chapter04_getting-started-with-neural-networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n",
    "\n",
    "**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n",
    "\n",
    "This notebook was generated for TensorFlow 2.6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "# Getting started with neural networks: Classification and regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "## Classifying movie reviews: A binary classification example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### The IMDB dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Loading the IMDB dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-31 18:41:15.095955: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-12-31 18:41:15.096035: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-12-31 18:41:15.166244: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2023-12-31 18:41:15.320831: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-12-31 18:41:16.653298: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import imdb\n",
    "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\n",
    "    num_words=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/bin/python3.11'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.executable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000,)\n",
      "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]\n"
     ]
    }
   ],
   "source": [
    "print(train_data.shape)\n",
    "print(train_data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train_labels.shape)\n",
    "train_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9999"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max([max(sequence) for sequence in train_data])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Decoding reviews back to text**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88584\n"
     ]
    }
   ],
   "source": [
    "word_index = imdb.get_word_index()\n",
    "reverse_word_index = dict(\n",
    "    [(value, key) for (key, value) in word_index.items()])\n",
    "decoded_review = \" \".join(\n",
    "    [reverse_word_index.get(i - 3, \"?\") for i in train_data[0]])\n",
    "print(len(reverse_word_index))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Preparing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Encoding the integer sequences via multi-hot encoding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def vectorize_sequences(sequences, dimension=10000):\n",
    "    results = np.zeros((len(sequences), dimension))\n",
    "    for i, sequence in enumerate(sequences):\n",
    "        for j in sequence:\n",
    "            results[i, j] = 1.\n",
    "    return results\n",
    "x_train = vectorize_sequences(train_data)\n",
    "x_test = vectorize_sequences(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000, 10000)\n",
      "[0. 1. 1. ... 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "y_train = np.asarray(train_labels).astype(\"float32\")\n",
    "y_test = np.asarray(test_labels).astype(\"float32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Building your model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Model definition**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-31 18:35:22.197703: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 18:35:23.330948: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 18:35:23.331308: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 18:35:23.335023: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 18:35:23.335342: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 18:35:23.335699: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 18:35:24.145190: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 18:35:24.145536: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 18:35:24.145551: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2022] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.\n",
      "2023-12-31 18:35:24.145854: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 18:35:24.145881: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4071 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2060, pci bus id: 0000:01:00.0, compute capability: 7.5\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(16, activation=\"relu\"),\n",
    "    layers.Dense(16, activation=\"relu\"),\n",
    "    layers.Dense(1, activation=\"sigmoid\")\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Compiling the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"rmsprop\",\n",
    "              loss=\"binary_crossentropy\",\n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Validating your approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Setting aside a validation set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "x_val = x_train[:10000]\n",
    "partial_x_train = x_train[10000:]\n",
    "y_val = y_train[:10000]\n",
    "partial_y_train = y_train[10000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Training your model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-31 18:35:38.061582: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 600000000 exceeds 10% of free system memory.\n",
      "2023-12-31 18:35:40.406363: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 600000000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-31 18:35:42.273154: I external/local_tsl/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2023-12-31 18:35:42.716931: I external/local_xla/xla/service/service.cc:168] XLA service 0x7f9a2c495a60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-12-31 18:35:42.716980: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2060, Compute Capability 7.5\n",
      "2023-12-31 18:35:42.741612: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2023-12-31 18:35:42.791333: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1704018942.851008     453 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26/30 [=========================>....] - ETA: 0s - loss: 0.5290 - accuracy: 0.7578"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-31 18:35:45.226612: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 400000000 exceeds 10% of free system memory.\n",
      "2023-12-31 18:35:45.729770: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 400000000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 6s 110ms/step - loss: 0.5129 - accuracy: 0.7713 - val_loss: 0.3923 - val_accuracy: 0.8565\n",
      "Epoch 2/20\n",
      "30/30 [==============================] - 1s 21ms/step - loss: 0.3071 - accuracy: 0.8995 - val_loss: 0.3418 - val_accuracy: 0.8590\n",
      "Epoch 3/20\n",
      "30/30 [==============================] - 1s 18ms/step - loss: 0.2298 - accuracy: 0.9223 - val_loss: 0.2791 - val_accuracy: 0.8899\n",
      "Epoch 4/20\n",
      "30/30 [==============================] - 1s 19ms/step - loss: 0.1879 - accuracy: 0.9363 - val_loss: 0.3196 - val_accuracy: 0.8706\n",
      "Epoch 5/20\n",
      "30/30 [==============================] - 1s 21ms/step - loss: 0.1551 - accuracy: 0.9503 - val_loss: 0.3188 - val_accuracy: 0.8725\n",
      "Epoch 6/20\n",
      "30/30 [==============================] - 1s 18ms/step - loss: 0.1313 - accuracy: 0.9573 - val_loss: 0.2981 - val_accuracy: 0.8807\n",
      "Epoch 7/20\n",
      "30/30 [==============================] - 1s 17ms/step - loss: 0.1137 - accuracy: 0.9647 - val_loss: 0.2983 - val_accuracy: 0.8833\n",
      "Epoch 8/20\n",
      "30/30 [==============================] - 1s 18ms/step - loss: 0.0985 - accuracy: 0.9706 - val_loss: 0.3138 - val_accuracy: 0.8791\n",
      "Epoch 9/20\n",
      "30/30 [==============================] - 1s 23ms/step - loss: 0.0851 - accuracy: 0.9759 - val_loss: 0.3437 - val_accuracy: 0.8745\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-31 18:35:50.644377: W tensorflow/core/kernels/data/prefetch_autotuner.cc:52] Prefetch autotuner tried to allocate 20482048 bytes after encountering the first element of size 20482048 bytes.This already causes the autotune ram budget to be exceeded. To stay within the ram budget, either increase the ram budget or reduce element size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/20\n",
      "30/30 [==============================] - 1s 19ms/step - loss: 0.0682 - accuracy: 0.9835 - val_loss: 0.3646 - val_accuracy: 0.8789\n",
      "Epoch 11/20\n",
      "30/30 [==============================] - 1s 18ms/step - loss: 0.0631 - accuracy: 0.9849 - val_loss: 0.3972 - val_accuracy: 0.8740\n",
      "Epoch 12/20\n",
      "30/30 [==============================] - 1s 18ms/step - loss: 0.0560 - accuracy: 0.9847 - val_loss: 0.3867 - val_accuracy: 0.8779\n",
      "Epoch 13/20\n",
      "30/30 [==============================] - 1s 24ms/step - loss: 0.0442 - accuracy: 0.9897 - val_loss: 0.4129 - val_accuracy: 0.8789\n",
      "Epoch 14/20\n",
      "30/30 [==============================] - 1s 18ms/step - loss: 0.0368 - accuracy: 0.9930 - val_loss: 0.4455 - val_accuracy: 0.8676\n",
      "Epoch 15/20\n",
      "30/30 [==============================] - 1s 17ms/step - loss: 0.0323 - accuracy: 0.9936 - val_loss: 0.5881 - val_accuracy: 0.8447\n",
      "Epoch 16/20\n",
      "30/30 [==============================] - 1s 18ms/step - loss: 0.0283 - accuracy: 0.9951 - val_loss: 0.4831 - val_accuracy: 0.8734\n",
      "Epoch 17/20\n",
      "30/30 [==============================] - 1s 19ms/step - loss: 0.0253 - accuracy: 0.9953 - val_loss: 0.4975 - val_accuracy: 0.8733\n",
      "Epoch 18/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-31 18:35:55.184354: W tensorflow/core/kernels/data/prefetch_autotuner.cc:52] Prefetch autotuner tried to allocate 20482048 bytes after encountering the first element of size 20482048 bytes.This already causes the autotune ram budget to be exceeded. To stay within the ram budget, either increase the ram budget or reduce element size\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30/30 [==============================] - 1s 19ms/step - loss: 0.0210 - accuracy: 0.9963 - val_loss: 0.5264 - val_accuracy: 0.8679\n",
      "Epoch 19/20\n",
      "30/30 [==============================] - 1s 17ms/step - loss: 0.0184 - accuracy: 0.9975 - val_loss: 0.5400 - val_accuracy: 0.8691\n",
      "Epoch 20/20\n",
      "30/30 [==============================] - 1s 17ms/step - loss: 0.0153 - accuracy: 0.9985 - val_loss: 0.5653 - val_accuracy: 0.8673\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(partial_x_train,\n",
    "                    partial_y_train,\n",
    "                    epochs=20,\n",
    "                    batch_size=512,\n",
    "                    validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_dict = history.history\n",
    "history_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Plotting the training and validation loss**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "history_dict = history.history\n",
    "loss_values = history_dict[\"loss\"]\n",
    "val_loss_values = history_dict[\"val_loss\"]\n",
    "epochs = range(1, len(loss_values) + 1)\n",
    "plt.plot(epochs, loss_values, \"bo\", label=\"Training loss\")\n",
    "plt.plot(epochs, val_loss_values, \"b\", label=\"Validation loss\")\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Plotting the training and validation accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "acc = history_dict[\"accuracy\"]\n",
    "val_acc = history_dict[\"val_accuracy\"]\n",
    "plt.plot(epochs, acc, \"bo\", label=\"Training acc\")\n",
    "plt.plot(epochs, val_acc, \"b\", label=\"Validation acc\")\n",
    "plt.title(\"Training and validation accuracy\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Retraining a model from scratch**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-31 22:01:43.531034: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 22:01:43.696149: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 22:01:43.696588: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 22:01:43.698106: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 22:01:43.698435: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 22:01:43.698701: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 22:01:43.972714: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 22:01:43.973116: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 22:01:43.973131: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2022] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.\n",
      "2023-12-31 22:01:43.973489: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
      "Your kernel may have been built without NUMA support.\n",
      "2023-12-31 22:01:43.973855: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3629 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2060, pci bus id: 0000:01:00.0, compute capability: 7.5\n",
      "2023-12-31 22:01:54.232589: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 359280000 exceeds 10% of free system memory.\n",
      "2023-12-31 22:01:55.015590: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 359280000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1401, in train_function  *\n        return step_function(self, iterator)\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1384, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1373, in run_step  **\n        outputs = model.train_step(data)\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1151, in train_step\n        loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1209, in compute_loss\n        return self.compiled_loss(\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/engine/compile_utils.py\", line 277, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/losses.py\", line 143, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/losses.py\", line 270, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/losses.py\", line 2532, in binary_crossentropy\n        backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/backend.py\", line 5822, in binary_crossentropy\n        return tf.nn.sigmoid_cross_entropy_with_logits(\n\n    ValueError: `logits` and `labels` must have the same shape, received ((None, 1) vs (None, 46)).\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[14], line 11\u001b[0m\n\u001b[1;32m      3\u001b[0m model \u001b[38;5;241m=\u001b[39m keras\u001b[38;5;241m.\u001b[39mSequential([\n\u001b[1;32m      4\u001b[0m     layers\u001b[38;5;241m.\u001b[39mDense(\u001b[38;5;241m16\u001b[39m, activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelu\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m      5\u001b[0m     layers\u001b[38;5;241m.\u001b[39mDense(\u001b[38;5;241m16\u001b[39m, activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelu\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m      6\u001b[0m     layers\u001b[38;5;241m.\u001b[39mDense(\u001b[38;5;241m1\u001b[39m, activation\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msigmoid\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      7\u001b[0m ])\n\u001b[1;32m      8\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrmsprop\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      9\u001b[0m               loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary_crossentropy\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     10\u001b[0m               metrics\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m---> 11\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m512\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     12\u001b[0m results \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mevaluate(x_test, y_test)\n",
      "File \u001b[0;32m~/.local/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[0;32m/tmp/__autograph_generated_filesnf964ht.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m     17\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1401, in train_function  *\n        return step_function(self, iterator)\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1384, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1373, in run_step  **\n        outputs = model.train_step(data)\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1151, in train_step\n        loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1209, in compute_loss\n        return self.compiled_loss(\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/engine/compile_utils.py\", line 277, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/losses.py\", line 143, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/losses.py\", line 270, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/losses.py\", line 2532, in binary_crossentropy\n        backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),\n    File \"/home/rakescar/.local/lib/python3.11/site-packages/keras/src/backend.py\", line 5822, in binary_crossentropy\n        return tf.nn.sigmoid_cross_entropy_with_logits(\n\n    ValueError: `logits` and `labels` must have the same shape, received ((None, 1) vs (None, 46)).\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(16, activation=\"relu\"),\n",
    "    layers.Dense(16, activation=\"relu\"),\n",
    "    layers.Dense(1, activation=\"sigmoid\")\n",
    "])\n",
    "model.compile(optimizer=\"rmsprop\",\n",
    "              loss=\"binary_crossentropy\",\n",
    "              metrics=[\"accuracy\"])\n",
    "model.fit(x_train, y_train, epochs=4, batch_size=512)\n",
    "results = model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Using a trained model to generate predictions on new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Further experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Wrapping up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "## Classifying newswires: A multiclass classification example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### The Reuters dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Loading the Reuters dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-31 21:59:38.936054: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-12-31 21:59:38.936222: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-12-31 21:59:38.962939: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2023-12-31 21:59:39.055187: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-12-31 21:59:40.797171: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import reuters\n",
    "(train_data, train_labels), (test_data, test_labels) = reuters.load_data(\n",
    "    num_words=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8982"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2246"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8982,)\n",
      "[1, 245, 273, 207, 156, 53, 74, 160, 26, 14, 46, 296, 26, 39, 74, 2979, 3554, 14, 46, 4689, 4329, 86, 61, 3499, 4795, 14, 61, 451, 4329, 17, 12]\n"
     ]
    }
   ],
   "source": [
    "print(train_data.shape)\n",
    "print(train_data[10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Decoding newswires back to text**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "word_index = reuters.get_word_index()\n",
    "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
    "decoded_newswire = \" \".join([reverse_word_index.get(i - 3, \"?\") for i in\n",
    "    train_data[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "? ? ? said as a result of its december acquisition of space co it expects earnings per share in 1987 of 1 15 to 1 30 dlrs per share up from 70 cts in 1986 the company said pretax net should rise to nine to 10 mln dlrs from six mln dlrs in 1986 and rental operation revenues to 19 to 22 mln dlrs from 12 5 mln dlrs it said cash flow per share this year should be 2 50 to three dlrs reuter 3\n"
     ]
    }
   ],
   "source": [
    "print(decoded_newswire)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 245, 273, 207, 156, 53, 74, 160, 26, 14, 46, 296, 26, 39, 74, 2979, 3554, 14, 46, 4689, 4329, 86, 61, 3499, 4795, 14, 61, 451, 4329, 17, 12]\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "print(train_data[10])\n",
    "print(train_labels[10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Preparing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Encoding the input data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "x_train = vectorize_sequences(train_data)\n",
    "x_test = vectorize_sequences(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Encoding the labels**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "def to_one_hot(labels, dimension=46):\n",
    "    results = np.zeros((len(labels), dimension))\n",
    "    for i, label in enumerate(labels):\n",
    "        results[i, label] = 1.\n",
    "    return results\n",
    "y_train = to_one_hot(train_labels)\n",
    "y_test = to_one_hot(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical\n",
    "y_train = to_categorical(train_labels)\n",
    "y_test = to_categorical(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Building your model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Model definition**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(64, activation=\"relu\"),\n",
    "    layers.Dense(64, activation=\"relu\"),\n",
    "    layers.Dense(46, activation=\"softmax\")\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Compiling the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"rmsprop\",\n",
    "              loss=\"categorical_crossentropy\",\n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Validating your approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Setting aside a validation set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "x_val = x_train[:1000]\n",
    "partial_x_train = x_train[1000:]\n",
    "y_val = y_train[:1000]\n",
    "partial_y_train = y_train[1000:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Training the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-12-31 22:02:20.216861: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 319280000 exceeds 10% of free system memory.\n",
      "2023-12-31 22:02:20.450059: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 319280000 exceeds 10% of free system memory.\n",
      "2023-12-31 22:02:21.003054: W external/local_tsl/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 20480000 exceeds 10% of free system memory.\n",
      "2023-12-31 22:02:21.486465: I external/local_tsl/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2023-12-31 22:02:21.623666: I external/local_xla/xla/service/service.cc:168] XLA service 0x7f2b0c6cdd40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-12-31 22:02:21.623710: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 2060, Compute Capability 7.5\n",
      "2023-12-31 22:02:21.650501: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2023-12-31 22:02:21.699520: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8904\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1704031341.763657   24301 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16/16 [==============================] - 2s 40ms/step - loss: 2.8329 - accuracy: 0.4878 - val_loss: 1.9178 - val_accuracy: 0.6230\n",
      "Epoch 2/20\n",
      "16/16 [==============================] - 0s 15ms/step - loss: 1.6095 - accuracy: 0.6760 - val_loss: 1.4086 - val_accuracy: 0.6890\n",
      "Epoch 3/20\n",
      "16/16 [==============================] - 0s 15ms/step - loss: 1.2142 - accuracy: 0.7414 - val_loss: 1.2209 - val_accuracy: 0.7350\n",
      "Epoch 4/20\n",
      "16/16 [==============================] - 0s 14ms/step - loss: 0.9776 - accuracy: 0.7933 - val_loss: 1.0839 - val_accuracy: 0.7570\n",
      "Epoch 5/20\n",
      "16/16 [==============================] - 0s 15ms/step - loss: 0.8088 - accuracy: 0.8276 - val_loss: 1.0098 - val_accuracy: 0.7800\n",
      "Epoch 6/20\n",
      "16/16 [==============================] - 0s 14ms/step - loss: 0.6665 - accuracy: 0.8596 - val_loss: 0.9775 - val_accuracy: 0.7950\n",
      "Epoch 7/20\n",
      "16/16 [==============================] - 0s 14ms/step - loss: 0.5568 - accuracy: 0.8849 - val_loss: 0.9119 - val_accuracy: 0.8050\n",
      "Epoch 8/20\n",
      "16/16 [==============================] - 0s 19ms/step - loss: 0.4683 - accuracy: 0.9032 - val_loss: 0.9001 - val_accuracy: 0.8060\n",
      "Epoch 9/20\n",
      "16/16 [==============================] - 0s 17ms/step - loss: 0.3957 - accuracy: 0.9169 - val_loss: 0.8756 - val_accuracy: 0.8130\n",
      "Epoch 10/20\n",
      "16/16 [==============================] - 0s 15ms/step - loss: 0.3363 - accuracy: 0.9287 - val_loss: 0.8847 - val_accuracy: 0.8110\n",
      "Epoch 11/20\n",
      "16/16 [==============================] - 0s 17ms/step - loss: 0.2895 - accuracy: 0.9374 - val_loss: 0.8869 - val_accuracy: 0.8200\n",
      "Epoch 12/20\n",
      "16/16 [==============================] - 0s 26ms/step - loss: 0.2608 - accuracy: 0.9415 - val_loss: 0.8702 - val_accuracy: 0.8260\n",
      "Epoch 13/20\n",
      "16/16 [==============================] - 0s 19ms/step - loss: 0.2262 - accuracy: 0.9479 - val_loss: 0.9021 - val_accuracy: 0.8120\n",
      "Epoch 14/20\n",
      "16/16 [==============================] - 0s 28ms/step - loss: 0.2016 - accuracy: 0.9503 - val_loss: 0.9216 - val_accuracy: 0.8140\n",
      "Epoch 15/20\n",
      "16/16 [==============================] - 0s 25ms/step - loss: 0.1872 - accuracy: 0.9504 - val_loss: 1.0338 - val_accuracy: 0.7990\n",
      "Epoch 16/20\n",
      "16/16 [==============================] - 0s 30ms/step - loss: 0.1710 - accuracy: 0.9525 - val_loss: 0.9175 - val_accuracy: 0.8180\n",
      "Epoch 17/20\n",
      "16/16 [==============================] - 0s 28ms/step - loss: 0.1603 - accuracy: 0.9541 - val_loss: 0.8992 - val_accuracy: 0.8180\n",
      "Epoch 18/20\n",
      "16/16 [==============================] - 0s 30ms/step - loss: 0.1479 - accuracy: 0.9557 - val_loss: 0.9425 - val_accuracy: 0.8150\n",
      "Epoch 19/20\n",
      "16/16 [==============================] - 0s 30ms/step - loss: 0.1404 - accuracy: 0.9580 - val_loss: 0.9552 - val_accuracy: 0.8180\n",
      "Epoch 20/20\n",
      "16/16 [==============================] - 0s 31ms/step - loss: 0.1367 - accuracy: 0.9573 - val_loss: 0.9793 - val_accuracy: 0.8160\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(partial_x_train,\n",
    "                    partial_y_train,\n",
    "                    epochs=20,\n",
    "                    batch_size=512,\n",
    "                    validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Plotting the training and validation loss**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "loss = history.history[\"loss\"]\n",
    "val_loss = history.history[\"val_loss\"]\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
    "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Plotting the training and validation accuracy**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "acc = history.history[\"accuracy\"]\n",
    "val_acc = history.history[\"val_accuracy\"]\n",
    "plt.plot(epochs, acc, \"bo\", label=\"Training accuracy\")\n",
    "plt.plot(epochs, val_acc, \"b\", label=\"Validation accuracy\")\n",
    "plt.title(\"Training and validation accuracy\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Retraining a model from scratch**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/9\n",
      "18/18 [==============================] - 1s 12ms/step - loss: 2.6936 - accuracy: 0.5163\n",
      "Epoch 2/9\n",
      "18/18 [==============================] - 0s 11ms/step - loss: 1.5025 - accuracy: 0.6821\n",
      "Epoch 3/9\n",
      "18/18 [==============================] - 0s 11ms/step - loss: 1.1557 - accuracy: 0.7430\n",
      "Epoch 4/9\n",
      "18/18 [==============================] - 0s 12ms/step - loss: 0.9382 - accuracy: 0.7977\n",
      "Epoch 5/9\n",
      "18/18 [==============================] - 0s 11ms/step - loss: 0.7719 - accuracy: 0.8343\n",
      "Epoch 6/9\n",
      "18/18 [==============================] - 0s 13ms/step - loss: 0.6332 - accuracy: 0.8707\n",
      "Epoch 7/9\n",
      "18/18 [==============================] - 0s 13ms/step - loss: 0.5243 - accuracy: 0.8910\n",
      "Epoch 8/9\n",
      "18/18 [==============================] - 0s 12ms/step - loss: 0.4419 - accuracy: 0.9057\n",
      "Epoch 9/9\n",
      "18/18 [==============================] - 0s 10ms/step - loss: 0.3729 - accuracy: 0.9203\n",
      "71/71 [==============================] - 0s 4ms/step - loss: 0.9143 - accuracy: 0.7916\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "  layers.Dense(64, activation=\"relu\"),\n",
    "  layers.Dense(64, activation=\"relu\"),\n",
    "  layers.Dense(46, activation=\"softmax\")\n",
    "])\n",
    "model.compile(optimizer=\"rmsprop\",\n",
    "              loss=\"categorical_crossentropy\",\n",
    "              metrics=[\"accuracy\"])\n",
    "model.fit(x_train,\n",
    "          y_train,\n",
    "          epochs=9,\n",
    "          batch_size=512)\n",
    "results = model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9143149852752686, 0.7916295528411865]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20213713268032057"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import copy\n",
    "test_labels_copy = copy.copy(test_labels)\n",
    "np.random.shuffle(test_labels_copy)\n",
    "hits_array = np.array(test_labels) == np.array(test_labels_copy)\n",
    "hits_array.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Generating predictions on new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71/71 [==============================] - 0s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46,)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99999994"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### A different way to handle the labels and the loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "y_train = np.array(train_labels)\n",
    "y_test = np.array(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"rmsprop\",\n",
    "              loss=\"sparse_categorical_crossentropy\",\n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### The importance of having sufficiently large intermediate layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**A model with an information bottleneck**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "63/63 [==============================] - 1s 11ms/step - loss: 1.8052 - accuracy: 0.6411 - val_loss: 1.1644 - val_accuracy: 0.7370\n",
      "Epoch 2/10\n",
      "63/63 [==============================] - 0s 8ms/step - loss: 0.9192 - accuracy: 0.7992 - val_loss: 0.9748 - val_accuracy: 0.7850\n",
      "Epoch 3/10\n",
      "63/63 [==============================] - 1s 9ms/step - loss: 0.6069 - accuracy: 0.8736 - val_loss: 0.8775 - val_accuracy: 0.8070\n",
      "Epoch 4/10\n",
      "63/63 [==============================] - 1s 10ms/step - loss: 0.4206 - accuracy: 0.9132 - val_loss: 0.8551 - val_accuracy: 0.8140\n",
      "Epoch 5/10\n",
      "63/63 [==============================] - 1s 9ms/step - loss: 0.3081 - accuracy: 0.9332 - val_loss: 0.8709 - val_accuracy: 0.8170\n",
      "Epoch 6/10\n",
      "63/63 [==============================] - 1s 9ms/step - loss: 0.2431 - accuracy: 0.9448 - val_loss: 0.8802 - val_accuracy: 0.8180\n",
      "Epoch 7/10\n",
      "63/63 [==============================] - 1s 9ms/step - loss: 0.2044 - accuracy: 0.9489 - val_loss: 0.9233 - val_accuracy: 0.8120\n",
      "Epoch 8/10\n",
      "63/63 [==============================] - 1s 10ms/step - loss: 0.1814 - accuracy: 0.9528 - val_loss: 1.0004 - val_accuracy: 0.8020\n",
      "Epoch 9/10\n",
      "63/63 [==============================] - 1s 9ms/step - loss: 0.1633 - accuracy: 0.9545 - val_loss: 0.9523 - val_accuracy: 0.8180\n",
      "Epoch 10/10\n",
      "63/63 [==============================] - 1s 9ms/step - loss: 0.1562 - accuracy: 0.9545 - val_loss: 1.0133 - val_accuracy: 0.8060\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7f2b5c219510>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    # layers.Dense(256, activation=\"relu\"),\n",
    "    # layers.Dense(128, activation=\"relu\"),\n",
    "    layers.Dense(96, activation=\"relu\"),\n",
    "    layers.Dense(64, activation=\"relu\"),\n",
    "    layers.Dense(46, activation=\"softmax\")\n",
    "])\n",
    "model.compile(optimizer=\"rmsprop\",\n",
    "              loss=\"categorical_crossentropy\",\n",
    "              metrics=[\"accuracy\"])\n",
    "model.fit(partial_x_train,\n",
    "          partial_y_train,\n",
    "          epochs=10,\n",
    "          batch_size=128,\n",
    "          validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Further experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Wrapping up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "## Predicting house prices: A regression example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### The Boston Housing Price dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Loading the Boston housing dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import boston_housing\n",
    "(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102, 13)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.2, 42.3, 50. , 21.1, 17.7, 18.5, 11.3, 15.6, 15.6, 14.4, 12.1,\n",
       "       17.9, 23.1, 19.9, 15.7,  8.8, 50. , 22.5, 24.1, 27.5, 10.9, 30.8,\n",
       "       32.9, 24. , 18.5, 13.3, 22.9, 34.7, 16.6, 17.5, 22.3, 16.1, 14.9,\n",
       "       23.1, 34.9, 25. , 13.9, 13.1, 20.4, 20. , 15.2, 24.7, 22.2, 16.7,\n",
       "       12.7, 15.6, 18.4, 21. , 30.1, 15.1, 18.7,  9.6, 31.5, 24.8, 19.1,\n",
       "       22. , 14.5, 11. , 32. , 29.4, 20.3, 24.4, 14.6, 19.5, 14.1, 14.3,\n",
       "       15.6, 10.5,  6.3, 19.3, 19.3, 13.4, 36.4, 17.8, 13.5, 16.5,  8.3,\n",
       "       14.3, 16. , 13.4, 28.6, 43.5, 20.2, 22. , 23. , 20.7, 12.5, 48.5,\n",
       "       14.6, 13.4, 23.7, 50. , 21.7, 39.8, 38.7, 22.2, 34.9, 22.5, 31.1,\n",
       "       28.7, 46. , 41.7, 21. , 26.6, 15. , 24.4, 13.3, 21.2, 11.7, 21.7,\n",
       "       19.4, 50. , 22.8, 19.7, 24.7, 36.2, 14.2, 18.9, 18.3, 20.6, 24.6,\n",
       "       18.2,  8.7, 44. , 10.4, 13.2, 21.2, 37. , 30.7, 22.9, 20. , 19.3,\n",
       "       31.7, 32. , 23.1, 18.8, 10.9, 50. , 19.6,  5. , 14.4, 19.8, 13.8,\n",
       "       19.6, 23.9, 24.5, 25. , 19.9, 17.2, 24.6, 13.5, 26.6, 21.4, 11.9,\n",
       "       22.6, 19.6,  8.5, 23.7, 23.1, 22.4, 20.5, 23.6, 18.4, 35.2, 23.1,\n",
       "       27.9, 20.6, 23.7, 28. , 13.6, 27.1, 23.6, 20.6, 18.2, 21.7, 17.1,\n",
       "        8.4, 25.3, 13.8, 22.2, 18.4, 20.7, 31.6, 30.5, 20.3,  8.8, 19.2,\n",
       "       19.4, 23.1, 23. , 14.8, 48.8, 22.6, 33.4, 21.1, 13.6, 32.2, 13.1,\n",
       "       23.4, 18.9, 23.9, 11.8, 23.3, 22.8, 19.6, 16.7, 13.4, 22.2, 20.4,\n",
       "       21.8, 26.4, 14.9, 24.1, 23.8, 12.3, 29.1, 21. , 19.5, 23.3, 23.8,\n",
       "       17.8, 11.5, 21.7, 19.9, 25. , 33.4, 28.5, 21.4, 24.3, 27.5, 33.1,\n",
       "       16.2, 23.3, 48.3, 22.9, 22.8, 13.1, 12.7, 22.6, 15. , 15.3, 10.5,\n",
       "       24. , 18.5, 21.7, 19.5, 33.2, 23.2,  5. , 19.1, 12.7, 22.3, 10.2,\n",
       "       13.9, 16.3, 17. , 20.1, 29.9, 17.2, 37.3, 45.4, 17.8, 23.2, 29. ,\n",
       "       22. , 18. , 17.4, 34.6, 20.1, 25. , 15.6, 24.8, 28.2, 21.2, 21.4,\n",
       "       23.8, 31. , 26.2, 17.4, 37.9, 17.5, 20. ,  8.3, 23.9,  8.4, 13.8,\n",
       "        7.2, 11.7, 17.1, 21.6, 50. , 16.1, 20.4, 20.6, 21.4, 20.6, 36.5,\n",
       "        8.5, 24.8, 10.8, 21.9, 17.3, 18.9, 36.2, 14.9, 18.2, 33.3, 21.8,\n",
       "       19.7, 31.6, 24.8, 19.4, 22.8,  7.5, 44.8, 16.8, 18.7, 50. , 50. ,\n",
       "       19.5, 20.1, 50. , 17.2, 20.8, 19.3, 41.3, 20.4, 20.5, 13.8, 16.5,\n",
       "       23.9, 20.6, 31.5, 23.3, 16.8, 14. , 33.8, 36.1, 12.8, 18.3, 18.7,\n",
       "       19.1, 29. , 30.1, 50. , 50. , 22. , 11.9, 37.6, 50. , 22.7, 20.8,\n",
       "       23.5, 27.9, 50. , 19.3, 23.9, 22.6, 15.2, 21.7, 19.2, 43.8, 20.3,\n",
       "       33.2, 19.9, 22.5, 32.7, 22. , 17.1, 19. , 15. , 16.1, 25.1, 23.7,\n",
       "       28.7, 37.2, 22.6, 16.4, 25. , 29.8, 22.1, 17.4, 18.1, 30.3, 17.5,\n",
       "       24.7, 12.6, 26.5, 28.7, 13.3, 10.4, 24.4, 23. , 20. , 17.8,  7. ,\n",
       "       11.8, 24.4, 13.8, 19.4, 25.2, 19.4, 19.4, 29.1])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_targets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Preparing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Normalizing the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "mean = train_data.mean(axis=0)\n",
    "train_data -= mean\n",
    "std = train_data.std(axis=0)\n",
    "train_data /= std\n",
    "test_data -= mean\n",
    "test_data /= std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Building your model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Model definition**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    model = keras.Sequential([\n",
    "        layers.Dense(64, activation=\"relu\"),\n",
    "        layers.Dense(64, activation=\"relu\"),\n",
    "        layers.Dense(1)\n",
    "    ])\n",
    "    model.compile(optimizer=\"rmsprop\", loss=\"mse\", metrics=[\"mae\"])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Validating your approach using K-fold validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**K-fold validation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing fold #0\n",
      "Processing fold #1\n",
      "Processing fold #2\n",
      "Processing fold #3\n"
     ]
    }
   ],
   "source": [
    "k = 4\n",
    "num_val_samples = len(train_data) // k\n",
    "num_epochs = 100\n",
    "all_scores = []\n",
    "for i in range(k):\n",
    "    print(f\"Processing fold #{i}\")\n",
    "    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "    partial_train_data = np.concatenate(\n",
    "        [train_data[:i * num_val_samples],\n",
    "         train_data[(i + 1) * num_val_samples:]],\n",
    "        axis=0)\n",
    "    partial_train_targets = np.concatenate(\n",
    "        [train_targets[:i * num_val_samples],\n",
    "         train_targets[(i + 1) * num_val_samples:]],\n",
    "        axis=0)\n",
    "    model = build_model()\n",
    "    model.fit(partial_train_data, partial_train_targets,\n",
    "              epochs=num_epochs, batch_size=16, verbose=0)\n",
    "    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)\n",
    "    all_scores.append(val_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.0290744304656982, 2.367687463760376, 2.44972562789917, 2.4025332927703857]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3122552037239075"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(all_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Saving the validation logs at each fold**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing fold #0\n",
      "Processing fold #1\n",
      "Processing fold #2\n",
      "Processing fold #3\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 500\n",
    "all_mae_histories = []\n",
    "for i in range(k):\n",
    "    print(f\"Processing fold #{i}\")\n",
    "    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "    partial_train_data = np.concatenate(\n",
    "        [train_data[:i * num_val_samples],\n",
    "         train_data[(i + 1) * num_val_samples:]],\n",
    "        axis=0)\n",
    "    partial_train_targets = np.concatenate(\n",
    "        [train_targets[:i * num_val_samples],\n",
    "         train_targets[(i + 1) * num_val_samples:]],\n",
    "        axis=0)\n",
    "    model = build_model()\n",
    "    history = model.fit(partial_train_data, partial_train_targets,\n",
    "                        validation_data=(val_data, val_targets),\n",
    "                        epochs=num_epochs, batch_size=16, verbose=0)\n",
    "    mae_history = history.history[\"val_mae\"]\n",
    "    all_mae_histories.append(mae_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Building the history of successive mean K-fold validation scores**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "average_mae_history = [\n",
    "    np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Plotting validation scores**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ksylYCBERUSfGACSzy7vAuBwGERGRMhiAZHZZ/mEXGBERkUIYgGQmSZI9BHEQNBERkTIYgBTA5TCIiIiUxQCkgKaB0AxAREREymAAUkBTCxB7wIiIiJTBAKSApuUwOAuMiIhIGQxAClBxDBAREZGi2hyA/vKXv6C2ttb+/LvvvoPZbLY/r6ysxGOPPda+1XVQnAVGRESkrDYHoPnz56OystL+PDU1FWfOnLE/r6mpwb/+9a/2ra6D4iwwIiIiZbU5AImftVb8/Dm1HWeBERERKYtjgBTQ1ALELjAiIiJlMAApgC1AREREytI4s/N///tf+Pr6AgAaGhqQkZGB4OBgAHAYH0StYwsQERGRstocgKKjo/Gf//zH/jw8PBz/+9//rtiHftmlAKRwIURERJ1UmwNQYWGhC8voXJqmwbMLjIiISBntNgaooqICb7zxRnudrkOztwAxABERESnimgNQZmYm7r//fkRERGDhwoXtUVOHp+JSGERERIq6qgB06tQpvPTSS+jZsyfGjBkDSZKwbt06GAyG9q6vQ+IsMCIiImW1OQBZLBZ89NFHSElJQWxsLLKzs/Hqq69CpVLh+eefx9ixY+Hh4eHKWjsMzgIjIiJSVpsHQXft2hVxcXH4zW9+g9WrVyMwMBAAMG3aNJcV11FdWgxV4UKIiIg6qTa3ADU0NECSJEiSBLVa7cqaOjw1Z4EREREpqs0BqLi4GDNnzsQHH3yA8PBwTJo0CevWrYN0cTwLtV1TFxjXUyMiIlJGmwOQp6cn0tLSsHXrVhw+fBj9+vXDb3/7WzQ0NOCPf/wjtmzZAqvV6tSbb9++HePHj0dkZCQkScL69evt2ywWC37/+99j4MCB8PHxQWRkJB544AEUFxe3es5FixbZW6qaHnFxcU7V5WqcBUZERKSsq5oF1qtXL7z88ss4efIkNm7cCLPZjDvvvBNhYWFOnae6uhoJCQlYvnz5Fdtqampw4MABvPjiizhw4ADWrl2LnJwc3HXXXb943gEDBqCkpMT+2LFjh1N1uZpaxVlgRERESnJqLbCfU6lUSE1NRWpqKs6ePXvF0hi/pOnY5uj1emzZssXhtTfeeAM33ngjioqKWl12Q6PRIDw83Kla5MRZYERERMpqtztBh4SEYO7cue11umYZjUZIkoSAgIBW98vNzUVkZCRiYmKQlpaGoqKiVvc3m80wmUwOD1e6dB8gl74NERERtaDNLUAxMTFt2u/EiRNXXUxr6urq8Pvf/x7Tpk2Dv79/i/slJiYiIyMDsbGxKCkpweLFizFy5EgcOXIEfn5+zR6zZMkSLF682CV1N6dpLTAuhUFERKQMpxZD7d69O+6//36Ehoa6sqYrWCwWTJkyBUIIrFixotV9L+9SGzRoEBITE9G9e3esWbMGDz30ULPHzJ8/36H1ymQyISoqqn2Kb4Z9DBC7wIiIiBTR5gD04YcfYuXKlVi6dClSU1MxY8YM3HHHHVCp2q0XrVlN4efkyZPYunVrq60/zQkICEDfvn2Rl5fX4j46nQ46ne5aS20zLoVBRESkrDanl8mTJ2PTpk3Iy8vDsGHD8NRTTyEqKgrPPvsscnNzXVJcU/jJzc3F119/jS5dujh9jqqqKuTn5yMiIsIFFV4dDoImIiJSltPNN127dsXzzz+P3NxcvP/++9izZw/i4uJw4cIFp9+8qqoK2dnZyM7OBgAUFBQgOzsbRUVFsFgsuPfee7Fv3z689957sFqtMBgMMBgMqK+vt59j9OjReOONN+zPn376aWRlZaGwsBA7d+7ExIkToVar3WrJjqalMDgGiIiISBlXNQ2+rq4OH3/8MVauXIk9e/Zg8uTJ8Pb2dvo8+/btw+23325/3jQOJz09HYsWLcKGDRsAAIMHD3Y4btu2bbjtttsAAPn5+Th37px92+nTpzFt2jSUl5cjJCQEI0aMwO7duxESEuJ0fa6itt8IUeFCiIiIOimnAtCePXvw1ltvYc2aNYiJicGMGTPwySef2BdGddZtt93W6nIQbVkqorCw0OH56tWrr6oWOanZAkRERKSoNgegAQMGoKysDPfffz+ysrKQkJDgyro6NC6FQUREpKw2B6Bjx47Bx8cH77zzTqt3fD5//ny7FNaRqS+OvOIsMCIiImW0OQCtWrXKlXV0Kk0tQOwCIyIiUkabA1B6eror6+hUVLwRIhERkaJcexdDapaaLUBERESKYgBSAJfCICIiUhYDkALsY4CYf4iIiBTBAKSApllg7AIjIiJSBgOQAuyDoBmAiIiIFOH0UhhWqxUZGRnIzMxEWVkZbDabw/atW7e2W3EdlZo3QiQiIlKU0wHoiSeeQEZGBsaNG4f4+HhIF7/Mqe24FAYREZGynA5Aq1evxpo1a3DHHXe4op5OQWILEBERkaKcHgOk1WrRu3dvV9TSadi7wGy/sCMRERG5hNMBaN68eXjttdfatFI7NY+zwIiIiJTldBfYjh07sG3bNmzatAkDBgyAh4eHw/a1a9e2W3EdFZfCICIiUpbTASggIAATJ050RS2dhn0pDAYgIiIiRTgdgLgq/LXjLDAiIiJlOR2Ampw9exY5OTkAgNjYWISEhLRbUR2dyj4LTOFCiIiIOimnB0FXV1djxowZiIiIwKhRozBq1ChERkbioYceQk1NjStq7HDYAkRERKQspwPQ3LlzkZWVhc8++wwVFRWoqKjAp59+iqysLMybN88VNXY4XAqDiIhIWU53gX3yySf4+OOPcdttt9lfu+OOO+Dl5YUpU6ZgxYoV7Vlfh8SlMIiIiJTldAtQTU0NwsLCrng9NDSUXWBtdLEBiF1gRERECnE6ACUlJWHhwoWoq6uzv1ZbW4vFixcjKSmpXYvrqHgfICIiImU53QX22muvISUlBd26dUNCQgIA4IcffoCnpye+/PLLdi+wI7q0FAYDEBERkRKcDkDx8fHIzc3Fe++9h+PHjwMApk2bhrS0NHh5ebV7gR1R0ywwNgAREREp46ruA+Tt7Y1HHnmkvWvpNDgLjIiISFltCkAbNmxAamoqPDw8sGHDhlb3veuuu9qlsI6Ms8CIiIiU1aYANGHCBBgMBoSGhmLChAkt7idJEqxWa3vV1mFxNXgiIiJltSkA2Wy2Zv9MV0fFFiAiIiJFOT0N/p133oHZbL7i9fr6erzzzjvtUlRHx6UwiIiIlOV0AJo+fTqMRuMVr1dWVmL69OntUlRHxxYgIiIiZTkdgIQQkC5+gV/u9OnT0Ov17VJUR3dpFpjChRAREXVSbZ4GP2TIEEiSBEmSMHr0aGg0lw61Wq0oKCjA2LFjXVJkR9M0C0ywBYiIiEgRbQ5ATbO/srOzkZKSAl9fX/s2rVaLHj16YNKkSe1eYEekutjuxvsAERERKaPNAWjhwoUAgB49emDq1Knw9PR0WVEdHe8DREREpCyn7wSdnp7uijo6Fc4CIyIiUpbTAchqtWLZsmVYs2YNioqKUF9f77D9/Pnz7VZcR8XV4ImIiJTl9CywxYsXY+nSpZg6dSqMRiPmzp2Le+65ByqVCosWLXLqXNu3b8f48eMRGRkJSZKwfv16h+1CCCxYsAARERHw8vJCcnIycnNzf/G8y5cvR48ePeDp6YnExER8//33TtXlak1dYLynJBERkTKcDkDvvfce/vOf/2DevHnQaDSYNm0a/vvf/2LBggXYvXu3U+eqrq5GQkICli9f3uz2v/zlL3j99dfx5ptvYs+ePfDx8UFKSgrq6upaPOeHH36IuXPnYuHChThw4AASEhKQkpKCsrIyp2pzJTUXQyUiIlKU0wHIYDBg4MCBAABfX1/7TRHvvPNObNy40alzpaam4uWXX8bEiROv2CaEwN///ne88MILuPvuuzFo0CC88847KC4uvqKl6HJLly7FI488gunTp6N///5488034e3tjZUrVzpVmys13UaJXWBERETKcDoAdevWDSUlJQCAXr164auvvgIA7N27Fzqdrt0KKygogMFgQHJysv01vV6PxMRE7Nq1q9lj6uvrsX//fodjVCoVkpOTWzwGAMxmM0wmk8PDlTgImoiISFlOB6CJEyciMzMTAPD444/jxRdfRJ8+ffDAAw9gxowZ7VaYwWAAAISFhTm8HhYWZt/2c+fOnYPVanXqGABYsmQJ9Hq9/REVFXWN1bfOPgaILUBERESKcHoW2CuvvGL/89SpUxEdHY1du3ahT58+GD9+fLsWJ5f58+dj7ty59ucmk8mlIUjFMUBERESKcjoA/VxSUhKSkpLaoxYH4eHhAIDS0lJERETYXy8tLcXgwYObPSY4OBhqtRqlpaUOr5eWltrP1xydTteu3Xe/5FILkGxvSURERJdpUwDasGFDm0941113XXUxl+vZsyfCw8ORmZlpDzwmkwl79uzBrFmzmj1Gq9Vi2LBhyMzMtC/dYbPZkJmZiTlz5rRLXe2Bs8CIiIiU1aYA1BQmmkiSdMVCnk0rxFut1ja/eVVVFfLy8uzPCwoKkJ2djaCgIERHR+PJJ5/Eyy+/jD59+qBnz5548cUXERkZ6VDP6NGjMXHiRHvAmTt3LtLT03HDDTfgxhtvxN///ndUV1dj+vTpba7L1XgjRCIiImW1aRC0zWazP7766isMHjwYmzZtQkVFBSoqKrBp0yYMHToUmzdvdurN9+3bhyFDhmDIkCEAGsPLkCFDsGDBAgDA7373Ozz++OOYOXMmhg8fjqqqKmzevNlhHbL8/HycO3fO/nzq1Kn461//igULFmDw4MHIzs7G5s2brxgYraRLN0JkACIiIlKCJH7elPML4uPj8eabb2LEiBEOr3/77beYOXMmjh071q4FKsFkMkGv18NoNMLf37/dz19WWYcb/5gJSQIKloxr9/MTERF1Rs58fzs9DT4/Px8BAQFXvK7X61FYWOjs6TqlphYgIXBFVyIRERG5ntMBaPjw4Zg7d67DTKvS0lI888wzuPHGG9u1uI5K1XQraHAgNBERkRKcDkArV65ESUkJoqOj0bt3b/Tu3RvR0dE4c+YM3nrrLVfU2OE0DYIGOBWeiIhICU7fB6h37944dOgQtmzZguPHjwMA+vXrh+TkZPtMMGqd2iEAMQERERHJ7apuhChJEsaMGYMxY8a0dz2dgppdYERERIpqUwB6/fXXMXPmTHh6euL1119vdd/f/va37VJYR6a6rOOR9wIiIiKSX5sC0LJly5CWlgZPT08sW7asxf0kSWIAaoPLW4B4LyAiIiL5tSkAFRQUNPtnujqXjwFiFxgREZH8nJ4FRtdOkiQ0NQKxC4yIiEh+bWoBmjt3bptPuHTp0qsupjNRSxIahIDNpnQlREREnU+bAtDBgwfbdDJOg2+7xpshCrYAERERKaBNAWjbtm2urqPTUakAWDkImoiISAkcA6QQ+4rwbAEiIiKS3VXdCHHfvn1Ys2YNioqKUF9f77Bt7dq17VJYR9e0HAZngREREcnP6Rag1atX4+abb8axY8ewbt06WCwWHD16FFu3boVer3dFjR1S01R4tgARERHJz+kA9Kc//QnLli3DZ599Bq1Wi9deew3Hjx/HlClTEB0d7YoaO6SmLjArZ4ERERHJzukAlJ+fj3HjxgEAtFotqqurIUkSnnrqKfz73/9u9wI7KnaBERERKcfpABQYGIjKykoAQNeuXXHkyBEAQEVFBWpqatq3ug6Mg6CJiIiU4/Qg6FGjRmHLli0YOHAgJk+ejCeeeAJbt27Fli1bMHr0aFfU2CGp2QJERESkmDYHoCNHjiA+Ph5vvPEG6urqAADPP/88PDw8sHPnTkyaNAkvvPCCywrtaLgUBhERkXLaHIAGDRqE4cOH4+GHH8Z9990HAFCpVHj22WddVlxH1tQCJBiAiIiIZNfmMUBZWVkYMGAA5s2bh4iICKSnp+Pbb791ZW0dGmeBERERKafNAWjkyJFYuXIlSkpK8I9//AOFhYW49dZb0bdvX/z5z3+GwWBwZZ0dDmeBERERKcfpWWA+Pj6YPn06srKy8NNPP2Hy5MlYvnw5oqOjcdddd7mixg6Js8CIiIiUc01rgfXu3RvPPfccXnjhBfj5+WHjxo3tVVeHxxYgIiIi5VzVWmAAsH37dqxcuRKffPIJVCoVpkyZgoceeqg9a+vQ1BejJ2eBERERyc+pAFRcXIyMjAxkZGQgLy8PN998M15//XVMmTIFPj4+rqqxQ7J3gbEFiIiISHZtDkCpqan4+uuvERwcjAceeAAzZsxAbGysK2vr0NgFRkREpJw2ByAPDw98/PHHuPPOO6FWq11ZU6egsg+CVrgQIiKiTqjNAWjDhg2urKPTaboRYoONNwIiIiKS2zXNAqOrp704CrrByiYgIiIiuTEAKUSjbmwBsvBW0ERERLJjAFKIRnWxBYiDgIiIiGTHAKQQj4stQA1sASIiIpIdA5BCNBfHAFk4BoiIiEh2DEAK8eAsMCIiIsUwACnk0iBotgARERHJze0DUI8ePSBJ0hWP2bNnN7t/RkbGFft6enrKXPUvu9QFxhYgIiIiuV31Yqhy2bt3L6xWq/35kSNH8Otf/xqTJ09u8Rh/f3/k5OTYn0sX77rsTngfICIiIuW4fQAKCQlxeP7KK6+gV69euPXWW1s8RpIkhIeHu7q0a6K5OAbIwjFAREREsnP7LrDL1dfX491338WMGTNabdWpqqpC9+7dERUVhbvvvhtHjx5t9bxmsxkmk8nh4WoatgAREREp5roKQOvXr0dFRQUefPDBFveJjY3FypUr8emnn+Ldd9+FzWbDzTffjNOnT7d4zJIlS6DX6+2PqKgoF1TviPcBIiIiUo4khLhumiBSUlKg1Wrx2WeftfkYi8WCfv36Ydq0afjDH/7Q7D5msxlms9n+3GQyISoqCkajEf7+/tdcd3Ne+zoXy77+CfcnRuNPEwe65D2IiIg6E5PJBL1e36bvb7cfA9Tk5MmT+Prrr7F27VqnjvPw8MCQIUOQl5fX4j46nQ46ne5aS3SKhi1AREREirluusBWrVqF0NBQjBs3zqnjrFYrDh8+jIiICBdVdnUudYFdNw1wREREHcZ1EYBsNhtWrVqF9PR0aDSOjVYPPPAA5s+fb3/+0ksv4auvvsKJEydw4MAB/OY3v8HJkyfx8MMPy112q5oWQ61nCxAREZHsrosusK+//hpFRUWYMWPGFduKioqgUl3KcRcuXMAjjzwCg8GAwMBADBs2DDt37kT//v3lLPkXeWg4C4yIiEgp10UAGjNmDFoaq/3NN984PF+2bBmWLVsmQ1XXhmuBERERKee66ALriLgaPBERkXIYgBRiHwTNFiAiIiLZMQAppGkQNFuAiIiI5McApBDeB4iIiEg5DEAKaeoCYwsQERGR/BiAFOJhHwTNFiAiIiK5MQAppGkMUIONLUBERERyYwBSCFeDJyIiUg4DkEJ4HyAiIiLlMAApRMM7QRMRESmGAUghTYOguRYYERGR/BiAFKKxT4NnCxAREZHcGIAU4sE7QRMRESmGAUghHhqOASIiIlIKA5BCLl8LTAi2AhEREcmJAUghTfcBAgArb4ZIREQkKwYghTTdBwjg3aCJiIjkxgCkkKb7AAGcCUZERCQ3BiCFeFzeAsSZYERERLJiAFKIWiVButgIZOFMMCIiIlkxACmI9wIiIiJSBgOQgrgiPBERkTIYgBTEFeGJiIiUwQCkIHsLEMcAERERyYoBSEFNd4PmLDAiIiJ5MQApiCvCExERKYMBSEFaTePlr29gACIiIpITA5CCvLVqAECNxapwJURERJ0LA5CCvD00AIDaegYgIiIiOTEAKcirqQWIAYiIiEhWDEAKauoCq61vULgSIiKizoUBSEFsASIiIlIGA5CCmlqAqhmAiIiIZMUApCBvbdMgaHaBERERyYkBSEFeHuwCIyIiUgIDkIIuDYJmACIiIpITA5CCvDkImoiISBFuHYAWLVoESZIcHnFxca0e89FHHyEuLg6enp4YOHAgvvjiC5mqdZ7XxTFAvBM0ERGRvNw6AAHAgAEDUFJSYn/s2LGjxX137tyJadOm4aGHHsLBgwcxYcIETJgwAUeOHJGx4rbjfYCIiIiU4fYBSKPRIDw83P4IDg5ucd/XXnsNY8eOxTPPPIN+/frhD3/4A4YOHYo33nhDxorbjvcBIiIiUobbB6Dc3FxERkYiJiYGaWlpKCoqanHfXbt2ITk52eG1lJQU7Nq1q9X3MJvNMJlMDg85eHtwEDQREZES3DoAJSYmIiMjA5s3b8aKFStQUFCAkSNHorKystn9DQYDwsLCHF4LCwuDwWBo9X2WLFkCvV5vf0RFRbXbZ2hN032A2AJEREQkL7cOQKmpqZg8eTIGDRqElJQUfPHFF6ioqMCaNWva9X3mz58Po9Fof5w6dapdz9+SS11gHANEREQkJ43SBTgjICAAffv2RV5eXrPbw8PDUVpa6vBaaWkpwsPDWz2vTqeDTqdrtzrbyj4ImrPAiIiIZOXWLUA/V1VVhfz8fERERDS7PSkpCZmZmQ6vbdmyBUlJSXKU57SmAGSxClisNoWrISIi6jzcOgA9/fTTyMrKQmFhIXbu3ImJEydCrVZj2rRpAIAHHngA8+fPt+//xBNPYPPmzfjb3/6G48ePY9GiRdi3bx/mzJmj1EdoVVMXGMBxQERERHJy6y6w06dPY9q0aSgvL0dISAhGjBiB3bt3IyQkBABQVFQElepShrv55pvx/vvv44UXXsBzzz2HPn36YP369YiPj1fqI7RKq1ZBrZJgtQnU1luh9/JQuiQiIqJOQRJCCKWLcDcmkwl6vR5GoxH+/v4ufa+ExV/BWGvBlqdGoU+Yn0vfi4iIqCNz5vvbrbvAOoMgHy0A4Hx1vcKVEBERdR4MQAoL9G7s9rpQwwBEREQkFwYghV1qAbIoXAkREVHnwQCksKYAxBYgIiIi+TAAKSyQY4CIiIhkxwCksCBvBiAiIiK5MQApjC1ARERE8mMAUlhTCxDHABEREcmHAUhhbAEiIiKSHwOQwro0zQJjACIiIpINA5DCmlqAquutqOWCqERERLJgAFKYv6cGfp6Na9KeulCjcDVERESdAwOQwiRJQvcu3gCAwnPVCldDRETUOTAAuYHuXXwAAEXn2QJEREQkBwYgN9CjqQWonC1AREREcmAAcgPdgxpbgE6WswWIiIhIDgxAbqBpDBADEBERkTwYgNxA71BfAI2zwCp4R2giIiKXYwByA118degV4gMhgO8LzitdDhERUYfHAOQmEmO6AAD2MAARERG5HAOQm0jsGQQA2FNQrnAlREREHR8DkJu46WIL0I/FJpjqLApXQ0RE1LExALmJMH9P9OjiDZsA9hWyG4yIiMiVGIDcSGLPi+OATjAAERERuRIDkBtJ6tUYgNYePANjDbvBiIiIXIUByI2MjQ9HTIgPzlaa8ffMn5Quh4iIqMNiAHIjnh5qvDCuHwBgQ3YxGqw2hSsiIiLqmBiA3MzIPiEI9PZAeXU97wlERETkIgxAbsZDrcLY+HAAwOeHShSuhoiIqGNiAHJD4wZGAgA2HylhNxgREZELMAC5oZtigtDFR4sLNRZ8m3tO6XKIiIg6HAYgN6RRqzA+obEVaOGGo6jknaGJiIjalUbpAqh5TyX3xZYfS1F0vgaz3j2AUD8dvLRqvDwhHpIkKV0eERHRdY0tQG5K7+2B16cNhlolYUfeOaw9eAbv7SnCjyUmpUsjIiK67jEAubFh3YPw6r2D0DXAy/5a1k9nFayIiIioY2AAcnP3DO2G7579FV66ewAAYOuxMgghFK6KiIjo+sYAdJ24PTYUkgTsO3kB/Rd8ieXb8pQuiYiI6Lrl1gFoyZIlGD58OPz8/BAaGooJEyYgJyen1WMyMjIgSZLDw9PTU6aKXScqyBt/nDAQAFBrseLVL3Nw5z++xXd5nCZPRETkLLeeBZaVlYXZs2dj+PDhaGhowHPPPYcxY8bgxx9/hI+PT4vH+fv7OwSljjJr6v7EaIwbGIHU17aj2FiHI2dMSPvvHtzSuwu6BXhD7+2B/7upO6KCvJUulYiIqFlCCLf4XnbrALR582aH5xkZGQgNDcX+/fsxatSoFo+TJAnh4eGuLk8Rem8P/PGegZi+aq/9te/yygGUAwDe3lmIuwdHIkLvhQs19RifEInhPYIUqpaIOqN1B0/D0iAwZXiU0qU4yC2thCRJ6B3qq2gdQgjUWWzQqCUcPmNEkLcWPYJb/qXe3GDFnzYew/6iC7g9NhTzxsTCYKzDR/tOYdKwbsgtq8KhUxV4ZFQMgMaFtZt7zyNnGmcRD+ymd9hWaqrD+oNncN/waOi9Pa441mK14e2dhThYVIFZt/VCXLgfcsuqcLK8Bv5eGtzcKxiVdRZ8l1eOpJgu9nP8cKoCxRW1UKkk6L080DfMD89+cgiZx8vw+7GxmDmq11Vfw/YgietoRG1eXh769OmDw4cPIz4+vtl9MjIy8PDDD6Nr166w2WwYOnQo/vSnP2HAgAEtntdsNsNsNtufm0wmREVFwWg0wt/fv90/R3s4X10PtSRh05ESWGwCZ011+C6/HPtPXnDYz0+nwRtpQ9E71BeHTlVg9d5TqKyz4L/pwxHko8WxEhMq6xrQK8QH/9iah4OnKvBcahwSY7rYz9FgtWHtwTMY2ScYEXqvn5dCdF2z2QQWfXYUapWEBXf2d4vfTK9GUXkNgny18NVd/e+17+4+Ca1GhSk3tB5cqswNWHvgNBK6BaDWYsXz6w7juTv6YXS/MOw+UY77/r0bALD5yZGIDPCCr1YDlar16yqEQKnJjDB/nf3v4JVNx1F0vhpLpwxu9kt9/8kL2Jl3DmMGhCM23M+hvg/3nsKdgyIQ5t84BOJCdT1u+fNW2ITAit8Mw6ubc9AnzBe/7h+G5H5hV5xfCIHC8hoAgLHWgn4RftCqVXhl83EcPm3EC+P6I9hXa5+ZGxPii6HRAXh3TxEyj5Wib5gfPD3UePTWGJRX1WP3iXIMiQ5Ejy7e+O3qg/jisMHh/W7sEYRAHw/8un84auobUGqqww3dg9AnzBfPfnIYOy4b7vC3yQn4z7cncNxQecU16d7FGyvShuFkeTUGRwfgyBkTrDYb1h08gy+PlgIAIvWe9p/xB4su2D8nAHQN8MKQ6ACcqahFcUUtwv09UVheA2NtyzfkTRkQhp355aisawAAjOwTjBJjHfLKqlo8xkMt4fPHRzr8vbUHk8kEvV7fpu/v6yYA2Ww23HXXXaioqMCOHTta3G/Xrl3Izc3FoEGDYDQa8de//hXbt2/H0aNH0a1bt2aPWbRoERYvXnzF6+4cgJrTYLXh7V0nceSMEXtOlKPYWHdV5/FQS/hk1s0Y1C0AAPB6Zi6WbvkJN/YIwksTBqBPqB/Uv/DDjOjnPtxbhGMllZh/Rxx0miu/zNqDEAKm2oYrfostrqiFv5dHs+Fg7YHTmLvmBwDA54+PQHxX/RX7NOebnDLMXfMDft0vDIvuGgAv7bV9psxjpfjbVz9h8d0DWmy1NdVZoNOo8E3OWZRX1SO3rBLnq+sxqFsAlnxxDA02gR5dvDFleBQ0Kgn1DTY8PDIG7+4+iQabwP8bFYOzVWYEeWuhUTcOARVCoN5qw9FiE+75504AwNIpCbg9NhR+nhoUllcjJtgXlXUNOFdthqeHGlPe3IUzFbXQe3lAp1GhrLLxF8hlUxPw6uYch589WrUKo/oG47bYULz0+Y/4x7QhSOwZhFPna/GnL47B3GDF4KhA7Dt5HodOG5Ge1B2/GxuH9dln8Py6IwCAcQMjcL66Hn3DfDH7V71x5IwRr2w6jp9KL33B6jQq3NgzCAO76pGxsxA19VZEB3lj4fj+8PP0wKHTFXh547Fmr+tdCZF49NZeiAzwxFs7CrDxcAkMxjrU1Fvt+8QE+6BvmB82HzU0e4620KgkeHmoUWlucPpYTw8VQvx0OHW+9qrf3x1o1Sp4adUw1lqQ3C8U/00f3q7n75ABaNasWdi0aRN27NjRYpBpjsViQb9+/TBt2jT84Q9/aHaf67EFqC3OVprx583HkfXTWZytNKNvmC8abAInzla3eIyHWoLF2vhP4qERPRHo7YG/fvWTwz4Du+oR4N34ZfKbm7pjWPdA+29PdRYrdp1obAY11VoQ4qe74jdqi9WGI2eMGNhVb/8h/EtKjLUoMdZhaHSgw+s19Q3YdNiAlPjwa/rN191ZbQIXauoR7KuT/b2LK2pRZ7EiKsgbGpV0xd9nZZ0FZZVm9AppuVuhytyA+IVfAgCeTY3Do7f2ghACJ85Vo3uQN6rNVuwtPI9bY0PgoVbhvT0nodOoEd/VH//vf/sxJCoAi++Oh96rMdjU1Dfg8GkjvLUaPPLOPmg1Kiy4sz8Ky6vx8sZjeCYlFj26+GBbThm+OmqAqa4Bwb46vD2jseWzwSoQ7KtDrcWKlL9vx9mLX+CzbuuF34+Nu6L+bTllKCqvwW9u6g6rTeDrY6V47L0D9u2zb++FZ1LisLfwPGrqrbi1b4jD8UXlNXgtMxfHDSb8dXIC+kVc+rkihMCp87WY/K+dKDU11vHQiJ4I89ehzGSGh0aFzUcMKDjX8v/b1sSF+9lbCubc3hv/2p6PHl188Mb9Q9Et0AtPrD6IHXnnUGdpeeHlmBCfVn9uOMvfUwNTnfMhoL390udq/PcO+8/E5kQHeaNboBd25pe3+X09PVS4/8buiNB7oneoL6ZnNA5pGNhVjyPFRsSG+WFApB6Zx0tRUWPBjT2D8PKEeHTv4o15a37A54dKEOavw8LxA+zh/qujpfj6WGmr7zvv131RY7GipKIWPYN9oVFLCPP3RKmpDjvzz+G7vHKM7BOMMpMZVeYG/HFiPHadKMe/sk5gZJ9g/DNtKN7MysfRYhMW3Nkf3bv4YNmWn/DFkRIMjQ5Ecr8w/PGLH5HSPxy3xoagZ7APfLQanK+pxyubjsNYa8EjI2MQ39Uf/9iah2fGxCLQR9vm69YWHS4AzZkzB59++im2b9+Onj17On385MmTodFo8MEHH7Rpf2cu4PVACIGaeit8LgaEYyUm5J+twk+lVUjopsfnh0qw7uAZjBsUgYV39sftf/0G1Zf95vNL/D01GBCpR7jeEwXnqpF9qsK+LTrIG3+bkoBjJSZ8cbgEvjoNTl+oxXFDJcL8degT6oeyyjq8dHc8hvcIQnFFLTJ2FkIlAXUWGxpsAgnd9PjnN/koOl+D1+4bjLsHd7Wff877B/D5oRIAQHK/UNw9uCvGDYz4xeb2lpytNOOTA6dhEwJpid3tX7g/t/SrHHy47xSWThmMMH8deoe2TzPunhONP0QHdtPDy0NtDxvPrzuM978vwv9mJGJEn2CHY46cMSK6izf8PT1w6nwN8sqqcFtsiENQsdmEU9ekuKIWB4ouYGSfEIz+WxbOVTV+MQ+OCsC//28Y9hSch6eHGp9mn0FWzllUmhvw/0bF4NFbe8Fbp4b2YrA9WmxC71BffH2sFHPePwgAUKskxIb5odRUh/LqetzQPRAXauqRf7Yadw6KwK/iQu0tMoHeHrhQ09j0fkvvLnjw5p44fMaI9/cU2Wtq4uWhRq2l7f9uVRJga+an352DIvBt7jlEB3kjKsgLx0oqHcKHj1bd7P+PlAFh9i4GABgSHYBRfUKwp6AcewsvwHrxzby1aug0Knh6qKFRS7hQbUGVky0CXXy0aLAJ1NZb4aGWnPr/+kviwv1w4mw16q0tB6Iwfx1m394bCz49CqDx/3mwrxaHThsxaWg3/G5sLH738SHkllWhrLKu1XAFNHbVT0uMxv6TFxy68f10GtiEgAAQ5u8Js8WKElMdhAC6BXohuV8Yfju6Dz7NPoOCc9X2XxT2n7yA4T2CYKprQJmpDqZaC4qNdfBQS/hn2jB8sv80RvYNRlpidzz7ySGs3nvK4fM/dntvDIj0R9cAL3h6NLZWfLTvFI4bKjGseyCm3BAFlQTUW23QqlWQJAmvZ+bi71//hEdGxmDemFgYay0oq6zD/731PcL9PfHRo0n44XQFduSewz1DuzmMQ1p/8Ax8dRok9w+Dqc4CP50GkiShtt4KY60F4XrHmcy5pZXoFujdbKvjj8UmaNQSLFYb/vTFMUwdHo1benVBjqESSb26tNjFa7HaUFxRi+5dfCCEgBCw/8zIMVQiKsgL3lr3/yWzwwQgIQQef/xxrFu3Dt988w369Onj9DmsVisGDBiAO+64A0uXLm3TMR0tAP2SanMD1h48g7sSIqH38sD+kxfwXd45nK+uR52lMTiF+unwZlY+7h3WDRF6L3hp1dhXeAFZP5XhXFV9u9RxeetTa0b0Dsa5KjOKK2qb/S3SV6dBXLgfZv+qN27oHgiVJOG4wYTtP53DxsMl0Kgk9ArxxR8nxmNnfjm+PlaKwVEBKDOZ8e6ek6i4+IXbo4s3xsZHINRPh+m39EDWT2ex60Q5JEh4Myvf4T0fHtETW3PKMCQqEH+6Jx6HTxtRXl0Pg7EOo/qGoNRUhwGR/qgyN6C+wYaoQG8YTHXY8EMxjpwx4scSE7oHeWNbzqU7fQ+JDsBb6cOxZt8pvLLpOIDGAHLf8CgUna9B14u/dW48VAJvrRrdu/jgp9JKWG0CL97ZH6PjQvH1sVIcN1Tii8MlSOwZhP6R/sg8Voa+YX7oF+GP7l288au4UHz1Yyk2HirGwK56eHqosWzLT9f0pRoX7of+Ef5Ye/AMPD1UUEmSQ3dCe5MkoKWfZBqVhOfH9cP7e4qQe3FMglajQn1D45eyj1aN/z2ciIWfHsXhM8Y2v2d0kDcevbUX/rz5eKvjI5qM6huC0xdqWm1x6BrghfSbu+PU+VpU1Frg5aHCx/tPwyaAhKgA3J0QiUHd9IjvqodWrYK5wYYGmw1PrM5Gz2AfxIX7YUh0APLKqpDUKxgzMvZi/8kL6N7FGycvjvPQqlVIjAnCt7mNY0oCvD2Q2DMI/p4eGNEnGHcP7or6Bhtq6huwI+8c+oT64ZVNx/BdfjnqG2wI9tXinRmJ6B/pj4NFjYFl3KAIROi9YG6wXtG9KYRA5rEy/HC6AiF+Omw+YkBCVACSYrpAJUmICfGBn6cGfp4esFht2JlfDrUkITEmCJqLX8A2AXu3e53FimpzA7o40RoqhED2qQpoVKorBgBbbQInzlZBp1Hj5Plq3NIr+Kp/gaqss8DP0/GXpjqLFRqV1ObWbro2HSYAPfbYY3j//ffx6aefIjY21v66Xq+Hl1fjYNwHHngAXbt2xZIlSwAAL730Em666Sb07t0bFRUVePXVV7F+/Xrs378f/fv3b9P7drYAdC2sNoGsn8pQXlWPEmMdCs9VI/9sFX44bcTjv+qNLw6XIP9sNfpF+GNM/zCYG2wI8vFAVKA3Pj9cgqhAb3ybexZHiy+tcRYV1Ph3OzQ6EIHeWmTsLGy1hl4hPvDRaRDiq8PuE+XX/NtwkI8WNiHsQcgVgn11qKyzwNzQ+m/G7iQu3A9F52scgkxUkBfSk3rA38sD/9yW5zCY8uc0Kgmf/3YEfHUa/HDKiG05ZTh82oic0kr0DPbBvcO6YcU3+agyNyDUT4eySjNUEvDKpEEAgOfWHkaPYB/EhvkhzN8Tj94Wg39uy0eE3hNj48Nx5z922AdhShKg9/JA9yBvZEy/EYE+Wpw4W4VFn/2IO+LDMXV4FL7NPYcDRRcwdXgUIvResFht+MfWPLyemYu+Yb6YNLQbjhabEBXkhSAfHQK8PFBebUZ8pB4DIvX2cUb/2X4Cr2XmYnxCBNISu2P3iXKcrTRDpZJQeK4a8V31GDcwAj2CfWCqsyC3tAp+nhpU1jXAbLHip9JK1DXYsPFQCZ5NjcMtvR1b+EqMtfD39LC34Dqj2tyA8qp6RHfxxuHTRhSdr8HwHoEI9fe0tzp5eajbNKbPahMQQsAqhMvGcBFdqw4TgFpqqlu1ahUefPBBAMBtt92GHj16ICMjAwDw1FNPYe3atTAYDAgMDMSwYcPw8ssvY8iQIW1+Xwaga2OzCZRW1iFC74U6ixU19VYE/UI/b4PVhuOGSnhr1Yj52ViSjYdKsOVHAxbfFY9TF2rw+aESdPHR4rbYEHTx1Tmc+0J1PXJKK7Hlx1Ks+q7gii4OrUYFT43K3nLU9EUENP5m/P9ujcGDN/dAlbkBE/+5E+erL7VuqVWSvRsjvqs/bukVjC3HStErxBe78ssdujECvT0ufuk2/rZ/eQuFVq1qtntBrZLwxOg+yNhZ6PC+QOOsiv0nL6Cm3oowfx1G9wtDSUUtDp02IrlfGMYNioBNNI5reXnjj9h94jwAoH+EP0b1DUFlnQWfHypB1wAvpAwIh8Vqw/6TF7DrYpdbuL8n4rvqceJsFQSAtMRo3NgzCK9+mYNqcwNWPjgcAd5alFXW4e2dhRjROwQ2IXBDj0D7l6GxxoL56w5BrVKhvsGKL4+WYvKwbnhkVAystsbaQvwcf2sXQuC4oTEAeXqoUWaqQ4mxDglRAdh8xIBgXy1uuDgg+JfuHVJqqsPH+09jwpCu0GlUVz1eqqyyDl18dBzoT3Qd6jABSCkMQB3Dhep6WIWAxWpDiK8ODTZhH6x9oOgCTl+oxW2xIfBQqfD+90UY2ScYfcMujeU5X12PyjoLDhRdgLHGguT+YQCAVd8VIj2pB6K7XLrhpM0mcKaiFo+9dwCDowLw4p39odWocL66HjmGStwUE4SzlWZ4qFXw1qnxfcF5qFUSbugeBJsQ0KgkmOoaEOSjRbW5AWqVhPPV9Vi99xRS48PRL8Ifpy/U4NBpI34VF9rslOAmdRYr3szKh7HWgt+PjWt130OnK1BRY8HNvbq0exN9WWUdQnyvHARPROQqDEDXiAGIiIjo+uPM9zdHZREREVGnwwBEREREnQ4DEBEREXU6DEBERETU6TAAERERUafDAERERESdDgMQERERdToMQERERNTpMAARERFRp8MARERERJ0OAxARERF1OgxARERE1OkwABEREVGnwwBEREREnY5G6QLckRACAGAymRSuhIiIiNqq6Xu76Xu8NQxAzaisrAQAREVFKVwJEREROauyshJ6vb7VfSTRlpjUydhsNhQXF8PPzw+SJLXbeU0mE6KionDq1Cn4+/u323nJEa+zPHid5cNrLQ9eZ3m48joLIVBZWYnIyEioVK2P8mELUDNUKhW6devmsvP7+/vzP5cMeJ3lwessH15refA6y8NV1/mXWn6acBA0ERERdToMQERERNTpMADJSKfTYeHChdDpdEqX0qHxOsuD11k+vNby4HWWh7tcZw6CJiIiok6HLUBERETU6TAAERERUafDAERERESdDgMQERERdToMQDJZvnw5evToAU9PTyQmJuL7779XuqTryvbt2zF+/HhERkZCkiSsX7/eYbsQAgsWLEBERAS8vLyQnJyM3Nxch33Onz+PtLQ0+Pv7IyAgAA899BCqqqpk/BTub8mSJRg+fDj8/PwQGhqKCRMmICcnx2Gfuro6zJ49G126dIGvry8mTZqE0tJSh32Kioowbtw4eHt7IzQ0FM888wwaGhrk/Chub8WKFRg0aJD9ZnBJSUnYtGmTfTuvs2u88sorkCQJTz75pP01Xutrt2jRIkiS5PCIi4uzb3fLayzI5VavXi20Wq1YuXKlOHr0qHjkkUdEQECAKC0tVbq068YXX3whnn/+ebF27VoBQKxbt85h+yuvvCL0er1Yv369+OGHH8Rdd90levbsKWpra+37jB07ViQkJIjdu3eLb7/9VvTu3VtMmzZN5k/i3lJSUsSqVavEkSNHRHZ2trjjjjtEdHS0qKqqsu/z6KOPiqioKJGZmSn27dsnbrrpJnHzzTfbtzc0NIj4+HiRnJwsDh48KL744gsRHBws5s+fr8RHclsbNmwQGzduFD/99JPIyckRzz33nPDw8BBHjhwRQvA6u8L3338vevToIQYNGiSeeOIJ++u81tdu4cKFYsCAAaKkpMT+OHv2rH27O15jBiAZ3HjjjWL27Nn251arVURGRoolS5YoWNX16+cByGazifDwcPHqq6/aX6uoqBA6nU588MEHQgghfvzxRwFA7N27177Ppk2bhCRJ4syZM7LVfr0pKysTAERWVpYQovG6enh4iI8++si+z7FjxwQAsWvXLiFEY1hVqVTCYDDY91mxYoXw9/cXZrNZ3g9wnQkMDBT//e9/eZ1doLKyUvTp00ds2bJF3HrrrfYAxGvdPhYuXCgSEhKa3eau15hdYC5WX1+P/fv3Izk52f6aSqVCcnIydu3apWBlHUdBQQEMBoPDNdbr9UhMTLRf4127diEgIAA33HCDfZ/k5GSoVCrs2bNH9pqvF0ajEQAQFBQEANi/fz8sFovDtY6Li0N0dLTDtR44cCDCwsLs+6SkpMBkMuHo0aMyVn/9sFqtWL16Naqrq5GUlMTr7AKzZ8/GuHHjHK4pwH/T7Sk3NxeRkZGIiYlBWloaioqKALjvNeZiqC527tw5WK1Wh79UAAgLC8Px48cVqqpjMRgMANDsNW7aZjAYEBoa6rBdo9EgKCjIvg85stlsePLJJ3HLLbcgPj4eQON11Gq1CAgIcNj359e6ub+Lpm10yeHDh5GUlIS6ujr4+vpi3bp16N+/P7Kzs3md29Hq1atx4MAB7N2794pt/DfdPhITE5GRkYHY2FiUlJRg8eLFGDlyJI4cOeK215gBiIiaNXv2bBw5cgQ7duxQupQOKzY2FtnZ2TAajfj444+Rnp6OrKwspcvqUE6dOoUnnngCW7Zsgaenp9LldFipqan2Pw8aNAiJiYno3r071qxZAy8vLwUraxm7wFwsODgYarX6itHupaWlCA8PV6iqjqXpOrZ2jcPDw1FWVuawvaGhAefPn+ffQzPmzJmDzz//HNu2bUO3bt3sr4eHh6O+vh4VFRUO+//8Wjf3d9G0jS7RarXo3bs3hg0bhiVLliAhIQGvvfYar3M72r9/P8rKyjB06FBoNBpoNBpkZWXh9ddfh0ajQVhYGK+1CwQEBKBv377Iy8tz23/PDEAuptVqMWzYMGRmZtpfs9lsyMzMRFJSkoKVdRw9e/ZEeHi4wzU2mUzYs2eP/RonJSWhoqIC+/fvt++zdetW2Gw2JCYmyl6zuxJCYM6cOVi3bh22bt2Knj17OmwfNmwYPDw8HK51Tk4OioqKHK714cOHHQLnli1b4O/vj/79+8vzQa5TNpsNZrOZ17kdjR49GocPH0Z2drb9ccMNNyAtLc3+Z17r9ldVVYX8/HxERES4779nlwytJgerV68WOp1OZGRkiB9//FHMnDlTBAQEOIx2p9ZVVlaKgwcPioMHDwoAYunSpeLgwYPi5MmTQojGafABAQHi008/FYcOHRJ33313s9PghwwZIvbs2SN27Ngh+vTpw2nwPzNr1iyh1+vFN9984zCdtaamxr7Po48+KqKjo8XWrVvFvn37RFJSkkhKSrJvb5rOOmbMGJGdnS02b94sQkJCOGX4Z5599lmRlZUlCgoKxKFDh8Szzz4rJEkSX331lRCC19mVLp8FJgSvdXuYN2+e+Oabb0RBQYH47rvvRHJysggODhZlZWVCCPe8xgxAMvnHP/4hoqOjhVarFTfeeKPYvXu30iVdV7Zt2yYAXPFIT08XQjROhX/xxRdFWFiY0Ol0YvTo0SInJ8fhHOXl5WLatGnC19dX+Pv7i+nTp4vKykoFPo37au4aAxCrVq2y71NbWysee+wxERgYKLy9vcXEiRNFSUmJw3kKCwtFamqq8PLyEsHBwWLevHnCYrHI/Gnc24wZM0T37t2FVqsVISEhYvTo0fbwIwSvsyv9PADxWl+7qVOnioiICKHVakXXrl3F1KlTRV5enn27O15jSQghXNO2REREROSeOAaIiIiIOh0GICIiIup0GICIiIio02EAIiIiok6HAYiIiIg6HQYgIiIi6nQYgIiIiKjTYQAiIiKiTocBiIioBZIkYf369UqXQUQuwABERG7pwQcfhCRJVzzGjh2rdGlE1AFolC6AiKglY8eOxapVqxxe0+l0ClVDRB0JW4CIyG3pdDqEh4c7PAIDAwE0dk+tWLECqamp8PLyQkxMDD7++GOH4w8fPoxf/epX8PLyQpcuXTBz5kxUVVU57LNy5UoMGDAAOp0OERERmDNnjsP2c+fOYeLEifD29kafPn2wYcMG+7YLFy4gLS0NISEh8PLyQp8+fa4IbETknhiAiOi69eKLL2LSpEn44YcfkJaWhvvuuw/Hjh0DAFRXVyMlJQWBgYHYu3cvPvroI3z99dcOAWfFihWYPXs2Zs6cicOHD2PDhg3o3bu3w3ssXrwYU6ZMwaFDh3DHHXcgLS0N58+ft7//jz/+iE2bNuHYsWNYsWIFgoOD5bsARHT1XLbOPBHRNUhPTxdqtVr4+Pg4PP74xz8KIYQAIB599FGHYxITE8WsWbOEEEL8+9//FoGBgaKqqsq+fePGjUKlUgmDwSCEECIyMlI8//zzLdYAQLzwwgv251VVVQKA2LRpkxBCiPHjx4vp06e3zwcmIllxDBARua3bb78dK1ascHgtKCjI/uekpCSHbUlJScjOzgYAHDt2DAkJCfDx8bFvv+WWW2Cz2ZCTkwNJklBcXIzRo0e3WsOgQYPsf/bx8YG/vz/KysoAALNmzcKkSZNw4MABjBkzBhMmTMDNN998VZ+ViOTFAEREbsvHx+eKLqn24uXl1ab9PDw8HJ5LkgSbzQYASE1NxcmTJ/HFF19gy5YtGD16NGbPno2//vWv7V4vEbUvjgEiouvW7t27r3jer18/AEC/fv3www8/oLq62r79u+++g0qlQmxsLPz8/NCjRw9kZmZeUw0hISFIT0/Hu+++i7///e/497//fU3nIyJ5sAWIiNyW2WyGwWBweE2j0dgHGn/00Ue44YYbMGLECLz33nv4/vvv8dZbbwEA0tLSsHDhQqSnp2PRokU4e/YsHn/8cfzf//0fwsLCAACLFi3Co48+itDQUKSmpqKyshLfffcdHn/88TbVt2DBAgwbNgwDBgyA2WzG559/bg9gROTeGICIyG1t3rwZERERDq/Fxsbi+PHjABpnaK1evRqPPfYYIiIi8MEHH6B///4AAG9vb3z55Zd44oknMHz4cHh7e2PSpElYunSp/Vzp6emoq6vDsmXL8PTTTyM4OBj33ntvm+vTarWYP38+CgsL4eXlhZEjR2L16tXt8MmJyNUkIYRQuggiImdJkoR169ZhwoQJSpdCRNchjgEiIiKiTocBiIiIiDodjgEiousSe++J6FqwBYiIiIg6HQYgIiIi6nQYgIiIiKjTYQAiIiKiTocBiIiIiDodBiAiIiLqdBiAiIiIqNNhACIiIqJO5/8DRJ8ZsTt7IjMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Validation MAE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Plotting validation scores, excluding the first 10 data points**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gaikiIqKG4ndwY7PZoNPpoNPpYDCwQZ2/jHodABYUExERNRS/g5vjx4/j7rvvxhdffIGMjAxce+21mDNnDnQ6XSjH1+QZDbXBDaeliIiIGoTfwU10dDQmTJiARYsWYcuWLejWrRsefPBB2Gw2vPjii1iwYAHsdnsox9okmfTOU8xpKSIiooZRr9VSHTp0wN///nccOnQIP/30EywWC6688kqkp6cHe3xNnqF2Wspq57QUERFRQzinrar1ej3GjBmDMWPG4OTJkx7bMZBrWsrOzA0REVGDCFqH4latWuHRRx8N1sNFDKM8LcWaGyIiogbhd+amffv2fh23f//+eg8mEikFxVwtRURE1CAC2jizTZs2uPnmm5GWlhbKMUUUkxLcMHNDRETUEPwObr788ktMnz4dr7/+OsaMGYPbb78dl19+OfT6oM1sRSRD7fnh3lJEREQNw+/I5Prrr8e8efOwd+9e9OvXD4888ghycnLw5JNPYs+ePaEcY5Nm0ssFxZyWIiIiaggBp12ys7Px9NNPY8+ePZg5cyZWr16Nrl274uzZs6EYX5MnLwVnQTEREVHDqNdS8Orqanz99deYPn06Vq9ejeuvvx6xsbHBHltEMBrYxI+IiKghBRTcrF69Gh999BG++uortG/fHrfffju++eYbpKSkhGp8TZ5SUMwmfkRERA3C7+CmR48eKCoqws0334ylS5eiT58+oRxXxFA6FDNzQ0RE1CD8Dm527NiBuLg4fPLJJz47EZ85cyYoA4sUptppKXYoJiIiahh+Bzcff/xx0J986tSp+Pbbb7Fz507ExMRg8ODB+Mc//oEuXbr4vN+//vUvTJs2DYcPH0bLli1x3XXXYerUqYiOjg76GM8V95YiIiJqWH4HN5MmTQr6ky9duhT5+fkYMGAAbDYbnnrqKYwaNQrbt29HXFyc5n1mzpyJJ598EtOnT8fgwYOxe/du3HrrrdDpdHj99deDPsZzJe8KzswNERFRwzinjTPP1c8//6y6PGPGDKSlpWHdunUYNmyY5n1+//13DBkyBDfffDMAoG3bthg/fjxWr16tebzFYoHFYlEul5aWBmn0/lG2X+BScCIiogbRqNoLl5SUAABSU1O9HjN48GCsW7cOa9asAeDcy2ru3Lm4/PLLNY+fOnUqkpKSlK+cnJzgD9wHo1JQzGkpIiKihhDWzI3I4XDg4YcfxpAhQ9CzZ0+vx9188804deoULrzwQkiSBJvNhnvuuQdPPfWU5vGTJ09W7VZeWlraoAGO3OdGkgCHQ4K+NtghIiKi0Gg0mZv8/Hxs3boVs2bN8nnckiVL8NJLL+Hf//431q9fj2+//RY//fQT/va3v2kebzabkZiYqPpqSAYhmGH2hoiIKPQaRebm/vvvx48//ohly5ahdevWPo999tlnccstt+DOO+8EAPTq1QsVFRW4++678fTTTze6jTzlJn4Ai4qJiIgaQsDBjd1ux4wZM7Bw4UIUFRXB4ZaNWLRokd+PJUkSHnjgAcyZMwdLlixBu3bt6rxPZWWlRwBjMBiUx2tsjMJYuTM4ERFR6AUc3Dz00EOYMWMGrrjiCvTs2RM6Xf1rSPLz8zFz5kx8//33SEhIQGFhIQAgKSkJMTExAICJEyciOzsbU6dOBQCMHTsWr7/+Ovr27YtBgwZh7969ePbZZzF27FglyGlMjHpmboiIiBpSwMHNrFmz8NVXX3ldnRSIadOmAQCGDx+uuv7jjz/GrbfeCgA4fPiwKlPzzDPPQKfT4ZlnnsGxY8fQqlUrjB07Fi+++OI5jycU9Hod9DrAIXF/KSIiooYQcHATFRWFjh07BuXJ/ZlGWrJkieqy0WjElClTMGXKlKCMoSEY9XrU2B3cX4qIiKgBBFx9+9hjj+HNN99slPUtjZXcyM/OmhsiIqKQCzhzs3z5cixevBjz5s1Djx49YDKZVLd/++23QRtcpGAjPyIiooYTcHCTnJyMa665JhRjiVhG7gxORETUYAIObkKxO3ikM3JncCIiogZT7yZ+J0+exK5duwAAXbp0QatWrYI2qEgjBzfcPJOIiCj0Ai4orqiowO23347MzEwMGzYMw4YNQ1ZWFu644w5UVlaGYoxNnjwtZeO0FBERUcgFHNw8+uijWLp0Kf73v/+huLgYxcXF+P7777F06VI89thjoRhjkyevlmKfGyIiotALeFrqm2++wddff61qvHf55ZcjJiYGN9xwg9KYj1zkaSkWFBMREYVewJmbyspKpKene1yflpbGaSkv5P2l2MSPiIgo9AIObvLy8jBlyhRUV1cr11VVVeGFF15AXl5eUAcXKTgtRURE1HACnpZ68803MXr0aLRu3Rp9+vQBAGzatAnR0dGYP39+0AcYCVxLwZm5ISIiCrWAg5uePXtiz549+Pzzz7Fz504AwPjx4zFhwgRlJ29Si492dnEuq7aGeSRERESRr159bmJjY3HXXXcFeywRq2V8FADgdEVNmEdCREQU+fwKbn744QeMGTMGJpMJP/zwg89jr7rqqqAMLJK0jDcDAE6VWcI8EiIiosjnV3Azbtw4FBYWIi0tDePGjfN6nE6ng91uD9bYIkaLOGZuiIiIGopfwY1D2M3awZ2tA6ZkbsqZuSEiIgq1gJeCf/LJJ7BYPN+ka2pq8MknnwRlUJGmRW3NzalyZm6IiIhCLeDg5rbbbkNJSYnH9WVlZbjtttuCMqhII2duTjNzQ0REFHIBBzeSJEGn03lcf/ToUSQlJQVlUJFGCW4qauBgl2IiIqKQ8nspeN++faHT6aDT6TBixAgYja672u12HDhwAJdddllIBtnUpdYWFNsdEkqqrEipvUxERETB53dwI6+S2rhxI0aPHo34+HjltqioKLRt2xbXXntt0AcYCaKMeiTFmFBSZcWsP47g3uEdwj0kIiKiiOV3cDNlyhQAQNu2bXHjjTciOjo6ZIOKRK1TYlBSZcU/ft6JcX2zkJnEbs5EREShEHDNzaRJkxjY1MOr1/VRvj/NVVNEREQhE3BwY7fb8dprr2HgwIHIyMhAamqq6ou0dc9KRE6qM1tTw93BiYiIQibg4OaFF17A66+/jhtvvBElJSV49NFH8ac//Ql6vR7PP/98CIYYOcxGAwDAYmVwQ0REFCoBBzeff/45PvjgAzz22GMwGo0YP348PvzwQzz33HNYtWpVKMYYMaIMztPNzA0REVHoBBzcFBYWolevXgCA+Ph4paHflVdeiZ9++im4o4swUcba4MbG4IaIiChUAg5uWrdujYKCAgBAhw4d8MsvvwAA/vjjD5jN5uCOLsKYa4Mbi42bixIREYVKwMHNNddcg4ULFwIAHnjgATz77LPo1KkTJk6ciNtvvz3oA4wkzNwQERGFnt99bmQvv/yy8v2NN96I3NxcrFy5Ep06dcLYsWODOrhIoxQUM7ghIiIKmYCDG3d5eXnIy8sLxlginpmZGyIiopDzK7j54Ycf/H7Aq666qt6DiXSsuSEiIgo9v4IbeV8pmU6ngyRJHtcBziZ/pI01N0RERKHnV0Gxw+FQvn755Recd955mDdvHoqLi1FcXIx58+bh/PPPx88//xzq8TZpDG6IiIhCL+Cam4cffhj/+c9/cOGFFyrXjR49GrGxsbj77ruxY8eOoA4wkrimpRjcEBERhUrAS8H37duH5ORkj+uTkpJw8ODBIAwpckUxuCEiIgq5gIObAQMG4NFHH8WJEyeU606cOIHHH38cAwcODOrgIg2XghMREYVewMHN9OnTUVBQgNzcXHTs2BEdO3ZEbm4ujh07ho8++igUY4wYrLkhIiIKvYBrbjp27IjNmzdjwYIF2LlzJwCgW7duGDlypLJiirTJG2d+s/4oqq12vDvh/DCPiIiIKPLUq4mfTqfDqFGjMGrUqGCPJ6KZTa5E2U9bCvBPqx3RJkMYR0RERBR5/Apu3nrrLdx9992Ijo7GW2+95fPYBx98MCgDi0Ry5kZWWcPghoiIKNj8Cm7eeOMNTJgwAdHR0XjjjTe8HqfT6Rjc+GB2C2QqLDakxkWFaTRERESRya/g5sCBA5rfU2DcMzflFluYRkJERBS5Al4tRfUn1twAQGUNgxsiIqJg8ytz8+ijj/r9gK+//nq9BxPpzB6ZG+7DRUREFGx+BTcbNmzw68G4FNw3uc+NrILTUkREREHnV3CzePHiUI+jWZA7FMsY3BAREQUfa24aEDM3REREoVevJn5r167FV199hcOHD6OmpkZ127fffhuUgUUis3twU8OaGyIiomALOHMza9YsDB48GDt27MCcOXNgtVqxbds2LFq0CElJSaEYY8Rg5oaIiCj0Ag5uXnrpJbzxxhv43//+h6ioKLz55pvYuXMnbrjhBuTm5gb0WFOnTsWAAQOQkJCAtLQ0jBs3Drt27arzfsXFxcjPz0dmZibMZjM6d+6MuXPnBvpSGpxH5obBDRERUdAFHNzs27cPV1xxBQAgKioKFRUV0Ol0eOSRR/D+++8H9FhLly5Ffn4+Vq1ahQULFsBqtWLUqFGoqKjwep+amhpceumlOHjwIL7++mvs2rULH3zwAbKzswN9KQ3OPXPDpeBERETBF3DNTUpKCsrKygAA2dnZ2Lp1K3r16oXi4mJUVlYG9Fg///yz6vKMGTOQlpaGdevWYdiwYZr3mT59Os6cOYPff/8dJpMJANC2bdtAX0ZYuAc3bOJHREQUfAFnboYNG4YFCxYAAK6//no89NBDuOuuuzB+/HiMGDHinAZTUlICAEhNTfV6zA8//IC8vDzk5+cjPT0dPXv2xEsvvQS7XTsLYrFYUFpaqvoKF26/QEREFHp+Z262bt2Knj174p133kF1dTUA4Omnn4bJZMLvv/+Oa6+9Fs8880y9B+JwOPDwww9jyJAh6Nmzp9fj9u/fj0WLFmHChAmYO3cu9u7di/vuuw9WqxVTpkzxOH7q1Kl44YUX6j2uYNLpdPjf/Rfi+43H8OHyA6y5ISIiCgGdJEmSPwfq9XoMGDAAd955J2666SYkJCQEdSD33nsv5s2bh+XLl6N169Zej+vcuTOqq6tx4MABGAzOpnivv/46Xn31VRQUFHgcb7FYYLFYlMulpaXIyclBSUkJEhMTg/oa/PXbnpO45aM1AIA59w1G39yUsIyDiIioqSgtLUVSUpJf799+T0stXboUPXr0wGOPPYbMzExMmjQJv/322zkPFgDuv/9+/Pjjj1i8eLHPwAYAMjMz0blzZyWwAYBu3bqhsLDQo+cOAJjNZiQmJqq+wi3O7EqY3f3pOuw/WY4amyOMIyIiIoocfgc3Q4cOxfTp01FQUIC3334bBw8exEUXXYTOnTvjH//4BwoLCwN+ckmScP/992POnDlYtGgR2rVrV+d9hgwZgr1798LhcAUDu3fvRmZmJqKiogIeQzgkRpuU70+WWXDJP5fipbk7wjgiIiKiyBFwQXFcXBxuu+02LF26FLt378b111+Pd999F7m5ubjqqqsCeqz8/Hx89tlnmDlzJhISElBYWIjCwkJUVVUpx0ycOBGTJ09WLt977704c+YMHnroIezevRs//fQTXnrpJeTn5wf6UsKmQ6s4TMpro7puxu8HwzMYIiKiCON3zY03FRUV+PzzzzF58mQUFxd7XbWk+eRedhH/+OOPceuttwIAhg8fjrZt22LGjBnK7StXrsQjjzyCjRs3Ijs7G3fccQeeeOIJ1VSVN4HM2YXalqMlGPvOcgBAcqwJG58bFdbxEBERNVaBvH/XO7hZtmwZpk+fjm+++QZ6vR433HAD7rjjDlxwwQX1GnRDaUzBDQAcPl2JYa86d13f9sJoVT0OEREROQXy/h3QO+nx48cxY8YMzJgxA3v37sXgwYPx1ltv4YYbbkBcXNw5Dbq5ym0Ri6QYE0qqrDh6tgpdMoK7Co2IiKi58Tu4GTNmDH799Ve0bNkSEydOxO23344uXbqEcmzNRk5qDEqOWXHkTCWDGyIionPkd3BjMpnw9ddf48orr/SrtoX81zo5FluPleLo2cC2ryAiIiJPfgc3P/zwQyjH0ay1TokBABw5W1XHkURERFSXgJeCU/BlJjuDm6IySx1HEhERUV0Y3DQCKbHOpn5nKzw7LBMRETUFFpsd989cj1lrDod7KAxuGoOUOGdn5TMMboiIqIn6bNVh/Li5AE9+uyXcQ2Fw0xikxjqDm+JKBjdERNQ0bT9eGu4hKBjcNAIptcHNGQY3RETURBWUNJ5FMQxuGoGUOGfNTbXVgaoa/7evICKipu3AqQrc9P5KXPXOcljtjrrv0IgVlFQr35/jzk7njMFNIxBvNsJkcO6zdZbZGyKiZuPx2Zuwav8ZbD5agkOnw9Pr7GSZBd9vPIYa27kFV8eKXZmbmjAHagxuGgGdTueamqqowZEzlfjfpuNwOMIb+RIRUWjtPVmufB+uzP11//kdD83aiJmrD9X7MUqqrKrgqLqGwQ3BVXdztrIGQ19ZjAe+2IC5WwvCPCoiImBvURkemrUBe4vK6z6Y/CZJEkqrrMrlyhobJEnCT5sLsP9kOQpLqvH8D9uw72Roz7ucMVq251S9H+NEabXqcqXVdk5jOlfcgrqRkOtunvh6s3LdukNncWXvrHANiYgIAHDT+6twqrwG6w6dxfInLgn3cCJGRY0dYoK+ymrH4l1FyJ+5HgDQr00K1h06i5+2FOCPp0fW6zkOna5AQUk1LmjfQvN2MSjJTY2t13MAQIVFHcyEu36UwU0jkRTjDG6OCwVZidGmcA2HiEhxqtxZC3iUW8QElZi1AZwBwTZhOfW6Q2cBOGti6uuiV5cAAOY/PExzY+ZNR4qV7+1CpCV/b9DrfD7+JysPYkdBGa7olam6vpLBDQHaDfxio7hBKRFRpCpxC24qa+zQ1xFM1NeOglLN4Gbz0RLl+7Jq53isdgfGvPkbYkwG/HD/EOh0OhSUVCEtIVoJdt5dvBczVx9Wiojd36+qreENblhz00jcktfW47qqMP9yEBE1dj9tLsBfZm+Cxdb0/l66Z24qrXYYdIEHN5Ik4fe9p1BSqX48m7BiyWTQfrsXp6XKqp1TS9uPl2JvUTm2HCtBucWGxTuLkDd1EZ79fqty7Kvzd6lWR7mv9A135obBTSNxVZ8szL4nT3VduH85iIgau/yZ6/H1uqP4dGX9V/oEmyRJeHjWBtz1yVqf/V5Kq93rVGzwEoP49PW6o7j5w9W4b+Y61fUVFtd7iNGgHTRVCyuc5ODm4OkK5bpyiw1//2k7AGDmau97Rlms6tVR4f5wzmmpRqRzmjpl6F6gRUQUTiGaMQmKwpLqug9qILtPlOO7jccBAKcratAy3qx5nGfNjQMSAm8B8taiPQCAFXtPqx+/2vX43oIscfqotNqKvUXl+EQIFMurbSgqtXi9j+xUufqYcBcUM3PTiCTFqguIGdwQUWNi1DfetwxbI+oLtnhXkfK9r8Z4YvABOJdPe8vY7ygo9RqgHDmjXehdJmSGqq3a4xADlbJqG+76ZK1SyAwAZRYbytzei9yXfQMawQ1rbsibCk5LEVEjUtfKmXCyOcK/dcFPmwtw3+fr8OPm48p1vgpr3QuKq2rsXj/UjnnzN8xc4zkt5Ovxy4Tgydtx4nRSWbUVB05VqG4vq/YcT4FGlsx9RRczN6Ty6KWdle8ra5i5IaLGw9iYgxt7+DM3+TPXY+6WQmw95lrObfGVualS/42vrLH7rLV8f9l+j+vEpeNxbiuW1Jkb7cettonTUp7vOYeE+ht5RZRW5sajfoiZGxI9OKITpk04HwBQblH/cuw+UeYR6RMRNRSDl6LUxqAxTUuJfAY3tZmV9ERnTU6V1XvmBtDOnIltRCpq7KpeNWUWofux1a45reUt6GmdEgNAvVRcfmx/6puYuSEPcu1NpfBLvvVYCUa9sQyXv/lbuIZFRM2AJEl4bf4uzNlw1OO2xp25ceDhWRvwl9mb6rUj9Z4TZSHJllt8ZDDkguKMxGgAzoDAV+ZG6/y7j7mi9nJRaTW+XX9Muf6Vn3dh7DvLcbLMgmGvLMbUuTsAaNfiJJiNOD83BQBUWz9YbA7YHRIKNTI3shiToXZcDG7ITVyUcxGb+Mvx42bnPlNiXwHRu4v3YsKHq8LeOIko3Costnq9uZHT2kNn8c7ivXjky00A1KtsglFz897Sffh6nWfgdK5Oldfgu43H8fW6o0pHZX+tOXAGl76xDH/69+9ejympsuJ0ufdOwV5XI/mVuXEGN5U1NiU40WLQKOh2/5svT0X9adrv+M1tr6itx0rx6apDOHymEu8t249NR4o13zMSY0yIj3a+D7l3pa6ssWG7MBXmrkW8c59ETkuRhziz85dK/CWvquMTxavzd2HF3tOYu4WbbVLztedEGXpMmY/HvtoU7qE0We6N4MRplXNdLbXvZDmmztuJv8wOzs9HDCjEDIZWTYgvP2xyZjh2FpZ5fZ4+L/yCfn//1Wt2x9tqpMU7i7DnhPbjFtee68wkIXNjCTRzoz6+vNoGh0PyulXGukNnlO+n/LBNM8OSEG1EQu37kHuh8Pcbj2P1gTMweZmibFG77L2u96xQY3DTCMWZnWk9ce7VV4pPPK6xzjsTNQS54PLbDcfqOJK8EetqbHaH6pP9ua4EF2sGxe659WUViojLhb+DWqt5fIky+N7qRly5esxL0FBWrV0POeP3g7j0jWWaS8KLagOHNi3iADj/zvvK3GhtzeD+3rD1WAn2n/K+i7jYC2fjkWLlvKXGRSnXJ0abkBCt3Qbvme+cXYrvHtYe3+cPQc/sRNXtLeKYuSEvYmunpax2SfkPIf4Cu/9REKeqGsOKAaJwsTO4V5EkKeApOjE7UGW1q96kgjnbVxOE4EZc/i124y0oCWyDzyij661Q63yJQdmCHSc0p2W0VhqJlu89qbpcY3MoxcBtWzp3466yBl5z4z6t9NjsTbjzv2t9jkWLPDUGODM38WbvPX6zk2Nw/8Wd0CcnGXde2F51mxzcsOaGPIjL+eQUqJgKrXT7ZRY/Sbjv70HUnDBzqXbnf9fi6ndXBJQl0cEtuBHepKznGJCIcYN7u/76sNq0MzfeahO9EYMb94Z1gHqq7pWfd+HytzwXdnjL3Mh+2HhcdVluemfU65CV7FyZ5KvPDQDNfae0goiDpyt9jgVwBiii9q3ilO+dNTcm97sobuifg5ja9yn3DTPlaalw138yuGmEjAY9zLX/2eT/sOInB/c52aNnXb/IWruLEzUXzNy4OBwSFu4swuajJV5rSURfrDmMF/63TRXAVNc4VJkb6zlmhsWfj7cl0m8t3INL/rnEo9YDcAYQYmbF6lA3oJMVFAc2LSU+pnvNEeDZbA/wzKDXlbnZcKQYW46W4Kp3lmPZ7pPKlFSrBLOyiKTMYvO5dFxrc9D6Zkjkpd6yjq3ile/rytzIS9cBV42oLDslBh1axXkETw2NwU0jJf/CyL+4YvW/+5zsUXFnVrfgpqi0Gvkz12P1fvWeI0SRiMGNizjtU1fGRZIkTP52Cz5ecRCrDrj+VlRabapP4FYfb7z+EN+cve3i/fqC3dh/sgJv/Lpbdf2mI8Xo9fwvePo7187U4usSf/SBTkuJU1pnK2tQbbVj3aEzcNQ+qFZwc6ayBjU2B+ZvK0RptdUjcyPWsADAqTILrvn3Cmw+WoK/zN6Eotqi57QEM6Jrl0+LdTlv3nQeHhrRSfUYWnUs3jIk/++yLl5fL+AMQmQmgw65qbHK5cRoExK91NwAQJoQ3Lhnbi7pmoaFjw3Hv27q6/P5Q43BTSMlFxXLwYq4b4dn5sb7tNTT323FT5sLcOP7q0I1VKJGg9NSLmIGoK6gT5yKqRYyAVU1dlTVuB7nXOtkxKkoXxkKANjtlm36V22wI+5M7a3G8NjZqoBqjaqsrtd/ttKKtxbuwbXTVuKhLzdCkiSPDS4B4FRZDf65YBf+/Ok6PPvdVmUJdmK0Ec9c0Q2X98pQHV9RY1d+P4vKLELmJtojQDDqdbiqTxYeETrWA84Puw6HpC5T0ChAXv3UCNw3vCN+evBCDOvcSvM1t05xBTPRRoMqk5MQbVSWgstShL0P0xJc9TnumZtYk+/i7IbC4KaR6pbhrEBfse80KmvUm6m5Z24KhMzNGbeU6kG3fUKIIpm9Eewv1FiIWYC6ppNOCKuLrEIg5F5QfK41N2Kr/7pqbg6dUdeN6DXqTbwFW8dLqvHlH0f8HpeYuSmurMGvO04AAP636TjmbinUzNycrrDgvaXO1XnfbzyuBEAju6XjzqHtEW30/Sa/vnZzyrREM2KjDIgyuN6OY6MM0Gm83mqrHZM+XoNBLy7Eo19txLBXFmP+thMex8nFwT2yknD7kLaazy8GM2aTAa2FzE18tBFJMeqaG7HgOC3Be+YmJorBDfkwqocz6v9lWyGmLz+guq2qxo6SSitmrj6MotJqVeDjPi3lYDMzakYiKXNzrgWZ4hu/mJnQInacLa8W+2vZVZkBh3RuU3/qzI3n6xOzLSfLLKrLWsugfa0Off83z32YROsPn8Xy2iZ34t/Q4kqr6vJ3G49pBzdujQLlzI28hNps8v32+st2Z1CSlmCGTqdDKyFgSIzRLuY9VV6D3/acQpnFhm/XH8PhM3UXDkd7yaS419ykC89fbXWgdUqs0m0YUBddy0XDgKvpLADkpMYo9aLh1jhGQR5GdE2DQa/DzsIyvPaLeu65osaGD5fvx1NztmDgSwtVxYLu01KR86eeqG6REszP3VKArs/+jC80doH2l5i5qfDRGA5Q7xUk1o5UWe0eQVYg2Zsam0O1y7Q4FaU1LeV+nbjqSas5stZY5FU/WoXBzuew4+jZSvzp37/j/z5ajdPlFlXwd7LMguPC8y7dfVJ1WbbtuGvPpQSzUTlvCbWrjLxlbuTXIS8Wkad4WgrBhdzULxB3D3MuyX7wko6q670FG1lJruDGanfAKGSOYkwGGPQ6dExzFRmLmSSxU3Ws2fU6rzs/RzPjFA4MbhqplLgodMtMUC5nJUXj4i7OudNKi93rUseyapvqP3yE/K0n8kuk9Hm67/P1AIDJ326p92OImZG6NjEUO/qWuWVu3O+7YPsJPPPdFr8yS3d9shYXv7YEi3ae8BiTVuam3G0ZtNiMT2taSiu4SYl1FvJ6G98DMzfgwn8sVi4fL65WBX9bj5fAIQHRJj3atIhFjc2BBds9p34W7ixSvq+02lFcJQc3vjM3Qzupa2DkDEorIRsiTgH5a0TXNGx+fhQeHaUuJBYzN2JjPnHaSQ6EX7muN8b0zMCfzs8GAHRKdwU33joORxn06JWdhNYpMbjVyxRYODC4acR6ZScp31/QvgVihW0ZvH0qAdTZm0j5JEvkD3HKpLmvnFJlbupohV9Q4iW4sdo9+mo98MUGfLbqMGb8frDOMSzd7Wxc99kqZwZKlbnRqLkpd1tOXSz8nRODG3kZtlYtUXLtm3aVxi7YFptdmQ6SnSitVgVwS3Y5x9wmNU5ZQaTV+2b/SVdGyu6QlN4ycubG7CVzM7hDC9XltrXdiVudY+YmNsqIRI3eNGJw01IIoMRAR57CvKF/Dqb9Xz/lPmN7ZynHuP9sZDqdDt/nD8Gvj17kUacTTgxuGrGeQnDTPStRae5XWeP6lKCltMr1S8jYhpoTu9gDJcDpk8W7ijwyB02ZGNzU1QtFnbkRpqVq6/u0HC+uwjuL9uDV+TvrHIu8oqau1VLu579Y+KAmJm7kgEurOWFybebGIXkWHGt1Fj5RVo1KjZqkVglmVc1JXXYVOh9bXgLubTooTwhuTAYdspKjleeTZQhTRhe0T/Xr+b0V8kYLGSRxebo4DeXtg8DFXdPw1vi+mPfQUNwzvAMA4Ko+WR7H6fU6r7U94cLgphHr5RbcyNsynKmo8SgcFonpWIlVN9SMiH+kAykufmfxXtz28R/KdFAkEIObuqalxIJiMUtRZbV73WHbIUl47ZfdeHfxPlVwdOBUBWx2hyprIn8wq2tayr07r1jIK2ah5eO0VkslC0uWq2vUt288Uuxx/IlSi9Je45YL2ijN687PTfZYCeSLvHFmqwRnACG+2Yv1Qp3TXeUGsVFGJchQBTfCtNS0Cf3w0jW96nx+r8GNkEHqmpGgeYwvV/XJQrfMRNxyQRv8+MCFeO36PgE/RjgwuGnEugi/iD0yk5TeNx8tP4D9Gku85ZSg+EeDmRtqTsSam0Aazs2qLdxdtvtkHUf6b/meU9h30vsGhr6IWYqbP1iFI36sinFnsfs/LXW2whVEuNfcnKnw7BQMqANJOZD6YdNxXPzaEjw4a4MqSJI/mNVVUOw+zlPlNUoGTgzQ5OBGa1oq3mxU9mASl7F/v/EYXvjfdgDAIyM749HaHjLiitO7hrbHqqdG4K3xfXHXsPZKKUAg5KkfMXMjNszzFvSINTcZwrRUSlwUbh6UW+fzeusvIz7fFb0ycd/wDnjvln51Pp47nU6HntlJqlVTjVnTGGUzZTYa8OujF+Hnh4ciKdak/IHwRg5uxKZbDG6oOVF15Q2g502wawV2FJTi/z5ajRH/XFqv+4v1Jb/vO40nv90c8GMEkrnxti9SldWO016yxFr1Te8t3QcAmLulEKc0tk8QP3idLq/Bk99sxoq9p5Tryt1Wdf1n6T6MefM32B2SampNPk5rWiraZFDe0MUs9scrDirfX9y1lZIdKSipVoKgmCgD4s1GXNUnCwnRpno1pFOCG2E66C+juiCvfQu8foM66yH+nFvGu6aM6lNz4y1zIwZZJqMe/++yrhjdI0Pz2EgSeFhKDUpcildXilT+A62almJ0Q81IdT33QQp2cLPLj72ctNgdEgx6cetKJ3Gptr8sqoJi78GNJElea42qauwe/VyU2zTqZ8Q3a3E6Sw4exJqbNxfuAQDM+uMIDr58Bc5W1GD2Ws/Ge3uLynG8uEr1s630MS0VbdIj2mRAucXmel6bXam3mX1PHnq3Tlb24Tt02pUFjzOr/8a6Z26SYkzQ6dSFzrFRBiXwijEZlPoisaA4OzkGX9x9gXL5xv45+HLtETw6ytWBWJxOE6eo/OWtxkfsDyQXLzcHDG6aEF/LXI16nfIfU0zFNvMFIxQCT8/Zgv0nK/DZnYNU/S4aA/ENMJCdsMXgRg4wzoVJKNaUJMmv3h8FJVUY8+ZvuLpPVm2Q4PrP6/7f2Gp3wKjX+XxcdebG+7RUZY3d69+JSqsdp2unpfQ69d8Tse5PzsiIp03cMkZ+fl9bLtz/xXqs2Ku9B96x4iq3zI3z8bT+JkabDIiJcp5/+W/hzoIy1NgdSIk1oX+bFACu5dbiDtruvWncP1CufmoEqq12nPfXBcp1PbOTsObAGQBAywRX9kUs5HXPuv91XA9MuCAXPbNcdZUdWsXjzxe1R6t4s+r3x1++fheWPj4cFRa7R9D0t3E98ex3W/H05d0Cfr7GjsFNE+Lr72O0yaBU9lerghtGNxRcn9fu7bPmwBnVyo/GoFrIDASyWkrcR+d0hUW1d059mAyu/6wWm8OvlSTfrDuK4kor/rvykM8ur9VWOy56dTE6psXj8zsv8Hqcv6ulfK0QO1Neo5zTjMRoHBcySGLLCfkY8Q320a82Kt/LQYav3jhiYJORGK0qcj52tkr1oU2uzdH6GUeb9K6/hbWvWy4k7pOTrIwxw62XTGyUwaMLshjcxEY5p7ui3AKP3kJw0yLOFTyImRv3jJDZaEDv1smq63Q6HSaP8R5kRBn1qLE5kJsaq3QmTk8040Spdk2UqI2XjM0tF7TB5T0zVB2HIwVrbpqQ6/vn4MremZq3mY16zXlmhjYUKue6iWIo1HdaSlxZVeTHm0VdTEJwUle9i0z8dO+R4ZCcmSiHQ8LqA2dwotTiNcshq7GJwYD3MZR56V8CAEeLnW+iZqMeSbHqXa7P1JG5EQNNeQrLW+bGfeVUttvWAEfPVrkVFDu/t2qknKKNrg96ckAkdxMWA4qUuChV3xetaX/xZyL3hdHrdcqKKgDo1dqVfTEKJ0DMvgRjv6WfHrgQT13eVbXb900DcjG0U0ulOLo+IjGwARjcNClJMSa8c/P5mreZjXqP/9CAuqC4uTc1o3PnUBWRNq7gxmp3qIKUQDI3Yi3ISY1C2ECJtW51rVRSxlDH6q7b/7sWA19aqFoe7aumTrW3lI8xeCsmBpxBBQC0iIvyWCUjBjdamRuRa1pKO8g641a0nJ2sDm7e+HW3qrBZWS2lcc6iTQaYlQ96DtXraNsiVnVsj6xE5ft4jZVRYsAj3i5+L071aO1BBaj3X6qvTukJuHtYB6UDM+DcqfvTOwbhwRGdzvnxIw2npSKEWeM/tJP6j71B37gaLVHTIq5AamyJm3PZA0l80y0qC7x4152YNaorc2Ox2XHje6s0e7DI9p+qUNo/7BaKla12CVFG7YDC372lfE1LybFTi3gzogzq51Ev6/bM3Ihchb3aP5PT5TWqwty6Nl+UgxubRoBtNnl+0JO3q3EPmnpkJSpdlC9o7znFqgpuhO6/8dFGoFQ+xvU2KpYBiPcNpBlgXeKEwCquHkvVmwtmbpqgOy5sB0C9XNBb5kZM1jTGaQRqWsQCzsaWCax2a+cfyLSU+KbrTw1DXayqHjO+g5vle075DGzc1dUIz3WbOC1Uv2kpWWpclM8iVznz5b7O69bBbZ3PX+O5Wkp0styiGm9dHZXlcyr/jMX6GbH+sMpqh8MhKRtftk5VZ266ZroyN5f19FweLQYuidHamZu4KAOm/qkX4qIM+Ps4V7O9nNRYPD66C/4+rqfmjub1FRcl1vIwuPGGZ6YJenx0Fwxom4pWCVG4dtpKAM7MjVyd762gOJCmZkRaGndwcy6ZG9exwdiCQTxPlXVMS4l7FPlDDJZqfPyfVhcUex+Dtz2DRFnJ0The7D2jVV0bZFnczrmcKVGCGy/B2OHTlarfpzuGtsOag2e8ThG6mvg5ny8z2VWAbDbqlRqX6ho7isossNqdK+DS3VYLnZ+brHw/uENLj+fxNi0l7ssUazZi/MBc3NA/x2OVXf7F6h26g4GZG//wzDRB0SYDLuuZoerPIGZuxD/y4h+4pbtP4sreWU2mwyQ1PuK0lNaUQDi5BzeB7BBu8fJ/pr7EwKrSx5QQAGw+VhLQY4t7Pfmq0xFvq7Y6UGNzqP7vl1RZsWLvKZyqXeqdEG30msVpnRKLk2Xet3ypqrFj2pJ92OSWgZI77VZZ7bA7JK+PL3dyNup1+P3JS5CWGI1Fj10Ei82BE6XVuOKt5arjv11/DD2zk5TALCs5BhsOO5/bZHAtrjhVbsHRs86i6IzEaNV+SvLr+va+wUiKMWn+XfQW3Iir3+RMSkO1RRADGveVW+TC4KYJE/+DqVdLabc4f/SrTdh6rBTPje3ecIOkiCK+afta1hsqi3cW4eV5O/Hq9b09ltK6T0sFMg0rBjSBZHy8EafE3HfVduceENRFvQTbjl+3n8B5ucmqlT+A5+vff6ocrVNi8c6ivbiydyb+9uN2rK5dwgw4O+t6D25ilBVHWuZsOIZtGptSisHN+A9WochLJkbOXqXGRSGtdoopIdqEhNpxdc1IwM7aWqPkWBOKK6145rutyv3ThaX7zuDG+ab/3rL9+H7jceU1aDk/N8Xr6xKnpcR2AQahcDoYK6ECIU5L+dE+qdkKa9g3depUDBgwAAkJCUhLS8O4ceOwa9cuv+8/a9Ys6HQ6jBs3LnSDbMTEhlN6nQ7Rtb/0cgrYZnd4TB1MX3Gg4QZIEUfMhtS1uudc/by1AGsPnlFdd9uMP7DrRJnmBpfudSUBZW6E1xKMzI2Y1ar0Mc1VVm1Vepb466yQuflk5SHc+clajHt3hcdx7q9jR0Ep3vx1N/6zdB+ufHu5KrAB1O3/AXVxcE5qrM9aJK3ABnDVwlRbHUovGC17i5yZG2+dosXA7fmxPTxuN5v0+Pu4nnhwRCe0axmnKuCVp6vcl5f7I9YsfoB0fS9maRo6eyJmn9Lq0cm4uQhrcLN06VLk5+dj1apVWLBgAaxWK0aNGoWKirrnoA8ePIi//OUvGDp0aAOMtHES9y6RAI+CYhYQU7A1VOZm/8ly3PPZelz3n5Wat2ttCeC+5Lq+q6WCkbnxp4Hec99vRa/nfwn4sUuEzM2PmwsAuJY6A8Dmo8X416+7PWppHvlyEz74zfuHG/fMT5awsqh1Sgwu7Z4OALhpQI7fY02Ni/J5e5/aHjFyAOI9uHE9zri+2Vj7zEhV8GUy6PF/F7RR+r1orU5qnVyP4MYkBjSu68WMiT/dp4Ntxm0D8Op1vdG+VXzdBzdTYZ2W+vnnn1WXZ8yYgbS0NKxbtw7Dhg3zej+73Y4JEybghRdewG+//Ybi4mKvx1osFlgsrk8cpaXanzCaIvflku4FxcH4BEokEvvIuE8DnavZa49g0c4ivH7DecrSXW+0yhvcl1zXt89NIKusvBHPk7di3k9WHqrXY4uZG63zcNU76ixO98xEbC+o+++ee3Aj/n1pFW/Gn4e1x/X9WsMhOfeD8kddS6AfubQz8j9frxRJi/sriUb3yMB3G48rWZKW8Wb0zU3BukNnAQAmtxOhNVXUOiXW47q6iFkScSpKH+b5oOFd0sL6/E1Bo6pGKilxzummpqb6PO6vf/0r0tLScMcdd9T5mFOnTkVSUpLylZPj/6eOxk78xCBJkkdBcainDaj5qW/mxuGQcO9n6zB17g6vxzz+9WbM21qI95btU715aDWq03pzqbC4Z27qOS0VjJobH5mbuVsKcMtHq+v92OL0mz9vsn1ykv16XPfgRsyi6HTOfaxaxJsDqjHR63U+e9Zc0L4FRtZmhAAg0Uvm5rKeGfjP/52PXx+9SLmut9AZ2OT2HGaNoKo+01IicTl3I9tSjTQ0muDG4XDg4YcfxpAhQ9CzZ0+vxy1fvhwfffQRPvjgA78ed/LkySgpKVG+jhzx7xNHU+ReUMzMDQWbGDAEkrnZXlBaG7jsr3MJ+ZajJapuKVa7hMoaGz5dJWQ6NN5c3IMIf1dzORySKqDxJ+NzssyCl+buwIFT2lPoVlXmRj2u+z5fj9/2nFJd16FV/XZrFmMbb+Me0DYF1/drXedjiZs+AsBDIzsjLcGMh0equ98G2pBOXHHUKsGMN27sgws7tsT9F3dEtMmAiXltldu9Bcw6nQ6X9cxErtBhuF1L1zkzukUb7pcBzwZ+gRIDyWD2raHQaDSrpfLz87F161YsX77c6zFlZWW45ZZb8MEHH6BlS8+eBFrMZjPM5uZRdBXtVnPjraeE+5JQIn+JO237aiDny9nKGo8sgci9wLbG7sDbC/fgvWX7leu03lrca278De7dMzX+BDczVx/G+8v2o6rGjr+N8/wwploK7sf2C7dc0AblFhu6ZyXi9hlr/Ri1p+JKq8euz4AzGHn1+j7YdaIMm496X/Hk/jNp2yIWq58a4VFTYtDrlE0c/RFjMuAsnFNptw5ui2v6tsY1fV3BVr82Kbixfw6+XHsEFwcw3SIGN+5/z06XexY/ZybXbzPUVglmnCyzYEQ319juG94B364/hvEDc+v1mBR6jSK4uf/++/Hjjz9i2bJlaN3a+yeMffv24eDBgxg7dqxynaP205nRaMSuXbvQoUOHkI+3MZIkz5obb5+s524pQGm1FRMGtWmw3gwUGeqbuRFrUE6VW+oOboRfyxqbA8v3qjMdWskf934yNj+bDLp3zfXnTfvQGWfGRlyWrXruADoUA87VSCO6pdd5nDtx6bYzaPQs4JXf+OtaWeP+M4ky6r0Wy8aYDH4HN9FC5sbbcuyXr+2Fuy9qj3Zedq/W0lY41n2cWr9f4mqnQCx87CIUlVajY1qCcl3HtATs+Otlyt9canzCGtxIkoQHHngAc+bMwZIlS9CuXTufx3ft2hVbtmxRXffMM8+grKwMb775ZkTV09SHv6ulHv5yIwCgQ6t4DOnoXwaMCFBP9VQHkLkRpxtOllnQ1bPTvcJic3j0nclMilYtNy632GCzO1QFnx6rpfx883XPQNX4Uasjr07y1s04kL2lgPrvzCw+/9mKGs1tFuTgpq4395RYExLMRpTVPqav7RZiTAavm0QCzq1hnr3S2U9LrN3xVtSr0+nQIcCVP+JqLvdOxn86vzUOnK7Ae0v3u98tYInRJiRGe9YCNXR/GwpMWIOb/Px8zJw5E99//z0SEhJQWFgIAEhKSkJMjPMXd+LEicjOzsbUqVMRHR3tUY+TnJwMAD7rdJoDCZIyLSV/EvW2j4vs4OkKBjcUEFWfGz8Lih0OSRXcnNKYMnC4ZVnELQlqbA6kJ3pOKZRW21RLjd0zN1Z/MzduQZA/QdExObjx0vROtbdUHds5XNI1Db2yk3we44+zlTWa45FXGIlbBmhJiDbhvNxkpR7IV3CTlmhWlm9rWTl5hPL9E5d1xczVh5GZHI2+fhY3+0PMOrv/TkUZ9Zg8phsqLXZ8uuoQclLPrd6Gmp6w5tSmTZuGkpISDB8+HJmZmcrXl19+qRxz+PBhFBQUhHGUTYMkuTI3NXYH7pjxR501EQU+9ooh0lKjWi1VdxDw2FebcOE/FuGU0JfmlEYbf/cAQ3yzqrE7NKdPi92mhOROwPIMhbfamY9XHMA7i/YIzx3YEnKb3aG8sXvP3LgeY8ORYpRUWnHkTCVenrdTddzIbumYfusAv6aHs5J814ycrbQqWReRnLnJv7gjUmJNXpdbJ0Qb0Vfo1uurOd3Atr5XtIouaN8Cb43vi8ljugW9EPeBSzoiwWzE7UO0s/5PjOmKpy7vipl3XhDU56XGL+zTUnVZsmSJz9tnzJgRnME0cf3apKjSpAt3Fnld+qjXOWsWjtfRS4TInU1Vc1N35uab9UcBAN9tOKZcd1Ijc+MeYJRWu6Y8amwOzSyk2O8FcHUCTopxtue3aQQpVrsDL/xvOwDgmvNbIzs5RmM3cd/BTWFptbLiy1twI56nGpsD/++bTZi/7YTHcVpN6+T/n7L+bVIwukcGYqIMqi0H3J2pqNHcPkEObnJSY7HumUuxYMcJ/PnTdapjTAbnku0BbVNU13lzQfsW+HC5dkPAS7sHXjtUX4+N6oKHR3b2GhzGm424e1jzrMNs7hpFQTHV36+PXoRFO09gYl5bRJsMuOeiDvjP0n0AtJuEfX7nIJwqt+ChWRvrbJTWUKx2h88UODUeYs1NIH2UxIzPKY39hdwDjJIq15v07hNlKK7yzPaUuF0n19wk1wY3Wn1uxGmbqhobHA4J3288pjpGvt/OwlLsLSrHlb2zVLeL3YC9BTfy672sRwZ+3laoGdgA2sGN0eBaifTvCefjkq5piDYZPMbprtjLtFTrZFedi16vQwuNrsEJ0SbodDoM7tASI7qmITHG5LHJpGhAO8/MTYzJgMV/Ga5Z1BxKXBRBWviO0sR1TIvH3cM6KPU2T47pirfH91Vu75mdqGo4NaRjS6Xfw/ESdXBTY3Ng+vID2FtUFvqB15o6bwf6vPCL134h1LhY68jc7D5Rhr/M3oQjZypVdTRiNkQrc+P+WKVCsepDszZqBgfF7pmb2sJdOWDQysCIwUi11YFfthd6bEkgB22X/es33D9zA1btP626/ZgY3FTbNDPQcuZmSKeWPqeTYjWKUsVuu5f3ylT+b9dVEHy6ogblFvU5yUmNQZLbNJRW8bK847VBr8NHtw7AGzee5/O5kmJMuG94B9Vra50Sg4wkz523icKBv4UR6NLu6biiVybuGtoO39w7GLmp6hUK8iqDwpJq1RvQh8v3468/bsfI15cFdTwWmx23frwGHyzzXLnw3tL9qKyx41+/7g7qc5Kn+valEYlTPVqrpW58byW+XncUf/50nWrljiq40crc+JiWEv19XE9c0SvTeYzbah25cDcpNsrjOWVicFNusWHNgbPKZXn6xv1+uwrVwb6YubE5JM0MlpzhijLoMLRTK83XAgB2jcDIW3BgrmPZ8fHiKo9pqbFuWSdAe78nObgJxP+7rCv+e/tA5TIXJ1BjwuAmAkWbDHh3wvl4+oruMBs9+1GkJZhh0OtgtUuqws2V+067P5QHh0PCfZ+v8yiM9OWHjcexZNdJvOij9b6fC1uonjYeKUb35+bjrYV7vB6z7tAZbPeyu7PMqlFQbLM78PnqQzhwqkKpg9leUKrqzCv+Drr3htl2vATPutWSuAcussQYk5LtcO8f45650doVXAxuKiw21QoieYxWu0O1fNu9l8mxYnWTQa2pKXk5uVGvx6OjOmN0j3TcN9yz9kOrW7O3WpfoOjI3R864ghu9Dhg/MBcPjujkcVyixqqpulZSeRMtdCse2onBDTUeDG6aAfe+HUaDHlm13TrFDfXqaosPAGsPncXcLYX4z9J9fhWEA/4Vnjr8fKymzt9zFmzP/7ANdoeE1xdoZ8hOlVtw7bSVuPyt33yOUWtaasbvB/H0nK245J9LVMeKAYKYURCDnnKLDVe8tRx/HDyrum+plyXWZqMecbVZhrJqG8qEDI8ruHHertXnSaxJKbfYcKbCs5bHaneogn730yFmbtwfE3Cuxlq2+yQA555H6YnReO+W/ri8NuMkaqvRtM6or1/mpqCkSllBduOAXEz9Uy9V8CHTasxX3+BGzPgMat+iXo9BFAoMbpoBrfT8RZ2dqfJ5WwqV68RPut+sO6rZn0NcfqvVMEyLP3Pw4XrTb0jTluzDoJcW4ojb9gINwdeyXsC/IllAu6B4WW1fFPcfodhUT2z4VlVjV37e//39oObzeGsQF20yIM7sfMP+z9J9OP9vC/Dhb87pTnmbA38zN+UWm2bPHatdQpEwdeZ+PtwL8cXby6qtymosQF0/474E+56LOuCG/p4d2XsJG0KK6srcOCRgT1E5gMCDlfpMSwFASlwU3rm5Lz65fWC9H4MoFBjcNANawY38KfLnbYXK7eIn3cdmb8L4D1Z53E/81F1a5f1NUCSuZvDWsr0ZxDb4x887UVRmwavzdzX4c5uMrp/B+8v2eey9I2ZZzlZ47zxrdVvi7HBIXpveib8r4vc2YaPKPSe0i9e9/Z6YjXrERrneRK12CX//aQfmbSlQxpYc46wp0aoxcp+WOi3037l5kGufoMISVw8oMevkcEhKCwV5ekx8TPHxAHUjPLHWZUjHFnhyTFfNwP+la3ph/MBc/O/+C9Wv3UfmRi7s3XikGACQEGCgkaDRgddfV/bOwrDO3uuKiMKBwU0zoJX6HtSuBRKijSipsmLfSeenPfddlDcfLfGYqhI/6ZZ5Kfp0J+7QK24iKGZrmkNwIwvHFJyYuXlp7k48NGuj6naxDuaMl/2SAM9sSEWNzes2H762HZBvc+9VUxezUa+ZIXjim83K9zm1BfRy9kWVrVFNS9mV3+e3x/fFs1d0V24Te0CJwU1RmQVWuwSjXof2tTt5i4/pngkyCvUz4m7avvZlapVgxtQ/9fLI4GhNMck6pDm3LiioDcriA83c1HNaiqixYnDTDPx7wvkY0zND9UnQoNcpQc+RM84/5Fpp/PWH1bUQ4kqX0mqrX9NJYnykKjIV3hQb+g3/dLkFN72/UtVcrqHovWxGGErufYTcN6IUa0+8bQYJeGYBV+477bXpna/dsOXfg2If+xNpiTYZNJdPyzU6UUY92rRwBjfHi6vw8YoD6DllPuZvc06/lrkFOvKy9G6ZiaqdpQuEzM30FQfwz1+c2bajZ51TihlJ0UqG6FS5BVuPOXfbdl8JJgaVYq2LP/tXuYsWxidughlt0iMtwbUkOyHaiEHtfNe/uBcV17fmhqixYnDTDLRtGYdp/9fP45OgvN+KXAOi9SblvoJK/OO94XAxBry4UHOJt0j8lCq+4Ymf7Bt6tdRrv+zCqv1nlE1EG1I4eo6ZjL7/q58VgxuNIluZ1S2799OWAq9ZCF81WfLvgfsWCnURC4plYq+VxGij0pn7bKWr/kXuyCvWkZ0qtyhZmZbxUTDodcoUaoFbD6i3F+3FseIq7K2taclNjVVqf578dguufHs5XvxpO95atFd1P2/1Zv7uqC0yC5kbsb1DjMmAa/pmIyPRuVnlpudGoXtWos/H+uLuCzCscyu0qg2SAp3GImrsGNw0Y3L6/nBtcGPTiDDkws5PVx3CX2ZvUn2i/ftPO3Cq3OJziTegrn2oEDY3FN/8vE1thEpRqWchaUMJR+bGXEdBsTgV5WuqSM7uDaztULtwR5Fmy39A/bN2p2RuAp2WMhlUwU2UQa/8HgNAdnIMEqNNXt+sxSmkHzYdV76Xi5DlZdjHSzz3XSssqcKag2cAAOfnpng01fvgtwPYUaBeSm/0sqy7ri0etIiZGzG4iY0y4sJOLbHqqRG448J2fu3f1CMrCZ/cPhDPXNEN3TITWTNDEYfhejOWk+L8AylnbrSmpcpqp57c+5AEQvyUWuElc1NcWYOSSqtHN9VQaehgShTszQP9Udf2Fv5mbuQmfgPbpuLw6UoUllZ7XV11psJ7AFlZY4fdIXk060swGzU3f5RFG/WIE6alEmOMyBAyN3KDyuyUGOx0a763YPsJfLn2iObjylNGJoMe1VYHCjS2Jjl6tgp/1AY3A9ul+tUU0dsqtZ51ZFa0iFmg7lmJ+LZ2StW9D08grj4vG1efl13v+xM1VszcNGPyp7+FO4tw28drNN+kyi02ZQfk+lJNS1m0V9BsPlqCgS/96rGKJ1S0ArmGEo5pKa2snOiMkEHxWXNT+zhRRj3G9Mrw+ZgnfGTHqmrsKK2yehSSJ8f5Dm7NJoNqtVRCtAkZia7gRt5aRP5XdNcnazUf844LXTtKy8FIkUYX5TUHzuDImSoY9Dqc3yYFE/Pa4s4L22HBI8Mwystmke6Zm7kPDsVtQ9riubE9vL1En9686Tw8P7Y7emW7ppjjz2GlE1GkYnDTjImp7cW7Tmr2FimrtmHPiXK/H/N0uQXL95xSFRqLWRIxc+Pe3M9ic2DhziK/n+tc1GdaIFjCMS3lK1O1dPdJpekcUEdwUxuoGg06zaZ0ohMaQbG8cq6ixqb5PCmxvjdddF8tlRhtRLoY3NTW22RpBDda7rywHZ690rVKyleGSy5K7pKegHizETmpsXjmyu7olJ6At2/ui+fHdve4j/vjdc9KxJSxPTS3QPDH1edl49Yh7ZAi3F8MzojIidNSzVh2SgzSEsyan1Jl5Rab0hjMl2qrHdEmA256fxX2FJWjdUoMiiut6JKRoPqUKWZrtApOvdVvBMJmd2DNgTPo19azLkLW0MGNuCeTVofYUKvxMYUyafoa1WWtrr0yOQNk0uvRKzsJOp33ZfwnNH6vUuOiUFRmQWWNXVkplRJrUup8YnwsdwacwUKs2XVMnFk9LSVnbPwNbtq3ilddFldM6XXqQvdTtT1sOqWr7wM4N7XUqlsxeek2fK46pcXjlet6o13LOAxo67lDN1Fzx8xNM2Yy6DH3oaGYmNfG6zFl1Ta/dgmX9wKSA6GjZ6tQbrFh3aGzqhVX4moVrT4oweje+8hXm3Dzh6vx+arDXo+xNvC0lJg5Cce0lNbrtdkdqqBL5qvIVw4KjQYdok0GtIjz3GFaVqSRuZEzFlU1dmWllJxtAdQNH72JE6aljAa9ZuYmtY7pLZncq0Ym7uvkLWjo0MozuAGANhr9pMTmicGk0+lwQ/8cBjZEXjC4aeZaxpt9FhSWa0xLDWib4nFcSZX3nje7hC60dWVu9p+qqHPMvpRbbPhf7SqYT1cd8nqce8PCYLHZHXjsq02YuVodWFmsYnDTcNGN/DPRWnpcbrHhtEaWRmtLAplcqyQXt2YKWRN3WtNS8rTT2coavF27bFqcivInuBELaA06oFW8K8Bqneycak2uY3pL5hncuB470ODGoNdh9j15uFDYHdvbPlFEFFr8n0c4PzfZ621l1VaPaSmtYKi4yqr5RulOFdxoZG72n/S/vkfL3C0Fyved0rTfhAB1JsMRxCY7m4+V4Jv1R/HOIvXu2xYhuLDX0bBw89Fi5M9cj8Onzy2LVW21Y/S/luGxrzZpBjePfrUJg15aqFz++LYBAJzTL+6rmGRyUBhVm+EQsybutE5rarwz6Pj3kn3YcLjY43ajH8GNOK1n0OuQkxqDK3pn4ob+rZXVdt5qd9q1jMO/bjwPHVrF4bycZFVgBKinpbz1inEPiEQD2qZiXF/X/4+69vQiotDg/zyCTqfDM1d007ytosaOkior9Dpg2eMXY+njw1Wf1uVusCWVVlXLegBoneJZ96Bq4qeRuTlWXOXXLuLeHBQyP/5MrwDAgh0nfHbTDYS8jLqixr1Y2nVZay8mMet11Tsr8NPmAvzl603nNJadhWXYfaIc87YWwKIx/bRIKN7OSIzGxV3SkJ7ofLPf56XOSu6sK2ckumYkBDSmFrXTUmKw1SnN9RiGADMdep0OOp0O7958Pl65ro9yfYrQUkAMmPrmJmNc32zMf3gYvr13sEf9k5i5yUmJxeW9MtChVRzGD3TtO9WupffgBlDXDXnrc0NEocXghgA4V1xMm3C+qoeIqE2LOOS2iEWbFnHo3yYVaQlmXNUnS1lxVVJlxfFi9TRE/zae01cVXpaCyyRJvWlhoMQVX6d99FkRl4L/+dN1eCRInYrlwlitlWAy92LmgpIqDHxpIV75eafq+nOtP5JrXipr7Kj00TsGcPaLAYCOtdkuMVtXVm3F4p1Fqhod+U37zxe1R157363+ZUa9zqNG5099s3H7hW2Vy8M6t0QgvE1jidNSMcLvdPvawMRo0Gv2GxK3AWmdEoN/T+iHXx+9CA9c0hFJMSYMapfqc48nQD1tVld/ISIKDf7PIwDO7M2YXpmqokgxpd5RmOJJijVh1eQReGt8X+VNpLjKM3OTlhjtsfql0sdScFnBOQQ3pcJqK1+ddt2zRvO3naj3c4rkIllL7Y7ZMrHmxr24d8bvB3GyzIJ/L9mnuv5c9/s5KdTO+KqjAYDE2l4pHWvrScTMzSNfbsJtM/7AW4v2KkGh/KadEG3CF3dfgKGdXEGJnP1xlxwbpWxZAADdMxPx+o3noXVKLJY+PhyvXNcbNw/M1dw7yhtvDRGThcyNwyFhxm0DMH5gDu64sL3PxxO3F5EfQ6fTISs5BksfH47/3j6wzjGJwY+JmRuisGBwQyriJ1dxp2D3+hX5TSWp9hN/iUZwo9fpPHYbrqij5gbQLkT1l5i5OVtZ47GrOeCcAqqoI5NRX+JU2Ox1R5Q9ilTTUm6ZGzGIFKenEs+xOZv4Rl3X7tuJtdsPdEx3ThHtFYKbX3c4A793Fu1R9pZyr40Rg9geWeo9zGTJsSZVFmWokKVp0yION/TPgdGgr7PXDQAM7+Jcdn3r4Laat4sZE6tdwvAuaZj6p96q59cibsvhPmWVHBtVZ9YGcG3loPUYRNQwGNyQiljrKmYOtHp7AK4/5AXFVTjmFtxc2j3NY48fMaiorM2edMtMxKS8NriqTxYAnFNHZDG4kSRoNia02Bx1duytr+IqV1H1E99swZg3f1OeU+Ye3IhN6cSAJDHm3IIbb/2LtHrJyD9redpmR0Ep3l64B/uEAm+H5JrOc9+IU8y2dM/ULsRNiTWpjhvcQXsKKtmPLTg+nNgfKydf4tdS6EC22gjGthw9shIxfmAuHhnZ+Zwfi4jqh038SEXM3IifOfu30X4TSa2toZi97qhy3au1zcX6tUn1yNwcFupI5JqQa/pm4e5hHfCP2pqTc6m5KXULZs5U1Hh0g/W2F1IwuGdI5EyOOnOjDqzEOGvT0RLle3HJuCRJeGrOVsRFGfDMlZ6dcLWc1Ahu5j88DJ+sPIjP3Zaqy1kiObA4XlKNfy7YjZ0nymDU65RgUA7M3JvTRasyN9rBTXJsFM5UuM6PVksBQHul08C2qZg42NWPyWjQIzPJv0Z9DU2n02Hqn3qFexhEzRozN6QiBjcHhaXI4s7LorF9Mj1WRV3QvgX6136iFhuuAc433KKyany84gB+3eFcrSNnEuQ9goJVUAxod9v1NiX1v03HceN7KzWbz9XlizWH8a9fd6PEy/SPWHPjnh0Q65A2Hy123UcIiPafqsAXaw7jw+UH/F5NphXcRBn1aBHvWRMjFxS7T4UdO1ul6gAsB2vuq4DEgE1cQi0urU6OMWFYbW1O98xE1R5RIq3MzVf35OHK3lmaxwfTpbV7RN07vEPIn4uIQofBDalotWDx1XokLSEar1zXW7kcbdKrNi10z9wAwIq9p/D6L7sBOKdDBtc2PZPfRH/eVohPVh70uN+h0xX4eIX3N3dJkpTgRh6DVnDjbYuHB77YgNUHzuCV+bs0b/dGkiRM/nYL/vXrHqyp3TXana9pKXEF2WYhcyPWJImBSkmVFf9eshdbhGO1eAtuWsV7ZkbkoMZ9KuxkmUVVvyJPGboXypYJfXHEjIpYf5ISF4VO6QlY+vhwzL4nz+u4/am5CZU3bjwPH986AI9eyikloqaMwQ2p9BD2gZo8piv0OuCzOwf5vE/v1snK9/Fmk2oFi1hzI9dzPPfdNpRZbOiWmYhNz41SOr6Kuzs/9/02j47HY99ejhf+tx1vLNitmV2pqLErBcRyL5JAMjcy96mtulSLWRmNHjaAOgvjviO5t8yNuKJLLNb+ZOVBvPLzLox9Z7nXMUmSpB3cGLQzN3G1P6cEsxFiDezJMotmDyD3AERs+idma8R6Ijkj06ZFnPJ8WlL8qLkJlXizERd3TeMSbqImjjU3pPLCVT3QIi4K1/dvjR5ZSZg0uG2dK0TEN7ByizowEDM3fXNTsP9UBcpqg4uxfTJVgVBmsrrb7ckyC9KEgEde5v3esv14b9l+LHv8YuS2cE2XyVmbKINeyQKJBb6yyjqmdcx+rIgRlXnp5iuTJMln5kasARJrdsTMzbGzruBmZ4FrO4ui0mrVObLYnE0XzQaDZnFslEGPlhrBjRxu6fU6xJuNSnarxu5QNowc2DYVfdsk47zWyR4bTpZWaQeMYgGxv6u/xB41D17SESO6pft1P3cz7xyEp+ZswYvXsP6FqLnhxxNSSY2LwvNX9VCW8/qz9BWAsiPyLReoN+EUA59e2epC05wUdR1PWkI0nrisq3L5yFnfTewW7nQuUZ699gh+33dKqXdJjDEqz6uVpfG2BF0WaMv80jp2Mq+y2uuoudEej5i5OSoEN6eEbNSKfadU9/m/D1dj4IsLseHIWc3HdNbceE77iBtougchcjbsXzedh8ljumFMr0yP+z8xxvlzu+PCdqrrxR3hxR43vqQIm17eNDAXfXKS/bqfu8EdW2LJ4xdjSEftVVlEFLkY3FBQvHtzX7x+Qx/8ZXQX1fVi5qazW6t+re0Z7h3eAYPaOYuRj5yp8rhd9dhmI1bvP43Hv96Mmz9YrWRuEmNMytLmz1Ydxp3/XavKjtQZ3BgD+29R1+qr8mqbzz43YgA2uEMLvFy70kYMbsRl9vuFHjQr9rp2XLc7JPxx0BnUfOZl09Aoo3bmZlA7V5dhb0vQfTXXu6hzK6x/9lJlG4+Zdw7CDf1bq4JVbwXE7sTMDbcvIKL64LQUBUVCtAl/Or+1x/Vi5sZ9N+XWKdorsHJSY7H6wJk6tx8w6HVYsc/15j5vq3PTzKQYk/K8JVVW/LrjBD5Yth+PXNoZby3co9pcU4s5wOCmrmmpMotNPS1lk3DkTCWqrHZ0Tk9QMjcf3zoAF3dNw9HajJUYhB0VslhlQjC08Uix8r24zF6rv49Br4NBr0OiEHAOaJuCZ6/srlrhlOilM3JdDfDEJfeDO7bE4I4tVXVT/nYeFjNH3HiSiOqDwQ2FlBgoxEQZYDLolGXDLTWmRwDXdJU4LaVVqFtRY1dtlPnJSme2IinG5LFK62xlDTYfLcbrC3bXOWb3/jKA906z87YU4P99vdnn4zkzN+qam+v/sxKFpdWYdfcFqKgt2JXf/OUMh7yFw8r9p3HkrHYWa9/JcpRVW5EQbcLuE65anB21dTlpCWalmZ+8wkl8LYnRJlVBOKCduTHodfUKNMTn6uLnJptiQMzCXiKqDwY3FFLiFE+UQQ+jXg+r3ZmR8BYw5KQ6p6vEaSmt7EilxYaDpys8rh8/MNdjubhBr8OWY76XTstqasdXbbXj6ndWIDM5GjNu095T6N7P19f5eOUWGyzCeE4LNTMPzdqg1LTIK4jEDsKVVjse/nKj5jYSgHPpfq/nf8GHE/urtkyQp8p6t05S+glpBQpaRcdaHYxjTIZ6byXw2/+7GOUWG9ISous+GOq9qRjcEFF98C8HhVSUwSB8r/drakKerhLrTLSmWUqrrdhzolx13RW9MjG6R4bq0z8AGHQ61Zu/L3Lx7+KdRdh1ogxLdp30qJMJxNGzlR4dgWUnSi3KaiT53IjZrvWHzuJkmQVxUQbcNVRdrNtGWCn22epDqsyNrI+QlRFrg+Sdqy/Q2NFbK4yqa0rKl5zUWHTzsiWDluTYKEy/tT9m3DYg4PonIiKAwQ2FmNjsTa/X4Z839IFeBzx9eTev95E/uReVVSvTQlorkvacKPfY3VsOatyDG71eh61+Zm7kKaRdQrCwZNdJj521vWVT3L06f7dfe1nJY9brdUrwIW9aObBdqqoQWKdzZqhkcVFGj0APALKFom2xbdD8h4fhb1f3wJ1uAROg7lItC2Sn7mC4pGs6hndJa9DnJKLIwWkpCqm2tc30ZMO7pGHL86N9NnGTpy+qrQ6UWWzYfKQE//fRao/j9mhkYuRaG/eam6oaO7YdL/VrzBabHceKq/Df3w8q1931yVokRhux+fnRynVa2SQt7kGRrG2LWNUWF7HCOYkxGVBtdWBh7ZTSkI4tVauNWsSZcevgtli44wT+OHgWJVVW7D/leT5aJZhh0Os8ArE2LeJwS16cx/EANFM3WlNVRESNFTM3FFKd0xPw5k3n4Yu7LlCu8xXYAM4pELmzcVGpBfd+tk7zuAOnPOtt4pVOu+qi2MNnKr32k3FXbXXgxvdWemyCWVptw+ajxZi3pQDVVjvOVGgHLd5KU9y3sRjaqRUMwpViACF/L0/N9clJVvZ/AoD2reIQbTIoeyBtLyhVdUqWpSVEIz3Bc+m3L5JGdNPQmRsionPB4IZC7urzspHXwbO2w5dWwtRUWQC7eMv9bdwbxu076V+9DQDsLSpXNc0TXfPv33Hv5+tx96frcLrcs/sxoN5yQvTmTX1VlzOTo9Et07WCSBXouAUTyTEmJAhLpId0cDamS4pxrjiTt5nomKZebp+RFI30JP8KeWV/HuYMmC7p6poWOpeaGyKihsbghhqltNpsw80fqKejxvbJwtvj+2rdBYBQc+M2LeUtWNEiZ0u6ZSaq3uABV53Nst0nsfaQdhdgb03w3JvnZSXFoGuGdqGtezCRKPTuAYAhHZ3BovsO2p3T49GvTQrMRj1eva43kmJM6OrnEmxZn5xkrH/2Unw4sb9S+xNj4gw2ETUdDG6oUfK2bPhESbVHsbBInvIyG+vONNw7vANiTAalq6673tlJqsZ07j78bb/m9Qle9lBKiTOppqwykqJxQ/8c521uQYp7jUtitEk1FnlLgmS3QKpjq3jMvGsQ/nhmJK6vfewnLuuKoZ1a4l83nuf1tbhLjYuCXq9Tfg6cliKipoQfx6hRSvNSJ1Jts3u80aYnmnGi1Fn/4p6x8SWvfQs8dmlnGA16/P2nHR6398hO9Jnxca/JkXnr8JsYbYJR72pimJ0cg5zUWHxx1wUeW1GIe3oZa1dPtWsZh1ev643slBil/0uSW3DTIS0eZqNBFdwlx0bh0zt87+zuTasEMw6fqWRwQ0RNCjM31Ci5T7cAzjfyZ67o7lGQ3DndNe3ird5FS0yUAcbaIGH+w8Nwfm6y6vauGYma46iLt/skxZiUwAYAMmtrYfI6tEBOqnorijhhZVRijElpoHd9/xwM7uDaCNJo0Ktes/sWF+dKDjL93UCViKgxYHBDjZJ7z7wHR3TCxucuxcB2qargJsqgVxXR1pW5EbMq4tRPl4wE/G1cT9WxaQlmpAqbOJ7nZXfqP1/UHl/9OU+5/PjorkiONeGi2p3SAWexsHv2w+ij+664c7e3TJAsWdhFO9jBjZxR8jU9R0TU2HBaihqlGwa0xtfrj+D6fjkY1SMd7VrGKdmLOCFI6JQer5qa8VWPAwDtW8Urm026F+261+kkx5pgNrkCkPdu6Yf3lu7HDQNaY9y7K5Sl190zEzGwXSryL+4Ag06HjmnxWPfMpSi32NDnhV9qH1uv2r7A27Sb63ZXzZG3AmVlnDFROIIqZCfHBH1V051D2yMpxoSbhIaBRESNHYMbapQyk2Lw2/+7RPO2OLdpGDEoEfvbdEyL99hyoX2rOFdwY3IPbtSZlIRoExxCBik9MRrPje2uPK/cFFDupPv46K7Kse67b7vvdZWZrK6xcddKCH4S6src1E6DuS8DD4b0xGjcf0mnoD8uEVEocVqKmhwxKGnTIlbVdE7sbzP7z3n4+NYBqvuKy6J9BTcJ0UYY9DqM7J6OlvFmXN4rQ3XsX0Z3wZCOLTD3waEeRb0yMVPjvvtCdrLv3jNicJPoZfWVTH7+YE9JERE1VczcUJOjF5rddU5PwOEzri0MxDqWlLgoXCz0qWnbIla1TNvXtJQ8vZUUY8LKyZfA6NZe+OIuabj4HPY+6pmd5PP2QIKb4V3SsHT3SVzaPb3e4yEiiiQMbqhJuueiDthVWIrRPTLw3tJ9ft3nkq7pMAjZFPdpKLG+RlwdZPJR+BuoN286D0t2ncTtQzw3rBSJwU1dRdLX9WuNa8/PVmWKiIiaMwY31CQ9OcZV35Ke6HuK59FLO2PF3lN4aGQnLNh+QrnePRiIMmgHN8F09XnZuPq87DqPaymsltLYpNsDAxsiIhcGN9TkXXN+NjYdLcaQji01b39wRCc8OMJZFJvto5BXr9rEMjjZms/uGIRHvtqIF92WmddFnCKrtvm34ScRETmFtaB46tSpGDBgABISEpCWloZx48Zh165dPu/zwQcfYOjQoUhJSUFKSgpGjhyJNWvWNNCIqTEyGfR48ZpeuLxXZp3HXtA+FU9c1hUfTuzv87hgLam+sFNL/PH0SIzqkVH3wV64r7QiIiLfwhrcLF26FPn5+Vi1ahUWLFgAq9WKUaNGoaKiwut9lixZgvHjx2Px4sVYuXIlcnJyMGrUKBw7dqwBR05NlU6nw73DO2BkHcW37iupwmFw7U7q49ljhogoIDpJ8mdGv2GcPHkSaWlpWLp0KYYNG+bXfex2O1JSUvDOO+9g4sSJdR5fWlqKpKQklJSUIDFRe0dmar7aPvkTAOCK3pl49+bzwzoWi82OEyUW5LaIrftgIqIIF8j7d6OquSkpKQEApKam+n2fyspKWK1Wr/exWCywWCzK5dLS0nMbJDULjSFzYzYaGNgQEdVDo2ni53A48PDDD2PIkCHo2dP/4ssnnngCWVlZGDlypObtU6dORVJSkvKVk5MTrCFTBPPWmI+IiBq/RhPc5OfnY+vWrZg1a5bf93n55Zcxa9YszJkzB9HR2suBJ0+ejJKSEuXryJEjwRoyRaDHR3dBh1ZxuG94h3APhYiI6qlR1Nzcf//9+P7777Fs2TK0a+e7uZnstddew9///nf8+uuv6N/f98oXEWtuiIiImp4mU3MjSRIeeOABzJkzB0uWLPE7sHnllVfw4osvYv78+QEFNkRERBT5whrc5OfnY+bMmfj++++RkJCAwsJCAEBSUhJiYpzN1iZOnIjs7GxMnToVAPCPf/wDzz33HGbOnIm2bdsq94mPj0d8PDcOJCIiau7CWnMzbdo0lJSUYPjw4cjMzFS+vvzyS+WYw4cPo6CgQHWfmpoaXHfddar7vPbaa+F4CURERNTIhH1aqi5LlixRXT548GBoBkNEREQRodGsliIiIiIKBgY3REREFFEY3BAREVFEYXBDREREEYXBDREREUUUBjdEREQUURjcEBERUURhcENEREQRhcENERERRRQGN0RERBRRwrr9QjjIWz6UlpaGeSRERETkL/l925+tm5pdcFNWVgYAyMnJCfNIiIiIKFBlZWVISkryeYxO8icEiiAOhwPHjx9HQkICdDpd0B63tLQUOTk5OHLkCBITE4P2uKSN57th8Xw3PJ7zhsXz3bDqc74lSUJZWRmysrKg1/uuqml2mRu9Xo/WrVuH7PETExP5H6MB8Xw3LJ7vhsdz3rB4vhtWoOe7royNjAXFREREFFEY3BAREVFEYXATJGazGVOmTIHZbA73UJoFnu+GxfPd8HjOGxbPd8MK9fludgXFREREFNmYuSEiIqKIwuCGiIiIIgqDGyIiIoooDG6IiIgoojC4CYJ3330Xbdu2RXR0NAYNGoQ1a9aEe0hN0rJlyzB27FhkZWVBp9Phu+++U90uSRKee+45ZGZmIiYmBiNHjsSePXtUx5w5cwYTJkxAYmIikpOTcccdd6C8vLwBX0XTMXXqVAwYMAAJCQlIS0vDuHHjsGvXLtUx1dXVyM/PR4sWLRAfH49rr70WJ06cUB1z+PBhXHHFFYiNjUVaWhoef/xx2Gy2hnwpTcK0adPQu3dvpWlZXl4e5s2bp9zOcx1aL7/8MnQ6HR5++GHlOp7z4Hr++eeh0+lUX127dlVub9DzLdE5mTVrlhQVFSVNnz5d2rZtm3TXXXdJycnJ0okTJ8I9tCZn7ty50tNPPy19++23EgBpzpw5qttffvllKSkpSfruu++kTZs2SVdddZXUrl07qaqqSjnmsssuk/r06SOtWrVK+u2336SOHTtK48ePb+BX0jSMHj1a+vjjj6WtW7dKGzdulC6//HIpNzdXKi8vV4655557pJycHGnhwoXS2rVrpQsuuEAaPHiwcrvNZpN69uwpjRw5UtqwYYM0d+5cqWXLltLkyZPD8ZIatR9++EH66aefpN27d0u7du2SnnrqKclkMklbt26VJInnOpTWrFkjtW3bVurdu7f00EMPKdfznAfXlClTpB49ekgFBQXK18mTJ5XbG/J8M7g5RwMHDpTy8/OVy3a7XcrKypKmTp0axlE1fe7BjcPhkDIyMqRXX31Vua64uFgym83SF198IUmSJG3fvl0CIP3xxx/KMfPmzZN0Op107NixBht7U1VUVCQBkJYuXSpJkvP8mkwmafbs2coxO3bskABIK1eulCTJGZDq9XqpsLBQOWbatGlSYmKiZLFYGvYFNEEpKSnShx9+yHMdQmVlZVKnTp2kBQsWSBdddJES3PCcB9+UKVOkPn36aN7W0Oeb01LnoKamBuvWrcPIkSOV6/R6PUaOHImVK1eGcWSR58CBAygsLFSd66SkJAwaNEg51ytXrkRycjL69++vHDNy5Ejo9XqsXr26wcfc1JSUlAAAUlNTAQDr1q2D1WpVnfOuXbsiNzdXdc579eqF9PR05ZjRo0ejtLQU27Zta8DRNy12ux2zZs1CRUUF8vLyeK5DKD8/H1dccYXq3AL8/Q6VPXv2ICsrC+3bt8eECRNw+PBhAA1/vpvdxpnBdOrUKdjtdtUPAgDS09Oxc+fOMI0qMhUWFgKA5rmWbyssLERaWprqdqPRiNTUVOUY0uZwOPDwww9jyJAh6NmzJwDn+YyKikJycrLqWPdzrvUzkW8jtS1btiAvLw/V1dWIj4/HnDlz0L17d2zcuJHnOgRmzZqF9evX448//vC4jb/fwTdo0CDMmDEDXbp0QUFBAV544QUMHToUW7dubfDzzeCGiJCfn4+tW7di+fLl4R5KROvSpQs2btyIkpISfP3115g0aRKWLl0a7mFFpCNHjuChhx7CggULEB0dHe7hNAtjxoxRvu/duzcGDRqENm3a4KuvvkJMTEyDjoXTUuegZcuWMBgMHtXeJ06cQEZGRphGFZnk8+nrXGdkZKCoqEh1u81mw5kzZ/jz8OH+++/Hjz/+iMWLF6N169bK9RkZGaipqUFxcbHqePdzrvUzkW8jtaioKHTs2BH9+vXD1KlT0adPH7z55ps81yGwbt06FBUV4fzzz4fRaITRaMTSpUvx1ltvwWg0Ij09nec8xJKTk9G5c2fs3bu3wX/HGdycg6ioKPTr1w8LFy5UrnM4HFi4cCHy8vLCOLLI065dO2RkZKjOdWlpKVavXq2c67y8PBQXF2PdunXKMYsWLYLD4cCgQYMafMyNnSRJuP/++zFnzhwsWrQI7dq1U93er18/mEwm1TnftWsXDh8+rDrnW7ZsUQWVCxYsQGJiIrp3794wL6QJczgcsFgsPNchMGLECGzZsgUbN25Uvvr3748JEyYo3/Och1Z5eTn27duHzMzMhv8dD7gcmlRmzZolmc1macaMGdL27dulu+++W0pOTlZVe5N/ysrKpA0bNkgbNmyQAEivv/66tGHDBunQoUOSJDmXgicnJ0vff/+9tHnzZunqq6/WXAret29fafXq1dLy5culTp06cSm4F/fee6+UlJQkLVmyRLV0s7KyUjnmnnvukXJzc6VFixZJa9eulfLy8qS8vDzldnnp5qhRo6SNGzdKP//8s9SqVSsuldXw5JNPSkuXLpUOHDggbd68WXryySclnU4n/fLLL5Ik8Vw3BHG1lCTxnAfbY489Ji1ZskQ6cOCAtGLFCmnkyJFSy5YtpaKiIkmSGvZ8M7gJgrffflvKzc2VoqKipIEDB0qrVq0K95CapMWLF0sAPL4mTZokSZJzOfizzz4rpaenS2azWRoxYoS0a9cu1WOcPn1aGj9+vBQfHy8lJiZKt912m1RWVhaGV9P4aZ1rANLHH3+sHFNVVSXdd999UkpKihQbGytdc801UkFBgepxDh48KI0ZM0aKiYmRWrZsKT322GOS1Wpt4FfT+N1+++1SmzZtpKioKKlVq1bSiBEjlMBGkniuG4J7cMNzHlw33nijlJmZKUVFRUnZ2dnSjTfeKO3du1e5vSHPt06SJKneOSciIiKiRoY1N0RERBRRGNwQERFRRGFwQ0RERBGFwQ0RERFFFAY3REREFFEY3BAREVFEYXBDREREEYXBDREREUUUBjdE1CzpdDp899134R4GEYUAgxsianC33nordDqdx9dll10W7qERUQQwhnsARNQ8XXbZZfj4449V15nN5jCNhogiCTM3RBQWZrMZGRkZqq+UlBQAzimjadOmYcyYMYiJiUH79u3x9ddfq+6/ZcsWXHLJJYiJiUGLFi1w9913o7y8XHXM9OnT0aNHD5jNZmRmZuL+++9X3X7q1Clcc801iI2NRadOnfDDDz8ot509exYTJkxAq1atEBMTg06dOnkEY0TUODG4IaJG6dlnn8W1116LTZs2YcKECbjpppuwY8cOAEBFRQVGjx6NlJQU/PHHH5g9ezZ+/fVXVfAybdo05Ofn4+6778aWLVvwww8/oGPHjqrneOGFF3DDDTdg8+bNuPzyyzFhwgScOXNGef7t27dj3rx52LFjB6ZNm4aWLVs23Akgovo7xx3OiYgCNmnSJMlgMEhxcXGqrxdffFGSJEkCIN1zzz2q+wwaNEi69957JUmSpPfff19KSUmRysvLldt/+uknSa/XS4WFhZIkSVJWVpb09NNPex0DAOmZZ55RLpeXl0sApHnz5kmSJEljx46VbrvttuC8YCJqUKy5IaKwuPjiizFt2jTVdampqcr3eXl5qtvy8vKwceNGAMCOHTvQp08fxMXFKbcPGTIEDocDu3btgk6nw/HjxzFixAifY+jdu7fyfVxcHBITE1FUVAQAuPfee3Httddi/fr1GDVqFMaNG4fBgwfX67USUcNicENEYREXF+cxTRQsMTExfh1nMplUl3U6HRwOBwBgzJgxOHToEObOnYsFCxZgxIgRyM/Px2uvvRb08RJRcLHmhogapVWrVnlc7tatGwCgW7du2LRpEyoqKpTbV6xYAb1ejy5duiAhIQFt27bFwoULz2kMrVq1wqRJk/DZZ5/hX//6F95///1zejwiahjM3BBRWFgsFhQWFqquMxqNStHu7Nmz0b9/f1x44YX4/PPPsWbNGnz00UcAgAkTJmDKlCmYNGkSnn/+eZw8eRIPPPAAbrnlFqSnpwMAnn/+edxzzz1IS0vDmDFjUFZWhhUrVuCBBx7wa3zPPfcc+vXrhx49esBiseDHH39UgisiatwY3BBRWPz888/IzMxUXdelSxfs3LkTgHMl06xZs3DfffchMzMTX3zxBbp37w4AiI2Nxfz58/HQQw9hwIABiI2NxbXXXovXX39deaxJkyahuroab7zxBv7yl7+gZcuWuO666/weX1RUFCZPnoyDBw8iJiYGQ4cOxaxZs4Lwyoko1HSSJEnhHgQRkUin02HOnDkYN25cuIdCRE0Qa26IiIgoojC4ISIioojCmhsianQ4W05E54KZGyIiIoooDG6IiIgoojC4ISIioojC4IaIiIgiCoMbIiIiiigMboiIiCiiMLghIiKiiMLghoiIiCLK/weUYFy3f2TrhQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "truncated_mae_history = average_mae_history[10:]\n",
    "plt.plot(range(1, len(truncated_mae_history) + 1), truncated_mae_history)\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Validation MAE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Training the final model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 4ms/step - loss: 17.8158 - mae: 2.6850\n"
     ]
    }
   ],
   "source": [
    "model = build_model()\n",
    "model.fit(train_data, train_targets,\n",
    "          epochs=130, batch_size=16, verbose=0)\n",
    "test_mse_score, test_mae_score = model.evaluate(test_data, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6850478649139404"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_mae_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Generating predictions on new data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 3ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([8.75626], dtype=float32)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(test_data)\n",
    "predictions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Wrapping up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "## Summary"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "chapter04_getting-started-with-neural-networks.i",
   "private_outputs": false,
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}