01-MNIST_Fully_Connected_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST - Loading, Training and Evaluation\n",
    "\n",
    "This notebook covers the main stages in Deep Learning, acting as an introduction to both the PyTorch platform as well as some impotant steps when loading data, defining a model, training and evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.1+cu118\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "# importing a module with utilities for displaying stats and data\n",
    "import sys\n",
    "sys.path.insert(1, '../../util')\n",
    "import vcpi_util\n",
    "\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check GPU availability\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the dataset from torch collection of datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor()]) #, transforms.RandomHorizontalFlip(1.0)])\n",
    "\n",
    "\n",
    "train_set = torchvision.datasets.MNIST(root='./data', train=True,\n",
    "                                        download=True, transform=transform)\n",
    "\n",
    "test_set = torchvision.datasets.MNIST(root='./data', train=False,\n",
    "                                       download=True, transform=transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes:  ['0 - zero', '1 - one', '2 - two', '3 - three', '4 - four', '5 - five', '6 - six', '7 - seven', '8 - eight', '9 - nine']\n",
      "Dataset shape:  torch.Size([60000, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "print(\"Classes: \", train_set.classes)\n",
    "\n",
    "print(\"Dataset shape: \", train_set.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# these are the images we loaded\n",
    "vcpi_util.show_loaded_images(3,8, train_set, train_set.classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 128\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_set, batch_size=BATCH_SIZE,\n",
    "                                          shuffle=True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(test_set, batch_size=BATCH_SIZE,\n",
    "                                         shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set samples:  60000\n",
      "Training set batches 469\n",
      "Test set samples:  10000\n"
     ]
    }
   ],
   "source": [
    "print(\"Training set samples: \", len(train_loader.dataset))\n",
    "\n",
    "# this will give the total number of batches, but note that the last batch may not be complete\n",
    "print(\"Training set batches\", len(train_loader))\n",
    "\n",
    "print (\"Test set samples: \", len(test_loader.dataset))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch shape:  torch.Size([128, 1, 28, 28])\n",
      "Each image varies between  0.0  and  1.0\n",
      "Shape after channel transpose : (128, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "images, targets = next(iter(train_loader))\n",
    "print(\"Batch shape: \", images.shape)\n",
    "img_np = images.numpy()\n",
    "print(\"Each image varies between \",np.min(img_np),\" and \", np.max(img_np))\n",
    "\n",
    "# putting channels last so that the image can be displayed\n",
    "img_np = np.transpose(img_np,(0,2,3,1))\n",
    "print(\"Shape after channel transpose :\", img_np.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display a batch of images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# these are the transformed images\n",
    "\n",
    "images, targets = next(iter(train_loader))\n",
    "\n",
    "vcpi_util.show_images(4,8, images, targets, train_set.classes) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class distribution\n",
    "\n",
    "A dataset is considered balanced if no class has a significantly higher number of samples than some other class\n",
    "\n",
    "When training an unbalanced dataset there is always the danger that the model will pay more attention to the more represented classes as this will decrease the loss faster\n",
    "\n",
    "Checking the dataset to see if it is balanced can be easily done with an histogram of occurrences per class.\n",
    "\n",
    "Solutions to overcome this problem may be found with data augmentation, or class weighting, more on this in later lectures.\n",
    "\n",
    "MNIST is a fairly balanced dataset so we don't have to worry about this in here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({1: 6742, 7: 6265, 3: 6131, 2: 5958, 9: 5949, 0: 5923, 6: 5918, 8: 5851, 4: 5842, 5: 5421})\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vcpi_util.show_histogram(train_set.targets, train_set.classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create model\n",
    "\n",
    "A fully connected model, comprised of an input which is the flattened view of the image (28 x 28 pixels = 784 values), a single hidden layer with 64 units, and an output layer with as many units as there are classes in the training set, 10 for MNIST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FullyConnected_I(torch.nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = torch.nn.Linear(784, 64)\n",
    "        self.relu1 = torch.nn.ReLU()\n",
    "        self.fc2 = torch.nn.Linear(64,10)       \n",
    "\n",
    "    def forward(self, x):    \n",
    "\n",
    "        x = torch.flatten(x,1)\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu1(x)\n",
    "        x = self.fc2(x)\n",
    "        return(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FullyConnected_I(\n",
      "  (fc1): Linear(in_features=784, out_features=64, bias=True)\n",
      "  (relu1): ReLU()\n",
      "  (fc2): Linear(in_features=64, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = FullyConnected_I()\n",
    "model.to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "FullyConnected_I                         [128, 10]                 --\n",
       "├─Linear: 1-1                            [128, 64]                 50,240\n",
       "├─ReLU: 1-2                              [128, 64]                 --\n",
       "├─Linear: 1-3                            [128, 10]                 650\n",
       "==========================================================================================\n",
       "Total params: 50,890\n",
       "Trainable params: 50,890\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.MEGABYTES): 6.51\n",
       "==========================================================================================\n",
       "Input size (MB): 0.40\n",
       "Forward/backward pass size (MB): 0.08\n",
       "Params size (MB): 0.20\n",
       "Estimated Total Size (MB): 0.68\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchinfo import summary\n",
    "summary(model, input_size=(BATCH_SIZE, 1, 28, 28))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some predictions with an untrained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use an iterator on test_loader to get the first batch. Then ask the model for predictions on the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 0.0527,  0.1096,  0.1493,  0.0554, -0.1604, -0.0261,  0.2035, -0.0431,\n",
      "        -0.0256,  0.1195], device='cuda:0', grad_fn=<SelectBackward0>)\n",
      "[0.10039506 0.10628282 0.11058672 0.10067239 0.08113189 0.09278993\n",
      " 0.11673979 0.09122349 0.09284083 0.10733715]\n"
     ]
    }
   ],
   "source": [
    "images, targets = next(iter(test_loader))\n",
    "\n",
    "# feed the images to the model. The return value is the output layer outputs for each image in the batch\n",
    "logits = model(images.to(device))\n",
    "\n",
    "# the return value is a tensor residing on device, GPU if available\n",
    "print(logits[0])\n",
    "\n",
    "# convert to probabilities, moving to CPU, detaching from compute compute graph and convert to numpy\n",
    "predictions = torch.nn.functional.softmax(logits,dim=1).cpu().detach().numpy()\n",
    "print(predictions[0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing the first batch and the predictions. In blue is the true class, in red the predicted class. When the model is initialized with random weights it is normal to have all predictions wrong. Notice also that the model has no confidence in general in its predicitons, with the predicted class getting always a low score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vcpi_util.plot_predictions(images, predictions, targets, train_set.classes, 5, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, data_loader):\n",
    "\n",
    "    # sets the model in evaluation mode.\n",
    "    # although our model does not have layers which behave differently during training and evaluation\n",
    "    # this is a good practice as the models architecture may change in the future\n",
    "    model.eval()\n",
    "\n",
    "    correct = 0\n",
    "    \n",
    "    for i, (images, targets) in enumerate(data_loader):\n",
    "         \n",
    "        # forward pass, compute the output of the model for the current batch\n",
    "        outputs = model(images.to(device))\n",
    "\n",
    "        # \"max\" returns a namedtuple (values, indices) where values is the maximum \n",
    "        # value of each row of the input tensor in the given dimension dim; \n",
    "        # indices is the index location of each maximum value found (argmax).\n",
    "        # the argmax effectively provides the predicted class number        \n",
    "        _, preds = torch.max(outputs, dim=1)\n",
    "\n",
    "        correct += (preds.cpu() == targets).sum()\n",
    "\n",
    "    return (correct / len(data_loader.dataset)).item()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When evaluating a model prior to any training the accuracy should be approximately 1/#classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08919999748468399"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(model, test_loader)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To train a model a loss function and an optimizer are required. In here we are selection the Adam optimizer provided by Torch, and the sparse categorical cross entropy, which is common in multi-class classification problems.\n",
    "\n",
    "In torch, the optimizer receives the model's parameters, in order to be able to update all the learning parameters of the model.\n",
    "\n",
    "The Adam optimizer has a set of parameters, and all can be set when defining the optimizer. See [Torch Adam page](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html).\n",
    "\n",
    "For further info on Torch optimizers see [Torch optimizer documentation page](https://pytorch.org/docs/stable/optim.html).\n",
    "\n",
    "For the loss function we are using [Cross Entropy](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(model.parameters())\n",
    "loss_fn = torch.nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a typical training loop for a classification problem. It runs for a number of epochs, exposing the model to the full training set.\n",
    "\n",
    "In each epoch, it iterates through the batches. For each batch it feeds the model, computes the loss and the gradients, and finally uses the optimizer to update the weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, data_loader, epochs):\n",
    "\n",
    "    # sets the model in training mode.\n",
    "    # although our model does not have layers which behave differently during training and evaluation\n",
    "    # this is a good practice as the models architecture may change in the future\n",
    "    model.train()\n",
    "\n",
    "    history = {}\n",
    "    history['accuracy'] = []\n",
    "    history['loss'] = []\n",
    "\n",
    "    for epoch in range(epochs):  # loop over the dataset multiple times\n",
    "\n",
    "        start_time = time.time() \n",
    "        correct = 0\n",
    "        running_loss = 0.0\n",
    "        for i, (inputs, targets) in enumerate(data_loader, 0):\n",
    "            \n",
    "            # send data to device, hopefully the GPU\n",
    "            inputs = inputs.to(device)\n",
    "            targets = targets.to(device)\n",
    "\n",
    "            # forward pass, compute the output of the model for the current batch\n",
    "            outputs = model(inputs)\n",
    "\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "\n",
    "            #compute the loss between the model's outputs and the labels, or targets.\n",
    "            loss = loss_fn(outputs, targets)\n",
    "            \n",
    "            # zero the parameter gradients\n",
    "            # this function must be called before calling \"backward\" on the losses, otherwise the gradients get accumulated with previous batches.\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            # perform backpropagation\n",
    "            loss.backward()\n",
    "            #update the model's parameters\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss += loss\n",
    "            correct += (predicted == targets).sum()\n",
    "\n",
    "        accuracy = 100 * correct / len(train_set)\n",
    "        stop_time = time.time()\n",
    "        print(f'Epoch: {epoch:03d}; Accuracy = {accuracy:0.4f}; Elapsed time: {(stop_time - start_time):0.4f}')\n",
    "        history['accuracy'].append(accuracy.cpu().numpy())\n",
    "        history['loss'].append(running_loss.cpu().detach().numpy())\n",
    "        \n",
    "    print('Finished Training')\n",
    "\n",
    "    return(history)\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 000; Accuracy = 87.5317; Elapsed time: 6.1700\n",
      "Epoch: 001; Accuracy = 93.2650; Elapsed time: 6.2007\n",
      "Epoch: 002; Accuracy = 94.7600; Elapsed time: 6.2602\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "epochs = 3\n",
    "history = train(model, train_loader, epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving the model\n",
    "\n",
    "documentation: [Saving and Loading Models](https://pytorch.org/tutorials/beginner/saving_loading_models.html)\n",
    "\n",
    "The first option is to use the \"save\" function with a dictionary of items to be saved. This allows not only to save the state of the model, useful for inference, but also the optimizer, useful for continuing training. \n",
    "\n",
    "This allow to recreate the model and optimizer in exactly the same state as when they were saved. Note that when loading the model, the class which it instantiates must be known."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9502999782562256"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###### Saving ######\n",
    "torch.save({\n",
    "    'epoch': epochs,\n",
    "    'model':model.state_dict(),\n",
    "    'optimizer': optimizer.state_dict(),\n",
    "    'history' : history\n",
    "    },\n",
    "    f'model_IV_optimizer_{epochs}.pt')\n",
    "\n",
    "\n",
    "###### Loading ######\n",
    "# recreate the model and optimizer\n",
    "retrieved_model = FullyConnected_I()\n",
    "retrieved_optimizer = torch.optim.Adam(retrieved_model.parameters())\n",
    "\n",
    "#load the dict\n",
    "retrieved = torch.load(f'model_IV_optimizer_{epochs}.pt')\n",
    "\n",
    "# set the model and optimizer state to the saved state\n",
    "retrieved_model.load_state_dict(retrieved['model'])\n",
    "retrieved_model.to(device)\n",
    "retrieved_optimizer.load_state_dict(retrieved['optimizer'])\n",
    "\n",
    "\n",
    "\n",
    "evaluate(retrieved_model, test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy': [array(87.53167, dtype=float32), array(93.265, dtype=float32), array(94.76, dtype=float32)], 'loss': [array(226.78627, dtype=float32), array(111.07634, dtype=float32), array(86.001366, dtype=float32)]}\n"
     ]
    }
   ],
   "source": [
    "print(retrieved['history'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second option works only for inference, and uses TorchScript, which provides an inermediate representation of a model. This can then be loaded without any knowledge of the underlying class used to create the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9502999782562256"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scripted = torch.jit.script(model) # Export to TorchScript\n",
    "model_scripted.save('model_scripted.pt') # save\n",
    "\n",
    "\n",
    "model = torch.jit.load('model_scripted.pt') # load\n",
    "evaluate(model, test_loader) # evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## History graphs for accuracy and loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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He2OSSuZf0qEuNQDjrrBFUIvil4/+0bQB51+MDJsZIiKqlTQagag7Su38y5XkTJ39LdwdEdyiIYID3fCcJ+dfjBmbGSIiqjXyi9Q4Hp+GiJhk7I9NRrLq0fyLpYUMXXyctPd/8XLi/IupYDNDRERmTZlTiANXiq++HL5yH9ml5l/sbSzRu+T+Ly/4N4SiDudfTBGbGSIiMjsJD3K0Lx+duvlAZ/7F1VGunX/p6tsAcitLCSslQ2AzQ0REJk8I3fmXy0m68y8Bbg7a+7+08lRw/sXMsJkhIiKTVFCkQeT1NETEJGFfTAqSVHnafZYWMnRqUh/BgW4IbuGKxg04/2LO2MwQEZHJUOYW4tCVFOwtmX/Jyi/S7qtjY4lezYvv//JiQEPUq2MjYaVUk9jMEBGRUUtMz8G+mGRExCbj5PUHKCo1/9LQQY6gQFcEtyief7G15vxLbSR5M5OZmYnZs2dj165dSElJQbt27fDNN9+gU6dOZda+++67WL16Nb7++mtMmzat5oslIqJqJ4TApbsq7C2Zf4m9p9LZ39y1rjbAsbWnAhYWnH+p7SRvZiZMmIDo6Ghs3LgRHh4e2LRpE4KCghATEwNPT0/tul27duHEiRPw8PCQsFoiIqoOBUUanLxRfP+XfTHJuKt8NP9iIQM6NnFC35IBXu8G9hJWSsZI0mYmNzcXO3bswO7du9GzZ08AwNy5c/Hbb79h5cqVWLBgAQDgzp07eP/99xEeHo6QkBApSyYiIgNR5RXi0JX7iIhJxqHLKcgsNf9iZ22Jns2dERzohhcDGsLJnvMv9GSSNjNFRUVQq9WwtbXV2W5nZ4ejR48CADQaDUaNGoWZM2eiZcuWzzxmfn4+8vMf3dFRpVI9ZTUREdWkuxm5xVdfYpNx4noaCtWP5l+c68oRHFh8A7tuvs6cf6EKk7SZcXBwQNeuXTF//ny0aNECrq6u2LJlCyIjI+Hn5wcAWLRoEaysrPDBBx9U6JhhYWGYN29edZZNREQVJIRAzD2V9v4vl+7q/g+mX8O62vu/tG1Uj/MvpBfJZ2Y2btyI8ePHw9PTE5aWlmjfvj1GjhyJs2fP4uzZs/jmm29w7ty5Ct/gKDQ0FDNmzNB+rlKp4OXlVV3lExHRYwrVGpy68UDbwNzJyNXus5ABHbzrawd4fZw5/0JVJxNCiGcvq37Z2dlQqVRwd3fH8OHDkZWVheDgYMyYMQMWFo+i1tVqNSwsLODl5YWbN28+87gqlQoKhQJKpRKOjo7VeAZERLVXZsn8y77YZBy8nAJV3qP5F1trC/Rs9uj+Lw3qyiWslExFZZ6/Jb8y85C9vT3s7e2Rnp6O8PBwLF68GEOGDEFQUJDOun79+mHUqFEYN26cRJUSEREA3FPmYl9MMvbGlDf/YoM+AcUvH3VvxvkXql6SNzPh4eEQQsDf3x9xcXGYOXMmAgICMG7cOFhbW6NBgwY6662treHm5gZ/f3+JKiYiqp2EELiclKl9+SjqjlJnf1MXewQHuqJvoCvaetWHJedfqIZI3swolUqEhoYiMTERTk5OGDJkCBYuXAhra8awExFJrUitwambj+ZfEtMfzb/IZECHxsXzL0GBrvB1qSthpVSbGc3MTHXhzAwRUeVk5Rfh8JX7iIhJwsEr96HMLdTus7W2QHc/F/QNdMWLLRrCmfMvVE1McmaGiIikk6zK0159iYxPQ4Fao93nZG+DPgHF93/p0cwFdjacfyHjwmaGiKgWEkLganIWImKSEBGTjIuJuvMvPs722vu/tG/M+RcybmxmiIhqiSK1BqdvpmvvwHv7QY52n0wGtPOqh+BANwQHusKvIedfyHSwmSEiMmPZ+UU4crU4/+jAlRRk5Dyaf5FbWaC7n3Px/V9aNERDB9unHInIeLGZISIyMymqPOyLTUFETBKOxaehoOjR/Ev9OtZ4seT+Lz2bO6OODZ8GyPTxp5iIyMQJIXAtJUs7wHshIUNnf5MGdYrfPt3CFR2868PK0qL8AxGZKDYzREQmSK0ROPPw/i+xybiVlqOzv61XPe0N7Pwa1q1wvh2RKWIzQ0RkInIKinDkamrx/MvlZKSXmn+xsbLA874NEBzohqAWDdHQkfMvVHuwmSEiMmL3M/OxP7b45aOjcanILzX/Uq+ONV70b1gy/+ICezn/SafaiT/5RERGRAiB+PtZ2BuTjH0xyTifkIHS92lv7FRHe/+Xjpx/IQLAZoaISHJqjcC52+naAd4bqdk6+9s0UpQ0MG5o7sr5F6LHsZkhIpJAboEaf10ruf/L5RSkZRdo99lYWqCrbwPtO5DcFJx/IXoaNjNERDUkNSsfB2JTsDcmGUfj7iOv8NH8i8LOGi8GPJp/qcv5F6IK428LEVE1ir//6P4v526n68y/NKpvp51/6dTECdacfyHSC5sZIiIDUmsELiSkY29JA3P9vu78SytPhbaBCXBz4PwLkQGwmSEiqqK8QjWOXiu+/8v+y8lIzXo0/2JtKUNXX+eS+ZeGcFfYSVgpkXliM0NEpIe0rHzsv5yCiJhk/HVNd/7FwdZKO//Sq7kLHGytJayUyPyxmSEiqqAbqdmIiElCREwyzt5Kh6bU/ItnvUfzL519OP9CVJPYzBARPYFGI3AhMUM7wBuXkqWz/zlPRwS1KG5gAt0dOf9CJBE2M0REpeQVqnE8vnj+ZV9sCu5n5mv3WVnIdO7/4lGP8y9ExoDNDBHVeg+yC3DgcgoiYpJw5GoqcgvV2n0Ociv0Lpl/6e3vAkfOvxAZHTYzRFQr3UrLRkRMMvbGJOPMzQc68y8eClsElcy/dPFpABsrzr8QGTM2M0RUK2g0AhcTM7CvJIH6arLu/Eugu6N2gLelB+dfiEwJmxkiMlt5hWpExqdhb0wy9scmI+Wx+ZcuTZ0Q3MIVQYGuaFS/joSVElFVsJkhIrOSkfNw/iUZR67eR3bBo/mXunIr9PJ3Qd9AV/Ru3hCKOpx/ITIHbGaIyOQlPMgpiQ9Iwumb6VCXGoBxc7RFUGBDBAe64R9NnSC3spSwUiKqDmxmiMhkbTpxC5tO3MLlpEyd7QFuDugb6IrgQDc858n5FyJzx2aGiEzSxhO3MPuXaACApYUMnZs4aQd4vZw4/0JUm7CZISKTc+Tqfcz99RIAYFJvX7zTsynq1bGRuCoikgqbGSIyKdeSMzFl8zmoNQJD2jfCx/38+TISUS3HO0ERkclIy8rH+PWnkZlfhM5NnPDv155jI0NEbGaIyDTkFaoxceNZJDzIhXeDOlg1qgPfmUREANjMEJEJEEIgdGcUzt5Kh6OtFdaM6QQne87IEFExNjNEZPRWHIjDrvN3YGUhw8o3O8CvYV2pSyIiI8JmhoiM2u9/38XSiKsAgPmDn8Pzfs4SV0RExkbyZiYzMxPTpk2Dt7c37Ozs0K1bN5w+fVq7f+7cuQgICIC9vT3q16+PoKAgnDx5UsKKiaimnL+djg+3XQQATOjug5GdG0tcEREZI8mbmQkTJiAiIgIbN25EVFQU+vbti6CgINy5cwcA0Lx5c6xYsQJRUVE4evQomjRpgr59++L+/fsSV05E1SkxPQdvbziL/CINglo0ROhLLaQuiYiMlEwIIZ69rHrk5ubCwcEBu3fvRkhIiHZ7hw4dMGDAACxYsKDM16hUKigUCuzbtw99+vR55vd4uF6pVMLR0dGg9RNR9cjMK8TQVZG4nJSJFu6O2P5uV9jLeVssotqkMs/fkl6ZKSoqglqthq2trc52Ozs7HD16tMz6goICfP/991AoFGjTpk1NlUlENUitEfhgy3lcTsqEi4Mca8Z0ZCNDRE8l6b8QDg4O6Nq1K+bPn48WLVrA1dUVW7ZsQWRkJPz8/LTrfv/9d4wYMQI5OTlwd3dHREQEnJ3LHwLMz89Hfn6+9nOVSlXt50FEhrPgjxgcvHIfttYW+O/ojvCoZyd1SURk5CSfmdm4cSOEEPD09IRcLseyZcswcuRIWFg8Ku2FF17AhQsXcPz4cfTv3x/Dhg1DSkpKuccLCwuDQqHQfnh5edXUqRBRFW08cQs/HLsJAPhqWFu08aonaT1EZBoknZkpLTs7GyqVCu7u7hg+fDiysrLwxx9/lLu2WbNmGD9+PEJDQ8vsK+/KjJeXF2dmiIzckav3MW7daag1AjP7+WPKC37P/iIiMluVmZkxmhei7e3tYW9vj/T0dISHh2Px4sVPXKvRaHQaltLkcjnkcnl1lUlE1eDx8MjJvX2lLomITIjkzUx4eDiEEPD390dcXBxmzpyJgIAAjBs3DtnZ2Vi4cCFeeeUVuLu7IzU1Fd9++y3u3LmDoUOHSl06ERkAwyOJqKokb2aUSiVCQ0ORmJgIJycnDBkyBAsXLoS1tTXUajUuX76M9evXIzU1FQ0aNECnTp3w119/oWXLllKXTkRVlF+kxjsMjySiKjKamZnqwvvMEBknIQRmbLuIXefvwMHWCrsmP8/MJSLSMpn7zBBR7fUwPNLSQoaVbzA8koj0x2aGiGqcTnjkoOfQvRnDI4lIf2xmiKhGlQ6PfKu7D/7ZheGRRFQ1bGaIqMbcycjVCY/8lOGRRGQAbGaIqEZk5RfhrXWnkZqVjxbujvhmRDtYWvAt2ERUdWxmiKjaMTySiKoTmxkiqnYL/4jFgcspkFsxPJKIDI/NDBFVq00nbmHtsRsAgK+HMzySiAyPzQwRVZu/rt3H579eAgDM7OePl1q5S1wREZkjNjNEVC3iUjIxuSQ88rX2ngyPJKJqw2aGiAwuLSsf49adRmZecXhk2GutGB5JRNWGzQwRGVR+kRrvbioOj2zsxPBIIqp+bGaIyGCEEJi1Iwqnb6bDwdYKa8d2gpO9jdRlEZGZYzNDRAbz7UGGRxJRzWMzQ0QG8fvfd7Fkb3F45BeDWjI8kohqDJsZIqqyCwkZOuGRb3TxlrgiIqpN2MwQUZXcycjFhPVnkF+kQZ8AhkcSUc1jM0NEeisdHhng5oBvRjI8kohqHpsZItJL6fBI57pyrBnbCXUZHklEEmAzQ0R60QmPHNMRngyPJCKJsJkhokorHR751bC2aMvwSCKSEJsZIqqU0uGRH/VtjpDWDI8kImmxmSGiCtMJj2zniSkv+EldEhERmxkiqpgH2QUYv+4MMvOK0KlJfYQNYXgkERkHNjNE9Ez5RWq8s/EMbj/IQWOnOlg9qiPDI4nIaLCZIaKnEkIgVCc8siPDI4nIqLCZIaKn+u5QPHbqhEc6SF0SEZEONjNE9ER//H0P/xd+BQAw7xWGRxKRcWIzQ0TlupCQgRnbLgAAxj/vgzf/wfBIIjJObGaIqIzS4ZEvBjTEZyEMjyQi48Vmhoh0PB4euYzhkURk5NjMEJGWWiMwleGRRGRi2MwQkda//4zFfoZHEpGJYTNDRACAzSdvYc3R4vDIpcPaMDySiEwGmxkiwtFrqZizuzg88sPg5ni5tYfEFRERVRybGaJaLi4lE5M2n9WGR773IsMjici0SN7MZGZmYtq0afD29oadnR26deuG06dPAwAKCwvxySefoFWrVrC3t4eHhwdGjx6Nu3fvSlw1kXkoHR7Z0ZvhkURkmiRvZiZMmICIiAhs3LgRUVFR6Nu3L4KCgnDnzh3k5OTg3LlzmD17Ns6dO4edO3fiypUreOWVV6Qum8jklQ2P7MDwSCIySTIhhJDqm+fm5sLBwQG7d+9GSEiIdnuHDh0wYMAALFiwoMzXnD59Gp07d8atW7fQuHHjZ34PlUoFhUIBpVIJR0dHg9ZPZKqEEPjw54vYee4OHGytsGtyN2YuEZFRqczzt6Q3kCgqKoJarYatra3Odjs7Oxw9erTcr1EqlZDJZKhXr165+/Pz85Gfn6/9XKVSGaxeInPx3aF47DxXHB753Rvt2cgQkUmT9GUmBwcHdO3aFfPnz8fdu3ehVquxadMmREZG4t69e2XW5+Xl4ZNPPsHIkSOf2KWFhYVBoVBoP7y8vKr7NIhMyp9RuuGRPZq5SFwREVHVSD4zs3HjRggh4OnpCblcjmXLlmHkyJGwsNAtrbCwEMOGDYMQAitXrnzi8UJDQ6FUKrUfCQkJ1X0KRCbjYkIGpv90AQAw7vkmDI8kIrMg+X3KfX19cfjwYWRnZ0OlUsHd3R3Dhw9H06ZNtWseNjK3bt3CgQMHnvramVwuh1wur4nSiUzKnYxcTNjwKDzyXyGBUpdERGQQkl+Zecje3h7u7u5IT09HeHg4Bg0aBOBRI3Pt2jXs27cPDRo0kLhSItPzMDzyfibDI4nI/Eh+ZSY8PBxCCPj7+yMuLg4zZ85EQEAAxo0bh8LCQrz++us4d+4cfv/9d6jVaiQlJQEAnJycYGNjI3H1RMaP4ZFEZO4k/xdNqVQiNDQUiYmJcHJywpAhQ7Bw4UJYW1vj5s2b+PXXXwEAbdu21fm6gwcPonfv3jVfMJGJCSsVHvmf0R0YHklEZkfS+8zUBN5nhmqzH0/exqe7ogAAK/7ZjplLRGQyKvP8bTQzM0RkWEevpWL27mgADI8kIvPGZobIDMWlZGnDI19leCQRmTk2M0Rmpjg88rQ2PPJLhkcSkZljM0NkRvKL1Hh341ncfpADLyc7hkcSUa2gVzNz8OBBQ9dBRFUkhEDoziicuvkADnIrrB3TCQ3q8gaSRGT+9Gpm+vfvD19fXyxYsIBxAURGonR45LdvtEczV4ZHElHtoFczc+fOHbz33nvYvn07mjZtin79+mHbtm0oKCgwdH1EVAGlwyPnvtISPZszPJKIag+9mhlnZ2dMnz4dFy5cwMmTJ9G8eXNMnjwZHh4e+OCDD3Dx4kVD10lET3AxIQMztl0AUBweOYrhkURUy1R5ALh9+/YIDQ3Fe++9h6ysLKxduxYdOnRAjx49cOnSJUPUSERPcLckPDKvUIMX/F0YHklEtZLezUxhYSG2b9+Ol156Cd7e3ggPD8eKFSuQnJyMuLg4eHt7Y+jQoYaslYhKyc4vwlvrz2jDI5f/sz3DI4moVtIrm+n999/Hli1bIITAqFGjsHjxYjz33HPa/fb29liyZAk8PHjHUaLqoNYITN16HrH3VHCuK8d/x3RkeCQR1Vp6/esXExOD5cuX47XXXoNcXv5bP52dnfkWbqJqEvZnLPbFPgqPbFS/jtQlERFJRq9mZv/+/c8+sJUVevXqpc/hiegpfjx5G/89egMAsHRYG7RrXF/iioiIpKXXzExYWBjWrl1bZvvatWuxaNGiKhdFROU7FpeKOSXhkTMYHklEBEDPZmb16tUICAgos71ly5ZYtWpVlYsiorLiUrLw7qazKNIIDG7rgfcZHklEBEDPZiYpKQnu7u5ltru4uODevXtVLoqIdD3ILsBb64vDIzt418eXQ1ozPJKIqIRezYyXlxeOHTtWZvuxY8f4DiYiA3sYHnkrrTg88vtRHWBrzfBIIqKH9BoAfvvttzFt2jQUFhbixRdfBFA8FPzxxx/jww8/NGiBRLWZEAKf7oxmeCQR0VPo1czMnDkTaWlpmDx5sjaPydbWFp988glCQ0MNWiBRbfbdoXjsOJfI8EgioqeQCSGEvl+clZWF2NhY2NnZoVmzZk+854yUVCoVFAoFlEolHB0dpS6HqML+F3UPkzafAwDMH/wcM5eIqFapzPN3lW4ZWrduXXTq1KkqhyCicvydmIHpJeGRY7sxPJKI6Gn0bmbOnDmDbdu24fbt29qXmh7auXNnlQsjqq3uZuTirfWPwiNnv8zwSCKip9Hr3Uxbt25Ft27dEBsbi127dqGwsBCXLl3CgQMHoFAoDF0jUa2RnV+ECSXhkf6uDlg2sh3DI4mInkGvZubf//43vv76a/z222+wsbHBN998g8uXL2PYsGFo3LixoWskqhUehkfG3FPBua4N1oztCAdba6nLIiIyeno1M/Hx8QgJCQEA2NjYIDs7GzKZDNOnT8f3339v0AKJaosv/1ccHmljZYHvR3dkeCQRUQXp1czUr18fmZmZAABPT09ERxdnxWRkZCAnJ8dw1RHVEltO3cZ//ioJjxzaBu0ZHklEVGF6DQD37NkTERERaNWqFYYOHYqpU6fiwIEDiIiIQJ8+fQxdI5FZOxaXitm/PAqPHNiGd9EmIqoMvZqZFStWIC8vDwDw2WefwdraGsePH8eQIUPwr3/9y6AFEpmzuJQsTGJ4JBFRlVS6mSkqKsLvv/+Ofv36AQAsLCwwa9YsgxdGZO7SS8IjVQyPJCKqkkrPzFhZWeHdd9/VXpkhosrLL1LjnU3F4ZGN6tthNcMjiYj0ptcAcOfOnXHhwgUDl0JUO2jDI2+UhEeO7QRnhkcSEelNr5mZyZMnY8aMGUhISECHDh1gb2+vs79169YGKY7IHK08/Cg8csUb7dGc4ZFERFWiV9CkhUXZCzoymQxCCMhkMqjVaoMUZwgMmiRjohMeOaglRnVtIm1BRERGqtqDJm/cuKFXYUS1WZnwSDYyREQGoVcz4+3NBF+iyrinzMWEkvDI3v4u+FdIC6lLIiIyG3o1Mxs2bHjq/tGjR1foOJmZmZg9ezZ27dqFlJQUtGvXDt988w06deoEoDh9e9WqVTh79iwePHiA8+fPo23btvqUTCSZ7PwivLXuDFJKwiOXj2wHK0u9Zu+JiKgcejUzU6dO1fm8sLAQOTk5sLGxQZ06dSrczEyYMAHR0dHYuHEjPDw8sGnTJgQFBSEmJgaenp7Izs5G9+7dMWzYMLz99tv6lEokqeLwyAsMjyQiqkZ6NTPp6elltl27dg2TJk3CzJkzK3SM3Nxc7NixA7t370bPnj0BAHPnzsVvv/2GlStXYsGCBRg1ahQA4ObNm/qUSSS5RXsuY19sMsMjiYiqkcGudTdr1gxffvllmas2T1JUVAS1Wg1bW1ud7XZ2djh69KihyiKSzNZTt/H9kesAgCUMjyQiqjYGfeHeysoKd+/erdBaBwcHdO3aFfPnz8fdu3ehVquxadMmREZG4t69e3rXkJ+fD5VKpfNBVNOOxaXiXyXhkdODmuMVhkcSEVUbvV5m+vXXX3U+F0Lg3r17WLFiBZ5//vkKH2fjxo0YP348PD09YWlpifbt22PkyJE4e/asPmUBAMLCwjBv3jy9v56oquLvPwqPHNTWAx/0YXgkEVF1MshN82QyGVxcXPDiiy9i6dKlcHd3r9TxsrOzoVKp4O7ujuHDhyMrKwt//PGHdv/Nmzfh4+NToXcz5efnIz8/X/u5SqWCl5cXb5pHNSI9uwCDvzuGW2k56OBdH5sndGHmEhGRHqr9pnkajUavwp7E3t4e9vb2SE9PR3h4OBYvXqz3seRyOeRy5txQzSso0jA8kohIAno1M4YSHh4OIQT8/f0RFxeHmTNnIiAgAOPGjQMAPHjwALdv39bO4Vy5cgUA4ObmBjc3N8nqJnqcEAKf7opieCQRkQT0GgAeMmQIFi1aVGb74sWLMXTo0AofR6lUYsqUKQgICMDo0aPRvXt3hIeHw9q6+D4cv/76K9q1a4eQkBAAwIgRI9CuXTusWrVKn7KJqs2qw9ex/WwiLGRgeCQRUQ3Ta2bGxcUFBw4cQKtWrXS2R0VFISgoCMnJyQYrsKoYNEnVbU/0Pby7qTg88otBLTGamUtERFVWmedvva7MZGVlwcbGpsx2a2trvhWaapW/EzMw7acLAIrDI9nIEBHVPL2amVatWuGnn34qs33r1q0IDAysclFEpoDhkURExkGvAeDZs2fjtddeQ3x8PF588UUAwP79+7Flyxb8/PPPBi2QyBgxPJKIyHjo1cwMHDgQv/zyC/79739j+/btsLOzQ+vWrbFv3z706tXL0DUSGZXHwyP/O4bhkUREUtL7rdkhISHadxkR1SalwyNXj+oILyeGRxIRSUmv6+KnT5/GyZMny2w/efIkzpw5U+WiiIzV4+GRHbwZHklEJDW9mpkpU6YgISGhzPY7d+5gypQpVS6KyBgdLxUeOS2oGcMjiYiMhF7NTExMDNq3b19me7t27RATE1PlooiMTfz9LLxbEh75ShsPTO3TTOqSiIiohF7NjFwuL/fGePfu3YOVlaQJCUQGl55dgLfWnYYqrwjtG9fD4tdbQyaTSV0WERGV0KuZ6du3L0JDQ6FUKrXbMjIy8OmnnyI4ONhgxRFJraBIg3c3ncXNkvDI70d3ZHgkEZGR0esyypIlS9CzZ094e3ujXbt2AIALFy7A1dUVGzduNGiBRFIRQuCzXVE4eeMB6sqtsGYMwyOJiIyRXs2Mp6cn/v77b2zevBkXL16EnZ0dxo0bh5EjR2pDIolM3arD1/Hzw/DIf7aDvxvDI4mIjJHeAy729vbo3r07GjdujIKCAgDA//73PwDAK6+8YpjqiCSyJ/oeFu25DACY+0pL9PZvKHFFRET0JHo1M9evX8err76KqKgoyGQyCCF0BiLVarXBCiSqaVGJSm145Jiu3gyPJCIycnoNAE+dOhU+Pj5ISUlBnTp1EB0djcOHD6Njx444dOiQgUskqjn3lLl4a/1p5BVq0Ku5C2a/zOBUIiJjp9eVmcjISBw4cADOzs6wsLCApaUlunfvjrCwMHzwwQc4f/68oeskqnalwyObu9bF8n8yPJKIyBTo9S+1Wq2Gg0PxMKSzszPu3r0LAPD29saVK1cMVx1RDVFrBKb9VBwe2cDeBmvGdIIjwyOJiEyCXldmnnvuOVy8eBE+Pj7o0qULFi9eDBsbG3z//fdo2rSpoWskqnaL91xGRExxeOT3oxkeSURkSvRqZv71r38hOzsbAPDFF1/g5ZdfRo8ePdCgQQP89NNPBi2QqLptPXUbq0vCI//v9dYMjyQiMjEyIYQwxIEePHiA+vXrG91t3lUqFRQKBZRKJRwdHaUuh4zM8fhUjF5zCkUagWlBzTAtqLnUJRERESr3/G2wICUnJydDHYqoRly/n4VJm84xPJKIyMTxrRpUK6VnF2D8utNQ5hYyPJKIyMSxmaFap3R4pGc9O6wexfBIIiJTxmaGapXHwyPXju0EFweGRxIRmTI2M1SrrD7C8EgiInPDZoZqjT3RSdrwyM8HMjySiMhcsJmhWqE4PPI8hABGd/XGmG5NpC6JiIgMhM0Mmb0kZR4mbHgUHjmH4ZFERGaFzQyZtez8Iry1/jSSVQyPJCIyV/xXncyWpiQ88tJdhkcSEZkzNjNkthbphEd2YHgkEZGZYjNDZumn04+HRzJug4jIXLGZIbNzPD4Vn+2KBgBM7dMMg9p6SlwRERFVJzYzZFZKh0cObOOBaUEMjyQiMndsZshsZOQU4K31Z6DMLUS7xvXwfwyPJCKqFdjMkFkoKNLgnY1ncSM1G5717PA9wyOJiGoNyZuZzMxMTJs2Dd7e3rCzs0O3bt1w+vRp7X4hBObMmQN3d3fY2dkhKCgI165dk7BiMjZCCPzrF4ZHEhHVVpI3MxMmTEBERAQ2btyIqKgo9O3bF0FBQbhz5w4AYPHixVi2bBlWrVqFkydPwt7eHv369UNeXp7ElZOxWH3kOradKQ6PXM7wSCKiWkcmhBBSffPc3Fw4ODhg9+7dCAkJ0W7v0KEDBgwYgPnz58PDwwMffvghPvroIwCAUqmEq6sr1q1bhxEjRjzze6hUKigUCiiVSjg6OlbbuZA09kQnYdLmsxACmDswEGOf95G6JCIiMoDKPH9LemWmqKgIarUatra2Otvt7Oxw9OhR3LhxA0lJSQgKCtLuUygU6NKlCyIjI8s9Zn5+PlQqlc4HmafoO0pM/+mCNjySjQwRUe0kaTPj4OCArl27Yv78+bh79y7UajU2bdqEyMhI3Lt3D0lJSQAAV1dXna9zdXXV7ntcWFgYFAqF9sPLy6vaz4NqXpIyD2+tP43cQjV6MjySiKhWk3xmZuPGjRBCwNPTE3K5HMuWLcPIkSNhYaFfaaGhoVAqldqPhIQEA1dMUsspeBQe2axhXaxgeCQRUa0m+TOAr68vDh8+jKysLCQkJODUqVMoLCxE06ZN4ebmBgBITk7W+Zrk5GTtvsfJ5XI4OjrqfJD50GgEpm19FB65dizDI4mIajvJm5mH7O3t4e7ujvT0dISHh2PQoEHw8fGBm5sb9u/fr12nUqlw8uRJdO3aVcJqSSqLwi9jL8MjiYioFCupCwgPD4cQAv7+/oiLi8PMmTMREBCAcePGQSaTYdq0aViwYAGaNWsGHx8fzJ49Gx4eHhg8eLDUpVMN++n0baw+zPBIIiLSJXkzo1QqERoaisTERDg5OWHIkCFYuHAhrK2LXzr4+OOPkZ2djYkTJyIjIwPdu3fHnj17yrwDisxbZHyaNjzyA4ZHEhFRKZLeZ6Ym8D4zpu/6/Sy8+t1xKHMLMbCNB5aNaMvMJSIiM2cy95khehaGRxIR0bOwmSGjVVCkwbubGB5JRERPx2aGjNLD8MgT14vDI9eM7cjwSCIiKhebGTJK3z8WHhngxnknIiIqH5sZMjrhl5Lw5Z7LAIA5LwfiBf+GEldERETGjM0MGZXoO0pM21ocHjnqH94Y062J1CUREZGRYzNDRqN0eGSPZs74fGAg37lERETPxGaGjEJOQREmbHgUHvntG+0ZHklERBXCZwuS3MPwyOg7KjgxPJKIiCqJzQxJbnH4leLwSEsLfD+K4ZFERFQ5bGZIUtvOJGDV4XgAwOLXW6NjE4ZHEhFR5bCZIclExqfh051RAIrDIwe3Y3gkERFVHpsZksSN1Gy8u+ksijQCL7d2x/SgZlKXREREJorNDNW4jJwCjF93GsrcQrT1qoclQ9vwLdhERKQ3NjNUowqKNJi06Zw2PPI/oxkeSUREVcNmhmqMEAKzf4lG5PU02NtYMjySiIgMgs0M1Zj//HUdP51JgIUMWPHP9gyPJCIig2AzQzUi/FISwv5XHB45++VAvBDA8EgiIjIMNjNU7R4PjxzL8EgiIjIgNjNUrRgeSURE1Y3NDFUbhkcSEVFN4DMLVQuNRmD6T4/CI9eMYXgkERFVDzYzVC0Wh19B+KVH4ZGNGzA8koiIqgebGTI4hkcSEVFNYjNDBnXieho+21USHvmiH8MjiYio2rGZIYN5GB5ZqBYIae2OaUHNpS6JiIhqATYzZBAZOQV4a91pZOQUh0cuHdoGFhZ8CzYREVU/NjNUZQ/DI6+XhEd+P7oDwyOJiKjGsJmhKnk8PPK/YzqioYOt1GUREVEtwmaGqqR0eOTyf7ZDC3eGRxIRUc1iM0N621sqPPJfIYF4McBV4oqIiKg2YjNDeom+o8TUkvDIN//RGOOebyJ1SUREVEuxmaFKS1LmYcL6M6XCI1syPJKIiCTDZoYq5WF4ZJIqD34N62LFP9vDmuGRREQkIT4LUYVpNAIzfrqoDY9cO6YTFHYMjyQiImlJ2syo1WrMnj0bPj4+sLOzg6+vL+bPnw8hhHZNcnIyxo4dCw8PD9SpUwf9+/fHtWvXJKy69vq/vVew51ISwyOJiMioWEn5zRctWoSVK1di/fr1aNmyJc6cOYNx48ZBoVDggw8+gBACgwcPhrW1NXbv3g1HR0d89dVXCAoKQkxMDOzt7aUsv1bZdiYBKw8Vh0cuer0VwyOJiMhoSNrMHD9+HIMGDUJISAgAoEmTJtiyZQtOnToFALh27RpOnDiB6OhotGzZEgCwcuVKuLm5YcuWLZgwYYJktdcmpcMj33/RD6+2ayRxRURERI9I+jJTt27dsH//fly9ehUAcPHiRRw9ehQDBgwAAOTn5wMAbG0f3VHWwsICcrkcR48erfmCa6HHwyOnMzySiIiMjKRXZmbNmgWVSoWAgABYWlpCrVZj4cKFeOONNwAAAQEBaNy4MUJDQ7F69WrY29vj66+/RmJiIu7du1fuMfPz87VNEACoVKoaORdzVDo8sg3DI4mIyEhJemVm27Zt2Lx5M3788UecO3cO69evx5IlS7B+/XoAgLW1NXbu3ImrV6/CyckJderUwcGDBzFgwABYWJRfelhYGBQKhfbDy8urJk/JbBSqdcMj/8PwSCIiMlIyUfqtQzXMy8sLs2bNwpQpU7TbFixYgE2bNuHy5cs6a5VKJQoKCuDi4oIuXbqgY8eO+Pbbb8scs7wrM15eXlAqlXB0ZG5QRQghELozCltPJ8DexhLbJ3Vj5hIREdUolUoFhUJRoedvSV9mysnJKXOFxdLSEhqNpsxahUIBoHgo+MyZM5g/f365x5TL5ZDL5YYvthb57183sPU0wyOJiMg0SNrMDBw4EAsXLkTjxo3RsmVLnD9/Hl999RXGjx+vXfPzzz/DxcUFjRs3RlRUFKZOnYrBgwejb9++ElZuvvZeSsK//xcLgOGRRERkGiRtZpYvX47Zs2dj8uTJSElJgYeHB9555x3MmTNHu+bevXuYMWMGkpOT4e7ujtGjR2P27NkSVm2+SodHvtGF4ZFERGQaJJ2ZqQmVec2tNktW5WHQimNIUuWhRzNnrB3biZlLREQkmco8f/PZipBboMaE9WcYHklERCaJz1i1nEYjMP2nC4i6o0T9OtYMjyQiIpPDZqaW0wmPHN2R4ZFERGRy2MzUYj8/Fh7ZieGRRERkgtjM1FInrqfhU4ZHEhGRGWAzUwvdLB0e2YrhkUREZNrYzNQyypxCjC8dHjmM4ZFERGTa2MzUIoVqDSZtPovrqdnwUNgyPJKIiMwCm5laQgiBObujcTw+DfY2llgzthMaOthKXRYREVGVsZmpJf771w1sOVUcHrlsJMMjiYjIfLCZqQUiYpK14ZGfhQSiTwuGRxIRkflgM2PmLt1VYurW89rwyPEMjyQiIjPDZsaMJavy8Na6M8gpUKNHM2fMfaUlZDK+c4mIiMwLmxkzlVugxtsbisMjfV3sGR5JRERmi89uZkijEZix7QL+TiwJjxzL8EgiIjJfbGbM0JK9V/C/6Efhkd4N7KUuiYiIqNqwmTEzP59JwHcl4ZFhrzE8koiIzB+bGTNyslR45Hsv+GFIB4ZHEhGR+WMzYyZupmbjnZLwyJdauWFGMMMjiYiodmAzYwaUOYUYv74kPLKRAkuHtmV4JBER1RpsZkycNjzyfkl45JiOsLNheCQREdUebGZMWHF45CVteOR/xzA8koiIah82MyZszdEb2HLqNmQl4ZGBHgyPJCKi2ofNjImKiEnGwj9LwiNfasHwSCIiqrXYzJig0uGR/+zSGG9195G6JCIiIsmwmTExKao8TFhfHB7Z3c8Z8xgeSUREtRybGROSW6DGhA1ncE9ZHB757RsMjyQiIuIzoYlgeCQREVH52MyYiKURxeGR1pYyrB7F8EgiIqKH2MyYgO1nE/HtweLwyC9fa43OPgyPJCIieojNjJE7eT0NoTv/BgBMecGX4ZFERESPYTNjxB4Pj/ww2F/qkoiIiIwOmxkjxfBIIiKiimEzY4QK1RpM/rFUeORohkcSERE9CZsZI/MwPPJYXBrqPAyPdGR4JBER0ZOwmTEyOuGRIxgeSURE9CySNjNqtRqzZ8+Gj48P7Ozs4Ovri/nz50MIoV2TlZWF9957D40aNYKdnR0CAwOxatUqCauuPvseC48MCmR4JBER0bNYSfnNFy1ahJUrV2L9+vVo2bIlzpw5g3HjxkGhUOCDDz4AAMyYMQMHDhzApk2b0KRJE+zduxeTJ0+Gh4cHXnnlFSnLN6hLd5X4oCQ8cmRnhkcSERFVlKRXZo4fP45BgwYhJCQETZo0weuvv46+ffvi1KlTOmvGjBmD3r17o0mTJpg4cSLatGmjs8bUlQ6PfN6vAb4YxPBIIiKiipK0menWrRv279+Pq1evAgAuXryIo0ePYsCAATprfv31V9y5cwdCCBw8eBBXr15F3759yz1mfn4+VCqVzocxKx0e2dTFHt/9swPDI4mIiCpB0peZZs2aBZVKhYCAAFhaWkKtVmPhwoV44403tGuWL1+OiRMnolGjRrCysoKFhQX+85//oGfPnuUeMywsDPPmzaupU6gSjUbgw58fhUf+MLYTFHUYHklERFQZkl4C2LZtGzZv3owff/wR586dw/r167FkyRKsX79eu2b58uU4ceIEfv31V5w9exZLly7FlClTsG/fvnKPGRoaCqVSqf1ISEioqdOptKURV/BnVHF45Ko3OzA8koiISA8yUfqtQzXMy8sLs2bNwpQpU7TbFixYgE2bNuHy5cvIzc2FQqHArl27EBISol0zYcIEJCYmYs+ePc/8HiqVCgqFAkqlEo6OxvM25x1nE/HhzxcBAEuHtmHmEhERUSmVef6W9MpMTk4OLCx0S7C0tIRGowEAFBYWorCw8KlrTNGpGw8wi+GRREREBiHpzMzAgQOxcOFCNG7cGC1btsT58+fx1VdfYfz48QAAR0dH9OrVCzNnzoSdnR28vb1x+PBhbNiwAV999ZWUpevtZmo23tl4huGRREREBiLpy0yZmZmYPXs2du3ahZSUFHh4eGDkyJGYM2cObGxsAABJSUkIDQ3F3r178eDBA3h7e2PixImYPn16hd6+bEwvMylzCvHqymO4fj8brRsp8NPErsxcIiIiKkdlnr8lbWZqgrE0M4VqDcb+cArH4tLgrrDF7inPM3OJiIjoCUxmZqa2KBse2ZGNDBERkYGwmakBj4dHtvRQSF0SERGR2WAzU80YHklERFS92MxUo5i7KoZHEhERVTM2M9UkRZWHt9afZngkERFRNWMzUw1yC9R4m+GRRERENYLPsAb2MDzyYqIS9epYY+0YhkcSERFVJzYzBvZVxFVteOTqNzugiTPDI4mIiKoTmxkD2nE2ESsOxgEAwl5rjS5NG0hcERERkfljM2MgpcMjJ/f2xesMjyQiIqoRbGYM4Fbao/DIAc+54aO+DI8kIiKqKWxmqkiZW4jx604jPacQrRsp8NWwtrCw4FuwiYiIagqbmSooVGswZfM5xN/PhrvCFv8d3ZEp2ERERDWMzYyehBD4/NdLOBqXyvBIIiIiCbGZ0dPaYzfx48ni8MhvGB5JREQkGSupCzBVDrZWsLKQ4ZP+AQhmeCQREZFk2MzoaVhHL7RvXB++LrwpHhERkZTYzFSBX8O6UpdARERU63FmhoiIiEwamxkiIiIyaWxmiIiIyKSxmSEiIiKTxmaGiIiITBqbGSIiIjJpbGaIiIjIpLGZISIiIpPGZoaIiIhMGpsZIiIiMmlsZoiIiMiksZkhIiIik8ZmhoiIiEya2admCyEAACqVSuJKiIiIqKIePm8/fB5/GrNvZjIzMwEAXl5eEldCRERElZWZmQmFQvHUNTJRkZbHhGk0Gty9excODg6QyWQGPbZKpYKXlxcSEhLg6Oho0GMbA56f6TP3c+T5mT5zP0een/6EEMjMzISHhwcsLJ4+FWP2V2YsLCzQqFGjav0ejo6OZvlD+hDPz/SZ+zny/EyfuZ8jz08/z7oi8xAHgImIiMiksZkhIiIik8Zmpgrkcjk+//xzyOVyqUupFjw/02fu58jzM33mfo48v5ph9gPAREREZN54ZYaIiIhMGpsZIiIiMmlsZoiIiMiksZkhIiIik8ZmppRvv/0WTZo0ga2tLbp06YJTp049df3PP/+MgIAA2NraolWrVvjzzz919gshMGfOHLi7u8POzg5BQUG4du1adZ7CU1Xm/P7zn/+gR48eqF+/PurXr4+goKAy68eOHQuZTKbz0b9//+o+jaeqzDmuW7euTP22trY6a0z5Mezdu3eZ85PJZAgJCdGuMabH8MiRIxg4cCA8PDwgk8nwyy+/PPNrDh06hPbt20Mul8PPzw/r1q0rs6ayv9fVqbLnuHPnTgQHB8PFxQWOjo7o2rUrwsPDddbMnTu3zGMYEBBQjWfxZJU9v0OHDpX7M5qUlKSzzlgew8qeX3m/XzKZDC1bttSuMabHLywsDJ06dYKDgwMaNmyIwYMH48qVK8/8OmN4LmQzU+Knn37CjBkz8Pnnn+PcuXNo06YN+vXrh5SUlHLXHz9+HCNHjsRbb72F8+fPY/DgwRg8eDCio6O1axYvXoxly5Zh1apVOHnyJOzt7dGvXz/k5eXV1GlpVfb8Dh06hJEjR+LgwYOIjIyEl5cX+vbtizt37uis69+/P+7du6f92LJlS02cTrkqe45A8V0rS9d/69Ytnf2m/Bju3LlT59yio6NhaWmJoUOH6qwzlscwOzsbbdq0wbfffluh9Tdu3EBISAheeOEFXLhwAdOmTcOECRN0nuz1+ZmoTpU9xyNHjiA4OBh//vknzp49ixdeeAEDBw7E+fPndda1bNlS5zE8evRodZT/TJU9v4euXLmiU3/Dhg21+4zpMazs+X3zzTc655WQkAAnJ6cyv4PG8vgdPnwYU6ZMwYkTJxAREYHCwkL07dsX2dnZT/wao3kuFCSEEKJz585iypQp2s/VarXw8PAQYWFh5a4fNmyYCAkJ0dnWpUsX8c477wghhNBoNMLNzU383//9n3Z/RkaGkMvlYsuWLdVwBk9X2fN7XFFRkXBwcBDr16/XbhszZowYNGiQoUvVW2XP8YcffhAKheKJxzO3x/Drr78WDg4OIisrS7vN2B7DhwCIXbt2PXXNxx9/LFq2bKmzbfjw4aJfv37az6v6d1adKnKO5QkMDBTz5s3Tfv7555+LNm3aGK4wA6nI+R08eFAAEOnp6U9cY6yPoT6P365du4RMJhM3b97UbjPWx08IIVJSUgQAcfjw4SeuMZbnQl6ZAVBQUICzZ88iKChIu83CwgJBQUGIjIws92siIyN11gNAv379tOtv3LiBpKQknTUKhQJdunR54jGriz7n97icnBwUFhbCyclJZ/uhQ4fQsGFD+Pv7Y9KkSUhLSzNo7RWl7zlmZWXB29sbXl5eGDRoEC5duqTdZ26P4Zo1azBixAjY29vrbDeWx7CynvU7aIi/M2Oj0WiQmZlZ5vfw2rVr8PDwQNOmTfHGG2/g9u3bElWon7Zt28Ld3R3BwcE4duyYdru5PYZr1qxBUFAQvL29dbYb6+OnVCoBoMzPW2nG8lzIZgZAamoq1Go1XF1ddba7urqWee32oaSkpKeuf/jfyhyzuuhzfo/75JNP4OHhofMD2b9/f2zYsAH79+/HokWLcPjwYQwYMABqtdqg9VeEPufo7++PtWvXYvfu3di0aRM0Gg26deuGxMREAOb1GJ46dQrR0dGYMGGCznZjegwr60m/gyqVCrm5uQb5uTc2S5YsQVZWFoYNG6bd1qVLF6xbtw579uzBypUrcePGDfTo0QOZmZkSVlox7u7uWLVqFXbs2IEdO3bAy8sLvXv3xrlz5wAY5t8uY3H37l3873//K/M7aKyPn0ajwbRp0/D888/jueeee+I6Y3kuNPvUbKq6L7/8Elu3bsWhQ4d0BmRHjBih/XOrVq3QunVr+Pr64tChQ+jTp48UpVZK165d0bVrV+3n3bp1Q4sWLbB69WrMnz9fwsoMb82aNWjVqhU6d+6ss93UH8Pa5Mcff8S8efOwe/dunZmSAQMGaP/cunVrdOnSBd7e3ti2bRveeustKUqtMH9/f/j7+2s/79atG+Lj4/H1119j48aNElZmeOvXr0e9evUwePBgne3G+vhNmTIF0dHRks3vVBavzABwdnaGpaUlkpOTdbYnJyfDzc2t3K9xc3N76vqH/63MMauLPuf30JIlS/Dll19i7969aN269VPXNm3aFM7OzoiLi6tyzZVVlXN8yNraGu3atdPWby6PYXZ2NrZu3VqhfxilfAwr60m/g46OjrCzszPIz4Sx2Lp1KyZMmIBt27aVuaT/uHr16qF58+Ym8RiWp3PnztrazeUxFEJg7dq1GDVqFGxsbJ661hgev/feew+///47Dh48iEaNGj11rbE8F7KZAWBjY4MOHTpg//792m0ajQb79+/X+T/30rp27aqzHgAiIiK06318fODm5qazRqVS4eTJk088ZnXR5/yA4gn0+fPnY8+ePejYseMzv09iYiLS0tLg7u5ukLorQ99zLE2tViMqKkpbvzk8hkDx2ybz8/Px5ptvPvP7SPkYVtazfgcN8TNhDLZs2YJx48Zhy5YtOm+rf5KsrCzEx8ebxGNYngsXLmhrN5fH8PDhw4iLi6vQ/1BI+fgJIfDee+9h165dOHDgAHx8fJ75NUbzXGiwUWITt3XrViGXy8W6detETEyMmDhxoqhXr55ISkoSQggxatQoMWvWLO36Y8eOCSsrK7FkyRIRGxsrPv/8c2FtbS2ioqK0a7788ktRr149sXv3bvH333+LQYMGCR8fH5Gbm2v05/fll18KGxsbsX37dnHv3j3tR2ZmphBCiMzMTPHRRx+JyMhIcePGDbFv3z7Rvn170axZM5GXl1fj56fPOc6bN0+Eh4eL+Ph4cfbsWTFixAhha2srLl26pF1jyo/hQ927dxfDhw8vs93YHsPMzExx/vx5cf78eQFAfPXVV+L8+fPi1q1bQgghZs2aJUaNGqVdf/36dVGnTh0xc+ZMERsbK7799lthaWkp9uzZo13zrL+zmlbZc9y8ebOwsrIS3377rc7vYUZGhnbNhx9+KA4dOiRu3Lghjh07JoKCgoSzs7NISUkx+vP7+uuvxS+//CKuXbsmoqKixNSpU4WFhYXYt2+fdo0xPYaVPb+H3nzzTdGlS5dyj2lMj9+kSZOEQqEQhw4d0vl5y8nJ0a4x1udCNjOlLF++XDRu3FjY2NiIzp07ixMnTmj39erVS4wZM0Zn/bZt20Tz5s2FjY2NaNmypfjjjz909ms0GjF79mzh6uoq5HK56NOnj7hy5UpNnEq5KnN+3t7eAkCZj88//1wIIUROTo7o27evcHFxEdbW1sLb21u8/fbbkj1JPFSZc5w2bZp2raurq3jppZfEuXPndI5nyo+hEEJcvnxZABB79+4tcyxjewwfvk338Y+H5zRmzBjRq1evMl/Ttm1bYWNjI5o2bSp++OGHMsd92t9ZTavsOfbq1eup64Uofju6u7u7sLGxEZ6enmL48OEiLi6uZk+sRGXPb9GiRcLX11fY2toKJycn0bt3b3HgwIEyxzWWx1Cfn9GMjAxhZ2cnvv/++3KPaUyPX3nnBkDn98pYnwtlJSdAREREZJI4M0NEREQmjc0MERERmTQ2M0RERGTS2MwQERGRSWMzQ0RERCaNzQwRERGZNDYzREREZNLYzBBRrXPo0CHIZDJkZGRIXQoRGQCbGSIiIjJpbGaIiIjIpLGZIaIap9FoEBYWBh8fH9jZ2aFNmzbYvn07gEcvAf3xxx9o3bo1bG1t8Y9//APR0dE6x9ixYwdatmwJuVyOJk2aYOnSpTr78/Pz8cknn8DLywtyuRx+fn5Ys2aNzpqzZ8+iY8eOqFOnDrp164YrV65U74kTUbVgM0NENS4sLAwbNmzAqlWrcOnSJUyfPh1vvvkmDh8+rF0zc+ZMLF26FKdPn4aLiwsGDhyIwsJCAMVNyLBhwzBixAhERUVh7ty5mD17NtatW6f9+tGjR2PLli1YtmwZYmNjsXr1atStW1enjs8++wxLly7FmTNnYGVlhfHjx9fI+RORYTFokohqVH5+PpycnLBv3z507dpVu33ChAnIycnBxIkT8cILL2Dr1q0YPnw4AODBgwdo1KgR1q1bh2HDhuGNN97A/fv3sXfvXu3Xf/zxx/jjjz9w6dIlXL16Ff7+/oiIiEBQUFCZGg4dOoQXXngB+/btQ58+fQAAf/75J0JCQpCbmwtbW9tq/lsgIkPilRkiqlFxcXHIyclBcHAw6tatq/3YsGED4uPjtetKNzpOTk7w9/dHbGwsACA2NhbPP/+8znGff/55XLt2DWq1GhcuXIClpSV69er11Fpat26t/bO7uzsAICUlpcrnSEQ1y0rqAoiodsnKygIA/PHHH/D09NTZJ5fLdRoafdnZ2VVonbW1tfbPMpkMQPE8DxGZFl6ZIaIaFRgYCLlcjtu3b8PPz0/nw8vLS7vuxIkT2j+np6fj6tWraNGiBQCgRYsWOHbsmM5xjx07hubNm8PS0hKtWrWCRqPRmcEhIvPFKzNEVKMcHBzw0UcfYfr06dBoNOjevTuUSiWOHTsGR0dHeHt7AwC++OILNGjQAK6urvjss8/g7OyMwYMHAwA+/PBDdOrUCfPnz8fw4cMRGRmJFStW4LvvvgMANGnSBGPGjMH48eOxbNkytGnTBrdu3UJKSgqGDRsm1akTUTVhM0NENW7+/PlwcXFBWFgYrl+/jnr16qF9+/b49NNPtS/zfPnll5g6dSquXbuGtm3b4rfffoONjQ0AoH379ti2bRvmzJmD+fPnw93dHV988QXGjh2r/R4rV67Ep59+ismTJyMtLQ2NGzfGp59+KsXpElE147uZiMioPHynUXp6OurVqyd1OURkAjgzQ0RERCaNzQwRERGZNL7MRERERCaNV2aIiIjIpLGZISIiIpPGZoaIiIhMGpsZIiIiMmlsZoiIiMiksZkhIiIik8ZmhoiIiEwamxkiIiIyaWxmiIiIyKT9P/V6qjgB5cMEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vcpi_util.show_history(history)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate the model on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9502999782562256"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(  model, test_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show 15 examples from the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images, targets = next(iter(test_loader))\n",
    "logits = model(images.to(device))\n",
    "\n",
    "predictions = torch.nn.functional.softmax(logits, dim=1).cpu().detach().numpy()\n",
    "vcpi_util.plot_predictions(images, predictions, targets, train_set.classes, 5, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some misclassified samples from test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "k = 0\n",
    "iters = 0\n",
    "\n",
    "preds = []\n",
    "ground_truth = []\n",
    "imgs = torch.Tensor(15, 1, 28,28)\n",
    "\n",
    "iterator = iter(test_loader)\n",
    "\n",
    "max_iters = test_set.__len__() / BATCH_SIZE\n",
    "while k < 15 and iters < max_iters:\n",
    "\n",
    "    images, targets = next(iterator)\n",
    "    #print(images[0].shape)\n",
    "    logits = model(images.to(device))\n",
    "    \n",
    "    #print(predictions[0])\n",
    "    predictions = torch.nn.functional.softmax(logits, dim=1).cpu().detach().numpy()\n",
    "    for i in range(len(predictions)):\n",
    "\n",
    "        if np.argmax(predictions[i]) != targets[i] and k < 15:\n",
    "\n",
    "            preds.append(predictions[i])\n",
    "            ground_truth.append(targets[i])\n",
    "            imgs[k, :, :, :] = images[i]\n",
    "            k += 1\n",
    "\n",
    "    iters += 1\n",
    "\n",
    "vcpi_util.plot_predictions(imgs, preds, ground_truth, train_set.classes, 5, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = []\n",
    "ground_truth = []\n",
    "\n",
    "for images, targets in test_loader:\n",
    "\n",
    "    logits = model(images.to(device))\n",
    "    preds_sparse = [np.argmax(x) for x in logits.cpu().detach().numpy()]\n",
    "    preds.extend(preds_sparse)\n",
    "    ground_truth.extend(targets.numpy())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vcpi_util.show_confusion_matrix(ground_truth, preds, len(train_set.classes))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "vscode": {
   "interpreter": {
    "hash": "8ca9a0304c78ff1aed536949a52a998bc579376efcff4fb61d932d04b9b39864"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}