4f_HSMA_neural_nets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Networks with Tensorflow/Keras (Titanic Dataset)\n",
    "\n",
    "TIP - this workbook contains extensive text to explain the code.  To save you having to constantly scroll up and down to refer back, if you're using VSCode you can right click on the notebook's name tab at the top and click one of the split options (e.g. \"Split Right\") to see have a second view of the same notebook that you can scroll independently.\n",
    "\n",
    "In this workbook we build a neural network to predict passenger survival on the Titanic, using the same dataset we used for the Logistic Regression example. The two common frameworks used for neural networks are TensorFlow and PyTorch. Both are excellent frameworks. TensorFlow frequently requires fewer lines of code, but PyTorch is more natively \"Pythonic\" in its syntax. Here we use TensorFlow and Keras which is integrated into TensorFlow and makes it simpler and faster to build TensorFlow models.\n",
    "\n",
    "**You should install and switch to the supplied tf_hsma environment for this exercise.  This environment contains an installation of TensorFlow version 2.16.1 which you will need.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The neural network unit - a neuron or perceptron\n",
    "\n",
    "The building block of a neural network is a neuron, which is essentially the same as the 'perceptron' described by Frank Rosenblatt in 1958.\n",
    "\n",
    "The neuron, or perceptron, takes inputs *X* and weights *W* (each individual input has a weight; a bias weight is also introduced by creating a dummy input with value 1). The neuron sums the *input* multiplied by the *weight* and passes the output to an activation function. The simplest activation function is a step function, whereby if the output is >0 the output of the activation function is 1, otherwise the output is 0.\n",
    "\n",
    "![](./images/perceptron.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural networks\n",
    "\n",
    "Having understood a neuron - which calculates the weighted sum of its inputs and passes it through an activation function, neural networks are easy(ish)!\n",
    "\n",
    "They are 'just' a network of such neurons, where the output of one becomes one of the inputs to the neurons in the next layer.\n",
    "\n",
    "This allows any complexity of function to be mimicked by a neural network (so long as the network includes a non-linear activation function, like ReLU - see below).\n",
    "\n",
    "Note the output layer may be composed of a single neuron, to predict a single value or single probability, or may be multiple neurons, to predict multiple values or multiple probabilities.\n",
    "\n",
    "![](./images/net_1.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activation functions\n",
    "\n",
    "Each neuron calculates the weighted sum of its inputs and passes that sum to an activation function. The two simplest functions are:\n",
    "\n",
    "* *Linear*: The weighted output is passed forward with no change.\n",
    "\n",
    "* *Step*: The output of the activation function is 0 or 1 depending on whether a threshold is reached.\n",
    "\n",
    "Other common activation functions are:\n",
    "\n",
    "* *Sigmoid*: Scales output 0-1 using a logistic function. Note that our simple single perceptron becomes a logistic regression model if we use a sigmoid activation function. The sigmoid function is often used to produce a probability output at the final layer.\n",
    "\n",
    "* *tanh*: Scales output -1 to 1. Commonly used in older neural network models. Not commonly used now.\n",
    "\n",
    "* *ReLU (rectifying linear unit)*: Simply converts all negative values to zero, and leaves positive values unchanged. This very simple method is very common in deep neural networks, and is sufficient to allow networks to model non-linear functions.\n",
    "\n",
    "* *Leaky ReLU* and *Exponential Linear Unit (ELU)*: Common modifications to ReLU that do not have such a hard constraint on negative inputs, and can be useful if we run into the Dying ReLU problem (in which - typically due to high learning rates - our weights are commonly set to negative values, leading to them effectively being switched off (set to 0) under ReLU). Try them out as replacements to ReLU.\n",
    "\n",
    "* *Maxout*: A generalised activation function that can model a complex non-linear activation function. \n",
    "\n",
    "* *SoftMax*: SoftMax is the final layer to use if you wish to normalise probability outputs from a network which has multiple class outputs (e.g. you want the total of your probabilities for \"this is dog\", \"this is a cat\", \"this is a fish\" etc to add up to 1).\n",
    "\n",
    "![](./images/activation.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss functions\n",
    "\n",
    "Loss functions are critical to neural networks as they provide the measure by which the neural network is in error, allowing modification of the network to reduce error.\n",
    "\n",
    "The most common loss functions are:\n",
    "\n",
    "* *Mean Squared Error Loss*: Common loss function for regression (predicting values rather than class).\n",
    "\n",
    "* *Cross Entropy Loss*: Common loss function for classification. *Binary Cross Entropy Loss* is used when the output is a binary classifier (like survive/die in the Titanic model)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How do neural networks learn? Backpropagation and optimisation\n",
    "\n",
    "*Backpropagation* is the process by which the final loss is distributed back through the network, allowing each weight to be updated in proportion to its contribution to the final error.\n",
    "\n",
    "For more on backpropagation see: https://youtu.be/Ilg3gGewQ5U\n",
    "\n",
    "For deeper maths on backpropagation see: https://youtu.be/tIeHLnjs5U8\n",
    "\n",
    "*Optimisation* is the step-wise process by which weights are updated. The basic underlying method, *gradient descent*, is that weights are adjusted in the direction that improves fit, and that weights are adjust more when the gradient (how much the output changes with each unit change to the weight) is higher.\n",
    "\n",
    "Common optimisers used are:\n",
    "\n",
    "* *Stochastic gradient descent*: Updates gradients based on single samples. Can be inefficient, so can be modified to use gradients based on a small batch (e.g. 8-64) of samples. *Momentum* may also be added to avoid becoming trapped in local minima.\n",
    "\n",
    "* *RMSprop*: A 'classic' benchmark optimiser. Adjusts steps based on a weighted average of all weight gradients.\n",
    "\n",
    "* *Adam*: The most common optimiser used today. Has complex adaptive momentum for speeding up learning.\n",
    "\n",
    "For more on optimisers see: https://youtu.be/mdKjMPmcWjY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a neural network - the practicalities\n",
    "\n",
    "The training process of a neural network consists of three general phases which are repeated across all the data. All of the data is passed through the network multiple times (the number of iterations, which may be as few as 3-5 or may be 1000+) until all of the data has been fed forward and backpropogated - this then represents an \"Epoch\". The three phases of an iteration are :\n",
    "\n",
    "1. Pass training X data to the network and predict y\n",
    "\n",
    "1. Calculate the 'loss' (error) between the predicted and observed (actual) values of y\n",
    "\n",
    "1. Backpropagate the loss and update the weights (the job of the optimiser).\n",
    "\n",
    "The learning is repeated until maximum accuracy is achieved (but keep an eye on accuracy of test data as well as training data as the network may develop significant over-fitting to training data unless steps are taken to offset the potential for over-fitting, such as use of 'drop-out' layers described below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Architectures\n",
    "\n",
    "The most common fully connected architecture design is to have the same number of neurons in each layer, and adjust that number and the number of layers. This makes exploring the size of the neural net relatively easy (if sometimes slow). \n",
    "\n",
    "As a rough guide - the size of the neural net should be increased until it over-fits data (increasing accuracy of training data with reducing accuracy of test data), and then use a form of *regularisation* to reduce the over-fitting (we will go through this process below).\n",
    "\n",
    "Some common architecture designs, which may be mixed in a single larger network, are:\n",
    "\n",
    "* *Fully connected*: The output of each neuron goes to all neurons in the next layer.\n",
    "\n",
    "* *Convolutional*: Common in image analysis. Small 'mini-nets' that look for patterns across the data - like a 'sliding window', but that can look at the whole picture at the same time. May also be used, for example, in time series to look for fingerprints of events anywhere in the time series.\n",
    "\n",
    "* *Recurrent*: Introduce the concept of some (limited) form of memory into the network - at any one time a number of input steps are affecting the network output. Useful, for example, in sound or video analysis.\n",
    "\n",
    "* *Transformers*: Sequence-to-sequence architecture. Convert sequences to sequences (e.g. translation).  Big in Natural Language Processing - we'll cover them in the NLP module.\n",
    "\n",
    "* *Embedding*: Converts a categorical value to a vector of numbers, e.g. word-2-vec converts words to vectors such that similar meaning words are positioned close together.\n",
    "\n",
    "* *Encoding*: Reduce many input features to fewer. This 'compresses' the data. De-coding layers may convert back to the original data.\n",
    "\n",
    "* *Generative*: Rather than regression, or classification, generative networks output some form of synthetic data (such as fake images; see https://www.thispersondoesnotexist.com/).\n",
    "\n",
    "For the kind of classification problem we're looking at here, a Fully Connected Neural Network is the most commonly used architecture now, and typically you keep all layers the same size (the same number of Neurons) apart from your output layer.  This makes it easy to test different sizes of network.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional resources\n",
    "\n",
    "Also see the excellent introductory video (20 minutes) from 3brown1blue: https://youtu.be/aircAruvnKk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's go !!!!!!!!!!!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this first cell, we're going to be a bit naughty, and turn off warnings (such as \"you're using an out-of-date version of this\" etc).  This will make the notebook cleaner and easier to interpret as you learn this, but in real-world work you shouldn't really do this unless you know what you're doing.  But we'll do it here because we do (I think).\n",
    "\n",
    "Don't forget to select the tf_hsma environment when you run the first cell.  If you're prompted that you need to install the ipykernel, click that you want to do it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn warnings off to keep notebook tidy\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to import the packages we're going to use.  The first three (MatPlotLib, NumPy and Pandas) are the stuff we use in pretty much everything in data science.  From SciKitLearn, we import functions to automatically split our data into training and test data (as we did for the Logistic Regression example) and to min-max normalise our data (remember we said that normalising our data is typical with Neural Networks (\"Neural Networks are Normal\"), and standardising our data - what we did last time - is typical with Logistic Regression).  Remember, when we normalise we'll scale all our feature values so they fall between 0 and 1.\n",
    "\n",
    "Then, we import a load of things we'll need from TensorFlow (and particularly Keras).  TensorFlow is the Neural Network architecture developed by Google, but the interface (API) for TensorFlow is not easy to use.  So instead, we use Keras, which sits on top of TensorFlow, and allows us to interact with TensorFlow in a much more straightforward way.  Don't worry about what each of things that we import are at this stage - we'll see them in use as we move through the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# sklearn for pre-processing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# TensorFlow sequential model\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download data if not previously downloaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This cell downloads the Titanic data that we're going to use.  You don't need to do this if you've already downloaded the data, but if you're unsure, run the cell anyway (it takes seconds!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "download_required = True\n",
    "\n",
    "if download_required:\n",
    "\n",
    "    # Download processed data:\n",
    "    address = 'https://raw.githubusercontent.com/MichaelAllen1966/' + \\\n",
    "                '1804_python_healthcare/master/titanic/data/processed_data.csv'\n",
    "\n",
    "    data = pd.read_csv(address)\n",
    "\n",
    "    # Create a data subfolder if one does not already exist\n",
    "    import os\n",
    "    data_directory ='./data/'\n",
    "    if not os.path.exists(data_directory):\n",
    "        os.makedirs(data_directory)\n",
    "\n",
    "    # Save data\n",
    "    data.to_csv(data_directory + 'processed_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define function to scale data\n",
    "\n",
    "In neural networks it is common to normalise (scale input data 0-1) rather than use standardise (subtracting mean and dividing by standard deviation) each feature.  As with the Logistic Regression example, we'll set up a function here that we can call whenever we want to do this (the only difference being that in the Logistic Regression example we standardised our data, rather than normalising it)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_data(X_train, X_test):\n",
    "    \"\"\"Scale data 0-1 based on min and max in training set\"\"\"\n",
    "\n",
    "    # Initialise a new scaling object for normalising input data\n",
    "    sc = MinMaxScaler()\n",
    "\n",
    "    # Apply the scaler to the training and test sets\n",
    "    train_sc = sc.fit_transform(X_train)\n",
    "    test_sc = sc.fit_transform(X_test)\n",
    "\n",
    "    return train_sc, test_sc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to load up and do a bit of initial prep on our data, much as we did before for the Logistic Regression.  We're going to load our data (which is stored in a .csv file) into a Pandas DataFrame.  We'll convert all the data into floating point numbers so everything is consistent.  We'll drop the Passenger ID column, as that isn't part of the original data, and we don't want the machine to learn anything from this.\n",
    "\n",
    "Then we define our input (X) and output (y) data.  Remember we're trying to predict y from X.  X is all of our columns (features) except for the \"Survived\" column (which is our label - the thing we're trying to predict).  The `axis=1` argument tells Pandas we're referring to columns when we tell it to drop stuff.\n",
    "\n",
    "We also set up NumPy versions of our X and y data - this is a necessary step if we were going to do k-fold splits (remember we talked about those in the last session - it's where we split up our data into training and test sets in multiple different ways to try to avoid biasing the data) as it requires the data to be in NumPy arrays, not Pandas DataFrames.  We're not actually going to use k-fold splits in this workbook, but we'll still go through the step of getting the data into the right format for when we do.  Because, in real world applications, you *should* use k-fold splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/processed_data.csv')\n",
    "# Make all data 'float' type\n",
    "data = data.astype(float)\n",
    "data.drop('PassengerId', inplace=True, axis=1)\n",
    "X = data.drop('Survived',axis=1) # X = all 'data' except the 'survived' column\n",
    "y = data['Survived'] # y = 'survived' column from 'data'\n",
    "# Convert to NumPy as required for k-fold splits\n",
    "X_np = X.values\n",
    "y_np = y.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set up neural net\n",
    "\n",
    "We're going to put construction of the neural net into a separate function, which is what we've written in this next cell.  The function that we've written will build a network of any size, but the one we set up here will be relatively simple.  As you read each bit in this description, you should scroll down to look at the corresponding code in the next cell.\n",
    "\n",
    "Here we use the `sequential` method to set up a TensorFlow neural network. This simpler method assumes each layer of the Neural Network occurs in sequence (one layer of neurons feeds into the next layer of neurons etc).  There are more complex architectures, but the simple sequential architecture is common.  Though simpler, it lacks some flexibility.  But don't worry about that for the moment.\n",
    "\n",
    "The inputs into the function are as follows :\n",
    "\n",
    "- The number of features in our input data (the columns of data from which we hope to make a prediction)\n",
    "- The number of hidden layers - these are the layers between our input layer and our output layer.  Determining how many works best for your problem is a bit of an art-form, but you'll probably find something between 1 and 7 hidden layers works well for classification problems.  We set a default of 3, so if we don't specify a number when calling the function, it'll default to 3 hidden layers. (The assignment operator `=` denotes we are specifying a default value)\n",
    "- The number of neurons in each hidden layer.  We default to 128 if no number is specified when the function is called.\n",
    "- The dropout rate (the proportion of neurons that will be randomly \"switched off\" in each epoch to try to prevent overfitting.  We default to no dropout if no value is specified when the function is called.\n",
    "- A learning rate, that will be used by the optimiser to determine how much it should change each time in response to the estimated error.  In other words, how sensitive will it be.  As with most things in a Neural Network, you will spend a lot of time playing around with these parameters to see if it improves things, but a good default is 0.003 (although for some non-Adam optimisers, that can be considered quite high, so you might want to try much lower learning rates to see if they help).  We use 0.003 as the default if no value is specified when the function is called.\n",
    "\n",
    "In the function, we first clear the session.  There used to be issues with TensorFlow keeping old models hanging around in memory - whilst it probably isn't an issue now, we still do it just to be on the safe side.\n",
    "\n",
    "Then we create a new Sequential Neural Network.\n",
    "\n",
    "Next, we use a loop to set up the hidden layers of our network (the input layer is added automatically and we don't need to explicitly define it).  For each pass of the *for loop* (you'll see we go around the loop the number of times we have passed in as being the number of hidden layers we want) we :\n",
    "\n",
    "- Add a fully-connected (dense) layer (one in which all of the neurons in the layer are connected to all of the neurons in the next layer), using the ReLU activation function for its neurons (you could of course change this to another activation function of your choosing)\n",
    "- Add a dropout layer, to enable dropout\n",
    "\n",
    "So, we end up with a set of fully-connected layers, each with a dropout layer after it.\n",
    "\n",
    "Once we've added all our hidden layers we then add our final output layer.  Here, we add a densely connected output layer with 1 neuron (which will calculate the output - the prediction) using the Sigmoid activation function (because we want our output to be a kind of \"likelihood\" of it being a classified as a '1' - whatever that represents in our model.  By default, anything over 0.5 will be classified as 1, whilst everything else will be classified as 0).  Note of caution - do not interpret this number as being a probability that something belongs to a certain class, it's subtly different (so for example, you shouldn't see that a passenger has an output value of 0.6, and interpret that that they had a 60% probability of survival.  That's NOT correct.  Rather, the model has classified them as likely a survivor with a small amount of confidence (it's only just past the threshold of 0.5)).\n",
    "\n",
    "Next we add our optimiser engine.  Remember - if in doubt, use Adam :)\n",
    "\n",
    "Then we \"compile\" the Neural Network (this is just a fancy way of saying we'll tell TensorFlow to build what we've set up above).  When we tell it to compile, we tell it the loss function we want to use (here - binary crossentropy, because it's a classification problem with two possible outputs - for the Titanic problem, this is \"survived\" or \"died\").  We also specify the metrics that we want TensorFlow to monitor as the model is being \"fitted\" (learning) - here we tell it to monitor accuracy.\n",
    "\n",
    "Finally, we get the function to return the network so we can use it (remember - the purpose of this function is to build a network to our specifications when we call it)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_net(number_features,\n",
    "             hidden_layers=3,\n",
    "             hidden_layer_neurones=128,\n",
    "             dropout=0.0,\n",
    "             learning_rate=0.003):\n",
    "\n",
    "    \"\"\"Make TensorFlow neural net\"\"\"\n",
    "\n",
    "    # Clear Tensorflow\n",
    "    K.clear_session()\n",
    "\n",
    "    # Set up neural net\n",
    "    net = Sequential()\n",
    "\n",
    "    # Add hidden hidden_layers using a loop\n",
    "    for i in range(hidden_layers):\n",
    "        # Add fully connected layer with ReLu activation\n",
    "        net.add(Dense(\n",
    "            hidden_layer_neurones,\n",
    "            input_dim=number_features,\n",
    "            activation='relu'))\n",
    "        # Add droput layer\n",
    "        net.add(Dropout(dropout))\n",
    "\n",
    "    # Add final sigmoid activation output\n",
    "    net.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "    # Compiling model\n",
    "    opt = Adam(learning_rate=learning_rate)\n",
    "\n",
    "    net.compile(loss='binary_crossentropy',\n",
    "                optimizer=opt,\n",
    "                metrics=['accuracy'])\n",
    "\n",
    "    return net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show summary of the model structure\n",
    "\n",
    "Here we will create an arbitrary model (that we won't use) with 10 input features, just to show the function we wrote above being used and so you can see how you can use the summary() function of a model to see an overview of the structure of it.\n",
    "\n",
    "We can see what the layers are in order.  Remember we have five main layers in total (input, 3 x hidden, output) but you won't see the input layer here.  When you run the below cell, you should see three hidden layers (each with a dropout layer immediately after) with 128 neurons in each layer followed by a final output layer with just one neuron.  You'll also see that there are over 34,500 parameters (weights) that it needs to optimise, just in a very simple network like this on a very small dataset with 10 features.  Now you can see why they're so complicated (and magical!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\Sammi\\Anaconda3\\envs\\ml_sammi\\Lib\\site-packages\\keras\\src\\backend\\common\\global_state.py:82: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
      "\n"
     ]
    },
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,408</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │         \u001b[38;5;34m1,408\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m16,512\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m16,512\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m129\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">34,561</span> (135.00 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m34,561\u001b[0m (135.00 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">34,561</span> (135.00 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m34,561\u001b[0m (135.00 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = make_net(10)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split and Scale data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, as we did before with the Logistic Regression, we split our data into training and test sets.  We've got 25% carved off for our test set.  But what is this `random_state=42` thing?  So, pulling back the curtain now, but the random numbers we tend to generate in our computers are not *strictly* random.  They are *pseudo-random* - they use complex algorithms to generate numbers that appear random (and which are good enough for the vast majority of things you will ever do).  Because they are pseudo-random, this means that we can fix the random number generator to use a pre-defined *seed* - a number that feeds into the algorithm which will ensure we always get the same random numbers being generated.  This can be useful if we're 1) teaching, and you want everyone to get the same thing, or 2) validating our outputs whilst we build our model.  Since we're doing both of those things here, we use a fixed seed.\n",
    "\n",
    "But why the number 42?  Those of you who have read, watched and / or listened to The Hitchiker's Guide to the Galaxy will know why.  Those that haven't, go off and read, watch or listen to it and then you'll get the \"joke\" (Computer Scientists love doing stuff like this..)\n",
    "\n",
    "Once we've established our training and testing data, we scale the data by normalising it, using the function we wrote earlier (which uses min-max normalisation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_np, y_np, test_size = 0.25, random_state=42)\n",
    "\n",
    "# Scale X data\n",
    "X_train_sc, X_test_sc = scale_data(X_train, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's just have a look at the scaled data for the first two records (passengers) in our input data.  We *should* see that all of the feature values have scaled between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.34656949, 0.        , 0.        , 0.05953204,\n",
       "        1.        , 0.        , 0.        , 0.828125  , 0.        ,\n",
       "        1.        , 0.        , 0.        , 1.        , 0.        ,\n",
       "        0.        , 0.        , 1.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        ],\n",
       "       [1.        , 0.30887158, 0.        , 0.        , 0.01376068,\n",
       "        0.        , 0.        , 1.        , 0.        , 1.        ,\n",
       "        1.        , 0.        , 0.        , 1.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 1.        ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sc[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare this with the unscaled data for the same two passengers to see the original values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.  ,  28.  ,   0.  ,   0.  ,  30.5 ,   1.  ,   0.  ,   0.  ,\n",
       "        106.  ,   0.  ,   1.  ,   0.  ,   0.  ,   1.  ,   0.  ,   0.  ,\n",
       "          0.  ,   1.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ],\n",
       "       [  3.  ,  25.  ,   0.  ,   0.  ,   7.05,   0.  ,   0.  ,   1.  ,\n",
       "          0.  ,   1.  ,   1.  ,   0.  ,   0.  ,   1.  ,   0.  ,   0.  ,\n",
       "          0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   1.  ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to calculate accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're now going to write a little function that will report the accuracy of the model on the training set and the test set.  This will help us assess how well our model is performing.  We pass into the function the model, the (normalised) input data for both the training and test sets, and the output data for both the training and test sets.\n",
    "\n",
    "The function uses the `predict` function of the model to grab out the probability predictions based on the input data for the training set.  We specify that a classification of 1 (in the case of Titanic, this means \"survived\") should be made if the probability predicted is greater than 0.5.  Then we \"flatten\" the data to get it in the right shape (because it comes out as a complex shape - a tensor.  Don't worry about this.  Just imagine a blob of data, and we squish it so we can read it).  Then we use `y_pred_train == y_train` to return boolean `True` values for each time where the prediction (survived or died) matched the real answer, and take the average of those matches (that effectively gives us accuracy - what proportion of times did prediction match real answer).  (Python interprets Trues and Falses as 1s and 0s, in case you're wondering how that works!).\n",
    "\n",
    "Then we do the same as above but for the test set.\n",
    "\n",
    "Finally we print the accuracy on both the training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_accuracy(model, X_train_sc, X_test_sc, y_train, y_test):\n",
    "    \"\"\"Calculate and print accuracy of training and test data fits\"\"\"\n",
    "\n",
    "    ### Get accuracy of fit to training data\n",
    "    probability = model.predict(X_train_sc)\n",
    "    y_pred_train = probability >= 0.5\n",
    "    y_pred_train = y_pred_train.flatten()\n",
    "    accuracy_train = np.mean(y_pred_train == y_train)\n",
    "\n",
    "    ### Get accuracy of fit to test data\n",
    "    probability = model.predict(X_test_sc)\n",
    "    y_pred_test = probability >= 0.5\n",
    "    y_pred_test = y_pred_test.flatten()\n",
    "    accuracy_test = np.mean(y_pred_test == y_test)\n",
    "\n",
    "    # Show acuracy\n",
    "    print (f'Training accuracy {accuracy_train:0.3f}')\n",
    "    print (f'Test accuracy {accuracy_test:0.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll also write a little function to plot the accuracy on the training set and the test set over time.  Keras keeps a \"history\" (which is a dictionary) of the learning which allows us to do this easily.  It's quite useful to plot the performance over time, as it allows us to look for indications as to when the model is becoming overfitted etc.\n",
    "\n",
    "In our function, we'll grab out the values from the passed in history dictionary, and then plot them using standard matplotlib plotting methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_training(history_dict):\n",
    "    acc_values = history_dict['accuracy']\n",
    "    val_acc_values = history_dict['val_accuracy']\n",
    "    epochs = range(1, len(acc_values) + 1)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    ax.set_xlabel('Time')\n",
    "    ax.set_ylabel('Accuracy')\n",
    "\n",
    "    ax.plot(epochs, acc_values, color='blue', label='Training acc')\n",
    "    ax.plot(epochs, val_acc_values, color='red', label='Test accuracy')\n",
    "    ax.set_title('Training and validation accuracy')\n",
    "\n",
    "    ax.legend()\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've now defined everything that will allow us to build the model.  So we'll now define the model we want and train it!\n",
    "\n",
    "To work out how many features we need (which we then need to pass into the `make_net` function we defined earlier), we can simply look at the number of columns in our X (input) data (where we've removed the 'label' (output) column).  We can grab this from the standardised training data, by looking at index 1 of the shape tuple (index 0 would be rows (passengers in the Titanic data), and index 1 would be columns).  We can see this if we run the code `X_train_sc.shape`.  Try it yourself (just insert a code cell below this markdown cell)!  You should see there are 668 rows, and 24 columns.  Therefore, we've got 668 passengers and 24 features.\n",
    "\n",
    "Next we call our `make_net` function, passing in the number of features we calculated above.  This will create our Neural Network.  As we've passed in nothing else, we'll have defaults for the rest of the network - 3 hidden layers, 128 neurons per layer, a learning rate of 0.003 and no dropout (although, we will still have dropput layers, they just won't do anything).\n",
    "\n",
    "Then, we fit (train) the model.  To do that, we call the `fit` method of the model, and pass it :\n",
    "\n",
    "- the standardised training data\n",
    "- the output (label) data\n",
    "- the number of epochs (training generations - full passes of all of the data through the network).  Initially, we want enough epochs that we see overfitting start to happen (the training accuracy starts to plateau) because then we know we've trained \"enough\" (albeit a bit too much) and can then look to reduce it back a bit\n",
    "- the batch size (how much data we shunt through the network at once.  Yann LeCun (French Computer Scientist) advises \"Friends shouldn't let friends use batch sizes of more than 32\".  But we will here... :))\n",
    "- the data we want to use as our \"validation data\" (which we use to fine tune the parameters of the model).  Keras will check performance on this validation data.  Here we just use our test set, but you should really have a separate \"validation set\" that you'd use whilst tuning the model.\n",
    "- whether we want to see all the things it's doing as it's learning.  If we set `verbose` to 0, all of this will be hidden (keeping things tidier), but as we're experimenting with our model, it's a good idea to set `verbose` to 1 so we can monitor what it's doing.\n",
    "\n",
    "You'll also see that we not only call `model.fit` but we store the output of that function in a variable called `history`.  This allows us to access all the useful information that keras was keeping track of whilst the model was training.  We'll use that later.\n",
    "\n",
    "Note - when you run the cell below, the model will be built and then start training.  How long this takes will depend on your computer specs, including whether you have a CUDA-enabled GPU (if you're running locally) or your priority in the queue for cloud computing (if you're running this on CoLab).\n",
    "\n",
    "Dan has a very fast computer with a high performance CUDA-enabled GPU, and the below (with 250 epochs) takes about 6 seconds on the GPU and about 11 seconds on the CPU.  It might take a little while longer on yours - don't worry, as long as you can see it moving through the epochs.\n",
    "\n",
    "For each epoch, you'll see various information, including the epoch number, the loss (error) that's been calculated in that epoch (for both the training and validation data), and the accuracy (for both the training and validation data).  You should see loss gradually reduce, and accuracy gradually increase.  But you'll likely see that training accuracy tends to keep getting better (before it reaches a plateau) and validation accuracy gets better but then starts to drop a bit.  That's a sign of overfitting (our model's become increasingly brilliant for the training data, but starting to get increasingly rubbish at being more generally useful)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - accuracy: 0.5894 - loss: 0.6207 - val_accuracy: 0.7489 - val_loss: 0.5343\n",
      "Epoch 2/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7645 - loss: 0.5116 - val_accuracy: 0.7803 - val_loss: 0.4798\n",
      "Epoch 3/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7654 - loss: 0.4894 - val_accuracy: 0.8117 - val_loss: 0.4555\n",
      "Epoch 4/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8346 - loss: 0.4073 - val_accuracy: 0.8027 - val_loss: 0.4448\n",
      "Epoch 5/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8469 - loss: 0.4044 - val_accuracy: 0.7758 - val_loss: 0.5106\n",
      "Epoch 6/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8175 - loss: 0.4148 - val_accuracy: 0.8117 - val_loss: 0.4318\n",
      "Epoch 7/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8279 - loss: 0.3935 - val_accuracy: 0.8027 - val_loss: 0.4375\n",
      "Epoch 8/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8366 - loss: 0.4046 - val_accuracy: 0.8027 - val_loss: 0.4640\n",
      "Epoch 9/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8236 - loss: 0.4184 - val_accuracy: 0.8072 - val_loss: 0.4712\n",
      "Epoch 10/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8344 - loss: 0.3743 - val_accuracy: 0.8072 - val_loss: 0.4901\n",
      "Epoch 11/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8338 - loss: 0.3994 - val_accuracy: 0.8072 - val_loss: 0.4832\n",
      "Epoch 12/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8273 - loss: 0.3678 - val_accuracy: 0.8206 - val_loss: 0.4622\n",
      "Epoch 13/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8438 - loss: 0.3705 - val_accuracy: 0.7937 - val_loss: 0.5043\n",
      "Epoch 14/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8478 - loss: 0.3726 - val_accuracy: 0.7982 - val_loss: 0.5243\n",
      "Epoch 15/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8387 - loss: 0.3699 - val_accuracy: 0.8251 - val_loss: 0.5106\n",
      "Epoch 16/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8551 - loss: 0.3550 - val_accuracy: 0.7578 - val_loss: 0.4942\n",
      "Epoch 17/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8415 - loss: 0.3626 - val_accuracy: 0.7848 - val_loss: 0.5928\n",
      "Epoch 18/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8400 - loss: 0.3567 - val_accuracy: 0.8161 - val_loss: 0.4823\n",
      "Epoch 19/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8661 - loss: 0.3454 - val_accuracy: 0.8117 - val_loss: 0.5576\n",
      "Epoch 20/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8629 - loss: 0.3278 - val_accuracy: 0.8206 - val_loss: 0.5223\n",
      "Epoch 21/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8526 - loss: 0.3550 - val_accuracy: 0.8117 - val_loss: 0.5888\n",
      "Epoch 22/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8647 - loss: 0.3301 - val_accuracy: 0.8206 - val_loss: 0.5603\n",
      "Epoch 23/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8622 - loss: 0.3260 - val_accuracy: 0.8072 - val_loss: 0.6828\n",
      "Epoch 24/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8717 - loss: 0.3087 - val_accuracy: 0.8251 - val_loss: 0.5943\n",
      "Epoch 25/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8460 - loss: 0.3446 - val_accuracy: 0.8027 - val_loss: 0.6073\n",
      "Epoch 26/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8601 - loss: 0.3467 - val_accuracy: 0.8161 - val_loss: 0.6106\n",
      "Epoch 27/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8680 - loss: 0.3236 - val_accuracy: 0.8072 - val_loss: 0.5850\n",
      "Epoch 28/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8689 - loss: 0.3169 - val_accuracy: 0.7803 - val_loss: 0.7162\n",
      "Epoch 29/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8634 - loss: 0.3264 - val_accuracy: 0.8161 - val_loss: 0.6224\n",
      "Epoch 30/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8903 - loss: 0.2943 - val_accuracy: 0.8117 - val_loss: 0.6422\n",
      "Epoch 31/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9017 - loss: 0.2637 - val_accuracy: 0.7892 - val_loss: 0.7711\n",
      "Epoch 32/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8693 - loss: 0.3187 - val_accuracy: 0.7848 - val_loss: 0.6392\n",
      "Epoch 33/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8680 - loss: 0.3148 - val_accuracy: 0.8072 - val_loss: 0.6474\n",
      "Epoch 34/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8742 - loss: 0.3211 - val_accuracy: 0.8117 - val_loss: 0.7169\n",
      "Epoch 35/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.3133 - val_accuracy: 0.8251 - val_loss: 0.7245\n",
      "Epoch 36/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8893 - loss: 0.3021 - val_accuracy: 0.7982 - val_loss: 0.7685\n",
      "Epoch 37/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9001 - loss: 0.2708 - val_accuracy: 0.8027 - val_loss: 0.8393\n",
      "Epoch 38/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8821 - loss: 0.2675 - val_accuracy: 0.7982 - val_loss: 0.7583\n",
      "Epoch 39/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8499 - loss: 0.3350 - val_accuracy: 0.7982 - val_loss: 0.9216\n",
      "Epoch 40/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8934 - loss: 0.2763 - val_accuracy: 0.7803 - val_loss: 0.7980\n",
      "Epoch 41/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8644 - loss: 0.3112 - val_accuracy: 0.8027 - val_loss: 0.8078\n",
      "Epoch 42/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8864 - loss: 0.2734 - val_accuracy: 0.7982 - val_loss: 0.9689\n",
      "Epoch 43/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8827 - loss: 0.3064 - val_accuracy: 0.8027 - val_loss: 0.8839\n",
      "Epoch 44/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9008 - loss: 0.2696 - val_accuracy: 0.8027 - val_loss: 0.8412\n",
      "Epoch 45/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8761 - loss: 0.3005 - val_accuracy: 0.7892 - val_loss: 1.1601\n",
      "Epoch 46/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8918 - loss: 0.3082 - val_accuracy: 0.8072 - val_loss: 0.7309\n",
      "Epoch 47/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8803 - loss: 0.2903 - val_accuracy: 0.7758 - val_loss: 0.7280\n",
      "Epoch 48/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8889 - loss: 0.2922 - val_accuracy: 0.7982 - val_loss: 0.7266\n",
      "Epoch 49/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8941 - loss: 0.2913 - val_accuracy: 0.7848 - val_loss: 0.8113\n",
      "Epoch 50/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8766 - loss: 0.2874 - val_accuracy: 0.8117 - val_loss: 0.7758\n",
      "Epoch 51/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8967 - loss: 0.2838 - val_accuracy: 0.7803 - val_loss: 0.8528\n",
      "Epoch 52/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8900 - loss: 0.2808 - val_accuracy: 0.7937 - val_loss: 0.8781\n",
      "Epoch 53/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8819 - loss: 0.2898 - val_accuracy: 0.8027 - val_loss: 0.9223\n",
      "Epoch 54/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8991 - loss: 0.2740 - val_accuracy: 0.8027 - val_loss: 0.9023\n",
      "Epoch 55/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8974 - loss: 0.2591 - val_accuracy: 0.7848 - val_loss: 1.0537\n",
      "Epoch 56/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8879 - loss: 0.2733 - val_accuracy: 0.8027 - val_loss: 0.9573\n",
      "Epoch 57/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9012 - loss: 0.2500 - val_accuracy: 0.7982 - val_loss: 0.9256\n",
      "Epoch 58/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8798 - loss: 0.2910 - val_accuracy: 0.7982 - val_loss: 0.9879\n",
      "Epoch 59/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9055 - loss: 0.2531 - val_accuracy: 0.7848 - val_loss: 1.0956\n",
      "Epoch 60/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8856 - loss: 0.2910 - val_accuracy: 0.8027 - val_loss: 0.9856\n",
      "Epoch 61/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8928 - loss: 0.2812 - val_accuracy: 0.8117 - val_loss: 0.9860\n",
      "Epoch 62/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8784 - loss: 0.2847 - val_accuracy: 0.7892 - val_loss: 1.0311\n",
      "Epoch 63/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8993 - loss: 0.2482 - val_accuracy: 0.8161 - val_loss: 1.0434\n",
      "Epoch 64/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8928 - loss: 0.2728 - val_accuracy: 0.7892 - val_loss: 0.9807\n",
      "Epoch 65/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9025 - loss: 0.2681 - val_accuracy: 0.8027 - val_loss: 1.0414\n",
      "Epoch 66/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8994 - loss: 0.2493 - val_accuracy: 0.8117 - val_loss: 1.0447\n",
      "Epoch 67/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8953 - loss: 0.2612 - val_accuracy: 0.8027 - val_loss: 1.0741\n",
      "Epoch 68/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9051 - loss: 0.2460 - val_accuracy: 0.8161 - val_loss: 1.1610\n",
      "Epoch 69/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8952 - loss: 0.2547 - val_accuracy: 0.8027 - val_loss: 1.1435\n",
      "Epoch 70/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8987 - loss: 0.2439 - val_accuracy: 0.8206 - val_loss: 1.1246\n",
      "Epoch 71/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9031 - loss: 0.2437 - val_accuracy: 0.8072 - val_loss: 1.1499\n",
      "Epoch 72/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8842 - loss: 0.2726 - val_accuracy: 0.8117 - val_loss: 1.1043\n",
      "Epoch 73/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8934 - loss: 0.2641 - val_accuracy: 0.8027 - val_loss: 1.1882\n",
      "Epoch 74/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8886 - loss: 0.2595 - val_accuracy: 0.7937 - val_loss: 1.1686\n",
      "Epoch 75/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8918 - loss: 0.2583 - val_accuracy: 0.8027 - val_loss: 1.1956\n",
      "Epoch 76/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9068 - loss: 0.2362 - val_accuracy: 0.8117 - val_loss: 1.1993\n",
      "Epoch 77/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8918 - loss: 0.2342 - val_accuracy: 0.8251 - val_loss: 1.2751\n",
      "Epoch 78/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9055 - loss: 0.2424 - val_accuracy: 0.7937 - val_loss: 1.3184\n",
      "Epoch 79/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8898 - loss: 0.2682 - val_accuracy: 0.8161 - val_loss: 1.2269\n",
      "Epoch 80/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8871 - loss: 0.2632 - val_accuracy: 0.8117 - val_loss: 1.2805\n",
      "Epoch 81/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8793 - loss: 0.2542 - val_accuracy: 0.8206 - val_loss: 1.2283\n",
      "Epoch 82/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9033 - loss: 0.2421 - val_accuracy: 0.7713 - val_loss: 1.5658\n",
      "Epoch 83/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8944 - loss: 0.2464 - val_accuracy: 0.8117 - val_loss: 1.3082\n",
      "Epoch 84/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8859 - loss: 0.2661 - val_accuracy: 0.8117 - val_loss: 1.3584\n",
      "Epoch 85/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9031 - loss: 0.2413 - val_accuracy: 0.8117 - val_loss: 1.1927\n",
      "Epoch 86/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9046 - loss: 0.2347 - val_accuracy: 0.7848 - val_loss: 1.6775\n",
      "Epoch 87/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8836 - loss: 0.2844 - val_accuracy: 0.8027 - val_loss: 1.4149\n",
      "Epoch 88/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8927 - loss: 0.2857 - val_accuracy: 0.8206 - val_loss: 0.9820\n",
      "Epoch 89/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8876 - loss: 0.2678 - val_accuracy: 0.7937 - val_loss: 1.1708\n",
      "Epoch 90/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8873 - loss: 0.3120 - val_accuracy: 0.8117 - val_loss: 1.0113\n",
      "Epoch 91/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9057 - loss: 0.2408 - val_accuracy: 0.7937 - val_loss: 1.0703\n",
      "Epoch 92/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9058 - loss: 0.2399 - val_accuracy: 0.8027 - val_loss: 1.0774\n",
      "Epoch 93/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9008 - loss: 0.2366 - val_accuracy: 0.8027 - val_loss: 1.0887\n",
      "Epoch 94/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8977 - loss: 0.2540 - val_accuracy: 0.7848 - val_loss: 1.2006\n",
      "Epoch 95/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8891 - loss: 0.2580 - val_accuracy: 0.7937 - val_loss: 1.1843\n",
      "Epoch 96/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9007 - loss: 0.2438 - val_accuracy: 0.8161 - val_loss: 1.0627\n",
      "Epoch 97/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8920 - loss: 0.2779 - val_accuracy: 0.8072 - val_loss: 1.1149\n",
      "Epoch 98/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9096 - loss: 0.2227 - val_accuracy: 0.8027 - val_loss: 1.1924\n",
      "Epoch 99/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9080 - loss: 0.2331 - val_accuracy: 0.7937 - val_loss: 1.1596\n",
      "Epoch 100/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9054 - loss: 0.2379 - val_accuracy: 0.8027 - val_loss: 1.2739\n",
      "Epoch 101/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9096 - loss: 0.2241 - val_accuracy: 0.8117 - val_loss: 1.2690\n",
      "Epoch 102/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9056 - loss: 0.2272 - val_accuracy: 0.8161 - val_loss: 1.2559\n",
      "Epoch 103/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8976 - loss: 0.2425 - val_accuracy: 0.8161 - val_loss: 1.2372\n",
      "Epoch 104/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9137 - loss: 0.2138 - val_accuracy: 0.8027 - val_loss: 1.3317\n",
      "Epoch 105/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8869 - loss: 0.2747 - val_accuracy: 0.8072 - val_loss: 1.3751\n",
      "Epoch 106/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9027 - loss: 0.2292 - val_accuracy: 0.7982 - val_loss: 1.3915\n",
      "Epoch 107/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9002 - loss: 0.2418 - val_accuracy: 0.7982 - val_loss: 1.3141\n",
      "Epoch 108/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9095 - loss: 0.2260 - val_accuracy: 0.8117 - val_loss: 1.3647\n",
      "Epoch 109/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9018 - loss: 0.2362 - val_accuracy: 0.8027 - val_loss: 1.3624\n",
      "Epoch 110/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9090 - loss: 0.2283 - val_accuracy: 0.8027 - val_loss: 1.3470\n",
      "Epoch 111/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9253 - loss: 0.1949 - val_accuracy: 0.8072 - val_loss: 1.4330\n",
      "Epoch 112/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8980 - loss: 0.2377 - val_accuracy: 0.8027 - val_loss: 1.4227\n",
      "Epoch 113/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9182 - loss: 0.2017 - val_accuracy: 0.7982 - val_loss: 1.4266\n",
      "Epoch 114/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9243 - loss: 0.1946 - val_accuracy: 0.8072 - val_loss: 1.4049\n",
      "Epoch 115/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9201 - loss: 0.2086 - val_accuracy: 0.8117 - val_loss: 1.5301\n",
      "Epoch 116/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9081 - loss: 0.2193 - val_accuracy: 0.8072 - val_loss: 1.4580\n",
      "Epoch 117/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9265 - loss: 0.1897 - val_accuracy: 0.8117 - val_loss: 1.6142\n",
      "Epoch 118/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9002 - loss: 0.2341 - val_accuracy: 0.8117 - val_loss: 1.4237\n",
      "Epoch 119/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9007 - loss: 0.2468 - val_accuracy: 0.8072 - val_loss: 1.4527\n",
      "Epoch 120/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9050 - loss: 0.2552 - val_accuracy: 0.7982 - val_loss: 1.4548\n",
      "Epoch 121/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9180 - loss: 0.2204 - val_accuracy: 0.8072 - val_loss: 1.5674\n",
      "Epoch 122/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8948 - loss: 0.2526 - val_accuracy: 0.8206 - val_loss: 1.4663\n",
      "Epoch 123/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9069 - loss: 0.2285 - val_accuracy: 0.7982 - val_loss: 1.6611\n",
      "Epoch 124/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9029 - loss: 0.2316 - val_accuracy: 0.8027 - val_loss: 1.4079\n",
      "Epoch 125/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9042 - loss: 0.2263 - val_accuracy: 0.8027 - val_loss: 1.5330\n",
      "Epoch 126/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9162 - loss: 0.2163 - val_accuracy: 0.8296 - val_loss: 1.5034\n",
      "Epoch 127/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9054 - loss: 0.2326 - val_accuracy: 0.8161 - val_loss: 1.4424\n",
      "Epoch 128/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9115 - loss: 0.2303 - val_accuracy: 0.7848 - val_loss: 1.5685\n",
      "Epoch 129/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9084 - loss: 0.2286 - val_accuracy: 0.8161 - val_loss: 1.5663\n",
      "Epoch 130/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9012 - loss: 0.2440 - val_accuracy: 0.8251 - val_loss: 1.6786\n",
      "Epoch 131/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9033 - loss: 0.2283 - val_accuracy: 0.8027 - val_loss: 1.6477\n",
      "Epoch 132/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9100 - loss: 0.2287 - val_accuracy: 0.8027 - val_loss: 1.5885\n",
      "Epoch 133/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9024 - loss: 0.2255 - val_accuracy: 0.8072 - val_loss: 1.5587\n",
      "Epoch 134/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9128 - loss: 0.2038 - val_accuracy: 0.7803 - val_loss: 1.7733\n",
      "Epoch 135/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9135 - loss: 0.2249 - val_accuracy: 0.8206 - val_loss: 1.4879\n",
      "Epoch 136/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8921 - loss: 0.2508 - val_accuracy: 0.8161 - val_loss: 1.5537\n",
      "Epoch 137/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9112 - loss: 0.2282 - val_accuracy: 0.7982 - val_loss: 1.7163\n",
      "Epoch 138/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9134 - loss: 0.1975 - val_accuracy: 0.8072 - val_loss: 1.6882\n",
      "Epoch 139/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9144 - loss: 0.2140 - val_accuracy: 0.8027 - val_loss: 1.7327\n",
      "Epoch 140/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9187 - loss: 0.1955 - val_accuracy: 0.8161 - val_loss: 1.6430\n",
      "Epoch 141/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9207 - loss: 0.2046 - val_accuracy: 0.8117 - val_loss: 1.6671\n",
      "Epoch 142/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9009 - loss: 0.2251 - val_accuracy: 0.7937 - val_loss: 1.7145\n",
      "Epoch 143/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9103 - loss: 0.2087 - val_accuracy: 0.8117 - val_loss: 1.7463\n",
      "Epoch 144/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9042 - loss: 0.2122 - val_accuracy: 0.8161 - val_loss: 1.6424\n",
      "Epoch 145/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9137 - loss: 0.2045 - val_accuracy: 0.8027 - val_loss: 1.8534\n",
      "Epoch 146/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9114 - loss: 0.2300 - val_accuracy: 0.8296 - val_loss: 1.6790\n",
      "Epoch 147/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9051 - loss: 0.2112 - val_accuracy: 0.8072 - val_loss: 1.7015\n",
      "Epoch 148/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8965 - loss: 0.2250 - val_accuracy: 0.8206 - val_loss: 1.6936\n",
      "Epoch 149/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9083 - loss: 0.2216 - val_accuracy: 0.8206 - val_loss: 1.7359\n",
      "Epoch 150/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9207 - loss: 0.2102 - val_accuracy: 0.8251 - val_loss: 1.7592\n",
      "Epoch 151/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9028 - loss: 0.2250 - val_accuracy: 0.7982 - val_loss: 1.8319\n",
      "Epoch 152/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9255 - loss: 0.1886 - val_accuracy: 0.8117 - val_loss: 1.8389\n",
      "Epoch 153/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9227 - loss: 0.1878 - val_accuracy: 0.8117 - val_loss: 1.8696\n",
      "Epoch 154/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9283 - loss: 0.1935 - val_accuracy: 0.8117 - val_loss: 1.8943\n",
      "Epoch 155/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9197 - loss: 0.2009 - val_accuracy: 0.8117 - val_loss: 1.8556\n",
      "Epoch 156/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9152 - loss: 0.1983 - val_accuracy: 0.8341 - val_loss: 1.8383\n",
      "Epoch 157/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9130 - loss: 0.2079 - val_accuracy: 0.7892 - val_loss: 1.9779\n",
      "Epoch 158/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9167 - loss: 0.1999 - val_accuracy: 0.8161 - val_loss: 1.9083\n",
      "Epoch 159/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9226 - loss: 0.1932 - val_accuracy: 0.8072 - val_loss: 1.9361\n",
      "Epoch 160/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9228 - loss: 0.1930 - val_accuracy: 0.8161 - val_loss: 1.8877\n",
      "Epoch 161/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9087 - loss: 0.2185 - val_accuracy: 0.8206 - val_loss: 1.8714\n",
      "Epoch 162/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9219 - loss: 0.2077 - val_accuracy: 0.7982 - val_loss: 1.8634\n",
      "Epoch 163/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9236 - loss: 0.1848 - val_accuracy: 0.8206 - val_loss: 1.9356\n",
      "Epoch 164/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9014 - loss: 0.2278 - val_accuracy: 0.8251 - val_loss: 1.9316\n",
      "Epoch 165/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9297 - loss: 0.1821 - val_accuracy: 0.8117 - val_loss: 2.0098\n",
      "Epoch 166/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9080 - loss: 0.2141 - val_accuracy: 0.8296 - val_loss: 1.9840\n",
      "Epoch 167/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9241 - loss: 0.2003 - val_accuracy: 0.8117 - val_loss: 1.9231\n",
      "Epoch 168/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9093 - loss: 0.2033 - val_accuracy: 0.8161 - val_loss: 1.9485\n",
      "Epoch 169/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9049 - loss: 0.2183 - val_accuracy: 0.8251 - val_loss: 1.9557\n",
      "Epoch 170/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9057 - loss: 0.2173 - val_accuracy: 0.8117 - val_loss: 1.9372\n",
      "Epoch 171/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9197 - loss: 0.1989 - val_accuracy: 0.8206 - val_loss: 1.9695\n",
      "Epoch 172/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9055 - loss: 0.2403 - val_accuracy: 0.8117 - val_loss: 1.9416\n",
      "Epoch 173/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9126 - loss: 0.2024 - val_accuracy: 0.8161 - val_loss: 2.0270\n",
      "Epoch 174/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9190 - loss: 0.2114 - val_accuracy: 0.8117 - val_loss: 2.0630\n",
      "Epoch 175/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9069 - loss: 0.2111 - val_accuracy: 0.8206 - val_loss: 2.1022\n",
      "Epoch 176/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9156 - loss: 0.2135 - val_accuracy: 0.7982 - val_loss: 2.1598\n",
      "Epoch 177/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9094 - loss: 0.2158 - val_accuracy: 0.8206 - val_loss: 2.1567\n",
      "Epoch 178/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9122 - loss: 0.2049 - val_accuracy: 0.8072 - val_loss: 2.1527\n",
      "Epoch 179/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8964 - loss: 0.2311 - val_accuracy: 0.8072 - val_loss: 2.1787\n",
      "Epoch 180/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9075 - loss: 0.2177 - val_accuracy: 0.8027 - val_loss: 2.2352\n",
      "Epoch 181/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9073 - loss: 0.2131 - val_accuracy: 0.8117 - val_loss: 1.9330\n",
      "Epoch 182/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8977 - loss: 0.2284 - val_accuracy: 0.8117 - val_loss: 1.9407\n",
      "Epoch 183/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9147 - loss: 0.2004 - val_accuracy: 0.8296 - val_loss: 1.9101\n",
      "Epoch 184/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9151 - loss: 0.2007 - val_accuracy: 0.8117 - val_loss: 2.0112\n",
      "Epoch 185/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9225 - loss: 0.1961 - val_accuracy: 0.8206 - val_loss: 2.0660\n",
      "Epoch 186/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9253 - loss: 0.1922 - val_accuracy: 0.8117 - val_loss: 2.1485\n",
      "Epoch 187/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9210 - loss: 0.2142 - val_accuracy: 0.8206 - val_loss: 2.1587\n",
      "Epoch 188/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9134 - loss: 0.2054 - val_accuracy: 0.8161 - val_loss: 1.9631\n",
      "Epoch 189/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9137 - loss: 0.2075 - val_accuracy: 0.7982 - val_loss: 2.1524\n",
      "Epoch 190/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9022 - loss: 0.2224 - val_accuracy: 0.8027 - val_loss: 2.2020\n",
      "Epoch 191/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9206 - loss: 0.2107 - val_accuracy: 0.8117 - val_loss: 2.2168\n",
      "Epoch 192/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9074 - loss: 0.2091 - val_accuracy: 0.8117 - val_loss: 2.2054\n",
      "Epoch 193/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9178 - loss: 0.2138 - val_accuracy: 0.8251 - val_loss: 2.1201\n",
      "Epoch 194/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9078 - loss: 0.2185 - val_accuracy: 0.8206 - val_loss: 2.1451\n",
      "Epoch 195/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9156 - loss: 0.1962 - val_accuracy: 0.8206 - val_loss: 2.1810\n",
      "Epoch 196/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9142 - loss: 0.1864 - val_accuracy: 0.8072 - val_loss: 2.1932\n",
      "Epoch 197/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9102 - loss: 0.2077 - val_accuracy: 0.8206 - val_loss: 2.1306\n",
      "Epoch 198/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9122 - loss: 0.2230 - val_accuracy: 0.8027 - val_loss: 2.2611\n",
      "Epoch 199/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9082 - loss: 0.2241 - val_accuracy: 0.8027 - val_loss: 2.1842\n",
      "Epoch 200/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8993 - loss: 0.2270 - val_accuracy: 0.7982 - val_loss: 2.2389\n",
      "Epoch 201/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9177 - loss: 0.1908 - val_accuracy: 0.8161 - val_loss: 2.2516\n",
      "Epoch 202/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9275 - loss: 0.1922 - val_accuracy: 0.7982 - val_loss: 2.3478\n",
      "Epoch 203/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9113 - loss: 0.2057 - val_accuracy: 0.8251 - val_loss: 2.3609\n",
      "Epoch 204/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9170 - loss: 0.1957 - val_accuracy: 0.7982 - val_loss: 2.2785\n",
      "Epoch 205/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9049 - loss: 0.2291 - val_accuracy: 0.8251 - val_loss: 2.1180\n",
      "Epoch 206/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9051 - loss: 0.2010 - val_accuracy: 0.8161 - val_loss: 2.1736\n",
      "Epoch 207/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9332 - loss: 0.1805 - val_accuracy: 0.8206 - val_loss: 2.0803\n",
      "Epoch 208/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9142 - loss: 0.2080 - val_accuracy: 0.8072 - val_loss: 2.1005\n",
      "Epoch 209/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9203 - loss: 0.2045 - val_accuracy: 0.7892 - val_loss: 2.3324\n",
      "Epoch 210/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9069 - loss: 0.2234 - val_accuracy: 0.7848 - val_loss: 2.7736\n",
      "Epoch 211/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9192 - loss: 0.2368 - val_accuracy: 0.8027 - val_loss: 1.5454\n",
      "Epoch 212/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8978 - loss: 0.2573 - val_accuracy: 0.7803 - val_loss: 1.1517\n",
      "Epoch 213/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9071 - loss: 0.2551 - val_accuracy: 0.7758 - val_loss: 1.0374\n",
      "Epoch 214/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9016 - loss: 0.2485 - val_accuracy: 0.8072 - val_loss: 0.9112\n",
      "Epoch 215/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9007 - loss: 0.2578 - val_accuracy: 0.7982 - val_loss: 1.1066\n",
      "Epoch 216/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9042 - loss: 0.2257 - val_accuracy: 0.8072 - val_loss: 1.1030\n",
      "Epoch 217/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9149 - loss: 0.2167 - val_accuracy: 0.7848 - val_loss: 1.2617\n",
      "Epoch 218/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9233 - loss: 0.2147 - val_accuracy: 0.8251 - val_loss: 1.1500\n",
      "Epoch 219/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9081 - loss: 0.2371 - val_accuracy: 0.7937 - val_loss: 1.2920\n",
      "Epoch 220/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9214 - loss: 0.2137 - val_accuracy: 0.7982 - val_loss: 1.2232\n",
      "Epoch 221/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9008 - loss: 0.2419 - val_accuracy: 0.8161 - val_loss: 1.1188\n",
      "Epoch 222/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9204 - loss: 0.2116 - val_accuracy: 0.8072 - val_loss: 1.2259\n",
      "Epoch 223/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8947 - loss: 0.2215 - val_accuracy: 0.7982 - val_loss: 1.3546\n",
      "Epoch 224/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9186 - loss: 0.1976 - val_accuracy: 0.8117 - val_loss: 1.3808\n",
      "Epoch 225/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9148 - loss: 0.2067 - val_accuracy: 0.8206 - val_loss: 1.3904\n",
      "Epoch 226/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9233 - loss: 0.1872 - val_accuracy: 0.8161 - val_loss: 1.4109\n",
      "Epoch 227/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8977 - loss: 0.2221 - val_accuracy: 0.8072 - val_loss: 1.4569\n",
      "Epoch 228/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9299 - loss: 0.1682 - val_accuracy: 0.7937 - val_loss: 1.4599\n",
      "Epoch 229/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9222 - loss: 0.1951 - val_accuracy: 0.8161 - val_loss: 1.4171\n",
      "Epoch 230/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9279 - loss: 0.1891 - val_accuracy: 0.8206 - val_loss: 1.4438\n",
      "Epoch 231/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9115 - loss: 0.1979 - val_accuracy: 0.8117 - val_loss: 1.4886\n",
      "Epoch 232/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9193 - loss: 0.1959 - val_accuracy: 0.7982 - val_loss: 1.5116\n",
      "Epoch 233/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9065 - loss: 0.2083 - val_accuracy: 0.8072 - val_loss: 1.5837\n",
      "Epoch 234/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9154 - loss: 0.1979 - val_accuracy: 0.7937 - val_loss: 1.6042\n",
      "Epoch 235/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9243 - loss: 0.1860 - val_accuracy: 0.7982 - val_loss: 1.6524\n",
      "Epoch 236/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9287 - loss: 0.1729 - val_accuracy: 0.7982 - val_loss: 1.6007\n",
      "Epoch 237/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9362 - loss: 0.1780 - val_accuracy: 0.8072 - val_loss: 1.5806\n",
      "Epoch 238/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9251 - loss: 0.1722 - val_accuracy: 0.7892 - val_loss: 1.6355\n",
      "Epoch 239/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9195 - loss: 0.1850 - val_accuracy: 0.8161 - val_loss: 1.6360\n",
      "Epoch 240/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9123 - loss: 0.1994 - val_accuracy: 0.7937 - val_loss: 1.6234\n",
      "Epoch 241/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9266 - loss: 0.1822 - val_accuracy: 0.8072 - val_loss: 1.6250\n",
      "Epoch 242/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9271 - loss: 0.1808 - val_accuracy: 0.7937 - val_loss: 1.6264\n",
      "Epoch 243/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9291 - loss: 0.1882 - val_accuracy: 0.7982 - val_loss: 1.6230\n",
      "Epoch 244/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9061 - loss: 0.2033 - val_accuracy: 0.8161 - val_loss: 1.6164\n",
      "Epoch 245/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9224 - loss: 0.1934 - val_accuracy: 0.8027 - val_loss: 1.6767\n",
      "Epoch 246/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9153 - loss: 0.1981 - val_accuracy: 0.7937 - val_loss: 1.7148\n",
      "Epoch 247/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9193 - loss: 0.2082 - val_accuracy: 0.8161 - val_loss: 1.6781\n",
      "Epoch 248/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9271 - loss: 0.1777 - val_accuracy: 0.8117 - val_loss: 1.7112\n",
      "Epoch 249/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9212 - loss: 0.1772 - val_accuracy: 0.8161 - val_loss: 1.7084\n",
      "Epoch 250/250\n",
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9130 - loss: 0.2118 - val_accuracy: 0.8072 - val_loss: 1.6142\n"
     ]
    }
   ],
   "source": [
    "# Define network\n",
    "number_features = X_train_sc.shape[1]\n",
    "model = make_net(number_features)\n",
    "\n",
    "### Train model (and store training info in history)\n",
    "history = model.fit(X_train_sc,\n",
    "                    y_train,\n",
    "                    epochs=250,\n",
    "                    batch_size=64,\n",
    "                    validation_data=(X_test_sc, y_test),\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate and print the final accuracy scores for both the training and test (validation) data.  Remember, we'll call the function we wrote to do this earlier.  You should see training accuracy is much better than test accuracy.  We've overfitted.  Don't worry - we'll try and improve that in a moment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "Training accuracy 0.918\n",
      "Test accuracy 0.807\n"
     ]
    }
   ],
   "source": [
    "# Show acuracy\n",
    "calculate_accuracy(model, X_train_sc, X_test_sc, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get training history\n",
    "\n",
    "`history` is a dictionary containing data collected during training.  Remember - we stored it when we called the `model.fit()` method.  Let's take a look at the keys in this dictionary (these are the metrics monitored during training)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['accuracy', 'loss', 'val_accuracy', 'val_loss'])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_dict = history.history\n",
    "history_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see from the above that we have four keys in our history dictionary - loss, accuracy, validation loss and validation accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot training history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot our history data using the plotting function we wrote earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see from the plot above that the training accuracy gets better and better before reaching a plateau, but for the test data the accuracy initially improves, but then reduces a bit and plateaus at poorer performance.  As we thought, we've overfitted.  So let's look at how we can now try to reduce the overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving fit by avoiding or reducing-over fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the lecture, we discussed a number of strategies we can take to try to reduce overfitting.  Let's look at each in turn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Reduce complexity of model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple initial strategy is to reduce the complexity of the model, so that the \"dividing line\" it learns becomes less complex (and less likely to be an overfit).\n",
    "\n",
    "Here, we create a new model where we reduce the number of hidden layers to 1 (from the default we used of 3), and we reduce the number of neurons on each hidden layer to 32 (from the default we used of 128).\n",
    "\n",
    "Then we fit (train) this new model, exactly as we did before.  We'll set `verbose` to 0 though, so we don't see everything as it trains (if you'd rather see it, just change `verbose` to 1 below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define network\n",
    "number_features = X_train_sc.shape[1]\n",
    "model = make_net(number_features,\n",
    "                hidden_layers=1,\n",
    "                hidden_layer_neurones=32)\n",
    "\n",
    "### Train model (and stote training info in history)\n",
    "history = model.fit(X_train_sc,\n",
    "                    y_train,\n",
    "                    epochs=250,\n",
    "                    batch_size=64,\n",
    "                    validation_data=(X_test_sc, y_test),\n",
    "                    verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate, print and plot accuracy as we did before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "Training accuracy 0.880\n",
      "Test accuracy 0.825\n"
     ]
    }
   ],
   "source": [
    "# Show acuracy\n",
    "calculate_accuracy(model, X_train_sc, X_test_sc, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FixezXeacfEa//PJL3HvvvWjUqBGGDh2KihUr4uTJk1i+fHm6v4X+/fujZ8+eAIB333035xeT7g4O73dG5EQZdYOvXbu23e03btyo7rnnHuXt7a3CwsLUqFGj1KpVq7LsWq119Z00aVK6YwIwdbnNqBv88OHD0+2btgu1UkpFRkaqhg0bKg8PD1W5cmX17bffqldffVV5eXllcBV0+/fvV+3atVO+vr4qMDBQDRkyxNbdPm035WLFiqXb317ZL1++rJ588knl5+en/P391ZNPPql27tyZrW7wmiNHjigACoDasGFDuvVXr15VgwYNUoGBgcrX11d16NBBHTx4MN31yU43+JyU+cyZM6pHjx4qICBA+fv7q169eqlz586le0+VUurdd99VZcqUUS4uLqYu8fbew2PHjqmePXuqgIAA5eXlpZo2bap+++030zbaufz000+m5fa6lduT3WumXY/nn39elSlTRnl4eKiyZcuqAQMGqJiYGNs2iYmJ6q233lIVK1ZU7u7uKiQkRPXs2VMdO3bMts2lS5fUo48+qnx8fFSJEiXUM888o/bu3Zvtz5dS2f+MKqXU3r17be+Pl5eXql69unr77bfTHTMpKUmVKFFC+fv7qxs3bmR63ejuZVGqAP07S0R5onv37ti3b5/d/BSioi4lJQVhYWHo2rUrZsyY4ezikJMwB4iokEs7JcCRI0ewYsUKtG3b1jkFIirgli5dikuXLpkSq6noYQ0QUSEXGhpqm5/q1KlT+Prrr5GUlISdO3eiatWqzi4eUYGxZcsW7N69G++++y4CAwNzPXgl3R2YBE1UyHXs2BHz5s1DdHQ0PD090bx5c3zwwQcMfojS+Prrr/HDDz+gQYMGpslYqWhiDRAREREVOcwBIiIioiKHARAREREVOcwBssNqteLcuXMoXrz4Hc1sTERERI6jlMK1a9cQFhaW5SCiDIDsOHfuHMqVK+fsYhAREVEunD59GmXLls10GwZAdhQvXhyAXEA/Pz8nl4aIiIiyIz4+HuXKlbP9jmeGAZAdWrOXn58fAyAiIqJCJjvpK05Pgp4yZQrCw8Ph5eWFZs2aYevWrRlue+vWLbzzzjuoXLkyvLy8UL9+faxcufKOjklERERFj1MDoAULFmDEiBEYN24cduzYgfr166NDhw4ZzhY8ZswYfPPNN5g8eTL279+PZ599Fj169MDOnTtzfUwiIiIqepw6EGKzZs3QpEkTfPXVVwCk91W5cuXwwgsv4I033ki3fVhYGN566y0MHz7ctuzRRx+Ft7c3fvjhh1wd0574+Hj4+/sjLi6OTWBERESFRE5+v52WA5ScnIzt27dj9OjRtmUuLi5o164dNm/ebHefpKQkeHl5mZZ5e3tjw4YNuT6mdtykpCTb8/j4+GydQ2pqKm7dupWtbYkAwMPDI8uumURElP+cFgDFxMQgNTUVwcHBpuXBwcE4ePCg3X06dOiATz/9FK1bt0blypURGRmJJUuWIDU1NdfHBICJEydiwoQJ2S67UgrR0dGIjY3N9j5EgATkFStWhIeHh7OLQkRUpBWqXmBffPEFhgwZgho1asBisaBy5coYNGgQZs6ceUfHHT16NEaMGGF7rnWjy4gW/AQFBcHHx4eDJVK2aANsnj9/HuXLl+fnhojIiZwWAAUGBsLV1RUXLlwwLb9w4QJCQkLs7lO6dGksXboUN2/exOXLlxEWFoY33ngDlSpVyvUxAcDT0xOenp7ZKndqaqot+ClVqlS29iHSlC5dGufOnUNKSgrc3d2dXRwioiLLackIHh4eiIiIQGRkpG2Z1WpFZGQkmjdvnum+Xl5eKFOmDFJSUrB48WJ069btjo+ZXVrOj4+PT54cj4oWrelLa7YlIiLncGoT2IgRIzBgwAA0btwYTZs2xeeff46EhAQMGjQIANC/f3+UKVMGEydOBABs2bIFZ8+eRYMGDXD27FmMHz8eVqsVo0aNyvYx8wqbLyg3+LkhIioYnBoA9e7dG5cuXcLYsWMRHR2NBg0aYOXKlbYk5qioKFOPmZs3b2LMmDE4fvw4fH190blzZ3z//fcICAjI9jGJiIiInDoOUEGV2TgCN2/exIkTJ1CxYsV0XfKLmvDwcLz88st4+eWXs7X9+vXrcd999+Hq1aumoLUo4eeHiCj/5GQcIA5IUgRYLJZMb+PHj8/Vcbdt24ahQ4dme/sWLVrg/Pnz8Pf3z9XrERER5ZVC1Q2ecuf8+fO2xwsWLMDYsWNx6NAh2zJfX1/bY6UUUlNT4eaW9UejdOnSOSqHh4dHpr3xiIjIuaxWICkJ8PZ2dknyH2uAioCQkBDbzd/fHxaLxfb84MGDKF68OH7//XdERETA09MTGzZswLFjx9CtWzcEBwfD19cXTZo0wdq1a03HDQ8Px+eff257brFY8O2336JHjx7w8fFB1apVsWzZMtv69evXw2Kx2AaQnD17NgICArBq1SrUrFkTvr6+6NixoylgS0lJwYsvvoiAgACUKlUKr7/+OgYMGIDu3btneL6XL19G3759UaZMGfj4+KBu3bqYN2+eaRur1YqPPvoIVapUgaenJ8qXL4/333/ftv7MmTPo27cvSpYsiWLFiqFx48bYsmVLLq4+EVHh0bEjUK4cUBTG+WUAlAeUAhISHH/Ly+ytN954Ax9++CEOHDiAevXq4fr16+jcuTMiIyOxc+dOdOzYEV27dkVUVFSmx5kwYQIee+wx7N69G507d0a/fv1w5cqVDLdPTEzExx9/jO+//x5//fUXoqKiMHLkSNv6//3vf5g7dy5mzZqFjRs3Ij4+HkuXLs20DDdv3kRERASWL1+OvXv3YujQoXjyySexdetW2zajR4/Ghx9+iLfffhv79+/Hjz/+aEuUv379Otq0aYOzZ89i2bJl+O+//zBq1ChYrdZsXEkiosIpPh5Yswa4fBn47z9nl8YBFKUTFxenAKi4uLh0627cuKH279+vbty4YVt2/bpSEo449nb9es7PbdasWcrf39/2fN26dQqAWrp0aZb71q5dW02ePNn2vEKFCuqzzz6zPQegxowZY7gu1xUA9fvvv5te6+rVq7ayAFBHjx617TNlyhQVHBxsex4cHKwmTZpke56SkqLKly+vunXrlt1TVkop1aVLF/Xqq68qpZSKj49Xnp6eavr06Xa3/eabb1Tx4sXV5cuXc/Qa2WHv80NEVBD8+af++/Ldd84uTe5k9vudFnOACADQuHFj0/Pr169j/PjxWL58Oc6fP4+UlBTcuHEjyxqgevXq2R4XK1YMfn5+uHjxYobb+/j4oHLlyrbnoaGhtu3j4uJw4cIFNG3a1Lbe1dUVERERmdbGpKam4oMPPsDChQtx9uxZJCcnIykpyTZ45YEDB5CUlIQHHnjA7v67du1Cw4YNUbJkyUzPlYjobrJ9u/44i6/6uwIDoDzg4wNcv+6c180rxYoVMz0fOXIk1qxZg48//hhVqlSBt7c3evbsieTk5EyPk3Z6B4vFkmmwYm97dYdte5MmTcIXX3yBzz//HHXr1kWxYsXw8ssv28runUV2X1briYjuRjt26I9PnXJeORyFAVAesFiANPFDobdx40YMHDgQPXr0ACA1QidPnnRoGfz9/REcHIxt27ahdevWAKR2Z8eOHWjQoEGG+23cuBHdunXDE088AUASng8fPoxatWoBAKpWrQpvb29ERkbi6aefTrd/vXr18O233+LKlSusBSKiIsMYABWFGiAmQZNdVatWxZIlS7Br1y78999/ePzxx52SBPzCCy9g4sSJ+OWXX3Do0CG89NJLuHr1aqZTSlStWhVr1qzBpk2bcODAATzzzDOmCXK9vLzw+uuvY9SoUfjuu+9w7Ngx/PPPP5gxYwYAoG/fvggJCUH37t2xceNGHD9+HIsXL8bmzZvz/XyJiDKzYwdQqRLw00+52//yZaBJEyDt/34JCcDBg/rztAFQSgowciRQvnzmCdLz5gFlywLTpuWufI7EAIjs+vTTT1GiRAm0aNECXbt2RYcOHdCoUSOHl+P1119H37590b9/fzRv3hy+vr7o0KFDpqMojxkzBo0aNUKHDh3Qtm1bWzBj9Pbbb+PVV1/F2LFjUbNmTfTu3duWe+Th4YHVq1cjKCgInTt3Rt26dfHhhx/C1dU1P0+ViPJAfDzwww/AzZvOLkn+mD4dOHEC+Oab3O3/9tvAv/8CM2eaUzf++0/GANKGgIuKknToefOATz4BunSR+9OngZUrMz7+7NnA2bPAM88AI0bovZXXrAH2789dmfNN/udkFz457QVGjpOamqqqVatm6m1WmPDzQ5S/unSRXkzvvOPskuSPOnXk/EqUUMpqzdm+O3Yo5eKi9/TasEFf9+WXsqxdO339vHn2eyC/9JL941utSgUGmrddskSpzZvlcXCwUjdv5vrUsyUnvcBYA0QF2qlTpzB9+nQcPnwYe/bswbBhw3DixAk8/vjjzi4aEeWh778HGjQAjh+XmogePYABA3J2jN9+A5Yvl8c//5znRbxjKSnAqFFAw4ZSS5JTV64Ae/fK46tXgbRpmXv3As2aAUFBQFiYXkv0wQdASAhw771ybTXGXl/btsl9y5ayPyC1RABQqxYwZAjw1FPy3DBWrcnp00BMjNQiPf+8LJs2TW8Ou3AB+OWXHJ92/snfWKxwYg1QwREVFaVatGih/Pz8VPHixVXz5s3Vn3/+6exi5Ro/P0T2tW4ttQSffKLU4cN6DcKFC9nb/8YNpSpXNtc+nDmTd+U7eVKpWbOUmjlTqf/+y/n+8fFKdeqkl+3LLzPe9tIlpZYvVyo11bz811/N57dokb5u5Uqlihc3r/fxkX2MtT4BAUoNHiyPBwyQfY8cUcrDQ5b98YdSjRvLY22/2bNlu/nz5XmrVvbL/fPPsr5+faWOHpXHFotSXl766z/4YM6vXU6wBojuGuXKlcPGjRsRFxeH+Ph4bNq0ydYjjIjuHlrS7eHDctPs25e9/T/9FDh2DAgNBerXl2UrVuRN2ZYvB+rUAQYNklqQli0laTgnxowBfv9df27scWW0c6fUhHXpIvk3Rn//bX6uHeP4caB7d+DaNaBNGznGvfcCiYlAt256jdrevbJtt27m/V9+GUhOBjp0ANq2BSpUkOVabVGrVnIfFib3GdUAaTVKjRoBlSsD998vYc/Nm5IYbbFILtCJE/b3dzQGQERE5DBWKzB4MDBhgr4sNRU4c0Ye5yYAOn0a0KbymzQJ6NlTHmvNYYmJQK9ewJtv2t9/3Digb1/g1q306376CXj4YUkYrlsX8POTx//8Y94uNVWO0759+mTfxERgzhx5rPW+MjY/abZskcBFax5bssS8fsMGub/nHvMxRoyQIKNNG2D1agmgvvoKcHGR6+3tDXz+OVC7NlCihAQogJRz0SK5Tu7uwBdfSJBSvrz+mqGhQMWK+mNAAiClgGefBZ57Tk901gKqiAi5HzJEP86IEUC7dvL4dodb58vfyqjCiU1glF/4+aHCbO9epWJi7uwY//6rN4ecPy/LzpzRl5Upo9Szz+rPn31Wkms3b1YqOdn+MXv3lm3vvVe23bFDbwK6cUOpt9/Wj7dqlRxn0yalkpKUWrNGX7d+ffpjN28u6/r3l/369pXn48fr21y7ptTDD+vH8fdXavp0aRKKjlZqzhxZXrGiNKUBSrm6KpWYKM19Z8/KcTp3lnVaorOvryQNHzig1OLFejOVlpxcurQ0fWnH27vXXPZXXpF1//ufebnVqlRQkKzTms1GjdLXf/aZfi6PPaYvN077tH+/uanRapUkZ0CurVJy7cPDlSpVSpr1fvhB1jdunMWH5A7kpAmMAZAdDIAov/DzQ4XVli2SE9K4cc57HxnNnq3/cM6cKcs2bTLnrjRrpj9u1Uqpb7+Vx337pj/e9u16vsrOnbLMalUqLEyWP/64Up6e+vGqV5djAkq1bKlUjRr6uk8+MR87MVEpd3dZd+yYLPu//9N7Syml1OnTSjVoIMs8PfXHxpybatXk8XvvSdlKl5bnCxZIfkxwsOQVaTk3hw4pFRIij7/+2pxDExIigYWbm94bDFDq5ZfTX5vUVAme7L1fHTvqxwwNlRwlzeLFGecqaQHT11+bg8qzZ/X3ISFB3/7yZT2PS8sL8vDIOJi9U8wBIiIqBFauBB59VHrHFHT/93/SnPLvv+bmnx9+AB57zP7UCbt2Se7Jnj36MmOT1m+/yX3afbduNW+v5cLMmwesW2feVuvp9Nhj0vQDSDPOe+/J/Y8/AklJQOvW0rvp0CE9l2bjRvPgfzt2SPPWk08CCxdKOW7dktwXrRno3nvlfvNm4MABoGlTOc+gIGD9erk2r70GNG8uAxbGxkqTnqur5BBZLHoT1MiR0nR14QLw4INyfdu2BapVAzp3lm2ef17PoWnZUpr4vLykOQuQ3mANGwLjx6e//i4uQI0a8pppac1UgByzeHH9uZYDBOj5PxqtGeyvv/Rl+/bpzV81a5qnaSpZUu9VVqkS4O8v+UbZze3KV/kTgxVurAGi/MLPDxlpNRFTpmS8TXKyUidOOKxIttc01hxcvaqUt7f+H/+gQbI8JUUf9yUoSJqpjLp3l3X16il165Ys05p5jE08//uf/fFmtJuxF1OdOvqxrl2TY2i9l9JaulSawby8pMnmu+/0pqilS5WqVEmea01oNWvqNTwlSig1Zkz6ZqDUVKnV0Y4DKFW7tv336MYNpZ54Qrbp2VNfPnp0xuc6d65ss2SJvszNTd4Po5dflnXdusl1yKk//pD927ZNX0MUFyfnHx4u77FR27ayn1bDBkivMu1a9e+f+eved59sN2NGzsucHWwCu0MMgCi/8PNDGqtVb74w5l+kpeWv/PKLY8p19qxSERHymj//LMuMQYGWWxMXpw9wp938/JSKjZV9bt5UqlgxfZ0W5FWoYN5nzRqlhg9PHwiULKlUlSr68/LlZRmgd8ueMUOeV6mScbNcdLRSx4/rz/fskVwWpeR+zx7ZBpAu223a6K+plX/yZPMxtcEWAQmujMdPy2qV5kNjE9OiRfr+gYF6oFiihARNSsn2WvPbyJHpj5uYKMdN21U+J3bsMDdXGZ06pdS5c+mXazlQxts99+jXbdq0zF/z1Vdlu+HDc1/uzLAJjIiogIuOluYLIPOJJ1etkvvFi+X+q6+kR489a9YAr7yS82kg4uOle3eXLkDjxnrvIq3pafp0uR87Vpo4EhOlaUlrwurSRbo9x8fr5f3zT+kqrjW/jBkjTV1ac5fWFfu33/TzN/Y+qlpVb+YBgEceAV58UR5rgxxq5Xr6afvNPAAQHKw3XwHSnV2bvLpYMXkeHCzNXEpJuTVaV3et2UtjfD5qlPn4aVks0kxmbGIyzio0YAAwZQrQsaN05ddm+SleXHq29ewp1z0tb285rssd/Io3bGhurjIqX15v7jKyt2zvXunBBqS/Vmlp526vF5zD5U8MVrjdbTVAADK9jRs37o6O/bP2byJlqTB+fihjN2/qtQk5Zex91KKF/W2sVr2Jp1IlaWbR9tGaXC5f1pspqlbN/L/wjAYVfPFF83/0oaF6jY5Wy+PhIT3AtB5C5copVauWPP7uO6mlAJR68knzMQcOVKpuXXms1XSEhOiJtpUrSxMZoNRTT+llePJJpd56S3++dq25d9e2bXrzkNab7E507WquldGa3fz80jcDaeUID8+4BiUzVqvs6+6evmmroJs0KePmu1Klsk6QP3hQtvX21psy8xJrgMjk/Pnzttvnn38OPz8/07KRI0c6u4gOl5yc7OwiUCGnlMyqXbu2eVLJ7DImgWZUAxQVpR/7+HG9xgOQMWH++AMIDJQJLqOjgSNHZJ02/o3m5k2gXz+p6Xj8cXMN0d69UgMByJQJ8+dLcm9goNToPPOMrHv0UaBUKWDoUEmSPX1axpGxWKT24qGHZLvff5cxcbTaoW7dpFYKAJYulfvatSXp191dBi/UroU2TgwgicBaDVDx4pKM26CB1NQkJspYQgDQtatM83CnjLUyvXrJOQFAixaSwGzUsKEkUG/cmHENSmYsFnnvtm6VJOXCxFgD5OVlLv+992ZcE6epWhXw9QVu3DAnoDtF3sdfhd/dVgNkNGvWLOXv729aNn36dFWjRg3l6empqlevrqYYMjKTkpLU8OHDVUhIiPL09FTly5dXH3zwgVJKqQoVKphqkipUqJDh644aNUpVrVpVeXt7q4oVK6oxY8ao5DT9IJctW6YaN26sPD09ValSpVT37t1t627evKlGjRqlypYtqzw8PFTlypXVt99+m+E5/fzzz8r48R43bpyqX7++mj59ugoPD1cWi0UppdTvv/+uWrZsqfz9/VXJkiVVly5d1NGjR03HOn36tOrTp48qUaKE8vHxUREREeqff/5RJ06cUBaLRW3bts20/WeffabKly+vUu00zhf2zw/pjGPXLFkiOTEvvKBUv34yds3p05nv//TT5iRfe92Cly83/4ft46M/HjpUT7ANDlZq4ULzdtpHLDZWH8vGmLMRHS2vqeVuPPKI+bX79zfvY0wyNnaTbt5cliUny/g3Wg2UVmt07ZrUkvn56fu8+KLsY5x4E9BzcQDpIh4bK0mzxu7pQ4aY91mxIifvWsaWLdOPuXy5jFfUqJGMs0M6LXkakJq9Xr305x9/nL1j3HuvbD9nTt6XLyc1QG7ODL7uGkrJvySO5uOTdbidhblz52Ls2LH46quv0LBhQ+zcuRNDhgxBsWLFMGDAAHz55ZdYtmwZFi5ciPLly+P06dM4ffo0AGDbtm0ICgrCrFmz0LFjR7im/TfJoHjx4pg9ezbCwsKwZ88eDBkyBMWLF8eoUaMAAMuXL0ePHj3w1ltv4bvvvkNycjJWGMax79+/PzZv3owvv/wS9evXx4kTJxATE5Ojcz169CgWL16MJUuW2MqakJCAESNGoF69erh+/TrGjh2LHj16YNeuXXBxccH169fRpk0blClTBsuWLUNISAh27NgBq9WK8PBwtGvXDrNmzULjxo1trzNr1iwMHDgQLnfSOE955uZNPa8iLxlHK16+HNi9G5g8WV/m7w98+GHG+xtrgKxWGf03PDzjbQDz18yff8rEk4B0o/7iC/N2f/4pUxu89ZZ02Q4IkJqi996TrtrNmknuyp9/yvX55BPza3XpAnz3nTyuXFlGGdb06CE1OGvWyGNAanM6dgQWLNBrjdq1k//2Aal5mjpVHtepI/cPPQSsXSuPS5SQGqqqVaUmq149uYZ//GEu10MP6TVh5cvLyMt5oUkTwMNDvlbvu09ybApEnkoBY6wB0mrpfvpJnmeV/6OJiJAazB07gP79876M2Zb38Vfhl+MaIOPwmI685SL5IG1tSeXKldWPP/5o2ubdd99VzW//W/fCCy+o+++/X1kzaNhFLnOAJk2apCIiImzPmzdvrvr162d320OHDikAas2aNXbXZ7cGyN3dXV28eDHTcl26dEkBUHv27FFKKfXNN9+o4sWLq8uXL9vdfsGCBapEiRLq5s2bSimltm/friwWizqRQb9l1gA51ty5MkKu1rU4L02das6ZKVdOHmsD4XXrlvG+VqteI6INaGdvjt8BA2SdNjIwoI/gm9FNG433+eeV2rVLz2WJjJRjHjpk7l1VrJhSv/2W/rWvXtXLNnFi+vWxsXJdk5L0ZVo3c0AGHDT2jtIGLASU2rhRlh05oi+rX1+W7dtnvzya69f1gQ2NozHnhU2bcjfRaVESG6u/Z2+8odRPP+k5PcbPQmb+/FOpjz6SPK68xhwgypaEhAQcO3YMgwcPhq+vr+323nvv4dixYwCAgQMHYteuXahevTpefPFFrF69OlevtWDBArRs2RIhISHw9fXFmDFjEGVIfNi1axceeOABu/vu2rULrq6uaGP8FzQXKlSogNKlS5uWHTlyBH379kWlSpXg5+eH8Nv/gmtl27VrFxo2bIiSJUvaPWb37t3h6uqKn293S5k9ezbuu+8+23Eo7yglg/EZB2ADgG3bpPbCXlrXihWSj2KchDIrq1dLbYpSmW9nrAE6f15yYkqU0Oe4Mq7X3LoFTJwoPbri4wE3N6mJAfQ8oJQU4LPPpNZm715Z9uyz+jEee8ycd+HnZ34NrcfQwoXAE09I7VKvXjIxJSD/tf/zj9Sk1KkjeSxduqQva0AA8MILkhujzV9l5O8vtToeHvqyhx+W+bIefljKb+wd1aiR/LffurU+CF+VKkD16vJY6wFWq5b98miKFZOcovr19ZqmvNK8udQ8Ucb8/KR2DJDPUrt20nPw5ZfNn4XMtG4tg0UaKs6dgk1gecHHJ3dZkHnxunfg+u0yT58+Hc20b+HbtCaiRo0a4cSJE/j999+xdu1aPPbYY2jXrh0WLVqU7dfZvHkz+vXrhwkTJqBDhw7w9/fH/Pnz8Ymhzt1b+4uyI7N1AODi4gKV5tfqlp1ZDYtpfV8NunbtigoVKmD69OkICwuD1WpFnTp1bEnSWb22h4cH+vfvj1mzZuGRRx7Bjz/+iC+MbRGUIaVy1oL766/A8OHSpHLokCTDxsbKj+WlS9LU9dZb5n20pGDtHtCbkby9za+vlAQn2jEqVpQf8ozKrR3TzU2CFkBGENZ+QI8dk+DL2DI8fbp5Qs6qVeW2caMeAH32mXStLllSEkUB+ZGpWVOSk7t1k5GNtQTSceOAV1+Vx+Hh0q36tdeAixfl5u0NfPyx+RxKlZLrmZVPP816GyN/f2kKzIg2IahR9+7A//4n55ddEyfKjRzPYpEm0b17JdgNCJB/QgqlvK+AKvyKUhJ0WFiYeuedd7K9/8qVKxUAW5OQu7u7WrRoUab7fPzxx6pSpUqmZYMHDzaVo23bthk2gWnJxhk1ga1YsUJZLBZ13dAk+Oabb6ZrAquv1bHfFhMTowCov/76y7bs77//NjXrzZ49W/n5+WXYBKaUUvv371cuLi7qs88+U/7+/ioxMTHDbQv75yevWK2SdFulij5wntE338igd1u36suMg89pH5WXXtKXeXvL4G3G19BG7A0IkOc9e+rbt29vfk1tJFvt9tBD5vW3bkmSs6+vNCdVry7bPf64vs+ePdJlWmuiMTYBWa3p54nq2VOpsWP1pOazZ/Vu79rNw0Nee88epebPl2Npk2u6uEjX9CZN5PkTT8j6P/6Q8xkzxn7TWkGSmCjvdyZ/YlTAbN+ePwnMeYEjQd+hohQATZ8+XXl7e6svvvhCHTp0SO3evVvNnDlTfXK728Unn3yifvzxR3XgwAF16NAhNXjwYBUSEmLr4VS1alU1bNgwdf78eXXlyhW7r/nLL78oNzc3NW/ePHX06FH1xRdfqJIlS5rKsW7dOuXi4qLGjh2r9u/fr3bv3q0+/PBD2/qBAweqcuXKqZ9//lkdP35crVu3Ti1YsEAppdTly5dVsWLF1IsvvqiOHj2q5s6dq8LCwrIMgFJTU1WpUqXUE088oY4cOaIiIyNVkyZNTAFQUlKSqlatmmrVqpXasGGDOnbsmFq0aJHapE13fFuLFi2Uh4eHevbZZzO9/oX986OxWqWXzr//mpcfPCizYNsb3+PqVRlR9+xZpb7/3tzjJi1tgkpttNjTp/VcFotF7l94QfJ7AH1Kgp499XFILl40BxLHj+v7ajdtZvP9+/V8l9de04MLrSdXbKwETNp+vXrp2+/cKYFc7956+WvXlnXGHkTauDWenjLys6+vUj/+qE/02bGj3qtLOx9jbozRpUsyLtBTT8nzuXNlkkp700EQFSUMgO5QUQqAlFJq7ty5qkGDBsrDw0OVKFFCtW7dWi1ZskQppdS0adNUgwYNVLFixZSfn5964IEH1I4dO2z7Llu2TFWpUkW5ubll2g3+tddeU6VKlVK+vr6qd+/ettoSo8WLF9vKERgYqB4x9Mu9ceOGeuWVV1RoaKjy8PBQVapUUTO1qaSVJD1XqVJFeXt7q4ceekhNmzYtywBIKaXWrFmjatasqTw9PVW9evXU+vXr0yV2nzx5Uj366KPKz89P+fj4qMaNG6stW7aYjjNjxgwFQG01VlnYUdg/P5r58+XHuVQpvbt1YqIM7pZR0qwWWISF6Ym6gCRDGhkTY+vVk2XvvCPP27RJ3w26Rw9zsu9TT0ky5saN5u20+abKlJFpFbTEY6tV74798MPyeq1by/N335XASRvwTwt6tHsvL/tTEWgD/hln0n7mGb3GSCl9v9WrZbk2y7bFIsFSnz7yfMCA3L5LREUPA6A7dDcHQJQ/3nnnHVW3bt0styvIn5/jx2V8mA4dMp9f6Pp1pcqW1QMLrRPh+PHmnkWrV0vtRZ8+sr5aNfu9ltL+wH/+ub7OYpGmEW3+qO+/l/F2Ro9W6rnnZPRhrWPf1Kl6ENSzp1KzZplfp2FDPcjRJuT8v/9T6tdf9ZqZY8fkWFoNlcWiHzMsTAKTUqX0Y2b0lo8apffE6tNHbxIDlFq3zrztoUPmcj79tCyPjZVA8uTJbL19RKQYAN0xBkCUXdeuXVN79uxRQUFBalpWswAq535+Nm1Savdu++s2b1aqdGn9R3j16vTbHDmi1Bdf6DNna7f77pNpGby85HlISPogZ8ECveakc2cZqO/ZZ2VZ48bm13nwQfO+Wp5PQIDUMmXGOHDgwIH2A67x4/XaqOHDZWZz7XU0iYnmZqgmTWTgQ6X0ZipAqUcftV8OrVnLGChqx0k7okRior4+IEAP6Igo5xgA3SEGQJRdAwYMUB4eHuqxxx5TKWknDLLDWZ+fc+ckmdbfX2pQjK5e1YMfDw89x8XIatXzcrTbF1/oOTVak9J990mCpLZcO542i/f998vxbt6UOZAACYa0GifjDNgtWui5OIDk/GSHlhCs1bqknX3811/1JOK2bfUxcdKOKJyUpFRUlAQ+xqBl3jz9WKNH2y/DX3+ZX/PRR+VYGX1EtLm30s46TkQ5w3GAiBxk9uzZSEpKwoIFCzIdCduRIiOlm2pYmIyVEhUlcw4lJwNxcTLXk9G4cdKNvEYNYN06WbZ0qSzT/PWXdLv28QH69gU++kjGiOnQQdZHRUm38enTZbyX+fOla7M2a/eVK3KvzRfl6SljwHh4SLd0bYbwtWtlrJwqVWR2ckDGsQHsj0Vjj/YaSUnm55pGjfQ5prZtA44ela69zZubt/PwAMqVA8qUMXeX79BB79petar9MqRd/txzcqyMPiLTp0u3buN4P0SUvxgAERUCqanAqlUy7k1Wxo2TiTPPn5cxWebNkyHnNcYJNffs0SfCnDxZJn5s0kSCEG0aBOM+/foBP/4o48xYLPokly1bAlu2SOAFyGB9o0bJ1AjGwfCMA9y5uekD+mkD/mkTaHbpIpNfapo2zf4AdWkH0TM+Dw6Wofxr1pTyJyTI8nr1ZDyT7ChRAujZU6aPyGhszuBgmcATACpVAtq2zbrMb7wh14SIHIMBUC4ppZxdBCqEcvu5mTdPgomGDdPPD2W0f78MqufqKrUOgMy5Y5zT6N9/gV275PHYsRJcPfKIPhP3kCFyP3myDMR39SqgjXuprdO0bw+cOyc1RGkG2QYAuLjoNTdVqsjIsUZaTcy+fVLTo81i/tBDUosSFGT/dTPTsKE+X5GbmwQfWmARESGBj4+POTDL7hxGmu++k/m3KlWyv95i0YO7p5+W60BEBQv/LHPI3d0dAJDojMlPqdDTRph2dXVFfDzQuTPw7bdZ77dqldyfPCm1NMaRVxMTZQTi116TqSIACSAGDpTHGzfqAZBWQ/PNNxK4aKMBv/eefrzHHwfKlpVmqY8/lkAoKUlqSewNXR8amvkP/AsvSDD29dfp1xkDoB07JKjw9ZWh8i0WKeeIEXJ+2eXiItdVO19vbz1QadRI306bkBPIeQDk4ZF+Coq0PvwQeP55OX8iKnhY4ZpDrq6uCAgIwMWLFwEAPj4+sNzhjOxUNFitVly6dAk+Pj5wc3PDb7/JHFXHjpnzW65ckSkMjPM9bdgg9xUqSGAyZIgENa6u8kP7ww/m13r6aaBBA6npuHpVlrm4yBxXDz0EzJghU0ekpsqPv3EagmLFJPDp00fmtUpNleXPP5+zqSs0xYvrzWxpaUHIvn1681f79vqcQt27yy2nnngCmDlTZvUGZB6sI0eATp30bWrXBpYtk8c5DYCy4/779fm3iKjgYQCUCyEhIQBgC4KIssvFxQXly5eHxWKx1cpERUlfIS240CaSXL0aeOAB4MwZqflxcZEk5YgI4L//pHakY0dJSAYksTgpSZJ2O3aUZp/mzSUpGpCAqnNnCQJ+/x2YPVuW20sufuwxYOpUYP16eT52bPaTkHNCC4D27JHaHyB90nJutG0LnDghuTiAzK81erQ+4abxtStUkBovIipanB4ATZkyBZMmTUJ0dDTq16+PyZMno2nTphlu//nnn+Prr79GVFQUAgMD0bNnT0ycOBFeXl4AgPHjx2OCNh3zbdWrV8dBbebAPGCxWBAaGoqgoCC7k24SZcTDwwMut9uLtMTkmzelx1VQkARDGzfK8hdekEBHq/1p0EDyVt59V2pjXn9dgp+kJAmUvvhCJtocMEDPebn3Xj0AatRIgqzPP9d7W/n5yUzhaVksUks0ejTw6KMSEOWHSpUk/2jJEmmSA8y1NHeiQgX9sZeXOfgBgB49ZHby3NQwEVHh59QAaMGCBRgxYgSmTp2KZs2a4fPPP0eHDh1w6NAhBGnZjwY//vgj3njjDcycORMtWrTA4cOHMXDgQFgsFnxqmLa4du3aWLt2re25Wz51rXB1dS0wXZ+p4Dh/Xnphaby9JXgx5slYreaeWVFREgCtWKEvO3BA8m+0Y2m9op55RvKGdu0Crl+X5qIvvwRq1QJ++cVcFmNPqogIua9WTXpovf++dDX38bF/HpUqAQsW5OTMc85ikdcYMULOtXVr4HYFa77z9rY/OzkRFQ1ODYA+/fRTDBkyBIMGDQIATJ06FcuXL8fMmTPxxhtvpNt+06ZNaNmyJR5//HEAQHh4OPr27YstW7aYtnNzc7M1UxE50pw5kp+TtmLwww+lxkZz/DgQH68/j4qSBGMtD6ZhQ2DnTmDMGD3ZVstTcXOTGpy//5ZAqlo1CX7sadZM8oRSU80JwO+8IzUtTZrc2fnmBTc3CeCefDLjXlVERHnNab3AkpOTsX37drTT+t5C8iPatWuHzZs3292nRYsW2L59O7Zu3QoAOH78OFasWIHOWpeP244cOYKwsDBUqlQJ/fr1Q1RUVP6dCNFtH34oPa9u3ZKckipVJB8HkG7sRsbaH0ASmxMT9eaqWbMkQLlxQ8+NMSbqliolTTePPGLuzZSWr68EO088Ib3HNC4uMnaPlmxcEDRpIudFROQITqsBiomJQWpqKoK1LMXbgoODM8zXefzxxxETE4N7770XSimkpKTg2WefxZtvvmnbplmzZpg9ezaqV6+O8+fPY8KECWjVqhX27t2L4trIZGkkJSUhSRs2FkC88V9zuiskJEiS8J22hl6+LD203Nyki7XWrJWYKInCgOTNvPeerIuJkaat//6TZObgYAmQjOPyAFIDtG6d5AOVLy9dzn/5RfKAvvlGgpzcVmoa/jyIiOi2QjUO0Pr16/HBBx/g//7v/7Bjxw4sWbIEy5cvx7vvvmvbplOnTujVqxfq1auHDh06YMWKFYiNjcXChQszPO7EiRPh7+9vu5UrV84Rp0MOcugQULLknU8zcOCABCe1akmz04sv6uu2bJHApkwZya3RAqPAQOCee+Tx0qUycnBgoF4jpI1uHBWl5/906SK5Me7uMnZOZKQ+Xg8REeUNpwVAgYGBcHV1xQWtfv+2CxcuZJi/8/bbb+PJJ5/E008/jbp166JHjx744IMPMHHiRFi1CYPSCAgIQLVq1XD06NEMyzJ69GjExcXZbqdPn879iVGB88cfMg/WDz/oUx9k5ehRqcnRpp5QSgKexERJngVknBltvdZTq1Wr9GPlaN2633xTurffuAFoHzGtB1JUFPDnn/K4fXt9X4tFxpIJD89euYmIKHucFgB5eHggIiICkVrSA2SguMjISDRPOyvhbYmJibYuxBqtF1ZGUwxcv34dx44dQ6g2Nr4dnp6e8PPzM93o7nHkiNwnJUkwlB1Dh0pOz7hx8vznnyXx2NNT5q2qXVsCmR9/lPV//y339gbU0+aiunZN7rWeWb6++ojFBw/qU1y0bJn9cyMiotxxahPYiBEjMH36dMyZMwcHDhzAsGHDkJCQYOsV1r9/f4wePdq2fdeuXfH1119j/vz5OHHiBNasWYO3334bXbt2tQVCI0eOxJ9//omTJ09i06ZN6NGjB1xdXdG3b1+nnCM53+HD+mOtl1Vmjh7VZ0X//nsZmVmb9PO116SnkjY31fTpQEqK1OwA5m7nmnr19IH2GjWSY69aBaxZo88afv263NeoYX9OLSIiyltO7Qbfu3dvXLp0CWPHjkV0dDQaNGiAlStX2hKjo6KiTDU+Y8aMgcViwZgxY3D27FmULl0aXbt2xfvvv2/b5syZM+jbty8uX76M0qVL495778U///yD0vxVKbKMAdDy5fqoyxs3Ss3L4MHm7WfM0B9fvSpNUlFRQLly0iwGSJft11+XsXimT5cAxt9fn9vKyGIBRo6U0YinTZNu6Vozl1JSE6QFQPkxJQMREaVnUZzWPJ34+Hj4+/sjLi6OzWGF3K1bkrOTmqqPh7Nzp/TMqlZNcoI2b9YTlW/dkkDnwgWgaVPg9ogLAICFC82jJvfrJ01gFosEMp0767OZ50Tt2jKLOyDjCPXvn/vzpSLk2DGpLsyr76jDh6WqMqORMYkKgZz8fheqXmBEOXXihAQ9Pj76FAtffCE1MlpCtHEczeXLJfgJDpaeWloF5H33AT17mo/90UfSPV37FyK3tTfGKRpYA0TZcvIkUL163kycBgC7d8vxnnwyb45HVAgwAKJCZ8sWfcDArGgJ0FWr6pN5zp5tHpjQOCjh9OlyP3Cg5PoMHizd1r/6Kn3vrjJlpBmtWzeZ8fyRR3JzNnoAFBoqc30RZWn7donst26V4cDvlJaBv3PnnR+LqJBgAESFSkIC0K6dzHauTZ6ZGS3/p1o1CVR+/FF6cmnLAD0AOn0aWLlSHmt5QdOmycCHGU014ecn4/tcvSr/QOeGlgjdtm36IIvIrhMn5D4pCYiOvvPjXb0q9+fP61WaRHc5p88GT5QTa9fqCcNbt2Y9k7cWAGlBRt++EqisWSOzgVevLvk3iYky/YTVKoGItj2QvaDkTubEffZZGadowIDcH4OKGONsu8ePA2Fhd3Y8LQC6eROIiwMCAu7seESFAGuAqEA4flyCGyOlpNu69t0MmJOM086nZY+xBkjTqJH04KpaVXJ9rFap+dd6f2ld3B3F11cGSdTmDaNCRing7beBxYsd95paDVDax/Zcvw48/7xk8A8cqAdPCxcCEyZI+Y1/ZOfPZ68Me/fKcfOiBorICRgAkdMpBTz8MPDgg/KdrJk3D+jaFXj1VX27nAZAWg6QMQDSWCxARIQ8fust6epeokTuc3moiPr3X5n87bnnHPeaaWuAMvPLL8CUKcCiRdLNcPJk+WN69llg/HiZ4yU3AdC778pxjeNGEBUiDIDI6Q4f1nMwX31V7521aZPca1NE7NplzvtJO6EoIMnK4eHSlb1cOX3KCXsBECC1QcbXGDEC8PLK7ZlQkaRNs3PxIuCIiZStVukFpskqANL+CLS23KNHZXRPLeiJjtbndAGyl1wHSOCnHY+oEGIARE5nrNU5cwaYOFEea0HR8ePyXa1t9+CD0j09Otr8z+rRoxJAnTolxzlzRpbXqgWUKmX/tbUACJBeXyNH5s05URFiDECyao7KC+fOSdJYdl9T+yNp2FDujx83lzkmJuc1QFev6sfIKgAjKqAYAJHTaYFNu3Zy//HHMm/W3r36Njt36tNYPPaYTBkBmJvBXnlFfhfatZPaIe22bVvGr601gQEyUjNrf/JZdnoYZbZNSkr69UqZl926BVy+rN+0rPn8KCuQcT5O2nKlpEh5jLUt2RUfL/smJaUPOLIKQLSARptk7sQJGURRk1EAlNn5G7vLZxSApaRkXq6CKDU16/c9v3rJFebed4XxvQYDIHKyuDjgr7/k8ddfAxUqyHf8smXyvaxZuVIflblzZ73m5vvvJeCpU0cCJDc3SXFo1Ei/ZTawbfnyUuM0caLkG1E+Wr5chuX+4YeMt/n7b3nDpk5Nv+7iRentlHao7JdflsGaoqIkcq5USZ5rt4AA4KefclbWpCT58DRvnvU4Oxnl47z+uiSVHTki7bpVqkh5SpSQSeWya/JkmWclMBAICdFn9K1fX+7PnZPeWxnRmrTuuUeqTm/c0CevA+wHQJ99JoNbbdxo/5jG/zzOnJHrlXa9n5/kCTmT1Qq0bi09HrJqnkxKkuri++/PfJuICKBFi7wZf0mzb5+M6j1mTN4d01EuXpTP5VNPObskOaconbi4OAVAxcXFObsod72ffpJ/k6tXl+dPPCHPW7XS/n2Wm6+v3DdqJNt99pl5vXZ7/XWnnQplpV8/eZOqVlXKarW/zciRsk1YmFK3bpnXzZsn60qV0pedP6+Um5ssnzVLqT//tP/BGDgwZ2XVXgtQ6uDBzLcND9e3ff55WXbpklIeHrJs0iSl1q41l6dcueyX5cEHzfsWKyb3Q4bofxiZlbFSJdnm77+VqlBBHteurR/vhReU8vPTn7dpI39ogFJdu9o/Zp8+5jIdOmReP3asLG/aNPvnmR8iI/UyTp2a+bb79unb3rxpf5v58/VtoqLyrpyDB8sx69TJu2M6yq+/StmDg51dEqVUzn6/WQNETqVVBmgj+mtTQfz9t9z7+8u91orRpYvcG3N3HnwQWL1a9jHMi0sFjZa1fuSInnWellaDcu4csGKF/f0vX9ZzYGbP1qvfjbkt7drJ8u+/Nx83u7QhwYHMuxveuiU1T2nL//33ehl37NCPoc2Ce/o0cOlS9sqiHfPll+Ve6yVQqZLcjNukpZTepGUcalxLsANk7hdj7cjJk8CePfJ4+XLg7Nn0x03bAyFtM5h2vs7ODzK+j8bH9hhzny5fzvp4eXVu164B8+enL0NhoZX54sVC1xTGAIicJjJSeui6usrwJADQqpV5G+Pko4AeALVoIdMWvfmmfEc/+KAET3cyICHlo+vXgUOH9OcZ/RgZf0jTbmMMRKKjpQni22/N+2o/SpUqyYehcuX0x83KsWN6MxNgv7uhJirK3BRy/LgEHWkDKK3sbdumH4I8M6mpktUPAC++KANXaSpW1AOajM4vPl6avAAJgLSAyShtL65TpySwA+TcZs1Kf0xtfInmzeU+bTCgnVtMjPzAO0NMDLBkiTx2cZH3MbOpPozBh7H9XXPsmHkOnrxKeJ83Tw9qjcF9YWHMGbt40bllySGOBE1OcesW8MIL8vi55ySHB5Dk5pIlpZcuALRpA6xaJf8wly4NNGkiy93cgO++c3y5C5WTJ6WmpV8/uWDZsWGDBA7aD5vRwYMy70fa3Ac3N6B3b0ng0mzeDKxbJ4/vv19PXvb0lDyKxYsl30Lrmt2qlUSwxuTcFSuAd96RH+1+/cwBw/nz8sNt3N74I6z90Gv3Wp6KNg/K779LblDz5rJ84UIZjMrfXw+qtLLu2CEf2LlzJQIvXVoCgP/+06sofX0lyDt5UvJmDhzQ9z98WK/CjIiQ2pXDh+W4HTpk8mbcLndKCuDhIQlrgwYBH36on1tWNUDaj5O/v+RW2ZtsTgtm3NzM/8Fr5Z8xQ/7TsFjkx1rLCypXDmjaVN5rYzAQHW3uSn/iBFCvXvrXtVqlCrhlSz1Qza6DB2UId3uDdl26JF8OW7dKMNGokeQALVgguVft20tPivBwCfbWrQOeeCLrACjteEfHj0vu1Y8/ykzLoaHp91m8WAJ/d3f5GzHOfLx1q4z+mvaLLDravF1aiYlSY9SliwTEhw7J5804LH5qqmxz//32y6Vt88MP8iUbHp5+/apVkgfWokX6dQcPyuema1fzdTt3LuNRyVevlnXal31B4IAmuUKHOUD5b8YMaTYODFTqyhXzuq5d9Wb2HTuU6tZNHg8Y4IySFmIPPSQX7uefs7d9fLxS3t6Su3LuXPr1ERH282sApdq2NR+neHF9XfHiSr33np5TUr9++v2LFZPX1J43bmxeP3my+fmSJUoNG2beNjRUqRYt5PHChVIWq1UpHx9znkp0tFIuLpL3kpys1P/+p+fCKKVUxYry/O235d7f31z+1FTJYwKU6tRJ7jt0kGMCSrVvL/eDBilVtqy53JcuSU4QoNSjj2b9nvzxh2xbrZo8P3pUnru6KnX5slJffinPH3nE/v5aDkyNGvJ87tyM38OyZfX8Iu16+PvL402blNq40bz9I48o9cUX6c9l+XLzdhl9/rTtHnww6+tglJKi51399lv69VqumXb7+mul1qwxL3voIdn24Yfl+eLFSr38sr5+/vz0x9Xec+3z9sQTSv3f/2WcY7ZnT/rrpUlNlS8/bZ27u1IlSsjjzZszP3/tb2HwYHneoIE8//VXfZtPPpFljz2W8XF+/lm26dIl/bqzZ+UzFhCQPl/v1i39c71rl34NAaWWLbP/Wvv3K2WxSA5aRvl/eYQ5QFTgrV4t988/L51ijLRmMBcXqRF65RVZNmqUY8tY6Gl5HsZ8j8wcPSrNJcnJkltjlJioNx8MGAA8/bTcBg+WN2r9er2Ja948afYIC5PbtWvApEmyLiJCmlSefVY/RvHi0gSwbJlsExwsIxY/95z0XALS9445f16vuXj8cX3ZgQPyWKvpsFjSNxMdOiS1D/Hxsv2GDbJ840ZpgtC2e/FFqQWJi9PLv3y5/Nesvfbvv8t9tWr6f+3ah3vIEHOyWvny0pNLG3shO01gWs2Odg6VK0tt1dy5UlWqzZ+S0eCFxvwfwNwElrZ7ZECAubagZUv9j/Hff/WeY1Wrynvz/vt6uYw1UFnlB2ly+vnUrF6tDwT5zTfmdZcv6z3++vWTz81TTwEPPAB88oleS5L2tffty7wGKCVFP8e+feX++HH9mtg7h3/+kXtv7/TbHD0qr+HpKX8D8+bpTaNZ5QFpx9m8Wf62/vtPnmvXQin9sTaabGbHsVf2bdukhig2Nn3vueXL9UHW9uwxlzejsm/aJOU6dSr7A206AJvAyOGU0pOc27RJv/7BB+V3q2FD+e5o00bvKk/ZlJKiJ+dmN1fBuN2330o3bpfb/yPt3i1BQ1CQBDDGGWIvXJAxCL79VgIFLf/llVfkzR41SoIIQAKChg1lzAPNqVMyO+2iRfK8UiVpHpsyRQKUWrX0/TXnz+s/SBER0sQTF6d35zb+0FeqJF/y2vbG89yxQ//B3rNH/9HSuqzXqyc/BtrrW60SvKVVsaK8jvbDXKuWBG8REXpgpwU+2oCEJ05IW2/JkumPp9HKajwfY2KcFrBk9MOTNgAyNoFFROh/iID+n4iWExQRIc1Mv/0m10nr6j5ggMwdA8iPpLGcgB7Y+fnJj2dGzXPaPlo3/uwOwmXMr9KStLVAUEs+b9BAHhs/pyNGSDPU0qXyt5GUpOdXHT+eeQB05oycq4eH3lPjxAl9TCd7f2PadejSRT7bJ0/K50fLRwKknNr5aD1CsgqAtNc6eFCCdqXk+YoVUs5jx/RJEM+ckbycoKCMj3P6tDTxurunLzsg10Jr6gXS591lJwAyBsXbtxeYiQ9ZA0QOd/KkfOe5u0sKQVoNGsjftZa/SLlw+rT+45Td3ippx7PRcngA/QsxIsL8owLos8fOmSN5Df/+K2/ugAFyM36xGkeeTLtMez3jj33NmvoAfoDknWjnpwV4xlwYQH54jdWKaWuAjOe5YoX+H+mtW3o+hlZzk3aocEBPKk4bZBmfDxki18m4v/Y4IEDfNqtaoLQ1QGkZAyDth9BI+0HS8jKCgvSanzp1zEFHiRL6dn5+UkZjbZXxM6DRckdiY/XgU/ux0wbWyigAN74Pxqk9MhMdDfz6qzyuVMmcpG1MPteuf1qhoVLrkpoqNShazlPaH/K0AZBW1vBwCY4B2f7gQX37tMne2nXo1k3y6pKS9Newdy2zCmbTliVtgrr2PLPOA/aOk5qqT5eStuyA+VqcOWPunXn0qHky3IzKbiyDFkxnNnaVgzAAIofTWhwiIjIepLB588zzAPPctWvSQ+fjj/P/tVJSpNlm2DD50t67V37k16zJ3fGSk4GePc3NRGmDmdRUSRgtX16+xL/4QtYtXiyvfeqUvo8WsDzyiHzZL1qkfyEaf9A1nTvLD+elS9LUAEhTQ+nS8oPbrZssCw62n5CpHVML2NL+2GsBFqB3A9y6VU8ODgsz71OpkvnHL22isPHaLF1qfq2ff5Z77YfJ+AM1b54EL1qZP/jA/BpaGTw8pIti2v3tPX7sMYn4M5pPy9irzZ6QELlPTjYPZqjRgjvtulss+rEqV5ZaLk2JEvp2jRpJTYX23uzbp9cqGD8DxYrpPdNq1ZLPl/ZjqiUoG6/3qFFSC2NsUtK2+eknqe41BkyxsfL5LF9ebnXryr733CMTuQIyEW358hIc798v1cb9+tm/Xi4u+vtk7NF1/Li5aSYmRiYfbNpUkoGN70OJEhIgAuYOAcZy37qlN001a6Z/mWnbaAGB8VoaA6B58+T90c67fHlg6FD5GzEGi9rntWZNuX//fb1LvbZs+3bgyy/l7758efnbTHv9MxrGADAP1zB7tpyz9h2xbZs5cd54DadOlST/6Gj9Wmjl+fFHqQXSmpadJV+zkQopJkHnLatVck01Q4dKvtzIkc4rUzq//CKFKlUq35P01LJletLgli1K9eghjzt3zt3xtERZQJJklVJq2jR9mYuLJFYaEzJ9fZWKjZUB+QClJkxQqmNHefzyy/rggoBSVaroictLltgvwwcf6NtbLEr99Ze+bsMGSfJ8+mn7+x47Zi7bzJnm9QkJkvRavry8vnFbLTlYG0DRXkKwdr0bNpTnWqJ0Zrc1a/SyeXlJkrNSemL0jz/KYHlVqypVpoxSiYmSKAwo9dxz5tdv2lSpoCBztv+335pf75ln7F+boCBZv2OH/fVKKVWypGyzd2/6dW3a6OXVPPusntisJdBq7/uPP8rjiRNlW6tVqdKlzYnSaT36aPrr16KF/r56eclxjAMN/v23PlAkoNRXXynVpIk8HjpUP/YPP9h/f+bNk2tepkz6dcOGZXytlNIT1++5J+P3v107pV57TR5366bUm2+a31vjdbOX7P3ff7LMz08Snu+/X57PmSPXIiBAnu/cqe8zfbos69RJPjP2yrV1q/3lCxear8U99yj18cfyuH17fcBM7bZpk560D8j3hcbYGQGQAUY12gi1Tz1lvxyNG+vbVq4sy3r2NG8TFqZU8+bmz1keysnvNwMgOxgA5a2HHpK4YtUqeV6zpnz2f/nFueUy+fxz/Q/01Kn8fS1jN7euXfVgIyQkd8fTehUBSo0eLctGjzZ/6Ywapf8wab1ZjD9c3bpJMAFIQHX6tARNxt5cgFInT9ovQ2qq9HrZtk2pEyfSr79wwRwFG1mtem8jQKl169JvExcnvct27jSXp2NHWT9lir7s1VfN+2q9cQIC5HloaPov7rQ/OJcv6/vHxCh144Z+nqdP6+vi4+WmiY6WXkpGCQkSbKY95wMH5McFkOt87Zp5m2vX9PJcvWr/2imlj+q8enX6ddp7arymKSnSy0cp+aHXXmP8eFl2+rT5nwAtMAakx09ayclKbd8u7/22bUr9+69cr+Rk/Uf23DmlXnkl/edRu73wglKenvLY11e/Fto+jz+uH9846vTVq/rybdukV1LaEcTTGj5cjunqmnEAVL++/sNdrpw+8vWkSXIM7Z8W4+2TT/TX0N7XNm3k+dNPy/Nx4/TA0MNDqaQkfR+tV1ydOvq1+OUXOS/tbzbtddNuZ8/q1+Lff+Uzt26deZsqVZRq2dL+cd54Qy/Hb7+Z12nnnJqqB1JpRzbXrmWZMvpnzPhPFCC9SC0W/bmbm4zknsfYC4yc4tYt4NNPzc3HZ85IDuXly9JSMnSo3lHHmNrhdJn1YslLZ8/qs78Cks+gVSGnHT8lu4zV1bNmpa/eBvQE41atpNcJIM1fmm3b9Kr1SpWAsmWlmUHrYQVIsm5G7ZIuLpJT0rix/TFFgoLMuUBGaXNl7DX3+PlJb7G0TWhac0bafBx728TGyvXV8hS0JgLA3MwWHm5OTC5VSs+VcXGRa6MpXlxumuDg9KNx+viYk0gBOecaNWRes8qVpQk27XxlWrNEiRJ605s9meWOpM0BAqR82vPSpfXlWt5U2bLmJkR7eUxG7u6yvHFjuUVEyPVyd9c/LwcOmMe70T6PmuXL9STr69elpxugf7YffFA/vtZbCpDroi1v3FjmR8tqzCvt86A1udoTE6P/DZ0+rU9EmHZ8KUD/HNlLBNeulzEPTft+qVtXmks12vu4d69cCz8/GSK/cWNpRgP062b87IaEyPupXQstt0BLttcMGaL36kt7/e2V3XgtAOn5eP263jPFmL9Qq5bcawOUauNXGbVuDVSvrj/v2lVvwnUSBkCUbVarfGdkNAfgjBnAq6/KuFlz58oyLV9OyzvU8vPq1pXflQLDGDDYSxo8fz59oqDm+nXpmvv77/rNmBhopLWht2xp/iK399pRUeZjare//jK/CcaALTpafkzS5vNozxs1ksTktD8S585JHombm/kH3hgY2EuAzitaToy7e+Y9REqXNgcY9n6Q0gZAxjwVbYRnPz+ZLkPTs6ee12EvUTu/uLjoAelXX5nfZy2/I6P8H40xALp2TXJWfv9dhlnXEnMzGgwvbQ6QPRnlMWWHVvYPPjBPL5HR51Mzfbp8xu3lytyptNfTGJhrwfulS/YnubX3eevZU98mNlauvzbVi3a9jHlo9hKggfTvUcOGei9M7fy1crRqpQevGV0bf389YdvNTf7u0x7HeP2vX5c8RC0XUTu+FgBp5daCTGPeXYMG8t2Qmpr+2mkiIsznbPxucRIGQJQtO3bI96Obm/zDO2mS1GMaTZsm98nJMrDqrFlS+wNIfu4PP8hArK+/Lh2GCpTMaoASEuSLo25d+/M3DRwoyX6dO+u3e+7RpxPQWK36aLJDh+o/fMWL6wmj2mvHx8uXivGY2q1NG0lq1LbTklO1+USmT9f/o0s7onNEhAQDWmJy8+bmQKxCBXOAERGh/yeZlz9CaWnHTvv6abm4mKeD0H5YKlTQfyzs9ZjSttM+kMYeTlWqyH/PjjhPewYOlD+sHTvM7/O4cbI+ox5gGmMA9MQTQMeOsr825o2vr7mWyig7AVBWNUCZ0a67lnA8aJB5fdrPpzZq+T//6AGcl5dew5AX0gZAxjJoI1YnJ+td3I3S1jiWLi01G4D8zT38sFz/3btlmXa9tO1PnNDH5kl7LUuX1j/DgDlYSLtt5cr6+szeE21dt27yd5N2W+3cT5yQz2H79vrQCA8+KPdaAJS2I4TxOpYrpwdMxiEqjLXBjRrp+5Yvr8+L50x53gB3FyjqOUAHD0pOnTENoG/f9M3OTz2lN2H/+6/erP3MM3rKhTYIrzHXr8CxWmUEZO3EgoLMJz9zpr7u44/T76uN4Fq3rrRzayeddgTc1atlub+/tNHHxcmFnT1bn95ey7GYOlXfNiJCv1WvrrfnW6367Odly0puBGBObhw/Xn/s76+f18GDMgLsP/+YZ/Zu1y799dm4UXKV8jM36vp1SV6eMyfrbY0jUhuTg999VxJ87SWxv/WW+dr06CGv2a+fUosWyTbr1kle1MWLeXJKOfLJJ+b3Wbu1bGlOKLdHy1/r2VPP2apXTz/Gp59mvK8xd+rvvzPebswYPUcoJ7Ztk8TZiAjJJbp40ZxUbfx8AjJStZZfo432nNczysfFmV9zwgT98SuvmL8LjLeSJfVj3LghoyxPn67n9GifLVdXOd/nntM/ixcvmo9lsdjPlTPmp/3wQ8ZlXrhQ/i579LA/arvm33/lO0XLm0qbb2c8d638jRrJKNfz58vzFi1k3/vuk+czZsjzF1/U9/3qK72jxIoV+t/bsGHyGiNHymvHxMjf+fLluXzzssYk6DtUlAOg1FQ9eV/rGBATo3fYWL9eRr/X/lbuu0/yRbWgp29fyX8zznZQpkz+d6y6I+fP619KWjKfliSqlN5jAZAAxHgyx4/Lcnd3PRrUEgzT9urq1UuWDx+evgx//aUHMkrpP/Jpf7yuXdMTEdet0wOnbt1kvdbrB5DM86VL9ef33Wf//I1J1MYeOAWVMYk8bXJxRtL2NEubKF2YLVgg5xQSIvdeXlknAqfdF7Dfiyw/GJOqFy6UXkHa84MH5QfU+F5l1asrN0qVkmOXKGHulTlpkt4zUvu71h4bezgZGZO9tUA0LavV3BOrfXv7xzIG9wcOmNdpidCABDa5pQUygHw/GKfk0JK2lZIve0AS6Y2Bk/bfrLHjyOLF+vs6Y4b+H/NHH+W+nLnEJGjKtchIfX7JN9+UoUW0wVUbNpTWlxdflNxdX18Zuy4szDz+mKsrMHmyfsyHHsq/1JE8oVXXlitnHjsDkPFPNm+WankfH5lGQRvICNDbxY0JjVrT1sqVet7QpUv6mDP22r61NvQzZySHYPt283gyGl9fPTF5+vT01dLGY6cdnC+jqvKsEpALGq3Jp0SJ9MnFGalUyZzzk1WzUmGiXQ8t7yw7icCa7DSB5TXj500bQRuQz3bVqtI0Yky2z48mSWNTlvEzHxpqviZaM5Bxn7SMyd6A/b9v45QsGW2jvT4geWtVq5rXGZvE7uTzm/Y4aQfw1GjXQUsIj4uT76TatWV52uumJdYbm8AK+PcJAyAyMY5yHhMjv+X/93/yXPtdByTFYNMmaeJNSpL0lkaNZCxBQHL0nnlGmrQHDMijwq1aJdFXZiOIbtwoQ95rs28DMnLvqFH6SMNbtsh0At27y3QNWg5NxYr6l+2bb8p6bd6frl2BPn3k8ZAhkvj499/2ExqrVpULYbXKMbp3lwH8bt2S6ezr109f7uLF9Vycp56S+0ceMX8Za7QvqUWL9B5l2us/+qj+Q1axYvqpD+xJ+4NU0Nmb1yo70gaHd4u0ybM5SVS21wssv6UNuLXPXIMG8oXh6qr/DQD5k5Suvf8VK5rzVNIGQA0b2k98Tks7hwoVzIG2vdcsXVpyhezR3suGDdPnwmnXzd//zt6rtH/vWtlLlJDvD4322bh6Ve8FV6+enjht/K4IDdXLfu6c/SlcCiIH1EgVOkW1CeziRb3GV5tMWLt5e9tvbUhOVurIEbmlHebFajUPkXLHtAGEMpvdvFkz2ebdd/Vln34qy+rUkecPPmg+OW0clUGD9MHI0t5WrUo/CFmNGjJAHiAzThtp7edpb1r7uT3aWCHazd54OErJhTVWlXt4yDg7mldfleUffijPK1WSKnp7OQeaunVln/37M96moNCu7VNP5Wy/mzelucXdXamoqPwpmzNcv27+3Hz7bfb3vXpVPj8hIY5rpz5zRsaA0drGtQH7tDGslJL3x8tLmqqMY+XklfffN39PVKkifyNRUeaExxkzlBowQB7PnZvx8bSBOLW/OXveeUe2efPNjLfRvniN4/JotMFMW7XK1ilm6MQJOdeKFeX5Rx/Zbxa+dUu/DtrAh8YBOxMSpFnPz0/+trTvzlq19P0yG78qnzAH6A4V1QBI+x7Smro//1zy1R59VAZedar4eH0QLW1grrSSk/UBxMLDJaHJatUDJxcX+bHQkpbbtjX/cLz7rhxj4UJJgNJuxoS9yEilvvlGT3TWcoa2bDGXxWqVQM14nEWLpEwZuXJFkoCnTVNq5crMr8fp0/JDN22afDGmvQ7Ll+uD9x0+nHmCq1KSy/THH5lvU1DcuiWDtRkHK8yuY8ckkL3b+Pnpn+PMRo22Z8sWxwe+mzbpOS43bsiAf2kHgty1K//KlZAguT8JCfL8yBH9b+SFF8z/hFy+rNSvv2aeVxUfr9Tvv2f+952QIPlNmR3nxg0pV9proVm92jwQZ279/beeGH3jhnxf2BuoVPuuLFtW7r/5xrx+5049J8j4H7SWX+UEDIDuUFEMgKxWvYNR2s94gfD33/ofVtqpBjTa8PPGWpsNG8zLtKH+3d3lS884Mqmx10VWBg3S93N11YMNImfQ/njTji5MOWfsGZXRyOdFhTHxOjvJ1489pm8bEeGYMqbBJGjKsY0bJb/Xx0dPdSlQjAMEZjS7edrxe77+Wk9g0hhHYqxSRcbv0eSkvdqYEFW7tnlWbSJH0/Iv0o4uTDmn5b6kHRS0KDLmQ7m7y2jvmTHm2RWCfEIGQEVQfDzw3HMyIKFSskyLC/r00QfELVCMwU1GAZAWJGlJiEuXyqzDxmVaInRGvaayq3lzfXA2R44cTGSPFgDxs3jntB/98PDMB+UsCoxJ8nXqyJD+mbn/fvvT0xRQDIAKsdRUYNeuzKe0SevUKZmF4euvgY8+AhYskAFPtWmICsDo5PYZa4BOnrQ/H4cWJA0aBPTuLT1KXFykH/7w4eZttR+Krl0lOOrRQ+aryi6LRYb3L1MmD7u5EeXSI49IEKT1WqTcu/de+RF/4glnl8T5jDVA2RmOwMVFvhfLldOnCCnALEppdQCkiY+Ph7+/P+Li4uBXIKtDpGdiz54ytdGkScDIkVnvk5QkvRgPH5bazFu35Pf7nntkXszatYE9ewrgmD2JidJN3Bj0nD5trp5OTZVtbtyQiRdr1DAf4/Rp81gdW7YATZvmb7mJiAqz11+X/5QBYMoUaToo4HLy+80aoELoyhVpgdHmdVywIHv7ffqpBD+hoTK+X8WKMjn54sUSEE2cWACDH0Dm1bFaZS4b45w6RocOSfDj62t/ktGyZfX/Zlxd9Tl/iIjIPmMN0F3YvMoAqBD67jv5vdea/f/9VwbfzMyZM8B778njjz6Ssfq++EKelywpEwB37Zp/Zc6VmzdlSOn//U+eR0SYZ1U20pq/tMHU0rJY9D9gJi0TEWVNywG6S/9pZABUCGkzMbz4ItC4sTz+/feMt794UQY+TkyU/J9+/WR5167Azp3SYtSmTf6WOVd+/FFOUptCokmTjAOg9evlPrP/Upo1M98TEVHGypSR+7p1AW9v55YlH2RzwhgqKJSSGRgAydVLSpIaoN9+A1JSgKgo4K23ZCaId9+VmqEtWyQFJiBAkp+NzVwNGjjjLLJp/365b9RI5uR54QW9u5qxCezaNb0d0DiUe1ojR0rNz6BB+VNeIqK7yX33AR9+KPd3IQZAhczRo1Kj4+kpFSLe3sD48cDPP8sNAFavlnm8jDFClSoSJFWv7pRi545WyzNoEPD88/LYXg3QvHlAQoIkPt97b8bHK14cGD06f8pKRHS3cXOTROi7FAOgQkar/WnSRIKghg2BkBCZCNrVVXKAt22TbSpVkrk+ixWTXt4BAU4rdu7Ym1HYXgCk1Qo9/XQBzeImIqKCxuk5QFOmTEF4eDi8vLzQrFkzbNVmnc3A559/jurVq8Pb2xvlypXDK6+8gptpZgfP6TELtK+/lsGlrl4FoOf/aBUdLi4SoFerJhODb9sm3dq7dJGmr+eflwoUpwQ/8+dLQc+e1ZfFxsq4O199lfm+SulVWMYRRbXH589LFnhIiLQBursD/fvnafGJiOgulu8Tc2Ri/vz5ysPDQ82cOVPt27dPDRkyRAUEBKgLxpmtDebOnas8PT3V3Llz1YkTJ9SqVatUaGioeuWVV3J9THsK1Fxg2iR0M2cqpfSpWYzzcxZYDRumnyF59mxZFhaW+b4xMfqcMomJ+nKr1TzbsHYbNCh/zoGIiAqNQjMX2KeffoohQ4Zg0KBBqFWrFqZOnQofHx/MnDnT7vabNm1Cy5Yt8fjjjyM8PBzt27dH3759TTU8OT1mgXbxovRfB4Dt23HqFHDkiLTytGjh3KJlKSkJ2LtXHhtHcdYenzsn7XYZ0Wp/QkPNvQ8sFqnx2bVLv+3dqzeDERERZYPTAqDk5GRs374d7bQ5mgC4uLigXbt22Lx5s919WrRoge3bt9sCnuPHj2PFihXo3Llzro8JAElJSYiPjzfdCoQ0gcMbb8jDNm0KQT7P3r0y1DRgnsfL+Nh4fmnZy//ReHsD9evrt9q1OWcPERHliNOSoGNiYpCamorg4GDT8uDgYBw8eNDuPo8//jhiYmJw7733QimFlJQUPPvss3jzzTdzfUwAmDhxIiZMmHCHZ5QPDAFC6o5dWLg5FRaLKz75JAfHUArYulXGx3HL4u2+eVO6njdsaE4mPnIEKFFCRgVNSgL+/FO2DQqShCMAOHZMsq1DQtKVHceOSe5P8eJSY6PZvh1o21aOpwVLgAQ0mQVAREREd8jpSdA5sX79enzwwQf4v//7P+zYsQNLlizB8uXL8e67797RcUePHo24uDjb7fTp03lU4jtkqC1xTbqBGjiIoUOzNyedzWefSZByO0jM1PjxEigZm5OiomQW4Pvuk+ko3n4b6NAB6NZN5uNYuVKClTp15Lm9Wh9ARlw8fFi6q2t27JAs7c6d5XjarVEjfX9jAjQREVEecVoNUGBgIFxdXXHhwgXT8gsXLiBEq0VI4+2338aTTz6Jp59+GgBQt25dJCQkYOjQoXjrrbdydUwA8PT0hKen5x2eUT64XYti9faBy41ENHHZgffeq539/a1WmUoCkKBmwoTMR/Ncs0buv/hCpoW3WCRwSU6WJq0VK4AZM2SbsDDJ45kyRUYJvXlTZmn/7Tfpc6/VAPn4yBDUO3botUPaso0bAa25sXFjqaE6fFgmO1u8WJazBoiIiPKB02qAPDw8EBERgcjISNsyq9WKyMhING/e3O4+iYmJcEkzz5Pr7dwPpVSujllgXbkiAQWAE417AQA6lt5umpsuS5GRtmMgNlYPKuxJSpKp4AFpBtNypozj7Tz9tJSrfHlg7VpZtmIFMG2avs306VILtHu3PO/dW+63b9drdXrJ+eDSJXnd+vWlmW7zZuCdd2SdUnLPAIiIiPKBU5vARowYgenTp2POnDk4cOAAhg0bhoSEBAy6PVVB//79Mdowcm/Xrl3x9ddfY/78+Thx4gTWrFmDt99+G127drUFQlkds9DQalAqV8Zfbg8AAJq4ZZI0bI/WlKVlTGfWU2rfPnMejr0pJ7SataeeAmrWBFq3llqmy5cBf39Zt3Kl3JKSZJkW7OzYoZ9T27bmGdu12iZAJioz1lKxCYyIiPJDvnfKz8LkyZNV+fLllYeHh2ratKn6559/bOvatGmjBgwYYHt+69YtNX78eFW5cmXl5eWlypUrp5577jl19erVbB8zOwrEOED/+5+Mb9Orl3q48l6lAJXs5atUaqpSsbFKPfecUn//nX6/1FSlRo9WqlMnpdzd9UGDXFzk8cGDMpbOO+/INp07K7VggVLTpsn64GC59/aW1+nSxTzejsWiVFSUvNb33+vLR41S6r77zMdo21apCxf0/by85PF//ynVp4889vJSKs37p/r3l3UeHnI+RERE2ZCT32+nB0AFUYEIgJ54QilAJbz1vnJBiopDcQkK1q9X6u235XHz5un3W7vWHLBo2zz0kDwfOVKpLVvM2/j56UHHqFFKVasmjxcvVqpmTXncr5/cd+umv1ZiolJBQRJoHT6s1E8/mY/79tuyXfXq+rJSpZRKTlbqm2/k+dNPpz+HzZslYLrnnjy/rEREdPfKye835wIrqG7n3uy/VQVWuGKVf2/0ivsWmDpVnxBs1y6ZAt7YvV1ruuraFejZE2jfXp4PGSIJynPmyACLgMywfvgwcOoUMHeuLIuIkJlUDx+WnB2tCWzCBJlTIyJCfy1vb2DTJpmNvWpVmXF1+XI5vo8P8NBDst3KldLVXSmgWTOZtmLwYGneatUq/bnfc4/kBIWG3vl1JCIissOilJZtSpr4+Hj4+/sjLi4Ofn5+zilEWBhw/jy+fHIbXvq+Md57eCveWtYs/XZ798q4OYAELmXKSK+tnTuBBg307VJSgAoVpOeW5s8/ZXKxt97Slx05ItPJDx8u++/aJROO3bwpgQsREVEBlZPf70I1DlCRceOGTPYJYNVhSQIu26MJUK9e+m23b5ealXPnZOLU5GTpUm4MfgCpJTImglerJrUvAwfqoyj7+UmvK22gIW3QwnLlGPwQEdFdhQFQQaR1Xffzw/YTJQEAdetZpBlLozVtaYMJlikDjB0ry26Pk5TOU0/pj59+WnpehYXJ1PGABD4uLtIt3Ti1BLuiExHRXYY5QAXR7fyf1PBKuLBbuodXqgSgSn/J1albV2pvVq+W8XiOHJH9PDyAWrWAxx+3f9xKlYAXX5S8ncGD9eVjx8rYP888I8+9vaWbuzaZKQMgIiK6yzAAKohuB0AJpaX5KyBAG8rHTx+gcN8+871x+ojMfPFF+mUREXoQZVymBUAci4eIiO4ybAIrYC5cAFZOlZ5Xl4pLzYvdCpgaNcwDBmbU7JVbxgnHWANERER3GQZABcn8+VjzyW7c3C81QFFuEnjYrYBxddUTnX18Mm72yi1jAMQaICIiusuwCayg2LUL6NsXXX1CcBYlAACHb0ngkWEFzD33SJPYY4/pU1HklQYNpOdXaqqM70NERHQXYQBUUBw9CgDwT4yGP6IBAL8fyqQGCADefBMIDgaGDs378vj6Aj/9BCQkIGczsBIRERV8DIAKitvj/missGDlwQoAMqkBCgwEXn89/8rUrVv+HZuIiMiJmANUUBhHaAZwDmFIghcApuAQERHlNQZABUWaGqDjkGofi0VmsCAiIqK8wwCooLgdAP2IvkiBK9bgQQAywLOnpzMLRkREdPdhDlBBcTsAmo2B+LDCVOw/XRywcggeIiKi/MAaoILidg7QeYSibC0/1KwlU2Aw/4eIiCjvMQAqCJKTgcuXAUgAVL480LSprKpe3YnlIiIiukuxCawgiJZxf1Jc3HHZWgoVKgD9+sn4g9r8pERERJR3GAAVBLfzfy67hwJJFpQvD5QvD4we7eRyERER3aXYBFYQ3M7/OYdQABL8EBERUf5hAFQQ3K4BikpmAEREROQIDIAKgtsB0FkVChcXICzMyeUhIiK6yzEAKghuB0DnEYqwMJmEnYiIiPIPA6CCwJYDFMaJ14mIiByAAVBBYKgBCghwblGIiIiKAgZABQEDICIiIodiAORsKSnAxYsAGAARERE5CgMgZ7t+HVAKAHAVJRgAEREROQADIGe7cQMAYIUFyfBgAEREROQADICc7XYAlOzqDcDCAIiIiMgBGAA52+0AKMnFGwAYABERETkAAyBnS0wEANyw+ABgAEREROQIDICc7XYN0A2wBoiIiMhRGAA52+0AKMHKAIiIiMhRGAA5mxYApUoA5O/vzMIQEREVDQyAnE0LgBRrgIiIiByFAZCz3Q6AEuEDiwXw83NyeYiIiIoABkDOpvUCgzf8/AAXviNERET5jj+3zmboBcbmLyIiIsfIcQAUHh6Od955B1FRUflRnqKHARAREZHD5TgAevnll7FkyRJUqlQJDz74IObPn4+kpKQ7KsSUKVMQHh4OLy8vNGvWDFu3bs1w27Zt28JisaS7denSxbbNwIED063v2LHjHZUx3zAAIiIicrhcBUC7du3C1q1bUbNmTbzwwgsIDQ3F888/jx07duS4AAsWLMCIESMwbtw47NixA/Xr10eHDh1w8eJFu9svWbIE58+ft9327t0LV1dX9OrVy7Rdx44dTdvNmzcvx2VzCAZAREREDpfrHKBGjRrhyy+/xLlz5zBu3Dh8++23aNKkCRo0aICZM2dCKZWt43z66acYMmQIBg0ahFq1amHq1Knw8fHBzJkz7W5fsmRJhISE2G5r1qyBj49PugDI09PTtF2JEiVye6r5y9ALjAEQERGRY+Q6ALp16xYWLlyIhx9+GK+++ioaN26Mb7/9Fo8++ijefPNN9OvXL8tjJCcnY/v27WjXrp1eIBcXtGvXDps3b85WOWbMmIE+ffqgWLFipuXr169HUFAQqlevjmHDhuHy5csZHiMpKQnx8fGmm8OwBoiIiMjh3HK6w44dOzBr1izMmzcPLi4u6N+/Pz777DPUqFHDtk2PHj3QpEmTLI8VExOD1NRUBAcHm5YHBwfj4MGDWe6/detW7N27FzNmzDAt79ixIx555BFUrFgRx44dw5tvvolOnTph8+bNcHV1TXeciRMnYsKECVm+Xr4wdIMvHeCcIhARERU1OQ6AmjRpggcffBBff/01unfvDnd393TbVKxYEX369MmTAmZmxowZqFu3Lpo2bWpabnztunXrol69eqhcuTLWr1+PBx54IN1xRo8ejREjRtiex8fHo1y5cvlXcCPWABERETlcjgOg48ePo0KFCpluU6xYMcyaNSvLYwUGBsLV1RUXLlwwLb9w4QJCQkIy3TchIQHz58/HO++8k+XrVKpUCYGBgTh69KjdAMjT0xOenp5ZHidfGAIgzgNGRETkGDnOAbp48SK2bNmSbvmWLVvw77//5uhYHh4eiIiIQGRkpG2Z1WpFZGQkmjdvnum+P/30E5KSkvDEE09k+TpnzpzB5cuXERoamqPyOQSToImIiBwuxwHQ8OHDcfr06XTLz549i+HDh+e4ACNGjMD06dMxZ84cHDhwAMOGDUNCQgIGDRoEAOjfvz9Gjx6dbr8ZM2age/fuKFWqlGn59evX8dprr+Gff/7ByZMnERkZiW7duqFKlSro0KFDjsuX79gERkRE5HA5bgLbv38/GjVqlG55w4YNsX///hwXoHfv3rh06RLGjh2L6OhoNGjQACtXrrQlRkdFRcElzQRZhw4dwoYNG7B69ep0x3N1dcXu3bsxZ84cxMbGIiwsDO3bt8e7777rvGauzDAAIiIicrgcB0Cenp64cOECKlWqZFp+/vx5uLnl+HAAgOeffx7PP/+83XXr169Pt6x69eoZjjPk7e2NVatW5aocTmHoBcYAiIiIyDFy3ATWvn17jB49GnFxcbZlsbGxePPNN/Hggw/maeGKAsUkaCIiIofLcZXNxx9/jNatW6NChQpo2LAhAGDXrl0IDg7G999/n+cFvNupxBuwAEh1Zw0QERGRo+Q4ACpTpgx2796NuXPn4r///oO3tzcGDRqEvn372h0TiDKhFCw3pQaoeLAPXHI9LjcRERHlRK6SdooVK4ahQ4fmdVmKnuRkWG7nMpUq6+3kwhARERUductahvQGi4qKQnJysmn5ww8/fMeFKjJu5/8ADICIiIgcKVcjQffo0QN79uyBxWKx9cayWCwAgNTU1Lwt4d3sdgCUCheElGPzIRERkaPkOOvkpZdeQsWKFXHx4kX4+Phg3759+Ouvv9C4cWO7XdYpE4Yu8GXLWZxcGCIioqIjxzVAmzdvxh9//IHAwEC4uLjAxcUF9957LyZOnIgXX3wRO3fuzI9y3p0M02CUKePkshARERUhOa4BSk1NRfHixQHIZKbnzp0DAFSoUAGHDh3K29Ld7QxjAJUt6+SyEBERFSE5rgGqU6cO/vvvP1SsWBHNmjXDRx99BA8PD0ybNi3d6NCUOW0MoBvwZg0QERGRA+U4ABozZgwSEhIAAO+88w4eeughtGrVCqVKlcKCBQvyvIB3s7joGwiABECVCuBE9URERHerHAdAxhnVq1SpgoMHD+LKlSsoUaKErScYZc+VsxIApbh7w8PD2aUhIiIqOnKUA3Tr1i24ublh7969puUlS5Zk8JMLseekF5jy4hhAREREjpSjAMjd3R3ly5fnWD95JP7C7YEQfXycWxAiIqIiJse9wN566y28+eabuHLlSn6Up0i5fkkCIDdf1gARERE5Uo5zgL766iscPXoUYWFhqFChAooVK2Zav2PHjjwr3N0u4fLtAMiPARAREZEj5TgA6t69ez4Uo2i6eUUCIE9/BkBERESOlOMAaNy4cflRjiIpOU4CIO+SDICIiIgcKcc5QJQ3lNIDoGJBTIImIiJypBzXALm4uGTa5Z09xLLn6lXAPUW6wfsFsQaIiIjIkXIcAP3888+m57du3cLOnTsxZ84cTJgwIc8KdreLigK8ITVA7kyCJiIicqgcB0DdunVLt6xnz56oXbs2FixYgMGDB+dJwe52xgAI3gyAiIiIHCnPcoDuueceREZG5tXh7noMgIiIiJwnTwKgGzdu4Msvv0QZTmmebQyAiIiInCfHTWBpJz1VSuHatWvw8fHBDz/8kKeFu5udOmUIgDgVBhERkUPlOAD67LPPTAGQi4sLSpcujWbNmqFEiRJ5Wri7WVQU4APpBcYaICIiIsfKcQA0cODAfChG0cMAiIiIyHlynAM0a9Ys/PTTT+mW//TTT5gzZ06eFOpul5wMnD8PFMc1WeDv79wCERERFTE5DoAmTpyIwMDAdMuDgoLwwQcf5Emh7nZnz8pI0P6IkwV+fs4tEBERURGT4wAoKioKFStWTLe8QoUKiIqKypNC3e1OnQLckQxv3JQFDICIiIgcKscBUFBQEHbv3p1u+X///YdSpUrlSaHudlFRgB/i9QUMgIiIiBwqxwFQ37598eKLL2LdunVITU1Famoq/vjjD7z00kvo06dPfpTxrmMKgIoVA1xdnVsgIiKiIibHvcDeffddnDx5Eg888ADc3GR3q9WK/v37Mwcom0wBEBOgiYiIHC7HAZCHhwcWLFiA9957D7t27YK3tzfq1q2LChUq5Ef57kqxsUyAJiIicqYcB0CaqlWromrVqnlZliIjNdVQA8QAiIiIyOFynAP06KOP4n//+1+65R999BF69eqVJ4W626WkGGqA2ARGRETkcDkOgP766y907tw53fJOnTrhr7/+ypNC3e1YA0RERORcOQ6Arl+/Dg8Pj3TL3d3dER8fb2cPSis1lTVAREREzpTjAKhu3bpYsGBBuuXz589HrVq18qRQdzvWABERETlXjpOg3377bTzyyCM4duwY7r//fgBAZGQkfvzxRyxatCjPC3g3YgBERETkXDmuAeratSuWLl2Ko0eP4rnnnsOrr76Ks2fP4o8//kCVKlVyVYgpU6YgPDwcXl5eaNasGbZu3Zrhtm3btoXFYkl369Kli20bpRTGjh2L0NBQeHt7o127djhy5EiuypYf2ARGRETkXDkOgACgS5cu2LhxIxISEnD8+HE89thjGDlyJOrXr5/jYy1YsAAjRozAuHHjsGPHDtSvXx8dOnTAxYsX7W6/ZMkSnD9/3nbbu3cvXF1dTT3QPvroI3z55ZeYOnUqtmzZgmLFiqFDhw64efNmbk43z7EGiIiIyLlyFQAB0htswIABCAsLwyeffIL7778f//zzT46P8+mnn2LIkCEYNGgQatWqhalTp8LHxwczZ860u33JkiUREhJiu61ZswY+Pj62AEgphc8//xxjxoxBt27dUK9ePXz33Xc4d+4cli5dmtvTzVOsASIiInKuHAVA0dHR+PDDD1G1alX06tULfn5+SEpKwtKlS/Hhhx+iSZMmOXrx5ORkbN++He3atdML5OKCdu3aYfPmzdk6xowZM9CnTx8UK1YMAHDixAlER0ebjunv749mzZpleMykpCTEx8ebbvmJNUBERETOle0AqGvXrqhevTp2796Nzz//HOfOncPkyZPv6MVjYmKQmpqK4OBg0/Lg4GBER0dnuf/WrVuxd+9ePP3007Zl2n45OebEiRPh7+9vu5UrVy6np5IjDICIiIicK9sB0O+//47BgwdjwoQJ6NKlC1wLwAzmM2bMQN26ddG0adM7Os7o0aMRFxdnu50+fTqPSmgfm8CIiIicK9sB0IYNG3Dt2jVERESgWbNm+OqrrxATE3NHLx4YGAhXV1dcuHDBtPzChQsICQnJdN+EhATMnz8fgwcPNi3X9svJMT09PeHn52e65afUFMUaICIiIifKdgB0zz33YPr06Th//jyeeeYZzJ8/H2FhYbBarVizZg2uXbuW4xf38PBAREQEIiMjbcusVisiIyPRvHnzTPf96aefkJSUhCeeeMK0vGLFiggJCTEdMz4+Hlu2bMnymI7ilnITHrglT1gDRERE5HA57gVWrFgxPPXUU9iwYQP27NmDV199FR9++CGCgoLw8MMP57gAI0aMwPTp0zFnzhwcOHAAw4YNQ0JCAgYNGgQA6N+/P0aPHp1uvxkzZqB79+4oVaqUabnFYsHLL7+M9957D8uWLcOePXvQv39/hIWFoXv37jkuX37wviW1P8piAW4nbxMREZHj5HgkaKPq1avjo48+wsSJE/Hrr79m2HU9M71798alS5cwduxYREdHo0GDBli5cqUtiTkqKgouLuY47dChQ9iwYQNWr15t95ijRo1CQkIChg4ditjYWNx7771YuXIlvLy8cn6S+UALgFJ9isPNJdcjERAREVEuWZRSytmFKGji4+Ph7++PuLi4fMkH6lbmX/xyrgmSgsvBMzoqz49PRERUFOXk95vVD07gk3K7BqgYE6CJiIicgQGQExRLkS7w1uJMgCYiInIGBkBOoNUAWX1ZA0REROQMDICcwDdVaoAUAyAiIiKnYADkBMVSb9cA+bEJjIiIyBkYADmBr/X2KNDFWQNERETkDAyAnKC49XYTGGuAiIiInIIBkBP4WWNvP2ANEBERkTMwAHKCcuoUAMBatpyTS0JERFQ0MQBygorqOABAhVdyckmIiIiKJgZADqauXUcQLsmTihWdWxgiIqIiigGQg1mPnQAAXEZJuJRgEjQREZEzMAByMOtRaf46jkpwdXVyYYiIiIooBkAOpk5IDdAJVGQARERE5CQMgBztOGuAiIiInI0BkINZbtcAMQAiIiJyHgZADmY5KTVAbAIjIiJyHgZAjqQUXKNYA0RERORsDIAc6cIFWG7cQCpccBrlYbE4u0BERERFEwMgR7qdAH0a5aDc3J1cGCIioqKLAZAjsQcYERFRgcAAyJHYA4yIiKhAcHN2AYqUjh0Rc80TiyfVZQBERETkRAyAHKlJE8QUb4KVk4AABkBEREROwyYwB0tNlXs3hp5EREROwwDIwbQAiE1gREREzsMAyMEYABERETkfAyAHYwBERETkfAyAHIwBEBERkfMxAHIwBkBERETOxwDIwRgAEREROR8DIAdjAEREROR8DIAcjAEQERGR8zEAcjAGQERERM7HAMjBGAARERE5HwMgB2MARERE5HwMgByMARAREZHzMQByMAZAREREzscAyMEYABERETkfAyAHYwBERETkfE4PgKZMmYLw8HB4eXmhWbNm2Lp1a6bbx8bGYvjw4QgNDYWnpyeqVauGFStW2NaPHz8eFovFdKtRo0Z+n0a2paTIPQMgIiIi53Fz5osvWLAAI0aMwNSpU9GsWTN8/vnn6NChAw4dOoSgoKB02ycnJ+PBBx9EUFAQFi1ahDJlyuDUqVMICAgwbVe7dm2sXbvW9tzNzamnacIaICIiIudzamTw6aefYsiQIRg0aBAAYOrUqVi+fDlmzpyJN954I932M2fOxJUrV7Bp0ya4u7sDAMLDw9Nt5+bmhpCQkHwte25pAVABismIiIiKHKc1gSUnJ2P79u1o166dXhgXF7Rr1w6bN2+2u8+yZcvQvHlzDB8+HMHBwahTpw4++OADpGpRxW1HjhxBWFgYKlWqhH79+iEqKipfzyUnWANERETkfE6rh4iJiUFqaiqCg4NNy4ODg3Hw4EG7+xw/fhx//PEH+vXrhxUrVuDo0aN47rnncOvWLYwbNw4A0KxZM8yePRvVq1fH+fPnMWHCBLRq1Qp79+5F8eLF7R43KSkJSUlJtufx8fF5dJbpMQAiIiJyvkLVEGO1WhEUFIRp06bB1dUVEREROHv2LCZNmmQLgDp16mTbvl69emjWrBkqVKiAhQsXYvDgwXaPO3HiREyYMMEh58AAiIiIyPmc1gQWGBgIV1dXXLhwwbT8woULGebvhIaGolq1anA1RA81a9ZEdHQ0kpOT7e4TEBCAatWq4ejRoxmWZfTo0YiLi7PdTp8+nYszyh4GQERERM7ntADIw8MDERERiIyMtC2zWq2IjIxE8+bN7e7TsmVLHD16FFar1bbs8OHDCA0NhYeHh919rl+/jmPHjiE0NDTDsnh6esLPz890yy8MgIiIiJzPqeMAjRgxAtOnT8ecOXNw4MABDBs2DAkJCbZeYf3798fo0aNt2w8bNgxXrlzBSy+9hMOHD2P58uX44IMPMHz4cNs2I0eOxJ9//omTJ09i06ZN6NGjB1xdXdG3b1+Hn589DICIiIicz6k5QL1798alS5cwduxYREdHo0GDBli5cqUtMToqKgouLnqMVq5cOaxatQqvvPIK6tWrhzJlyuCll17C66+/btvmzJkz6Nu3Ly5fvozSpUvj3nvvxT///IPSpUs7/PzsYQBERETkfBallHJ2IQqa+Ph4+Pv7Iy4uLs+bwz74AHjrLWDwYODbb/P00EREREVaTn6/nT4VRlHDGiAiIiLnYwDkYAyAiIiInI8BkIMxACIiInI+BkAOxgCIiIjI+RgAORgDICIiIudjAORgDICIiIicjwGQgzEAIiIicj4GQA6WkiL3DICIiIichwGQg7EGiIiIyPkYADmYFgC5OXUSEiIioqKNAZCDsQaIiIjI+RgAORgDICIiIudjAORgDICIiIicjwGQgzEAIiIicj4GQA7GAIiIiMj5GAA5GAMgIiIi52MA5GAMgIiIiJyPAZCDMQAiIiJyPgZADsYAiIiIyPkYADkYAyAiIiLnYwDkYAyAiIiInI8BkIMxACIiInI+BkAOxgCIiIjI+RgAORgDICIiIudjAORgDICIiIicjwGQgzEAIiIicj4GQA6WkiL3DICIiIichwGQg2k1QG5uzi0HERFRUcYAyMHYBEZEROR8DIAcjAEQERGR8zEAcjAGQERERM7HAMjBGAARERE5HwMgB2MARERE5HwMgByMARAREZHzMQByMAZAREREzscAyMEYABERETkfAyAHYwBERETkfAyAHIwBEBERkfMxAHIwBkBERETOxwDIwRgAEREROR8DIAdjAEREROR8Tg+ApkyZgvDwcHh5eaFZs2bYunVrptvHxsZi+PDhCA0NhaenJ6pVq4YVK1bc0TEdiQEQERGR8zk1AFqwYAFGjBiBcePGYceOHahfvz46dOiAixcv2t0+OTkZDz74IE6ePIlFixbh0KFDmD59OsqUKZPrYzoaAyAiIiLnsyillLNevFmzZmjSpAm++uorAIDVakW5cuXwwgsv4I033ki3/dSpUzFp0iQcPHgQ7u7ueXJMe+Lj4+Hv74+4uDj4+fnl8uzSUwpwuR1yXrgABAXl2aGJiIiKvJz8fjutBig5ORnbt29Hu3bt9MK4uKBdu3bYvHmz3X2WLVuG5s2bY/jw4QgODkadOnXwwQcfIPV2tUpujgkASUlJiI+PN93yg9WqP2YNEBERkfM4LQCKiYlBamoqgoODTcuDg4MRHR1td5/jx49j0aJFSE1NxYoVK/D222/jk08+wXvvvZfrYwLAxIkT4e/vb7uVK1fuDs/OvpQU/TEDICIiIudxehJ0TlitVgQFBWHatGmIiIhA79698dZbb2Hq1Kl3dNzRo0cjLi7Odjt9+nQeldhMy/8BADe3fHkJIiIiygan/QwHBgbC1dUVFy5cMC2/cOECQkJC7O4TGhoKd3d3uBqqT2rWrIno6GgkJyfn6pgA4OnpCU9Pzzs4m+wxBkCsASIiInIep9UAeXh4ICIiApGRkbZlVqsVkZGRaN68ud19WrZsiaNHj8JqSKY5fPgwQkND4eHhkatjOhIDICIiooLBqU1gI0aMwPTp0zFnzhwcOHAAw4YNQ0JCAgYNGgQA6N+/P0aPHm3bftiwYbhy5QpeeuklHD58GMuXL8cHH3yA4cOHZ/uYzsQAiIiIqGBwaiZK7969cenSJYwdOxbR0dFo0KABVq5caUtijoqKgouLHqOVK1cOq1atwiuvvIJ69eqhTJkyeOmll/D6669n+5jOZAyAXApV9hUREdHdxanjABVU+TUO0PnzQFiYBD/GYIiIiIjuXKEYB6go4ijQREREBQMDIAdiAERERFQwMAByIAZAREREBQMDIAdiAERERFQwMAByIAZAREREBQMDIAdiAERERFQwMAByIAZAREREBQMDIAdiAERERFQwMAByIAZAREREBQMDIAdiAERERFQwMAByIAZAREREBQMDIAdKSZF7BkBERETOxQDIgbQaIDc355aDiIioqGMA5EBsAiMiIioYGAA5EAMgIiKigoEBkAMxACIiIioYGAA5EAMgIiKigoEBkAMxACIiIioYGAA5EAMgIiKigoEBkAMxACIiIioYGAA5EAMgIiKigoEBkAMxACIiIioYGAA5EAMgIiKigoEBkAMxACIiIioYGAA5EAMgIiKigoEBkAMxACIiIioYGAA5EAMgIiKigoEBkAMxACIiIioYGAA5EAMgIiKigoEBkAMxACIiIioYGAA5UEqK3Lu5ObccRERERR0DIAdydQW8vABPT2eXhIiIqGizKKWUswtR0MTHx8Pf3x9xcXHw8/NzdnGIiIgoG3Ly+80aICIiIipyGAARERFRkcMAiIiIiIocBkBERERU5DAAIiIioiKHARAREREVOQyAiIiIqMgpEAHQlClTEB4eDi8vLzRr1gxbt27NcNvZs2fDYrGYbl5eXqZtBg4cmG6bjh075vdpEBERUSHh9EkZFixYgBEjRmDq1Klo1qwZPv/8c3To0AGHDh1CUFCQ3X38/Pxw6NAh23OLxZJum44dO2LWrFm2554cfpmIiIhuc3oN0KeffoohQ4Zg0KBBqFWrFqZOnQofHx/MnDkzw30sFgtCQkJst+Dg4HTbeHp6mrYpUaJEfp4GERERFSJODYCSk5Oxfft2tGvXzrbMxcUF7dq1w+bNmzPc7/r166hQoQLKlSuHbt26Yd++fem2Wb9+PYKCglC9enUMGzYMly9fzvB4SUlJiI+PN92IiIjo7uXUACgmJgapqanpanCCg4MRHR1td5/q1atj5syZ+OWXX/DDDz/AarWiRYsWOHPmjG2bjh074rvvvkNkZCT+97//4c8//0SnTp2Qmppq95gTJ06Ev7+/7VauXLm8O0kiIiIqcJw6Geq5c+dQpkwZbNq0Cc2bN7ctHzVqFP78809s2bIly2PcunULNWvWRN++ffHuu+/a3eb48eOoXLky1q5diwceeCDd+qSkJCQlJdmex8fHo1y5cpwMlYiIqBApNJOhBgYGwtXVFRcuXDAtv3DhAkJCQrJ1DHd3dzRs2BBHjx7NcJtKlSohMDAww208PT3h5+dnuhEREdHdy6m9wDw8PBAREYHIyEh0794dAGC1WhEZGYnnn38+W8dITU3Fnj170Llz5wy3OXPmDC5fvozQ0NBsHVOrFGMuEBERUeGh/W5nq3FLOdn8+fOVp6enmj17ttq/f78aOnSoCggIUNHR0UoppZ588kn1xhtv2LafMGGCWrVqlTp27Jjavn276tOnj/Ly8lL79u1TSil17do1NXLkSLV582Z14sQJtXbtWtWoUSNVtWpVdfPmzWyV6fTp0woAb7zxxhtvvPFWCG+nT5/O8rfe6eMA9e7dG5cuXcLYsWMRHR2NBg0aYOXKlbbE6KioKLi46C11V69exZAhQxAdHY0SJUogIiICmzZtQq1atQAArq6u2L17N+bMmYPY2FiEhYWhffv2ePfdd7M9FlBYWBhOnz6N4sWL2x1jKDe0vKLTp0+ziS0f8To7Dq+1Y/A6Ow6vtWPk53VWSuHatWsICwvLclunJkEXJTlJzKLc43V2HF5rx+B1dhxea8coKNfZ6QMhEhERETkaAyAiIiIqchgAOYinpyfGjRvHOcnyGa+z4/BaOwavs+PwWjtGQbnOzAEiIiKiIoc1QERERFTkMAAiIiKiIocBEBERERU5DICIiIioyGEA5ABTpkxBeHg4vLy80KxZM2zdutXZRSr0xo8fD4vFYrrVqFHDtv7mzZsYPnw4SpUqBV9fXzz66KPpJt2l9P766y907doVYWFhsFgsWLp0qWm9Ugpjx45FaGgovL290a5dOxw5csS0zZUrV9CvXz/4+fkhICAAgwcPxvXr1x14FoVDVtd64MCB6T7jHTt2NG3Da521iRMnokmTJihevDiCgoLQvXt3HDp0yLRNdr4voqKi0KVLF/j4+CAoKAivvfYaUlJSHHkqBVp2rnPbtm3TfaafffZZ0zaOvM4MgPLZggULMGLECIwbNw47duxA/fr10aFDB1y8eNHZRSv0ateujfPnz9tuGzZssK175ZVX8Ouvv+Knn37Cn3/+iXPnzuGRRx5xYmkLh4SEBNSvXx9Tpkyxu/6jjz7Cl19+ialTp2LLli0oVqwYOnTogJs3b9q26devH/bt24c1a9bgt99+w19//YWhQ4c66hQKjayuNQB07NjR9BmfN2+eaT2vddb+/PNPDB8+HP/88w/WrFmDW7duoX379khISLBtk9X3RWpqKrp06YLk5GRs2rQJc+bMwezZszF27FhnnFKBlJ3rDABDhgwxfaY/+ugj2zqHX+cczVxKOda0aVM1fPhw2/PU1FQVFhamJk6c6MRSFX7jxo1T9evXt7suNjZWubu7q59++sm27MCBAwqA2rx5s4NKWPgBUD///LPtudVqVSEhIWrSpEm2ZbGxscrT01PNmzdPKaXU/v37FQC1bds22za///67slgs6uzZsw4re2GT9lorpdSAAQNUt27dMtyH1zp3Ll68qACoP//8UymVve+LFStWKBcXF9sk3Uop9fXXXys/Pz+VlJTk2BMoJNJeZ6WUatOmjXrppZcy3MfR15k1QPkoOTkZ27dvR7t27WzLXFxc0K5dO2zevNmJJbs7HDlyBGFhYahUqRL69euHqKgoAMD27dtx69Yt03WvUaMGypcvz+t+B06cOIHo6GjTdfX390ezZs1s13Xz5s0ICAhA48aNbdu0a9cOLi4u2LJli8PLXNitX78eQUFBqF69OoYNG4bLly/b1vFa505cXBwAoGTJkgCy932xefNm1K1b1zZJNwB06NAB8fHx2LdvnwNLX3ikvc6auXPnIjAwEHXq1MHo0aORmJhoW+fo6+z02eDvZjExMUhNTTW9mQAQHByMgwcPOqlUd4dmzZph9uzZqF69Os6fP48JEyagVatW2Lt3L6Kjo+Hh4YGAgADTPsHBwYiOjnZOge8C2rWz93nW1kVHRyMoKMi03s3NDSVLluS1z6GOHTvikUceQcWKFXHs2DG8+eab6NSpEzZv3gxXV1de61ywWq14+eWX0bJlS9SpUwcAsvV9ER0dbfdzr60jM3vXGQAef/xxVKhQAWFhYdi9ezdef/11HDp0CEuWLAHg+OvMAIgKpU6dOtke16tXD82aNUOFChWwcOFCeHt7O7FkRHmjT58+tsd169ZFvXr1ULlyZaxfvx4PPPCAE0tWeA0fPhx79+415QtS3svoOhvz0+rWrYvQ0FA88MADOHbsGCpXruzoYjIJOj8FBgbC1dU1XW+CCxcuICQkxEmlujsFBASgWrVqOHr0KEJCQpCcnIzY2FjTNrzud0a7dpl9nkNCQtIl+KekpODKlSu89neoUqVKCAwMxNGjRwHwWufU888/j99++w3r1q1D2bJlbcuz830REhJi93OvrSNdRtfZnmbNmgGA6TPtyOvMACgfeXh4ICIiApGRkbZlVqsVkZGRaN68uRNLdve5fv06jh07htDQUERERMDd3d103Q8dOoSoqChe9ztQsWJFhISEmK5rfHw8tmzZYruuzZs3R2xsLLZv327b5o8//oDVarV92VHunDlzBpcvX0ZoaCgAXuvsUkrh+eefx88//4w//vgDFStWNK3PzvdF8+bNsWfPHlPAuWbNGvj5+aFWrVqOOZECLqvrbM+uXbsAwPSZduh1zvO0ajKZP3++8vT0VLNnz1b79+9XQ4cOVQEBAaYsd8q5V199Va1fv16dOHFCbdy4UbVr104FBgaqixcvKqWUevbZZ1X58uXVH3/8of7991/VvHlz1bx5cyeXuuC7du2a2rlzp9q5c6cCoD799FO1c+dOderUKaWUUh9++KEKCAhQv/zyi9q9e7fq1q2bqlixorpx44btGB07dlQNGzZUW7ZsURs2bFBVq1ZVffv2ddYpFViZXetr166pkSNHqs2bN6sTJ06otWvXqkaNGqmqVauqmzdv2o7Ba521YcOGKX9/f7V+/Xp1/vx52y0xMdG2TVbfFykpKapOnTqqffv2ateuXWrlypWqdOnSavTo0c44pQIpq+t89OhR9c4776h///1XnThxQv3yyy+qUqVKqnXr1rZjOPo6MwBygMmTJ6vy5csrDw8P1bRpU/XPP/84u0iFXu/evVVoaKjy8PBQZcqUUb1791ZHjx61rb9x44Z67rnnVIkSJZSPj4/q0aOHOn/+vBNLXDisW7dOAUh3GzBggFJKusK//fbbKjg4WHl6eqoHHnhAHTp0yHSMy5cvq759+ypfX1/l5+enBg0apK5du+aEsynYMrvWiYmJqn379qp06dLK3d1dVahQQQ0ZMiTdP0681lmzd40BqFmzZtm2yc73xcmTJ1WnTp2Ut7e3CgwMVK+++qq6deuWg8+m4MrqOkdFRanWrVurkiVLKk9PT1WlShX12muvqbi4ONNxHHmdLbcLTkRERFRkMAeIiIiIihwGQERERFTkMAAiIiKiIocBEBERERU5DICIiIioyGEAREREREUOAyAiIiIqchgAEdFdZ+DAgejevbuzi0FEBRhngyeiQsVisWS6fty4cfjiiy/AMV6JKDMMgIioUDl//rzt8YIFCzB27FgcOnTItszX1xe+vr7OKBoRFSJsAiOiQiUkJMR28/f3h8ViMS3z9fVN1wTWtm1bvPDCC3j55ZdRokQJBAcHY/r06UhISMCgQYNQvHhxVKlSBb///rvptfbu3YtOnTrB19cXwcHBePLJJxETE+PgMyai/MAAiIiKhDlz5iAwMBBbt27FCy+8gGHDhqFXr15o0aIFduzYgfbt2+PJJ59EYmIiACA2Nhb3338/GjZsiH///RcrV67EhQsX8Nhjjzn5TIgoLzAAIqIioX79+hgzZgyqVq2K0aNHw8vLC4GBgRgyZAiqVq2KsWPH4vLly9i9ezcA4KuvvkLDhg3xwQcfoEaNGmjYsCFmzpyJdevW4fDhw04+GyK6U8wBIqIioV69erbHrq6uKFWqFOrWrWtbFhwcDAC4ePEiAOC///7DunXr7OYTHTt2DNWqVcvnEhNRfmIARERFgru7u+m5xWIxLdN6l1mtVgDA9evX0bVrV/zvf/9Ld6zQ0NB8LCkROQIDICIiOxo1aoTFixcjPDwcbm78qiS62zAHiIjIjuHDh+PKlSvo27cvtm3bhmPHjmHVqlUYNGgQUlNTnV08IrpDDICIiOwICwvDxo0bkZqaivbt26Nu3bp4+eWXERAQABcXfnUSFXYWxeFSiYiIqIjhvzFERERU5DAAIiIioiKHARAREREVOQyAiIiIqMhhAERERERFDgMgIiIiKnIYABEREVGRwwCIiIiIihwGQERERFTkMAAiIiKiIocBEBERERU5DICIiIioyPl/7Y0bliY/KMgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that the simplification of the model above has improved things a bit (though it may not, there's randomness at play here, and your network may have learned differently) - training accuracy has reduced, but test accuracy (our measure of how generally useful our model will be beyond the training set) has improved - a little bit.  But there's still a bit of a gap between them - we're still overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Reduce training time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the moment, let's do one change at a time, so we'll go back to our original model before trying our next strategy.\n",
    "\n",
    "Another approach we can use is simply to stop training for so long.  We can see from our earlier plots that things improve in the test set initially but then reduces.  So, by not training for so long, we can stop training before it significantly overfits.\n",
    "\n",
    "Here, we'll run the model exactly as we did the first time, except we'll only run it for 25 epochs, rather than 250 - just 10% of the original training time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define network\n",
    "number_features = X_train_sc.shape[1]\n",
    "model = make_net(number_features)\n",
    "### Train model (and stote training info in history)\n",
    "history = model.fit(X_train_sc,\n",
    "                    y_train,\n",
    "                    epochs=25,\n",
    "                    batch_size=64,\n",
    "                    validation_data=(X_test_sc, y_test),\n",
    "                    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 834us/step\n",
      "Training accuracy 0.868\n",
      "Test accuracy 0.798\n"
     ]
    }
   ],
   "source": [
    "# Show acuracy\n",
    "calculate_accuracy(model, X_train_sc, X_test_sc, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that reducing the training time has also led to an improvement in test accuracy, much as simplifying the model did, although you might not.  You might find that this measure is slightly more effective than the simplifying measure.  You should also see from the plot that the test set accuracy tends to plateau, and it doesn't get to the bit where it starts dropping significantly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Add dropout\n",
    "\n",
    "Using dropout, in each training epoch a random selection of weights are \"switched off\" (the selection changes from epoch to epoch).  It does this by using the Dropout layers after each hidden layer (remember when we added those earlier?), and randomly switching some of the incoming weights to 0.  When predicting (after fitting) all weights are used.  Dropout ensures that, during training, the model can't rely too much on any set of weights (because they'll occasionally be turned off), and looks to explore them more globally.\n",
    "\n",
    "This is probably the most common method for reducing overfitting.  Dropout values of 0.2 to 0.5 are common.\n",
    "\n",
    "Here, we'll use a dropout value of 0.5.  So 50% of the weights coming out of each hidden layer will be set to 0 in each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define network\n",
    "number_features = X_train_sc.shape[1]\n",
    "model = make_net(number_features,\n",
    "                dropout=0.5)\n",
    "\n",
    "### Train model (and stote training info in history)\n",
    "history = model.fit(X_train_sc,\n",
    "                    y_train,\n",
    "                    epochs=250,\n",
    "                    batch_size=64,\n",
    "                    validation_data=(X_test_sc, y_test),\n",
    "                    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "Training accuracy 0.883\n",
      "Test accuracy 0.803\n"
     ]
    }
   ],
   "source": [
    "# Show acuracy\n",
    "calculate_accuracy(model, X_train_sc, X_test_sc, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we should see that Dropout has improved performance on the test set over the base case (although it might not)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Combination of the above and with automatic early stopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than just doing one of these things above, we tend to combine these measures.  We'll also use a Keras callback called EarlyStopping to automate the measure where we try to stop the training sooner.  A callback is simply a function that Keras can use to perform various actions continually throughout the training.\n",
    "\n",
    "EarlyStopping will automatically stop the training when it appears the validation accuracy isn't getting any better.  It allows us to specify a `patience` level, which is the number of epochs we are prepared to wait (to give it a chance to improve) before EarlyStopping cuts things off.  We can also optionally specify the minimum level we want our metric(s) (e.g. accuracy) to improve between epochs to count as an \"improvement\" - this allows us to say that we don't consider a very small improvement as significant enough.  You'll see examples of this later in the course, but here we'll just specify `patience`, and we'll allow any improvement to count as improvement.\n",
    "\n",
    "Here, we specify a `patience` of 25 epochs - this means that we are prepared to wait 25 epochs to see if we can get a better accuracy score on the validation set.  By setting `restore_best_weights=True` we tell it that, once it stops (if it didn't manage to improve things in 25 epochs), then it should roll back the network to how it was when it reached its peak performance.\n",
    "\n",
    "So, here we set up our EarlyStopping callback.  Then we define a simpler network with 1 hidden layer and 64 neurons per layer, have a 50% dropout rate, and run for 250 epochs **but** add in the EarlyStopping callback so that Keras will stop the training when things stop improving in the validation set, and revert back to the best version it's seen.\n",
    "\n",
    "In the below, you'll see we've also added another callback called ModelCheckpoint.  This callback just automatically saves the model at its best point so we can easily retrieve it.  In combination with EarlyStopping, this means we have a model that won't keep going beyond when it should, and it'll save the best version for later use.\n",
    "\n",
    "Note that as well as creating and defining the callbacks, you also need to ensure you add them into the list of inputs you pass in when you call `model.fit`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define save checkpoint callback (only save if new best validation results)\n",
    "checkpoint_cb = keras.callbacks.ModelCheckpoint(\n",
    "    'model_checkpoint.keras', save_best_only=True)\n",
    "\n",
    "# Define early stopping callback\n",
    "# Stop when no validation improvement for 25 epochs\n",
    "# Restore weights to best validation accuracy\n",
    "early_stopping_cb = keras.callbacks.EarlyStopping(\n",
    "    patience=25, restore_best_weights=True)\n",
    "\n",
    "# Define network\n",
    "number_features = X_train_sc.shape[1]\n",
    "model = make_net(\n",
    "    number_features,\n",
    "    hidden_layers=1,\n",
    "    hidden_layer_neurones=64,\n",
    "    dropout=0.5)\n",
    "\n",
    "### Train model (and stote training info in history)\n",
    "history = model.fit(X_train_sc,\n",
    "                    y_train,\n",
    "                    epochs=250,\n",
    "                    batch_size=64,\n",
    "                    validation_data=(X_test_sc, y_test),\n",
    "                    verbose=0,\n",
    "                    callbacks=[checkpoint_cb, early_stopping_cb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "Training accuracy 0.832\n",
      "Test accuracy 0.812\n"
     ]
    }
   ],
   "source": [
    "# Show accuracy\n",
    "calculate_accuracy(model, X_train_sc, X_test_sc, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see from the above, where we've combined the three anti-overfitting measures, that we get quite a decent improvement in test accuracy and a closing of the gap between training and test accuracy.  This indicates that our model is far less overfitted than it was originally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving and reloading the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we'll look at how we can save our models so we can come back to them another time, and we don't have to retrain them each time.  For a small model like this, it's not hugely inconvenient, but if we had a large model (that could take hours or even days to run) we don't want to have to retrain it every time we want to use it!\n",
    "\n",
    "Here, we can use the `save()` function of the model to easily save a model.  We just pass in a filename - we use the new .keras file extension.  The model will be saved in the present working directory for the code.\n",
    "\n",
    "You can also see in the cell below how to load a model back in, and then use it again.  You can verify this if you run the two cells below, which will save the model, then load it back up, and recalculate its accuracy - you should see that the reported training and test accuracies are the same as you had above (because that's the model we saved and then loaded back up)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n",
      "[0.09658381 0.15201195 0.15485787 0.85435104 0.68767977]\n"
     ]
    }
   ],
   "source": [
    "# Save model\n",
    "model.save('titanic_tf_model.keras')\n",
    "\n",
    "# Load and use saved model - we need to first set up a model\n",
    "restored_model = keras.models.load_model('titanic_tf_model.keras')\n",
    "\n",
    "# Predict classes as normal\n",
    "predicted_proba = restored_model.predict(X_test_sc)\n",
    "\n",
    "# Show examples of predicted probability\n",
    "print(predicted_proba[0:5].flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "Training accuracy 0.832\n",
      "Test accuracy 0.812\n"
     ]
    }
   ],
   "source": [
    "calculate_accuracy(restored_model, X_train_sc, X_test_sc, y_train, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}