unit-7-intro-to-neural-networks.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Intro to Neural Networks\n",
    "\n",
    "<iframe src=\"https://giphy.com/embed/2bYewTk7K2No1NvcuK\" width=\"480\" height=\"269\" style=\"\" frameBorder=\"0\" class=\"giphy-embed\" allowFullScreen></iframe><p><a href=\"https://giphy.com/gifs/2bYewTk7K2No1NvcuK\">via GIPHY</a></p>\n",
    "\n",
    "In this unit we will discuss the following:\n",
    "1. Taking inspiration from the neuron \n",
    "2. Combining Neurons\n",
    "3. Stochastic gradient descent (SGD) and Loss\n",
    "4. Hyper Parameter Tuning\n",
    "5. Over/Under Fitting\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taking Inspiration from the Neuron\n",
    "\n",
    "We still do not understand 100% of how the brain works, however, naturally it seems, that if you want to have a computer learn you would try to simulate a brain. \n",
    "\n",
    "Our brain is made of neurons:\n",
    "\n",
    "![https://www.geeksforgeeks.org/single-layer-perceptron-in-tensorflow/](./images/Biological-Neuron-and-similarity-with-neural-network.png)\n",
    "\n",
    "The dendrite is the input of the neuron, the input can be an inhibitory signal, or a excitatory signal. Depending on the input the neuron will send a signal out of the axon, the output of the neuron. Inhibitory help prevent the neuron from firing and excitatory signals help the neuron to fire. These signals can be received from many oth neurons. Once the excitatory signals out weigh the inhibitory signals in some threshold the axon will fire. \n",
    "\n",
    "\n",
    "Lets try to model this with math! \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Perceptron\n",
    "\n",
    "Lets build something called a perceptron. \n",
    "\n",
    "Let us start with one dendrite, a nucleus (which will hold our threshold), and an axon. \n",
    "\n",
    "The simplest neuron we can make is one that has no threshold and passes the input to the output\n",
    "\n",
    "The mathmatical equivlent is $y=x$\n",
    "\n",
    "<img src=\"./images/perceptron1.png\" width=\"600\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we can add the threshold. Ideally we want to send out the axon if this is activate or not. One of our trusty friends we can use is the sigmoid!\n",
    "\n",
    "This will take our input and create an output signal between 0 and 1. For our neuron output we can think of the neuron output of 1 as activating or the neuron firing. This can be thought of as the activation step of our output - so the sigmoid function in this case is called the activation function.\n",
    "\n",
    "The mathematical equivalent is $y = sigmoid(x)$\n",
    "\n",
    "<img src=\"./images/perceptron2.png\" width=\"600\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How ever we can take in multiple inputs from other neurons. Then before our sigmoid function or activation function we accumulate the total and feed it in. \n",
    "\n",
    "This is now mathematically equivalent to \n",
    "$y = sigmoid( x1 + x2 + x3  )$\n",
    "\n",
    "<img src=\"./images/perceptron3.png\" width=\"600\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we may value certain neurons inputs differently we give a weight to each input.\n",
    "\n",
    "Note that our weights can be negative! - This is where we get our inhibitory and excitatory behavior. In this specific example a positive weight would be excitatory and a negative weight would be inhibitory. (This is not always true and depend on your input value and threshold value as described below)\n",
    "\n",
    "This is now mathematically equivalent to \n",
    "$y = sigmoid( w1 * x1 + w2 * x2 + w3 * x3  )$\n",
    "\n",
    "<img src=\"./images/perceptron4.png\" width=\"600\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to be able to alter our threshold. Rather an changing the activation function directly, we can change the threshold by adding a bias to the neuron. This is represented by B. \n",
    "\n",
    "This is also sometimes represented as a weight * 1. \n",
    "\n",
    "$y = sigmoid( w1 * x1 + w2 * x2 + w3 * x3 + B )$\n",
    "\n",
    "<img src=\"./images/perceptron5.png\" width=\"600\">\n",
    "\n",
    "One interesting thing to see here is that if we only had one input it would look like this, which looks very familiar:\n",
    "\n",
    "$y = sigmoid(w1 * x1 + b)$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combining Perceptron - The Multilayer Perceptron (MLP)\n",
    "\n",
    "The real power comes from when we start to connect these neurons together like a brain. Building a network of perceptrons. Ideally we want our \"brain\" to learn patterns that are valuable to us. We can combine these perceptron in multiple layers, creating our first neural network the Multilayered Perceptron!\n",
    "\n",
    "<img src=\"./images/MLP.png\" width=\"600\">\n",
    "\n",
    "But first lets build our brain in code!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[-0.19192582,  1.00478842]]), array([[ 1.28674291],\n",
      "       [-0.03658972]])]\n",
      "0: (1, 2)\n",
      "1: (2, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\trent\\Documents\\DSC412\\units\\.venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:1105: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "c:\\Users\\trent\\Documents\\DSC412\\units\\.venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Lets build a classifier first, but there is also a regression model too (the last layer you dont have a sigmoid)\n",
    "# https://scikit-learn.org/1.5/modules/neural_networks_supervised.html\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import numpy as np\n",
    "\n",
    "layers = [2]\n",
    "\n",
    "skeleton_model = MLPClassifier(hidden_layer_sizes=layers)\n",
    "# A bunch of defaults are decided for us\n",
    "# Hidden Layer Size is how many layers and how wide those layers are\n",
    "# Alpha is how much we wan to punish the weights from getting too big\n",
    "# Learning rate is how big of a step we take down the the local loss minimum\n",
    "# Read the documentation to learn more!\n",
    "\n",
    "x = np.array([0]).reshape(1, 1)\n",
    "y = np.array([1]).reshape(1, 1)\n",
    "skeleton_model.fit(x, y)\n",
    "\n",
    "\n",
    "print(skeleton_model.coefs_)\n",
    "for layer_num, width in enumerate(layers):\n",
    "    print(f\"{layer_num}: {skeleton_model.coefs_[layer_num].shape}\")\n",
    "print(f\"{len(layers)}: {skeleton_model.coefs_[-1].shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stochastic Gradient Descent - visualizing through loss\n",
    "\n",
    "Usually with neural networks we want to do batch training. This allows us to pass data through our model and have the weight and biases update to learn the patterns of our dataset. The process of passing the data through the model is called feed forward, while the learning and updating of the weights and basis are called back propagation. \n",
    "\n",
    "Unfortunately due to class time constraints we do not have time to go into depth about back propagation. I encourage those who are interested to look up videos and read the machine learning text books/research papers provided. 3 Brown 1 Blue has great visualizations. \n",
    "\n",
    "The main idea is we can take the derivative (or gradient) of the output of the model and based on the direction of the difference we can update our weights and biases to push our output in the correct direction.\n",
    "\n",
    "<img src=\"./images/mlp_back_prop.png\" width=\"600\">\n",
    "\n",
    "Photo Credit to: https://www.geeksforgeeks.org/backpropagation-in-neural-network/\n",
    "\n",
    "As discussed in our first machine learning lecture our goal is to step down to the local minimum. The big difference compared to our classic machine learning models is that we cannot find the local minimum instantly, due to hardware constraints we have to learn in batches. So for each batch we walk down the slope until we see that our local loss begins to converage. This idea is often referred to as Stochastic Gradient Descent\n",
    "\n",
    "Lets look at a MLP model learning to visual our loss converging.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this example we want to learn on our digits dataset we used earlier. MLP is supervised so we feed it the labels. \n",
    "from sklearn.datasets import fetch_openml\n",
    "X_mnist, y_mnist = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# digits: 10; # samples: 56000; # features 784\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import gen_batches\n",
    "X_train_mnist, X_test_mnist, y_train_mnist, y_test_mnist = train_test_split(\n",
    "    X_mnist, y_mnist, test_size=0.2, shuffle=True\n",
    ")\n",
    "\n",
    "(n_samples, n_features), n_digits = X_train_mnist.shape, np.unique(y_train_mnist).size\n",
    "\n",
    "print(f\"# digits: {n_digits}; # samples: {n_samples}; # features {n_features}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "def show_nums(data):\n",
    "    fig, axs = plt.subplots(4, 4)\n",
    "    for i in range(0, 4):\n",
    "        for j in range (0, 4):\n",
    "            axs[i, j].imshow(pd.DataFrame(data).iloc[(i*4)+j].values.reshape(28, 28 ))\n",
    "    plt.show()\n",
    "show_nums(X_train_mnist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\trent\\Documents\\DSC412\\units\\.venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=[10, 10, 10], max_iter=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(hidden_layer_sizes=[10, 10, 10], max_iter=50)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=[10, 10, 10], max_iter=50)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets make a basic model:\n",
    "layers = [10, 10, 10]\n",
    "model_mnist_basic = MLPClassifier(hidden_layer_sizes=layers, max_iter=50)\n",
    "model_mnist_basic.fit(X_train_mnist, y_train_mnist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.921\n"
     ]
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(model_mnist_basic.loss_curve_)\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.show()\n",
    "print(\"Score:\", model_mnist_basic.score(X_test_mnist, y_test_mnist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trained on 14000 data\n",
      "Trained on 28000 data\n",
      "Trained on 42000 data\n",
      "Trained on 56000 data\n",
      "Epoch: 0\n",
      "Loss 1.8074948535965831\n",
      "Score 0.40164285714285713\n",
      "\n",
      "Epoch: 10\n",
      "Loss 0.8654409343203189\n",
      "Score 0.778\n",
      "\n",
      "Epoch: 20\n",
      "Loss 0.41441126444742354\n",
      "Score 0.887\n",
      "\n",
      "Epoch: 30\n",
      "Loss 0.2410008392522477\n",
      "Score 0.9156428571428571\n",
      "\n",
      "Epoch: 40\n",
      "Loss 0.20887794717803942\n",
      "Score 0.9217142857142857\n",
      "\n",
      "Ding! Done Training!\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 50\n",
    "layers = [10, 10, 10]\n",
    "model_mnist_basic = MLPClassifier(hidden_layer_sizes=layers, max_iter=1)\n",
    "loss = []\n",
    "score = []\n",
    "\n",
    "batch_size = 100\n",
    "classes = np.unique(y_train_mnist)\n",
    "num_x_train_mod = len(X_train_mnist)/4\n",
    "\n",
    "\n",
    "# Train the model in batches\n",
    "for epoch in range(num_epochs):  # Number of epochs\n",
    "    count = 0\n",
    "    for batch_slice in gen_batches(len(X_train_mnist), batch_size):\n",
    "        X_batch = X_train_mnist[batch_slice]\n",
    "        y_batch = y_train_mnist[batch_slice]\n",
    "        model_mnist_basic.partial_fit(X_batch, y_batch, classes=classes) \n",
    "        count += len(X_batch)\n",
    "        if (not count%(num_x_train_mod) and (epoch == 0)):\n",
    "            print(f\"Trained on {count} data\")\n",
    "    \n",
    "    if not epoch%4:\n",
    "        loss.append(model_mnist_basic.loss_)\n",
    "        score.append(model_mnist_basic.score(X_test_mnist, y_test_mnist))\n",
    "\n",
    "    if not epoch%10:\n",
    "        print(\"Epoch:\", epoch)\n",
    "        print(\"Loss\", model_mnist_basic.loss_)\n",
    "        print(\"Score\", model_mnist_basic.score(X_test_mnist, y_test_mnist)) # This is a no no, you do not want to use the score as a learning metric, here it is for visualization\n",
    "        # if you want to visualize your score you can use a validation dataset to look at that. However this eats into your training and test dataset.\n",
    "        print()\n",
    "print(\"Ding! Done Training!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BAwagQ4cOxnslLeTtYoug9g44XVCO5PQLuLuXt9SRiMiIRFFEZnEm1Llq7M/dry9ZZ4vPNrq+m50bwr3D0cOjB2SCDFXaqtqbrurqnw28aWo0100vUKOrQY2uBhXVFbf8Gl1tXa87CuXp4NloibK3sb/l/RGR4QwuXPv27cPQoUP19+sGt0+YMAFLly5FTk4OMjMz9Y9PnDgRpaWl+OKLL/Cvf/0Lrq6uuPPOO284LYS5RQd74HRBOXaeKmThIrJi1dpqHC08qi9VdQWrqUHdga6BCPMOQ7h3OMK8wxDmHQY/Zz+THNXR6rQtLmz64qbVwFZhe93pPJVCdfMARCQpQbSE83o3kZWVBX9/f5w7dw5+fn5Gf/4/juTimf+loHN7B/z1ryFGf34iMr4STQkO5h3E/pz9+tOCh/MPo0pbdd26NjIb9PTsWVuqvMIQ7hOOPl594Grrav7gRG2IqT+/rYnJx3BZg9s6u0MmAOkF5cgpvgwfFzupIxHRFaIo4nzp+euOWp2+dLrR9Z1VztcdtQppHwKlXGnm5EREV7FwAXCxs0FvXxccyCrGzlMX8EhE227hRFLR6rQ4ceFEg7FW6lw1CioKGl3f39lfX6rqSlaAawAHehORxWHhuiI62AMHsoqx61QhCxeRmZwpOoM/0//EvvP7sD93Pw7lHcLlmsvXrScX5Oju0b3BkatQ71B42HPuPKLWZv78+fj444+Rm5uL0NBQfP755xgwYECj61ZXVyMxMRHLli1DdnY2unXrhg8//BB33323mVPfHAvXFdHBHvhyy2nsPF0IURT5L2QiEyjRlGDLmS3YdHoTNp3ehJMXT163joONA0K9QxHmdeWolU84erbvCTsbnuonau1WrlyJhIQELFiwAJGRkZg3bx5iY2Nx/PhxeHpef63HN954A8uXL8eiRYvQvXt3/PHHH3jwwQexa9cuhIeHS/AKmsZB81dUVmvR561NqKrR4c+EOxDs6WSS/RC1JVqdFvvO78Pm9M3YdHoTkrOSG8wbJRfkiPKPwiD/QQj3qT1yFewWzFnHiVqJus/vtLQ0+Pr66pdfe0WZOpGRkejfvz+++OILAIBOp4O/vz+ef/55zJgx47r1O3TogNdffx1Tp07VL3v44YdhZ2eH5cuXm+AVtRyPcF1hayNH/4B22HnqAnaeusDCRdRCZ4vO1h7BSt+EpPQkXKq81ODxYLdgDO88HMODhmNIwBC42LpIlJSIzOXa6yHPmTMHb775ZoNlVVVVSElJwcyZM/XLZDIZYmJikJyc3OjzajQa2NraNlhmZ2eHHTt2GCe4EbFw1TMwyONK4SrEhIEBUschsgqlmlL8feZvbD69GZvSN+HEhRMNHndRuSCmcwyGdR6GYUHD0LldZ4mSEpFUGjvCda3CwkJotVp4eXk1WO7l5YVjx441+ryxsbGYO3cu7rjjDgQFBSEpKQlr1qyBVqs17gswAhauegYFe+DjP44jOf0CarQ6KOQ8rUF0La1Oi5ScFP04rMZOE97mdxuGB9UexerXoR8UMv6qIWrLnJyc4OzsbPTn/b//+z9MnjwZ3bt3hyAICAoKQnx8PJYsWWL0fd0q/hasp5evC5xtFSiprMHh8yUI83eVOhKRRThbdFY/DuvP9D+vO00Y1C5IX7CGBgzlaUIiMpiHhwfkcjny8vIaLM/Ly4O3d+NXgWnfvj3WrVuHyspKXLhwAR06dMCMGTPQubPlHUln4apHLhNwW2d3bErLw85ThSxc1GaVakr13ybcnL4Zxy8cb/C4s8oZdwXeheFBwzGs8zAEuQVJlJSIWgulUomIiAgkJSVh1KhRAGoHzSclJWHatGk33NbW1ha+vr6orq7GTz/9hNGjR5shsWFYuK4xqIuHvnBNHRosdRwis9DqtEjNSdUPdt91bleD04QyQVZ7mvDKYPf+vv15mpCIjC4hIQETJkxAv379MGDAAMybNw/l5eWIj48HAMTFxcHX1xeJiYkAgN27dyM7OxthYWHIzs7Gm2++CZ1Oh3//+99SvoxG8TfmNQYG1U6kuO/sJVRWa2FrI5c4EZFpZBZn6ge6/5n+Jy5evtjg8UDXQMQGxdaeJgwcyusOEpHJjRkzBgUFBZg9ezZyc3MRFhaGjRs36gfSZ2ZmQia7Or66srISb7zxBtLT0+Ho6Ih77rkH//vf/+Dq6irRK2ga5+G6hiiKuC0xCXklGnw3KRLRwZzJmloHTY1GPw5r0+lNjZ4mvDPwTv1RLJ4mJKJbxYtXX8UjXNcQBAHRwR5Yk5qNHacKWbioVUg5n4Lxa8Y3KFkyQYZI30j9OKwBvgNgI7eRMCURUevFwtWI6KDawrXrVKHUUYhuiVanxUc7P8LsLbNRo6uBp4MnHuz+IIYHDcedgXfyNCERkZmwcDWi7qjWoexiFFdUw8We/+on63Om6Azi1sZhe+Z2AMDDPR7G1/d9DXd7d4mTERG1PZzZsxHeLrYIau8AnQgkp1+QOg6RQURRxPKDyxG6IBTbM7fDUemIbx74Bj8++iPLFhGRRFi4mlB3lGvXaZ5WJOtx6fIljFszDk+sfQIlmhJE+UXhwLMHMDFsIgRBkDoeEVGbxcLVhLrpIXZyHBdZiS1ntiB0QShWHF4BuSDH20Pexrb4bbx2IRGRBeAYriZEdXaHTABOF5Qjt7gS3i62N9+ISAKaGg1m/T0Ln+z6BCJEBLsFY/mDyxHpFyl1NCIiuoJHuJrgYm+D3r6114PjUS6yVGkFabht8W34eNfHECFiUvgk7H9mP8sWEZGFYeG6gYFXxnHt5DgusjCiKOKLPV8gYmEE1LlquNu5Y+2YtVh0/yI4Kh2ljkdERNdg4bqBQcFXx3FZwYT81EbkluXinu/vwfO/P4/KmkrEBsXi0JRDGNV9lNTRiIioCSxcNxDRqR2UChnySjQ4XVAudRwirD+2Hr2/6o2NpzZCJVfhP3f/BxvGb4CPk4/U0YiI6AZYuG7A1kaOfp3aAeD0ECSt8qpyPP3L0xi1chQKKwoR6hWKlKdT8Hzk85AJ/N+YiMjS8Tf1TdTNx7XjJAsXSWNP9h6Efx2ORamLIEDAy1EvY/ek3ejp2VPqaERE1EwsXDdRV7j+Sb8ArY7juMh8anQ1eHfbuxi4eCBOXjwJP2c//Bn3Jz4e/jFUCpXU8YiIyACch+smevu6wMlWgZLKGhzOLkaov6vUkagNyLiUgSfWPoGd53YCAEb3HI0F9y5AO7t2EicjIqKW4BGum5DLBER1rr3+3A7Ox0UmJooilqmXIXRBKHae2wknpRO+HfUtVjy8gmWLiMiKsXA1A6+rSOZw8fJFjFk9BhPXT0RpVSkGdRyEg1MO4onQJ3gdRCIiK8dTis0QHVx7hGvvmUuorNbC1kYucSJqbZLSkzBh3QRkl2ZDIVPgrSFv4dXoVyGX8e8aEVFrYPARrm3btmHkyJHo0KEDBEHAunXrbrqNRqPB66+/jk6dOkGlUiEgIABLlixpSV5JBLV3hJezClU1OqScvSR1HGpFNDUavLzpZcT8LwbZpdno6t4Vu57chdduf41li4ioFTH4CFd5eTlCQ0Px5JNP4qGHHmrWNqNHj0ZeXh4WL16M4OBg5OTkQKfTGRxWKoIgIDrIA2v2Z2PnqUL9KUaiW3E4/zDGrxmPg3kHAQDPRDyDT4d/Cgelg8TJiIjI2AwuXCNGjMCIESOavf7GjRuxdetWpKenw83NDQAQEBBww200Gg00Go3+fmlpqaExjW5g8JXCdfqC1FHIyulEHT7f/Tle/fNVaLQaeNh7YPH9i3F/t/uljkZERCZi8kHzP//8M/r164ePPvoIvr6+6Nq1K15++WVcvny5yW0SExPh4uKiv4WEhJg65k3VjeM6lFWE4svVEqcha3W+9DxGfDcCL/7xIjRaDe7pcg8OTTnEskVE1MqZvHClp6djx44dOHz4MNauXYt58+Zh9erVeO6555rcZubMmSguLtbf0tLSTB3zpnxc7NC5vQN0Yu0kqESGWnt0Lfp81QebTm+CrcIW8++Zj1/H/gpvR2+poxERkYmZ/FuKOp0OgiDgu+++g4uLCwBg7ty5eOSRR/Dll1/Czs7uum1UKhVUqqszaZeUlJg6ZrNEB3kgvaAcu04VIrYnPySpecqqyjD99+lYoq79oki4dzi+e+g79GjfQ+JkRERkLiY/wuXj4wNfX1992QKAHj16QBRFZGVlmXr3RqW/riInQKVm+ifrH4QtCMMS9RIIEPBq9Kv4Z9I/LFtERG2MyQtXdHQ0zp8/j7KyMv2yEydOQCaTwc/Pz9S7N6qozu6QCcDpgnLkFldKHYcsWI2uBm9teQuDlgzC6Uun4e/sj78n/I0PYj6AUq6UOh4REZmZwYWrrKwMarUaarUaAJCRkQG1Wo3MzEwAteOv4uLi9OuPGzcO7u7uiI+PR1paGrZt24ZXXnkFTz75ZKOnEy2Zi70NevnWHqnjrPPUmCptFdYeXYtBSwbhza1vQitqMbbXWBycchCDAwZLHY+IiCRi8Biuffv2YejQofr7CQkJAIAJEyZg6dKlyMnJ0ZcvAHB0dMTmzZvx/PPPo1+/fnB3d8fo0aPx7rvvGiG++UUHe+BgVjF2nCrEQ32t6wgdmY46V42l6qX47tB3KKyoLePOKmd8de9XGNd7nMTpiIhIagYXriFDhkAUxSYfX7p06XXLunfvjs2bNxu6K4sUHeSBr7acxq5TFyCKIq9x14YVVhTi+0Pf4xv1N1DnqvXLvR29EdcnDtMGTIO/i790AYmIyGLwWooG6hfQDkqFDLkllUgvLEdQe0epI5EZ1ehq8PvJ37H0wFL8cvwXVOtq52RTypW4v9v9iA+Lx/Cg4VDI+L8WERFdxU8FA9nayNGvUzvsOn0BO08VsnC1EUfyj+Ab9TdYfnA58srz9MsjfCIwMWwixvYaC3d7dwkTEhGRJWPhaoHoYA994YqLCpA6DpnIpcuX8MPhH7BUvRR7z+/VL29v3x6P93kcE8Mmoo9XHwkTEhGRtWDhaoGBQbVHMpJPX4BWJ0Iu4ziu1kKr02Jz+mZ8o/4G646tQ5W2CgCgkClwX9f7MDF0Iu7pcg9s5DYSJyUiImvCwtUCvX1d4GSrQEllDQ5nFyPU31XqSHSLjhcex1L1Unx78FucLz2vX97Hqw/iw+Ixrvc4eDp4SpiQiIisGQtXCyjkMtzW2R2b0/Kw83QhC5eVKq4sxqojq/CN+hskZyXrl7vZuWF87/GYGDYR4d7h/CYqERHdMhauFooOqi1cu05dwHNDgqWOQ82kE3X4K+MvLFUvxZqja3C55jIAQC7IcXfw3YgPi8d9Xe+DSqG6yTMRERE1HwtXCw3qUntdxb1nLqKyWgtbG7nEiehGTl88jWUHlmHZgWXILL46MW8Pjx6ID4vH430eh4+Tj4QJiYioNWPhaqGg9o7wdFIhv1SD1LOXMPDKha3JcpRVleHHIz9i6YGl2HZ2m365i8oFY3uNRXx4PPp36M9ThkREZHIsXC0kCAKigz2wdn82dp4uZOGyEDpRh+1nt+Mb9TdYnbYa5dXlAAABAoYHDcfEsIkY1X0UbBW2EiclIqK2hIXrFtQVrh2nLuCVWKnTtG1nis7g2wPfYql6KTKKMvTLu7h1QXxYPJ4IfQJ+zrz2JRERSYOF6xZEB9fOx3UoqwjFl6vhYse5mcxt0+lN+HDnh/gr4y/9MielE8b0HIOJYRMx0H8gTxkSEZHkWLhugY+LHTq3d0B6QTn+Sb+A2J7eUkdqU3449AMeX/s4dKIOAHBn4J2ID4vHg90fhIPSQeJ0REREV7Fw3aLoIA+kF5Rj16lCFi4zql+2xvcej/fufA+dXDtJHYuIiKhRMqkDWLu604o7T1+QOEnbUb9sPRn2JL598FuWLSIismgsXLcoqrMHBAE4lV+G3OJKqeO0eisOr2hQthbdvwgygX+NiYjIsvGT6ha52Nugt68LAGDX6UKJ07RuKw6vwPg146ETdYgPi2fZIiIiq8FPKyMYGFQ7B9fOUzytaCorD69sULb+e/9/WbaIiMhq8BPLCAYF1xWuQoiiKHGa1mfl4ZUYt2YcyxYREVktfmoZQb+AdlAqZMgtqUR6YbnUcVoVHtkiIqLWgJ9cRmBrI0dEx3YAgF2nOI7LWOrKllbUYmLYRJYtIiKyWvz0MpJBXWpPK+5g4TKKVUdWNSxbI1m2iIjIevETzEgGBtXOx5V8+gK0Oo7juhWrjqzCuJ/GNShbcplc6lhEREQtxsJlJL19XeCkUqCksgZHzhdLHcdq/XjkR33ZmhA6gWWLiIhaBRYuI1HIZbjtylEunlZsmR+P/IixP43Vl63F9y9m2SIiamPmz5+PgIAA2NraIjIyEnv27Lnh+vPmzUO3bt1gZ2cHf39/vPTSS6istLyJyFm4jCj6SuHaxfm4DFa/bMWFxrFsERG1QStXrkRCQgLmzJmD1NRUhIaGIjY2Fvn5+Y2u//3332PGjBmYM2cOjh49isWLF2PlypV47bXXzJz85li4jCj6ynxce89cRGW1VuI01mN12uoGZWvJ/UtYtoiI2qC5c+di8uTJiI+PR0hICBYsWAB7e3ssWbKk0fV37dqF6OhojBs3DgEBARg+fDjGjh1706NiUmDhMqJgT0d4OqmgqdEh9ewlqeNYhdVpq/HY6sdYtoiIWrHS0lKUlJTobxqN5rp1qqqqkJKSgpiYGP0ymUyGmJgYJCcnN/q8AwcOREpKir5gpaenY8OGDbjnnntM80JuAQuXEQmCoD/KtZPXVbyp+mXriT5PsGwREbVSISEhcHFx0d8SExOvW6ewsBBarRZeXl4Nlnt5eSE3N7fR5x03bhzefvttDBo0CDY2NggKCsKQIUN4SrEtqJsegtdVvLGf0n5qULa+eeAbli0iolYqLS0NxcXF+tvMmTON8rxbtmzB+++/jy+//BKpqalYs2YNfvvtN7zzzjtGeX5jUkgdoLWpO8J1MKsIxZer4WJnI3Eiy/NT2k8Ys3oMyxYRURvh5OQEZ2fnG67j4eEBuVyOvLy8Bsvz8vLg7e3d6DazZs3CE088gUmTJgEAevfujfLycjz99NN4/fXXIZNZznElg5Ns27YNI0eORIcOHSAIAtatW9fsbXfu3AmFQoGwsDBDd2s1OrjaobOHA3QisDudR7mu9VPaT3jsp9ojW4/3eZxli4iIAABKpRIRERFISkrSL9PpdEhKSkJUVFSj21RUVFxXquTy2s8UUbSsScgNLlzl5eUIDQ3F/PnzDdquqKgIcXFxuOuuuwzdpdUZGHxleojTLFz1rTm6Bo/99BhqdDV4vM/jWPrAUpYtIiLSS0hIwKJFi7Bs2TIcPXoUU6ZMQXl5OeLj4wEAcXFxDU5Hjhw5El999RVWrFiBjIwMbN68GbNmzcLIkSP1xctSGHxKccSIERgxYoTBO3r22Wcxbtw4yOXymx4V02g0Db7BUFpaavD+pDQo2APL/8nkBKj1rDm6BmNWj2HZIiKiJo0ZMwYFBQWYPXs2cnNzERYWho0bN+oH0mdmZjY4ovXGG29AEAS88cYbyM7ORvv27TFy5Ei89957Ur2EJpllDNc333yD9PR0LF++HO++++5N109MTMRbb71lhmSmcVtndwgCcCq/DHkllfBytpU6kqTql63xvcezbBERUZOmTZuGadOmNfrYli1bGtxXKBSYM2cO5syZY4Zkt8bko8lOnjyJGTNmYPny5VAomtfvZs6c2eDbDGlpaSZOaVyu9kr06uACANjVxqeHWHt0bYOytWzUMpYtIiJqc0xauLRaLcaNG4e33noLXbt2bfZ2KpUKzs7O+puTk5MJU5pG3bcVd5xsu+O41h5di9GrR6NGV4NxvcexbBERUZtl0sJVWlqKffv2Ydq0aVAoFFAoFHj77bdx4MABKBQK/PXXX6bcvaSi9QPnCy3umxLmcG3Z+nbUtyxbRETUZpl0DJezszMOHTrUYNmXX36Jv/76C6tXr0ZgYKApdy+pfp3coJTLkFNciYzCcnRu7yh1JLNZd2wdyxYREVE9BheusrIynDp1Sn8/IyMDarUabm5u6NixI2bOnIns7Gx8++23kMlk6NWrV4PtPT09YWtre93y1sZOKUdEp3ZITr+AnacK20zhWndsHR798VGeRiQiIqrH4FOK+/btQ3h4OMLDwwHUzpkRHh6O2bNnAwBycnKQmZlp3JRWqu60Ylu5zE/9sjW211gsG7UMChkvZkBERCSIVjDAKCsrC/7+/jh37hz8/PykjtNsqZmX8NCXu+BiZ4PUWcMglwlSRzKZ9cfW45EfH9GXrW8f/JZli4iojbPWz29TsJyLDLVCfXxd4KRSoPhyNY6cL5Y6jsmwbBEREd0YC5cJKeQyRHZu3acV1x9brz+N+Fivx1i2iIiIGsHCZWL1p4dobX4+/jMe/fFRVOuq8Vivx/C/B//HskVERNQIFi4TG3RlAtQ9GRdRWa2VOI3x/Hz8Zzyy6hGWLSIiomZg4TKxYE9HeDqpoKnRITXzktRxjKJ+2RrTcwzLFhER0U2wcJmYIAgYGHTltGIrGMf1y/FfGpSt5Q8tZ9kiIiK6CRYuM9BfV/GUdY/j+uX4L3h41cMsW0RERAZi4TKDusJ1MKsIJZXVEqdpme1nt+vL1uieo1m2iIiIDMDCZQYdXO0Q6OEAnQjsTr8odZwWeXvb26jWVePB7g/iu4e+Y9kiIiIyAAuXmVy9zI/1nVY8WnAUf6b/CZkgw2exn7FsERERGYiFy0yig2pPK1pj4fpy75cAgPu73Y9Orp0kTkNERGR9WLjMJCrIHYIAnMwvQ35JpdRxmq1EU4KlB5YCAKb1nyZtGCIiIivFwmUmrvZK9OrgAgDYaUWzzv/vwP9QVlWG7h7dcWfgnVLHISIiskosXGY0MNi6rqsoiiK+2PsFgNqjW4IgSJyIiIjIOrFwmVHdOK5dpwohiqLEaW7ur4y/cKzwGJyUTogLjZM6DhERkdVi4TKj/gFuUMplOF9ciYzCcqnj3FTd0a0JoRPgpHKSOA0REZH1YuEyIzulHH07uQIAdp627NOKZ4vO4ufjPwMApg6YKnEaIiIi68bCZWb1TytasgX7FkAn6hDTOQbdPbpLHYeIiMiqsXCZWXSXK4Xr9AVodZY5jquyphKLUhcB4FQQRERExsDCZWZ9fF3gpFKg+HI10s6XSB2nUSsPr8SFyxfQ0aUj7ut6n9RxiIiIrB4Ll5kp5DJEdr4yPYQFzscliiI+3/M5AOC5fs9BLpNLnIiIiMj6sXBJwJKvq7gnew9SclKgkqvwVN+npI5DRETUKrBwSSA6uHYc194zF6Gp0UqcpqG6qSDG9h4LD3sPidMQERG1DixcEuji6Yj2TipUVuuQerZI6jh6eWV5WHVkFQAOliciIjImFi4JCIKA6CDLO63439T/okpbhdv8bkNEhwip4xAREbUaLFwSGXjltKKlDJyv0dXgq31fAeDRLSIiImNj4ZJI3Tiug1nFKKmsljgNsP7YemSXZsPTwROPhDwidRwiIqJWhYVLIr6udgj0cIBWJ2J3+kWp4+gHyz/d92moFCqJ0xAREbUuLFwSGmgh47gO5x/GljNbIBfkeKbfM5JmISIiao1YuCQ0KLjuMj/SFq75e+YDAB7s8SD8nP0kzUJERNQasXBJKCrIHYIAnMgrQ35JpSQZiiqL8O3BbwFwsDwREZGpGFy4tm3bhpEjR6JDhw4QBAHr1q274fpr1qzBsGHD0L59ezg7OyMqKgp//PFHS/O2Kq72SvTs4Ayg9mLWUlimXoaK6gr08uyFOzrdIUkGIiKi1s7gwlVeXo7Q0FDMnz+/Wetv27YNw4YNw4YNG5CSkoKhQ4di5MiR2L9/v8FhW6O6bytKMY5LJ+owf2/tf8dp/adBEASzZyAiImoLFIZuMGLECIwYMaLZ68+bN6/B/ffffx/r16/HL7/8gvDw8Ea30Wg00Gg0+vulpaWGxrQa0UEe+HprOnaeKoQoimYtPZtPb8bJiyfhonLB+D7jzbZfIiKitsbsY7h0Oh1KS0vh5ubW5DqJiYlwcXHR30JCQsyY0Lz6B7hBKZfhfHElzlyoMOu+66aCiA+Lh6PS0az7JiIiakvMXrg++eQTlJWVYfTo0U2uM3PmTBQXF+tvaWlpZkxoXnZKOfp2cgVg3tOK6ZfS8duJ3wAAz/V/zmz7JSIiaovMWri+//57vPXWW1i1ahU8PT2bXE+lUsHZ2Vl/c3JyMmNK84sOMv84rq/2fgURIu4Ovhtd3LuYbb9ERERtkdkK14oVKzBp0iSsWrUKMTEx5tqtVai7rmJy+gXodKLJ91dRXYHF+xcD4FQQRERE5mCWwvXDDz8gPj4eP/zwA+69915z7NKqhPq5wFGlQFFFNdJySky+vx8O/YBLlZfQuV1n3B18t8n3R0RE1NYZXLjKysqgVquhVqsBABkZGVCr1cjMzARQO/4qLi5Ov/7333+PuLg4fPrpp4iMjERubi5yc3NRXFxsnFfQCijkMtzWufZLBDtMfFpRFEX9YPnn+j0HuUxu0v0RERFRCwrXvn37EB4erp/SISEhAeHh4Zg9ezYAICcnR1++AGDhwoWoqanB1KlT4ePjo79Nnz7dSC+hdRhopnFcu87tgjpXDTuFHeLD4026LyIiIqpl8DxcQ4YMgSg2Pc5o6dKlDe5v2bLF0F20SYO61BauvWcuQlOjhUphmiNPdUe3xvceDze7pqfmICIiIuPhtRQtRBdPR7R3UqGyWofUs0Um2UdOaQ5Wp60GAEwdMNUk+yAiIqLrsXBZCEEQMDDIHQCw67RpTisuTFmIGl0NBnUchDDvMJPsg4iIiK7HwmVBTHldxSptFRakLADAqSCIiIjMjYXLgtQVrgNZxSitrDbqc689uha5ZbnwcfTBgz0eNOpzExER0Y2xcFkQX1c7BLjbQ6sTsTv9olGfu26w/DMRz0ApVxr1uYmIiOjGWLgsTN1RLmPOx6XOVWNH5g4oZAo8HfG00Z6XiIjI2ObPn4+AgADY2toiMjISe/bsaXLdIUOGQBCE626WOMk6C5eFqT+O60bTbxhi/p75AIBHQh6Bj5OPUZ6TiIjI2FauXImEhATMmTMHqampCA0NRWxsLPLz8xtdf82aNcjJydHfDh8+DLlcjkcffdTMyW+OhcvCDAxyh41cwMn8MvyUmn3Lz3fx8kV8d+g7ABwsT0RE0igtLUVJSYn+ptFoGl1v7ty5mDx5MuLj4xESEoIFCxbA3t4eS5YsaXR9Nzc3eHt762+bN2+Gvb09CxfdnKu9Ei/GdAUAzFl/GGcKy2/p+b7Z/w0u11xGmHcYBvoPNEZEIiIig4SEhMDFxUV/S0xMvG6dqqoqpKSkICYmRr9MJpMhJiYGycnJzdrP4sWL8dhjj8HBwcFo2Y3F4JnmyfSeHRyEbScKsDvjIl5cqcaPz0bBRm54N9bqtPhy35cAao9uCYJg7KhEREQ3lZaWBl9fX/19lUp13TqFhYXQarXw8vJqsNzLywvHjh276T727NmDw4cPY/Hixbce2AR4hMsCyWUCPhsTBmdbBdTnivCfpJMtep6NpzYi/VI62tm2w9jeY42ckoiIqHmcnJzg7OysvzVWuG7V4sWL0bt3bwwYMMDoz20MLFwWqoOrHd5/qDcAYP7fp7Anw/BpIuqmgngq/CnY29gbNR8REZExeXh4QC6XIy8vr8HyvLw8eHt733Db8vJyrFixAk899ZQpI94SFi4Ldl+fDngkwg86EXhppRrFl5s/GerJCyex8dRGCBAwpf8UE6YkIiK6dUqlEhEREUhKStIv0+l0SEpKQlRU1A23/fHHH6HRaPD444+bOmaLsXBZuDfv74mObvbILrqMWesON3uqiC/31o7durfrvejcrrMpIxIRERlFQkICFi1ahGXLluHo0aOYMmUKysvLER8fDwCIi4vDzJkzr9tu8eLFGDVqFNzd3c0dudk4aN7COaoU+L/HwvDIgmT8fOA8hnZvjwfD/W64TVlVGb5RfwOAU0EQEZH1GDNmDAoKCjB79mzk5uYiLCwMGzdu1A+kz8zMhEzW8FjR8ePHsWPHDmzatEmKyM0miMaaXdOEsrKy4O/vj3PnzsHP78Zlo7X6POkkPt18Ao4qBTa8cDs6ujc9JuvrfV/j2d+eRRe3Ljg27RhkAg9kEhGR+fHz+yp+EluJ54YGo39AO5RpavDiyv2o0eoaXU8URf1g+an9p7JsERERWQB+GluJuqkinFQKpGYW4Yu/TzW63raz23A4/zAcbBwwIWyCmVMSERFRY1i4rIhfO3u8+2AvAMB/kk4i5ez1U0XUHd16os8TcLV1NWc8IiIiagILl5V5IMwXD4b7QicC01eoUVp5daqIrJIsrD26FgAwdcBUqSISERHRNVi4rNDbD/SEv5sdsi5dxuz1R/TLv973NbSiFkMChqCXZy8JExIREVF9LFxWyMnWBvPGhEEuE7B2fzbWq7OhqdFgYepCAJwKgoiIyNKwcFmpiE5ueP7OYADAG2sP4+u93yG/PB9+zn54oPsDEqcjIiKi+li4rNi0ocGI6NQOpZoavPXXXADAsxHPQiHjfLZERESWhIXLiinkstpTi6p0XKw5Arlgg8kRk6WORURERNdg4bJy/m726NRpOwDArmYQsi7YSJyIiIiIrsXCZeUKKwqx6/zPAADH6vvw4go1yjQ1EqciIiKi+li4rNzi1MXQaDUI945AoHMoMi9WYE69qSKIiIhIeixcVkyr0+LLfV8CAKZHPo//GxsOmQD8lJqFXw6clzgdERER1WHhsmK/nvgVmcWZcLdzx5heY9A/wA3ThtZOFfH62kPILroscUIiIiICWLisWt11Eyf3nQxbhS0A4Pm7uiDM3xUllTV4aaUaWp0oZUQiIiJCCwrXtm3bMHLkSHTo0AGCIGDdunU33WbLli3o27cvVCoVgoODsXTp0hZEpfqOFhzFn+l/QibI8Gy/Z/XLbeQy/N9jYXBQyrEn4yIWbD0tYUoiIiICWlC4ysvLERoaivnz5zdr/YyMDNx7770YOnQo1Go1XnzxRUyaNAl//PGHwWHpqi/31o7dur/b/ejk2qnBY53cHfDWA7XXUvxs8wkcOFdk7nhERERUj8FTko8YMQIjRoxo9voLFixAYGAgPv30UwBAjx49sGPHDnz22WeIjY1tdBuNRgONRqO/X1paamjMVq1EU4KlB5YCaPq6iQ/39cXfx/Px28EcTF+xH7+9cDscVJyBnoiISAomH8OVnJyMmJiYBstiY2ORnJzc5DaJiYlwcXHR30JCQkwd06r878D/UFZVhu4e3XFn4J2NriMIAt4f1RsdXGxx5kIF3v4lzcwpiYiIqI7JC1dubi68vLwaLPPy8kJJSQkuX278W3QzZ85EcXGx/paWxrJQRxRF/WD5af2nQRCEJtd1sbfB3DFhEARg5b5z+P1QjrliEhERUT0W+S1FlUoFZ2dn/c3JyUnqSBbjr4y/cKzwGJyUTogLjbvp+rd1dseUwUEAgBlrDiGnmFNFEBERmZvJC5e3tzfy8vIaLMvLy4OzszPs7OxMvftWp+7o1oTQCXBSNa+IvjSsK/r4uaD4cjUSVh7gVBFERERmZvLCFRUVhaSkpAbLNm/ejKioKFPvutU5W3QWPx+vvW7i1AFTm71d7VQR4bBXypGcfgGLtqebKiIRERE1wuDCVVZWBrVaDbVaDaB22ge1Wo3MzEwAteOv4uKunup69tlnkZ6ejn//+984duwYvvzyS6xatQovvfSScV5BG7Jg3wLoRB1iOsegu0d3g7YN9HDAmyN7AgA+3XQch7KKTRGRiIiIGmFw4dq3bx/Cw8MRHh4OAEhISEB4eDhmz54NAMjJydGXLwAIDAzEb7/9hs2bNyM0NBSffvop/vvf/zY5JQQ1rrKmEotSFwFoeiqIm3m0nx9G9PJGtVbE9BX7UVFVY8yIRERE1ARBFEWLH9CTlZUFf39/nDt3Dn5+flLHkcQy9TJMXD8RHV06Iv2FdMhl8hY9T1FFFe6etx25JZUYO6AjEh/qbeSkREREtfj5fZVFfkuRGhJFEZ/v+RwA8Fy/51pctgDA1V6JuWNCIQjAD3sysfFwrrFiEhERURNYuKzAnuw9SMlJgUquwlN9n7rl5xsY5IGn7+gMAJix5iDySipv+TmJiIioaSxcVqBuKoixvcfCw97DKM/5r2Hd0MvXGUUV1fjXqgPQcaoIIiIik2HhsnB5ZXlYdWQVgJYPlm+MUlE7VYStjQw7ThVi8Y4Moz03ERERNcTCZeH+m/pfVGmrcJvfbYjoEGHU5w5q74jZ99VOFfHRH8dw5DyniiAiIjIFFi4LVqOrwVf7vgJg3KNb9Y0d4I/hIV6o1op44Yf9uFylNcl+iIiI2jIWLgu2/th6ZJdmw9PBE4+EPGKSfQiCgA8e7gNPJxVOF5TjvQ28UDgREZGxsXBZsLrB8k/3fRoqhcpk+3FzUGLu6DAAwPJ/MvFnWt6NNyAiIiKDsHBZqMP5h7HlzBbIBTme6feMyfc3qIsHJt8eCAD4908Hkc+pIoiIiIyGhctCzd8zHwDwYI8H4edsntl5X47thhAfZ1wsr8K/fuRUEURERMbCwmWBiiqL8O3BbwGYbrB8Y1QKOf4zNgwqhQzbTxbim11nzLZvIiKi1oyFywItUy9DRXUFenn2wh2d7jDrvoM9nfDGfSEAgA9/P4ajOSVm3T8REVFrxMJlYURRxPy9tacTp/WfBkEQzJ7h8ciOiOnhiSqtDi/8sB+V1ZwqgoiI6FawcFmYQ/mHcPLiSdjb2GN8n/GSZBAEAR8+3AcejiqczC9D4oajkuQgIiJqLVi4LMxfGX8BAO7odAcclY6S5XB3VOHT0aEAgGXJZ/H3sXzJshAREVk7Fi4LU1e47gq8S+IkwOCu7fFkdO1UEa+sPoCCUo3EiYiIiKwTC5cFqdHVYOvZrQCAOwPvlDhNrX/f3Q3dvZ1QWFaFV1YfgChyqggiIiJDsXBZkJTzKSjRlKCdbTuEeoVKHQcAYGsjx3/GhkOlkGHL8QIs41QRREREBmPhsiB1pxOHBAyBXCaXOM1VXb2c8No9PQAAib8fw8m8UokTERERWRcWLgvy1xnLGb91rbioThjctT00NTq8uFKNqhqd1JGIiIisBguXhdDUaLAjcwcAyxm/VZ8gCPj4kT5oZ2+DI+dL8NmfJ6SOREREZDVYuCxEclYyKmsq4e3oje4e3aWO0yhPZ1skPtQbALBg62nsybgocSIiIiLrwMJlIerGb90ZeKcks8s31929fPBohB9EEXhppRolldVSRyIiIrJ4LFwWQl+4AizvdOK15tzfEx3d7JFddBlv/nxE6jhEREQWj4XLApRVlWF39m4AwF2dLW/A/LUcVQp8NiYUMgFYk5qN3w7mSB2JiIjIorFwWYDtZ7ejRleDQNdABLgGSB2nWSI6uWHq0GAAwGtrDyG3uFLiRERERJaLhcsC1B+/ZU1euKsL+vi5oPhyNV7+8QB0Os5CT0RE1BgWLgtQN/+WtRUuG7kMn40Jg62NDDtOFWIpZ6EnIqJbNH/+fAQEBMDW1haRkZHYs2fPDdcvKirC1KlT4ePjA5VKha5du2LDhg1mStt8LFwSu3j5Ivbn7AdgfYULAILaO+L1e0MAAB9sPIbjuZyFnoiIWmblypVISEjAnDlzkJqaitDQUMTGxiI/P7/R9auqqjBs2DCcOXMGq1evxvHjx7Fo0SL4+vqaOfnNsXBJbOuZrRAhIqR9CLwdvaWO0yKPR3bE0G7tUXVlFnpNjVbqSEREZIXmzp2LyZMnIz4+HiEhIViwYAHs7e2xZMmSRtdfsmQJLl68iHXr1iE6OhoBAQEYPHgwQkMt43rE9bFwSSwpIwmAdUwH0RRBEPDhI33g5qDE0ZwSzN3EWeiJiOiq0tJSlJSU6G8ajea6daqqqpCSkoKYmBj9MplMhpiYGCQnJzf6vD///DOioqIwdepUeHl5oVevXnj//feh1VreP/xbVLgMPb86b948dOvWDXZ2dvD398dLL72Eykp+qw2w3gHz1/J0ssUHV2ahX7g9HcmnL0iciIiILEVISAhcXFz0t8TExOvWKSwshFarhZeXV4PlXl5eyM3NbfR509PTsXr1ami1WmzYsAGzZs3Cp59+infffdckr+NWKAzdoO786oIFCxAZGYl58+YhNjYWx48fh6en53Xrf//995gxYwaWLFmCgQMH4sSJE5g4cSIEQcDcuXON8iKsVU5pDo4WHoUAAUMChkgd55YN7+mNx/r7Y8Xec/jXKjV+f/EOuNjZSB2LiIgklpaW1mBclUqlMsrz6nQ6eHp6YuHChZDL5YiIiEB2djY+/vhjzJkzxyj7MBaDj3AZen51165diI6Oxrhx4xAQEIDhw4dj7NixNzwqptFoGhx6LC1tnQOx/z7zNwCgr09ftLNrJ3Ea45h1Xwg6udvjfHEl5qw/LHUcIiKyAE5OTnB2dtbfGitcHh4ekMvlyMvLa7A8Ly8P3t6Nj3H28fFB165dIZfL9ct69OiB3NxcVFVVGfdF3CKDCldLzq8OHDgQKSkp+oKVnp6ODRs24J577mlyP4mJiQ0OPYaEhBgS02okpV8Zv2XlpxPrc1Ap8NmYMMhlAtapz+PnA+eljkRERFZAqVQiIiICSUlJ+mU6nQ5JSUmIiopqdJvo6GicOnUKOp1Ov+zEiRPw8fGBUqk0eWZDGFS4WnJ+ddy4cXj77bcxaNAg2NjYICgoCEOGDMFrr73W5H5mzpyJ4uJi/S0tLc2QmFbDWuffupm+HdvpZ6F/Y+0hnC+6LHEiIiKyBgkJCVi0aBGWLVuGo0ePYsqUKSgvL0d8fDwAIC4uDjNnztSvP2XKFFy8eBHTp0/HiRMn8Ntvv+H999/H1KlTpXoJTTL5txS3bNmC999/H19++SVSU1OxZs0a/Pbbb3jnnXea3EalUjU49Ojk5GTqmGaXcSkDZ4rOQCFTYFDHQVLHMbrn7wxGqL8rSipr8K9VnIWeiIhubsyYMfjkk08we/ZshIWFQa1WY+PGjfoDPZmZmcjJuXr9Xn9/f/zxxx/Yu3cv+vTpgxdeeAHTp0/HjBkzpHoJTTJo0HxLzq/OmjULTzzxBCZNmgQA6N27N8rLy/H000/j9ddfh0zWNmemqPt24m1+t8FR6ShxGuOzkcswb0wY7vm/7UhOv4AlOzMw6fbOUsciIiILN23aNEybNq3Rx7Zs2XLdsqioKPzzzz8mTnXrDGo7LTm/WlFRcV2pqhvcJopt96iH/nSiFc+/dTOBHg6YdV/t+LuPNh7H0ZwSiRMRERFJw+DDS4aeXx05ciS++uorrFixAhkZGdi8eTNmzZqFkSNHNvhWQVsiimKrmX/rZsYO8Mdd3T1RpdXhpZVqVFZb3mR0REREpmbwPFxjxoxBQUEBZs+ejdzcXISFhV13frX+Ea033ngDgiDgjTfeQHZ2Ntq3b4+RI0fivffeM96rsDJHC48itywXdgo73OZ3m9RxTKpuFvq7523DsdxSfPLHcbxxX+v81ikREVFTBNEKzutlZWXB398f586dg5+fn9RxbtkXe77A878/j2Gdh2HTE5ukjmMWSUfz8NSyfQCA7ydFYmCwh8SJiIjI1Frb5/etaJsj1iXWVk4n1ndXDy+Mi+wIAPjXjwdQXFEtcSIiIiLzYeEyM61Oq59hvi0VLgB4494eCPRwQE5xJd7gLPRERNSGsHCZmTpXjaLKIjirnNHXp6/UcczKXnl1FvpfDpzHenW21JGIiIjMgoXLzOpOJw7uNBgKmcHfWbB6Yf6ueOHOLgCAN9YdRjZnoSciojaAhcvM6ubfuivwLomTSGfq0CCEd3RFaWUNElaqoeUs9ERE1MqxcJlRlbYK289uB9D2xm/Vp5DL8NnoMNgr5didcRH/3Z4udSQiIiKTYuEyoz3Ze1BeXY729u3R07On1HEkFeDhgNlX5uP6ZNNxpJ3nLPRERNR6sXCZUd34raGBQyET+NaP6e+PYSFeqNaKeHHlfs5CT0RErRY/9c2ornC15fFb9QmCgA8e6g0PRxVO5JXho43HpY5ERERkEixcZlJRXYHkrGQAbXv81rXcHVX4+JE+AIAlOzOw/WSBxImIiIiMj4XLTHZm7kSVtgr+zv4IahckdRyLMrS7Jx6/rXYW+pd/PICiiiqJExERERkXC5eZ1L+cjyAIEqexPK/fE4LO7R2QV6LB62sPwwou8UlERNRsLFxmUjf/Fk8nNs5OKce8MWFQyAT8digHa/dzFnoiImo9WLjMoLiyGPvO7wPAwnUjffxc8WJM7Sz0s9cfwbmLFRInIiIiMg4WLjPYdnYbdKIOXd27ws/ZT+o4Fu3ZwUGI6NQOZZoa/GvVAc5CT0RErQILlxkkZSQBAO4M4NGtm6mbhd5BKceeMxfx9bbTUkciIiK6ZSxcZlB/wDzdXEd3e8y5v3Ym/s82n8Dh7GKJExEREd0aFi4Tyy/Px6H8QwBqZ5in5nk0wg+xPetmoVdzFnoiIrJqLFwmtuXMFgBAqFcoPOw9pA1jRQRBQOJDfdDeSYVT+WX44PdjUkciIiJqMRYuE0tKvzJ+i6cTDebmoNTPQr901xlsPcFZ6ImIyDqxcJkY59+6NUO6eWJCVCcAtbPQXyznLPRERGR9WLhMKLM4E6cunoJckOOOTndIHcdqzRjRA0HtHVBQqsFraw5xFnoiIrI6LFwm9HfG3wCA/r794axyljiN9bJTyvF/j4VDIROw8UguVqdkSR2JiIjIICxcJsT5t4ynl68LXhrWFQDw5s9HkHmBs9ATEZH1YOEyEVEUOf+WkT07OAj9A9qhvEqLhFVqzkJPRERWg4XLRE5ePIns0myo5CoM9B8odZxWQS4TMHd0GBxVCuw7ewkLtnIWeiIisg4sXCZSd3RroP9A2NnYSZym9fB3s8db9WahP5hVJG0gIiKiZmDhMhGeTjSdh/r64p7e3qjR1c5Cf7mKs9ATEZFlY+EyAZ2oY+EyIUEQ8N6o3vB0UiG9oBzvbUiTOhIREdENsXCZwKG8Q7hw+QIclY7o36G/1HFapXYOSnzyaCgAYPk/mXhj3SFU1egkTkVERNQ4Fi4TqDu6dUenO2Ajt5E4Tet1R9f2mDmiOwShtnQ9tjAZucWVUsciIiK6TosK1/z58xEQEABbW1tERkZiz549N1y/qKgIU6dOhY+PD1QqFbp27YoNGza0KLA10F/Oh/Nvmdwzg4OweEI/ONsqkJpZhPs+34Hd6RekjkVERNSAwYVr5cqVSEhIwJw5c5CamorQ0FDExsYiPz+/0fWrqqowbNgwnDlzBqtXr8bx48exaNEi+Pr63nJ4S1Sjq8HWM1sBcPyWudzZ3Qu/PD8I3b2dUFimwbj/7saSHRm8BBAREVkMgwvX3LlzMXnyZMTHxyMkJAQLFiyAvb09lixZ0uj6S5YswcWLF7Fu3TpER0cjICAAgwcPRmhoaJP70Gg0KCkp0d9KS0sNjSmZfef3obSqFO1s2yHUu+nXSMbVyd0Ba54biAfCOkCrE/H2r2l4caUaFVU1UkcjIiIyrHBVVVUhJSUFMTExV59AJkNMTAySk5Mb3ebnn39GVFQUpk6dCi8vL/Tq1Qvvv/8+tNqmv8qfmJgIFxcX/S0kJMSQmJKqG781NHAoZAKHyJmTvVKBeWPCMPu+EMhlAtarz+OhL3fh7IVyqaMREVEbZ1AjKCwshFarhZeXV4PlXl5eyM3NbXSb9PR0rF69GlqtFhs2bMCsWbPw6aef4t13321yPzNnzkRxcbH+lpZmPV/7rytcdwXeJXGStkkQBDw5KBDfT4qEh6MKx3JLMfLzHfj7WOOnvImIiMzB5IdgdDodPD09sXDhQkRERGDMmDF4/fXXsWDBgia3UalUcHZ21t+cnJxMHdMoKmsqsfPcTgAcvyW1yM7u+PX5QQjv6IqSyho8uWwv5v15Ajpef5GIiCRgUOHy8PCAXC5HXl5eg+V5eXnw9vZudBsfHx907doVcrlcv6xHjx7Izc1FVVVVCyJbruRzyaisqYSPow+6uXeTOk6b5+1ii5VPR+GJ2zpBFIF5f57E5G/3ofhytdTRiIiojTGocCmVSkRERCApKUm/TKfTISkpCVFRUY1uEx0djVOnTkGnuzop5YkTJ+Dj4wOlUtnC2Jap/uzygiBInIYAQKmQ4Z1RvfDxI32gVMiQdCwf93+xA8dyS6SORkREbYjBpxQTEhKwaNEiLFu2DEePHsWUKVNQXl6O+Ph4AEBcXBxmzpypX3/KlCm4ePEipk+fjhMnTuC3337D+++/j6lTpxrvVViIuvm3OH7L8jzazx9rpgyEr6sdzl6owIPzd2G9OlvqWERE1EYoDN1gzJgxKCgowOzZs5Gbm4uwsDBs3LhRP5A+MzMTMtnVHufv748//vgDL730Evr06QNfX19Mnz4dr776qvFehQUo1ZRiT3btBLAcv2WZevm64NfnB+GFFfux/WQhpq9Q42BWMWaM6A4bOb9RSkREpiOIVjA7ZFZWFvz9/XHu3Dn4+flJHadRG05uwL3f34vO7Trj9AunpY5DN6DVifh003F8uaX2v9OAQDfMH9cX7Z1UEicjImpdrOHz21z4z3oj0Y/f4uV8LJ5cJuDfd3fHgscj4KhSYE/GRdz3+XakZl6SOhoREbVSLFxGUn/APFmHu3t5Y93UaAS1d0BeiQZjvk7G8n/O8pJARERkdCxcRnCh4gLUuWoALFzWJtjTEeunDcKIXt6o1op4Y91h/Hv1QVRWN30lBCIiIkOxcBnB1rNbIUJEz/Y94eXodfMNyKI4qhT4cnxfzBjRHTIB+DElC48s2IWsSxVSRyMiolaChcsIktJr5yXj0S3rJQgCnh0chP89FYl29jY4nF2CkZ/vwPaTBVJHIyKiVoCFywjq5t9i4bJ+0cEe+OX5Qejt64JLFdWYsGQPvtxyiuO6iIjolrBw3aLzpedxrPAYZIIMQwKGSB2HjMCvnT1+fDYKo/v5QScCH208jinLU1FayUsCERFRy7Bw3aK/M/4GAPT16QtXW1dpw5DR2NrI8eHDffD+g71hIxew8UguRs3fiVP5ZVJHIyIiK8TCdYuSMq6M3+L8W62OIAgYF9kRq56JgrezLU4XlOOBL3Zg4+EcqaMREbVa8+fPR0BAAGxtbREZGYk9e/Y0ue7SpUshCEKDm62trRnTNh8L1y0QRfFq4eL4rVYrvGM7/PL8IEQGuqG8Sotnl6fiw43HoNVxXBcRkTGtXLkSCQkJmDNnDlJTUxEaGorY2Fjk5+c3uY2zszNycnL0t7Nnz5oxcfOxcN2CjKIMZBZnwkZmg0EdB0kdh0yovZMK302KxKRBgQCAr7acxoQle3CxvEriZERElq+0tBQlJSX6m0ajaXS9uXPnYvLkyYiPj0dISAgWLFgAe3t7LFmypMnnFgQB3t7e+lvdtZ0tDQvXLaibXf42v9vgoHSQOA2ZmkIuwxv3heDzseGws5Fjx6lCjPx8Bw5lFUsdjYjIooWEhMDFxUV/S0xMvG6dqqoqpKSkICYmRr9MJpMhJiYGycnJTT53WVkZOnXqBH9/fzzwwAM4cuSISV7DrWLhugU8ndg2jQztgHVToxHgbo/sost4eMEurNp7TupYREQWKy0tDcXFxfrbzJkzr1unsLAQWq32uiNUXl5eyM3NbfR5u3XrhiVLlmD9+vVYvnw5dDodBg4ciKysLJO8jlvBwtVCoijy+oltWDdvJ6yfNggxPTxRVaPDv386iNfWHoKmhpcEIiK6lpOTE5ydnfU3lUpllOeNiopCXFwcwsLCMHjwYKxZswbt27fH119/bZTnNyYWrhZKK0hDfnk+7BR2iPSNlDoOScDFzgYLn+iHfw3rCkEAvt+diTFf/4Oc4stSRyMisjoeHh6Qy+XIy8trsDwvLw/e3t7Neg4bGxuEh4fj1KlTpoh4S1i4Wqju6NbtnW6HSmGcpk7WRyYT8PxdXbBkYn+42NlAfa4IIz/fgeTTF6SORkRkVZRKJSIiIpCUlKRfptPpkJSUhKioqGY9h1arxaFDh+Dj42OqmC3GwtVC+sv5cP4tAjC0myd+mTYIPXycUVhWhccX78ac9YdxOLuYlwUiImqmhIQELFq0CMuWLcPRo0cxZcoUlJeXIz4+HgAQFxfXYPzX22+/jU2bNiE9PR2pqal4/PHHcfbsWUyaNEmql9AkhdQBrJFWp8WWM1sAcPwWXdXR3R5rpgzEa2sPYe3+bCxLPotlyWfR1csRD/X1w6gwX3i7WOaEfERElmDMmDEoKCjA7NmzkZubi7CwMGzcuFE/kD4zMxMy2dVjRZcuXcLkyZORm5uLdu3aISIiArt27UJISIhUL6FJgmgF//zOysqCv78/zp07Bz8/P6njYN/5fei/qD9cVC4o/HchFDL2VrpKFEVsPVGA1SlZ2JSWh6oaHQBAEIBBwR54uK8fhvf0gr2Sf2+IqHWztM9vKfE3fgvUjd8aHDCYZYuuIwgChnTzxJBunii+XI0Nh3KwJjULe89cwvaThdh+shAOSjlG9PbBQ319cVugO2QyQerYRERkQmwLLVBXuO4KvEviJGTpXOxsMHZAR4wd0BFnL5Rj7f5srEnNRubFCqxOycLqlCz4utphVHgHPNTXD0HtHaWOTEREJsBTigaq0lah3YftUFFdgUNTDqGXZy9J85D1EUUR+85ewprULPx6MAellTX6x8L8XfFwX1/c16cD2jkoJUxJRHTrLOnzW2o8wmWg3Vm7UVFdgfb27dGzfU+p45AVEgQB/QPc0D/ADXNG9sSfR/OwJjUbW08UQH2uCOpzRXj71zTc2d0TD/X1w9BunlAq+IViIiJrxsJloPqzywsCx93QrbG1keO+Ph1wX58OKCjVYL269pRjWk4J/jiShz+O5KGdvQ3uD6095djHz4V/74iIrBALl4Hq5t/i+C0ytvZOKky6vTMm3d4ZR3NKsHZ/Ntbuz0ZBqUY/xURQewc81NcPD4b7ooOrndSRiYiomTiGywAV1RVw/cAV1bpqnHr+FILcgiTLQm1DjVaHnacvYE1qFv44kovK6qtTTER1dsdDff0wopc3HFT8txMRWR5L+fy2BPwtbYAdmTtQratGR5eO6Nyus9RxqA1QyGUY3LU9Bndtj9LKavx+OBc/pWRhd8ZF7Dp9AbtOX8CsdYcxopc3Hurrh6ggd8g5xQQRkcVh4TIAx2+RlJxsbTC6nz9G9/PHuYsVWLc/G2v2ZyOjsBxrrvzZ29kWo8J98XBfX3TxcpI6MhERXcHCZQDOv0WWwt/NHs/f1QXT7gzG/nNFWJOahV8O5CC3pBILtp7Ggq2n0dvXBQ/39cXI0A5wd+QF1omIpMQxXM1UVFkE94/coRN1yHopC77OvpLkIGqKpkaLv47m46fUbGw5no8aXe3/2gpZ7cz3D/f1xZ09PKFSyCVOSkRthSV8flsKHuFqpq1ntkIn6tDNvRvLFlkklaL2ckEjevvgQpkGvxw4jzX7s3Ewqxh/Hs3Dn0fz4O6gxFePR2BAoJvUcYmI2pQWzaY4f/58BAQEwNbWFpGRkdizZ0+ztluxYgUEQcCoUaNasltJ1R+/RWTp3B1VmBgdiJ+nDcLml+7As4OD4O1siwvlVYhbshvbThRIHZGIqE0xuHCtXLkSCQkJmDNnDlJTUxEaGorY2Fjk5+ffcLszZ87g5Zdfxu23397isFKqm3+LhYusTRcvJ8wY0R1bXhmCod3ao7Jah0nL9uGPI7lSRyMiajMMLlxz587F5MmTER8fj5CQECxYsAD29vZYsmRJk9totVqMHz8eb731Fjp3vvl0ChqNBiUlJfpbaWmpoTGNKq8sD4fzDwMAhgYMlTQLUUvZ2sjx9RP9MKKXN6q0Ojz3XSrWq7OljkVE1CYYVLiqqqqQkpKCmJiYq08gkyEmJgbJyclNbvf222/D09MTTz31VLP2k5iYCBcXF/0tJCTEkJhGt+XMFgBAmHcY3O3dJc1CdCuUChk+HxuOh/r6QqsT8eJKNVbsyZQ6FhFRq2dQ4SosLIRWq4WXl1eD5V5eXsjNbfz0xI4dO7B48WIsWrSo2fuZOXMmiouL9be0tDRDYhpdUkYSAODOAJ5OJOunkMvwySOhePy2jhBFYMaaQ1i8I0PqWERErZpJv6VYWlqKJ554AosWLYKHh0ezt1OpVFCprs4bVFJSYop4zcYB89TayGQC3nmgF+yVCizclo53fk3D5aoaTLuzi9TRiIhaJYMKl4eHB+RyOfLy8hosz8vLg7e393Xrnz59GmfOnMHIkSP1y3S62mvBKRQKHD9+HEFBln09wrNFZ3H60mnIBTnu6HSH1HGIjEYQBMwc0R0OSgU++/MEPtl0AuVVWvw7thuvpEBEZGQGnVJUKpWIiIhAUlKSfplOp0NSUhKioqKuW7979+44dOgQ1Gq1/nb//fdj6NChUKvV8Pf3v/VXYGJ/n/kbADDAdwCcVLxUCrUugiBgekwXvH5PDwDAV1tO482fj0Cns/j5kImIrIrBpxQTEhIwYcIE9OvXDwMGDMC8efNQXl6O+Ph4AEBcXBx8fX2RmJgIW1tb9OrVq8H2rq6uAHDdckulH7/F04nUik2+ozPsVXK8se4wliWfRUWVFh883IcXwiYiMhKDC9eYMWNQUFCA2bNnIzc3F2FhYdi4caN+IH1mZiZkshbNp2pxRFHk+C1qM8ZHdoKdjRwv/3gAP6Zk4XK1Fp+NCYONvHX8/0xEJCVeS/EGjhceR/f53aGSq3Dp1Uuws7Ez276JpLLxcA6e/2E/qrUiYnp44otxfWFrw+svEpHheC3Fq/hP1xuoO7oV3TGaZYvajLt7+WBhXD+oFDL8eTQfTy3bi4qqGqljERFZNRauG+D8W9RWDe3miaXxA+CglGPnqQuIW7wHJZXVUsciIrJaLFxN0Ik6/TcUOX6L2qKoIHcsnxQJZ1sF9p29hHGL/sHF8iqpYxERWSUWriYczDuIi5cvwlHpiH4d+kkdh0gS4R3bYcXTUXB3UOJwdgnGfJ2M/JJKqWMREVkdFq4m1I3fGtxpMGzkNhKnIZJOSAdnrHwmCl7OKpzML8OjXycj61KF1LGIiKwKC1cTOB0E0VXBno748ZmB8Hezw9kLFRi9IBkZheVSxyIishosXI2o1lZj69mtAFi4iOp0dLfHqmei0Lm9A84XV+LRBck4nlsqdSwiIqvAwtWIfef3oayqDG52bujj1UfqOEQWw8fFDqueiUJ3bycUlmkwZmEyDmUVSx2LiMjisXA1ou504tCAoZAJfIuI6vNwVGHF07ch1N8VRRXVGLfoH+w9c1HqWEREFo1tohF/naktXHcF3iVxEiLL5GqvxHeTIjEg0A2lmhrELd6DHScLpY5FRGSxWLiucbn6MnZm7gTA8VtEN+KoUmBZ/AAM7toel6u1eHLpXvyZlid1LCIii8TCdY3krGRotBp0cOqAru5dpY5DZNHslHIsjItAbE8vVGl1eHZ5Cn45cF7qWEREFoeF6xr1p4MQBEHiNESWT6WQY/64vngw3Bc1OhEvrNiPVXvPSR2LiMiisHBdo65wcfwWUfMp5DJ8+mgoxg7oCFEE/v3TQXyzM0PqWEREFoOFq55STSn2ZO8BUPsNRSJqPplMwPsP9sKkQYEAgLd+ScP8v09JnIqIyDKwcNWz7ew2aEUtgtoFoZNrJ6njEFkdQRDw+r098MJdXQAAH/9xHB//cQyiKEqcjIhIWixc9fByPkS3ThAEJAzripkjugMA5v99Gm/9kgadjqWLiNouFq566ubfYuEiunXPDA7COw/0BAAs3XUGM9ccgpali4jaKBauKy5UXIA6Vw2A47eIjOWJqAB88mgoZAKwct85vLhSjWqtTupYRERmx8J1xd9n/gYA9PLsBS9HL4nTELUej0T44fOxfaGQCfjlwHlMWZ6Kymqt1LGIiMyKhesK/fitAJ5OJDK2e/v4YGFcBJQKGf48mofJ3+5DRVWN1LGIiMyGhesKDpgnMq07u3th6cT+sFfKsf1kISYs2YOSymqpYxERmQULF4Dskmwcv3AcMkGGwQGDpY5D1GoNDPbA/56KhJOtAnvPXML4RbtxqbxK6lhERCbHwoWr47cifCLgausqbRiiVi6iUzv8MPk2uDkocSi7GI8t/Adp50uQU3wZl8qrUFmt5bxdRNTqKKQOYAmSMpIA8HQikbn08nXByqdvw/j/7sbxvFLc85/t161jayODrY0cdjZy2OpvMv19Oxs5VNfcr9um/nZ2ShlsFXLYKuWwVchhp2z4PCqFjNdNJSKTa/OFSxRFjt8ikkAXLyf8+GwUXlqpxsn8MlRWa1GtvXpkq7Jah8pqHYpg+nFe1xc5ORxVcgR6OKCLpxOCvRzR1csJHVxsWc6IqEXafOFKv5SOzOJM2MhsMKjjIKnjELUpndwdsOa5aP39Gq0OlTU6VFZrcblKC02NFperdKisqb1fWa3F5WotNNU6XK6+er+2nNW/r8XlessarFelRWVN4+UO15S7vWcuNbjvoJQj2NMRXbyc0MXTEV28HNHF0wm+rnaQyVjEiKhpbb5w1R3divKPgr2NvcRpiNo2hVwGR7kMjirT/2qqK3d1Ra7ySiGrK2zFl6txKr8Mp/LLcDK/FBmF5Siv0uJAVjEOZBU3eC47m7oiVlvAunjWHhHza8ciRpZBqxNxqaIKhWUaFJZe+VmmQUGZBgWlGhSWVeHFmC7o27Gd1FFbrTZfuPTjtzj/FlGbYmi5q9bqcPZCOU7mleFEXm0JO5VfhvSCclyu1uJQdjEOZTcsYrY2MgS1ry1fwZ6OV46KOaGjmz3kLGJ0i7Q6ERfKGxao2lsVCksblqmL5Rrc7Mpao8I6sHCZUJsuXBy/RUTNZSOXIdjTCcGeThjR++ryGq0OZy9W4GReGU7ll14pY2U4XVCGymodjpwvwZHzJQ2eS6moLWK1R8IcEezphC5ejujkZg+FnF8eb8tqtDpcLK9CwZXiVFuYNCgsrVemrhSri+VVNy1R13JzUMLDUQkPR9XVm1Pt/X6d3EzzoghAGy9cRwqOoKCiAPY29oj0i5Q6DhFZIYW8tjwFtXcE4K1frtWJyLxYgZN5pTiZX6b/eSq/DJoaHY7mlOBozjVFTC5D5/YOCL5ySrJunFgndwfY3GIR0+lEVGl10NTooKmpHQdXpdVBU33lfo0OVTVXH9f/uVpbb71rHqu3jUImwEYhg41cgFIug41cBqXiyk+5ABu57Mrj9e5fWaaUy6BU1Fsmr11mc2WZUr9cqPecMpOertXpRGhFEVrdlZsoQqut/anTiajRNXzsRsvqnqtGV/sctaf26p3Wq1emLlVUwZBZUQQBcLOvLUztnVRXy5RTXaG6+pibg/KW/x6Zw/z58/Hxxx8jNzcXoaGh+PzzzzFgwICbbrdixQqMHTsWDzzwANatW2f6oAZqUeEy5M1YtGgRvv32Wxw+fBgAEBERgffff79Zb56p1R3dur3j7VDKlRKnIaLWRC4TEOjhgEAPBwzveXW5Vici61LtEbGTV8aH1R4dK8Plai2O5ZbiWG4pgBz9NjZyQf+NSTcHZROlR3ulINUVqYYlqqoVXjRcLhNgI7+mlNUraTJBgE68WnrqClH9ZfWLVP1lUk4FJxMAN4fastS+XnG6+uerR6bc7JWt6qjoypUrkZCQgAULFiAyMhLz5s1DbGwsjh8/Dk9Pzya3O3PmDF5++WXcfvvtZkxrGIMLl6FvxpYtWzB27FgMHDgQtra2+PDDDzF8+HAcOXIEvr6+RnkRLcXTiURkbnKZgE7uDujk7oCYEC/9cp1ORHbRZX0Bq39UrKJKixNXxo4ZgyAAKoUMKoUcSoXsyp+vuX9ljjJlvcdU9de1kUMpl0FlU1t0tDoR1VrdlZsITc2VP1/5WaWt/7gOVTXilZ/1ltVfp+b6bep/sxSA/mhS7TdMzUcuEyAXBMhkgEImg0y4skwmg7xumQyQC8KV5QJkggCFvHY7F/va03rtrzmlV1eo2tkr2+wYv7lz52Ly5MmIj48HACxYsAC//fYblixZghkzZjS6jVarxfjx4/HWW29h+/btKCoqMmPi5hNEA6d0joyMRP/+/fHFF18AAHQ6Hfz9/fH88883+WbUp9Vq0a5dO3zxxReIi4trdB2NRgONRqO/n52djZCQEJw7dw5+fn6GxL2hDSc3YMPJDXgm4hn09up98w2IiMxMpxORU1JZW77yylBaWa0vQ/oiZCPTl5+GJeqa0nRlO4VMsMr5xHQ6EdW62uJ1tchduV+vvNX+rD1SpahXdmRXClDdsroypF92pUjplwsC5HLhmuIEq3zvpJKVlQV/f3+kpaU1OMiiUqmgUqkarFtVVQV7e3usXr0ao0aN0i+fMGECioqKsH79+kb3MWfOHBw8eBBr167FxIkTUVRUZP2nFKuqqpCSkoKZM2fql8lkMsTExCA5OblZz1FRUYHq6mq4uTU9OC8xMRFvvfWWIdFa5J4u9+CeLveYfD9ERC0lkwnwdbWDr6sdhnRr+pRKWyCTCVDJ5FApAKhuujpZkJCQkAb358yZgzfffLPBssLCQmi1Wnh5eTVY7uXlhWPHjjX6vDt27MDixYuhVquNGdckDCpcLXkzrvXqq6+iQ4cOiImJaXKdmTNnIiEhQX+/7ggXERERWZ/GjnDdqtLSUjzxxBNYtGgRPDw8bvn5TM2s31L84IMPsGLFCmzZsgW2trZNrnftocaSkpIm1yUiIiLL5uTkBGdn5xuu4+HhAblcjry8vAbL8/Ly4O3tfd36p0+fxpkzZzBy5Ej9Mp2udjyfQqHA8ePHERQUZIT0xmHQVxsMfTPq++STT/DBBx9g06ZN6NOnj+FJiYiIqNVSKpWIiIhAUlKSfplOp0NSUhKioqKuW7979+44dOgQ1Gq1/nb//fdj6NChUKvV8Pf3N2f8mzLoCFf9N6NuQFvdmzFt2rQmt/voo4/w3nvv4Y8//kC/fv1uKTARERG1TgkJCZgwYQL69euHAQMGYN68eSgvL9d/azEuLg6+vr5ITEyEra0tevXq1WB7V1dXALhuuSUw+JSiIW8GAHz44YeYPXs2vv/+ewQEBCA3NxcA4OjoCEdHRyO+FCIiIrJmY8aMQUFBAWbPno3c3FyEhYVh48aN+rHjmZmZkMmsc94xg6eFAIAvvvhCP/FpWFgY/vOf/yAysnam9iFDhiAgIABLly4FAAQEBODs2bPXPUdj31BoSt3XSo09LQQRERGZDj+/r2pR4TI3/gcjIiKyPvz8vso6j8sRERERWREWLiIiIiITY+EiIiIiMjEWLiIiIiITY+EiIiIiMjEWLiIiIiITM+u1FFuq7tpIOTk5EichIiKi5qr73K77HG/LrKJw1V27ccCAARInISIiIkPl5eWhY8eOUseQlFVMfFpTU4P9+/fDy8vLqFP6l5aWIiQkBGlpaXBycjLa87ZWfL+aj+9V8/G9aj6+V83H96r5TPle6XQ65OXlITw8HAqFVRzjMRmrKFymUlJSAhcXFxQXF8PZ2VnqOBaP71fz8b1qPr5Xzcf3qvn4XjUf3yvz4KB5IiIiIhNj4SIiIiIysTZduFQqFebMmQOVSiV1FKvA96v5+F41H9+r5uN71Xx8r5qP75V5tOkxXERERETm0KaPcBERERGZAwsXERERkYmxcBERERGZGAsXERERkYmxcBERERGZWJsuXPPnz0dAQABsbW0RGRmJPXv2SB3J4iQmJqJ///5wcnKCp6cnRo0ahePHj0sdyyp88MEHEAQBL774otRRLFJ2djYef/xxuLu7w87ODr1798a+ffukjmWRtFotZs2ahcDAQNjZ2SEoKAjvvPMO+CVzYNu2bRg5ciQ6dOgAQRCwbt26Bo+LoojZs2fDx8cHdnZ2iImJwcmTJ6UJK7EbvVfV1dV49dVX0bt3bzg4OKBDhw6Ii4vD+fPnpQvcyrTZwrVy5UokJCRgzpw5SE1NRWhoKGJjY5Gfny91NIuydetWTJ06Ff/88w82b96M6upqDB8+HOXl5VJHs2h79+7F119/jT59+kgdxSJdunQJ0dHRsLGxwe+//460tDR8+umnaNeundTRLNKHH36Ir776Cl988QWOHj2KDz/8EB999BE+//xzqaNJrry8HKGhoZg/f36jj3/00Uf4z3/+gwULFmD37t1wcHBAbGwsKisrzZxUejd6ryoqKpCamopZs2YhNTUVa9aswfHjx3H//fdLkLSVEtuoAQMGiFOnTtXf12q1YocOHcTExEQJU1m+/Px8EYC4detWqaNYrNLSUrFLly7i5s2bxcGDB4vTp0+XOpLFefXVV8VBgwZJHcNq3HvvveKTTz7ZYNlDDz0kjh8/XqJElgmAuHbtWv19nU4nent7ix9//LF+WVFRkahSqcQffvhBgoSW49r3qjF79uwRAYhnz541T6hWrk0e4aqqqkJKSgpiYmL0y2QyGWJiYpCcnCxhMstXXFwMAHBzc5M4ieWaOnUq7r333gZ/v6ihn3/+Gf369cOjjz4KT09PhIeHY9GiRVLHslgDBw5EUlISTpw4AQA4cOAAduzYgREjRkiczLJlZGQgNze3wf+LLi4uiIyM5O/6ZiguLoYgCHB1dZU6SqugkDqAFAoLC6HVauHl5dVguZeXF44dOyZRKsun0+nw4osvIjo6Gr169ZI6jkVasWIFUlNTsXfvXqmjWLT09HR89dVXSEhIwGuvvYa9e/fihRdegFKpxIQJE6SOZ3FmzJiBkpISdO/eHXK5HFqtFu+99x7Gjx8vdTSLlpubCwCN/q6ve4waV1lZiVdffRVjx46Fs7Oz1HFahTZZuKhlpk6disOHD2PHjh1SR7FI586dw/Tp07F582bY2tpKHcei6XQ69OvXD++//z4AIDw8HIcPH8aCBQtYuBqxatUqfPfdd/j+++/Rs2dPqNVqvPjii+jQoQPfLzK66upqjB49GqIo4quvvpI6TqvRJk8penh4QC6XIy8vr8HyvLw8eHt7S5TKsk2bNg2//vor/v77b/j5+UkdxyKlpKQgPz8fffv2hUKhgEKhwNatW/Gf//wHCoUCWq1W6ogWw8fHByEhIQ2W9ejRA5mZmRIlsmyvvPIKZsyYgcceewy9e/fGE088gZdeegmJiYlSR7Nodb/P+bu++erK1tmzZ7F582Ye3TKiNlm4lEolIiIikJSUpF+m0+mQlJSEqKgoCZNZHlEUMW3aNKxduxZ//fUXAgMDpY5kse666y4cOnQIarVaf+vXrx/Gjx8PtVoNuVwudUSLER0dfd30IidOnECnTp0kSmTZKioqIJM1/HUtl8uh0+kkSmQdAgMD4e3t3eB3fUlJCXbv3s3f9Y2oK1snT57En3/+CXd3d6kjtSpt9pRiQkICJkyYgH79+mHAgAGYN28eysvLER8fL3U0izJ16lR8//33WL9+PZycnPTjHlxcXGBnZydxOsvi5OR03dg2BwcHuLu7c8zbNV566SUMHDgQ77//PkaPHo09e/Zg4cKFWLhwodTRLNLIkSPx3nvvoWPHjujZsyf279+PuXPn4sknn5Q6muTKyspw6tQp/f2MjAyo1Wq4ubmhY8eOePHFF/Huu++iS5cuCAwMxKxZs9ChQweMGjVKutASudF75ePjg0ceeQSpqan49ddfodVq9b/v3dzcoFQqpYrdekj9NUkpff7552LHjh1FpVIpDhgwQPznn3+kjmRxADR6++abb6SOZhU4LUTTfvnlF7FXr16iSqUSu3fvLi5cuFDqSBarpKREnD59utixY0fR1tZW7Ny5s/j666+LGo1G6miS+/vvvxv9HTVhwgRRFGunhpg1a5bo5eUlqlQq8a677hKPHz8ubWiJ3Oi9ysjIaPL3/d9//y119FZBEEVOVUxERERkSm1yDBcRERGRObFwEREREZkYCxcRERGRibFwEREREZkYCxcRERGRibFwEREREZkYCxcRERGRibFwEREREZkYCxcRERGRibFwEREREZkYCxcRERGRif0/1p6Tr95k6HYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(loss)\n",
    "ax2.plot(score, 'g')\n",
    "fig.legend([\"Loss\", \"Score\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper parameter tuning - pulling the levers\n",
    "\n",
    "Our model learning pretty quickly! However there are levers we can pull to see if we can have our model learn more quickly or even have better accuracy. \n",
    "\n",
    "These parameters (literally the ones you pass into the machine learning model) are the levers we can pull. These paramters also extend outside the model - anything that can be changed can be thought of as a learning parameter. Profiling these parameters is known as Hyperparameter Tuning. \n",
    "\n",
    "Over time and getting more experience with models you will gain a better understanding of which levels you may want to pull to reduce the amount of profiling you will need to do. \n",
    "\n",
    "Lets look at a small example, lets say I want to hyper parameter tune on hidden layer width vs hidden layer depth. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Depth:  1 Width:  5\n",
      "Layers: [5]\n",
      "Loss: 2.3007623625748583\n",
      "Validation Score: 0.10910714285714286\n",
      "\n",
      "\n",
      "Depth:  1 Width:  10\n",
      "Layers: [10]\n",
      "Loss: 1.5969393099086808\n",
      "Validation Score: 0.38410714285714287\n",
      "\n",
      "\n",
      "Depth:  1 Width:  15\n",
      "Layers: [15]\n",
      "Loss: 1.0817790717442743\n",
      "Validation Score: 0.5733928571428571\n",
      "\n",
      "\n",
      "Depth:  1 Width:  20\n",
      "Layers: [20]\n",
      "Loss: 0.7722582124663605\n",
      "Validation Score: 0.7526785714285714\n",
      "\n",
      "\n",
      "Depth:  2 Width:  5\n",
      "Layers: [5 5]\n",
      "Loss: 1.6778829816031982\n",
      "Validation Score: 0.30089285714285713\n",
      "\n",
      "\n",
      "Depth:  2 Width:  10\n",
      "Layers: [10 10]\n",
      "Loss: 1.1517478609661043\n",
      "Validation Score: 0.5601785714285714\n",
      "\n",
      "\n",
      "Depth:  2 Width:  15\n",
      "Layers: [15 15]\n",
      "Loss: 1.2744113027317139\n",
      "Validation Score: 0.5367857142857143\n",
      "\n",
      "\n",
      "Depth:  2 Width:  20\n",
      "Layers: [20 20]\n",
      "Loss: 0.9425076622641106\n",
      "Validation Score: 0.6553571428571429\n",
      "\n",
      "\n",
      "Depth:  3 Width:  5\n",
      "Layers: [5 5 5]\n",
      "Loss: 1.7365390617399743\n",
      "Validation Score: 0.29428571428571426\n",
      "\n",
      "\n",
      "Depth:  3 Width:  10\n",
      "Layers: [10 10 10]\n",
      "Loss: 0.6486082915793852\n",
      "Validation Score: 0.7617857142857143\n",
      "\n",
      "\n",
      "Depth:  3 Width:  15\n",
      "Layers: [15 15 15]\n",
      "Loss: 0.5260165406064258\n",
      "Validation Score: 0.8044642857142857\n",
      "\n",
      "\n",
      "Depth:  3 Width:  20\n",
      "Layers: [20 20 20]\n",
      "Loss: 0.3218194905289867\n",
      "Validation Score: 0.8953571428571429\n",
      "\n",
      "\n",
      "Depth:  4 Width:  5\n",
      "Layers: [5 5 5 5]\n",
      "Loss: 2.0047006220887393\n",
      "Validation Score: 0.1980357142857143\n",
      "\n",
      "\n",
      "Depth:  4 Width:  10\n",
      "Layers: [10 10 10 10]\n",
      "Loss: 0.5142373769542035\n",
      "Validation Score: 0.8069642857142857\n",
      "\n",
      "\n",
      "Depth:  4 Width:  15\n",
      "Layers: [15 15 15 15]\n",
      "Loss: 0.5028133590573546\n",
      "Validation Score: 0.8603571428571428\n",
      "\n",
      "\n",
      "Depth:  4 Width:  20\n",
      "Layers: [20 20 20 20]\n",
      "Loss: 0.26518825119576106\n",
      "Validation Score: 0.9194642857142857\n",
      "\n",
      "\n",
      "Depth:  5 Width:  5\n",
      "Layers: [5 5 5 5 5]\n",
      "Loss: 1.5145877814982192\n",
      "Validation Score: 0.38357142857142856\n",
      "\n",
      "\n",
      "Depth:  5 Width:  10\n",
      "Layers: [10 10 10 10 10]\n",
      "Loss: 0.5702761226533662\n",
      "Validation Score: 0.8117857142857143\n",
      "\n",
      "\n",
      "Depth:  5 Width:  15\n",
      "Layers: [15 15 15 15 15]\n",
      "Loss: 0.2550138713536874\n",
      "Validation Score: 0.9271428571428572\n",
      "\n",
      "\n",
      "Depth:  5 Width:  20\n",
      "Layers: [20 20 20 20 20]\n",
      "Loss: 0.23897593314379734\n",
      "Validation Score: 0.92375\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "max_layer_depth = 5\n",
    "max_layer_width = 20\n",
    "max_layer_width_step = 5\n",
    "max_layer_depth_step = 1\n",
    "num_iter = 10\n",
    "# For now lets just do two iterations on the dataset\n",
    "models = []\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"sklearn\")\n",
    "for d_i, depth in enumerate(range(max_layer_depth_step, max_layer_depth+1, max_layer_depth_step)):\n",
    "    for w_i, width in enumerate(range(max_layer_width_step, max_layer_width+1, max_layer_width_step)):\n",
    "        print()\n",
    "        print(\"Depth: \", depth, \"Width: \", width)\n",
    "        layers = np.ones([depth], dtype=int) * width\n",
    "        print(\"Layers:\", layers)\n",
    "        model = MLPClassifier(layers, max_iter=num_iter, early_stopping=True, n_iter_no_change=num_iter)\n",
    "        model.fit(X_train_mnist, y_train_mnist)\n",
    "        print(\"Loss:\", model.loss_)\n",
    "        print(\"Validation Score:\", model.validation_scores_[-1])\n",
    "        print()\n",
    "        models.append(model)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_rows = int(max_layer_depth/max_layer_depth_step)\n",
    "num_columns = int(max_layer_width/max_layer_width_step)\n",
    "\n",
    "first_losses = [model.loss_curve_[0] for model in models]\n",
    "max_loss = np.array(first_losses).max()\n",
    "\n",
    "fig, ax = plt.subplots(num_rows, num_columns)\n",
    "fig.set_size_inches(10, 10)\n",
    "fig.tight_layout()\n",
    "for row in range(num_rows):\n",
    "    for col in range(num_columns):\n",
    "        ax[row, col].set_title(f\"D: {(row+1)*max_layer_depth_step}, W: {(col+1)*max_layer_width_step}\")\n",
    "        model_temp =  models[row * num_columns + col]\n",
    "        ax[row, col].plot(model_temp.loss_curve_)\n",
    "        ax[row, col].set_ylim(0, max_loss)\n",
    "        ax[row, col].text(0.2, 0.2, f\"Score: {round(model_temp.best_validation_score_, 3)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Under Fitting and Over Fitting\n",
    "\n",
    "One problem we can run into with our data is under and over training. \n",
    "\n",
    "Under training occurs when our model is capable of learning more about the data but is stopped before it's loss converges. \n",
    "\n",
    "Over training occurs when the model is trained on the training dataset too much - often training past convergence. Getting great scores on the training dataset but gets lower scores on the test dataset. This is usually a result of the model not having enough generalization of the patter you want it to learn and instead it is learning the pattern of the training dataset itself - which does not exist in test data. This tends to happen more with more complex models, as they can learn more complex patterns.\n",
    "\n",
    "Lets look at an example of what an over fitting graph would look like.\n",
    "\n",
    "Here is some advice on what IBM recommends for overfitting:\n",
    "https://www.ibm.com/topics/overfitting\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./images/Overfitting Example Graph.png \" width=\"800\">\n",
    "\n",
    "Photo Credit: https://towardsdatascience.com/dont-overfit-how-to-prevent-overfitting-in-your-deep-learning-models-63274e552323"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./images/Overfitting Example with Data.png\" width=\"800\">\n",
    "\n",
    "Photo Credit: https://www.kdnuggets.com/2022/08/avoid-overfitting.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Citations:\n",
    "- https://mpfi.org/balancing-act-in-the-brain-excitatory-and-inhibitory-activity/\n",
    "- Listed throughout notebook as comments"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}