qVcAs4KzAA1Q1sHFPYN+yqWcBs1S1UlU34MbcTQpirI2b/meQCHjr5mZN2Lt8Sj96psTyhzdWUFZV0w4BGmM6MlWNUNUk75Uc8EpS1eRQxxd2YhLh4ufdyqjPXAB7NrTo9IGZibzygymcMqo7763YyY3PLWLSH9/n9L9/wrxNe4IUtDEm3AQzQe4FbAn4nONt24+IXCsi63A9yNe38NyrRGSuiMzNzc1ts8D3k9obpt0Kq9+GlW80eXhslI/bzhjBiu1FzHjoS3KLK4MTlzHGmIYldYNLXwZ/DTx1DpS07PdDl8QY7rloPPN/eSKv//AYbj1lGPml1Zz/zy/445srrNqFMZ1AyCfpqeoDqjoQ+Bnwyxae+5C3olR2ZmZmcAIEV1Ko60g3ri2v6VrH00f14F/fzmbVzmLO+cdnrN3VuhWfjDHGtFLGYNeTXLTdJckVLR+NEhEhjOqVwve/MZB3bjyOiyb14aGP13PafZ+wcEtB28dsjAkbwUyQtwK9Az5nedsaMws4u5XnBpcvCs59GGqr4fHTm5UknziiG7OuOoryqlrO/cfnfLU+rx0CNcYYs1fvSXDhk27lvVkXQ3XrxxInxkTyx2+N5j9XTNrbrt/73hpqalu3mp8xJrwFM0GeAwwWkf4iEo2bdLffsqgiMjjg42nAGu/9bOAir7RRf9wEla+DGGvTuo2Ay1+D2kp4/DTYvbbJU8b1TuWVHxxNl8RoLn3kK16Yu6XJc4wxxrShwSfC2f9wNe1f/q7r6DgExw3J5O0bj+OMMT3423urufChL60snDEdkGgzJp61+uIipwL3AD7gUVX9g4jcDsxV1dkici9wAlAN5APXqeoy79xfAFcANcCPVPWtg90rOztb586dG7TvstfO5fDEGRAR6RLmzCFNnlJYVs21z8zn07W7+f5xA7h5+jB8ERL8WI0xYUtE5qlqdqjjaGvt1ha31Jf/gLdvccPlTr8b+hx5yJf8v4Vb+eUrS/GrMr5PGrFRPuKjfaTGRzF1aCZHD8ogJrL9CzAZY5qvsbY4qAlye2rXRnnXCpck11a5kkKDvtnkKdW1fm5/bTlPfrmJE4Z35d6LxpMQE9kOwRpjwpElyCGw8g1462duUZFxl8KJv3XVLg5BTn4Zd7y9iq35ZZRX+6mormVXUQWlVbUkxURywohuzJjUh0n909voSxhj2pIlyG0tfyM8e7FbSOSk38ORP3DLnzbhP19s5LevLWdi3zSe+M4k4qKtd8GYzsgS5BCpKoWP/wKf/92V8Ow2CnpNgF4TYeDxkNT90G9R4+ezdbt5a8l2/rt8J0Xl1fzxW6O5aFKfNvgCxpi2ZAlyMFSWwKtXw4rXYOzFcPIfIL7pXoLZi7bxo1kLmDIwg4cvzyY2ypJkYzobS5BDbNdKWPg0bFsA2xZCVTH4YmDCZXDMjZByQGXRVimrquEHT8/nw1W53HTSEK6dNghpRmeKMaZ9WIIcLH4/fHwnfPgniIiCYae6R3cDj3erOjXixXk53PTCIo4f1pV/XjqR6MiQV9wzxrQjS5DDiN/vngZ+9S+XNEsEjL8Upv4cEg+9hGh1rZ+bX1zMKwu2cvlRfbnxxCFU1yo1fj/Rvgi6JMa0wZcwxrSGJcjBtnMZLHgKFj8HZXmQ1h8uecHV4mzE019t4hevLOXkkd144OIJRPosSTams7AEOUwVbIZP/wbzn4S4VFcBY/CJ+/b7/bD2XYhJhr5HNfuyfr/yp7dW8O9PDlzZ74ZvDuZHJwy2nmVjQsAS5PZSUwWr3oQ3b3JLU1/6EvQc1+jhj366gdtfX86F2b3587mjrYE0ppOwBDnM7VwGL33X1VCe9H2Yegssexm+eBD2rHNPDC96Boac1KLL/nfZDnLyy4mKjCAqQvhyfR6vLtzGWeN6cse5Y2zInTHtrLG22MootLXIaBh5tpv48eTZrtrFjFnQ7+gGD7/imP7kl1Xx9/+tpVtyDD8+aWi7hmuMMaYB3UbC9/4H7/0GvvonzPk3qB96ToBvPQRfPgjPXQoXPwcDpzX7sieN3H8S4IVH9GZI9yTufHsVOfnl3HX+WLYVlLN0ayErthfROz2eC7J70zs9vo2/oDHmYKwHOZgKc+DJb7lHdmf/A0Z+q8FKF6rKLS8t4bm5W/jd2aP49pF9QxCsMaY9WQ/yYWTNe7D6bRh1rqufLAJle9zKqvkb3JPCvlMO6RZvLtnOjc8tpLJm38p83ZJjyC2uxK9wzKAMLjyiNycM72bVj4xpQzbEIlRKd8PT57mZ0v2OdSXhGhhyUVPr5+qn5vH+yl38cNoguibHEu2LIDoyghE9kxncNdGGXxjTgViC3AGU7HIrqxZtd4uPjDznwMnZ1RUQ4QNfVJOXW76tiM/X7WZo9yRG9kwhPSGabQXlvDgvh+fmbGFrQTlxUT6mDctk+qgeTBnYhShfBHi/xpNiI4mwRaiMaRFLkEOpthrmPe4qXZTtgbEzYNQ5bhhGUve9vcrlVbXMfOxrvtqw54BL9EiJ5djBGZwwvBsnjuhmybIxhzlLkDuIom3w9Pmwcymk9IGjfuCeFm78FFbMhjXvQnSCG6/ce1Krb+P3K1+uz+OtpTt4e9kOcosrDzhmSLdE/nTOGCb2TTuUb2RMp2IJcjgoL4BP/urGs9VWuW3xGe6R3al3QXIPVJWCsmqqa/1U1fopr6pl3qZ8Pl6Ty6drdlNUUcMtpwzj6m8MDOlXMcYcGkuQOxC/3w3B+Pw+2PzFvu0JXWHYabD+Q5dIn/UAjDn/kG9X61fmb85ncU4hAAJU1fr5z+cb2V5UweVH9eOmk4eS2MBqrdsKynl14VZiI31cPLmPTQo0nZ4lyOGkohB2LHU9DjsWw9JXIC4NLnneTQxpRE2tnx89t5A3lmznX5dOPGCyhzHm8GEJcge1ZY5LiPsdDb0nu+EVZXvchL5Nn8FxN8PUWyGi7ct6llTWcNc7q3jii410T45l6tBMeqfH0yc9nppa5aX5OXy6djd1v/Z7psTyk5OG8q3xvWxohum0LEEOZ9sXwzMXuJX5LngcBp3Q6KEV1bVc+K8vWLOrhBeuPoqRPVPaL05jTJuxBLmTqamC12+EhU/BkFNcb3JCl6Dcav7mfO58eyVrdpaQV1q1d3uv1DjOnZjF+ROzyMkv549vrmDJ1kKG90hmTK8Uavxu8RJfhDC6VwoT+6YxvEeyG+dsTAdlCXK4K9wKz1zoam5OvQXGXQwpWQ0euquogjPv/4wIgeevPoo9pVUs3VrEyh1FpMVHM75PKuN6p5IaH93OX8IY01yWIHdCqm6I3X9/BQkZcM5D0P+4oN6ypLKGnPwyyqtqGZuVul9Psd+vvLZ4Gw98sJbC8moiIyKI9AnlVbXs8sY4x0ZFcOKI7vz+7FGkxO0/0fC1Re7c204fwZRBGUH9HsYEiyXIh4PKYnj5KrfQCLjHcyPOdhM7ugx0wzA8S7cWcv4/v6C8unbvtoRoH2XVtXsfnw3qmsiVx/Tn/IlZtkqfMWHGEuRObPsiePEKyFsHR98Aw8+A9AEQnx7qyPbaXljO/E0FfLUhj2e+2kxWWhwPXZbNkG5J+P3K3e+u5v4P1hLti0AE/vXtiUwd2jXUYRvTYpYgH07y1sGyV2DZq7Bzyb7tcemQOQxGnAWjz2NubgQfr85lWI9kRvZMpndaPKVVNSzJKWTBlgL+u3wni7YUMCAzgZ+eNJTpo7pb9QtjwoQlyJ1cZQm8/TNY8NS+bXFprmPkpD9AxqCWXW/HEoiKd50pbWzOxj384On5lFbW8PuzR/HW0h28u3wnF2b35scnDeGKx+ewZmcJ91883ubGmMOOJciHqz3rYdcKlzTvWQdb57mGMCISBp3oZkhnDHa9DwmZ+y1Eoqq8u3wnf3lnFWt2lTAwM4FJ/dMZm5XK2N6pDO2WZBMzjAkRS5AN4Nr23avdn3lr3XLW1RVw3E9d73JkE0Pl/H5XPeP92yEyBs64F8Zc0OZh7iyq4Oqn5rFgcwG+COFXpw3n8in9EBEKy6q57LGvWba1kJ+fOpyRPZPpkhhNekIMsVH7P730RQiRERH47HePCROWIHckO5fD4lmw+Hko3r5ve3SSe1Q39RZI27caX61feXl+DrMXbWPRlgKKKmoAOKJfGv/6djbpCTZW2Zj21p4JsohMB+4FfMDDqvrnevuvBq4FaoES4CpVXS4i/YAVwCrv0C9V9eqD3atTtcXBULzT9SwvewUyh8Mpd7hxyg09/SvbA69e40rMjTjLLUy16TOYdJXrhW4quW6hyppa/v3xeib0TWPKwP3HHBdXVHPl43P5euOBdfwbIgJZaXHcc+H4g9ZtrvUrbyzZzvJtRVx2VF96psYd0ncwpj5LkDsify3kb3S9zHnrXNm4JS+47RNnuh6IpG77naKqbMwr46NVu/jjWyvpkRLLozOPYGBmYki+gjGdVXslyCLiA1YDJwI5wBxghqouDzgmWVWLvPdnAj9Q1elegvy6qo5q7v06ZVscDKvehjdvgsIt0Oco+MbNMGCa27d7jUuEP7nbdZKc/EeY9D3w18B7v4Ev7ndDNU74DfQ+Migl5RpSU+tn1c5i9pRW7X1VBSydrbiEt9av1NT6eXXhNrYXlnPbGSO5dHKf/YYAVtbU8tK8rfzr43VsyisDID7ax49OGMx3ju5vlTVMm7EEubMo3Aof/wXm/8cNw+g9yXsdCX0mQ+y+snDzNuVz1X/mUl3r55+XTqR7SiyLcwpZuKWAoopqzhzbk2MHZ9qjMGOCoB0T5KOA36jqyd7nWwFU9U+NHD8DuExVT7EEOcSqK2DBk/Dp36BoK3Qd4Za3Ltvt9qcPgHMehqyJ+5+39GWYfT1UFUNSDzfZu98xbrGSvLVuuF50Igw9FYactN8E8PZUWFbNDc8t4MNVuZw3MYuLJ/dh/qZ85m/O56v1e8grrWJsVgrXTB3EiB7J3P76Mt5bsYsh3RK58YQhHDWwi1VrMofMEuTOJm8dzHkYNn3uxixrLUTGurFpk6+BbiMA2LKnjO88Poe1u0r2nhoX5SPKJxRV1NArNY7zs7MY3SuF4ooaiiuqKa2qZWi3JCb1TyehgZWajDFNa8cE+Txguqp+1/v8bWCyql5X77hrgR8D0cDxqrrGS5CX4Xqgi4BfquonDdzjKuAqgD59+kzctGlTEL9RJ1RTCQufgUXPuqS47xToM8VNyGts4nVlMax+xyXLa9/dt3prdKK7RskuKNkB4nOLmnQb5ZLp5J5uXkuPse3y1fx+5Z7313Df+2v2bstKi2Ni3zTOn9ibowd12a9n+d3lO/nN7GVsLSgH3PLaE/umIwK5xZXkFldS4/dz0RF9OD87i5jIhlcKrKn189An63ni8430TU9gfJ9UxvdJZWLfdDKTYoL7pU1YsQS5M6sscZP7lr0Mi56DmnLo/w0Y9E2Iz6A0MoU319cQ1aUvQwcMYHC3JGq9CX7PzdnCJ2t2N3jZyAhhXO9UpgzswsR+6Yzvk0pybFSDxwKs3VXMU19uZuaUfvTLSAjWtzXmsBBuCXLA8RcDJ6vq5SISAySqap6ITAReBUbWDcdoiLXFYaiiEHJXQWpfSOzqkmq/H7bNh5VvwJr/wp4NUF2675xjb4Ljf9l4At7G5m3KZ1dRBRP6ptEtOfagx1bW1LJoSyFzNu7hqw17WLg5n+jICDISY8hMiqGgrJolWwvpnhzLNVMHcuERvfdbUnvljiJ++sJilmwt5OhBXSitrGXZtkKqa10+NLxHMt8YkslxQzKIi/KxeU8Zm/PKyC2pZGLfNI4f1pWkg/yuM4cXS5CNU7YH5j8BXz8MRTkH7o9OhLR+0GMcfPNXkNSdrQXl7CqqICUuiqTYKGKiIli8pZDP1u3m87W7WbK1EL+6dnRI1ySmj+rONVMH7tcgzduUz5VPzKGgrJqYyAh+fOIQrjymv9VnNp1WGA+xiADyVfWAZTpF5EPgJlVttLG1tvgwpQqVRVC0Hb58wA3TG38pnH4v+LwnhVVlbkJgr4n7TQQPN6rKZ2vzuPf91czZmE9khNArLY6+XRJIj4/ijSXbSY6N4ndnj+LU0T0At0rtsm1FfLUhj49W5TJvUz41/v3zo7goH+XVtUT7Ijh2cAYnjezGlIEZ9E6PP2gsld447MDfiSZ8WIJs9qcKVSVu1nPZHijdBfmb3KS//A2w/kOIioPT/gqjzj3opUoqa1i0pYB5m/L5esMePl27mwEZCfzpnNFMHtCF/63cyQ+enk/35FjuOn8sD328nv8u38moXsn8+ZwxjOply2WbzqcdE+RI3BCJbwJbcZP0LlbVZQHHDFbVNd77M4Bfq2q2iGQCe1S1VkQGAJ8Ao1W10VIF1hZ3AKrw4Z/hoz/D4JPh+F/Awmdh0TOuNzo+Ay553iXKYUxV+WrDHj5Zk8vGPNcLnJNfxjeGZHLbGSMPWsGpuKKar9a7/8z7dImnd1o8MZERzNucz1tLdvD20u1sK6wA3BLekwekkxIXxc6iCnYUVrCzqJLiimrKqmqp8SsRAqN7pTBlUAZHD8ygT3o8JZU1lFbVUFJZQ63Xe61AUmwkk/unN7huwYbdrpe/b3q8lWltIyFJkJtRWujHwHeBGiAXuEJVN3n7aoG6VTI2q+qZB7uXNcptbPcaeOX7bmjGyHNg8vehqtQl1ZUlbjybv8ZVzPDX7HfqivJUrp+bzpp85YTh3fhg1S5G9Ejmse8cQUZiDKrK20t38Kv/W0ZeaSXnjM/iJycNaVH5npLKGmIiI2wmszlstXOZt1OBe3Bt8aOq+gcRuR2Yq6qzReRe4ASgGsgHrlPVZSJyLnC7t92PS5xfO9i9rC3uQOY84ippqB8iomDEma6U6Lu3uc6V859wk/wAKopcFaXKYpjyQ4io11uq6mr6p/WF6MN/iJ2qsnpnCV9tyOPL9Xl8vWEP5VW1dEuJpUdKLN2SYkmOiyIhxkd8dCTlVbV8uT6PhVsKDuiZbsjUoZncce6YvcNNyqtqufOdlTz++UZUITEmkhE9kxnVM4XRWcmM7pXKgIwES5pbod0T5GaWFpoGfKWqZSJyDTBVVS/09pWoarNrj1mjHAS1NfDZPa4nwV/dolM1Kp4ViUfywK5RVPT9JvdefgyJ9Sb0FZZX8+AHa3ns840I8J2j+zOudwq7S1x5oPLqWk4a0Y1xvVP3/ku6orqWRz7dwAMfrGVQ10Qem3kEXRJtQoU5/NhCIeawsOY9yF0BYy5045fB1Wp++jzYucyNU96zHpa+BNWuHBvDz3DVNaK8scRVZfDaDbDkebfa35CTYeS3XCm6/I2uQyZvjRveN3aGe3rZQZVU1jBnwx52l1SSGBNJYmwk8dGRRPkEwf2em7NxD3e+s5KYSB+3nzWSrLQ4bnphMRt2l3LZUX0Z1TOFJVsLWbqtkOXbivYO4UiI9jGyZwrDeyQxrEcyw3skM6pncquGMu4prSI1LqpFCXdNrZ/KGj8iEB99+EzgD0WC3NJxb+OB+1X1aO+zJcjhIm+dawBjktwY5ZhE8MW4MnIRPq+noO5/IoXti12R+xWzoTQX9cUg/Y+FIdNh8EmQ2me/iR85+WX89b+reWXB1v1u64sQav3KqF7JXDq5L3HRPu58exVbC8o5dnAGX2/YQ6+0OJ68cjK9rHi8OcxYgmwOa5XF8PxlsO5/EJUAo8919fc3fwnv/Bz6Hg0XPe2Om3WJq6Z09A3u8/L/21eqrk5EpHsamZAJk6+GI74Lcamh+GZhYX1uCT95YRELNhcAbhjHX84bw5RB+y/QUlPrZ21uCUtyClmytZBl24pYub2I0qpaACb2TePRmUeQErf/pMJP1+zmvRU7mdg3jWMGZZCWEE1lTS1vL93Bk19sYu6mfMZmpXDbGSOY2De90Ti/WJfHLS8vZmt++d6ecV+EcOroHlx17ABGZ7XdEMrCsmqS4yIbHHpyKEKRILd05vT9wA5V/b33uQZYiBt+8WdVfbWBc6y0UDjz17oyc6veglVvurHN4MrNpfZxM6rju7je6dpqSiurKM6ciEy4nNQuGVTV+Hl1wVae+nIzq3YWA2528a9OH86UgS5BvvKJOSTGRPLklZMY1DWpwTBUlapaP34/1Pj9iMgBvdnGtDdLkM1hr7Ya1n0AfY6E2OR925e8CK9c7crJle12x537sOs5Bvd0cuMnkLsS0ge6snKpfVxy/enfXFm66CTInglH/sCVnuuEav3Ko59uYFdxBTecMKTZv7f8fiUnv5yP1uRy+2vLGNItif9cMYku3hDHhz/ZwJ/eWoGI64QSb3z0toJydpdU0a9LPCeP6s6rC7ays6iSM8b25GfTh5KVtm8yYq1fue/9Nfz9f2vol5HA9JHdiY3yERMZwc6iSp6fu4WSyhqOHJDOddMGc8zgjINE3LiK6lpeX7ydJ7/cxKItBZwwvCu/O3sUPVIO7BSrrKlttKzfwYR1giwilwLXAd9Q1UpvWy9V3epNDPkf8E1VXdfY/axRDnOqsHs1bPjYPVIr8CYEVhS6sW2+KJdQ561xvdTjLnHjnrsMRFWZszGfPaVVnDii234LlyzfVsRlj35Njd/PtKFd6Z0WR1a6m0yxfFsRi3MKWbq1kOLK/cdJnzcxi9vPGnlYPQYyHYslyKZDW/8RPHcpJHWHi55xSXBz7VgCn97jSpOKzw3vmPJD6DosaOF2VB+s2sXVT86jd3o8j15+BPe8t5qXF2zl1NHduePcMazdVcIna3bz6drdpMZFcemRfTlmUAYREUJZVQ3//Gg9D328jsoaP0O7JTGxbxoT+qTxwrwtfLl+D+dOcL9L66+JUFRRzXNfb+HRzzawvbCC8ydm8cvTRxzQk+33a4PDOLYWlPPkF5t4bs5m8suqGZiZwLGDM5k1ZzORERH8bPpQZkzqw+Kthby7fCfvLd/JmKxU/npBy+t3h+0QCxE5Afg7Ljne1ci1Hset5vRiY/ezRrmD2L4IvvyH64HwV0NiN8gcCpnDXePYfaxb5CRgjNqmvFJ+PXsZa3aWsL2wnLr5D9G+CIb3SGJ0Vgo9UuKIjBB8EUJOfjlPfLGRQZmJPHjJBAZ3O7DnuW4Cxtcb91BaWbN3AElKXBTnTMgiOtImB5pDYwmy6fDK9rgxx1EHr2vcqPyN8Pn9sOApV78/YygMnQ5DTnErxNafCBhMfj/sWOR+B7X10t2FOW6RliB9n6/W53HlE3Mpq6rBr/DjE4fww+MHNXuowraCcl6al8OcTfnM35RPSWUN8dE+fnfWKM6dmHXQcytrarnv/TX886P1ZCRG86dzRtM1KZb3V+zifyt3snRbESN7JnPc4EyO9XqZH/98I+8s2wHAiSO6cdlR/Zgy0C0YszmvjF+8uoRP1uzeW3bPFyFM7p/O2eN7cUF27xb/fEKRIDentNB44EVcT/OagO1pQJmqVopIBvAFcFbgBL/6rFHuYIq2u0kfu5a7x3C5q1wFDQCJgIwh0HO8KzPUcwJ0HwWRMVTX+tleUEFZRTkDdQtRO+bDtgWudzqtP6S712dFXbn+xZWUVdXyk5OGkJ4QTUllDcUVNazaUczn6/LYXVLZYGiT+qfzj0sm2ORAc0gsQTammUp3u06T1W/Bxk/dWOXYVBgwFQYe714pWfsvarJnvZsLs+xVl6TPeBbi642l3bPBdch4TysbtWslvP4j2PwFTPo+nHpn23yv6gr43+/giwdg0vfg1L+0zXUbsGhLAbe/vpzvHduf6aN6tPo6tX5l9c5iuiRE07WJBV0CLc4p4KYXFrF6p/s9LgLjeqcyvncai3MKWLClgFqvdyslLoqLJvXm20f23W9YRx1V5ZUFW/lyfR5TBmYwbWhXUuJbv3BLqMq8NVVa6D1gNLDdO2Wzqp4pIlOAf+HKCkUA96jqIwe7lzXKHZyqG5axfTHsWOz+3DYfSnPdfvFBZMDEwaoyqPUS3Lg0N+65ePu+60VEUp05kv8V9+bdwl5s8Hdns3Yll1Qyk2KZMrALRw/M4KiBXeiSGL03hHeX7+RnLy0mMymGhy/PZlj3ZFpqw+5S/vTmCkqrahiTlcrYrFRG9UomKSaKiAg3wSHKZyXsOjpLkI1phYpCWPu+e617f1+77otxE/wSurgOkZ1L3fZeE2HHUsgcApfN3pckb1/sKnGU7HSTDE+90w3tC0yyq8vh47/AZ/e5yem9j3RJ+il3uqQ6UHW5+/3ja2aitn0RvHyV6wDqOsJ1Bn3nLbeMeAdVWVPL83O2EBvlY9qwrmQEdDIVllfzxbrdlFfXMn1kD+Ki2+/pgC0UYjoeVfdoatt8N2atutw1jFrrEuIeY70Vn/q5Rq+qzD2yy1vrepW3zkW3zkfqeqYBjYyFhAwkIgp80RAZ7Rrd5F7uld6fJQlHceVzayitrOHyKf3YUVjB2twSNuSWkpEUw8S+aWT3TSO7Xxr9MxL3jpmurvXz70/Wc+97a4iOjKBPejyrdhQ3WhMzKSaS1IQo0uOjyUyKoVuyq6/ZKy2OE0d0b3DChqriV/Ybp23CkyXIxhwiVZdgbvgYCrdAaZ6bFFhTCYNPhBFnucl/a96DWRe7J4+X/Z9LRmdd7CoznfUAfPJXN2lw5LfguJth61w3hnr9B1CWB2MvhpN+5zpbnrvUrSY44zlXA9pfC/Medz3Bid1gxiz3pLIxezbA3Edcz3V8Bpz9APQ5Ch48yiXXV3/aocvchSNLkI1piL/WJc17NrgqG/kb3bg5f7VbDKWmEkp2QdFW9ycKkbGUDz6D32/L5umdWXRPjmNQ10T6ZySwvbCceZvyyS9zdaNjoyIY3DWJod2TWLatiBXbi5g+sju/PWsk3ZJjqaiuZfl2t72i2o/fr9SqUlntJ7+sioKyKvJKq8gtrmRnUcXe6/bPSOD+i8czsue+EjpzNu7hphcWUVBWzcWT+3D5Uf3ontLKsX/1VNX42ZJfRtekGJJiW/8oy+xjCbIx7Wjt+y4pTu7lOlbS+sG3X3ZDM/y18Nm98MEf9i18ldjNDeEY/23of+y+61SWwGOnuCEcp/7FJbo7FrskN3clIHDhk9DvmH3nVFe4nud5j7tVaiUCRp8P0/+8r0d7/Yfwn7PgmBvhhN+0ww/E1LEE2ZhDVVMFO5fAgqe9FaOK8Cf1ICLrCMjKdr3V1RVo7gqKtiyjIncDW+nG0uqefF7cjR1RvbjmtKM4eUzLJxHUqaiu3ZsI55dV86vThnN+dm/uemcVj3y2gay0OEb0SObd5TuJEOGU0T1IiYtkU14ZG/NKKSitZvKALpw4oivHD+tGZpIbt72ruJLc4koGZiYckACvyy3hmqfm7R07lhQTSY/UWE4c0Y3rpg1u10dhHYklyMa0s3X/g2dnQPfRcPHzB45J3r7IrR7b5yjIHLb/cItARdvg38e74R3JveCk37ve5z3r4dmL3J/T/+x6qFe+4ZLz6lJI6Q0TLnNDOVJ6HXjd/7sOFj4D3/sf9BznttVUuaei9XuVVd196oZo1D0pNS1mCbIxbamqzC2EsuZd9zguf+P++xMyXc9E/kYoz99/X3wX1zuR1MMdk9rbfS7YArtXuQmJZXvcY7qMwdBlMHQdDt1GusZYhLySSn7ywiI+XJVLSlwUteWF/Hbwes72fY4vMopdI6/gn1v68sK8HHw+oW96PH27JBAf7eOTNbvZWlCOCHRJiCavtIq6ZiAlLoqrjhvAzCn9SIiJ5I3F27n5xUXERPm48YTBlFXVsr2wgvW7S/l4dS5ZaXH87uxRTBvatT1+6h2KJcjGhEBJrluApLljhRuTu8ol3BMu23/p7PICePEKNz4aXDs/9FQYfjr0/8bBK1WUF8ADk10y3GWQGw5YsMklw+n9XTWnzCFQsNmtMRA4ryaph0vsu45wn9UPKAw7zf2DoCmbvoCSHTDwm/vXtG6pikK39sHIc9wQxcOAJcjGBFNJLmxf6BrKjKFuogi4hq1klxvztme9m1RYsnPfsI3CnICJhhGu0kbmMIhPc8M+dq/etx8gNsU1gPFd0Kh4luf5yd+9naNq5+KrrXTn11S4hrP7aPSoHyJxaa6hzVsLxdvR2BTyNIUVxTGs0r4U9zyG7qlxpMZF8eK8HN5fuYv0hGgm90/nraU7mNAnlQcumXBAYfYv1+fxi1eWsC63lBNHdCMjMZptBRVsKygnyhfBT08eyrRh+yfOH67axYMfrsPvV1Ljo0lPiKJrUixje6cyoU9qiyqDbNhdytb8crqnxNIzNfawq2ltCbIxHVRtDax8DVL6uGpLLSkLt/odt8hKSi/XOdJlkEuqd61wr7y1bsnvvke7CX3dRroJiZu+8JLmbftfLzoRLnnh4JP/5j8Js38IqFuXoP9xLrEefX7DyXLOXPcPjB71ag77a+Hp890/Dnof6YaaJIZ/54klyMaEq+pylzAndXeVOOorz3e9FTuWwM5l7pFaRaEre1dV5mZODz/DFdPPynZjpxc/78bU5a3Zd53YFEjOcueW5u6r8tFrIkz9OQz6JoiwYHM+d/93FfPWbuXCKUO59dQRjdZ9rqyp5aGP1vPPj9YRF+1jeFIFJ0YuoKZwO48UTWbUyFHcdsZIqmv8/P6N5by3Yhd90uPJSosjv6ya/NIqdpdU7p2o2K9LPJP7d2HasK4cMzjjgImI2wvLeX3RdmYv2saSrYX77UuLj2JwtyRG90phTFYK43qn0rdLAoeitLKGW15eQnWNn2umDmRs79RDul4gS5CNMS1WW+MS5oaGU6i6VQslwr1Kd8ETZ7iOmIuf338sdZ05j8AbP4ZBJ7jxz6vfhhWvuzk5sSlw5LWuYkdcKuTMgw9+73rOfTFuAZjBJ+y71v9+76p+jL8UlrzkhrBc+BT0mhC0H0dbsATZmM7G74dNn7qGrMsg11jVNaqqUFnk6oR+fJebAZ41yY1jy1sLeeug0lvlMKm7e8Wlu4Y5wufK6kXFuzF2sckogqz/EHLmAK5N8RPBO/5JPKGnscTfjz6+PK4dH830Pkpk5mA3xi4yhorqWpZuLWTepnzmbsrny/V5FFfUEOUTjuiXTqQvgp2FFWwvLKeowk2gGZOVwpljezKyZwo7iyrYWlBOTn45K3cUsXxbEZU1fgBOH9ODX5424qCTFbfsKePTtbs5dnDGfjU3dxVXcOXjc1m2rZCEmEiKK2o4dnAGP5g6iEFdE1H2tZ1dEmJaXDnEEmRjTNAV74T/nAn5m+DiWW7iYZ0v/wFv3+IWXrngiX0dNKqwdb6r7rHqDYhJdr3FGz9xvwem/NCtcpi7Ci582lXzWPmGmwQ5/lI4837XoTPrEpekH3Wtq1tdV4aVgLYyMgYGnwTJ9Woz717jJjUm94Ls7wS1soclyMaYhtVUwYIn4fO/u0dkXQa4hDoly42JK9nphmyUF+wro+evcT3flUVQWezGu/UYC0NPg2GnunJIXz+Ef85jRFQVNXxfX4x7/Nj7CPcoMX0ApA+gOqEbczcV8sGqXXy6ZjeRPqF7cizdU2LJSovjhOHdGJCZuP+1cle7xjwmiZqhp7E6dgxvL9/Nvz5ejy9CuOGbg/nO0f2JjoygptZPSWUNH6zaxQtzc/h8XR4AUT7h/OzeXDttEOVVtcx87GvySqq4/+LxTB7Qhae+3MTDn6xnd0nVAV9l7i9P2K+mZ3NYgmyMaRcluS5JzlsLid0BdUlwUQ4MPxPOfaTx8cLbF7te4a3zIXsmTL7adYyU7YEnz4ady90kxQ/+4BZb+c7b+1ZOLN3txmNv+Ojg8UmE68Eed4nryPniQVf1IyLS/a5J6gHH/dSN9z7UseMN3d4SZGNMUKi6cngNLSdbWQKLn3NDOlL77puQmLsSNn8JW75yM8drA5JOiXCP9mJT3WO9yDiv5zrSFevvezQMOtE1xuX58OGfYc7DrkfbX+OWpI1NhSEnk584iOfWCG/kxFIWmUy0v5IofwXxUkm5RhOT0o2pE0Zy9PDevDBvC8/N2QJAbKSPmKgIHr1kFGNqlrr7xKVTEZXCxzl+dvq6gcjefpBzJ2S1uJqHJcjGmHZTmgcf3eE6NSQCEPfE8JgftT7pLM+HJ89xaxHEd4GrPnJtfH1VZV7HivfaL65cWPI8LHx23/jp+C5wxPfgiO+63xX/+537XZHSx004TOjiakinD3C1rg9lUiGWIBtjwpW/1o2R27PODe0o2eka3vICqChwybff67UuzXVj48Al3BWFrsGf+B2Y9nOXJK/7H6x4zf1Zuqt5MUQnQpeBlKUO4aOCDHaU+LkofQ1xWz93kx7rS+ntxn0PPwN6Tz74zPRGWIJsjDnsVRTCu7fB2BnQ58jWX8df6y3Msse1q4FDKlRdxaiv/+VK7JXluZe/xrXdYy+C7Cuh24hW3doSZGNMx5C/Eda+51bH8kW6CYaNNYyVxW7sXV25veh4t6xsVKwr3l+22yXdxTtcxZBdK/aVTkof6MbGDT7RJcTl+e5VvA1W/9cl4LWVkNAVrvzvwVfPaoAlyMYY00qqrmb1nEdg6UuuLR5zIZzzUIsv1VhbfHjVRTLGmLR+7tHbEd9t+tiYJOg+yr2aqzzfDQ1p6FFhnewrXPK9+h3X65Hat/nXN8YYc2hEXNWmrGw3BnrhU25oXRuyBNkYYwLFpblXU2KSYPR57mWMMSY0ErrA0Te0+WVbUL3aGGOMMcaYjs8SZGOMMcYYYwJYgmyMMcYYY0wAS5CNMcYYY4wJYAmyMcYYY4wxASxBNsYYY4wxJoAlyMYYY4wxxgSwBNkYY4wxxpgAHWapaRHJBTa14tQMYHcbh3OoLKbmsZiax2JqnvaOqa+qZrbj/dqFtcVBF24xhVs8YDE1l8XkNNgWd5gEubVEZG5Da3CHksXUPBZT81hMzROOMXUm4fjzt5iaFm7xgMXUXBbTwdkQC2OMMcYYYwJYgmyMMcYYY0wAS5DhoVAH0ACLqXkspuaxmJonHGPqTMLx528xNS3c4gGLqbkspoPo9GOQjTHGGGOMCWQ9yMYYY4wxxgSwBNkYY4wxxpgAnTpBFpHpIrJKRNaKyC0hiuFREdklIksDtqWLyLsissb7M60d4+ktIh+IyHIRWSYiN4RBTLEi8rWILPJi+q23vb+IfOX9/T0nItHtFVNAbD4RWSAir4dDTCKyUUSWiMhCEZnrbQvZ3513/1QReVFEVorIChE5KsT/PQ31fj51ryIR+VGof06dlbXDjcZkbXHLYrO2uOmYrC1ugU6bIIuID3gAOAUYAcwQkREhCOVxYHq9bbcA76vqYOB973N7qQF+oqojgCOBa72fSyhjqgSOV9WxwDhguogcCdwB/E1VBwH5wJXtGFOdG4AVAZ/DIaZpqjouoJZkKP/uAO4F3lbVYcBY3M8rZDGp6irv5zMOmAiUAa+EMqbOytrhg7K2uGWsLW6atcUtDLBTvoCjgHcCPt8K3BqiWPoBSwM+rwJ6eO97AKtC+HP6P+DEcIkJiAfmA5Nxq+1ENvT32U6xZOH+5z0eeB2QMIhpI5BRb1vI/u6AFGAD3oTgcIipXhwnAZ+FU0yd6WXtcIvis7a48VisLW46HmuLW/jqtD3IQC9gS8DnHG9bOOimqtu99zuAbqEIQkT6AeOBr0Idk/f4bCGwC3gXWAcUqGqNd0go/v7uAW4G/N7nLmEQkwL/FZF5InKVty2Uf3f9gVzgMe/x58MikhDimAJdBDzrvQ+XmDoTa4ebwdriJt2DtcVNsba4hTpzgnxYUPdPqHavxSciicBLwI9UtSjUMalqrbrHMFnAJGBYe96/PhE5HdilqvNCGUcDjlHVCbhH1teKyHGBO0PwdxcJTAD+oarjgVLqPS4L4X/j0cCZwAv194UqJhOeQvnfg7XFB2dtcbNZW9xCnTlB3gr0Dvic5W0LBztFpAeA9+eu9ry5iEThGuSnVfXlcIipjqoWAB/gHpmlikikt6u9//6OBs4UkY3ALNyjvXtDHBOqutX7cxduLNckQvt3lwPkqOpX3ucXcY10OPz3dAowX1V3ep/DIabOxtrhg7C2uFmsLW4ea4tbqDMnyHOAwd5M12hc9/7sEMdUZzZwuff+ctzYs3YhIgI8AqxQ1bvDJKZMEUn13sfhxuGtwDXO54UiJlW9VVWzVLUf7r+d/6nqJaGMSUQSRCSp7j1uTNdSQvh3p6o7gC0iMtTb9E1geShjCjCDfY/0IDxi6mysHW6EtcXNY21x81hb3AqhGPgcLi/gVGA1bgzVL0IUw7PAdqAa9y+8K3Hjp94H1gDvAentGM8xuMcZi4GF3uvUEMc0BljgxbQUuM3bPgD4GliLezQTE6K/w6nA66GOybv3Iu+1rO6/6VD+3Xn3HwfM9f7+XgXSwiCmBCAPSAnYFtKYOuvL2uFGY7K2uOXxWVt88LisLW7By5aaNsYYY4wxJkBnHmJhjDHGGGPMASxBNsYYY4wxJoAlyMYYY4wxxgSwBNkYY4wxxpgAliAbY4wxxhgTwBJk02mISK2ILAx43dL0Wc2+dj8RWdpW1zPGmI7I2mFzuIhs+hBjOoxydUukGmOMCQ1rh81hwXqQTacnIhtF5E4RWSIiX4vIIG97PxH5n4gsFpH3RaSPt72biLwiIou81xTvUj4R+beILBOR/3orTRljjGmCtcMm3FiCbDqTuHqP9i4M2FeoqqOB+4F7vG1/B55Q1THA08B93vb7gI9UdSxuLftl3vbBwAOqOhIoAM4N6rcxxpjDj7XD5rBgK+mZTkNESlQ1sYHtG4HjVXW9iEQBO1S1i4jsBnqoarW3fbuqZohILpClqpUB1+gHvKuqg73PPwOiVPX37fDVjDHmsGDtsDlcWA+yMY428r4lKgPe12Jj/I0xpiWsHTZhwxJkY5wLA/78wnv/OXCR9/4S4BPv/fvANQAi4hORlPYK0hhjOjBrh03YsH9Zmc4kTkQWBnx+W1XrSgylichiXO/DDG/bD4HHROSnQC7wHW/7DcBDInIlrofiGmB7sIM3xpgOwNphc1iwMcim0/PGvmWr6u5Qx2KMMZ2RtcMm3NgQC2OMMcYYYwJYD7IxxhhjjDEBrAfZGGOMMcaYAJYgG2OMMcYYE8ASZGOMMcYYYwJYgmyMMcYYY0wAS5CNMcYYY4wJ8P/yLahtqUBmZAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", "\n", "axes[0].set_title('Sequential API: loss')\n", "axes[0].plot(sequential_history.history['loss'], label='Training')\n", "axes[0].plot(sequential_history.history['val_loss'], label='Validation')\n", "axes[0].set_xlabel('Epoch')\n", "axes[0].set_ylabel('Loss (MSE)')\n", "axes[0].legend(loc='best')\n", "\n", "axes[1].set_title('Sequential API: MAE')\n", "axes[1].plot(sequential_history.history['mae'], label='Training')\n", "axes[1].plot(sequential_history.history['val_mae'], label='Validation')\n", "axes[1].set_xlabel('Epoch')\n", "axes[1].set_ylabel('Mean absolute error')\n", "axes[1].legend(loc='best')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f9f32761", "metadata": {}, "source": [ "### 3.4. Test set evaluation" ] }, { "cell_type": "code", "execution_count": 16, "id": "fa2e6fa3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sequential API model R² on test set: 0.7865\n" ] } ], "source": [ "sequential_predictions = sequential_model.predict(testing_df[features], verbose=0).flatten()\n", "\n", "ss_res = np.sum((testing_df[label] - sequential_predictions) ** 2)\n", "ss_tot = np.sum((testing_df[label] - np.mean(testing_df[label])) ** 2)\n", "sequential_rsquared = 1 - (ss_res / ss_tot)\n", "\n", "print(f'Sequential API model R² on test set: {sequential_rsquared:.4f}')" ] }, { "cell_type": "markdown", "id": "8a8be77f", "metadata": {}, "source": [ "### 3.5. Performance analysis" ] }, { "cell_type": "code", "execution_count": 17, "id": "97451643", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", "\n", "axes[0].set_title('Sequential API predictions')\n", "axes[0].scatter(\n", " testing_df[label], sequential_predictions,\n", " c='black', s=0.5, alpha=0.5\n", ")\n", "axes[0].plot(\n", " [testing_df[label].min(), testing_df[label].max()],\n", " [testing_df[label].min(), testing_df[label].max()],\n", " color='red', linestyle='--'\n", ")\n", "axes[0].set_xlabel('True values (standardized)')\n", "axes[0].set_ylabel('Predicted values (standardized)')\n", "\n", "axes[1].set_title('Residuals vs predicted values')\n", "axes[1].scatter(\n", " sequential_predictions, testing_df[label] - sequential_predictions,\n", " c='black', s=0.5, alpha=0.5\n", ")\n", "axes[1].axhline(0, color='red', linestyle='--')\n", "axes[1].set_xlabel('Predicted values (standardized)')\n", "axes[1].set_ylabel('Residuals (standardized)')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "27dfbd83", "metadata": {}, "source": [ "## 4. Keras Functional API model\n", "\n", "The Functional API provides more flexibility than the Sequential API. It allows you to create models with non-linear topology, shared layers, and multiple inputs or outputs.\n", "\n", "### 4.1. Build model" ] }, { "cell_type": "code", "execution_count": 18, "id": "6e3eb1bc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Model: \"functional_mlp\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ input_features (InputLayer)     │ (None, 8)              │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ hidden_1 (Dense)                │ (None, 64)             │           576 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (Dropout)             │ (None, 64)             │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ hidden_2 (Dense)                │ (None, 32)             │         2,080 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (Dropout)             │ (None, 32)             │             0 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ output (Dense)                  │ (None, 1)              │            33 │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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 Total params: 2,689 (10.50 KB)\n",
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 Trainable params: 2,689 (10.50 KB)\n",
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 Non-trainable params: 0 (0.00 B)\n",
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\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Define input layer\n", "inputs = keras.Input(shape=(8,), name='input_features')\n", "\n", "# Define hidden layers\n", "x = layers.Dense(64, activation='relu', name='hidden_1')(inputs)\n", "x = layers.Dropout(0.2, name='dropout_1')(x)\n", "x = layers.Dense(32, activation='relu', name='hidden_2')(x)\n", "x = layers.Dropout(0.2, name='dropout_2')(x)\n", "\n", "# Define output layer\n", "outputs = layers.Dense(1, name='output')(x)\n", "\n", "# Create model\n", "functional_model = Model(inputs=inputs, outputs=outputs, name='functional_mlp')\n", "\n", "functional_model.compile(\n", " optimizer=keras.optimizers.Adam(learning_rate=5e-4),\n", " loss='mse',\n", " metrics=['mae']\n", ")\n", "\n", "functional_model.summary()" ] }, { "cell_type": "markdown", "id": "58042a68", "metadata": {}, "source": [ "### 4.2. Train model" ] }, { "cell_type": "code", "execution_count": 19, "id": "50a40114", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.6101 - mae: 0.5943 - val_loss: 0.3551 - val_mae: 0.4213\n", "Epoch 2/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 877us/step - loss: 0.3749 - mae: 0.4537 - val_loss: 0.3120 - val_mae: 0.3960\n", "Epoch 3/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 839us/step - loss: 0.3410 - mae: 0.4281 - val_loss: 0.3036 - val_mae: 0.3859\n", "Epoch 4/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 841us/step - loss: 0.3170 - mae: 0.4063 - val_loss: 0.2910 - val_mae: 0.3782\n", "Epoch 5/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 855us/step - loss: 0.3042 - mae: 0.3994 - val_loss: 0.2852 - val_mae: 0.3749\n", "Epoch 6/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 836us/step - loss: 0.2873 - mae: 0.3864 - val_loss: 0.2833 - val_mae: 0.3717\n", "Epoch 7/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 844us/step - loss: 0.2833 - mae: 0.3813 - val_loss: 0.2719 - val_mae: 0.3667\n", "Epoch 8/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 855us/step - loss: 0.2808 - mae: 0.3791 - val_loss: 0.2700 - val_mae: 0.3658\n", "Epoch 9/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 838us/step - loss: 0.2768 - mae: 0.3749 - val_loss: 0.2673 - val_mae: 0.3606\n", "Epoch 10/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 880us/step - loss: 0.2684 - mae: 0.3697 - val_loss: 0.2611 - val_mae: 0.3568\n", "Epoch 11/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 843us/step - loss: 0.2691 - mae: 0.3690 - val_loss: 0.2608 - val_mae: 0.3581\n", "Epoch 12/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 859us/step - loss: 0.2640 - mae: 0.3650 - val_loss: 0.2596 - val_mae: 0.3570\n", "Epoch 13/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 858us/step - loss: 0.2591 - mae: 0.3627 - val_loss: 0.2529 - val_mae: 0.3488\n", "Epoch 14/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 853us/step - loss: 0.2547 - mae: 0.3595 - val_loss: 0.2501 - val_mae: 0.3480\n", "Epoch 15/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 836us/step - loss: 0.2561 - mae: 0.3602 - val_loss: 0.2489 - val_mae: 0.3481\n", "Epoch 16/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 864us/step - loss: 0.2540 - mae: 0.3582 - val_loss: 0.2477 - val_mae: 0.3451\n", "Epoch 17/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 870us/step - loss: 0.2552 - mae: 0.3586 - val_loss: 0.2474 - val_mae: 0.3482\n", "Epoch 18/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 846us/step - loss: 0.2517 - mae: 0.3557 - val_loss: 0.2463 - val_mae: 0.3444\n", "Epoch 19/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.2434 - mae: 0.3502 - val_loss: 0.2434 - val_mae: 0.3427\n", "Epoch 20/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 853us/step - loss: 0.2477 - mae: 0.3535 - val_loss: 0.2474 - val_mae: 0.3429\n", "Epoch 21/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 833us/step - loss: 0.2484 - mae: 0.3525 - val_loss: 0.2431 - val_mae: 0.3449\n", "Epoch 22/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 857us/step - loss: 0.2460 - mae: 0.3498 - val_loss: 0.2415 - val_mae: 0.3392\n", "Epoch 23/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 861us/step - loss: 0.2426 - mae: 0.3486 - val_loss: 0.2406 - val_mae: 0.3433\n", "Epoch 24/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 909us/step - loss: 0.2471 - mae: 0.3526 - val_loss: 0.2396 - val_mae: 0.3388\n", "Epoch 25/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 851us/step - loss: 0.2411 - mae: 0.3490 - val_loss: 0.2413 - val_mae: 0.3405\n", "Epoch 26/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 865us/step - loss: 0.2429 - mae: 0.3498 - val_loss: 0.2446 - val_mae: 0.3403\n", "Epoch 27/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 839us/step - loss: 0.2393 - mae: 0.3466 - val_loss: 0.2372 - val_mae: 0.3382\n", "Epoch 28/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 849us/step - loss: 0.2439 - mae: 0.3500 - val_loss: 0.2370 - val_mae: 0.3367\n", "Epoch 29/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 847us/step - loss: 0.2437 - mae: 0.3508 - val_loss: 0.2374 - val_mae: 0.3362\n", "Epoch 30/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 878us/step - 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loss: 0.2295 - mae: 0.3376 - val_loss: 0.2257 - val_mae: 0.3266\n", "Epoch 61/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 847us/step - loss: 0.2258 - mae: 0.3357 - val_loss: 0.2283 - val_mae: 0.3250\n", "Epoch 62/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 844us/step - loss: 0.2268 - mae: 0.3363 - val_loss: 0.2248 - val_mae: 0.3256\n", "Epoch 63/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 835us/step - loss: 0.2277 - mae: 0.3366 - val_loss: 0.2236 - val_mae: 0.3240\n", "Epoch 64/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 847us/step - loss: 0.2321 - mae: 0.3394 - val_loss: 0.2225 - val_mae: 0.3251\n", "Epoch 65/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 854us/step - loss: 0.2303 - mae: 0.3397 - val_loss: 0.2235 - val_mae: 0.3229\n", "Epoch 66/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 876us/step - loss: 0.2300 - mae: 0.3390 - val_loss: 0.2227 - val_mae: 0.3213\n", "Epoch 67/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 845us/step - loss: 0.2261 - mae: 0.3353 - val_loss: 0.2250 - val_mae: 0.3225\n", "Epoch 68/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 857us/step - loss: 0.2279 - mae: 0.3370 - val_loss: 0.2221 - val_mae: 0.3251\n", "Epoch 69/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 840us/step - loss: 0.2237 - mae: 0.3335 - val_loss: 0.2229 - val_mae: 0.3250\n", "Epoch 70/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 848us/step - loss: 0.2284 - mae: 0.3395 - val_loss: 0.2235 - val_mae: 0.3253\n", "Epoch 71/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 847us/step - loss: 0.2241 - mae: 0.3335 - val_loss: 0.2225 - val_mae: 0.3195\n", "Epoch 72/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 855us/step - loss: 0.2271 - mae: 0.3361 - val_loss: 0.2210 - val_mae: 0.3203\n", "Epoch 73/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 848us/step - loss: 0.2296 - mae: 0.3385 - val_loss: 0.2226 - val_mae: 0.3246\n", "Epoch 74/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 855us/step - loss: 0.2304 - mae: 0.3372 - val_loss: 0.2225 - val_mae: 0.3205\n", "Epoch 75/75\n", "\u001b[1m774/774\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 872us/step - loss: 0.2251 - mae: 0.3322 - val_loss: 0.2241 - val_mae: 0.3253\n", "Training complete.\n", "Final training loss: 0.2277\n", "Final validation loss: 0.2241\n" ] } ], "source": [ "functional_history = functional_model.fit(\n", " training_df[features],\n", " training_df[label],\n", " epochs=75,\n", " batch_size=16,\n", " validation_split=0.2,\n", " verbose=1\n", ")\n", "\n", "print('Training complete.')\n", "print(f'Final training loss: {functional_history.history[\"loss\"][-1]:.4f}')\n", "print(f'Final validation loss: {functional_history.history[\"val_loss\"][-1]:.4f}')" ] }, { "cell_type": "markdown", "id": "15632d7d", "metadata": {}, "source": [ "### 4.3. Learning curves" ] }, { "cell_type": "code", "execution_count": 20, "id": "72ba22ca", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", "\n", "axes[0].set_title('Functional API: loss')\n", "axes[0].plot(functional_history.history['loss'], label='Training')\n", "axes[0].plot(functional_history.history['val_loss'], label='Validation')\n", "axes[0].set_xlabel('Epoch')\n", "axes[0].set_ylabel('Loss (MSE)')\n", "axes[0].legend(loc='best')\n", "\n", "axes[1].set_title('Functional API: MAE')\n", "axes[1].plot(functional_history.history['mae'], label='Training')\n", "axes[1].plot(functional_history.history['val_mae'], label='Validation')\n", "axes[1].set_xlabel('Epoch')\n", "axes[1].set_ylabel('Mean absolute error')\n", "axes[1].legend(loc='best')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "de85777b", "metadata": {}, "source": [ "### 4.4. Test set evaluation" ] }, { "cell_type": "code", "execution_count": 21, "id": "001e2f1c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Functional API model R² on test set: 0.7849\n" ] } ], "source": [ "functional_predictions = functional_model.predict(testing_df[features], verbose=0).flatten()\n", "\n", "ss_res = np.sum((testing_df[label] - functional_predictions) ** 2)\n", "ss_tot = np.sum((testing_df[label] - np.mean(testing_df[label])) ** 2)\n", "functional_rsquared = 1 - (ss_res / ss_tot)\n", "\n", "print(f'Functional API model R² on test set: {functional_rsquared:.4f}')" ] }, { "cell_type": "markdown", "id": "c9a0827c", "metadata": {}, "source": [ "### 4.5. Performance analysis" ] }, { "cell_type": "code", "execution_count": 22, "id": "322b1929", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n", "\n", "axes[0].set_title('Functional API predictions')\n", "axes[0].scatter(\n", " testing_df[label], functional_predictions,\n", " c='black', s=0.5, alpha=0.5\n", ")\n", "\n", "axes[0].plot(\n", " [testing_df[label].min(), testing_df[label].max()],\n", " [testing_df[label].min(), testing_df[label].max()],\n", " color='red', linestyle='--'\n", ")\n", "\n", "axes[0].set_xlabel('True values (standardized)')\n", "axes[0].set_ylabel('Predicted values (standardized)')\n", "\n", "axes[1].set_title('Residuals vs predicted values')\n", "axes[1].scatter(\n", " functional_predictions, testing_df[label] - functional_predictions,\n", " c='black', s=0.5, alpha=0.5\n", ")\n", "\n", "axes[1].axhline(0, color='red', linestyle='--')\n", "axes[1].set_xlabel('Predicted values (standardized)')\n", "axes[1].set_ylabel('Residuals (standardized)')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "717d75c2", "metadata": {}, "source": [ "## 5. Model comparison" ] }, { "cell_type": "code", "execution_count": 23, "id": "905307f8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Linear regression R² on test set: 0.6586\n", "Sequential API model R² on test set: 0.7865\n", "Functional API model R² on test set: 0.7849\n" ] } ], "source": [ "print(f'Linear regression R² on test set: {linear_rsquared:.4f}')\n", "print(f'Sequential API model R² on test set: {sequential_rsquared:.4f}')\n", "print(f'Functional API model R² on test set: {functional_rsquared:.4f}')" ] }, { "cell_type": "code", "execution_count": 24, "id": "74cc4e6d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(3, 2, figsize=(8, 9))\n", "\n", "# Linear regression\n", "axes[0, 0].set_title('Linear regression predictions')\n", "axes[0, 0].scatter(\n", " testing_df[label], linear_predictions,\n", " c='black', s=0.5, alpha=0.5\n", ")\n", "\n", "axes[0, 0].plot(\n", " [testing_df[label].min(), testing_df[label].max()],\n", " [testing_df[label].min(), testing_df[label].max()],\n", " color='red', linestyle='--'\n", ")\n", "\n", "axes[0, 0].set_xlabel('True values (standardized)')\n", "axes[0, 0].set_ylabel('Predicted values (standardized)')\n", "\n", "axes[0, 1].set_title('Linear regression residuals')\n", "axes[0, 1].scatter(\n", " linear_predictions, testing_df[label] - linear_predictions,\n", " c='black', s=0.5, alpha=0.5\n", ")\n", "\n", "axes[0, 1].axhline(0, color='red', linestyle='--')\n", "axes[0, 1].set_xlabel('Predicted values (standardized)')\n", "axes[0, 1].set_ylabel('Residuals (standardized)')\n", "\n", "# Sequential API\n", "axes[1, 0].set_title('Sequential API predictions')\n", "axes[1, 0].scatter(\n", " testing_df[label], sequential_predictions,\n", " c='black', s=0.5, alpha=0.5\n", ")\n", "\n", "axes[1, 0].plot(\n", " [testing_df[label].min(), testing_df[label].max()],\n", " [testing_df[label].min(), testing_df[label].max()],\n", " color='red', linestyle='--'\n", ")\n", "\n", "axes[1, 0].set_xlabel('True values (standardized)')\n", "axes[1, 0].set_ylabel('Predicted values (standardized)')\n", "\n", "axes[1, 1].set_title('Sequential API residuals')\n", "axes[1, 1].scatter(\n", " sequential_predictions, testing_df[label] - sequential_predictions,\n", " c='black', s=0.5, alpha=0.5\n", ")\n", "\n", "axes[1, 1].axhline(0, color='red', linestyle='--')\n", "axes[1, 1].set_xlabel('Predicted values (standardized)')\n", "axes[1, 1].set_ylabel('Residuals (standardized)')\n", "\n", "# Functional API\n", "axes[2, 0].set_title('Functional API predictions')\n", "axes[2, 0].scatter(\n", " testing_df[label], functional_predictions,\n", " c='black', s=0.5, alpha=0.5\n", ")\n", "\n", "axes[2, 0].plot(\n", " [testing_df[label].min(), testing_df[label].max()],\n", " [testing_df[label].min(), testing_df[label].max()],\n", " color='red', linestyle='--'\n", ")\n", "\n", "axes[2, 0].set_xlabel('True values (standardized)')\n", "axes[2, 0].set_ylabel('Predicted values (standardized)')\n", "\n", "axes[2, 1].set_title('Functional API residuals')\n", "axes[2, 1].scatter(\n", " functional_predictions, testing_df[label] - functional_predictions,\n", " c='black', s=0.5, alpha=0.5\n", ")\n", "\n", "axes[2, 1].axhline(0, color='red', linestyle='--')\n", "axes[2, 1].set_xlabel('Predicted values (standardized)')\n", "axes[2, 1].set_ylabel('Residuals (standardized)')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": ".venv (3.12.3)", "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }