02_pytorch_computer_vision.ipynb

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Computer vision libraries in PyTorch\n","\n","* `torchvision` - base domain library for PyTorch computer vision\n","* `torchvision.datasets` -  get datasets and data laoding functions for computer vision here\n","* `torchvision.models` - get pretrained computer vision models that you can leverage for your own problems\n","* `torchvision.transforms` - functions for manipulating your vision data (images) to be suitable for use with an ML model\n","* `torch.utils.data.Datasets` - Base dataset class for PyTorch\n","* `torch.utils.data.DataLoader` - Creates a Python iterable over a dataset"],"metadata":{"id":"9f2yjNMwDC3S"}},{"cell_type":"code","source":["# Import PyTorch\n","import torch\n","from torch import nn\n","\n","# Import torchvision\n","import torchvision\n","from torchvision import datasets\n","from torchvision import transforms\n","from torchvision.transforms import ToTensor\n","\n","# Import matplotlib for visualization\n","import matplotlib.pyplot as plt\n","\n","# Check versions\n","print(torch.__version__)\n","print(torchvision.__version__)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iYZQUaAmDRr_","executionInfo":{"status":"ok","timestamp":1711936331032,"user_tz":-330,"elapsed":5716,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"27c2ceb1-11c0-4e42-eb77-c3db841b996a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["2.2.1+cu121\n","0.17.1+cu121\n"]}]},{"cell_type":"code","source":["# Setup device-agnostic code\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","\n","# For mac silicon gpu\n","device = \"mps\" if torch.backends.mps.is_available() else device\n","device"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"F5C20TsWQTwG","executionInfo":{"status":"ok","timestamp":1711936331032,"user_tz":-330,"elapsed":6,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"b9efe4d2-888d-4fc9-dee0-ffc10dd64936"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'cuda'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":3}]},{"cell_type":"markdown","source":["## 1. Getting  a dataset\n","\n","The dataset we'll be using is FashionMNIST from torchvision.datasets"],"metadata":{"id":"GzdcMrQMFM8O"}},{"cell_type":"code","source":["# Setup the training data\n","train_data = datasets.FashionMNIST(\n","    root=\"data\",\n","    train=True,\n","    download=True,\n","    transform=ToTensor(),\n","    target_transform=None\n",")\n","\n","# Setup the testing data\n","test_data = datasets.FashionMNIST(\n","    root=\"data\",\n","    train=False,\n","    download=True,\n","    transform=ToTensor(),\n","    target_transform=None\n",")"],"metadata":{"id":"oowJRe3tGt2p","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1711936338332,"user_tz":-330,"elapsed":7305,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"e24e2777-4a40-4d82-ab0a-10fb0bfacb3f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n","Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 26421880/26421880 [00:02<00:00, 10456380.73it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw\n","\n","Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n","Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 29515/29515 [00:00<00:00, 197535.15it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n","\n","Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n","Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 4422102/4422102 [00:01<00:00, 3710662.00it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw\n","\n","Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n","Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 5148/5148 [00:00<00:00, 24370515.79it/s]"]},{"output_type":"stream","name":"stdout","text":["Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n","\n"]},{"output_type":"stream","name":"stderr","text":["\n"]}]},{"cell_type":"code","source":["# Check the length of the training and testing data\n","len(train_data), len(test_data)"],"metadata":{"id":"ddLlXhxVbK1Q","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1711936338333,"user_tz":-330,"elapsed":22,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"0489a9dc-c9ed-4ade-b5d3-2aa14e186612"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(60000, 10000)"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["# See the first training example (unpacking train data to give image and its lable)\n","image, label = train_data[0]\n","image, label"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qhGr2uGJ0zNm","executionInfo":{"status":"ok","timestamp":1711936338333,"user_tz":-330,"elapsed":20,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"802cb1e0-48e9-4d57-89a7-6ac90e0e7081"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(tensor([[[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n","           0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n","           0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n","           0.0000, 0.0000, 0.0000, 0.0000],\n","          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n","           0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n","           0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n","           0.0000, 0.0000, 0.0000, 0.0000],\n","          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 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0.8588, 0.8667, 0.8431, 0.8510, 0.8745,\n","           0.8745, 0.8784, 0.8980, 0.1137],\n","          [0.2941, 0.8000, 0.8314, 0.8000, 0.7569, 0.8039, 0.8275, 0.8824,\n","           0.8471, 0.7255, 0.7725, 0.8078, 0.7765, 0.8353, 0.9412, 0.7647,\n","           0.8902, 0.9608, 0.9373, 0.8745, 0.8549, 0.8314, 0.8196, 0.8706,\n","           0.8627, 0.8667, 0.9020, 0.2627],\n","          [0.1882, 0.7961, 0.7176, 0.7608, 0.8353, 0.7725, 0.7255, 0.7451,\n","           0.7608, 0.7529, 0.7922, 0.8392, 0.8588, 0.8667, 0.8627, 0.9255,\n","           0.8824, 0.8471, 0.7804, 0.8078, 0.7294, 0.7098, 0.6941, 0.6745,\n","           0.7098, 0.8039, 0.8078, 0.4510],\n","          [0.0000, 0.4784, 0.8588, 0.7569, 0.7020, 0.6706, 0.7176, 0.7686,\n","           0.8000, 0.8235, 0.8353, 0.8118, 0.8275, 0.8235, 0.7843, 0.7686,\n","           0.7608, 0.7490, 0.7647, 0.7490, 0.7765, 0.7529, 0.6902, 0.6118,\n","           0.6549, 0.6941, 0.8235, 0.3608],\n","          [0.0000, 0.0000, 0.2902, 0.7412, 0.8314, 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'Trouser',\n"," 'Pullover',\n"," 'Dress',\n"," 'Coat',\n"," 'Sandal',\n"," 'Shirt',\n"," 'Sneaker',\n"," 'Bag',\n"," 'Ankle boot']"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["# Get the class names with their index\n","class_to_idx = train_data.class_to_idx\n","class_to_idx"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K4aFwKC71cnV","executionInfo":{"status":"ok","timestamp":1711936338333,"user_tz":-330,"elapsed":17,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"37632641-4d63-4823-ace9-f5bb5e499c94"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'T-shirt/top': 0,\n"," 'Trouser': 1,\n"," 'Pullover': 2,\n"," 'Dress': 3,\n"," 'Coat': 4,\n"," 'Sandal': 5,\n"," 'Shirt': 6,\n"," 'Sneaker': 7,\n"," 'Bag': 8,\n"," 'Ankle boot': 9}"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["# Get the classification label index\n","train_data.targets"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sp9e4yxF1pNq","executionInfo":{"status":"ok","timestamp":1711936338333,"user_tz":-330,"elapsed":16,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"07dec922-df6d-4bcd-af3c-7ad43ed62387"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([9, 0, 0,  ..., 3, 0, 5])"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["# Shape of the image [color_channels, height, width] and label\n","image.shape, label"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"al9XNzs31126","executionInfo":{"status":"ok","timestamp":1711936338333,"user_tz":-330,"elapsed":16,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"d6e58888-cbfe-434a-badc-155aee3941c4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.Size([1, 28, 28]), 9)"]},"metadata":{},"execution_count":10}]},{"cell_type":"markdown","source":["### 1.1 Checking the `Input and Output shapes` of the data"],"metadata":{"id":"udBGERmc4d6V"}},{"cell_type":"code","source":["# Check the shape of our image\n","print(f\"Image shape: {image.shape} -> [color_channels, height, width]\")\n","print(f\"Image label: {class_names[label]}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K3AfZ3n72GaT","executionInfo":{"status":"ok","timestamp":1711936338333,"user_tz":-330,"elapsed":15,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"7c47c363-5bff-4ccc-b5da-1a84a938fa16"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Image shape: torch.Size([1, 28, 28]) -> [color_channels, height, width]\n","Image label: Ankle boot\n"]}]},{"cell_type":"markdown","source":["### 1.2 Visualizing our data"],"metadata":{"id":"lv1UixeQ2Npx"}},{"cell_type":"code","source":["# Use imshow to convert the numericals into image\n","import matplotlib.pyplot as plt\n","image, label = train_data[0]\n","print(f\"Image shape: {image.shape}\")\n","plt.imshow(image.squeeze())\n","plt.title(f\"Image label name: {class_names[label]}, Image label index: {label}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"cO6-p28v4Yd0","executionInfo":{"status":"ok","timestamp":1711936339368,"user_tz":-330,"elapsed":1050,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"2e8ec88c-83c4-4439-8cb9-def893e84833"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Image shape: torch.Size([1, 28, 28])\n"]},{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Image label name: Ankle boot, Image label index: 9')"]},"metadata":{},"execution_count":12},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Convert the image to gray scale\n","plt.imshow(image.squeeze(), cmap=\"gray\")\n","plt.title(class_names[label])\n","plt.axis(False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":446},"id":"VWfdm6h-4YbA","executionInfo":{"status":"ok","timestamp":1711936339368,"user_tz":-330,"elapsed":9,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"6cb1b43e-0786-4798-8379-cdd77898d7ec"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(-0.5, 27.5, 27.5, -0.5)"]},"metadata":{},"execution_count":13},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Plot more images (set of 16 images [4 rows 4 columns])\n","torch.manual_seed(42)\n","fig = plt.figure(figsize=(9, 9))\n","rows, cols = 4, 4\n","for i in range(1, rows * cols + 1):\n","    # TODO: what is `size` in it\n","    # INFO: `item` is used to convert a tensor into a `integer value`\n","    random_idx = torch.randint(0, len(train_data), size=[1]).item()\n","    img, label = train_data[int(random_idx)]\n","    fig.add_subplot(rows, cols, i)\n","    plt.imshow(img.squeeze(), cmap=\"gray\")\n","    plt.title(class_names[label])\n","    plt.axis(False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":752},"id":"kmYfc5a34YYG","executionInfo":{"status":"ok","timestamp":1711936340441,"user_tz":-330,"elapsed":1079,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"d892bab7-190f-480d-e94e-0e905c8a0834"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 900x900 with 16 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## 2. Prepare DataLoader\n","\n","Right now. our data is in the form of PyTorch Datasets\n","\n","`DataLoader` turns our dataset inot a `Python Iterable`\n","\n","More specifically, we want to turn our data into `batches (or mini-batches)`\n","\n","**Why would we do this?**\n","\n","1. It is more computationally efficient, as in, your computing hardware may not be able to look (store in memory) at 60000 images in on hit. So we break it down to 32 images at a time (batch size of 32).\n","2. It gives our neural network more chances to update its gradients per epoch.\n"],"metadata":{"id":"0e74zC1J4YLE"}},{"cell_type":"code","source":["# Import PyTorch DataLoader\n","from torch.utils.data import DataLoader\n","\n","# Setup the batch size hyperparameter\n","BATCH_SIZE = 32\n","\n","# Turn train dataset into iterables (batches) - with shuffle\n","train_dataloader = DataLoader(\n","    dataset=train_data,\n","    batch_size=BATCH_SIZE,\n","    shuffle=True\n",")\n","\n","# Turn test dataset into iterables (batches) - without shuffle\n","test_dataloader = DataLoader(\n","    dataset=test_data,\n","    batch_size=BATCH_SIZE,\n","    shuffle=False\n",")\n","\n","train_dataloader, test_dataloader"],"metadata":{"id":"Kmre6tTO4YIQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1711936340441,"user_tz":-330,"elapsed":15,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"c78a9c8a-5e80-45b0-9877-0d13efa62198"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(<torch.utils.data.dataloader.DataLoader at 0x7b79502381f0>,\n"," <torch.utils.data.dataloader.DataLoader at 0x7b795023b0a0>)"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["# Let's check what we have created\n","print(f\"DataLaoders: {train_dataloader, test_dataloader}\")\n","print(f\"Length of train_dataloader: {len(train_dataloader)} batches of {BATCH_SIZE}...\")\n","print(f\"Length of test_dataloader: {len(test_dataloader)} batches of {BATCH_SIZE}...\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hNdKDMCu8KcE","executionInfo":{"status":"ok","timestamp":1711936340441,"user_tz":-330,"elapsed":13,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"ab515bce-569a-4884-ef6b-ab3932814348"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["DataLaoders: (<torch.utils.data.dataloader.DataLoader object at 0x7b79502381f0>, <torch.utils.data.dataloader.DataLoader object at 0x7b795023b0a0>)\n","Length of train_dataloader: 1875 batches of 32...\n","Length of test_dataloader: 313 batches of 32...\n"]}]},{"cell_type":"code","source":["# Check out what's inside the training dataloader\n","train_features_batch, train_labels_batch = next(iter(train_dataloader))\n","print(f\"Train features batch shape: {train_features_batch.shape}\")\n","print(f\"Train features label shape: {train_labels_batch.shape}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zYbQkeHC9Mb7","executionInfo":{"status":"ok","timestamp":1711936340441,"user_tz":-330,"elapsed":12,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"3dae7b49-badf-408d-c437-821c670d168c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train features batch shape: torch.Size([32, 1, 28, 28])\n","Train features label shape: torch.Size([32])\n"]}]},{"cell_type":"code","source":["# Show a sample\n","torch.manual_seed(42) # To maintain the randomness of the code\n","random_idx = torch.randint(0, len(train_features_batch), size=[1]).item()\n","img, label = train_features_batch[random_idx], train_labels_batch[random_idx]\n","plt.imshow(img.squeeze(), cmap=\"gray\")\n","plt.title(class_names[label])\n","plt.axis(False)\n","print(f\"Image size: {img.shape}\")\n","print(f\"Label: {label}, label size: {label.shape}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":464},"id":"b_Wqe332_QQy","executionInfo":{"status":"ok","timestamp":1711936340441,"user_tz":-330,"elapsed":11,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"fddd5c6d-abb9-45ce-803c-424fcce92a3f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Image size: torch.Size([1, 28, 28])\n","Label: 6, label size: torch.Size([])\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## 3. Model 0: Build a baseline model\n","\n","When starting to build a series of machine learning modelling experiments, it's best practice to start with a baseline model.\n","\n","A baseline model is a simple model you will try and improve upon with subsequent models/experiments.\n","\n","In other words: start simplt and add complexity when necessary."],"metadata":{"id":"pwK0mjEk_iH3"}},{"cell_type":"code","source":["# Create a flatten layer\n","flatten_model = nn.Flatten()\n","\n","# Get a single sample\n","x = train_features_batch[0]\n","\n","# Flatten the sample\n","output = flatten_model(x) # perform forward pass\n","\n","# Print out what happened\n","print(f\"Shape before flattening: {x.shape} -> [color_channels, height, weight]\")\n","print(f\"Shape after flattening: {output.shape} -> [color_channels, height*weight]\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7al2HQGUj241","executionInfo":{"status":"ok","timestamp":1711936340441,"user_tz":-330,"elapsed":10,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"b6421aa2-2864-4ce3-b732-e1164b076629"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape before flattening: torch.Size([1, 28, 28]) -> [color_channels, height, weight]\n","Shape after flattening: torch.Size([1, 784]) -> [color_channels, height*weight]\n"]}]},{"cell_type":"code","source":["from torch import nn\n","\n","# Create the FashionMNIST Version 0 Model\n","class FashionMNISTModelV0(nn.Module):\n","    def __init__(self, input_shape: int, hidden_units: int, output_shape: int):\n","        super().__init__()\n","\n","        # Neural network layer stack\n","        self.layer_stack = nn.Sequential(\n","            nn.Flatten(),\n","            nn.Linear(in_features=input_shape, out_features=hidden_units),\n","            nn.Linear(in_features=hidden_units, out_features=output_shape)\n","        )\n","\n","    # Foward pass method to override the nn.Module forward method\n","    def forward(self, x: torch.Tensor):\n","        return self.layer_stack(x)"],"metadata":{"id":"0sLg7SswlPaU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Set the randomness of the model\n","torch.manual_seed(42)\n","\n","# Setup model with input parameters\n","model_0 = FashionMNISTModelV0(\n","    input_shape=784, # this is 28*28\n","    hidden_units=10, # how many units in the hidden layer\n","    output_shape=len(class_names) # one for every class\n",").to(\"cpu\")\n","\n","model_0"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CtH8_vCWmHVy","executionInfo":{"status":"ok","timestamp":1711936340441,"user_tz":-330,"elapsed":9,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"329ac770-d66d-41ea-db1b-ecf355125245"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["FashionMNISTModelV0(\n","  (layer_stack): Sequential(\n","    (0): Flatten(start_dim=1, end_dim=-1)\n","    (1): Linear(in_features=784, out_features=10, bias=True)\n","    (2): Linear(in_features=10, out_features=10, bias=True)\n","  )\n",")"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["# Dummy tensor to check model_0 is working fine\n","dummy_x = torch.rand([1, 1, 28, 28])\n","\n","# Predict using model_0 and check the output\n","model_0(dummy_x)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"upVV2Jygpcyq","executionInfo":{"status":"ok","timestamp":1711936340441,"user_tz":-330,"elapsed":8,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"fea5924a-cc84-459c-b432-7137b2be20af"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[-0.0315,  0.3171,  0.0531, -0.2525,  0.5959,  0.2112,  0.3233,  0.2694,\n","         -0.1004,  0.0157]], grad_fn=<AddmmBackward0>)"]},"metadata":{},"execution_count":22}]},{"cell_type":"markdown","source":["### 3.1 Setup loss, optimizer and evaluation metrics\n","\n","* Loss function - since we're working with multi-class data, out loss function will be `nn.CrossEntropyLoss()`\n","* Optimizer - our optimizer `torch.optim.SGD()` (stochastic gradient descent)\n","* Evaluation metric - since we're working on a classification problem, let's use accuracy as our evaluation metric"],"metadata":{"id":"w2YwUJ_qnr1D"}},{"cell_type":"code","source":["# Import the modules used to download the helper functions from the Learn PyTorch repo\n","from timeit import default_timer as timer\n","import requests\n","from pathlib import Path\n","\n","# Helper functions file download start time\n","start_time: float = timer()\n","\n","# Download helper functions from Learn PyTorch repo\n","if Path(\"helper_functions.py\").is_file():\n","    print(\"helper_functions.py already exists, skipping download...\")\n","else:\n","    print(\"Downloading helper_functions.py\")\n","    request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py\")\n","    with open(\"helper_functions.py\", \"wb\") as f:\n","        f.write(request.content)\n","\n","# # Helper functions file download end time\n","end_time: float = timer()\n","# Calculate the total time taken to download the file and print it\n","total_time: float = end_time - start_time\n","print(f\"Time taken to download the helper_functions.py: {total_time:.3f} seconds\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fMaeRGxhoxdx","executionInfo":{"status":"ok","timestamp":1711936341270,"user_tz":-330,"elapsed":836,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"2618ed90-8556-46f9-e915-c58af2228cab"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading helper_functions.py\n","Time taken to download the helper_functions.py: 0.213 seconds\n"]}]},{"cell_type":"code","source":["# Import accuracy metic\n","from helper_functions import accuracy_fn\n","\n","# Setup loss function and optimizer\n","loss_fn = nn.CrossEntropyLoss()\n","optmizer = torch.optim.SGD(\n","    params=model_0.parameters(),\n","    lr=0.01\n",")"],"metadata":{"id":"-aNHNCVmq8pm"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 3.2 Creating a function to time our experiments\n","\n","Machine learning is very experimental.\n","\n","Two of the main things you'll often want to track are:\n","1. Model's performance (loss and accuracy values etc)\n","2. How fast it runs"],"metadata":{"id":"wX0vfJA5s0Cl"}},{"cell_type":"code","source":["# Import default timer to calculate the time taken by model to train for\n","# different devices\n","from timeit import default_timer as timer\n","\n","def print_train_time(start: float, end: float, device: str | None = None) -> float:\n","    \"\"\"Prints difference between model train start and end time.\"\"\"\n","    total_time: float = end - start\n","    print(f\"Train time on {device}: {total_time:.3f} seconds\")\n","    return total_time"],"metadata":{"id":"b-6Gg31Ztb7d"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["start_time = timer()\n","# some code...\n","end_time = timer()\n","print_train_time(start=start_time, end=end_time, device=\"cpu\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GZCdzy5HuZCu","executionInfo":{"status":"ok","timestamp":1711936341270,"user_tz":-330,"elapsed":7,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"4872966b-00f1-4898-ad4d-53133b788c86"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train time on cpu: 0.000 seconds\n"]},{"output_type":"execute_result","data":{"text/plain":["5.862700004399812e-05"]},"metadata":{},"execution_count":26}]},{"cell_type":"markdown","source":["### 3.3 Creating a training loop and traning a model on batches of data\n","1. Loop through epochs.\n","2. Loop through training batches, perform training steps, calculate the train loss **`per batch`**.\n","3. Loop through testing batches, perform testing steps, calculate the test loss **`per batch`**.\n","4. Print out what's happening.\n","5. Time it all (for fun)."],"metadata":{"id":"TTKUISexvu2u"}},{"cell_type":"code","source":["# Import tqdm for progress bar\n","from tqdm.auto import tqdm\n","\n","# Set the seed and start the timer\n","torch.manual_seed(42)\n","train_time_start_on_cpu = timer()\n","\n","# Set the number of epochs (we'll keep this small for faster training time)\n","epochs = 3\n","\n","# Store the train, test loss and test accuracy for model evaluation\n","train_loss_per_batch = []\n","test_loss_per_batch = []\n","test_acc_per_batch = []\n","\n","# Create training and testing loop\n","for epoch in tqdm(range(epochs)):\n","    print(f\"Epoch: {epoch}\\n------\")\n","    ### Training\n","    train_loss = 0\n","    # Add a loop to loop through the training batches\n","    for batch, (X_train, y_train) in enumerate(train_dataloader):\n","        # Set model for training\n","        model_0.train()\n","\n","        # 1. Forward pass\n","        y_pred = model_0(X_train)\n","\n","        # 2. Calculate the train loss (per batch)\n","        loss = loss_fn(y_pred, y_train)\n","        train_loss += loss # accumulate train loss\n","\n","        # 3. Set optmizer zero grad\n","        optmizer.zero_grad()\n","\n","        # 4. Backward propagation\n","        loss.backward()\n","\n","        # 5. Gradient descent\n","        optmizer.step()\n","\n","        # Print out what's happening\n","        if batch % 400 == 0:\n","            print(f\"Looked at {batch * len(X_train)}/{len(train_dataloader.dataset)} samples.\")\n","\n","    # Calculate the train loss per batch\n","    average_train_loss = train_loss / len(train_dataloader)\n","    train_loss_per_batch.append(average_train_loss)\n","\n","    ### Testing\n","    test_loss, test_acc = 0, 0\n","    # Add a loop to loop through the test batches\n","    with torch.inference_mode():\n","        for X_test, y_test in test_dataloader:\n","            # Predict from the model\n","            test_pred = model_0(X_test)\n","\n","            # Calculate the test loss (per batch)\n","            test_loss += loss_fn(test_pred, y_test) # accumulate test loss\n","\n","            # Calculate accuracy\n","            test_acc += accuracy_fn(y_true=y_test, y_pred=test_pred.argmax(dim=1))\n","\n","        # Calculate the test loss per batch\n","        average_test_loss = test_loss / len(test_dataloader)\n","        test_loss_per_batch.append(average_test_loss)\n","\n","        # Calculate the test accuracy per batch\n","        average_test_acc = test_acc / len(test_dataloader)\n","        test_acc_per_batch.append(average_test_acc)\n","\n","    # Print out what's happening\n","    print(f\"\\nTrain loss: {average_train_loss:.4f} | Test loss: {average_test_loss:.4f} | Test acc: {average_test_acc:.4f}\")\n","\n","    # Calculate the training time\n","    train_time_end_on_cpu = timer()\n","    total_train_time_model_0 = print_train_time(\n","        start=train_time_start_on_cpu,\n","        end=train_time_end_on_cpu,\n","        device=str(next(model_0.parameters()).device)\n","    )\n","\n","    print(\"=\" * 50)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":638,"referenced_widgets":["9ad1fe3379384f60966724682e242574","e2623914ac3946e8b19e0a1948bb9163","1419b51f9a09448498878e9c84f4a26b","304db711f22f468aadc1e0a2e2810dfc","81e3a614f83743cdb4c9494750c6899f","106873ca261b44998318c01bffd38a80","ce5006abfc8943dcb7f120498b8460b7","be12d077c1484939915e5f2ce3285d62","1f3a6534d73f4834b4331a62422c04b8","5621340792504eacb03340f8ffc944a3","22587793fa2d466db3ac9f5719c60b38"]},"id":"S4YlXiJMjMZV","outputId":"fd0b23fc-885f-4ea2-9964-86b8520036d6","executionInfo":{"status":"ok","timestamp":1711936370785,"user_tz":-330,"elapsed":29520,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}}},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9ad1fe3379384f60966724682e242574"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Epoch: 0\n","------\n","Looked at 0/60000 samples.\n","Looked at 12800/60000 samples.\n","Looked at 25600/60000 samples.\n","Looked at 38400/60000 samples.\n","Looked at 51200/60000 samples.\n","\n","Train loss: 0.9096 | Test loss: 0.6290 | Test acc: 78.6342\n","Train time on cpu: 11.785 seconds\n","==================================================\n","Epoch: 1\n","------\n","Looked at 0/60000 samples.\n","Looked at 12800/60000 samples.\n","Looked at 25600/60000 samples.\n","Looked at 38400/60000 samples.\n","Looked at 51200/60000 samples.\n","\n","Train loss: 0.5573 | Test loss: 0.5441 | Test acc: 81.1302\n","Train time on cpu: 20.928 seconds\n","==================================================\n","Epoch: 2\n","------\n","Looked at 0/60000 samples.\n","Looked at 12800/60000 samples.\n","Looked at 25600/60000 samples.\n","Looked at 38400/60000 samples.\n","Looked at 51200/60000 samples.\n","\n","Train loss: 0.5027 | Test loss: 0.5137 | Test acc: 81.9988\n","Train time on cpu: 30.081 seconds\n","==================================================\n"]}]},{"cell_type":"markdown","source":["## 4. Make predictions and get Model 0 results"],"metadata":{"id":"irHGSlVOs1nP"}},{"cell_type":"code","source":["from tqdm.auto import tqdm\n","\n","torch.manual_seed(42)\n","\n","def eval_model(model: torch.nn.Module,\n","               data_loader: torch.utils.data.DataLoader,\n","               loss_fn: torch.nn.Module,\n","               accuracy_fn,\n","               device: torch.device=device):\n","    \"\"\"Returns a dictionary containing the results of model prediction on data_loader.\"\"\"\n","    loss, acc = 0, 0\n","\n","    # Put model on the target device\n","    model.to(device)\n","\n","    model.eval()\n","    with torch.inference_mode():\n","        for X, y in tqdm(data_loader):\n","            # Put data on the target device\n","            X, y = X.to(device), y.to(device)\n","\n","            # Make predictions\n","            y_pred = model(X)\n","\n","            # Accumulate the loss and acc values per batch\n","            loss += loss_fn(y_pred, y)\n","            acc += accuracy_fn(y_true=y, y_pred=y_pred.argmax(dim=1))\n","\n","        # Scale loss and acc to find the average loss and acc per batch\n","        loss /= len(data_loader)\n","        acc /= len(data_loader)\n","\n","    return {\n","        \"model_name\": model.__class__.__name__, # only works when model was created with a class\n","        \"model_acc\": acc,\n","        \"model_loss\": loss.item(), # item convert the loss type tensor value to float\n","    }\n","\n","# Calculate model - returns on test dataset\n","model_0_results = eval_model(model=model_0,\n","                             data_loader=test_dataloader,\n","                             loss_fn=loss_fn,\n","                             accuracy_fn=accuracy_fn)\n","\n","model_0_results"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":103,"referenced_widgets":["d421eb0b1689432ca335be9581603ded","ff37768d364144b3b2ac4e14344795a6","313cdeea228a46f8b3513c9cf6dbfbd7","bb28031c4d744e8bb4c8e9acf099dc43","db141e5bc3694181bd8aa86572fcc02f","112c1c10a3ae4e2b9a17d35bfa7f556d","64aa927a59004fd0a56d3677094b01e1","534724e60dc94fd08bda6e38576be635","c82e9308ad934fb9bfd444870872c5d2","cd640e2e55b54283b102f7f32fc56ef6","6bcd04f20bc84296854fc394ca515d1e"]},"id":"wQG8GYNaHtgX","executionInfo":{"status":"ok","timestamp":1711936372872,"user_tz":-330,"elapsed":2104,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"bdc6e9c4-45ef-41ce-82c0-175c17a3f081"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/313 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d421eb0b1689432ca335be9581603ded"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'model_name': 'FashionMNISTModelV0',\n"," 'model_acc': 81.9988019169329,\n"," 'model_loss': 0.5136752128601074}"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","source":["## 5. Setup device agnostic-code (for using a GPU if there is one)"],"metadata":{"id":"e1N13nGnIAX3"}},{"cell_type":"code","source":["!nvidia-smi"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ujr0f3rDJOv3","executionInfo":{"status":"ok","timestamp":1711936372873,"user_tz":-330,"elapsed":12,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"90e5717d-817d-4ffc-bece-1b214732403a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mon Apr  1 01:52:51 2024       \n","+---------------------------------------------------------------------------------------+\n","| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n","|-----------------------------------------+----------------------+----------------------+\n","| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n","|                                         |                      |               MIG M. |\n","|=========================================+======================+======================|\n","|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n","| N/A   44C    P0              25W /  70W |    151MiB / 15360MiB |      3%      Default |\n","|                                         |                      |                  N/A |\n","+-----------------------------------------+----------------------+----------------------+\n","                                                                                         \n","+---------------------------------------------------------------------------------------+\n","| Processes:                                                                            |\n","|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n","|        ID   ID                                                             Usage      |\n","|=======================================================================================|\n","+---------------------------------------------------------------------------------------+\n"]}]},{"cell_type":"code","source":["torch.cuda.is_available()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V4onf-iOJWxI","executionInfo":{"status":"ok","timestamp":1711936372873,"user_tz":-330,"elapsed":10,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"bb6c3850-9bd3-4ad4-94d8-c1283acb09e4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","source":["# Setup device-agnostic code\n","device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","\n","# For mac silicon gpu\n","device = \"mps\" if torch.backends.mps.is_available() else device\n","device"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"IoYVyRXVJf1y","executionInfo":{"status":"ok","timestamp":1711936372873,"user_tz":-330,"elapsed":9,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"804ed771-115d-4a77-9e18-91469b96a54a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'cuda'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":31}]},{"cell_type":"markdown","source":["## 6. Model 1: Building a better model with non-linearity"],"metadata":{"id":"n3Xl0cSESYmY"}},{"cell_type":"code","source":["# Import the nn module from pytorch\n","from torch import nn\n","\n","# Create the pytorch model with non-linear and linear layer\n","class FashionMNISTModelV1(nn.Module):\n","    def __init__(self, input_shape: int, hidden_units: int, output_shape: int):\n","        super().__init__()\n","\n","        # Neural network layer stack\n","        self.layer_stack = nn.Sequential(\n","            nn.Flatten(),\n","            nn.Linear(in_features=input_shape, out_features=hidden_units),\n","            nn.ReLU(),\n","            nn.Linear(in_features=hidden_units, out_features=output_shape),\n","            nn.ReLU()\n","        )\n","\n","    # Foward pass method to override the nn.Module forward method\n","    def forward(self, x: torch.Tensor):\n","        return self.layer_stack(x)"],"metadata":{"id":"A94SSphaKjjD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Set the seed to fix the randomness\n","torch.manual_seed(42)\n","\n","# Setup model with input parameters\n","model_1 = FashionMNISTModelV1(\n","    input_shape=784, # this is the ouput of the flatten later after our 28 * 28 images goes in\n","    hidden_units=10,\n","    output_shape=len(class_names)\n",").to(device) # send to the GPU it it's available\n","\n","model_1, next(model_1.parameters()).device"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WgWVj-hBQ45p","executionInfo":{"status":"ok","timestamp":1711936372873,"user_tz":-330,"elapsed":8,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"c61c343d-33a8-493d-e7e2-1ace130f1b2b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(FashionMNISTModelV1(\n","   (layer_stack): Sequential(\n","     (0): Flatten(start_dim=1, end_dim=-1)\n","     (1): Linear(in_features=784, out_features=10, bias=True)\n","     (2): ReLU()\n","     (3): Linear(in_features=10, out_features=10, bias=True)\n","     (4): ReLU()\n","   )\n"," ),\n"," device(type='cuda', index=0))"]},"metadata":{},"execution_count":33}]},{"cell_type":"markdown","source":["### 6.1 Setup loss, optimizer and evaluation metrics"],"metadata":{"id":"xTHD5sv6RIOQ"}},{"cell_type":"code","source":["from helper_functions import accuracy_fn\n","loss_fn = nn.CrossEntropyLoss() # measure how wrong our model is\n","optimizer = torch.optim.SGD(\n","    params=model_1.parameters(),\n","    lr=0.01\n",") # tries to update our model's parameters to reduce the loss"],"metadata":{"id":"cuc3AkikdL7N"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Let's creata function for:\n","* training loop - `train_step()`\n","* test loop - `test_step()`"],"metadata":{"id":"Acpgm_zdVkHa"}},{"cell_type":"code","source":["# Set the seed\n","torch.manual_seed(42)\n","\n","# Create a function for training and testing\n","### Training\n","def train_step(\n","        model: torch.nn.Module,\n","        data_loader: torch.utils.data.DataLoader,\n","        loss_fn: torch.nn.Module,\n","        optimizer: torch.optim.Optimizer,\n","        accuracy_fn, device:\n","        torch.device=device\n","    ):\n","    \"\"\"Performs a training with model trying to learn on data_loader\"\"\"\n","\n","    # Store the train loss and train accuracy\n","    train_loss, train_acc = 0, 0\n","\n","    # Set model for training\n","    model.train()\n","\n","    # Train the model for each batch\n","    for batch, (X, y) in enumerate(train_dataloader):\n","        # Put data on target device\n","        X, y = X.to(device), y.to(device)\n","\n","        # 1. Forward pass (output the raw logits from the model)\n","        y_pred = model(X)\n","\n","        # 2. Calculate the loss and acc per batch\n","        loss = loss_fn(y_pred, y)\n","        train_loss += loss\n","\n","        accuracy = accuracy_fn(y_true=y,\n","                               y_pred=y_pred.argmax(dim=1)) # go from logits -> prediction labels\n","        train_acc += accuracy\n","\n","        # 3. Zero the gradient\n","        optimizer.zero_grad()\n","\n","        # 4. Backpropagation\n","        loss.backward()\n","\n","        # 5. Gradient descent\n","        optimizer.step()\n","\n","        # Print out what's happening\n","        if batch % 400 == 0:\n","            print(f\"Processing for {batch * len(X)}/{len(data_loader.dataset)} samples\")\n","\n","    print(f\"Processed all samples\")\n","\n","    # Calculate the train loss per batch\n","    train_loss = train_loss / len(data_loader)\n","\n","    # Calculate the train accuracy per batch\n","    train_acc = train_acc / len(data_loader)\n","\n","    # Print out the metrics\n","    print(f\"\\nTrain loss: {train_loss} | Train accuracy: {train_acc}\")\n","\n","\n","### Test\n","def test_step(model: nn.Module,\n","              data_loader: torch.utils.data.DataLoader,\n","              loss_fn: torch.nn.Module,\n","              accuracy_fn,\n","              device: torch.device=device):\n","    \"\"\"Performs a testing loop step on model going over data_loader\"\"\"\n","\n","    # # Test the model\n","    # print(f\"Test the model\\n-------\")\n","\n","    # Store the test loss and test accuracy\n","    test_loss, test_acc = 0, 0\n","\n","    # Put model to the target device\n","    model.to(device)\n","\n","    # Put model to the eval mode\n","    model.eval()\n","\n","    # Model inference mode disable the gradient tracking\n","    with torch.inference_mode():\n","        # Evaluate the model for each test batch\n","        for X, y in data_loader:\n","            # Put data to the target device\n","            X, y = X.to(device), y.to(device)\n","\n","            # 1. Forward pass\n","            y_pred = model(X)\n","\n","            # 2. Calculate the loss\n","            loss = loss_fn(y_pred, y)\n","            test_loss += loss\n","\n","            accuracy = accuracy_fn(y_true=y, y_pred=y_pred.argmax(dim=1))\n","            test_acc += accuracy\n","\n","        # Calculat the test loss per batch\n","        test_loss = test_loss / len(data_loader)\n","\n","        # Calculate the test accuracy\n","        test_acc = test_acc / len(data_loader)\n","\n","        # Print out the metrics per batch\n","        print(f\"Test loss: {test_loss} | Test accuracy: {test_acc}\\n\")"],"metadata":{"id":"Jrc9Myf8dqYF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Set the seed\n","torch.manual_seed(42)\n","torch.cuda.manual_seed(42)\n","\n","# Import the timer\n","from timeit import default_timer as timer\n","train_time_start_on_gpu = timer()\n","\n","# Set the epochs\n","epochs = 3\n","\n","# Loop through the epochs to train the model\n","for epoch in tqdm(range(epochs)):\n","    # Print out the epochs\n","    print(f\"Epoch: {epoch}\\n------\")\n","\n","    # Train the model\n","    train_step(model_1, train_dataloader, loss_fn, optimizer, accuracy_fn, device)\n","\n","    # Test the model\n","    test_step(model_1, test_dataloader, loss_fn, accuracy_fn, device)\n","\n","    print(\"=\" * 50)\n","\n","train_time_end_on_gpu = timer()\n","total_train_time_model_1 = print_train_time(start=train_time_start_on_gpu,\n","                                            end=train_time_end_on_gpu,\n","                                            device=device)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":763,"referenced_widgets":["488214e609734c4d87a84cc6750cea84","afd1d9bf2825488cbbcff868f346144e","feb6b71d9bdc4f4f992bc8f9d30d606d","718c6f71a7d4451f91b21b7484e7648e","8534b7238e484d4290db62c131d34846","88a918162c0c45549b1b902e0f5ae962","2b0ee7f85b0e43c8a0147285d478edce","a3f38e9749c94c41afae99656191b071","65990298025643beb05aa7c747941bae","dbc8235a2a6043c6a93c3a8b0ab741a4","5f05969ba2cc4827913c97c21147fd77"]},"id":"WUF_rK6RkPTm","executionInfo":{"status":"ok","timestamp":1711936423298,"user_tz":-330,"elapsed":50432,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"fb61c02c-e68d-4d85-e2c2-a85346c2cd5d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"488214e609734c4d87a84cc6750cea84"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Epoch: 0\n","------\n","Processing for 0/60000 samples\n","Processing for 12800/60000 samples\n","Processing for 25600/60000 samples\n","Processing for 38400/60000 samples\n","Processing for 51200/60000 samples\n","Processed all samples\n","\n","Train loss: 1.4217853546142578 | Train accuracy: 54.28666666666667\n","Test loss: 1.1216751337051392 | Test accuracy: 62.15055910543131\n","\n","==================================================\n","Epoch: 1\n","------\n","Processing for 0/60000 samples\n","Processing for 12800/60000 samples\n","Processing for 25600/60000 samples\n","Processing for 38400/60000 samples\n","Processing for 51200/60000 samples\n","Processed all samples\n","\n","Train loss: 1.033589243888855 | Train accuracy: 64.61166666666666\n","Test loss: 1.0001887083053589 | Test accuracy: 64.47683706070288\n","\n","==================================================\n","Epoch: 2\n","------\n","Processing for 0/60000 samples\n","Processing for 12800/60000 samples\n","Processing for 25600/60000 samples\n","Processing for 38400/60000 samples\n","Processing for 51200/60000 samples\n","Processed all samples\n","\n","Train loss: 0.9599606990814209 | Train accuracy: 66.05166666666666\n","Test loss: 0.9633837938308716 | Test accuracy: 65.40535143769968\n","\n","==================================================\n","Train time on cuda: 50.442 seconds\n"]}]},{"cell_type":"markdown","source":["> **NOTE:** Sometimes, depending on your data/hardware you might find that your model trains faster on CPU then GPU.\n",">\n","> Why is this?\n",">\n","> 1. It could be that the overhead for copying data/model to and from th4e GPU outweighs the compute benefits offered by the GPU.\n","> 2. The hardware you're using has a better CPU in terms compute capability than the GPU."],"metadata":{"id":"MwTucJ-0ITOQ"}},{"cell_type":"markdown","source":["### 6.2 Evaluate the model"],"metadata":{"id":"17Wbq95lke6w"}},{"cell_type":"code","source":["model_1_results = eval_model(model=model_1,\n","                             data_loader=test_dataloader,\n","                             loss_fn=loss_fn,\n","                             accuracy_fn=accuracy_fn,\n","                             device=device)\n","\n","model_1_results"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":103,"referenced_widgets":["35f6e427ca1f42aabeea00e2908f8459","44e63018a54b43a38e7d486fbff9a5fd","5dee7dd3fee44a4b8b49e0d6561307b3","f5c0fe799b374d0a95636e9f34313f70","9ab20b03e4b04117ac9a9e061a095e53","182c8096514b4cff8d9175194bd22e87","13df46efcdcc484eba548a2d30a2f0db","bc132e6cb3564488bad634d4e567407e","d21ca355c4574b4fa563345568934233","bcc35e8c1ff841d09a1ee6e4cf10118f","8e2eea06bd434d769cae06ecd3acbf89"]},"id":"plSZ3BNPObBb","executionInfo":{"status":"ok","timestamp":1711936427458,"user_tz":-330,"elapsed":4167,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"7581cca5-3904-41e6-de90-18afa73a238d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/313 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"35f6e427ca1f42aabeea00e2908f8459"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'model_name': 'FashionMNISTModelV1',\n"," 'model_acc': 65.40535143769968,\n"," 'model_loss': 0.9633837938308716}"]},"metadata":{},"execution_count":37}]},{"cell_type":"code","source":["model_0_results"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iveA_dQPOq7u","executionInfo":{"status":"ok","timestamp":1711936427458,"user_tz":-330,"elapsed":12,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"4481a445-d9a8-468e-df1e-f52887a41f1a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'model_name': 'FashionMNISTModelV0',\n"," 'model_acc': 81.9988019169329,\n"," 'model_loss': 0.5136752128601074}"]},"metadata":{},"execution_count":38}]},{"cell_type":"markdown","source":["## Model 2: Building a Convolutional Neural Network (CNN)\n","\n","CNN's are also known ConvNets\n","\n","CNN's are known for their capabilities to find patterns in visual data.\n","\n","To find out what's happening inside a CNN, see this website: https://poloclub.github.io/cnn-explainer"],"metadata":{"id":"s2N1XKvmOvAb"}},{"cell_type":"code","source":["import torch\n","from torch import nn\n","\n","# Create a convolutional neural network\n","class FashionMNISTModelV2(nn.Module):\n","    \"\"\"\n","    Model architecture that replicates the TinyVGG\n","    Model from CNN explainer website.\n","    \"\"\"\n","    def __init__(self, input_shape: int, hidden_units: int, output_shape: int):\n","        super().__init__()\n","        self.conv_block_1 = nn.Sequential(\n","            nn.Conv2d(in_channels=input_shape, # number of channels to the image\n","                      out_channels=hidden_units,\n","                      kernel_size=3, # it can be tuple as well (3, 3)\n","                      stride=1,\n","                      padding=1), # values we can set ourselves in our NN's are called hyperparameters\n","            nn.ReLU(),\n","            nn.Conv2d(in_channels=hidden_units,\n","                      out_channels=hidden_units,\n","                      kernel_size=3,\n","                      stride=1,\n","                      padding=1),\n","            nn.ReLU(),\n","            nn.MaxPool2d(kernel_size=2)\n","        )\n","        self.conv_block_2 = nn.Sequential(\n","            nn.Conv2d(in_channels=hidden_units,\n","                      out_channels=hidden_units,\n","                      kernel_size=3,\n","                      stride=1,\n","                      padding=1),\n","            nn.ReLU(),\n","            nn.Conv2d(in_channels=hidden_units,\n","                      out_channels=hidden_units,\n","                      kernel_size=3,\n","                      stride=1,\n","                      padding=1),\n","            nn.ReLU(),\n","            nn.MaxPool2d(kernel_size=2)\n","        )\n","        self.classifier = nn.Sequential(\n","            nn.Flatten(),\n","            nn.Linear(in_features=hidden_units*7*7,\n","                      out_features=output_shape)\n","        )\n","\n","    def forward(self, x: torch.Tensor):\n","        x = self.conv_block_1(x)\n","        # print(f\"Output shape of conv_block_1: {x.shape}\")\n","        x = self.conv_block_2(x)\n","        # print(f\"Output shape of conv_block_2: {x.shape}\")\n","        x = self.classifier(x)\n","        # print(f\"Output shape of classifier: {x.shape}\")\n","        return x"],"metadata":{"id":"plCDwDTMYVaA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["image.shape[0] # this is the channels"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OcYHiE9feSsN","executionInfo":{"status":"ok","timestamp":1711936427458,"user_tz":-330,"elapsed":11,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"3f3c1072-a479-43c0-c027-111cd7556cd1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1"]},"metadata":{},"execution_count":40}]},{"cell_type":"code","source":["class_names, len(class_names)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-Jl_FGdYejFA","executionInfo":{"status":"ok","timestamp":1711936427458,"user_tz":-330,"elapsed":10,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"05e8d564-55bf-4219-c167-001567b25498"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(['T-shirt/top',\n","  'Trouser',\n","  'Pullover',\n","  'Dress',\n","  'Coat',\n","  'Sandal',\n","  'Shirt',\n","  'Sneaker',\n","  'Bag',\n","  'Ankle boot'],\n"," 10)"]},"metadata":{},"execution_count":41}]},{"cell_type":"code","source":["# Set the seed\n","torch.manual_seed(42)\n","torch.cuda.manual_seed(42)\n","\n","model_2 = FashionMNISTModelV2(input_shape=1,\n","                              hidden_units=10,\n","                              output_shape=len(class_names)).to(device)"],"metadata":{"id":"mf2ng2jLa1Lk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.imshow(image.squeeze(), cmap=\"gray\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"do2D-l6IIeQq","executionInfo":{"status":"ok","timestamp":1711936427458,"user_tz":-330,"elapsed":9,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"57f0871a-1a1b-4070-c9e1-3657b2daa93a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7b793da05e40>"]},"metadata":{},"execution_count":43},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Random tensor to test the forward pass\n","random_image_tensor = torch.randn(size=(1, 28, 28))\n","\n","random_image_tensor.unsqueeze(dim=0).shape, \\\n","random_image_tensor.unsqueeze(dim=1).shape, \\\n","random_image_tensor.unsqueeze(dim=2).shape, \\\n","random_image_tensor.unsqueeze(dim=3).shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Jp5ULvPlJVCR","executionInfo":{"status":"ok","timestamp":1711936427458,"user_tz":-330,"elapsed":8,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"83cb969e-f995-44d1-8398-82adcb9cdd1c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(torch.Size([1, 1, 28, 28]),\n"," torch.Size([1, 1, 28, 28]),\n"," torch.Size([1, 28, 1, 28]),\n"," torch.Size([1, 28, 28, 1]))"]},"metadata":{},"execution_count":44}]},{"cell_type":"code","source":["# Pass through the model\n","model_2(random_image_tensor.unsqueeze(dim=0).to(device))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t1RmXwZpI5P6","executionInfo":{"status":"ok","timestamp":1711936427458,"user_tz":-330,"elapsed":7,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"7b3b33f8-a387-469c-fab8-956a8c81bad8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[ 0.0366, -0.0940,  0.0686, -0.0485,  0.0068,  0.0290,  0.0132,  0.0084,\n","         -0.0030, -0.0185]], device='cuda:0', grad_fn=<AddmmBackward0>)"]},"metadata":{},"execution_count":45}]},{"cell_type":"code","source":["model_2.state_dict()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_Kz0MYY-hGAC","executionInfo":{"status":"ok","timestamp":1711936427982,"user_tz":-330,"elapsed":530,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"5ed3ecfa-991e-4fd4-ac40-51a759514ac4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["OrderedDict([('conv_block_1.0.weight',\n","              tensor([[[[ 0.2548,  0.2767, -0.0781],\n","                        [ 0.3062, -0.0730,  0.0673],\n","                        [-0.1623,  0.1958,  0.2938]]],\n","              \n","              \n","                      [[[-0.2445,  0.2897,  0.0624],\n","                        [ 0.2463,  0.0451,  0.1607],\n","                        [-0.0471,  0.2570,  0.0493]]],\n","              \n","              \n","                      [[[-0.1556,  0.0850, -0.1536],\n","                        [-0.0391, -0.1354,  0.2211],\n","                        [-0.2631, -0.1537, -0.0941]]],\n","              \n","              \n","                      [[[-0.2004,  0.0315, -0.3292],\n","                        [ 0.3010, -0.2832,  0.2573],\n","                        [ 0.0555, -0.1082,  0.2060]]],\n","              \n","              \n","                      [[[ 0.0520,  0.2693,  0.0364],\n","                        [-0.1051,  0.0896, -0.0904],\n","                        [ 0.1403,  0.2976,  0.1927]]],\n","              \n","              \n","                      [[[-0.1457,  0.1924,  0.0596],\n","                        [ 0.1693, -0.2032, -0.3300],\n","                        [-0.1288, -0.2557,  0.2735]]],\n","              \n","              \n","                      [[[ 0.0960,  0.1381,  0.1054],\n","                        [-0.0058,  0.2609, -0.2368],\n","                        [ 0.0210, -0.2275,  0.1028]]],\n","              \n","              \n","                      [[[-0.1148,  0.1021, -0.0694],\n","                        [ 0.2765, -0.1976, -0.1988],\n","                        [-0.1988,  0.2998,  0.1111]]],\n","              \n","              \n","                      [[[ 0.3208, -0.2751, -0.3306],\n","                        [-0.2608, -0.2242,  0.1350],\n","                        [ 0.1194,  0.2770, -0.1721]]],\n","              \n","              \n","                      [[[-0.2272,  0.1769, -0.1347],\n","                        [ 0.2023, -0.0791,  0.1907],\n","                        [-0.2590, -0.1682,  0.1016]]]], device='cuda:0')),\n","             ('conv_block_1.0.bias',\n","              tensor([ 0.0705, -0.0850,  0.1987,  0.2266, -0.2417, -0.1780,  0.3052, -0.1125,\n","                      -0.1182, -0.3225], device='cuda:0')),\n","             ('conv_block_1.2.weight',\n","              tensor([[[[-0.0604,  0.0263, -0.0139],\n","                        [-0.0765,  0.0025, -0.0720],\n","                        [-0.0894, -0.0580, -0.0923]],\n","              \n","                       [[-0.0671,  0.1054,  0.0199],\n","                        [ 0.0325, -0.0983, -0.0692],\n","                        [-0.0351,  0.0165, -0.0928]],\n","              \n","                       [[-0.0454, -0.0631,  0.0003],\n","                        [-0.0392, -0.0073, -0.0714],\n","                        [-0.0724, -0.0615, -0.0361]],\n","              \n","                       [[-0.0832,  0.0884, -0.0209],\n","                        [ 0.0907,  0.0328, -0.0893],\n","                        [ 0.0729, -0.0290, -0.0404]],\n","              \n","                       [[-0.0875, -0.1048,  0.0302],\n","                        [-0.0230,  0.0410, -0.0865],\n","                        [ 0.0783, -0.0774, -0.0182]],\n","              \n","                       [[ 0.0220,  0.0544,  0.0851],\n","                        [ 0.0960, -0.0836,  0.0265],\n","                        [-0.0453, -0.0116, -0.0789]],\n","              \n","                       [[ 0.0960, -0.0774,  0.0563],\n","                        [ 0.0370,  0.0343, -0.0570],\n","                        [ 0.0958,  0.0232,  0.0136]],\n","              \n","                       [[-0.0929,  0.0442, -0.0158],\n","                        [-0.0483,  0.0905,  0.0235],\n","                        [-0.0583, -0.0534, -0.0050]],\n","              \n","                       [[ 0.0589, -0.0269, -0.0601],\n","                        [-0.0361, -0.0787,  0.0376],\n","                        [ 0.0816, -0.0992,  0.0245]],\n","              \n","                       [[ 0.0545,  0.0191, -0.0375],\n","                        [ 0.0550,  0.0554,  0.0394],\n","                        [-0.0185, -0.0279,  0.0113]]],\n","              \n","              \n","                      [[[-0.0186, -0.0314,  0.0674],\n","                        [ 0.0906, -0.0104, -0.0236],\n","                        [ 0.0015, -0.0063,  0.0253]],\n","              \n","                       [[ 0.0295, -0.0957, -0.0389],\n","                        [ 0.0888,  0.0411, -0.0052],\n","                        [-0.0636, -0.0645, -0.0944]],\n","              \n","                       [[-0.0344,  0.0356,  0.0672],\n","                        [ 0.0487, -0.0932, -0.0634],\n","                        [-0.0166,  0.1020,  0.0152]],\n","              \n","                       [[-0.0273,  0.0436, -0.0401],\n","                        [-0.0682,  0.0769, -0.0479],\n","                        [-0.0211, -0.1049,  0.0705]],\n","              \n","                       [[ 0.0799,  0.0384, -0.0735],\n","                        [-0.1040, -0.0856,  0.0786],\n","                        [ 0.0506,  0.0887,  0.0552]],\n","              \n","                       [[ 0.0267, -0.0010, -0.0802],\n","                        [-0.0903, -0.0986,  0.0432],\n","                        [-0.0518, -0.0212, -0.0607]],\n","              \n","                       [[-0.0192, -0.0742, -0.0689],\n","                        [ 0.0350, -0.0313,  0.0651],\n","                        [-0.0338, -0.0773, -0.0186]],\n","              \n","                       [[-0.0511, -0.0322, -0.1003],\n","                        [ 0.0590, -0.0734,  0.0530],\n","                        [ 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1.7727e-02],\n","                        [-6.3437e-02, -5.7241e-02,  8.3964e-02],\n","                        [ 7.5834e-02,  6.1033e-02, -8.2189e-02]]],\n","              \n","              \n","                      [[[ 8.2092e-02, -1.0076e-02,  7.7661e-02],\n","                        [ 9.1553e-02,  1.1554e-02, -4.3863e-02],\n","                        [ 9.9153e-02, -5.4931e-02,  6.8876e-02]],\n","              \n","                       [[-1.0108e-01, -3.3153e-02, -9.1902e-02],\n","                        [-4.7284e-02,  4.4759e-02, -7.5529e-02],\n","                        [-9.1158e-02,  7.5371e-02,  5.6270e-02]],\n","              \n","                       [[-1.1527e-03, -7.4309e-02, -2.7927e-02],\n","                        [-3.4129e-02,  6.5100e-02, -3.4478e-02],\n","                        [-3.0360e-02, -7.4720e-02, -4.9646e-02]],\n","              \n","                       [[ 5.7074e-02,  6.7914e-02,  1.5315e-02],\n","                        [-3.9549e-02,  1.0124e-01,  2.0806e-02],\n","                        [-4.0688e-02, -3.6535e-02, -1.4752e-02]],\n","              \n","                       [[ 4.9974e-02,  3.8555e-02,  7.6418e-02],\n","                        [-4.7494e-03,  8.7183e-02, -4.2816e-02],\n","                        [-4.8547e-02, -3.8927e-02, -9.8896e-02]],\n","              \n","                       [[-6.9195e-02, -9.5382e-02, -6.2294e-03],\n","                        [ 9.9374e-04, -2.7358e-02, -7.2035e-02],\n","                        [ 9.5637e-02, -3.4926e-02,  5.0233e-02]],\n","              \n","                       [[ 7.3408e-02, -6.9291e-02, -1.3179e-02],\n","                        [ 6.0923e-02,  1.0218e-01, -1.3299e-02],\n","                        [ 7.6382e-02, -8.2732e-02, -6.8489e-02]],\n","              \n","                       [[ 8.6682e-02, -9.9801e-03,  1.0414e-01],\n","                        [ 7.6651e-03, -4.3714e-02,  1.0011e-01],\n","                        [ 9.2179e-02,  9.7826e-03, -6.3900e-02]],\n","              \n","                       [[-4.5639e-03, -5.0693e-02,  7.6810e-02],\n","                        [ 4.8829e-03,  2.2191e-02,  6.3927e-02],\n","                        [ 3.4916e-02, -6.5803e-02,  8.7566e-02]],\n","              \n","                       [[ 6.4758e-02, -6.5073e-02,  7.9700e-02],\n","                        [ 2.9905e-02, -2.0750e-02, -7.5385e-02],\n","                        [-1.7490e-02, -1.0335e-01,  6.0163e-02]]],\n","              \n","              \n","                      [[[ 7.6343e-02, -3.0347e-02,  9.7720e-02],\n","                        [-3.9032e-02,  1.8051e-02, -7.3459e-02],\n","                        [-4.4565e-03,  4.2610e-02,  4.5403e-02]],\n","              \n","                       [[-3.5346e-03, -5.3154e-02,  7.3680e-02],\n","                        [ 6.9788e-02,  1.6916e-02, -4.8475e-02],\n","                        [ 2.2349e-02,  2.8186e-04,  9.6302e-02]],\n","              \n","                       [[ 1.5621e-02,  8.1301e-03,  7.2057e-03],\n","                        [ 5.6079e-02, -1.3024e-03,  9.0351e-02],\n","                        [ 5.4917e-02, -7.9650e-02, -1.2063e-06]],\n","              \n","                       [[-8.9472e-02, -8.0934e-02,  2.0480e-02],\n","                        [ 2.3687e-02, -9.2246e-03,  1.0019e-01],\n","                        [-5.6627e-02, -4.4176e-02, -1.6881e-02]],\n","              \n","                       [[ 6.3911e-04, -8.9284e-03,  9.4909e-02],\n","                        [-4.4519e-02, -5.5137e-02,  9.0599e-03],\n","                        [ 7.9171e-02,  2.5019e-02,  5.6787e-02]],\n","              \n","                       [[ 2.0406e-02,  8.9839e-02,  6.3311e-02],\n","                        [ 7.5428e-02, -1.4198e-02, -8.7268e-02],\n","                        [-5.0002e-02,  3.5910e-02,  7.3950e-02]],\n","              \n","                       [[-4.1184e-02,  8.7218e-02,  1.5150e-02],\n","                        [ 4.1869e-04,  4.1093e-03, -1.8623e-02],\n","                        [ 9.8683e-02,  4.5784e-03,  6.4564e-02]],\n","              \n","                       [[-8.8967e-02, -5.4309e-02,  1.1852e-02],\n","                        [ 8.4169e-02,  5.0184e-02,  2.0076e-02],\n","                        [-1.0414e-01,  1.9816e-03, -6.9581e-02]],\n","              \n","                       [[-9.0006e-02,  1.4414e-02, -6.6693e-02],\n","                        [ 9.5674e-02, -5.7294e-02,  3.3970e-02],\n","                        [ 6.1871e-02, -8.1928e-02,  5.3946e-02]],\n","              \n","                       [[-1.4114e-02,  5.4619e-02,  1.0201e-01],\n","                        [-4.4922e-02, -4.5653e-02,  8.3753e-02],\n","                        [ 1.1722e-02, -1.0513e-02,  7.9971e-02]]],\n","              \n","              \n","                      [[[-5.0928e-02, -5.2047e-03,  7.2403e-02],\n","                        [ 4.1195e-02, -6.8180e-02,  2.7398e-02],\n","                        [-8.0368e-02, -5.7245e-02,  6.7779e-02]],\n","              \n","                       [[-2.8093e-02, -5.3691e-02,  7.4717e-03],\n","                        [ 2.5759e-02, -6.5524e-02, -7.1084e-02],\n","                        [-1.0209e-01,  2.7236e-02, -6.8013e-02]],\n","              \n","                       [[ 8.0331e-03, -2.3576e-02, -6.8923e-02],\n","                        [-3.3636e-02, -8.1027e-02, -5.5797e-02],\n","                        [-3.2857e-03, -9.0116e-02, -9.2447e-02]],\n","              \n","                       [[ 7.8958e-02,  9.9188e-03, -4.6618e-02],\n","                        [-3.5047e-03,  7.8168e-02, -8.7939e-02],\n","                        [-5.5886e-02, -7.6226e-02, -7.6634e-03]],\n","              \n","                       [[-3.6274e-03, -8.2146e-02,  7.3163e-02],\n","                        [-8.0946e-02,  9.8414e-02, -7.2560e-02],\n","                        [-1.4446e-02,  1.9710e-02, -4.6852e-02]],\n","              \n","                       [[ 9.6939e-02, -7.2673e-02, -5.8427e-03],\n","                        [-7.7398e-02,  2.9261e-02,  8.9871e-02],\n","                        [ 9.7776e-02,  1.2514e-02, -5.2773e-02]],\n","              \n","                       [[ 1.0244e-01,  7.8667e-03,  7.1317e-02],\n","                        [-5.4751e-02, -4.8920e-02, -8.7504e-02],\n","                        [ 9.6990e-02,  1.7486e-02, -7.5704e-02]],\n","              \n","                       [[ 9.0535e-03, -4.5211e-02,  5.2659e-03],\n","                        [ 3.4988e-02, -5.2308e-02,  1.8394e-02],\n","                        [-6.6553e-02,  2.0312e-02, -1.0178e-01]],\n","              \n","                       [[ 1.6797e-02,  1.0473e-01,  9.7094e-02],\n","                        [ 3.8451e-02,  7.7563e-02,  1.0248e-01],\n","                        [ 2.9870e-02,  3.5156e-02,  1.3707e-02]],\n","              \n","                       [[ 9.3322e-02,  9.0551e-02, -4.9570e-02],\n","                        [-4.3333e-03, -5.3110e-02,  3.7824e-02],\n","                        [-1.0214e-01,  3.7301e-02, -2.8929e-02]]],\n","              \n","              \n","                      [[[ 3.8227e-02,  3.2899e-02, -5.2454e-02],\n","                        [ 5.4687e-02,  4.4762e-02, -8.9602e-02],\n","                        [ 1.0517e-01,  9.0731e-02,  6.5584e-02]],\n","              \n","                       [[-1.0699e-02,  3.7345e-02, -5.7028e-02],\n","                        [-3.5818e-02,  4.9749e-02,  4.6925e-02],\n","                        [ 4.1741e-02, -1.0053e-01,  8.7350e-02]],\n","              \n","                       [[-4.4028e-02,  9.1223e-02,  8.6852e-02],\n","                        [ 3.9070e-02,  1.0502e-01,  6.0528e-02],\n","                        [ 6.1821e-02, -3.5794e-02,  9.7766e-02]],\n","              \n","                       [[ 2.7627e-02,  6.2280e-02, -2.3834e-02],\n","                        [ 7.6340e-02,  9.3509e-02, -8.0770e-02],\n","                        [ 8.6415e-02, -6.9664e-02, -7.2571e-02]],\n","              \n","                       [[-8.8089e-02,  3.0459e-02, -7.9144e-02],\n","                        [-3.9680e-02, -5.2988e-02,  2.8172e-02],\n","                        [-1.0349e-01, -4.8324e-02,  7.7112e-04]],\n","              \n","                       [[ 9.4660e-03, -4.7605e-02,  3.7764e-02],\n","                        [-6.9544e-02, -8.9270e-02, -1.4986e-02],\n","                        [-5.6989e-02,  6.6443e-02, -7.2049e-02]],\n","              \n","                       [[-8.8494e-03,  4.3782e-02, -9.2311e-02],\n","                        [ 8.1599e-02, -4.7895e-02, -2.8684e-02],\n","                        [-6.4480e-02, -3.9279e-02, -4.0645e-02]],\n","              \n","                       [[-9.3801e-02,  3.6019e-02, -3.3768e-04],\n","                        [ 1.0311e-01,  7.1117e-02,  9.1699e-02],\n","                        [ 3.1014e-02,  5.5388e-02,  9.8704e-02]],\n","              \n","                       [[ 8.6545e-02, -8.0996e-02, -2.3636e-02],\n","                        [-1.0166e-01,  3.9877e-03, -3.7229e-02],\n","                        [ 9.1486e-02,  1.6666e-02,  1.1601e-03]],\n","              \n","                       [[-7.6248e-02, -8.2718e-02,  1.6594e-02],\n","                        [-5.2376e-02, -4.8409e-02,  7.3938e-02],\n","                        [-5.4952e-02, -4.6918e-02,  8.0934e-02]]]], device='cuda:0')),\n","             ('conv_block_2.2.bias',\n","              tensor([ 0.0412, -0.0599,  0.0319,  0.0531, -0.0936,  0.0197,  0.0241, -0.0041,\n","                       0.1011, -0.0697], device='cuda:0')),\n","             ('classifier.1.weight',\n","              tensor([[ 0.0245, -0.0240, -0.0387,  ...,  0.0094, -0.0015, -0.0225],\n","                      [ 0.0228,  0.0067, -0.0439,  ..., -0.0302,  0.0368,  0.0293],\n","                      [ 0.0303,  0.0347, -0.0211,  ...,  0.0207, -0.0423, -0.0240],\n","                      ...,\n","                      [-0.0359, -0.0343,  0.0166,  ...,  0.0324,  0.0113, -0.0143],\n","                      [-0.0294, -0.0316,  0.0251,  ..., -0.0056,  0.0300, -0.0396],\n","                      [-0.0246, -0.0035, -0.0046,  ..., -0.0146, -0.0358,  0.0175]],\n","                     device='cuda:0')),\n","             ('classifier.1.bias',\n","              tensor([ 0.0320, -0.0445,  0.0246, -0.0357, -0.0442,  0.0156, -0.0010, -0.0277,\n","                       0.0404,  0.0037], device='cuda:0'))])"]},"metadata":{},"execution_count":46}]},{"cell_type":"code","source":["next(iter(model_2.parameters()))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GeIrY5ZOe3zr","executionInfo":{"status":"ok","timestamp":1711936427982,"user_tz":-330,"elapsed":24,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"24554f6c-75ef-4b99-b1b7-672faf12b7f1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Parameter containing:\n","tensor([[[[ 0.2548,  0.2767, -0.0781],\n","          [ 0.3062, -0.0730,  0.0673],\n","          [-0.1623,  0.1958,  0.2938]]],\n","\n","\n","        [[[-0.2445,  0.2897,  0.0624],\n","          [ 0.2463,  0.0451,  0.1607],\n","          [-0.0471,  0.2570,  0.0493]]],\n","\n","\n","        [[[-0.1556,  0.0850, -0.1536],\n","          [-0.0391, -0.1354,  0.2211],\n","          [-0.2631, -0.1537, -0.0941]]],\n","\n","\n","        [[[-0.2004,  0.0315, -0.3292],\n","          [ 0.3010, -0.2832,  0.2573],\n","          [ 0.0555, -0.1082,  0.2060]]],\n","\n","\n","        [[[ 0.0520,  0.2693,  0.0364],\n","          [-0.1051,  0.0896, -0.0904],\n","          [ 0.1403,  0.2976,  0.1927]]],\n","\n","\n","        [[[-0.1457,  0.1924,  0.0596],\n","          [ 0.1693, -0.2032, -0.3300],\n","          [-0.1288, -0.2557,  0.2735]]],\n","\n","\n","        [[[ 0.0960,  0.1381,  0.1054],\n","          [-0.0058,  0.2609, -0.2368],\n","          [ 0.0210, -0.2275,  0.1028]]],\n","\n","\n","        [[[-0.1148,  0.1021, -0.0694],\n","          [ 0.2765, -0.1976, -0.1988],\n","          [-0.1988,  0.2998,  0.1111]]],\n","\n","\n","        [[[ 0.3208, -0.2751, -0.3306],\n","          [-0.2608, -0.2242,  0.1350],\n","          [ 0.1194,  0.2770, -0.1721]]],\n","\n","\n","        [[[-0.2272,  0.1769, -0.1347],\n","          [ 0.2023, -0.0791,  0.1907],\n","          [-0.2590, -0.1682,  0.1016]]]], device='cuda:0', requires_grad=True)"]},"metadata":{},"execution_count":47}]},{"cell_type":"markdown","source":["### 7.1 Stepping through `nn.Conv2d()`"],"metadata":{"id":"uCofshZEfe9s"}},{"cell_type":"code","source":["# Set the seed\n","torch.manual_seed(42)\n","\n","# Create a batch of images\n","images = torch.randn(size=(32, 3, 64, 64))\n","test_image = images[0]\n","\n","print(f\"Image batch shape: {images.shape}\")\n","print(f\"Single image shape: {test_image.shape}\")\n","print(f\"Test image:\\n {test_image}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"m8JcyWWPgjH1","executionInfo":{"status":"ok","timestamp":1711936427982,"user_tz":-330,"elapsed":7,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"a05e10e1-bf3f-453d-f7fc-86808bb007b9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Image batch shape: torch.Size([32, 3, 64, 64])\n","Single image shape: torch.Size([3, 64, 64])\n","Test image:\n"," tensor([[[ 1.9269,  1.4873,  0.9007,  ...,  1.8446, -1.1845,  1.3835],\n","         [ 1.4451,  0.8564,  2.2181,  ...,  0.3399,  0.7200,  0.4114],\n","         [ 1.9312,  1.0119, -1.4364,  ..., -0.5558,  0.7043,  0.7099],\n","         ...,\n","         [-0.5610, -0.4830,  0.4770,  ..., -0.2713, -0.9537, -0.6737],\n","         [ 0.3076, -0.1277,  0.0366,  ..., -2.0060,  0.2824, -0.8111],\n","         [-1.5486,  0.0485, -0.7712,  ..., -0.1403,  0.9416, -0.0118]],\n","\n","        [[-0.5197,  1.8524,  1.8365,  ...,  0.8935, -1.5114, -0.8515],\n","         [ 2.0818,  1.0677, -1.4277,  ...,  1.6612, -2.6223, -0.4319],\n","         [-0.1010, -0.4388, -1.9775,  ...,  0.2106,  0.2536, -0.7318],\n","         ...,\n","         [ 0.2779,  0.7342, -0.3736,  ..., -0.4601,  0.1815,  0.1850],\n","         [ 0.7205, -0.2833,  0.0937,  ..., -0.1002, -2.3609,  2.2465],\n","         [-1.3242, -0.1973,  0.2920,  ...,  0.5409,  0.6940,  1.8563]],\n","\n","        [[-0.7978,  1.0261,  1.1465,  ...,  1.2134,  0.9354, -0.0780],\n","         [-1.4647, -1.9571,  0.1017,  ..., -1.9986, -0.7409,  0.7011],\n","         [-1.3938,  0.8466, -1.7191,  ..., -1.1867,  0.1320,  0.3407],\n","         ...,\n","         [ 0.8206, -0.3745,  1.2499,  ..., -0.0676,  0.0385,  0.6335],\n","         [-0.5589, -0.3393,  0.2347,  ...,  2.1181,  2.4569,  1.3083],\n","         [-0.4092,  1.5199,  0.2401,  ..., -0.2558,  0.7870,  0.9924]]])\n"]}]},{"cell_type":"code","source":["# Set the seed\n","torch.manual_seed(42)\n","\n","# Create a single conv2d layer\n","conv_layer = nn.Conv2d(in_channels=3,\n","                       out_channels=10,\n","                       kernel_size=(3, 3),\n","                       stride=1,\n","                       padding=1)\n","\n","# Pass the data through the convolutional layer\n","conv_output = conv_layer(test_image)\n","conv_output.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Rw5_St8HhkQT","executionInfo":{"status":"ok","timestamp":1711936427982,"user_tz":-330,"elapsed":5,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"befb1e86-fc61-464b-dfd2-6d6a4f8ba00a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([10, 64, 64])"]},"metadata":{},"execution_count":49}]},{"cell_type":"markdown","source":["### 7.2 Stepping through `nn.MaxPool2d`"],"metadata":{"id":"iQtEwe5Njdcp"}},{"cell_type":"code","source":["# Set the seed\n","torch.manual_seed(42)\n","\n","# Print out original image shape without unsqueezed dimension\n","print(f\"Test image original shape: {test_image.shape}\")\n","print(f\"Test image with unsqueezed dimension: {test_image.unsqueeze(0).shape}\")\n","\n","# Create a sample nn.MaxPool2d layer\n","max_pool_layer = nn.MaxPool2d(kernel_size=2)\n","\n","# Pass data through just the conv_layer\n","test_image_through_conv = conv_layer(test_image)\n","print(f\"Shape after going through conv_layer(): {test_image_through_conv.shape}\")\n","\n","# Pass data through the max_pool_layer\n","test_image_through_conv_and_max_pool = max_pool_layer(test_image_through_conv)\n","print(f\"Shape after going through conv_layer() and max_pool_layer(): {test_image_through_conv_and_max_pool.shape}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1tGw_3ZLkMUN","executionInfo":{"status":"ok","timestamp":1711936427982,"user_tz":-330,"elapsed":4,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"cc139f2f-562f-4d91-b15c-4ff0fc6bb691"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Test image original shape: torch.Size([3, 64, 64])\n","Test image with unsqueezed dimension: torch.Size([1, 3, 64, 64])\n","Shape after going through conv_layer(): torch.Size([10, 64, 64])\n","Shape after going through conv_layer() and max_pool_layer(): torch.Size([10, 32, 32])\n"]}]},{"cell_type":"markdown","source":["Experimenting with smaller tensor's"],"metadata":{"id":"EOlPDYLBm0pY"}},{"cell_type":"code","source":["# Set the seed\n","torch.manual_seed(42)\n","\n","# Create a random tensor with a similar number of dimensions to our images\n","random_tensor = torch.randn(size=(1, 1, 2, 2))\n","print(f\"Shape of the random tensor: {random_tensor.shape}\")\n","print(f\"Value of the random tensor:\\n{random_tensor}\")\n","\n","# Creata a max_pool_layer\n","max_pool_layer = nn.MaxPool2d(kernel_size=2)\n","\n","# Pass the random tensor through the max_pool_layer\n","random_tensor_through_max_pool_layer = max_pool_layer(random_tensor)\n","\n","print(f\"\\nShape of the random tensor through max_pool_layer(): {random_tensor_through_max_pool_layer.shape}\")\n","print(f\"Value of the random tensor through max_pool_layer():\\n{random_tensor_through_max_pool_layer}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dCPCIbtYD2KW","executionInfo":{"status":"ok","timestamp":1711936428434,"user_tz":-330,"elapsed":456,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"0f7f33fc-c8e8-42d7-be9b-2ffb69bcd6e0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of the random tensor: torch.Size([1, 1, 2, 2])\n","Value of the random tensor:\n","tensor([[[[0.3367, 0.1288],\n","          [0.2345, 0.2303]]]])\n","\n","Shape of the random tensor through max_pool_layer(): torch.Size([1, 1, 1, 1])\n","Value of the random tensor through max_pool_layer():\n","tensor([[[[0.3367]]]])\n"]}]},{"cell_type":"markdown","source":["Experimenting with the actual size of the tensor (same as per image size - (28, 28))"],"metadata":{"id":"mKf50pJfHZfr"}},{"cell_type":"code","source":["# Set the seed\n","torch.manual_seed(42)\n","\n","# Create the same shape random tensor as our images\n","random_tensor = torch.randn(size=(1, 1, 28, 28))\n","\n","# Create a max_pool_layer\n","max_pool_layer = nn.MaxPool2d(kernel_size=2)\n","\n","# Pass the random tensor through max_pool_layer twice (same as our model)\n","random_tensor_through_max_pool_layer_once = max_pool_layer(random_tensor)\n","print(f\"Shape of the random tensor through max pool layer first time: {random_tensor_through_max_pool_layer_once.shape}\")\n","\n","random_tensor_through_max_pool_layer_twice = max_pool_layer(random_tensor_through_max_pool_layer_once)\n","print(f\"Shape of the random tensor through max pool layer second time: {random_tensor_through_max_pool_layer_twice.shape}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i9W6lXR5ED70","executionInfo":{"status":"ok","timestamp":1711936428434,"user_tz":-330,"elapsed":9,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"4be7a038-ac85-4d9f-ada9-7dc5a604968d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of the random tensor through max pool layer first time: torch.Size([1, 1, 14, 14])\n","Shape of the random tensor through max pool layer second time: torch.Size([1, 1, 7, 7])\n"]}]},{"cell_type":"markdown","source":["### 7.3 Setup a loss function and optimizer for `model_2`"],"metadata":{"id":"sKzKx39AHXH-"}},{"cell_type":"code","source":["# Setup loss function/eval metrics/optimizer\n","from helper_functions import accuracy_fn\n","\n","loss_fn = nn.CrossEntropyLoss()\n","optimizer = torch.optim.SGD(params=model_2.parameters(),\n","                            lr=0.1)"],"metadata":{"id":"gbwV9dW_M_xm"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 7.4 Training and testing `model_2` using our training and test functions"],"metadata":{"id":"YErleBnQNgCn"}},{"cell_type":"code","source":["# Set the seed\n","torch.manual_seed(42)\n","torch.cuda.manual_seed(42)\n","\n","# Calculate the time taken by model\n","from timeit import default_timer as timer\n","train_time_start_model_2 = timer()\n","\n","# Train and test model\n","epochs = 3\n","for epoch in tqdm(range(epochs)):\n","    print(f\"{'=' * 10}\\nEpoch: {epoch}\\n{'-' * 7}\")\n","    train_step(model=model_2,\n","               data_loader=train_dataloader,\n","               loss_fn=loss_fn,\n","               optimizer=optimizer,\n","               accuracy_fn=accuracy_fn,\n","               device=device)\n","    test_step(model=model_2,\n","              data_loader=test_dataloader,\n","              loss_fn=loss_fn,\n","              accuracy_fn=accuracy_fn,\n","              device=device)\n","\n","train_time_end_model_2 = timer()\n","total_train_time_model_2 = print_train_time(start=train_time_start_model_2,\n","                                            end=train_time_end_model_2,\n","                                            device=device)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":763,"referenced_widgets":["8e75dbeaca674d9a815c94626efcbfb6","32cd29f3b4204207a362b29771050825","6d1108300e4848d98192280005be03a8","44450337499840e9afe7cfa77a73d585","bef63065cfc942338fe3161bed3638f5","8c409aa72f954b2a925e0bfd3c71881f","cafe38f08453472b86b169d4014e1d7a","048da38a95f1419ab02aa055be2a7b8b","3ea298ec31c54786969b9fe4dceeb1c9","39ef3c16134544449d444768a868a039","c547881e4b7a464fab6fed48919b2f7c"]},"id":"tl6ugLRGNv5G","executionInfo":{"status":"ok","timestamp":1711936478768,"user_tz":-330,"elapsed":50342,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"795c358e-4543-459c-9542-206d0a7c16f4"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8e75dbeaca674d9a815c94626efcbfb6"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["==========\n","Epoch: 0\n","-------\n","Processing for 0/60000 samples\n","Processing for 12800/60000 samples\n","Processing for 25600/60000 samples\n","Processing for 38400/60000 samples\n","Processing for 51200/60000 samples\n","Processed all samples\n","\n","Train loss: 0.5880289077758789 | Train accuracy: 78.62666666666667\n","Test loss: 0.38489386439323425 | Test accuracy: 86.42172523961662\n","\n","==========\n","Epoch: 1\n","-------\n","Processing for 0/60000 samples\n","Processing for 12800/60000 samples\n","Processing for 25600/60000 samples\n","Processing for 38400/60000 samples\n","Processing for 51200/60000 samples\n","Processed all samples\n","\n","Train loss: 0.3638988137245178 | Train accuracy: 86.865\n","Test loss: 0.35299620032310486 | Test accuracy: 86.81110223642173\n","\n","==========\n","Epoch: 2\n","-------\n","Processing for 0/60000 samples\n","Processing for 12800/60000 samples\n","Processing for 25600/60000 samples\n","Processing for 38400/60000 samples\n","Processing for 51200/60000 samples\n","Processed all samples\n","\n","Train loss: 0.32466381788253784 | Train accuracy: 88.21833333333333\n","Test loss: 0.32050246000289917 | Test accuracy: 88.46845047923323\n","\n","Train time on cuda: 49.561 seconds\n"]}]},{"cell_type":"code","source":["# Get model_2 results\n","model_2_results = eval_model(model=model_2,\n","                             data_loader=test_dataloader,\n","                             loss_fn=loss_fn,\n","                             accuracy_fn=accuracy_fn,\n","                             device=device)\n","\n","model_2_results"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":103,"referenced_widgets":["c0c3449e3abb4720a6e9a5c0ed19d1aa","600af4500b8e4d98955a0ebba4ebcc40","59e225bfb46d4bc2adc8756622f3e670","6d34ae787a3240738c7e0b8e59a15114","1129ee94103446eaaac97a4fad3ffa4e","cd72ae19be7b4feca5344d3c328e8520","ffd8ef3553de4c56b76ea8ef1a310c12","7f37aeddc8274766a4708076fafad260","77967bd9c0b248fc8a136c94012ed086","1019552661714658a93ddf5bf89cee63","6805a23233324e17b1d37ddaf405ab38"]},"id":"s_mBd0IdO-8J","executionInfo":{"status":"ok","timestamp":1711936480869,"user_tz":-330,"elapsed":2107,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"bdab9ffd-1e6a-407e-f512-7b427029cbb2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/313 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c0c3449e3abb4720a6e9a5c0ed19d1aa"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'model_name': 'FashionMNISTModelV2',\n"," 'model_acc': 88.46845047923323,\n"," 'model_loss': 0.32050246000289917}"]},"metadata":{},"execution_count":55}]},{"cell_type":"markdown","source":["## 8. Compare model results and training time"],"metadata":{"id":"kt9ilYgTSMmW"}},{"cell_type":"code","source":["import pandas as pd\n","\n","compare_results = pd.DataFrame([model_0_results,\n","                                model_1_results,\n","                                model_2_results])\n","compare_results"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"ElGKGolsSQ83","executionInfo":{"status":"ok","timestamp":1711936480870,"user_tz":-330,"elapsed":8,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"9ec430b9-dffd-43d9-954d-d557f8da3ad0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            model_name  model_acc  model_loss\n","0  FashionMNISTModelV0  81.998802    0.513675\n","1  FashionMNISTModelV1  65.405351    0.963384\n","2  FashionMNISTModelV2  88.468450    0.320502"],"text/html":["\n","  <div id=\"df-aac02dc8-758d-4f69-a6fd-eedd60dba867\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>model_name</th>\n","      <th>model_acc</th>\n","      <th>model_loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>FashionMNISTModelV0</td>\n","      <td>81.998802</td>\n","      <td>0.513675</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>FashionMNISTModelV1</td>\n","      <td>65.405351</td>\n","      <td>0.963384</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>FashionMNISTModelV2</td>\n","      <td>88.468450</td>\n","      <td>0.320502</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aac02dc8-758d-4f69-a6fd-eedd60dba867')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-aac02dc8-758d-4f69-a6fd-eedd60dba867 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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\"num_unique_values\": 3,\n        \"samples\": [\n          81.9988019169329,\n          65.40535143769968,\n          88.46845047923323\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.32986106409355725,\n        \"min\": 0.32050246000289917,\n        \"max\": 0.9633837938308716,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5136752128601074,\n          0.9633837938308716,\n          0.32050246000289917\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":56}]},{"cell_type":"code","source":["# Add training time to results comparison\n","compare_results[\"training_time\"] = [total_train_time_model_0,\n","                                    total_train_time_model_1,\n","                                    total_train_time_model_2]\n","compare_results"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"F_BogjkbTig5","executionInfo":{"status":"ok","timestamp":1711936480870,"user_tz":-330,"elapsed":7,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"0e854cf4-cb0f-45d3-9d04-05020e6bc68d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            model_name  model_acc  model_loss  training_time\n","0  FashionMNISTModelV0  81.998802    0.513675      30.081401\n","1  FashionMNISTModelV1  65.405351    0.963384      50.441894\n","2  FashionMNISTModelV2  88.468450    0.320502      49.560647"],"text/html":["\n","  <div id=\"df-38077a2e-1de2-4a7a-8def-851edf9a24f5\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    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\"num_unique_values\": 3,\n        \"samples\": [\n          81.9988019169329,\n          65.40535143769968,\n          88.46845047923323\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.32986106409355725,\n        \"min\": 0.32050246000289917,\n        \"max\": 0.9633837938308716,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5136752128601074,\n          0.9633837938308716,\n          0.32050246000289917\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"training_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.509179599316221,\n        \"min\": 30.08140144799995,\n        \"max\": 50.44189431500001,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          30.08140144799995,\n          50.44189431500001,\n          49.5606474409999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":57}]},{"cell_type":"code","source":["# Visualize our model results\n","compare_results.set_index(\"model_name\")[\"model_acc\"].plot(kind=\"barh\")\n","plt.xlabel(\"accuracy (%)\")\n","plt.ylabel(\"model\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"id":"EIQ2u3VCUMmm","executionInfo":{"status":"ok","timestamp":1711936483375,"user_tz":-330,"elapsed":2512,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"642ee7c6-cec5-4713-dc27-fb6a1665fd93"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'model')"]},"metadata":{},"execution_count":58},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["compare_results.set_index(\"model_name\")[\"training_time\"].plot(kind=\"barh\")\n","plt.xlabel(\"training time\")\n","plt.ylabel(\"model\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"id":"MlA0RspQVDUT","executionInfo":{"status":"ok","timestamp":1711936483375,"user_tz":-330,"elapsed":8,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"947d7d80-0b7e-470d-c279-f17924761189"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'model')"]},"metadata":{},"execution_count":59},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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p6enoMgAAQC3jTi8AAACMR+gFAACA8Qi9AAAAMB6hFwAAAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8Qi9AAAAMB6hFwAAAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8Qi9AAAAMB6hFwAAAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxnNydAHAjeS21DWq4+Lm6DIAADDK/vRYR5fAnV4AAACYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPGcqtuwcePGslgs1Wp78uTJGhcEAAAA1LZqh945c+ZcxzIAAACA66faoXfkyJHXsw4AAADguqnxnN49e/boiSee0NChQ3X8+HFJ0vvvv6/vvvuu1ooDAAAAakONQu+GDRvUqVMnffnll8rNzVVJSYkkacuWLUpNTa3VAgEAAIBrVaPQO2XKFM2YMUNr166Vs7OzbXu/fv30xRdf1FpxAAAAQG2oUejdtm2b7rrrrkrbW7Roof/7v/+75qIAAACA2lSj0NuoUSMdOXKk0vZvvvlGt9xyyzUXBQAAANSmGoXe++67T5MnT9bRo0dlsVhUUVGh/Px8paSkKCEhobZrBAAAAK5JjULvM888o6CgIPn4+KikpETBwcHq06ePIiIi9MQTT9R2jQAAAMA1qfZ7en/N2dlZr776qp588kl9++23KikpUefOndWuXbvarg8AAAC4ZjUKvRf5+vrK19e3tmoBAAAArotqh95HHnmk2p0+99xzNSqmNqxfv16RkZH66aef1KhRoyrbTJ06VW+//bYKCwt/19puNtX5LH7Lz89PycnJSk5Ovq61AQCAm0u15/R+8803dsvChQv1yiuvaP369Vq/fr3+9a9/aeHChVcVJEeNGiWLxVJp2b17d02updpSUlL08ccf12qf69evl8ViUePGjXXu3Dm7fZs2bbJd22/bd+zYUeXl5XbtGzVqpJycHNu6n5+f5syZY1vfsmWLBg4cqBYtWqh+/fry8/PTkCFDdPz4cU2dOrXKMf31Iv137MeOHVvpWpKSkmSxWDRq1KhrH5gaOn/+vJo1a6b09PQq90+fPl1eXl4qKyvTkSNHNGzYMAUGBqpOnToEZgAAUEm1Q++6detsS1xcnPr27asffvhBmzdv1ubNm3Xo0CFFRkYqNjb2qgqIjo7WkSNH7Ja2bdte9YVcDQ8PDzVt2vS69N2gQQOtXLnSbtvChQsvOQ1k7969eu2116rd/4kTJ9S/f381adJEa9as0Y4dO5SdnS1vb2+dPn1aKSkpdmPZunVrPf3003bbLvLx8dHSpUt19uxZ27Zz585pyZIlDp+24uzsrBEjRig7O7vSPqvVqpycHCUkJKhevXoqLS1V8+bN9cQTTyg0NNQB1QIAgBtdjd7ekJmZqbS0NDVu3Ni2rXHjxpoxY4YyMzOvqi8XFxe1bNnSbpk7d646deokd3d3+fj46MEHH7T9qWNJOnDggOLi4tS4cWO5u7urY8eOWr16tV2/BQUF6tKli9zc3BQREaGdO3fa9k2dOlVhYWG29YqKCj399NNq3bq1XFxcFBYWpg8++MC2f//+/bJYLMrNzVVkZKTc3NwUGhqqjRs3VrqekSNHatGiRbb1s2fPaunSpRo5cmSV1//QQw8pNTVVpaWl1Rqv/Px8FRUVacGCBercubPatm2ryMhIPf/882rbtq08PDzsxrJu3bpq0KCB3baLwsPD5ePjo9zcXNu23Nxc+fr6qnPnznbnLS0t1fjx4213l//0pz9p06ZNdm1Wr16twMBAubq6KjIyUvv3769Uf15ennr37i1XV1f5+Pho/PjxOn36dJXXmpiYqO+//155eXl22zds2KC9e/cqMTFR0i93wufOnauEhAQ1bNiwWuMIAABuLjUKvcXFxTpx4kSl7SdOnNCpU6euvag6dTRv3jx99913Wrx4sT755BNNmjTJtj8pKUmlpaX69NNPtW3bNs2aNUseHh52fTz++OPKzMzU119/LScnJ40ePfqS55s7d64yMzOVkZGhrVu3KioqSgMHDtSuXbsq9ZmSkqLCwkIFBgZq6NChunDhgl2b+Ph4ffbZZzp48KAkacWKFfLz81N4eHiV505OTtaFCxc0f/78ao1Ny5YtdeHCBa1cuVJWq7Vax1zO6NGj7e6mLlq0SPfff3+ldpMmTdKKFSu0ePFibd68WQEBAYqKitLJkyclSYcOHdKgQYMUFxenwsJCjRkzRlOmTLHrY8+ePYqOjtbdd9+trVu3atmyZcrLy9O4ceOqrK1Tp07q2rWr3ZcIScrOzlZERISCgoJqfN2lpaUqLi62WwAAgLlqFHrvuusu3X///crNzdUPP/ygH374QStWrFBiYqIGDRp0VX2tWrVKHh4etmXw4MFKTk5WZGSk/Pz81K9fP82YMUPLly+3HXPw4EH16tVLnTp10q233qoBAwaoT58+dv3OnDlTffv2VXBwsKZMmaLPP/+80lzbizIyMjR58mTdd999at++vWbNmqWwsDC7ebTSL3OBY2NjFRgYqGnTpunAgQOV5h+3aNFCMTExtjm5ixYtumzgdnNzU2pqqtLS0lRUVHTF8erRo4cee+wxDRs2TM2aNVNMTIxmz56tY8eOXfHYqowYMUJ5eXk6cOCADhw4oPz8fI0YMcKuzenTp/Xyyy9r9uzZiomJUXBwsF599VW5urpq4cKFkqSXX35Z/v7+yszMVPv27TV8+PBKc4LT0tI0fPhwJScnq127doqIiNC8efP02muvXfKzSUxM1Jtvvmm703/q1Cm99dZblx3T6khLS1PDhg1ti4+PzzX1BwAAbmw1Cr1ZWVmKiYnRsGHD1KZNG7Vp00bDhg1TdHS0XnrppavqKzIyUoWFhbZl3rx5+uijj9S/f3/dcsstatCggeLj4/Xjjz/qzJkzkqTx48drxowZ6tWrl1JTU7V169ZK/YaEhNh+btWqlSTp+PHjldoVFxfr8OHD6tWrl932Xr16aceOHTXqc/To0crJydHevXu1ceNGDR8+/LJjkJiYqKZNm2rWrFmXbXfRzJkzdfToUWVlZaljx47KyspSUFCQtm3bVq3jf6158+aKjY1VTk6OsrOzFRsbq2bNmtm12bNnj8rKyuzGqF69eurWrZttjHbs2KHu3bvbHdezZ0+79S1btignJ8fuS05UVJQqKiq0b9++KusbOnSoysvLbV96li1bpjp16mjIkCFXfa2/9uijj6qoqMi2HDp06Jr6AwAAN7YahV43Nze99NJL+vHHH21vczh58qReeuklubu7X1Vf7u7uCggIsC2lpaUaMGCAQkJCtGLFChUUFOjFF1+U9MsT/ZI0ZswY7d27V/Hx8dq2bZu6dOlSaXpAvXr1bD9ffGNBRUVFTS73qvuMiYnR2bNnlZiYqLi4uCs+NOfk5KSZM2dq7ty5Onz4cLVqadq0qQYPHqyMjAzt2LFD3t7eysjIuIqr+a+LIX3x4sXXfAf1ckpKSvTAAw/YfcnZsmWLdu3aJX9//yqP8fT01D333GObgpGdna1777230nSWq+Xi4iJPT0+7BQAAmKtGofcid3d3NWnSRE2aNLnqsHspBQUFqqioUGZmpnr06KHAwMAqg6CPj4/Gjh2r3NxcTZgwQa+++mqNzufp6Slvb2/l5+fbbc/Pz1dwcHCN+nRyclJCQoLWr19f7RA5ePBgdezYUdOmTbvq8zk7O8vf3/+SD4RdSXR0tM6fP6+ysjJFRUVV2u/v7y9nZ2e7MSorK9OmTZtsY9ShQwd99dVXdsd98cUXduvh4eHavn273Zeci4uzs/Ml60tMTFReXp5WrVqlzz//3PYAGwAAQHXVKPRefNtBw4YNbdMbGjVqpOnTp1/z3dSAgACVlZVp/vz52rt3r/79738rKyvLrk1ycrLWrFmjffv2afPmzVq3bp06dOhQ43NOnDhRs2bN0rJly7Rz505NmTJFhYWFevjhh2vc5/Tp03XixIkqQ+SlpKena9GiRZcNr6tWrdKIESO0atUqff/999q5c6cyMjK0evVq3XnnnTWqtW7dutqxY4e2b9+uunXrVtrv7u6uf/zjH5o4caI++OADbd++XX//+9915swZWwAdO3asdu3apYkTJ2rnzp1asmSJ3buGJWny5Mn6/PPPNW7cOBUWFmrXrl165513Lvkg20V9+vRRQECAEhISFBQUpIiIiEptLt45Likp0YkTJ1RYWKjt27fXaDwAAIB5avRniB9//HEtXLhQ6enptnmeeXl5mjp1qs6dO6eZM2fWuKDQ0FA999xzmjVrlh599FH16dNHaWlpSkhIsLUpLy9XUlKSfvjhB3l6eio6OlrPP/98jc85fvx4FRUVacKECTp+/LiCg4P17rvvql27djXu09nZudLc2Cvp16+f+vXrpw8//PCSbYKDg+Xm5qYJEybo0KFDcnFxUbt27bRgwQLFx8fXuN4r/d/76enpqqioUHx8vE6dOqUuXbpozZo1ttfW+fr6asWKFfrnP/+p+fPnq1u3bnrmmWfs7nSHhIRow4YNevzxx9W7d29ZrVb5+/tfcX6uxWLR6NGj9dhjj+nRRx+tss2vX7FWUFCgJUuWqE2bNlW+Ng0AANx8LNYavPfK29tbWVlZGjhwoN32d955Rw8++KD+3//7f7VWIPB7KC4u/uUtDsnLVcfFzdHlAABglP3pV/fHy6rr4u/voqKiK97Aq9H0hpMnT1b5jtSgoCDbe1sBAACAG0WNQm9oaKheeOGFSttfeOEF/gwsAAAAbjg1mtP77LPPKjY2Vh999JHtXawbN27UwYMH9f7779dqgQAAAMC1qtGd3r59+2rnzp0aNGiQfv75Z/38888aNGiQvv/+e/Xu3bu2awQAAACuSY3u9Eq//HGEgQMHqkePHrbXlH399deSVOkBNwAAAMCRahR6P/jgAyUkJOjHH3/Ub1/+YLFYVF5eXivFAQAAALWhRtMbHnroIQ0ePFiHDx9WRUWF3ULgBQAAwI2mRqH32LFjeuSRR+Tl5VXb9QAAAAC1rkah95577tH69etruRQAAADg+qjRnN4XXnhBgwcP1meffaZOnTqpXr16dvvHjx9fK8UBAAAAtaFGofeNN97Qhx9+qPr162v9+vWyWCy2fRaLhdALAACAG0qNQu/jjz+uadOmacqUKapTp0YzJAAAAIDfTY0S6/nz5zVkyBACLwAAAP4QapRaR44cqWXLltV2LQAAAMB1UaPpDeXl5Xr22We1Zs0ahYSEVHqQ7bnnnquV4gAAAIDaUKPQu23bNnXu3FmS9O2339rt+/VDbQAAAMCNoEahd926dbVdBwAAAHDd8CQaAAAAjEfoBQAAgPEIvQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOPV6D29gKm+nRYlT09PR5cBAABqGXd6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8Qi9AAAAMB6hFwAAAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8Qi9AAAAMB6hFwAAAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADCek6MLAG4kt6WuUR0XN0eXAcBQ+9NjHV0CcNPiTi8AAACMR+gFAACA8Qi9AAAAMB6hFwAAAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8YwLvevXr5fFYtHPP/98yTZTp05VWFjY71bTzao6n8Vv+fn5ac6cOdetJgAAcHNyaOgdNWqULBZLpWX37t3X9bwpKSn6+OOPa7XPiwGvcePGOnfunN2+TZs22a7tt+07duyo8vJyu/aNGjVSTk6Obf23QXDLli0aOHCgWrRoofr168vPz09DhgzR8ePHNXXq1CrH9NeL9N+xHzt2bKVrSUpKksVi0ahRo659YGro/PnzatasmdLT06vcP336dHl5eamsrEzSL+MZHh4uFxcXBQQE2I0fAACAw+/0RkdH68iRI3ZL27Ztr+s5PTw81LRp0+vSd4MGDbRy5Uq7bQsXLpSvr2+V7ffu3avXXnut2v2fOHFC/fv3V5MmTbRmzRrt2LFD2dnZ8vb21unTp5WSkmI3lq1bt9bTTz9tt+0iHx8fLV26VGfPnrVtO3funJYsWXLJen8vzs7OGjFihLKzsyvts1qtysnJUUJCgurVq6d9+/YpNjZWkZGRKiwsVHJyssaMGaM1a9Y4oHIAAHAjcnjodXFxUcuWLe2WuXPnqlOnTnJ3d5ePj48efPBBlZSU2I45cOCA4uLi1LhxY7m7u6tjx45avXq1Xb8FBQXq0qWL3NzcFBERoZ07d9r2/XZ6Q0VFhZ5++mm1bt1aLi4uCgsL0wcffGDbv3//flksFuXm5ioyMlJubm4KDQ3Vxo0bK13PyJEjtWjRItv62bNntXTpUo0cObLK63/ooYeUmpqq0tLSao1Xfn6+ioqKtGDBAnXu3Flt27ZVZGSknn/+ebVt21YeHh52Y1m3bl01aNDAbttF4eHh8vHxUW5urm1bbm6ufH191blzZ7vzlpaWavz48ba7y3/605+0adMmuzarV69WYGCgXF1dFRkZqf3791eqPy8vT71795arq6t8fHw0fvx4nT59usprTUxM1Pfff6+8vDy77Rs2bNDevXuVmJgoScrKylLbtm2VmZmpDh06aNy4cbrnnnv0/PPPX3IcS0tLVVxcbLcAAABzOTz0VqVOnTqaN2+evvvuOy1evFiffPKJJk2aZNuflJSk0tJSffrpp9q2bZtmzZolDw8Puz4ef/xxZWZm6uuvv5aTk5NGjx59yfPNnTtXmZmZysjI0NatWxUVFaWBAwdq165dlfpMSUlRYWGhAgMDNXToUF24cMGuTXx8vD777DMdPHhQkrRixQr5+fkpPDy8ynMnJyfrwoULmj9/frXGpmXLlrpw4YJWrlwpq9VarWMuZ/To0XZ3UxctWqT777+/UrtJkyZpxYoVWrx4sTZv3qyAgABFRUXp5MmTkqRDhw5p0KBBiouLU2FhocaMGaMpU6bY9bFnzx5FR0fr7rvv1tatW7Vs2TLl5eVp3LhxVdbWqVMnde3a1e5LhCRlZ2crIiJCQUFBkqSNGzfqjjvusGsTFRVV5ZeSi9LS0tSwYUPb4uPjc5lRAgAAf3QOD72rVq2Sh4eHbRk8eLCSk5MVGRkpPz8/9evXTzNmzNDy5cttxxw8eFC9evVSp06ddOutt2rAgAHq06ePXb8zZ85U3759FRwcrClTpujzzz+vNNf2ooyMDE2ePFn33Xef2rdvr1mzZiksLKzSA1UpKSmKjY1VYGCgpk2bpgMHDlSaf9yiRQvFxMTY5pQuWrTosoHbzc1NqampSktLU1FR0RXHq0ePHnrsscc0bNgwNWvWTDExMZo9e7aOHTt2xWOrMmLECOXl5enAgQM6cOCA8vPzNWLECLs2p0+f1ssvv6zZs2crJiZGwcHBevXVV+Xq6qqFCxdKkl5++WX5+/srMzNT7du31/DhwyvNCU5LS9Pw4cOVnJysdu3aKSIiQvPmzdNrr712yc8mMTFRb775pu1O/6lTp/TWW2/ZjenRo0fl5eVld5yXl5eKi4vtpm782qOPPqqioiLbcujQoasaNwAA8Mfi8NB7cR7mxWXevHn66KOP1L9/f91yyy1q0KCB4uPj9eOPP+rMmTOSpPHjx2vGjBnq1auXUlNTtXXr1kr9hoSE2H5u1aqVJOn48eOV2hUXF+vw4cPq1auX3fZevXppx44dNepz9OjRysnJ0d69e7Vx40YNHz78smOQmJiopk2batasWZdtd9HMmTN19OhRZWVlqWPHjsrKylJQUJC2bdtWreN/rXnz5oqNjVVOTo6ys7MVGxurZs2a2bXZs2ePysrK7MaoXr166tatm22MduzYoe7du9sd17NnT7v1LVu2KCcnx+5LTlRUlCoqKrRv374q6xs6dKjKy8ttX3qWLVumOnXqaMiQIVd9rb/m4uIiT09PuwUAAJjL4aHX3d1dAQEBtqW0tFQDBgxQSEiIVqxYoYKCAr344ouSfnmiX5LGjBmjvXv3Kj4+Xtu2bVOXLl0qTQ+oV6+e7eeLbyyoqKi4plqr22dMTIzOnj2rxMRExcXFXfGhOScnJ82cOVNz587V4cOHq1VL06ZNNXjwYGVkZGjHjh3y9vZWRkbGVVzNf10M6YsXL77sXelrVVJSogceeMDuS86WLVu0a9cu+fv7V3mMp6en7rnnHtsUjOzsbN17771201latmxZ6U73sWPH5OnpKVdX1+t2PQAA4I/D4aH3twoKClRRUaHMzEz16NFDgYGBVQZBHx8fjR07Vrm5uZowYYJeffXVGp3P09NT3t7eys/Pt9uen5+v4ODgGvXp5OSkhIQErV+/vtohcvDgwerYsaOmTZt21edzdnaWv7//JR8Iu5Lo6GidP39eZWVlioqKqrTf399fzs7OdmNUVlamTZs22caoQ4cO+uqrr+yO++KLL+zWw8PDtX37drsvORcXZ2fnS9aXmJiovLw8rVq1Sp9//rntAbaLevbsWekVdGvXrq10pxkAANy8nBxdwG8FBASorKxM8+fPV1xcnPLz85WVlWXXJjk5WTExMQoMDNRPP/2kdevWqUOHDjU+58SJE5Wamip/f3+FhYUpOztbhYWFev3112vc5/Tp0zVx4sSrejVaenp6laHz11atWqWlS5fqvvvuU2BgoKxWq9577z2tXr26ytd7VUfdunVt0xTq1q1bab+7u7v+8Y9/aOLEiWrSpIl8fX317LPP6syZM7YAOnbsWGVmZmrixIkaM2aMCgoKKr0rd/LkyerRo4fGjRunMWPGyN3dXdu3b9fatWv1wgsvXLK+Pn36KCAgQAkJCQoKClJERITd/rFjx+qFF17QpEmTNHr0aH3yySdavny5/vOf/9RoPAAAgHluuDu9oaGheu655zRr1izddtttev3115WWlmbXpry8XElJSerQoYOio6MVGBiol156qcbnHD9+vB555BFNmDBBnTp10gcffKB3331X7dq1q3Gfzs7Oatasmd0fpLiSfv36qV+/fpXeCPFrwcHBcnNz04QJExQWFqYePXpo+fLlWrBggeLj42tc75Xmtaanp+vuu+9WfHy8wsPDtXv3bq1Zs0aNGzeWJPn6+mrFihV6++23FRoaqqysLD3zzDN2fYSEhGjDhg36/vvv1bt3b3Xu3FlPPfWUvL29L1ubxWLR6NGj9dNPP1V557xt27b6z3/+o7Vr1yo0NFSZmZlasGDBFb9AAACAm4fFWhvvvQL+4IqLi395dVnyctVxcXN0OQAMtT891tElAEa5+Pu7qKjoig+l33B3egEAAIDaRugFAACA8Qi9AAAAMB6hFwAAAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOM5OboA4Eby7bQoeXp6OroMAABQy7jTCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8Qi9AAAAMB6hFwAAAMYj9AIAAMB4hF4AAAAYj9ALAAAA4xF6AQAAYDxCLwAAAIxH6AUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QCwAAAOMRegEAAGA8Qi8AAACMR+gFAACA8Qi9AAAAMJ6TowsAbgRWq1WSVFxc7OBKAABAdV38vX3x9/jlEHoBST/++KMkycfHx8GVAACAq3Xq1Ck1bNjwsm0IvYCkJk2aSJIOHjx4xf9oUHuKi4vl4+OjQ4cOydPT09Hl3DQY998fY+4YjLtj/J7jbrVaderUKXl7e1+xLaEXkFSnzi/T2xs2bMj/MDqAp6cn4+4AjPvvjzF3DMbdMX6vca/uzSoeZAMAAIDxCL0AAAAwHqEXkOTi4qLU1FS5uLg4upSbCuPuGIz7748xdwzG3TFu1HG3WKvzjgcAAADgD4w7vQAAADAeoRcAAADGI/QCAADAeIReAAAAGI/QC0h68cUX5efnp/r166t79+766quvHF2SUT799FPFxcXJ29tbFotFb7/9tt1+q9Wqp556Sq1atZKrq6vuuOMO7dq1yzHFGiItLU1du3ZVgwYN1KJFC/3tb3/Tzp077dqcO3dOSUlJatq0qTw8PHT33Xfr2LFjDqrYDC+//LJCQkJsL+Xv2bOn3n//fdt+xvz6S09Pl8ViUXJysm0b4177pk6dKovFYrcEBQXZ9t+IY07oxU1v2bJleuSRR5SamqrNmzcrNDRUUVFROn78uKNLM8bp06cVGhqqF198scr9zz77rObNm6esrCx9+eWXcnd3V1RUlM6dO/c7V2qODRs2KCkpSV988YXWrl2rsrIy/c///I9Onz5ta/PPf/5T7733nt58801t2LBBhw8f1qBBgxxY9R9f69atlZ6eroKCAn399dfq16+f7rzzTn333XeSGPPrbdOmTXrllVcUEhJit51xvz46duyoI0eO2Ja8vDzbvhtyzK3ATa5bt27WpKQk23p5ebnV29vbmpaW5sCqzCXJunLlStt6RUWFtWXLltbZs2fbtv38889WFxcX6xtvvOGACs10/PhxqyTrhg0brFbrL2Ncr14965tvvmlrs2PHDqsk68aNGx1VppEaN25sXbBgAWN+nZ06dcrarl0769q1a619+/a1Pvzww1arlX/r10tqaqo1NDS0yn036phzpxc3tfPnz6ugoEB33HGHbVudOnV0xx13aOPGjQ6s7Oaxb98+HT161O4zaNiwobp3785nUIuKiookSU2aNJEkFRQUqKyszG7cg4KC5Ovry7jXkvLyci1dulSnT59Wz549GfPrLCkpSbGxsXbjK/Fv/XratWuXvL29deutt2r48OE6ePCgpBt3zJ0cdmbgBvB///d/Ki8vl5eXl912Ly8v/e///q+Dqrq5HD16VJKq/Awu7sO1qaioUHJysnr16qXbbrtN0i/j7uzsrEaNGtm1Zdyv3bZt29SzZ0+dO3dOHh4eWrlypYKDg1VYWMiYXydLly7V5s2btWnTpkr7+Ld+fXTv3l05OTlq3769jhw5omnTpql379769ttvb9gxJ/QCgOGSkpL07bff2s23w/XTvn17FRYWqqioSG+99ZZGjhypDRs2OLosYx06dEgPP/yw1q5dq/r16zu6nJtGTEyM7eeQkBB1795dbdq00fLly+Xq6urAyi6N6Q24qTVr1kx169at9ETpsWPH1LJlSwdVdXO5OM58BtfHuHHjtGrVKq1bt06tW7e2bW/ZsqXOnz+vn3/+2a49437tnJ2dFRAQoNtvv11paWkKDQ3V3LlzGfPrpKCgQMePH1d4eLicnJzk5OSkDRs2aN68eXJycpKXlxfj/jto1KiRAgMDtXv37hv23zqhFzc1Z2dn3X777fr4449t2yoqKvTxxx+rZ8+eDqzs5tG2bVu1bNnS7jMoLi7Wl19+yWdwDaxWq8aNG6eVK1fqk08+Udu2be3233777apXr57duO/cuVMHDx5k3GtZRUWFSktLGfPrpH///tq2bZsKCwttS5cuXTR8+HDbz4z79VdSUqI9e/aoVatWN+y/daY34Kb3yCOPaOTIkerSpYu6deumOXPm6PTp07r//vsdXZoxSkpKtHv3btv6vn37VFhYqCZNmsjX11fJycmaMWOG2rVrp7Zt2+rJJ5+Ut7e3/va3vzmu6D+4pKQkLVmyRO+8844aNGhgm0fXsGFDubq6qmHDhkpMTNQjjzyiJk2ayNPTUw899JB69uypHj16OLj6P65HH31UMTEx8vX11alTp7RkyRKtX79ea9asYcyvkwYNGtjmql/k7u6upk2b2rYz7rUvJSVFcXFxatOmjQ4fPqzU1FTVrVtXQ4cOvXH/rTvsvRHADWT+/PlWX19fq7Ozs7Vbt27WL774wtElGWXdunVWSZWWkSNHWq3WX15b9uSTT1q9vLysLi4u1v79+1t37tzp2KL/4Koab0nW7OxsW5uzZ89aH3zwQWvjxo2tbm5u1rvuust65MgRxxVtgNGjR1vbtGljdXZ2tjZv3tzav39/64cffmjbz5j/Pn79yjKrlXG/HoYMGWJt1aqV1dnZ2XrLLbdYhwwZYt29e7dt/4045har1Wp1UN4GAAAAfhfM6QUAAIDxCL0AAAAwHqEXAAAAxiP0AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQDcEPz8/DRnzpxqt1+/fr0sFot+/vnn61bT5VxtvQAci7/IBgCokT//+c8KCwurteB34sQJubu7y83NrVrtz58/r5MnT8rLy0sWi6VWaqhKTk6OkpOTK4Xrq60XgGM5OboAAIC5rFarysvL5eR05V83zZs3v6q+nZ2d1bJly5qWds2utl4AjsX0BgDAVRs1apQ2bNiguXPnymKxyGKxaP/+/bYpB++//75uv/12ubi4KC8vT3v27NGdd94pLy8veXh4qGvXrvroo4/s+vztdAGLxaIFCxborrvukpubm9q1a6d3333Xtv+30xtycnLUqFEjrVmzRh06dJCHh4eio6N15MgR2zEXLlzQ+PHj1ahRIzVt2lSTJ0/WyJEj9be//a3K61y/fr3uv/9+FRUV2a5z6tSpl6z3lVde0YABA+Tm5qYOHTpo48aN2r17t/785z/L3d1dERER2rNnj9053nnnHYWHh6t+/fq69dZbNW3aNF24cOHqPxQAl0XoBQBctblz56pnz576+9//riNHjujIkSPy8fGx7Z8yZYrS09O1Y8cOhYSEqKSkRH/961/18ccf65tvvlF0dLTi4uJ08ODBy55n2rRpuvfee7V161b99a9/1fDhw3Xy5MlLtj9z5owyMjL073//W59++qkOHjyolJQU2/5Zs2bp9ddfV3Z2tvLz81VcXKy33377kv1FRERozpw58vT0tF3nr/v7renTpyshIUGFhYUKCgrSsGHD9MADD+jRRx/V119/LavVqnHjxtnaf/bZZ0pISNDDDz+s7du365VXXlFOTo5mzpx52XEBUANWAABqoG/fvtaHH37Ybtu6deuskqxvv/32FY/v2LGjdf78+bb1Nm3aWJ9//nnbuiTrE088YVsvKSmxSrK+//77duf66aefrFar1ZqdnW2VZN29e7ftmBdffNHq5eVlW/fy8rLOnj3btn7hwgWrr6+v9c4777xkndnZ2daGDRtW2n6lejdu3GiVZF24cKFt2xtvvGGtX7++bb1///7WZ555xq7ff//739ZWrVpdsh4ANcOcXgBArevSpYvdeklJiaZOnar//Oc/OnLkiC5cuKCzZ89e8U5vSEiI7Wd3d3d5enrq+PHjl2zv5uYmf39/23qrVq1s7YuKinTs2DF169bNtr9u3bq6/fbbVVFRcVXXV516vby8JEmdOnWy23bu3DkVFxfL09NTW7ZsUX5+vt2d3fLycp07d05nzpzhITmgFhF6AQC1zt3d3W49JSVFa9euVUZGhgICAuTq6qp77rlH58+fv2w/9erVs1u3WCyXDahVtbf+ji8p+vX5L75RoqptF6+hpKRE06ZN06BBgyr1Vb9+/etZKnDTIfQCAGrE2dlZ5eXl1Wqbn5+vUaNG6a677pL0S9jbv3//dayusoYNG8rLy0ubNm1Snz59JP1yV3Xz5s0KCwu75HFXc51XKzw8XDt37lRAQMB16R/AfxF6AQA14ufnpy+//FL79++Xh4eHmjRpcsm27dq1U25uruLi4mSxWPTkk0/W2pSCq/HQQw8pLS1NAQEBCgoK0vz58/XTTz9d9j2/fn5+Kikp0ccff6zQ0FC5ubnV2rSDp556SgMGDJCvr6/uuece1alTR1u2bNG3336rGTNm1Mo5APyCtzcAAGokJSVFdevWVXBwsJo3b37Z+bnPPfecGjdurIiICMXFxSkqKkrh4eG/Y7W/mDx5soYOHaqEhAT17NlTHh4eioqKuuxUgoiICI0dO1ZDhgxR8+bN9eyzz9ZaPVFRUVq1apU+/PBDde3aVT169NDzzz+vNm3a1No5APyCv8gGALhpVVRUqEOHDrr33ns1ffp0R5cD4DpiegMA4KZx4MABffjhh+rbt69KS0v1wgsvaN++fRo2bJijSwNwnTG9AQBw06hTp45ycnLUtWtX9erVS9u2bdNHH32kDh06OLo0ANcZ0xsAAABgPO70AgAAwHiEXgAAABiP0AsAAADjEXoBAABgPEIvAAAAjEfoBQAAgPEIvQAAADAeoRcAAADG+/+Zj1Rq7uP+6gAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## 9. Make and evaluate random predictions with best model"],"metadata":{"id":"d8urCpZoVtKf"}},{"cell_type":"code","source":["def make_predictions(model: torch.nn.Module,\n","                     data: list,\n","                     device: torch.device=device):\n","    pred_probs = []\n","    model.to(device)\n","    model.eval()\n","    with torch.inference_mode():\n","        for sample in data:\n","            # Prepare the sample (add a batch dimension and pass to target device)\n","            # print(f\"Shape of sample before squeeze: {sample.shape}\")\n","            sample = torch.unsqueeze(sample, dim=0).to(device)\n","            # print(f\"Shape of sample after squeeze: {sample.shape}\")\n","\n","            # Forward pass (model outputs raw logits)\n","            pred_logit = model(sample)\n","\n","            # Get prediction probability (logit -> prediction probability)\n","            pred_prob = torch.softmax(pred_logit.squeeze(), dim=0)\n","\n","            # This will also work bcoz we just need the max value index\n","            # pred_prob = pred_logit.squeeze()\n","\n","            # Get pre_prod off the GPU for further calculations\n","            pred_probs.append(pred_prob.cpu())\n","\n","    # Stack the pred_probs to turn list into a tensor\n","    return torch.stack(pred_probs)"],"metadata":{"id":"_V7CSDPSV0A9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import random\n","random.seed(42)\n","test_samples = []\n","test_labels = []\n","\n","for sample, label in random.sample(list(test_data), k=9):\n","    test_samples.append(sample)\n","    test_labels.append(label)\n","\n","# View the first sample shape\n","test_samples[0].shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tHwfZPBTXAIN","executionInfo":{"status":"ok","timestamp":1711936485505,"user_tz":-330,"elapsed":2135,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"8bb4ca4e-4063-4f9f-93dc-01f121aad334"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1, 28, 28])"]},"metadata":{},"execution_count":61}]},{"cell_type":"code","source":["plt.imshow(test_samples[0].squeeze(), cmap=\"gray\")\n","plt.title(class_names[test_labels[0]])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":470},"id":"HfLaNXJfXmg9","executionInfo":{"status":"ok","timestamp":1711936485506,"user_tz":-330,"elapsed":11,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"1f1a3372-3585-4c50-cd28-758a0ba5471e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Sandal')"]},"metadata":{},"execution_count":62},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Make predictions\n","pred_probs = make_predictions(model=model_2,\n","                              data=test_samples)\n","\n","# View first two predictions probabilities\n","pred_probs[:2]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oZ-4HgrwXt6p","executionInfo":{"status":"ok","timestamp":1711936485506,"user_tz":-330,"elapsed":7,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"f549860d-5455-482e-a2d2-c0e8969f3103"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[6.5882e-08, 8.4146e-09, 4.1745e-08, 4.1440e-08, 2.8894e-09, 9.9993e-01,\n","         4.9442e-08, 5.3047e-06, 1.4485e-05, 4.5633e-05],\n","        [5.8502e-02, 5.0363e-01, 2.5407e-03, 3.5434e-01, 3.3311e-02, 2.2700e-04,\n","         4.5737e-02, 9.4196e-04, 5.1980e-04, 2.4757e-04]])"]},"metadata":{},"execution_count":63}]},{"cell_type":"code","source":["# Convert prediction probabilities to labels\n","pred_classes = pred_probs.argmax(dim=1)\n","pred_classes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ooj2oMj9YRCs","executionInfo":{"status":"ok","timestamp":1711936485506,"user_tz":-330,"elapsed":6,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"cbb45473-8c2a-44ea-e421-3b1c7bc75398"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([5, 1, 7, 4, 3, 0, 4, 7, 1])"]},"metadata":{},"execution_count":64}]},{"cell_type":"code","source":["[test_label == pred_class\n"," for test_label, pred_class in zip(test_labels, pred_classes)]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1XwarBTxZX6x","executionInfo":{"status":"ok","timestamp":1711936485506,"user_tz":-330,"elapsed":5,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"dce1f573-ce4c-401a-f4b0-dae8f45be077"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[tensor(True),\n"," tensor(True),\n"," tensor(True),\n"," tensor(True),\n"," tensor(True),\n"," tensor(True),\n"," tensor(True),\n"," tensor(True),\n"," tensor(True)]"]},"metadata":{},"execution_count":65}]},{"cell_type":"code","source":["# Plot predictions\n","plt.figure(figsize=(9, 9))\n","nrows, ncols = 3, 3\n","\n","for i, sample in enumerate(test_samples):\n","    # Create subsplot\n","    plt.subplot(nrows, ncols, i+1)\n","\n","    # Plot the target image\n","    plt.imshow(sample.squeeze(), cmap=\"gray\")\n","\n","    # Find the prediction (in texr form, eg. \"Sandal\")\n","    pred_label = class_names[pred_classes[i]]\n","\n","    # Get the truth label (in text form)\n","    truth_label = class_names[test_labels[i]]\n","\n","    # Create a title for the plot\n","    title_text = f\"Pred: {pred_label} | Truth: {truth_label}\"\n","\n","    # Check for equality between pred and truth and change color of title text\n","    if pred_label == truth_label:\n","        plt.title(title_text, fontsize=10, c=\"g\")\n","    else:\n","        plt.title(title_text, fontsize=10, c=\"r\")\n","\n","    plt.axis(False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":749},"id":"66Q2srvHZdXz","executionInfo":{"status":"ok","timestamp":1711936486588,"user_tz":-330,"elapsed":1087,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"34470187-381e-4f26-da09-3269e71b1498"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 900x900 with 9 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## 10. Making a confusion matrix for further prediction evaluation\n","\n","A confusion matrix is a fantastic way of evaluation your classification models visually.\n","\n","1. Make prediction with our trained model on the test dataset.\n","2. Make a confusion matrix `torchmetrics.ConfusionMatrix`\n","3. Plot the confusion matrix using `mlextent.plotting.plot_confusion_matrix()`"],"metadata":{"id":"NCaxXFmBai8E"}},{"cell_type":"code","source":["# Import tqdm.auto\n","from tqdm.auto import tqdm\n","\n","# 1. Make predictions with trained model\n","y_preds = []\n","model_2.eval()\n","with torch.inference_mode():\n","    for X, y in tqdm(test_dataloader, desc=\"Making predictions...\"):\n","        # Send data to the target device\n","        X, y = X.to(device), y.to(device)\n","\n","        # Forward pass (model outputs raw logits)\n","        y_logits = model_2(X)\n","\n","        # Convert logits to the prediction probability\n","        y_pred = torch.softmax(y_logits.squeeze(), dim=1).argmax(dim=1)\n","\n","        # Put prediction on CPU for further evalutation\n","        y_preds.append(y_pred.cpu())\n","\n","# Concatinate list of predictions into a tensor\n","# print(y_preds)\n","y_pred_tensor = torch.cat(y_preds)\n","y_pred_tensor"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":67,"referenced_widgets":["1f2de2a0bcc14c96957ebe40f2afdf62","98fa1253ac4143548076452fe15c2a9c","a55d4ecd596c40b89aa5e11f603a93ec","4bf9fc33b6d24d6ca0fe01554fde1005","6409e72e7b274f6dafc06a9130098b31","6b517f64276549b491e5e097f05368c4","5e46e0ae0eed4cc8bef54eb10d852188","c2b7130e9c53435eacc33d3ca59da9c4","5ed1102445a8416eab590d354408f742","14566ea66a6940c6a468dfc209cd1c0a","f108c5e04a284465852e3669db5e0ad9"]},"id":"IC1Cmyem3783","executionInfo":{"status":"ok","timestamp":1711936491393,"user_tz":-330,"elapsed":4810,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"59c118f0-45a3-4d9d-f0d6-90b2805e2d0b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Making predictions...:   0%|          | 0/313 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1f2de2a0bcc14c96957ebe40f2afdf62"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["tensor([9, 2, 1,  ..., 8, 1, 5])"]},"metadata":{},"execution_count":67}]},{"cell_type":"code","source":["len(y_pred_tensor)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Tp5xI4u-6iRn","executionInfo":{"status":"ok","timestamp":1711936491393,"user_tz":-330,"elapsed":8,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"f58aeed4-c940-47b4-94af-8f5023c91d62"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["10000"]},"metadata":{},"execution_count":68}]},{"cell_type":"code","source":["# See if required packages are installed and if not, install them...\n","try:\n","    import torchmetrics, mlxtend\n","    print(f\"mixtend version: {mlxtend.__version__}\")\n","    assert int(mlxtend.__version__.split(\".\")[1]) >= 19, \"mlxtent version should be 0.19.0 or higher\"\n","except:\n","    !pip install -q torchmetrics -U mlxtend\n","    import torchmetrics, mlxtend\n","    print(f\"mixtend version: {mlxtend.__version__}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WvgmWX0-7lfq","executionInfo":{"status":"ok","timestamp":1711936578285,"user_tz":-330,"elapsed":86899,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"02bc6b1a-509a-4211-c64a-38a09681898f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m841.5/841.5 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     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Jain","userId":"07916721077419186041"}},"outputId":"fcc60a3c-b252-4395-9c3c-1545da2622b4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([9, 2, 1,  ..., 8, 1, 5])"]},"metadata":{},"execution_count":71}]},{"cell_type":"code","source":["from torchmetrics import ConfusionMatrix\n","from mlxtend.plotting import plot_confusion_matrix\n","\n","# 2. Setup confusion matrix instance and compare predictions to targets\n","conf_mat = ConfusionMatrix(task=\"multiclass\", num_classes=len(class_names))\n","conf_mat_tensor = conf_mat(preds=y_pred_tensor,\n","                           target=test_data.targets)\n","\n","# 3. Plot the confusion matrix\n","fig, ax = plot_confusion_matrix(\n","    conf_mat=conf_mat_tensor.numpy(), # matplotlib likes working with numpy\n","    class_names=class_names,\n","    figsize=(10, 7)\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":667},"id":"wHV6jUUZ9Y-N","executionInfo":{"status":"ok","timestamp":1711936580589,"user_tz":-330,"elapsed":2308,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"c754472c-e13c-4053-915f-45fa483a9a3c"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x700 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAApYAAAKKCAYAAACH5hvqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADY4klEQVR4nOzddVRU6R8G8GdopJEyQEAsVFBBsQO7MbADXXV/roWY2N2r2K6Jurq2iL224Joo6KproK5FijCEEjP8/mB3dkdRiQuXYZ/POfcc59Z8X96Z68ONF0lmZmYmiIiIiIjySU3sAoiIiIioeGCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJQkPsAij/5HI53r59CwMDA0gkErHLISIiomImMzMTiYmJKF26NNTUvnxeksGyGHj79i2sra3FLoOIiIiKuVevXqFs2bJfXM5gWQwYGBgAAEp0XA6Jpq7I1Qjr2aY+YpdQIOTy4vkHr9Iy5GKXUCC0NYvfXUPF9erGu8RUsUsoECUNtMUuoUAU12Ohmlrx+34lSqVwsLNWZI4vYbAsBv7+D0KiqVvsgqWhoaHYJRSI4nowZbBUHcU1WKZJimewNGSwVCnFMVj+7VvHjuJ3tCQiIiIiUTBYEhEREZEgGCyJiIiISBAMlkREREQkCAZLIiIiIhIEgyURERERCYLBkoiIiIgEwWBJRERERIJgsCQiIiIiQTBYEhEREZEgGCyJiIiISBAMlkREREQkCAZLIiIiIhIEgyURERERCYLBkoiIiIgEwWBJRERERIJgsCQiIiIiQTBYEhEREZEgGCyJiIiISBDFMljOmjULNWrU+Oo6TZs2hbe3d6HUU9SpSSSY1qMG7q3qiugdfRG2sismdnVSWse3uzNCfvRApH8fvNzcC4FTW8LVwUyxvKGjJRL3DMx2qmVfsrCblGPBQZfRzaMj7GxKQ1dTgsAjAWKXlGvBQZfRvUsnlLctAz1tNRz9pA2ZmZmYO3sG7MuVRkmjEmjfpiWePnkiTrF5tGLZYpjoacB3go9i3sePHzF+7CjYW1ugrIURBvTxRHRUlIhV5k3lCnYooaX22eQ9eoTYpQliw7q1qORgC2N9HTSq74abN26IXVKuJCUmYpbveNR1qgCH0sbwaN0UobdvKZZbm+pkO21YtVzEqvNO1fvr32QyGebMmg7HivYoaVQC1So7YNGCucjMzBS7NEEU1b4qEsFSIpF8dZo1a5bg73no0CHMnTv3q+u8ePECEokEoaGh2S6fPXs2+vXrByCrDQEBAQJXWTh8OlfDkBaVMH7bdbiOC8CM3SHw7lgN/2tTWbHO0wgpxm27jroTA9Fq1im8jElCwJSWMDPQBgBcfxSD8t/vVZr8zz3G86hE3H72TqymfVNycjKqOznDb9VasUvJs6w2OGHFyjXZLl/+4xKsX7saq1avx8Xga9DT00PnDm3w8ePHQq40b26H3IT/1k2oWk35l50pk8bh1Ilj8N+5B8dOn0dkxFv079NdpCrzLui3G3j28q1iOnbyVwBA126eIleWf/v37cWkCT6YOm0mrt64DScnZ3Rq3xrR0dFil5ZjE8YMR9DFc/DbsBVngkPQuFlz9OnSDhFv3wAAQh6+UJqWrf4JEokEbTt5iFt4HhSH/vq35csWY/PGDVjutxq3wx5g7oJFWPHjUqxfu1rs0vKtKPdVkQiWERERisnPzw+GhoZK88aPHy/4e5qamsLAwOCLy9PS0r65jyNHjqBTp05CliUKt4rmOB7yCqfvvMHLmGQcuf4nzt99C5fy/5yR3H/lOS7+HoEX0Un443U8fHfeglEJLVQtZwIASJfJEZ3wUTHFJaWivas1fr70VKxm5UjrNm0xa848dPboInYpeda6TVvMnD0PnTp/3obMzEysXb0SEydPRYdOnVG9uhM2bd2OiIi3OBoYUPjF5lJSUhKGDR6AlWs2wNjEWDE/ISEBP2/fivmLlqFxU3fUqOmCNRu24Ma1q7h545p4BeeBubk5rKysFNPJE8dgX748GjVuInZp+bbKbzkGfTcUA7wGoYqjI1av2wDdEiWw3X+r2KXlyIcPH3Dy6GFMmb0Ades3gp19efhMng5b+/LYuW0jAMDC0kpp+vXkMdRv1ATlbO1Frj73VL2/PnXt6lW079gJbdq1RzlbW3Tp2h3NW7TCrVs3xS4t34pyXxWJYPnvg6qRkREkEonSPH19/c+2uXjxIurUqQM9PT0YGxujQYMG+PPPP5XW2blzJ2xtbWFkZIRevXohMTFRsezTS+G2traYO3cuBgwYAENDQwwbNgx2dnYAgJo1a0IikaBp06aK9V+9eoX79++jTZs2sLW1BQB06dIFEolE8RoA1q9fj/Lly0NLSwuVKlXCzp07lWqUSCRYv3492rZtC11dXdjb2+PAgQN5/EnmzfXHMWhSrRQcShkCAKrZmKBeJQucCX2T7fqa6moY1Lwi4pPT8Puf77Ndp52LNUwNtPHzxaIdLIu7F8+fIyoyEs2at1DMMzIyQu06brh+7aqIleXMhLGj0Kp1WzR1b6E0P+xOCNLT09G0WXPFvIqVKqOstQ1uXletYPlvaWlp2LN7FwYMHASJRCJ2OfmSlpaGO7dD4P6vz56amhrc3Vvghgp89gBAlpEBmUwGbW1tpfk6Ojq4ee23z9aPiY7C+V9Pomc/r0KqUDjFob8+VbdePVy8cB5PHj8GANy9G4bffgtGq9ZtRK4sf4p6X2mIXUBeZGRkwMPDA0OHDsUvv/yCtLQ03LhxQ+lAHB4ejoCAABw7dgzv379Hjx49sGjRIsyfP/+L+122bBlmzJiBmTNnAgBGjBiBOnXq4OzZs6hatSq0tLQU6wYGBqJp06YwNDTEzZs3YWFhgW3btqFNmzZQV1cHABw+fBhjxoyBn58fWrRogWPHjmHQoEEoW7YsmjVrptjX9OnTsWjRIqxcuRI7d+5Er169cO/ePVSpUiXbOlNTU5Gamqp4LZVK8/aD/MuPR+7BQFcTIT96QCbPhLqaBHP23sa+K8+V1mtTqyy2jW6MEloaiIz/gM7zf8W7xNRs9zmgWQWcDXuLt3Ep+aqN8icqKhIAYGFhqTTfwsKyyN+PeHD/XoSF3sH5oM+DYlRUFLS0tGBkbKw038LCQtFmVXT0SADi4+PRb4CX2KXkW2xsLGQy2eefPUtLPHr0h0hV5Y6+gQFcatfFymUL4VCxMswtLHHk4F6E3LwOW/vyn61/YM/P0NM3QNsOHoVfbD4Vh/761LgJkyGVSlHTqQrU1dUhk8kwc8489OrdV+zS8qWo95VKBkupVIqEhAR06NAB5ctnfbk/DWFyuRz+/v6Ky939+/fHuXPnvhos3d3dMW7cOMXrvwNiyZIlYWVlpbTukSNH0LlzZwBZl7IAwNjYWGm9ZcuWwcvLCz/88AMAwMfHB9euXcOyZcuUgqWnpyeGDBkCAJg7dy7OnDmD1atXY926ddnWuXDhQsyePfuL7citrnVt0aOhPQavvoyHr+PhZGuKxQNqI+L9B+y+HK5Y7/L9SDSYdBQlDbTh1bwitns3QbNpJxArVb5Xr7RpCbRwLo0BfpcEq5H+W16/fgXfCWNx6Ogp6OjoiF1OodnuvxWtWrdF6dKlxS6F/uK3YQvGj/oetavaQ11dHdWca6Jztx64F3rns3X37tqOLp69/lOf2aLs4IF92LtnN7bt2IUqjlVxNywUk8aPRalSpdGv/0Cxyyu2isSl8K95+fIl9PX1FdOCBQtgamoKLy8vtG7dGh07dsTKlSsRERGhtJ2tra3SPZSlSpX65k2trq6uOapJKpXi0qVL37y/8uHDh2jQoIHSvAYNGuDhw4dK8+rVq/fZ60/X+TdfX18kJCQoplevXuWo7i+Z188Vy4/cw8GrL/DgVTz2BD3DmhMPMa5zdaX1UlIz8CwqETefxmLET78hQ5aJgc0cPttfv6YOiEtMxYmQ/NVF+WdpmfWLTnS08tnJ6OgoWFhaZrdJkRB25zZiYqLRtEFtmBlqw8xQG1eCLuOn9athZqgNCwsLpKWlISE+Xmm76OhoRZtVzcs//8T5c2fhNfg7sUsRhJmZGdTV1T//7EVFffaLelFma1ceB46dxaNX73D93lMcOxuM9PQM2NjaKa13/Wowwp88Ru/+g0SqNH+KS3/921TfiRg3fhI8e/RCtWrV0advf4wc7Y0flywSu7R8Kep9VeSDZenSpREaGqqY/ve//wEAtm3bhqtXr6J+/frYu3cvKlasiGvX/rlkpqmpqbQfiUQCuVz+1ffS09PLUU0nT56Eo6MjrK2tc9kaYWhra8PQ0FBpyo8SWuqQfzL6glwuh9o3Ph1qahJoaap/Nr9fEwf8EvQMGbLiMaSDKrO1s4OllRUunj+nmCeVSnHzxnW41a33lS3F1bipO67cCMXlqyGKqWYtV3j27IPLV0NQo5YrNDU1ceniecU2Tx4/wutXL1Hbra6Ilefdju3bYG5hgbbt2otdiiC0tLRQs5YLLvzrsyeXy3HhwjnUKcKfvS8poacHS6tSiI9/j8vnz6BV2w5Ky/f87I/qNWrB8ZPRC1RFcesvAPiQkgK1T/4jU1NX/2YWKOqKel8V+UvhGhoacHD4/KwYkPVQTc2aNeHr64t69eph9+7dqFtXuP9U/r6nUiaTKc3/92Xwv2lqan62XpUqVXDlyhUMHPjPKfcrV67A0dFRab1r165hwIABSq9r1qwpSBty4uTt15jgUR2vY5Pw8HU8nG1LYmT7qth5MWuswxLaGpjQpTpO3HqFyPgPKGmgjWGtKqO0SQkcvqb8wFSTalawszTA9vOPC63+/EhKSkL4038eMHrx/DnCQkNhYmoKGxsbESvLuaSkJISH/6sNL54jLCwUpiamsLaxwYhRY7Bk0Xw4OFRAOTs7zJ01A6VKlUbHIjwcioGBARyrVlOaV0KvBExNSyrm9xs4GFMnj4eJiQkMDA0xcdwY1Hari9p1VC9YyuVy7Nzhj379BkBDo8gflnNstLcPhg4eCBcXV7jWroM1q/yQkpyMAQNV56zexXNnkJmZifIVKuDFs3DMnzkF5StUQo++/xzXE6VSHD9yCNPnLhax0vwrDv31b23bd8SSxQtgbW2DKo5VERZ2B2tWrkB/FW3PvxXlvlLJI9jz58+xceNGdOrUCaVLl8ajR4/w5MkTpXAmBAsLC+jq6uLUqVMoW7YsdHR0oKenh5MnT342BJKtrS3OnTuHBg0aQFtbGyYmJpgwYQJ69OiBmjVrokWLFjh69CgOHTqEs2fPKm27f/9+uLq6omHDhti1axdu3LiBLVu2CNqWrxm/7Tqm9aiJ5YPrwtxIBxHvP2Dr2cdYdDAMACCTy1GxtBH6+DigpIE24hJTcftZLFrPOok/Xscr7WtAswq49igaj9/m74GiwnI75BZat/jnftdJfw3A3a//QGza6i9SVblzO+QW2rZyV7yePDHrPuG+/Qdi4+Zt8Bk3ESnJyRg54nskxMejXv2GCDh6UuXvA1uw+EeoqalhQN8eSEtNhXuLVli2IvuxPIu68+fO4tXLlxjgNVjsUgTl2aMnYmNiMGf2DERFRsLJuQaOHDsFyyJ8G8anEqUJWDR3OiLfvoGxiSnadvTAxGmzla6KBR7ah8zMTHTu1kPESvOvOPTXv/24YhXmzJoO7zEjEBMdjVKlSmPwkGHwnTpD7NLyrSj3lSSziA1B7+/vD29vb8R/cu/Uv0VFReF///sfrl+/jnfv3qFUqVIYOHAgZs6cCTU1NcyaNQsBAQFKA5v7+fnBz88PL168AJA13FCNGjXg5+cHICsYent7f/bXeDZv3ow5c+bgzZs3aNSoEaZPnw4vL6/P7ms8evQofHx88OLFC5QpU0bxPuvXr8eyZcvw6tUr2NnZYdq0aejfv79iO4lEgrVr1yIgIACXL19GqVKlsHjxYvTokfMDlFQqhZGREfS6rodEUzfH26mCmJ+L5w3W8k/vPSgm0jJU+xLTl2hrFvm7hnJN1Ycz+pLYL4xUoer+/mMUxU1xPRaqqRW/75dUKoVlSSMkJCR89Ra8Ihcsi7rRo0cjIyPji09s55ZEIsHhw4fh4eGR530wWKqe4nowZbBUHQyWqoXBUrX8l4OlSl4KF1O1atU+e4qbiIiIiBgsc23YsGFil0BERERUJDFYiox3IhAREVFxUfxuHCIiIiIiUTBYEhEREZEgGCyJiIiISBAMlkREREQkCAZLIiIiIhIEgyURERERCYLBkoiIiIgEwWBJRERERIJgsCQiIiIiQTBYEhEREZEgGCyJiIiISBAMlkREREQkCAZLIiIiIhIEgyURERERCYLBkoiIiIgEwWBJRERERIJgsCQiIiIiQWiIXQAJ59mmPjA0NBS7DEGZuI0Ru4QC8f76SrFLKBAa6hKxSygQEknxbFdxZGagLXYJlAtqavxuFTc8Y0lEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBkvKsQ3r1qKSgy2M9XXQqL4bbt64IXZJX6VfQhtLx3XBo2MzEXdlKS5s9YaLo41i+YeQldlOY/u7f7YvLU11XNs9AR9CVsKpYpnCbEaeqVp/fSo46DI8u3ZCBbuyMNBRx9HAAKXl0VFR+H7IIFSwKwsLE3106dgWT58+EafYfNi4YT1q13SChakhLEwN0aRhPZw+dVLssgSj6p/DT7G/VEtw0GV08+gIO5vS0NWUIPBIgNglCaao9lWxDJYSieSr06xZs8QuUeXs37cXkyb4YOq0mbh64zacnJzRqX1rREdHi13aF62f3gvubpUwePrPcO25GGev/YHj639AaXMjAIBtq2lK07BZuyGXy3H4fNhn+1owpjMiYqSF3YQ8U8X++lRKSjKqV3fGj36rP1uWmZmJXj264sXz59iz/zCCr4fA2qYcOrVtheTkZBGqzbsyZcti7oJF+O16CK5cu4Wmzdzh2bUzHty/L3Zp+VYcPoefYn+pluTkZFR3cobfqrVilyKootxXkszMzEyxixBaZGSk4t979+7FjBkz8OjRI8U8fX196OvrA8j6D0omk0FDQ6PQ6/yWtLQ0aGlpfXM9qVQKIyMjRL1LgKGhYYHU0qi+G1xca8Nv1RoAgFwuh4OdNYaPGIUJEycXyHsCgInbmDxtp6OtiZjLi+E5bjNOBT9QzL/y83j8euUBZq8/8dk2+378DvoldNBuuPIBqFX9Kljs44HeE7bizoEpcOu9BHcfv8lTXX97f31lvrb/FrH6K0MmL5D9GuioY/e+g+jYyQMA8OTJY9SqXgU3bt9FFceqALLaWL5cacycPQ9eg4cI+v4a6oX7O3hpC1MsWLQUXoO/K9T3FZpYn8PCxv5SDbqaEuw9cBidOnuIXUq+idFXUqkUliWNkJDw9axRLM9YWllZKSYjIyNIJBLF6z/++AMGBgY4efIkXFxcoK2tjeDgYKSmpmL06NGwsLCAjo4OGjZsiJs3byr26e/vD2NjY6X3CQgIgEQiUbwOCwtDs2bNYGBgAENDQ7i4uODWrVuK5cHBwWjUqBF0dXVhbW2N0aNHK51dsbW1xdy5czFgwAAYGhpi2LBhBfdDyoW0tDTcuR0C9+YtFPPU1NTg7t4CN65dFbGyL9NQV4OGhjo+pmYozf+Ymo76New/W9/C1ABtGlbF9iPXPpu/blovfDf9Z6R8TC/QmoWiiv2VW2mpqQAAbW0dxTw1NTVoa2nj6m9XxCor32QyGfbt3YPk5GS41a0ndjn58l/4HLK/SAxFva+KZbDMicmTJ2PRokV4+PAhnJycMHHiRBw8eBDbt2/H7du34eDggNatWyMuLi7H++zbty/Kli2LmzdvIiQkBJMnT4ampiYAIDw8HG3atEG3bt1w9+5d7N27F8HBwRg5cqTSPpYtWwZnZ2fcuXMH06dPz/Z9UlNTIZVKlaaCFBsbC5lMBgsLS6X5FpaWSmeHi5KklFRcC3sO3yGtUMrMEGpqEvRq6wq36rawMvv8N61+HWojMfkjAj65DL5xVh9sOngFtx++KqzS800V+yu3KlaqDGtrG8yaMQXv379HWloali9bgjdvXiMqMkLs8nLt93v3YGasDyM9bYwe8T/sPXAYVRwdxS4rX4rz55D9RWIq6n31nw2Wc+bMQcuWLVG+fHloa2tj/fr1WLp0Kdq2bQtHR0ds2rQJurq62LJlS473+fLlS7Ro0QKVK1dGhQoV4OnpCWdnZwDAwoUL0bdvX3h7e6NChQqoX78+Vq1ahR07duDjx4+Kfbi7u2PcuHEoX748ypcvn+37LFy4EEZGRorJ2to6fz+MYmrwjJ2QSCR4dnouEq7+iBG9GmPf6duQZ3P3x4DOdbH3ZAhS0/45w/lDr8Yw0NPB0m1nCrNsygFNTU3s2nsAT588gU0pM1iY6CPo0gW0at0Gamqqd1irWKkSrt8KxeUr1zH0++EYOnggHj548O0NSRTsL6IvK3o3FhYSV1dXxb/Dw8ORnp6OBg0aKOZpamqiTp06ePjwYY736ePjgyFDhmDnzp1o0aIFPD09FeEwLCwMd+/exa5duxTrZ2ZmQi6X4/nz56hSpcpndX2Jr68vfHx8FK+lUmmBhkszMzOoq6sjOjpKaX50VBSsrKwK7H3z6/nrd2g1bDVK6GjBUF8HkbFS7Fw4EM/fvFNar0ENe1SytUT/yf5K85vWrgC36rZIuPqj0vwrO8dhz6kQDJ25C0WRqvZXbtWs5YLfbtxGQkIC0tLSYG5ujmaN6qFmLRexS8s1LS0tlHdwAADUcnFByK2bWLt6Jdas/0nkyvKuOH8O2V8kpqLeV6r3q71A9PT0crW+mpoaPn3OKT1d+Z67WbNm4f79+2jfvj3Onz8PR0dHHD58GACQlJSE77//HqGhoYopLCwMT548UTozmZO6tLW1YWhoqDQVJC0tLdSs5YIL588p5snlcly4cA51VOC+opSPaYiMlcLYQBct6lXGsYv3lJYP9KiLkAcvce/JW6X545YeQp3eS+DWZync+iyFx5is/zT6+27HrLXHCq3+3FL1/sotIyMjmJub4+nTJ7gdcgvtO3QSu6R8k8vlSP3rPlJV9V/6HLK/qDAV9b76z56x/Lfy5ctDS0sLV65cQbly5QBkhcabN2/C29sbAGBubo7ExEQkJycrwl9oaOhn+6pYsSIqVqyIsWPHonfv3ti2bRu6dOmCWrVq4cGDB3D467dcVTPa2wdDBw+Ei4srXGvXwZpVfkhJTsaAgYPELu2LWtSrDAmAx39Go7y1ORaM6YTHL6Kx4+h1xToGetro2qIGJq848tn2ryLfK71OSsn6j+PZ61i8iU4o0NrzSxX761NJSUl4Fv5U8frPFy9wNywUJiamsLaxweGD+2FmZo6y1ja4f/8eJo0biw6dOqN5y1YiVp1706f6onWbtrC2tkFiYiL27tmNy5cu4uiJ02KXlm/F4XP4KfaXaklKSkL403+OIy+eP0dYaChMTE1hY2PzlS2LtqLcVwyWyDpLOHz4cEyYMAGmf33YlixZgpSUFHz3XdbwEW5ubihRogSmTJmC0aNH4/r16/D391fs48OHD5gwYQK6d+8OOzs7vH79Gjdv3kS3bt0AAJMmTULdunUxcuRIDBkyBHp6enjw4AHOnDmDNWvWiNHsXPHs0ROxMTGYM3sGoiIj4eRcA0eOnYKlpeW3NxaJkb4O5ozsiDIWxoiTJuPIuTDMXHccGRn/DInj2aoWJBIJ9p0OEbFS4alif33qTsgttGvdXPHad+I4AECffgPw0+ZtiIyMhO/E8YiOjoKVVSn07tsfk6ZME6vcPIuJjsZ3gwYgMiICRkZGqFbdCUdPnEbzFi3FLi3fisPn8FPsL9VyO+QWWrdopng9aULWbWT9+g/Epq3+IlWVf0W5r4rlOJb/5u/vD29vb8THxwMALl68iGbNmuH9+/dKwwd9/PgREydOxC+//ILExES4urpixYoVqF27tmKdgIAATJgwAW/evEHz5s3RqVMnDBs2DJmZmUhLS8PAgQNx5coVREVFwczMDF27dsXSpUuho5M1JMrNmzcxdepUXL16FZmZmShfvjx69uyJKVOmAMgabsjb21txljSnCmMcS7HkdRzLoq6gx7EUS0GNYym2wh7HkoioqMnpOJbFPlj+FzBYqh4GS9XCYElE/3X/6QHSiYiIiKjwMVgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIDbELIOHI5JmQyTPFLkNQ76+vFLuEAlHJ56jYJRSIB8s6iF1CgXj7/oPYJQiutImu2CUUiHeJqWKXUCBKGmiLXQJRjvCMJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLP9ia2sLPz8/xWuJRIKAgADR6hFTcNBleHbpBAfbMtDXVsPRIwGKZenp6Zg+ZRLq1HKChYk+HGzLYOjggYh4+1a8gvMhOOgyunl0hJ1NaehqShD4r7YWVcEzm+PPVR0/m+Z6VgMA7BlV77Nl83tUV9pHaRNdbPu+Dv5Y1g4h81thSucqUFeTiNGcL/ra5/BTo0f8D/raali7yq/Q6supG1eDMbRfN9Srbo/yFiXw64lAxbL09HQsnjMNbZvURjVbM9Srbo9xI4YgKlL5+/T73TsY0L0DajiUgkulspgybgSSk5IKuyl5smHdWlRysIWxvg4a1XfDzRs3xC4pV5ISEzHTdzzcnCqgfGljdG7dFKG3bymW/7hoLpq4OaFCWVNUtbNCry5tcfuWarXx31S9vz6lisf4nCqqfVVsgqWXlxckEgkkEgm0tLTg4OCAOXPmICMjQ+zSVE5KcjKqOTlh+co1ny9LSUHonTuYNGUagq+FYPfeg3jy+BF6dOssQqX5l5ycjOpOzvBbtVbsUnKs049BcJ36q2Lqs+YqAOD4nQjFOruv/Km0zsLAh4plahJg2/d1oKmuhq4rgjHu51B0d7OGT7tKhd6Wr/na5/DfAo8cxs0b11GqdOlCqix3UlKSUblqdcxatOKzZR8/pOD+3VCM9JmMwLO/Yd22PXge/hjD+nsq1omKfIsB3TugnJ09Dp26hG17AvDkj4eYOHpYYTYjT/bv24tJE3wwddpMXL1xG05OzujUvjWio6PFLi3HJowZjqCL57Byw1acDQ5B42bN0btLO0S8fQMAsHeogHmLV+Bs8C0cOnEeZa3LoW+3DngXGyNy5blXHPrrU6p4jM+JotxXGmIXIKQ2bdpg27ZtSE1NxYkTJzBixAhoamrC19dX7NLyJC0tDVpaWoX+vq3atEWrNm2zXWZkZISjJ39Vmvej32o0aeCGVy9fwtrGpjBKFEzrNm3R+gttLariktKUXg9v6YAXMcm49vSdYt6HdBliElOz3b5xZQtUsDJA37VXEZuYhgdvpPjx+CNM7lQFficfIV2WWaD159TXPod/e/vmDcaPHY2AY6fQ3aNDIVWWO02bt0bT5q2zXWZgaIQdB44pzZu1cDm6tG6Mt69foXRZa5z/9SQ0NDQxe7Ef1NSyzgXMW7oK7ZrWwYtn4bC1L1/gbcirVX7LMei7oRjgNQgAsHrdBpw8eRzb/bdiwsTJIlf3bR8+fMCJo4exddcB1K3fCAAwbvJ0nD19Aju3bcTEqbPRpXsvpW1mzluCPT/74+H9e2jYxF2MsvNM1fsrO6p4jM+JotxXxeaMJQBoa2vDysoK5cqVw/Dhw9GiRQsEBgaiadOm8Pb2VlrXw8MDXl5eOd73vXv34O7uDl1dXZQsWRLDhg1D0l+Xon799Vfo6OggPj5eaZsxY8bA3f2fA0twcDAaNWoEXV1dWFtbY/To0UhOTlYst7W1xdy5czFgwAAYGhpi2LCif0YCAKQJCZBIJDAyNha7lP8cTXUJuriWxb5rL5Xme7iWwZ0FrfHr5CaY2LEydDTVFctq2Zngj7dSxCb+E1AvP4yGoa4mKpYyKLTa80sul2PI4AEYM3Y8HB2ril2OYBKlUkgkEhgYGQHI+gVTU0tTESoBQFtHFwBw68ZvotSYE2lpabhzOwTuzVso5qmpqcHdvQVuXLsqYmU5J8vIgEwmg7a2ttJ8HR0d3Lj2+c8+LS0Nu7ZvgaGhERyrORVWmYIoDv31X1HU+6pYBctP6erqIi0t7dsrfkNycjJat24NExMT3Lx5E/v378fZs2cxcuRIAEDz5s1hbGyMgwcPKraRyWTYu3cv+vbtCwAIDw9HmzZt0K1bN9y9exd79+5FcHCwYh9/W7ZsGZydnXHnzh1Mnz4923pSU1MhlUqVJrF8/PgR06dOhmfP3jA0NBStjv+qVk5WMNTVwP7rrxTzjoS8gfeOO+i1+jesO/sUXWuXxcoBNRXLzQ20EfvJ2cyYv0KmuYFO4RQugOXLFkNDXQM/jBwtdimCSf34EYvnTkPHLj1gYJD1farXsAlio6Owcc0KpKWlISH+PZbOyzo2xERFilnuV8XGxkImk8HCwlJpvoWlJSIji27d/6ZvYACX2nXht2whIiPeQiaT4eC+3Qi5eR3R//rZnz19AhWtS6J8KSNs2rAauw8dh2lJMxErz73i0F//FUW9r4plsMzMzMTZs2dx+vRppTOGebV79258/PgRO3bsQLVq1eDu7o41a9Zg586diIqKgrq6Onr16oXdu3crtjl37hzi4+PRrVs3AMDChQvRt29feHt7o0KFCqhfvz5WrVqFHTt24OPHj4rt3N3dMW7cOJQvXx7ly2d/iWvhwoUwMjJSTNbW1vluY16kp6djQJ+eyMzMhN/qdaLU8F/Xs64NLj6MRrT0n6D4y28vcfmPGDyKSETArTfw2XkHbZxLwcashIiVCuvO7RCsW7MKP23eBomkaD10lFfp6ekYNbQfkJmJOUtXKuZXrOyIpas3Ycv6lahWriTqVrNDWRtbmJlbQE1SLA/hRcrKDVuQmZkJ16r2sLcyxNaN69C5Ww+ln339hk1w+tINBJy6iKbuLTF8cF/Exoh/rxuRGIrVUenYsWPQ19eHjo4O2rZti549e2LWrFn53u/Dhw/h7OwMPT09xbwGDRpALpfj0aNHAIC+ffvi4sWLePvX09G7du1C+/btYfzX5eGwsDD4+/tDX19fMbVu3RpyuRzPnz9X7NfV1fWb9fj6+iIhIUExvXr16pvbCC09PR39+/TEy5d/IvDErzxbKYIyJrpoWMkce66+/Op6d/6MBwDYmmV9fmMSU2FmoHxpz9xA669lH6EKfgsOQkx0NCo7lINRCU0YldDEyz//hO+k8XCsaCd2ebmWnp6OUUP64c2rV9i+/5jibOXfOnXriev3X+C3sKe49cdrjJkwFXHvYmFtW3TbamZmBnV1dURHRynNj46KgpWVlUhV5Z6tXXkcPHYWj1+9w417T3H8bDAy0jNg86+ffQk9PdjZl4dLbTf8uPonqGtoYM/P/uIVnQfFpb/+C4p6XxWrYNmsWTOEhobiyZMn+PDhA7Zv3w49PT2oqakhM1P5gYT09HRB37t27dooX7489uzZgw8fPuDw4cOKy+AAkJSUhO+//x6hoaGKKSwsDE+ePFE6M/nv8Pol2traMDQ0VJoK09+hMvzpExw9eQYlS5Ys1PenLJ51rfEuMRXn73/9zEjVMlmfj2hpVmi8/fw9Kpc2REn9fx4Ma1jZHNIP6XgSqRpD2PTq2x/XQsLw2807iqlU6dLw9hmPgKOnxC4vV/4OlS+eh2PHgWMwMf3y98nMwhJ6+vo4fuQAtHV0ivTDIVpaWqhZywUXzp9TzJPL5bhw4Rzq1K0nYmV5U0JPD5ZWpRAf/x6Xzp9Bq7ZfflgsUy5Hamr2D88VVcWtv4qzot5XxeqpcD09PTg4OHw239zcHBER/wzFIpPJ8Pvvv6NZs2Y52m+VKlXg7++P5ORkRfC7cuUK1NTUUKnSP0O09O3bF7t27ULZsmWhpqaG9u3bK5bVqlULDx48yLa+oiYpKQnPwp8qXv/54jnuhoXCxMQUVqVKoV8vT4SG3saBw0chl8kQ9dc9HSampqI8xZ4fSUlJCH/6T1tfPH+OsNBQmJiawqYIP+EukQCebtY4cOMVZPJ/fmmyMSsBD5cyOP8gGvHJaahc2hAzulbFtafv8MfbRADA5T+i8SQyESv618TCIw9hbqiN8e0rY0fQC6RlyMVq0me+9jm0trH57BcaTU1NWFpaoWKlojVsUnJSEv58Hq54/frln3hwLwzGJqYwt7TCyO/64Pe7odj880HIZTLFfZNGJv98n3ZsWY9atetCT08fwRfPYdGcqZgwbS4MjYzFaFKOjfb2wdDBA+Hi4grX2nWwZpUfUpKTMWDgILFLy7GL584gMzMT5StUwItn4Zg3cwrKV6iEnn0HIiU5GauWL0LLNh1gaWWFuHfvsH3zBkRGvEWHzt3ELj3XikN/fUpVj/HfUpT7qlgFyy9xd3eHj48Pjh8/jvLly2P58uWfPcH9NX379sXMmTMxcOBAzJo1CzExMRg1ahT69+8PS0tLpfVmzZqF+fPno3v37kpPEk6aNAl169bFyJEjMWTIEOjp6eHBgwc4c+YM1qz5+jh9he12yC20a/XPmZDJE8cBAPr2H4gp02bi+LGsAZ7r1a6ptN2JX8+jcZOmhVanEG6H3ELrFv/8gjFpgg8AoF//gdi01V+kqr6tYSVzlDUtgX3XlG+DSM+Qo0Elcwxuag9dLXVEvP+Ak6ERWP3rE8U68kxg8E83ML9HdRz2aYiUtAwcvP4ay088KuxmfNXXPoc/bd4mVlm5di/sNvp2aaN4PX/GJABA1579MGbCVJw9dRwA0MG9rtJ2uw6fQt0GjQEAYbdDsHLJfKQkJ8HeoRLmLV2NLj36FFIL8s6zR0/ExsRgzuwZiIqMhJNzDRw5dkrpuFnUJUoTsGjudES8fQNjE1O07eiBSdNmQ1NTEzKZDE+fPMb+Pb3x/l0sTExLwrmmCw4eP4dKVRzFLj3XikN/fUpVj/HfUpT7SpL56TViFeXl5YX4+Phs/1pOeno6xowZg71790JDQwNjx47FtWvXYGxsDH9/fwBZQ/14e3srhiWSSCQ4fPgwPDw8AGQNNzRmzBhcvXoVJUqUQLdu3bB8+XLo6+srvZebmxtu3LiB8+fPf3ZG9ObNm5g6dSquXr2a9Rtw+fLo2bMnpkyZkm0NOSWVSmFkZIS3MfHF7l7HovbXYIRSyeeo2CUUiAfLiuZYkvkVlaAa957mRmkTXbFLKBDvvjB+q6or+cl90USFTSqVwrKkERISEr6aNYpNsPwvY7BUPQyWqoXBUnUwWBIVjJwGy2L18A4RERERiYfBkoiIiIgEwWBJRERERIJgsCQiIiIiQTBYEhEREZEgGCyJiIiISBAMlkREREQkCAZLIiIiIhIEgyURERERCYLBkoiIiIgEwWBJRERERIJgsCQiIiIiQTBYEhEREZEgGCyJiIiISBAMlkREREQkCAZLIiIiIhIEgyURERERCYLBkoiIiIgEwWBJRERERIJgsCQiIiIiQWiIXQAJJzMzE5mZmWKXIahi1hyFh8s6iF1CgSjZZqHYJRSImJOTxS6BcshQV1PsEoj+03jGkoiIiIgEwWBJRERERIJgsCQiIiIiQTBYEhEREZEgGCyJiIiISBAMlkREREQkCAZLIiIiIhIEgyURERERCYLBkoiIiIgEwWBJRERERIJgsCQiIiIiQTBYEhEREZEgGCyJiIiISBAMlkREREQkCAZLIiIiIhIEgyURERERCYLBkoiIiIgEwWBJRERERIJgsCQiIiIiQTBYEhEREZEgGCyJiIiISBAMlvSZZUsWoUkDN5QyM4KdtRV6eXbB48ePFMvj4uIwfuxo1KxeBebGeqjiYIsJPmOQkJAgYtX5t2zJIpTQUsOEcd5il5IrwUGX0b1LJ5S3LQM9bTUcPRKgtPxIwCF0bNca1qXMoKethrCwUFHq/BZ9XS0sHdECj34ZgbiTE3Bh9QC4VCqlWN65USUcXdILrw9748P5KXAqb/HZPixN9LDFtyOeHxiN2OPj8dtPg+HRqFJhNuObgoMuw7NrJ1SwKwsDHXUcDQxQWm6go57t5Ld8mTgF59OGdWtRycEWxvo6aFTfDTdv3BC7pBz7cWnWsbC0uRHsbazQ27MLnvzrWAgA7Vq5w1BXXWnyHjVcpIrzT5X7KzsbN6xH7ZpOsDA1hIWpIZo0rIfTp06KXZYgimpfMVj+xcvLCxKJBBKJBJqamrC0tETLli2xdetWyOVyscsrVFeCLmHo98Nx/vJvCDx+Gunp6fBo3wbJyckAgMiIt4iIeIv5i5bgeshdbNi0FWd+PY0R/xsicuV5d+vWTWzZvBHVqzuJXUquJScno7qTE1asXPPF5fUbNMDc+YsKubLcWT++Hdxd7DB4YSBcv9uMs7ee4/jS3ihtpg8AKKGjid/uvca0TRe+uI/Nvh1R0bokPKfth+uQzTgS9Ag/z+gCZwfLwmrGN6WkJKN6dWf86Lc62+VPX7xRmtb9tBkSiQSdPboWcqX5t3/fXkya4IOp02bi6o3bcHJyRqf2rREdHS12aTkSHHQJw/43HOcu/YYjx04jPSMdHh3+ORb+zWvwEDx5/kYxzZm/WKSK80fV+ys7ZcqWxdwFi/Db9RBcuXYLTZu5w7NrZzy4f1/s0vKlKPeVJDMzM1PsIooCLy8vREVFYdu2bZDJZIiKisKpU6ewcOFCNGrUCIGBgdDQ0Phsu/T0dGhqaopQ8T+kUimMjIzwJvo9DA0NBd9/TEwM7K2tcPLMBTRs1DjbdQ4f3I8hgwYgKi4x259TXqmrSQTb15ckJSWhfh0X+K1ei8UL58PJ2RlLf/Qr0PcsqG+dnrYa9uw7hI6dPT5b9ueLF3CsZI/fbtyGs3ONAnn/km0W5mk7HS0NxBwfD89p+3Hqerhi/pUNg/DrjWeYvfWSYp6NpREe/TICbkM342648kE05vh4jPY7hV/O/K6Y9/qwN6ZtugD/E2F5qg0AYk5OzvO2X2Ogo47d+w6iYyePL67Ty7MLkhKTcOzUGUHfW0O94M8rNKrvBhfX2vBblfVLj1wuh4OdNYaPGIUJEwvmZ5qeUXAnAmJjYmBvk3UsbNAw61jYrpU7qjs5Y/GyFQX2vgCgqVE8+0sMpS1MsWDRUngN/k7sUvJMjL6SSqWwLGmEhISEr2YNnrH8F21tbVhZWaFMmTKoVasWpkyZgiNHjuDkyZPw9/cHAEgkEqxfvx6dOnWCnp4e5s+fDwA4cuQIatWqBR0dHdjb22P27NnIyMgAAGRmZmLWrFmwsbGBtrY2SpcujdGjRyved926dahQoQJ0dHRgaWmJ7t27F3rbv0YqzbrEbWpq+sV1EhISYGBoKGioLCxjR49Em3bt4N68hdil/GdpqKtBQ10NH9NkSvM/pmagfrWyOd7Ptfuv0b1pFZgY6EAiATybOUJHSwOXQ18KXXKhiI6KwumTJzDAa5DYpeRaWloa7twOUfpeqampwd29BW5cuypiZXmX8Nex0MRE+Vi4b+9u2Ja1gJuLE2ZNn4KUlBQxysuX4thfn5LJZNi3dw+Sk5PhVree2OXkWVHvK9VLAYXM3d0dzs7OOHToEIYMybrUO2vWLCxatAh+fn7Q0NBAUFAQBgwYgFWrVqFRo0YIDw/HsGHDAAAzZ87EwYMHsWLFCuzZswdVq1ZFZGQkwsKyzp7cunULo0ePxs6dO1G/fn3ExcUhKCjoqzWlpqYiNTVV8VoqlRZQ67N+C5o0fizq1msAx6rVsl0nNjYWSxbOx6DBQwusjoKyf+8ehN65jaCrRePelP+qpA9puHb/NXz7N8Cjl7GIep+MHu6OcHMsg/C373O8n36zD2PnjC54e8QH6RkypHxMR8+ZB/EsF/soSnb9vAMGBgbopIKXwWNjYyGTyWBhoXwbgoWlJR49+kOkqvJOLpdj8oTPj4WePXvB2qYcSpUqjd/v3cXMab548vgRdu09KGK1uVfc+uvffr93D00b1cPHjx+hr6+PvQcOo4qjo9hl5VlR7ysGyxyoXLky7t69q3jdp08fDBr0zxmEwYMHY/LkyRg4cCAAwN7eHnPnzsXEiRMxc+ZMvHz5ElZWVmjRogU0NTVhY2ODOnXqAABevnwJPT09dOjQAQYGBihXrhxq1qz51XoWLlyI2bNnF0BLP+czZiQe3r+PX89fzna5VCqFZ5eOqFylCqZMn1koNQnl9atXmDDOG0dP/AodHR2xy/nPG7wwED9N6IBn+0cjQyZH6JNI7Dv/ADUrWuV4HzMHN4GxvjbajtuNdwkp6NiwIn6e0QUtxuzE/ecxBVh9wdi5fRt69OrDz2cRMM4761h4+pzysXDQd8MU/65arTqsSpVCx7Yt8exZOOztyxd2mZSNipUq4fqtUCQkJODwoQMYOnggfj13SaXDZVHGS+E5kJmZCYnkn3v9XF1dlZaHhYVhzpw50NfXV0xDhw5FREQEUlJS4OnpiQ8fPsDe3h5Dhw7F4cOHFZfJW7ZsiXLlysHe3h79+/fHrl27vnkZxdfXFwkJCYrp1atXwjcawDjvUTh14jiOnz6HMmU/vxyZmJiILp3aQV/fALv3HRL9XtPcun07BNHR0ajv5gIDXU0Y6Goi6PIlrFuzGga6mpDJZN/eCQnm+dt4tBr7M0q2W4oKPVej0Q/+0NRQw/OI+Bxtb1faGMO7uOL7pcdx8c4L3HsWjQU7gnH7UQS+7+xSsMUXgCvBQXjy+BEGDlLN+8DMzMygrq6O6OgopfnRUVGwssr5LwtFwd/HwmNfOBb+m2ttNwDAs/CnhVGaYIpTf31KS0sL5R0cUMvFBXPnL0R1J2esXb1S7LLyrKj3FYNlDjx8+BB2dnaK13p6ekrLk5KSMHv2bISGhiqme/fu4cmTJ9DR0YG1tTUePXqEdevWQVdXFz/88AMaN26M9PR0GBgY4Pbt2/jll19QqlQpzJgxA87OzoiPj/9iPdra2jA0NFSahJSZmYlx3qNwNDAAx06fhe2/2v43qVSKzh3aQEtTC3sPBqjkGZVm7s1x8/ZdXLt5RzHVcnFFr959ce3mHairq4td4n9Sysd0RMYlw1hfBy1q2+PYlcc52q6EdtYvNnK58pNRMnkm1ArhITCh7fDfipq1XFDdyVnsUvJES0sLNWu54ML5c4p5crkcFy6cQx0Vub/t72PhscAAHD11Fra2nx8LP3Xvr+G8rKxKfX3FIqY49FdOyeVypdvJVE1R7yteCv+G8+fP4969exg7duwX16lVqxYePXoEBweHL66jq6uLjh07omPHjhgxYgQqV66Me/fuoVatWtDQ0ECLFi3QokULzJw5E8bGxjh//jy6dhXnviqfMSOxf+8v2LP/MAz0DRAVGQkAMDQygq6uriJUfkhJweatO5AolSLxr/s8zczNVSaQGRgYoGo15ftG9fT0YFrS9LP5RVlSUhLC/3V25MWL5wgLC4WpiSmsbWwQFxeHV69eIuLtWwBQjMNnaWlVJH67/VsLVztIJBI8fvUO5cuYYMH3zfH45TvsOJV1G4qJgQ6sLQxRyswAAFDRuiQAICouGVHvk/Ho5Ts8fR2HNT5t4bvhHN5JP6BTg4po7mKHrlP3idauTyUlJSmdzfrzxQvcDQuFyV/9BWT94hZw6AAWLF4qVpmCGO3tg6GDB8LFxRWutetgzSo/pCQnY8BA1XgYycd7JA7s/QW/fOFY+OxZOPbv/QWtWreFacmSuH/vLiZPHIcGDRujmgoOXabq/ZWd6VN90bpNW1hb2yAxMRF79+zG5UsXcfTEabFLy5ei3FcMlv+SmpqKyMjIz4Yb6tChAwYMGPDF7WbMmIEOHTrAxsYG3bt3h5qaGsLCwvD7779j3rx58Pf3h0wmg5ubG0qUKIGff/4Zurq6KFeuHI4dO4Znz56hcePGMDExwYkTJyCXy1GpkniDOm/euAEA0LaVu9L89Ru3oN8AL4TduY1bN64DAJyrVlRa5/c/wlHO1rZQ6qQst0NuKfXV5InjAAB9+w/Exs3bcPxYIP43dLBi+cB+vQEAU6bNwNTpswq11q8x0tPBnKFNUcbMAHGJH3Ek6A/M3HIJGbKs4WPa16+ATZM6KtbfOaMLAGDe9iDM3x6EDJkcHr57MW9oMxyY1wP6upoIf/seQxYfxel/DWEktjsht9CudXPFa9+/+qtPvwH4afM2AMCBfXuQmZmJ7j16i1KjUDx79ERsTAzmzJ6BqMhIODnXwJFjp2BpWXTGFf2aLX8dC9tlcyzs298LWppauHj+HNatWYmU5GSUKWuNzh5dMWHyVDHKzTdV76/sxERH47tBAxAZEQEjIyNUq+6EoydOo3mLlmKXli9Fua84juVfvLy8sH37dgCAhoYGTExM4OzsjD59+mDgwIFQU8u6a0AikeDw4cPw8PBQ2v706dOYM2cO7ty5A01NTVSuXBlDhgzB0KFDERAQgEWLFuHhw4eQyWSoXr065s2bh+bNmyM4OBjTpk3D3bt38fHjR1SoUAFTp05Fjx49clx7QY9jKabCGMdSDMX1W5fXcSyLuoIax1JMhTGOpRgKchxLMRXGOJZEX5PTcSwZLIsBBkvVU1y/dQyWqoPBUrUwWJLYOEA6ERERERUqBksiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJQkPsAkg4Gupq0FDn7wqqIC1DJnYJBSLi2CSxSygQVv13iF2C4GJ3e4ldQoFITs0Qu4QCYayhJXYJRDnCFEJEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJeXYhnVrUcnBFsb6OmhU3w03b9wQu6R8Wbp4IRrUrQ1zEwPYlLaAZzcPPH70SOyycm3Lxg2oX6cmrC1NYG1pgpZNG+DM6ZMAgPdxcZjgMwauzo6wMtVHtYp2mDjOGwkJCSJXnTsrli2GiZ4GfCf4KOb5b92EDm3cYWNlAhM9DSTEx4tX4BeoSSSY3rMmfl/TDTE/98PdVV0xqZvTF9dfObQekvZ54Yd2jkrz76/pjqR9XkqTT+fqBV2+IFT5uCGTybB43izUcaoIOysj1K1RGcuXLEBmZqZinczMTCyZPxvOlcrBzsoIPTq3wbPwJyJWnT+q3F9fUhzbBBTddmnkZKXAwMAc77BTp055LqYoi4yMxPz583H8+HG8efMGFhYWqFGjBry9vdG8eXNB3sPW1hbe3t7w9vYWZH9C2r9vLyZN8MHqtRtQu44b1qzyQ6f2rRF2/xEsLCzELi9Pgi5fwv+Gj4CLa21kZGRg5vQp6NCuFe7cfQA9PT2xy8ux0mXKYNac+SjvUAGZmZn45ecd6NOjKy5fvYXMzExERrzF3AWLUbmKI16+/BM+o0cgMuItduzeJ3bpOXI75Cb8t25C1WrKgexDSgqat2iN5i1aY87MqSJV93U+HtUwpGUlDFsbjIev41HLviTW/9AQ0pR0rD/5UGndjrVtULuCOd7GJWe7r7l7b2Pb2X8CS9LH9AKtXQiqftxY47cM27duxKr1m1GpsiPCQm/De8RQGBoaYsj/RgIA1q78EVt+WouV6zfDppwdlsyfhd5dO+DS9TDo6OiI3ILcUfX+yk5xbBNQtNslyfz3r15foKaWsxObEokEMpks30UVNS9evECDBg1gbGyMOXPmoHr16khPT8fp06exceNG/PHHH4K8T16DpVQqhZGREaLeJcDQ0FCQWj7VqL4bXFxrw2/VGgCAXC6Hg501ho8YhQkTJxfIexa2mJgY2JS2wJnzl9CwUeMCfa/U9IL9ntiWMcec+YsxwGvwZ8sCDh3AsMED8DZWCg2NHP1umWPfPprkTlJSEpo2qI1lK1Zj2ZIFqF69BhYuXa60TvDli+jYtgVevImFkbGxsAX8pazXzjxtt39Sc0QnfMCIDb8p5u0a1xQf0mQYsjpIMa+USQlcXNAeHvPP4MDkFlh74gHWnXigWH5/TffP5uVX7G4vwfb1JWIcN+KT0wTbV/+eHjA3t8TyNT8p5n3Xvyd0dHWxdqM/MjMzUaOyLf43cgyGj8o6my5NSIBTRWv4rdsMj249BKvFWE9LsH19SXE8zhfHNgHitEsqlcKypBESEr6eNXKUGOVyeY6m4hgqAeCHH36ARCLBjRs30K1bN1SsWBFVq1aFj48Prl27BgB4+fIlOnfuDH19fRgaGqJHjx6IiopS7CM8PBydO3eGpaUl9PX1Ubt2bZw9e1axvGnTpvjzzz8xduxYSCQSSCSSQm/nl6SlpeHO7RC4N2+hmKempgZ39xa4ce2qiJUJS/rX5WETE1ORK8k7mUyGg/v3IiU5GXXc6ma7jjQhAQaGhoKHyoIwYewotGrdFk3dW3x75SLo+uNoNK1WGg6lsg7C1cqZoF4lS/x6541iHYkE2DyqEVYG/o6Hr+O/uK9xHtXx55ZeuLK4I8Z0rAp1taJzjMhOcThuuNaph6BLFxD+9DEA4P69u7hx7Te4t2gNAHj553NER0WiUZN/rloZGhmhpksd3LpxTZSa86o49NenimObgKLfrnz9z/Lx40eVO9WfW3FxcTh16hTmz5+f7eVRY2NjyOVyRai8dOkSMjIyMGLECPTs2RMXL14EkHXmpV27dpg/fz60tbWxY8cOdOzYEY8ePYKNjQ0OHToEZ2dnDBs2DEOHDv1qTampqUhNTVW8lkqlgrb5U7GxsZDJZLCwsFSab2FpiUePhDlbKza5XI4J47xRr34DVK1WTexycu3+7/fQqllDfPz4EXr6+vh5zwFUruL42XrvYmOxZNF8eA0aIkKVuXNw/16Ehd7B+SDV+g/6334MuAcDXS3cXtEFMnkm1NUkmL3nNvYFP1Os49O5OjJkcqz75NL4v60/+QBhz+MQl5SKupUsMKt3LViZlIDvjpuF0Yw8KQ7HjVFjJyApUYpGtZ2grq4OmUyGydPnoFuP3gCA6L9OHph/cunR3MICMdFRn+2vKCsO/fWp4tgmoOi3K9fBUiaTYcGCBdiwYQOioqLw+PFj2NvbY/r06bC1tcV3331XEHWK5unTp8jMzETlypW/uM65c+dw7949PH/+HNbW1gCAHTt2oGrVqrh58yZq164NZ2dnODs7K7aZO3cuDh8+jMDAQIwcORKmpqZQV1eHgYEBrKysvlrTwoULMXv2bGEaSAAA71EjcP/+7zh3MVjsUvKkQsVKCLoWAmlCAo4EHMTwYYNx/PR5pXAplUrRo2tHVK5cBZOnzRSx2m97/foVfCeMxaGjp1T6l9du9ezQs6E9Bq+6jIev3qO6rSkWe9VBxPsU7L4Ujhp2JfFDO0c0mPT1+9jXHP/XZfGX75GWIcOqofUxc3cI0jLkBd2M/6zAwwdwaP8erNu8A5UqO+L3e2GY6TseVlal0KNPf7HLIyqScv1U+Pz58+Hv748lS5ZAS+ufez6qVauGzZs3C1pcUZCDW1Dx8OFDWFtbK0IlADg6OsLY2BgPH2adhUhKSsL48eNRpUoVGBsbQ19fHw8fPsTLly9zXZOvry8SEhIU06tXr3K9j9wwMzODuro6oj/5DTw6KuqbIVgVeI8eiRMnjuH0mQsoW7as2OXkiZaWFuzLO6BGLRfMnLMA1ao7YcPa1YrliYmJ6N65HfQNDPDz3oPQ1NQUsdpvC7tzGzEx0WjaoDbMDLVhZqiNK0GX8dP61TAz1FaZ227m9XPF8iP3cOC357j/Kh57gp5h7fEHGO+R9SBS/SqWMDfUwR/rPBH/ywDE/zIA5Sz0sXCAK+6v6f7F/d56EgtNDTWUM9cvrKbkWnE4bsyd4YuR3uPh0a0HqlStBs9efTH0h9FYtWIJgKwzRAAQEx2ttF1MdDTMPzmbVNQVh/76VHFsE1D025XrYLljxw5s3LgRffv2hbq6umK+s7OzYA+xFCUVKlSARCLJd9vGjx+Pw4cPY8GCBQgKCkJoaCiqV6+OtLTc32iura0NQ0NDpakgaWlpoWYtF1w4f04xTy6X48KFc6hTt16BvndByszMhPfokQg8chinfj0PWzs7sUsSjFwuR2pa1u0SUqkUXTu2gaaWFn7ZH6ASZwAbN3XHlRuhuHw1RDHVrOUKz559cPlqiNKxpyjT1VaHXK78y6lMnom/b6HeczkcdSccQf2JgYrpbVwy/ALvw2P+r1/cb3VbU8jkcsRIPxZk+flSHI4bH1JSPnt4VV1dHZnyrLPENuXsYGFpheBL5xXLE6VS3Am5Adc62d/jXFQVh/76VHFsE1D025XrS+Fv3ryBg4PDZ/PlcjnS04v+8Be5ZWpqitatW2Pt2rUYPXr0Z/dZxsfHo0qVKnj16hVevXqlOGv54MEDxMfHw9Ex61LklStX4OXlhS5dugDIOoP54sULpX1paWkV2TMxo719MHTwQLi4uMK1dh2sWeWHlORkDBg4SOzS8sx71Ajs3bMb+w8dgb6BASIjIwEARkZG0NXVFbm6nJs9YwpatGqDstY2SEpMxIF9vyD48iUcCjyhCJUpHz5g49YdSJRKkfjXPblm5uZFNqAZGBjAsaryva4l9ErA1LSkYn5UZCSioyLx7Fk4AOD+/Xsw0DdAWWsbmJgWjQewToa8xoSuTngVm4yHr+PhbGuKUR2qYseFrGGD4pJSEZeUqrRNekYmouI/4ElEVj/VqWAO1wrmuHw/Akkf0lGnogUWD6yNPUHPBH0CuiCo+nGjZZv2WPnjYpQpa41KlR1x724Yflq7Er37DQSQNRLK0OGj4LdsEezKO8CmnB0Wz58FS6tSaNNe9YbeU/X+yk5xbBNQtNuV62Dp6OiIoKAglCtXTmn+gQMHULNmTcEKK0rWrl2LBg0aoE6dOpgzZw6cnJyQkZGBM2fOYP369Xjw4AGqV6+Ovn37ws/PDxkZGfjhhx/QpEkTuLq6Asg683no0CF07NgREokE06dPh1yufG+Ura0tLl++jF69ekFbWxtmZmZiNDdbnj16IjYmBnNmz0BUZCScnGvgyLFTsLRUrcs9/7bxp/UAgFbNmyrP37wN/Qd6FX5BeRQTHYP/DRmEqMgIGBoZoWq16jgUeALNmrdE0OWLuHUza9DcmtUqKW0X9vApypWzFaFiYWzb8hMWL5ireN2+VTMAwNoNW9Cn/0CxylIyfus1TO9ZCyuG1IW5kQ4i4lKw9cwjLDwQluN9pGbI0L2+HaZ41oC2phr+jE7CmuMPsPrY/QKsXBiqftyYv2QFFs+fhcnjxuBdbDQsrUqh/6Ah8Jn4z7ipI8aMQ0pyMiZ4j4A0IR516tbH7oNHVeLKwKdUvb+yUxzbBBTtduVoHMt/O3LkCAYOHAhfX1/MmTMHs2fPxqNHj7Bjxw4cO3YMLVu2LKhaRRUREYH58+fj2LFjiIiIgLm5OVxcXDB27Fg0bdoUL1++xKhRo3Du3DmoqamhTZs2WL16taKTX7x4gcGDB+PatWswMzPDpEmTsH//ftSoUQN+fn4AgGvXruH777/Ho0ePkJqamqP7O4HCGceShFXQ41iKRehxLIuKvI5jWZQVxjiWYijqZ3HzqjDGsST6mpyOY5nrYAkAQUFBmDNnDsLCwpCUlIRatWphxowZaNWqVb6KprxhsFQ9DJaqhcFSdTBYEhWMnAbLPI1j2ahRI5w5cybPxRERERFR8ZPnAdJv3bqlGErH0dERLi4ughVFRERERKon18Hy9evX6N27N65cuQLjv/4ub3x8POrXr489e/ao7DiARERERJQ/uR7HcsiQIUhPT8fDhw8RFxeHuLg4PHz4EHK5HEOGFP0/E0dEREREBSPXZywvXbqE3377DZUq/TN0SaVKlbB69Wo0atRI0OKIiIiISHXk+oyltbV1tgOhy2QylC5dWpCiiIiIiEj15DpYLl26FKNGjcKtW7cU827duoUxY8Zg2bJlghZHRERERKojR5fCTUxMIPn7j9sCSE5OhpubGzQ0sjbPyMiAhoYGBg8eDA8PjwIplIiIiIiKthwFy7//MgwRERER0ZfkKFgOHFg0/u4uERERERVdeR4gHQA+fvyItDTlP5/FPylIRERE9N+U64d3kpOTMXLkSFhYWEBPTw8mJiZKExERERH9N+U6WE6cOBHnz5/H+vXroa2tjc2bN2P27NkoXbo0duzYURA1EhEREZEKyPWl8KNHj2LHjh1o2rQpBg0ahEaNGsHBwQHlypXDrl270Ldv34Kok4iIiIiKuFyfsYyLi4O9vT2ArPsp4+LiAAANGzbE5cuXha2OiIiIiFRGroOlvb09nj9/DgCoXLky9u3bByDrTKaxsbGgxRERERGR6sh1sBw0aBDCwsIAAJMnT8batWuho6ODsWPHYsKECYIXSERERESqIdf3WI4dO1bx7xYtWuCPP/5ASEgIHBwc4OTkJGhxRERERKQ68jWOJQCUK1cO5cqVE6IWIiIiIlJhOQqWq1atyvEOR48enediiP4rtDXVxS6BciF2t5fYJQjOpPZIsUsoEO9vrhG7hAIhl2eKXUKBUFOTiF1CgcjMLH79ldM25ShYrlixIkc7k0gkDJZERERE/1E5CpZ/PwVORERERPQluX4qnIiIiIgoOwyWRERERCQIBksiIiIiEgSDJREREREJgsGSiIiIiASRp2AZFBSEfv36oV69enjz5g0AYOfOnQgODha0OCIiIiJSHbkOlgcPHkTr1q2hq6uLO3fuIDU1FQCQkJCABQsWCF4gEREREamGXAfLefPmYcOGDdi0aRM0NTUV8xs0aIDbt28LWhwRERERqY5cB8tHjx6hcePGn803MjJCfHy8EDURERERkQrKdbC0srLC06dPP5sfHBwMe3t7QYoiIiIiItWT62A5dOhQjBkzBtevX4dEIsHbt2+xa9cujB8/HsOHDy+IGomIiIhIBeTob4X/2+TJkyGXy9G8eXOkpKSgcePG0NbWxvjx4zFq1KiCqJGIiIiIVECug6VEIsHUqVMxYcIEPH36FElJSXB0dIS+vn5B1EdEREREKiLXwfJvWlpacHR0FLIWIiIiIlJhuQ6WzZo1g0Qi+eLy8+fP56sgIiIiIlJNuQ6WNWrUUHqdnp6O0NBQ/P777xg4cKBQdRERERGRisl1sFyxYkW282fNmoWkpKR8F0REREREqilPfys8O/369cPWrVuF2h0RERERqRjBguXVq1eho6Mj1O6IiIiISMXkOlh27dpVaerSpQvq1q2LQYMG4fvvvy+IGqmI2LBuLSo52MJYXweN6rvh5o0bYpckCLZLdQQHXUY3j46wsykNXU0JAo8EiF2SYFStv/RLaGPp+G54dGIO4q4uxwV/H7g42iiW6+lqYcUkTzw9NRdxV5fj9sGpGNK94Rf3F7BmOD7cWYOOTZ0Ko/x8U7X++pbExERMGOeNyhVsUdKoBNybNEDIrZtil5VvxfWYMW/OLJTQUlOaalSrInZZAPIQLI2MjJQmU1NTNG3aFCdOnMDMmTMLokaV5uXlBQ8Pjxyv/+LFC0gkEoSGhhZYTXmxf99eTJrgg6nTZuLqjdtwcnJGp/atER0dLXZp+cJ2qZbk5GRUd3KG36q1YpciKFXsr/Uz+sC9bmUMnrYdrj0W4OzVP3B8wyiUNjcCACwe1w0t6zti0NQdqNF1HtbsuogVkzzRvkn1z/Y1qm8zZGYWdgvyThX761tG/G8oLpw7i81bd+BGyF00b9ESHdq2xNs3b8QuLV+K6zEDABwdq+LZy7eK6ezFILFLApDLYCmTyTBo0CAsX74c27Ztw7Zt27BlyxYsWrQIrVq1KqgaBRETE4Phw4fDxsYG2trasLKyQuvWrXHlyhWxS1MJq/yWY9B3QzHAaxCqODpi9boN0C1RAtv9Vfu+WrZLtbRu0xaz5sxDZ48uYpciKFXrLx1tTXg0r4GpfgG4cjscz17FYv5PJxD+KgZDPRsBAOo62+HnY9cRFPIELyPisPXQFdx9/AauVcsp7cupYhmM6e+O/836WYym5Imq9de3fPjwAQGHD2LegsVo2Kgxyjs4YOr0WbAv74BNG9eLXV6+FNdjBgCoa2jAyspKMZmZmYldEoBcBkt1dXW0atUK8fHxBVROwenWrRvu3LmD7du34/HjxwgMDETTpk3x7t07sUsr8tLS0nDndgjcm7dQzFNTU4O7ewvcuHZVxMryh+2iokAV+0tDXQ0aGur4mJauNP9jajrq1ywPALgW9hwdmlRXnMFs7FoBFcpZ4Oy1h4r1dXU04b/QC96L9iHqXWLhNSAfVLG/viUjIwMymQzanzwnoauri6u/8eRLURX+9Ansy5WBY6XyGDSgH169fCl2SQDycCm8WrVqePbsWUHUUmDi4+MRFBSExYsXo1mzZihXrhzq1KkDX19fdOrUCQCwfPlyVK9eHXp6erC2tsYPP/ygNHySv78/jI2Ncfr0aVSpUgX6+vpo06YNIiIiFOvIZDL4+PjA2NgYJUuWxMSJE5H5yfWdU6dOoWHDhop1OnTogPDw8ML5QeRRbGwsZDIZLCwsleZbWFoiMjJSpKryj+2iokAV+yspJRXXwp7Bd2hblDI3gpqaBL3a1Yabkx2szAwBAD6L9+Phs0iE/zof0hsrEbj2B3gv2ocrt/853i0Z1w3Xwp7j2MV7YjUl11Sxv77FwMAAbnXrYfHCeYh4+xYymQy/7P4Z169dReS//o+joqN2HTds3LwNR46exMrV6/DixXO0cG+MxETxf0HLdbCcN28exo8fj2PHjiEiIgJSqVRpKor09fWhr6+PgIAApKamZruOmpoaVq1ahfv372P79u04f/48Jk6cqLROSkoKli1bhp07d+Ly5ct4+fIlxo8fr1j+448/wt/fH1u3bkVwcDDi4uJw+PBhpX0kJyfDx8cHt27dwrlz56CmpoYuXbpALpfnuD2pqakq8XMnouJr8LQdkEiAZ7/OR8J1P4zo3QT7Tt2CXJ71y/QPvZqgTnVbdBuzAfX7Lsbk5YfhN7kHmrlVAgC0b1IdTetUxISlB8RsBv1l89YdyMzMhINdWZgY6GD92tXw7NkbamqCDR5DAmrdpi26dvdEdScntGzVGocDjyMhPh4HD+wTu7TcD5Derl07AECnTp2U/rRjZmYmJBIJZDKZcNUJRENDA/7+/hg6dCg2bNiAWrVqoUmTJujVqxecnLKeQPT29lasb2tri3nz5uF///sf1q1bp5ifnp6ODRs2oHz5rEs9I0eOxJw5cxTL/fz84Ovri65duwIANmzYgNOnTyvV0q1bN6XXW7duhbm5OR48eIBq1arlqD0LFy7E7Nmzc/4DyCczMzOoq6sjOjpKaX50VBSsrKwKrQ6hsV1UFKhqfz1/HYtWQ1aihI4WDPV1EBkrxc5Fg/D8TSx0tDUxe1RH9PTZhFPB9wEAvz95C6dKZeHdvzkuXH+EprUrwr6sGSIvL1Xa7y/LhuDKnXC0HrpSjGZ9k6r217fYly+P02cvIjk5GVKpFKVKlcKAvr1ga2cvdmmUA8bGxnCoUBHPnj4Vu5Tcn7G8cOGCYjp//rxi+vt1UdWtWze8ffsWgYGBaNOmDS5evIhatWrB398fAHD27Fk0b94cZcqUgYGBAfr37493794hJSVFsY8SJUooQiUAlCpVSvEUYEJCAiIiIuDm5qZYrqGhAVdXV6U6njx5gt69e8Pe3h6GhoawtbUFALzMxb0Rvr6+SEhIUEyvXr3K7Y8jV7S0tFCzlgsunD+nmCeXy3HhwjnUqVuvQN+7ILFdVBSoen+lfExDZKwUxga6aFG/Co5dvAdNDXVoaWpA/smtQDKZHGpqWScklm37FbV7LIRbr0WKCQAm/ngQw2YW3Qd5VL2/vkVPTw+lSpXC+/fvcfbMaXTo2EnskigHkpKS8PxZOKxKlRK7lNyfsbSzs4O1tbXS2Uog64xlQQec/NLR0UHLli3RsmVLTJ8+HUOGDMHMmTPRtGlTdOjQAcOHD8f8+fNhamqK4OBgfPfdd0hLS0OJEiUAAJqamkr7k0gkn91D+S0dO3ZEuXLlsGnTJpQuXRpyuRzVqlVDWlpajvehra0NbW3tXL1vfo329sHQwQPh4uIK19p1sGaVH1KSkzFg4KBCrUNobJdqSUpKQvi/fiN/8fw5wkJDYWJqChsbm69sWbSpYn+1qFcFEgnw+EU0ylubY8FYDzx+HoUdgVeRkSHH5VtPsMDbAx8+puNlRBwauTigb4c6mLT8EAAg6l1itg/svIp4jz/fFu2HKlWxv77lzK+nkZmZiYoVKyE8/Cmm+k5ExUqV0V+F2wQU32OG76TxaNe+I2xsyiEi4i3mzZkFdXV1ePbsLXZpeQuWERERsLCwUJofFxcHOzu7Inkp/EscHR0REBCAkJAQyOVy/Pjjj4r7Sfbty919CkZGRihVqhSuX7+Oxo0bA8h60i4kJAS1atUCALx79w6PHj3Cpk2b0KhR1pAcwcHBArao4Hj26InYmBjMmT0DUZGRcHKugSPHTsHS0vLbGxdhbJdquR1yC61bNFO8njTBBwDQr/9AbNrqL1JV+aeK/WWkr4M5ozqhjKUx4hJScORcKGauPYqMjKz7xQdM3oo5ozrDf8FAmBiWwMuIOMxaewyb9qvGMe9rVLG/vkUqTcDMaVPw5s1rmJiawsOjK2bOmf/ZCRVVU1yPGW9ev8HA/n0Q9+4dzMzNUb9+Q1wMugpzc3OxS4MkM5en3NTU1BAVFfVZ8X/++SccHR2RnJwsaIFCePfuHTw9PTF48GA4OTnBwMAAt27dwqhRo9C+fXuMHj0aNWrUgJ+fHzp27IgrV67A19cXb968wfv372FsbAx/f394e3srDbUUEBCALl26KM5aLl68GEuWLMGWLVtQuXJlLF++HHv27IG7uzsCAgIgl8thYWGBtm3bYubMmXj58iUmT56Mmzdv4vDhw/Dw8MCLFy9gZ2eHO3fuoEaNGjlqn1QqhZGREaLeJcDQ0LAAfoJEVNyY1B4pdgkF4v3NNWKXUCD+fiiquPn71ojiJrdXM1WBVCqFlZkxEhK+njVyfMbSxycr5UskEkyfPl1xeRjIGmbn+vXrOQ5ChU1fXx9ubm5YsWIFwsPDkZ6eDmtrawwdOhRTpkyBrq4uli9fjsWLF8PX1xeNGzfGwoULMWDAgFy9z7hx4xAREYGBAwdCTU0NgwcPRpcuXZCQkAAgK5Tv2bMHo0ePRrVq1VCpUiWsWrUKTZs2LYBWExERERWuHJ+xbNYs61TypUuXUK9ePWhpaSmWaWlpwdbWFuPHj0eFChUKplL6Ip6xJKLc4hlL1cIzlqqFZyxz4MKFCwCAQYMGYeXKlQwwRERERKQk1w/vbNu2rSDqICIiIiIVxyH1iYiIiEgQDJZEREREJAgGSyIiIiISBIMlEREREQmCwZKIiIiIBMFgSURERESCYLAkIiIiIkEwWBIRERGRIBgsiYiIiEgQDJZEREREJAgGSyIiIiISBIMlEREREQmCwZKIiIiIBMFgSURERESCYLAkIiIiIkEwWBIRERGRIBgsiYiIiEgQGmIXQMJ5E5eChPTi1aXWJUuIXUKBSP6YIXYJBUIiEbuCgpGWIRe7BMG9v7lG7BIKRKPFF8UuoUAETWoqdgkFIkNW/L5bAKChXvzO20lyeIAvfi0nIiIiIlEwWBIRERGRIBgsiYiIiEgQDJZEREREJAgGSyIiIiISBIMlEREREQmCwZKIiIiIBMFgSURERESCYLAkIiIiIkEwWBIRERGRIBgsiYiIiEgQDJZEREREJAgGSyIiIiISBIMlEREREQmCwZKIiIiIBMFgSURERESCYLAkIiIiIkEwWBIRERGRIBgsiYiIiEgQDJZEREREJAgGSyIiIiISBIMl4ebVYHzfvzsaOpdHRSs9nDl5VGn5qqXz0bphTTjbmcO1UhkM9GyPsNs3lda5f/cOvHp0gEvF0qhTxRrTxo9EcnJSYTYjzzasW4tKDrYw1tdBo/puuHnjhtgl5criBXNgZqCpNNWtVU2xvFPb5p8tHzfmBxErzj2/H5egpL4mpkz0AQC8/PMFSuprZjsdOXRA5Gq/rnb1iihlrP3Z5Dt+NADgxfNwDOrriarly6CCtRmGefVBTHSUyFXnnap9v8wNtDCnUxWcGdsAQRMb4ZehrqhSykBpHduSJfCjZzVcGNcQlyc0wvZBtWBpqK1Y7tu2Ig7/4IagiY3wq3d9LPOshnIlSxR2U/JE1frrU8FBl+HZtRMq2JWFgY46jgYGKC030FHPdvJbvkycgvOhqPYVg6UAJBIJAgICvrj84sWLkEgkiI+PL7SaciMlJRmVq1bHjIUrsl1uV94BMxb8iKMXb+CXI2dQxrocBvXshLjYGABAVGQEvHp0RDnb8th/4iI27w7A00cPMXn094XZjDzZv28vJk3wwdRpM3H1xm04OTmjU/vWiI6OFru0XKlcpSruP32lmI7/elFpeX+v75SWz5q7SJxC8+B2yE1s37oJVatVV8wrU9YaD8JfKU2Tp86Enr4+mrdqI2K133bywhWEPfpTMe0NOAEA6Ni5G1KSk9GrS3tIJBIcCDyNwFMXkZaWhgG9ukIul4tcee6p2vfLQEcDmwfUQoZcjjF776LnTzfhdzYc0g/pinXKGOtg04CaePEuBd//HIrem25iS/CfSMv4p3/+iEzEnKN/oMdPNzFqz11IAKzp7QQ1iQiNygVV66/spKQko3p1Z/zotzrb5U9fvFGa1v20GRKJBJ09uhZypflTlPtKkpmZmSl2EUVdTEwMZsyYgePHjyMqKgomJiZwdnbGjBkz0KBBA0gkEhw+fBgeHh7Zbp+Wloa4uDhYWlpCIvnykcXLywvx8fFfDanZkUqlMDIywu0nEdA3MMzVtp+qaKWHtdv2oGXbjl9cJylRiloVSsF//zHUb9QMe3ZuxcrFc3Dl7jOoqWX9rvLo4e/o2MwNZ67eRTm78nmux7qAf8tvVN8NLq614bdqDQBALpfDwc4aw0eMwoSJkwvsfZM/Zgi2r8UL5uDksSO4+FtItss7tW2O6k7OmL94uWDv+SVf+XjnSVJSEtwb1sGSFauxfPECVHNyxoIl2bejaX1XONWoiVXrNglbBKAUGoQ2ffI4nD19Ar/dfoBLF86ib/dO+ONFFAwMs77L0oQEVLa1xJ7Dx9G4aXPB3tdYT0uwfX2JGN+vRosv5nnbkc3s4VTWEMN2hn5xnfkejsiQyzEz8I8c79fBQg+/DK0Nj7XX8Cb+Y55qC5rUNE/b5YYY/ZUhK7jvloGOOnbvO4iOnTy+uE4vzy5ISkzCsVNnBH1vDfWCPW8nRl9JpVJYljRCQkICDA2/nDV4xjIHunXrhjt37mD79u14/PgxAgMD0bRpU7x79y5H22tpacHKyuqLoVImk6nM2Yi0tDTs3bkVBoZGqOyYdQYpLTUVmlpailAJADo6ugCAkOu/iVJnTqSlpeHO7RC4N2+hmKempgZ39xa4ce2qiJXl3rPwp6hawQYu1Svi++/64/Wrl0rLD+z9BRXLWaFhnRqYO3MqUlJSRKo0dyb6jELL1m3RtNnXA1XonRDcuxuGfgMGFVJlwkhLS8PBfb+gVz8vSCQSpKWmQiKRQEv7n8uq2jo6UFNTw42rRfe7lB1V/H41qlASDyMSsbCrI05718fP37nAo0YpxXIJgAYOpngZ9wGrejnhtHd9bPOqhSYVzb64Tx1NNXR0ssKb9x8QJU0thFbkjSr2V35FR0Xh9MkTGOCleseNotxXDJbfEB8fj6CgICxevBjNmjVDuXLlUKdOHfj6+qJTp06K9WJjY9GlSxeUKFECFSpUQGBgoGLZp5fC/f39YWxsjMDAQDg6OkJbWxuDBw/G9u3bceTIEUgkEkgkEly8eDHbmlJTUyGVSpWmgnbh15OoYW+B6uVMsW3jGmzbexSmJbMOpvUaNkFsdBQ2r12BtLQ0JMS/x7J5MwAA0dGRBV5bXsXGxkImk8HCwlJpvoWlJSIji27dn3JxrYPVG7Zg3+FjWLpiDV6+eIEOrZshMTERANDNsxfWb96OgBNnMGbcROzbswvDhwwUuepvO7R/L+6G3sH02fO/ue7P27ehYqUqqFO3fiFUJpxTxwMhTYhHzz79AQC1aruhhJ4e5s2cgpSUFKQkJ2POtEmQyWSIiooQudrcUcXvVxkTXXRzKYNXcR8w6pe7OHj7Lca1ckD76lltMNXTgp62BgbWs8HVZ3EY9ctdXHwUiyXdq6KWjZHSvrq7lMalCY0QNLEx6pcviRG7w5AhL7oXCFWxv/Jr1887YGBggE4qdhm8qPcVg+U36OvrQ19fHwEBAUhN/fJvm7Nnz0aPHj1w9+5dtGvXDn379kVcXNwX109JScHixYuxefNm3L9/H6tWrUKPHj3Qpk0bREREICIiAvXrZ/+f5MKFC2FkZKSYrK2t893Ob3Fr0BhHzl3F3mPn0bhZS3gP6493MVn3clSo7IjFqzZi64ZVcLYzQ30ne5S1KQczcwuoSfgRK2gtWrVB5y7dUbWaE9xbtMKeg0eRkBCPI4f2AwAGDh4K9xat4Fi1Ojx79sHajdtw/GgAnj8LF7nyL3vz+hWmTPTBT1t3QEdH56vrfvjwAQf370G/gap11gEAdu/cBvcWrWFVqjQAwMzMHBv9d+PMqeNwKGOKijbmSEhIQHXnmkpXBKhgqEmAR5GJWHfxOR5HJeHwnQgEhEaga62s/vn7otOlx7H45cZrPI5KwvarLxH85J1inb+d/D0K/TbfwrAdd/AyLgULu1aFVgFfHqXc2bl9G3r06vPNYwzlDj/l36ChoQF/f39s374dxsbGaNCgAaZMmYK7d+8qrefl5YXevXvDwcEBCxYsQFJSEm585Qmt9PR0rFu3DvXr10elSpVgaGgIXV1daGtrw8rKClZWVtDSyv4eKF9fXyQkJCimV69eCdrm7JTQ00M5u/Ko4VIHC1ash7qGBvb/sl2xvGPXnvjt3nMEhT7BjYevMGr8VMS9i4V1ObsCry2vzMzMoK6ujuhPnriNjoqClZWVSFXln5GxMco7VPhicHRxrQMARTpYht65jZiYaDRrUAcWRjqwMNLBleDL2Lh+DSyMdCCTyRTrBgYcxIeUFPTs3U/EinPv1cs/EXTxPPp8cvm+qXtLXAv9A/eevsb98LdYs3EbIiPeopxt0f0uZUcVv1+xSWl4Fqt8m8iL2BRYGWUFj/iUdGTI5Hj+yTrPY1NgZagcTpJTZXj1/gPuvErApIP3YVuyBJpW+vIlc7GpYn/lx5XgIDx5/AgDB30ndim5VtT7isEyB7p164a3b98iMDAQbdq0wcWLF1GrVi34+/sr1nFyclL8W09PD4aGhl99OktLS0tpm9zQ1taGoaGh0lTY5HI50lLTPptvZm4JPT19nDhyANraOmjQxL3Qa8spLS0t1KzlggvnzynmyeVyXLhwDnXq1hOxsvxJSkrCi+fPYPmFA8zvd0MB4IvLi4LGTd0RfP0OLv12SzHVqOWC7j1749Jvt6Curq5Yd9f2bWjTriPMzM1FrDj39u7aATNzC7Ro3S7b5SVLmsHI2BjBly4gNiYardp2KOQK80cVv19hrxJQzlRXaZ6NqS4iE7IeuMmQZ+JBRCLKlfxknZK6iEj48kM5EknWpKVRdP/LVcX+yo8d/ltRs5YLqjs5i11KrhX1vtIQuwBVoaOjg5YtW6Jly5aYPn06hgwZgpkzZ8LLywsAoKmpqbS+RCL56gM5urq6X31CvDAlJyfhz+f/nL16/fIFHvweBmNjUxibmGL9yiVo3ro9zC2s8D7uHXZt+wlRkW/RtmMXxTY7t2z46/4wfVy5dB5L5k7F+KlzYGhkLEKLcm60tw+GDh4IFxdXuNaugzWr/JCSnIwBKnRZdcaUiWjdrgOsrW0QGfEWixfMgbqaOrp274Xnz8JxcP8etGjVBqamJXH/93uY7jse9Ro0QtVqefvFpjAYGBigStVqSvP0SujB1LSk0vxn4U/x25Ug7D109NNdFGlyuRx7du1Aj979oKGhfBje8/N2VKhUGSXNzHDrxnXMmDwOw34YDYcKlUSqNu9U7fv1y43X2DKwJrzq2+DswxhULW2ALjVLY8GJR4p1dl57hQVdHHHnZQJu/RmPeuVN0aiCGf7315PkZYx10NLRAteexeF9SjosDbQxsL4NPqbLceVpzh74FIuq9Vd2kpKS8Cz8qeL1ny9e4G5YKExMTGFtYwMg6+nmgEMHsGDxUrHKzLei3FcMlnnk6OiY62GBvkVLS0vpEl9h+T30Nvp3a6t4vXBm1lAFXXr0xZwlq/Ds6WMc3rcL7+PewcTEFNVruGB3wBlUqOyo2ObunVtYvWw+kpOTYO9QEXOWrIKHZ59Cb0tuefboidiYGMyZPQNRkZFwcq6BI8dOwdLS8tsbFxFv377BsEH98D7uHUqamcOtXgOcOh8MM3NzpKZ+xKUL5/DT2lVISUlG6bLW6NCpC8ZNnCJ22YLYtdMfpcuURbPmLcUuJVcuXzyHN69fole/zx+iCn/6GAvmTEf8+zhY25TD6HGT8P2IMSJUmX+q9v16EJGICQfuY0QzOwxpZIu38R+w/MxTnLr/z9Wni49isfDkY3jVt8G4Vg54GfcBkw7+jrDXCQCA1Aw5algboVftsjDU1UBcchruvEzAkO238T4l/UtvXSSoWn9l507ILbRr/c8oEr4TxwEA+vQbgJ82bwMAHNi3B5mZmejeo7coNQqhKPcVx7H8hnfv3sHT0xODBw+Gk5MTDAwMcOvWLYwaNQrt27fHli1bsh3H0tjYGH5+fvDy8sLFixfRrFkzvH//HsbGxvD394e3t/dnA6YvWLAAP/30E3799VeULFkSRkZGn50JzY6Q41gWNQU9jqVYhBzHsigpIifhBVeQ41iKpTDGsRRDfsaxLMoKYxxLMRTkOJZiKuhxLMWQ03EsecbyG/T19eHm5oYVK1YgPDwc6enpsLa2xtChQzFlirBnfYYOHYqLFy/C1dUVSUlJuHDhApo2bSroexAREREVFJ6xLAZ4xlL18IylauEZS9XBM5aqhWcsVQf/8g4RERERFSoGSyIiIiISBIMlEREREQmCwZKIiIiIBMFgSURERESCYLAkIiIiIkEwWBIRERGRIBgsiYiIiEgQDJZEREREJAgGSyIiIiISBIMlEREREQmCwZKIiIiIBMFgSURERESCYLAkIiIiIkEwWBIRERGRIBgsiYiIiEgQDJZEREREJAgGSyIiIiISBIMlEREREQlCQ+wCSDhlTEvA0LCE2GVQDujp8KunSkpoi12B8DJkcrFLKBBBk5qKXUKBMGk0WewSCsT7oEVil1Ag0jOK3/crp23iGUsiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFgSERERkSAYLImIiIhIEAyWRERERCQIBksiIiIiEgSDJREREREJgsGSiIiIiATBYElEREREgmCwJCIiIiJBMFhSjm1YtxaVHGxhrK+DRvXdcPPGDbFLEgTbpVrYrqJn2ZJFaNLADaXMjGBnbYVenl3w+PEjxfK4uDiMHzsaNatXgbmxHqo42GKCzxgkJCSIWHX+qFp/6ZfQwlLvDnh0aBLiLs7FhY3D4VKlrGL5xmme+HB1kdJ0ZMUgxXIbKxOsn9INDw9ORNzFubi/fwKmDWkBTQ11MZqTY8FBl9HNoyPsbEpDV1OCwCMBYpeUaz8uzfp+lTY3gr2NFXp7dsGTf32/AKBdK3cY6qorTd6jhotSL4OlQF68eAGJRILQ0FCxSykQ+/ftxaQJPpg6bSau3rgNJydndGrfGtHR0WKXli9sl2phu4qmK0GXMPT74Th/+TcEHj+N9PR0eLRvg+TkZABAZMRbRES8xfxFS3A95C42bNqKM7+exoj/DRG58rxRxf5a79sN7rUrYPCcfXDt54ez15/g+KohKG1uqFjn9NVHsG0/TzENnLFHsaySrTnUJBKMXHwYtfqswMSVxzCkixvmDG8tRnNyLDk5GdWdnOG3aq3YpeRZcNAlDPvfcJy79BuOHDuN9Ix0eHT45/v1N6/BQ/Dk+RvFNGf+YlHqlWRmZmaK8s4CiomJwYwZM3D8+HFERUXBxMQEzs7OmDFjBho0aFAoNbx48QJ2dna4c+cOatSoUSjv+TepVAojIyNEvUuAoaHhtzfIg0b13eDiWht+q9YAAORyORzsrDF8xChMmDi5QN6zMLBdqoXtEk6GTF4g+wWyjsn21lY4eeYCGjZqnO06hw/ux5BBAxAVlwgNDQ3B3ltDveDPl4jRXyaN8r5fHW0NxJydDc9JO3Dqt3/OdF3ZNhK/Xn2M2Rt/xcZpnjDW10GPyTtzvN+xfRtjaBc3OHZfmufa3gctyvO2uaWrKcHeA4fRqbNHgb9XekbBfb9iY2Jgb5P1/WrQMOv71a6VO6o7OWPxshUF9r5SqRRlLU2QkPD1rFEszlh269YNd+7cwfbt2/H48WMEBgaiadOmePfundil5Ut6errYJQAA0tLScOd2CNybt1DMU1NTg7t7C9y4dlXEyvKH7VItbJfqkEqzLnGbmpp+cZ2EhAQYGBoKGioLgyr2l4a6GjQ01PExLUNp/sfUDNR3tlW8blTLHn8en4awPeOwcoIHTA1LfHW/hno6iJN+KIiS6SsS/vp+mZgof7/27d0N27IWcHNxwqzpU5CSkiJGeaofLOPj4xEUFITFixejWbNmKFeuHOrUqQNfX1906tQJACCRSLB582Z06dIFJUqUQIUKFRAYGKi0n99//x1t27aFvr4+LC0t0b9/f8TGxiqWnzp1Cg0bNoSxsTFKliyJDh06IDw8/It1yWQyDB48GJUrV8bLly8BAEeOHEGtWrWgo6MDe3t7zJ49GxkZ/3zRJRIJ1q9fj06dOkFPTw/z58/Pdt+pqamQSqVKU0GKjY2FTCaDhYWl0nwLS0tERkYW6HsXJLZLtbBdqkEul2PS+LGoW68BHKtWy3ad2NhYLFk4H4MGDy3k6vJPFfsrKSUN1+79Cd9BzVHKzABqahL0al0DbtVsYFXSAABw5tojDJmzD+1Gb8K0dSfRqKYdjqwYBDU1Sbb7tC9bEsM962NLwPXCbMp/nlwux+QJn3+/PHv2wqatO3D81Dn4jJ+EPbt/xtBB/UWpUeWDpb6+PvT19REQEIDU1NQvrjd79mz06NEDd+/eRbt27dC3b1/ExcUByAqn7u7uqFmzJm7duoVTp04hKioKPXr0UGyfnJwMHx8f3Lp1C+fOnYOamhq6dOkCufzz092pqanw9PREaGgogoKCYGNjg6CgIAwYMABjxozBgwcP8NNPP8Hf3/+z8Dhr1ix06dIF9+7dw+DBg7Nty8KFC2FkZKSYrK2t8/KjIyISnM+YkXh4/z78d+7OdrlUKoVnl46oXKUKpkyfWcjV/XcNnr0XEgnw7OhUJFyahxE9GmDfmTDI/7obbv/Zuzge/BD3w6Nw9PIDdB2/Ha6O1mhcy/6zfZU2N0TgikE4dP4etgXeLOym/KeN8876fm3bofz9GvTdMLRo2RpVq1VHz9598dMWfxwNDMCzZ18+AVZQVD5YamhowN/fH9u3b4exsTEaNGiAKVOm4O7du0rreXl5oXfv3nBwcMCCBQuQlJSEG389xbdmzRrUrFkTCxYsQOXKlVGzZk1s3boVFy5cwOPHjwFkXW7v2rUrHBwcUKNGDWzduhX37t3DgwcPlN4nKSkJ7du3R0xMDC5cuABzc3MAWcF28uTJGDhwIOzt7dGyZUvMnTsXP/30k9L2ffr0waBBg2Bvbw8bG5ts2+zr64uEhATF9OrVK0F+ll9iZmYGdXV1REdHKc2PjoqClZVVgb53QWK7VAvbVfSN8x6FUyeO4/jpcyhTtuxnyxMTE9GlUzvo6xtg975D0NTUFKHK/FHV/nr+Jg6tftiIks2mo4LHIjT6bi00NdTw/E1ctuu/eBuHmPdJKF+2pNL8UmYGOLVmKK7de4kRiw4VRun0l7+/X8e+8P36N9fabgCAZ+FPC6M0JSofLIGs0Pf27VsEBgaiTZs2uHjxImrVqgV/f3/FOk5OTop/6+npwdDQUPEEX1hYGC5cuKA4+6mvr4/KlSsDgOJy95MnT9C7d2/Y29vD0NAQtra2AKC4zP233r17Izk5Gb/++iuMjIwU88PCwjBnzhyl9xg6dCgiIiKU7oNwdXX9Znu1tbVhaGioNBUkLS0t1KzlggvnzynmyeVyXLhwDnXq1ivQ9y5IbJdqYbuKrszMTIzzHoWjgQE4dvosbO3sPltHKpWic4c20NLUwt6DAdDR0RGh0vxT9f5K+ZiOyHeJMDbQRQu3ijgW9CDb9cqYG6KkUQlExiYq5pU2N8TptcNw5483GDZvP4rBs78q4e/v17HAABw9dRa2tp9/vz51LywUAGBlVaqAq/ucat01/RU6Ojpo2bIlWrZsienTp2PIkCGYOXMmvLy8AOCz34wlEoniMnZSUhI6duyIxYs/fzS/VKmsTunYsSPKlSuHTZs2oXTp0pDL5ahWrRrS0tKU1m/Xrh1+/vlnXL16Fe7u7or5SUlJmD17Nrp27Zpt7X/T09PL2w+ggI329sHQwQPh4uIK19p1sGaVH1KSkzFg4KBvb1yEsV2qhe0qmnzGjMT+vb9gz/7DMNA3QNRf9xoaGhlBV1dXESo/pKRg89YdSJRKkfjXveFm5uZQVy/aYyF+ShX7q4VbBUgkEjz+Mwbly5bEgpHt8PjPGOw4dgt6ulqY+l1zBFz4HZHvkmBf1hTzR7RF+Ot3OHM966rd36HyZeR7+K45AXPjf/6viopLEqtZ35SUlITwp/+ctXvx/DnCQkNhYmr6xauCRY2P90gc2PsLfvnC9+vZs3Ds3/sLWrVuC9OSJXH/3l1MnjgODRo2RrXqTt/Yu/CKTbD8lKOjIwICAnK0bq1atXDw4EHY2tpm+4Tiu3fv8OjRI2zatAmNGjUCAAQHB2e7r+HDh6NatWro1KkTjh8/jiZNmije49GjR3BwcMhbg0Tm2aMnYmNiMGf2DERFRsLJuQaOHDsFS0vLb29chLFdqoXtKpo2b9wAAGjbyl1p/vqNW9BvgBfC7tzGrRtZD3k4V62otM7vf4Sj3F9XgFSFKvaXkb4O5vyvDcpYGCFOmoIjF3/HzA2nkSGTQ0MuR7XypdC3rQuMDXQQEZuIs9cfY87GM0hLlwEA3GtXgIO1GRyszRAeOEVp37r1iu5QX7dDbqF1i2aK15Mm+AAA+vUfiE1b/UWqKne2/PX9apfN96tvfy9oaWrh4vlzWLdmJVKSk1GmrDU6e3TFhMlTxShX9cexfPfuHTw9PTF48GA4OTnBwMAAt27dwqhRo9C+fXts2bIFEokEhw8fhoeHh2I7Y2Nj+Pn5wcvLC2/fvkWNGjXQpEkTTJw4Eaampnj69Cn27NmDzZs3QyKRwMLCAm3btsXMmTPx8uVLTJ48GTdv3lTs99NxLP38/DB9+nScPHkSDRs2xOnTp9GhQwdMmzYN3bt3h5qaGsLCwvD7779j3rx5AJBtnTlRGONYElHxUpDjWIqpMMaxFEN+xrEsygpzHMvCVJDjWIolp+NYqvwZS319fbi5uWHFihUIDw9Heno6rK2tMXToUEyZMuXbOwBQunRpXLlyBZMmTUKrVq2QmpqKcuXKoU2bNlBTU4NEIsGePXswevRoVKtWDZUqVcKqVavQtGnTL+7T29sbcrkc7dq1w6lTp9C6dWscO3YMc+bMweLFi6GpqYnKlStjyBDV/MsTRERERJ9S+TOWxDOWRJR7PGOpWnjGUrX8l89YFs9vIBEREREVOgZLIiIiIhIEgyURERERCYLBkoiIiIgEwWBJRERERIJgsCQiIiIiQTBYEhEREZEgGCyJiIiISBAMlkREREQkCAZLIiIiIhIEgyURERERCYLBkoiIiIgEwWBJRERERIJgsCQiIiIiQTBYEhEREZEgGCyJiIiISBAMlkREREQkCAZLIiIiIhIEgyURERERCUJD7AKIqPiQyzPFLqFAqKlJxC5BcBrqxfO8QnH9DMZdXih2CQXCpK632CUUiPfX/MQuQXCaGjk7ZhTPIwsRERERFToGSyIiIiISBIMlEREREQmCwZKIiIiIBMFgSURERESCYLAkIiIiIkEwWBIRERGRIBgsiYiIiEgQDJZEREREJAgGSyIiIiISBIMlEREREQmCwZKIiIiIBMFgSURERESCYLAkIiIiIkEwWBIRERGRIBgsiYiIiEgQDJZEREREJAgGSyIiIiISBIMlEREREQmCwZKIiIiIBMFgSURERESCYLCkb1q6eCEa1K0NcxMD2JS2gGc3Dzx+9EjssgQRHHQZ3Tw6ws6mNHQ1JQg8EiB2Sfm2ccN61K7pBAtTQ1iYGqJJw3o4feqk2GXlm0wmw5xZ0+FY0R4ljUqgWmUHLFowF5mZmWKXli/F8TMIFO/jRmJiIiaM80blCrYoaVQC7k0aIOTWTbHLEsyyJYtQQksNE8Z5i13KN+mX0MZSny54dHQG4oKX4MKWMXBxtFYs/3DLL9tpbP9minVMDEtg29x+iLq4CBEXFmL99F7Q09USozm5smHdWlRysIWxvg4a1XfDzRs3xC4JAIMl5UDQ5Uv43/ARuBR8DcdOnkFGejo6tGuF5ORksUvLt+TkZFR3cobfqrVilyKYMmXLYu6CRfjtegiuXLuFps3c4dm1Mx7cvy92afmyfNlibN64Acv9VuN22APMXbAIK35civVrV4tdWr4Ux88gULyPGyP+NxQXzp3F5q07cCPkLpq3aIkObVvi7Zs3YpeWb7du3cSWzRtRvbqT2KXkyPppveDuVhGDZ/wM115LcPb6Ixxf9wNKmxsBAGxbT1eahs3eDblcjsPn7yr2sW1uf1SxL4UOI9ajm/dGNKxZHmun9hSrSTmyf99eTJrgg6nTZuLqjdtwcnJGp/atER0dLXZpkGSq+q/7IvHy8sL27dsVr01NTVG7dm0sWbIETk6F+4WUSqUwMjJC1LsEGBoaFvj7xcTEwKa0Bc6cv4SGjRoX+PsVFl1NCfYeOIxOnT3ELkVwpS1MsWDRUngN/q5A30cuL7jDSTePjrCwtMD6n7Yo5vXp2R06urrY6r+zwN4XANTUJAW6/78V589gYR03CvIzCAAfPnyAZUlD7DsQgDbt2ivmN6jrilat22Dm7HkF8r6SQvgIJiUloX4dF/itXovFC+fDydkZS3/0K9D3NK03Ns/b6mhrIubSIniO24JTVx4o5l/ZOQ6//vYQs9ef+Gybfcu+g34JbbT7YR0AoJKtJUIP+KJB/x9x++ErAEDLepURsHIYHNrNQkSsNE+1vb/ml6ftcqpRfTe4uNaG36o1AAC5XA4HO2sMHzEKEyZOLpD3lEqlsCxphISEr2cNnrHMhzZt2iAiIgIRERE4d+4cNDQ00KFDB7HLKnDShAQAgImJqciV0LfIZDLs27sHycnJcKtbT+xy8qVuvXq4eOE8njx+DAC4ezcMv/0WjFat24hcGeVEcTluZGRkQCaTQVtHR2m+rq4urv52RaSqhDF29Ei0adcO7s1biF1Kjmioq0FDQx0f09KV5n9MTUf9GvafrW9hqo82DR2x/cg1xTw3J1u8l6YoQiUAnL/xGHJ5JmpXK1dwxedDWloa7twOUeonNTU1uLu3wI1rV0Ws7K9axC5AlWlra8PKygpWVlaoUaMGJk+ejFevXiEmJgYAMGnSJFSsWBElSpSAvb09pk+fjvR05S/AvHnzYGFhAQMDAwwZMgSTJ09GjRo1vvq+qampkEqlSlNhkcvlmDDOG/XqN0DVatUK7X0pd36/dw9mxvow0tPG6BH/w94Dh1HF0VHssvJl3ITJ6O7ZEzWdqsBITwv169TCiFFj0Kt3X7FLo28oTscNAwMDuNWth8UL5yHi7VvIZDL8svtnXL92FZEREWKXl2f79+5B6J3bmDNvodil5FhSSiquhT2H75DWKGVmCDU1CXq1dYFbdVtYmX1+Rq1fhzpITP6IgAv/XAa3LGmAmPdJSuvJZHLESVNgWbLgrwDmRWxsLGQyGSwsLJXmW1haIjIyUqSq/sFgKZCkpCT8/PPPcHBwQMmSJQFkHYD8/f3x4MEDrFy5Eps2bcKKFSsU2+zatQvz58/H4sWLERISAhsbG6xfv/6b77Vw4UIYGRkpJmtr629uIxTvUSNw//7v2LFrT6G9J+VexUqVcP1WKC5fuY6h3w/H0MED8fDBg29vWIQdPLAPe/fsxrYdu3Dlegg2bvHHqhU/4ued/2/vruOiyP8/gL+GVhHEQEVBwkBFUexuwTzsDgxsxS4MrDO+nnnGqWed3Z6nZ3digC12YGArKCDs6/cHv51jRU89wd3V9/Px4KE7M7v7np3dmfd8cvGnnyz06ns7b8z/fQlIIqdLdtiltcLsX2egUZNmMDExzkvqvbt30b9vAH5f/Aes3iuJNXTthv8BBcCNv0fh5ZH/oVvT8li9/fQHm0S0rlsCq/4+hZjYuG8f6A/ETN8BGLMtW7bA2toaQEID/KxZs2LLli3qySUwMFDd1tnZGf369cPKlSsxYMAAAMCMGTPQvn17+Pn5AQCGDx+OHTt2IDIyEv9m8ODB6NOnj/r41atX3yS5DOjZHVu3bsGuPQeQPXv2FH8/8d9ZWFjALWdOAIBXkSI4dTIYv86Yhpmz5+o5sv9u6OAB6NtvIBo1bgoA8PAogLt3bmPyxPFo2aqNnqMTH/M9njdc3dywfdc+REVF4dWrV8iaNStat2gKZ5ek1a/G4PTpU4iIiEDpEkXUZfHx8Th08ADmzPoVLyKjYWpqqscIP+5m+FNU7zQTqa0sYJPGCg+fvsLScW1wM/yJznZlCrkij3NmtBqseyP66OlrZLKz1llmamqC9Dap8ejpt6sN/BIZM2aEqakpIiIe6SyPePQIWbJk0VNU/zDO2ysDUalSJYSEhCAkJAQnTpyAt7c3atSogdu3bwMAVq1ahTJlyiBLliywtrZGYGAg7ty5oz7/ypUrKF68uM5rvv/4QywtLWFjY6Pzl5JIIqBnd2zetAF/79gDZxeXFH0/kfw0Gg1iYmL0HcZXefvmTZISIRNTU2g0Gj1FJP7Nj3DeSJMmDbJmzYrnz59j187tqF2nrr5D+k8qVa6C4NNncSz4jPrnVaQomjZrgWPBZww2qUzsTXQsHj59hXRpU6FqKXds2X9eZ32bn0ri1MU7OHf1vs7y42dvwc4mNQq7/3PTU7FoLpiYKAg+f/ubxP6lLCwsUNirCPbu2a0u02g02Lt3N4obQFt6KbH8CmnSpEHO/y8VAoD58+fD1tYW8+bNQ61atdCiRQsEBQXB29sbtra2WLlyJSZPnqzHiP+bgB7dsGrlcqxZvwnWadOqbThsbW2RKlUqPUf3dSIjI3H92jX18a2bNxEaEgK79Onh5OSkx8j+u2FDB8PbpwYcHZ3w+vVrrFq5HAf278OfW7frO7SvUqNWHUycMA6Ojk7Imy8/QkPPYOa0KWjVxk/foX2V7/E7CHzf542dO7aDJHLnzoPr169h6OAByJ3H3Wi/i2nTpk3S9jVNmjRInyG9wbeJrVrSHYoChN2OgJtjRozr+RPCbj3Cks3H1W3SprFE/aqeGDR1U5LnX7n1CNsPX8KvgU3Q8+c1MDczxZQBDbBmx5n/3CP8W+gZ0Acd27VBkSJFUbRYccycPhVvoqLQ2gC+g5JYJiNFUWBiYoK3b9/iyJEjyJEjB4YOHaqu15ZkauXJkwfBwcFo3bq1uiw42PAG2f1tbkK7z+pVKuoun78Qrdq0/fYBJaPTp07Cu+o/A+UO7J/QxKBlqzaY9/siPUX1dR5HRKC9X2s8fPAAtra28ChQEH9u3Y4qVavpO7SvMnnKdIwaOQwBvbrhcUQEsmZ1QLsO/hg8dLi+Q/sq3+N3EPi+zxuvXr3EiMAhCA+/B7v06eHrWx8jRo2Fubm5vkP74dhaW2FU99rIZp8Oz15FYdOesxjx61+Ii/+nJqNRdS8oioLVf5/+4Gv4DVuKKQMaYOusrtCQ2LjnLPpOWvetduE/adS4CZ48foxRQcPx6OFDFPQshE1b/kbmzJk//eQUJuNY/kdt27bFo0ePsHDhQgDA8+fPMXPmTMyePRt79uzBq1ev0KBBAyxduhTFihXDX3/9haCgIMTHx+PFixcAEjrvdOzYEbNnz0bp0qWxatUqTJo0Ca6urjhz5sxnx/Ktx7EU4mNSegxBfflW41iKr/e9fge/xTiW+vA141gaspQex1IfPnccSymx/Ap///03smbNCiChKsHd3R1r1qxBxYoVAQC9e/dG9+7dERMTg1q1amHYsGEYOXKk+vwWLVrgxo0b6NevH6Kjo9G4cWO0bdsWJwxkWiYhhBBCiC8hJZYGplq1asiSJQuWLv38mUSkxFIYiu+1tEhKLI3H9/odlBJL4yIllkIv3rx5gzlz5sDb2xumpqZYsWIFdu3ahZ07d+o7NCGEEEKILyaJpR4pioKtW7di7NixiI6ORp48ebBu3TpUrWoc02kJIYQQQiQmiaUepUqVCrt27dJ3GEIIIYQQyUIGSBdCCCGEEMlCEkshhBBCCJEsJLEUQgghhBDJQhJLIYQQQgiRLCSxFEIIIYQQyUISSyGEEEIIkSwksRRCCCGEEMlCEkshhBBCCJEsJLEUQgghhBDJQhJLIYQQQgiRLCSxFEIIIYQQyUISSyGEEEIIkSwksRRCCCGEEMlCEkshhBBCCJEsJLEUQgghhBDJQhJLIYQQQgiRLMz0HYAQ4vthYqLoOwTxmTQa6juEFPG9fgej38XrO4QU8fzYVH2HkCLsyg3SdwjJjnExn7WdlFgKIYQQQohkIYmlEEIIIYRIFpJYCiGEEEKIZCGJpRBCCCGESBaSWAohhBBCiGQhiaUQQgghhEgWklgKIYQQQohkIYmlEEIIIYRIFpJYCiGEEEKIZCGJpRBCCCGESBaSWAohhBBCiGQhiaUQQgghhEgWklgKIYQQQohkIYmlEEIIIYRIFpJYCiGEEEKIZCGJpRBCCCGESBaSWAohhBBCiGQhiaUQQgghhEgWklgKIYQQQohkIYmlEEIIIYRIFpJYCiGEEEKIZCGJpfhsc2b9ijw5nZHO2grlSpdA8IkT+g4pWch+GRfZL+Px+vVr9O8bAPdczshgmxqVK5TBqZPB+g4rWRjz8Vrw2xyUKV4YTpnt4JTZDtUrlsHO7dt0tjlx/Cjq1qiKbBlt4JTZDjWrVcTbt2/1FPF/N2nCzyhTshgy2aWFk4M9GjXwRdiVK/oO65OsU1tgUkBtXFk/EM/2jcbe37qgSN7s6vrfAhvh7dHxOn+bpvjpvMaaia0RtmEQnu8bjRt/DsGC4Y2RNWPaFI/9u0gsR44ciUKFCn10/aJFi5AuXbqveo+2bdvC19f3q17DmK1ZvQoD+/fB0MAROHriNAoW9ETdWt6IiIjQd2hfRfbLuMh+GZdunTti7+5dmP/7Epw4dRZVqlZD7RrVcD88XN+hfRVjP14O2bJhxKix2Hv4BPYcOo5yFSqhReP6uHTxAoCEpLLhT7VQqUo17DpwFLsPHkPHzt1gYmJ8KcPBA/vRuUs37D90DFu27UTcu3eoXbM6oqKi9B3av5o9uAEqF8uFdqNWo2jLqdh1/Cr+mt4BDpls1G22H70C51pj1L82w1fqvMaB0zfQMnAZPJtORvMhf8A1ewYsH9cyxWNXSDLF3+UTjh49irJly8LHxwd//fXXFz9/5MiR2LhxI0JCQj64ftGiRQgICMCLFy/+c4xt27bFixcvsHHjxv/8Gp+jYsWKKFSoEKZOnfrZz3n16hVsbW3x6OlL2NjYfPoJ/0G50iVQpGgxTJ0+EwCg0WiQ08URXbr1QP8Bg1LkPb8F2S/jIvuVfDSalD31v337Fpkz2GD12o3wqVlLXV6mZFFU9/bBiKAxKfK+JiZKirxuYvo4XtHv4lPkdbVcsmXCqLET0KptO1SrUBoVK1fF0BGjUvQ9AcDK3DTF3yOxx48fw8nBHjv37EfZcuVT7H3syv3374GVpRke7wpCo4FL8PeRf0pXDy/sjh1HwxD02w78FtgI6ayt0HjQ0s9+3Vpl82L1hFawLR+IuHjNF8fFuBjEnJyKly//PdcwiNuPBQsWoEePHjhw4ADu37+v73DEe2JjY3Hm9ClUrlJVXWZiYoLKlavixLGjeozs68h+GRfZL+MSFxeH+Ph4WFpZ6SxPlSoVjh45rKeovt73drzi4+Oxbs0qvImKQrESJfE4IgIng08gk709qlcqi9zODqhVvRKOHjmk71CTxauXLwEAdnbp9RzJx5mZmsDMzBTRsXE6y6Nj4lDa01l9XM7LFbf/CkToyr6Y1t8X6W1Sf/Q17WxSoal3IRw7d+c/JZVfQu+JZWRkJFatWoUuXbqgVq1aWLRokc76ffv2QVEU7N69G0WLFkXq1KlRunRpXPmXNhLXr1+Hq6srunfvjo8VyG7atAleXl6wsrKCq6srgoKCEBcX98FtEwsKCkKmTJlgY2ODzp07IzY2Vl0XExODnj17wt7eHlZWVihbtiyCg3XbE+3fvx/FixeHpaUlsmbNikGDBqnv27ZtW+zfvx/Tpk2DoihQFAW3bt1KEkNMTAxevXql85eSnjx5gvj4eNjbZ9ZZbp85Mx4+fJii752SZL+Mi+yXcUmbNi1KlCyFCT+PwYP79xEfH48Vy//A8WNH8fDBA32H9599L8frwvlzyJ7JFpnTpUafnl2xdOVauOfNh1u3bgAAxo8dhTZ+HbB241/wLFQYvjWr4/q1q3qO+utoNBr07xuAUqXLIL+Hh77D+ajIN7E4du42BvtVQdaMaWFioqCpdyGU8HBClgwJbSR3HruCDqNWo2bPeQictQ3lCrtg0xS/JCX2Y7r64MmeUbi/fQQcM6dDowFLUjx+vSeWq1evhru7O/LkyYOWLVvi999//2AyOHToUEyePBknT56EmZkZ2rVr98HXO3v2LMqWLYvmzZtj5syZUJSk1SIHDx5E69at0atXL1y8eBFz587FokWLMHbs2H+Ndffu3bh06RL27duHFStWYP369QgKClLXDxgwAOvWrcPixYtx+vRp5MyZE97e3nj27BkAIDw8HDVr1kSxYsUQGhqK2bNnY8GCBRgzJqFKaNq0aShVqhQ6duyIBw8e4MGDB3B0dEwSx88//wxbW1v170PbCCGEvs3/fQlIIqdLdtiltcLsX2egUZNmRtlW73uTK3ceHDh2Crv2H0G7jp3Q1b8dLl+6CI0moTSrbbuOaNG6LQoWKoxxE39Bztx58MeShXqO+usE9OiGCxfOY8mylZ/eWM/aBa2CogA3/hyKl/vHoFvjMli9MxSa/8+P1uw6i78OXcKF64/w54GLqN9vMYrmc0R5L1ed15my7ABKtpmOWj3nI15DzB/eOMVj1/uve8GCBWjZMqExqY+PD16+fIn9+/cn2W7s2LGoUKEC8uXLh0GDBuHIkSOIjo7W2ebIkSOoWLEi+vXrpyZrHxIUFIRBgwahTZs2cHV1RbVq1TB69GjMnTv3X2O1sLDA77//jvz586NWrVoYNWoUpk+fDo1Gg6ioKMyePRuTJk1CjRo1kC9fPsybNw+pUqXCggULAACzZs2Co6MjZs6cCXd3d/j6+iIoKAiTJ0+GRqOBra0tLCwskDp1amTJkgVZsmSBqWnS9ieDBw/Gy5cv1b+7d+9+8nP+GhkzZoSpqSkiIh7pLI949AhZsmRJ0fdOSbJfxkX2y/i4urlh+659iHj2Gleu38GBw8cR9+4dnF1cP/1kA/W9HC8LCwu4uuVEIa8iGDFqHDwKFMScX2cgS5asAIA8efPpbJ8njzvupfC1JiUF9OyOrVu3YPvOvciePfunn6BnN8OfoXrX35Ch0jDk8h2Pcu1/hbmZCW6GP/vg9rfuP8Pj55Fwy55BZ/nTl29w7e4T7Am+htbDlqNGGXeU8HBK0dj1mlheuXIFJ06cQLNmzQAAZmZmaNKkiZqIJVawYEH1/1mzJnzxE/fAu3PnDqpVq4bhw4ejb9++//q+oaGhGDVqFKytrdU/bSnhmzdvPvo8T09PpE79TxuGUqVKITIyEnfv3sX169fx7t07lClTRl1vbm6O4sWL49KlSwCAS5cuoVSpUjqlqGXKlEFkZCTu3bv3rzEnZmlpCRsbG52/lGRhYYHCXkWwd89udZlGo8HevbtRvGSpFH3vlCT7ZVxkv4xXmjRpkDVrVjx//hy7dm5H7Tp19R3Sf/a9Hi+NRoPY2Bg45XBG1qwOuBam29zs2tWrcHRM2YQkJZBEQM/u2LxpA/7esQfOLi76DumLvIl+h4dPXyNd2lSoWiI3thy8+MHtsmWyQQbb1Hj45PVHX0tbTW5hbpYisWql7Kt/woIFCxAXFwcHBwd1GUlYWlpi5syZsLW1VZebm5ur/9cmZtoiewDIlCkTHBwcsGLFCrRr1+5fk63IyEgEBQWhfv36SdZZvdfQXCToGdAHHdu1QZEiRVG0WHHMnD4Vb6Ki0LqN36efbMBkv4yL7Jdx2bljO0gid+48uH79GoYOHoDcedzRysj3y9iPV9DwIaha3QeOjk54/fo11q5egUMH9mPd5q1QFAU9evfFz2OC4FHQEwUKemLFH0twNewyFi9fpe/Qv1hAj25YtXI51qzfBOu0adV2sLa2tkiVKpWeo/u4qiVyQVEUhN1+DLfsGTCue02E3X6MJVtOIk0qCwxtXwUb957Hw6eRcM2eHmO71cD1e0+x83gYAKBYPkcUyZcdR0Jv4cXrt3DJlgEj/Kvh+r0nOH7+dorGrrfEMi4uDkuWLMHkyZNRvXp1nXW+vr5YsWIFOnfu/NmvlypVKmzZsgU1a9aEt7c3duzYgbRpPzwQqJeXF65cuYKcOXN+UcyhoaF4+/at+mU8duwYrK2t4ejoiIwZM8LCwgKHDx9Gjhw5AADv3r1DcHAwAgICAAB58+bFunXrQFJNjg8fPoy0adOqRfMWFhaIj0/ZYSX+i0aNm+DJ48cYFTQcjx4+REHPQti05W9kzpz50082YLJfxkX2y7i8evUSIwKHIDz8HuzSp4evb32MGDVWp6DAGBn78XoS8RhdOvjh0cMHsLG1RX6PAli3eSsqVakGAOjSvReio6MxZEBfvHj+DPkLFMT6LX/DxdVNz5F/ud/mzgYAVK9SUXf5/IVo1abttw/oM9laW2FUZx9ks7fFs1dvsGnfeYyYsx1x8RqYaTTwcMuKFjWKIF1aKzx48hq7jodh1G87Efv/w1K9iYnFTxU8ENihKtJYWeDh09fYcSwMExbtUbdJKXobx3Ljxo1o0qQJIiIidEomAWDgwIHYs2cPgoODsW/fPlSqVAnPnz9XBzkPCQlB4cKFcfPmTTg7O+uMYxkZGYkaNWqAJP7++29YW1snGcdy+/btqF27NgIDA9GwYUOYmJggNDQU58+f/2jbzLZt22LdunWoU6cOAgMDcevWLbRr1w5+fn74+eefAQABAQFYs2YNFixYACcnJ0ycOBGbN2/G9evXYWdnh/DwcOTOnRt+fn7o3r07rly5gg4dOqBbt24YOXIkAMDf3x8hISFYvXo1rK2tkT59+k82dP8W41gKIb4vKT2Opb58i3Es9SGlx7HUl289juW38jXjWBoqgx/HcsGCBahatWqSpBIAGjRogJMnT+Ls2bNf/LrW1tbYtm0bSKJWrVofHF3f29sbW7ZswY4dO1CsWDGULFkSU6ZMUUsaP6ZKlSrIlSsXypcvjyZNmqBu3bpqQggA48ePR4MGDdCqVSt4eXnh2rVr2L59O+zs7AAA2bJlw9atW3HixAl4enqic+fOaN++PQIDA9XX6NevH0xNTZEvXz5kypQJd+7c+eLPQAghhBBCHwxi5h3xdaTEUgjxpaTE0rhIiaVxkRJLIYQQQgghvpIklkIIIYQQIllIYimEEEIIIZKFJJZCCCGEECJZSGIphBBCCCGShSSWQgghhBAiWUhiKYQQQgghkoUklkIIIYQQIllIYimEEEIIIZKFJJZCCCGEECJZSGIphBBCCCGShSSWQgghhBAiWUhiKYQQQgghkoUklkIIIYQQIllIYimEEEIIIZKFJJZCCCGEECJZSGIphBBCCCGShSSWQgghhBAiWZjpOwDx9UgCAF6/eqXnSIQQxkKjob5DSBEmJoq+Q0gR0e/i9R1Ciog1N9V3CCmCcTH6DiHZMT5hn7Q5x8dIYvkdeP36NQAgp4ujniMRQgghxPfs9evXsLW1/eh6hZ9KPYXB02g0uH//PtKmTQtFSdm79VevXsHR0RF3796FjY1Nir7XtyT7ZTy+x30CZL+MjeyXcZH9+nok8fr1azg4OMDE5OMtKaXE8jtgYmKC7Nmzf9P3tLGx+a5+nFqyX8bje9wnQPbL2Mh+GRfZr6/zbyWVWtJ5RwghhBBCJAtJLIUQQgghRLKQxFJ8EUtLS4wYMQKWlpb6DiVZyX4Zj+9xnwDZL2Mj+2VcZL++Hem8I4QQQgghkoWUWAohhBBCiGQhiaUQQgghhEgWklgKIYQQQohkIYmlEEIIIYRIFpJYCiGEEEKIZCGJpRBImKoKAO7cuaPnSMSPjCQ0Go2+w0g2p0+f1ncIQnx3Eg/mY4jnC0kshQCgKAo2btyIRo0a4cKFC/oOJ0XIyGKGKyYmBkDC9/Du3bt6jiZ5HD16FEWLFsWvv/6q71BSTHx8vL5DSHba88T+/fuxc+dOPUcjPkRRFDx8+BCXLl2CiYkJ1q5di/Xr1+s7LJUkluKzGOJdUXLQnkTv3r2LadOmoUOHDsifP7+eo0oe2n27ePEi4uPjoSiKniMSH3L9+nUMHToUz58/x5o1a+Di4oLr16/rO6yvVqpUKYwZMwZ9+vTB7Nmz9R1OstCeB1+/fg0AMDU1RUhICB4+fKjPsJKF9nyhKAr27t2LmjVrIioqCnFxcXqO7Ou9f/0y9pvsly9fonnz5pgyZQqmTZuGxo0bIyoqSt9hqcz0HYAwfBqNBiYmCfcgf/75J8LDw+Hs7IxcuXLBzc1Nz9F9HUVRcPDgQWzatAm2trb46aef9B1SslEUBZs3b0afPn2wZMkSlC5dWt8hfTWSUBQFx44dQ1RUFKpUqaLvkL7auXPnMHfuXFy4cAH79u3DwoUL4ebmpu6rMRsyZAhMTU3RvXt3AECXLl30HNHXMTExwf379+Hv749u3bohNjYW9erVw/Hjx5ElSxZ9h/dVtN+1+/fv4+TJkxgyZAh8fX2NPgkDoF6/QkJCUKhQIaP/Xdna2qJ9+/YYOXIk5s+fj3HjxqFVq1aGc86gEJ9pwIABtLa2ZsGCBZkuXTqWL1+eixcv1ndYX+2XX36hoii0tbXlqVOn9B3OV9NoNCTJ+/fvs169epw1a5aeI0oe2v1at24dHRwc2LlzZ967d0/PUSWPwYMHU1EUVq5cWWeftPts7MaPH08TE5Pv4rt45swZNmjQgPnz56elpSWXL19OkoyPj9dzZF9Ho9Hw5s2bVBSF6dOn56RJk/Qd0ldLfEwOHjxIe3t79XgZK+054datW3R2dqajoyO7devG8+fPJ9lGX6QqXHyW4OBg7Nq1C9u3b0doaCh27tyJ3LlzY9q0aVi1apW+w/sqvXv3xrx582BiYoLff/8dt27d0ndIX0VRFBw4cAD9+vXDixcvUKlSJQDGX/2jKAp27tyJli1bYvTo0ZgyZQqyZcum77C+iraNnpWVFXr37o2rV69i7NixuHz5MoCEfTb24wYAAwcOxNixY9G9e3ejrRbn/3esKlSoEGrXro2LFy/CyckJadOmBZBQKmasTYb4/yVdzs7OmDJlCp4/f44zZ87gyZMn+g7tP0tc0/bHH39g+fLliIqKwsCBA7Fs2TI9R/ffaUsk7e3tsWfPHowePRpHjhzBtGnT1P4Bei+11GtaK4zC+PHj2a5dO7Zo0ULnDvD8+fNs0KABmzZtynfv3un9LulzaGMMCwvjiRMnuGvXLnXd9OnT6eDgwCFDhvD27dv6CjFZ7NmzhxkzZqSJiQnXrVunLjeGY/QxMTEx7NKlC/v27UuSfPHiBYODgxkQEMDhw4fz8uXLeo7w661YsYLZs2dn586ddfYnNDRUj1F9Pu3368KFCzx48CC3bdums37cuHFGX3K5cuVK1qlTh/Pnz2eLFi1YtmxZrlq1Sl1vTCWX2uP1fsyTJ0+moigcP348X758qY/Qks3AgQOZJUsWzp49m+PHj2e5cuWYK1cuLly4UN+hfRHtsbp9+zYvXrzI69evq+vmzZvHwoULs1OnTmrJ5ejRo7lhwwZ9hEpJLMUnDRs2jIqi0MXFhXfu3NFZt3z5cpqZmfHGjRt6iu7zJa5KdXd3p7u7O/Ply0cvLy/evXuXJDlt2jRmy5aNw4YN482bN/UY7dc7fPgwnZ2dWbt2bZ48eVJdbszJZbNmzVi4cGHevHmTrVq1YuXKlVm6dGlmypSJ9evX13d4n0X7+QcHB/OPP/7gzJkzefv2bfXivmLFCjo6OrJLly48cOAAR40aRUVR+OzZM4M+dtrY1q9fT0dHR+bPn59p06ZlvXr1eOnSJXW7cePG0dLSkpMnT9ZXqF9Mu2/Xrl2jtbU1Z8yYQTLhGDZu3Jhly5blmjVr1O23b9/Ohw8f6iXWz6Xdpz179rBXr15s164dAwMD1fWTJk2ioiicMGGC0SaX165do7u7u06CFRISwo4dO9LV1ZUrVqzQX3BfIPG1K2/evMyaNStz5szJunXrMiYmhmRCclm8eHFWrFiRTZo0oaIoemvaJYml0PGxu+1p06ZRURSOGjWKT548UZcfP36c7u7uvHLlyrcK8avs37+f1tbWnDdvHqOjo7l//34qisI5c+ao20yfPp1WVlYcPXo03717p8doP4/2pHP27Flu3LiRy5YtY0REBMmE/XVxcWHz5s15+vRpfYb5xbT7dfLkSbVk+ciRIyxcuDAtLS3ZqFEjrl+/nmRCMlOoUCE+e/ZMb/F+jsQXiPTp07Ny5crMnDkzq1atyoULFzIuLo4kuXr1aubNm5ceHh50dHTkiRMn9Bn2v0qc7O7YsYPp0qXjvHnzSCbc3CiKwlq1avHcuXPqdoGBgcyQIQOfP3/+rcP9zw4cOMBFixZx8ODBOstPnjzJJk2asGzZspwyZQpHjhxJRVGMov3v+vXraW1tzW7durF///7MmTMnCxUqxNjYWJIJJZcWFhYcOXIkX716pedov9zdu3dpZ2fHJUuW6Cw/c+YMnZ2dmTVrVi5dulRP0X2ZvXv3MlWqVJw9ezZ3797NtWvX0tXVlSVLllTPG6tWrWKvXr1Yv359nd/btyaJpVAlTipv3LjB8+fP6ySR2pKTvn37ct++fTx//jx9fHxYrFgxo6n+mTx5Mrt27UoyYR9z5MjBLl26JNlu1qxZDAsL+9bh/Wdr165ljhw56OXlxVKlStHa2pq7d+8mSe7bt48uLi5s1aqVQScoiSVOwBwdHdmvXz+Gh4fz3bt3jIqKSrIfPXv2ZI0aNRgVFaWPcL/Ivn37mDlzZs6fP58kee7cOZqZmbF48eKcM2eO+ls6d+4cjx07ppamG5r169fz4sWLJBOO16tXr9izZ0+OHDmSZMLvy9XVlS1atKCDgwMrVarE0NBQ9dgmPrcYmoCAAE6cOFF9/PLlS/r4+FBRFNarV48kdW46z5w5Q39/f7q7uzN//vw6NQSGKjw8nB4eHpw+fTpJ8ubNm8ySJQs7dOigs11QUBDt7OwM+niR/5wzEv/75MkTVqtWjX379k0Sf6NGjVi+fHkWK1aMO3fu/ObxfqmgoKAktTLXr1+ns7MzGzVqpLNce2OgL5JYCpK6pQ6DBw9mgQIFaGVlxTJlyqiJGEmOGTOGiqJQURS2adOG9evXV7/ExpBctmzZkn5+fnz69CkdHR3p7++v7vuiRYuMsifk8ePHaWdnp5YSXbhwgYqicNy4ceox2bdvH21sbNixY0dGR0frM9zP9vfffzNVqlScO3cu3759+8FtTp48yb59+zJdunRG0Q7x3bt3HD9+PAMCAkgmXBi0yZePjw9dXV05f/58tQTCUJ09e5aenp6sV6+eegMWExPDDRs2MCwsjM+ePWORIkXYvn17kuSWLVuoKArLli3LCxcu6DP0T4qLi+P8+fOTlPAfOnSIDRo0oI2NjbrPiS/gL1684KNHj9TaAkN38eJF5sqVi7Gxsbx37x6zZ8/OTp06qeu3bNmi/v/p06f6CPGzJb72vJ9U/fLLL0yXLh2nTZumHptXr16xYcOGnDVrFsuUKcMhQ4Z803j/izZt2rBo0aLqY+2NzcKFC5k/f36DugGVxFLomDBhAtOnT88tW7Zw7969HD16ND08PHTulGbOnElFUTh9+nS+ePGCJA3yQpi4ZOTNmzckE0pZvL29mSlTJnbs2JFkwkkpPj6e3bp1Y9euXdVtjcWyZcvYvHlzkgmlRNr2eVqvX78mmVCVd/XqVb3E+KWio6PZqlUrDhgwgGRCidHp06c5ePBgBgUF8enTpzx79ix79OjBwoULG0VSqXXp0iVevHiRkZGRLFOmDNu1a0cyYfiQdOnSMX/+/GpppiH7/fffWbFiRTZs2FAtudTetKxZs4bFixdX215v3LiRNWrUYN68eY2q7fLWrVs5YsQI9XFwcDArVapER0dHXrt2jSSNorlMYufPn2d8fDwfPHjAChUqcN26dXRycmKnTp3UfQkLC2PLli158OBBkobdLjtxUjlr1iw2btyYTZs25c8//6wuHz58OO3t7VmnTh36+/uzVKlS9PLyIplQ2FClShWD3keS/Ouvv+jm5saVK1fqLN+4cSNdXFwYHh6up8iSksTyB5f4x/Ty5UvWqlWLv/zyi7osKiqKK1euZL58+fi///1PXT527FgqisKpU6caZDsp7X5t3ryZ1atX586dOxkfH88rV66wbNmydHNz4/bt20kmlDQMHTqUWbJk0elkYKjePwEGBQWxcuXKvH37Np2cnOjv76+ebNevX8+AgACjS5ZJsnnz5ixXrhyvXbtGPz8/Vq5cmUWLFmWmTJnURPr8+fMG3UniQxcr7cX7wIED9PDwUEvwgoODWaVKFbZq1cqgRyVInEj99ttvrFmzJhs1aqTTS3XixIl0c3NTj83gwYP5888/G1USptFoOGvWLCqKwtGjR6vLg4OD6e3tTWdnZzVxNsQbazJp0nvu3Dlmz56dd+7c4fPnz1mxYkWamJiwRYsWOtv169ePJUuWNOjf1vu0vb+HDBnCfv360cXFhX5+fur6P/74g71792b16tXZpUsX9Saofv36DAgIMJgaN+05Izw8nNevX1fbjT969Ij16tVjrVq11E5HsbGxHDRoEIsXL25Q7cslsfyBvf9D0mg09PLy0qn6JhO+vA0aNGCzZs10lk+YMIGKonDWrFkGebenbZg+atQotXSBJE+dOsXChQvTw8OD7u7urFq1Kh0cHIyqc8uhQ4fUO/IjR46wYsWKTJ8+vXoi1R7bgIAANm/e3OAb3n/o+7Np0yYWK1aMpqambNSoEdeuXUsyoclCsWLFDL49pXafDh06xAkTJnDQoEHctWuXekHbuXMnXVxcuGnTJsbHx3PEiBFs3769WsJsqN7vTezh4UFTU1M2btxYrSIOCwujjY0NCxYsyPLly9PW1pYhISH6DPs/efPmDefOnUsTExO17ShJnjhxgjVr1qSNjY3BlsBOmjSJDRs21PmdHD16lO7u7mp18YULF5gxY0bWqlWLS5Ys4Y4dO9i9e3fa2toaVS3A8uXLmTt3bh47doxkQol56tSp1VEJtBJf8yIiIjh06FCmT59eLXHXt8QjK+TKlYsuLi60tbVljx49eOPGDd68eZMNGzZkjhw5mCdPHlaqVIl2dnYGd+2SxPIHFRwczMePH5NMmFFn0aJFJMkePXrQ29s7yQ9txIgRrFq1KqOjo3V+nFOmTDGYH2ViN2/epJubG2fOnEky4YQSGxvL48eP8+3bt3z69Ck3b97MAQMGcMWKFUYxXJJWTEwMAwICWLlyZZIJ7YW0HSTmz5/Pd+/e8cGDBxw8eDAzZsxo8G3aEidgI0eO5KBBg9RenK9fv+aRI0d0tu/SpQvr1Knz0XaXhmTt2rW0trZmhQoVWKJECSqKwn79+vHu3bt8+vSpOqZevnz5DPIC8TE7duygoiicMmUK//zzTw4cOJAFChRgw4YN1VL/c+fOsWPHjuzfv7/BfwfJf0od7927p1NzodFo+OuvvyZJLo8cOcIGDRoYbPOSv//+m5aWljo3K9u2baOnpyfJf5KsU6dOsXLlysyRIwfz5s2rdrIyZDExMToJ8+zZszl8+HCSCbVUdnZ2nDp1KufNm0dTU1O1ra/W06dP6efnR1dXV545c+Zbhv5J+/btY6pUqThlyhSeOnWKM2bMYKlSpVivXj3evHmTT5484YEDB9ivXz/OmDHDIDuZSmL5A4qIiKCiKOzRowc7derEtGnTqkMThISEMHPmzGzVqpU6Btbr169ZsWJFtU0iabgddbRJyuXLl1mkSBGeOnWKT5484aRJk1ihQgXa2tqyfPnyPHz4sJ4j/TonT56kpaWlWiXy/Plz1qpViwUKFGC6dOlYtmxZuri4GE2ism7dOtra2rJ58+Zs164d7ezskpSQnz9/nn369GG6dOl49uxZPUX6+a5evUonJyfOmzdP/V6uWLGCGTNmVAd5v337NufOnctp06YZ5AXifRqNhvHx8ezQoQObNm2qs+63335j3rx52bhxYzXZiouLM8jaDK1Zs2Zxz549apXxmjVr6OjoqI7DuWfPHrV0T5tcJq4WN/Sbm71799La2pp+fn6Mj4/nxo0bWahQIZK6tQTR0dF8+PAhIyIiDL7EfO3ataxfvz4LFy7MUaNGqctv3LjBJ0+e0MvLi+PHjyeZ8BvMli0bFUXhwIEDdV7n9u3bScZl1ift8ejbt69OKSv5T+2Nts25oZPE8gezf/9+3rx5k6dOnaKlpSVTpUrFPXv2kPznjv3YsWN0cXGhl5cXPTw8WKJECXp4eKgnWEO+UGirfG/fvs306dPT29ubmTNnpq+vL3/++Wdu376defPmNaqZPxJ/3vHx8erjPn36sEqVKurJMSoqiidPnuScOXO4d+9eg+ol+G+0PaO1pctXr15l+vTp6e/vr25z/Phxdu7cmZ6engZbpRoREcHg4GD1huzcuXN0dXVlSEiIzjFctmwZTUxMeODAAX2F+tW6devGqlWrJumBGxAQQCsrK3p7exv02Lba45EnTx46OTnxyJEjPHv2LF1cXDhp0iTu3buX3t7edHJy4po1a9RBqOfMmaMOGm4sdu/eTWtra/bs2ZOrV69mqVKluGPHDu7bt48XLlzgqVOnuHnzZj548EDfoX7SnDlzaGNjw969ezMgIICmpqb89ddf1fXHjx+nk5OTepN29epVNm/enDt37tRpB2vI17A+ffqwatWqjIuL0ynAmThxIjNmzGjwiT8pieUP5dWrV2zfvj0HDBjAo0eP0srKiiYmJuzduzfv379P8p8f3NWrV7lixQoOHDiQM2fOVO/oDbnxfUhICC0tLXn06FGSCdPgDRo0iJMnT9ZphF61alVOmzZNX2H+Jzt37uSGDRt0Okpt2rSJOXPmVHtuGqvTp0+zYMGCJBNuCLRTGmoFBweTTCil1X5PDc2FCxdYpkwZ+vj4sH79+oyLi2NwcDDNzc3Vdl+Jh3ny8PDQ6QxnbCZNmvTBEvElS5awQIECbNasmcHe2Lxf21KhQgW6u7tz8eLF7N+/v866Bg0aJEku58+fb5DNfxJ7P3HatWsX06RJw9SpU9PNzY0uLi7MmjUr8+TJw+zZs9PBwcHgmwPNmzeP5ubmOrPoNGvWjNOnT1fP79evX6ebmxt79OjBixcv0tvbm/Xq1VM/D0PtZJXYlClTaG1trU7NqI19x44dzJcvHx89eqTP8D6LJJY/mGXLljFHjhxq+8qdO3fSxMSE3bp1++Qdq6H/KG/dusXatWvTxsaGx48fJ6l7MY+Li+PgwYOZOXNmnc48hu7Nmzfs0aMHFUWhr6+vWs1Dkq1atdIZ28wYaE+Ue/fu5a5du3jx4kWWLl2aO3fuTDLsSWhoKFu2bGnQ84CfP3+e6dKlU+eYT5y4NGrUiPny5dPpMR0TE8MiRYrwt99+00e4X0R7rC5dusTQ0FCdJgjFihVj/vz5GRwcrLZ3GzBgAAcPHmyw4x5qj83Nmzc5Y8YM9TxQvHhxKopCb2/vJKWwDRo0oJubG//44w+9Dzz9Kdrj9erVK0ZGRuqs279/PzNlysSaNWvyzp07fPr0KV++fMknT56ow8YZqr1791JRFAYFBeks9/T0ZMGCBZk2bVqWKVOG06dP5+TJk5k9e3bmyJGDJUqUMNiaNu057vr167x06ZLOOU476kBoaKj62woICKCXl5fBHytSEssfRuIfVcuWLdmwYUN1/tc///yTJiYm7NmzpzoNWcOGDblq1Sq9xPq5Eu+T9v+3b99m48aNmSpVKnX2i/j4eP7+++/09fVltmzZjKbd4fuOHDnCIUOGMHPmzCxevDinT5/O9evXs2rVqvzzzz/1Hd4nJT5ee/fuZerUqbl+/Xpev36dRYsWpZWVFdu0aaPznD59+rBSpUrqjZChefr0KcuWLcuePXvqLNcmMIcOHaKPjw/z5MnD3bt3c//+/Rw6dCgzZsyok2wasjVr1tDe3p6Ojo50c3NTRyN4+/YtixcvThcXFxYrVozVq1enhYWFwXbU0R6Ts2fPMnfu3KxXr55O6Ve1atVoZ2fH3bt3J7mJrlatGgsUKGDQoytof19//fUXK1asSC8vL5YvX57nz59XS1v37NnD1KlTs3PnzkY1BFlYWBjLlSvHunXrqjUY9evXZ86cOblq1Spu27aN+fPnZ9GiRRkaGsrw8HAePXpUPeaGUtO2ePFidSILkly5ciUdHR1pb2/PnDlzsnHjxoyNjWVERAR9fHyYNm1aFitWjJUrV6atra3BdTT6GEksv3Mf6mSzd+9e1qtXT60yJhNmWbC0tGT16tXp5eXF3LlzG/zdOZlwF66twtGeWG/dusXGjRszderU6g/x3Llz7N27t0G3+9LS7kdoaCg3bNjANWvW6MzmERERwY4dO7JKlSpMlSqVOs2mod2Rf8y9e/c4adIkjhkzRl22detWmpmZ0d/fn9u3b+fJkycZEBBg8B11Lly4QDc3N+7fv/+jHdpOnDjBFi1a0NLSkjlz5mT+/PkN/uZG+116+vQp3d3duXDhQu7Zs4c///wzzc3NGRgYqG47a9YsDh06lP379zf4KuJLly7Rzs6OgwYN+uCA0mXKlKGzszMPHjyY5HgaatV+Yps2bWLatGk5dOhQ7t69m6VLl6anpye3bt2qJpe7d++moijs1q2b0ZwzyITk0sfHh7Vq1WKZMmXo5eWlM9TTqVOnqCgKN23apPM8Q+loGhERwdq1a7NEiRJcuXIl79+/TxcXF86ePZt79uzhypUrmT17dlauXFk9LvPmzeOYMWM4ZswYo+jcpyWJ5XcscZuZX375RR225d27d6xTpw4bNGigs/2+ffvYu3dv9u/f3yjaVL58+ZJVq1ZlxowZ1ROM9gcZFhbGQoUKMVOmTOodriHvy/u0pUS5cuWik5MTM2TIwD///FPtharRaBgeHs6JEyfS09NT7dVv6G7cuEFFUWhra5ukA8SqVavo5eXFDBky0MPDg8WKFTPYjjpay5Yto5mZmfq9S3wR05Z6RUVF8dKlS3z8+DFv375tsKWv79u1axcHDRrE7t27q0nJ69evOXPmTJqamiaZBs/Qk5S3b9+yUaNG7Natm87y2NhY3rhxQ7158/HxoZOTEw8fPmwwScnnuHHjBosWLcopU6aQJB8/fkwXFxfa29vT3t6eW7duVZsG7d+/3ygmg3hfWFgYq1atSltbW65evZrkPx0aT506xXz58vHQoUN6jvLjQkJC2LJlS1aqVIm9e/dmixYtdApwLl26RAcHB7Zs2VKPUX49SSy/UyEhIVQUhRs3bmSvXr2YPn16nfHWHjx4QHd3d3X8yg81bjaGROzo0aOsUaMGXVxckjQ+b9OmDU1MTJg1a1a+ffvWaC4Sp0+fpp2dHRcuXMiHDx/y4cOH7NChA62trblt2zaSuhdxQx4oPCoqio8fP+bevXvVZhbLly+noihs3LhxknmVHz58yEuXLvHGjRsGOaPT+w4fPkwrKyt18PYPmT59OqtVq2Y0c7STCe1Ahw4dSlNTUxYpUkRnnTa5tLKyUodNIg0/sXz37h3LlSvHGTNmqMv+/vtvBgQE0MbGhtmzZ2fDhg1JJiSXtra2ascrY3DlyhVOmDCBkZGRvH//PnPmzKlO7VqsWDF6enpy48aN6k2Csbp27Rq9vb1Zo0YNnZEVateuzYoVKxr8eT4kJIQtWrSgi4sLS5YsqS7XXm8XLFjAfPny8fbt2+pvytB/W++TxPI7FhQUxFSpUtHa2lqnOjEuLo7v3r1jUFAQu3fvzjdv3hj8j5H858cVGxur0zD93LlzrFKlCl1cXHjr1i11eUBAAFevXm3Qveh27NiRpNPUhg0b6OXlxefPn+ucUPz8/Jg1a1Y14TL0k86VK1fYunVruru708rKimnTpmWzZs0YHh7O9evXq1PlGUNj9I+5d+8e7e3tWbduXZ3vXuJj0rdvXw4aNMhgj1NiiWO8desWg4KC1Nm1EouMjOSkSZOYIUMGPn782Cj27eXLl3R3d2fHjh15+fJljhs3jnny5GGDBg04bdo0LliwgDly5FDHqaxSpYrBDn7+Mdp4u3btygYNGqhD07Rq1YqKojB37txJOvUYI221eM2aNXnw4EHWr19fp/mWoV/Pzp07x6ZNmzJ16tScM2eOzrrNmzcze/bsBj2t66dIYvmdSfyDGjNmDBVFoZmZGdetW5dk28OHDzNz5szcsmULScNNUEjdhun16tWjp6cnO3TowK1bt5IkL168yKpVq9LOzo7Dhg1jq1atmDVrVoMdQkM7b7m2rVPikrt58+YxderU6h2stqTr6tWrzJ49O3ft2qWXmL9EaGgos2bNys6dO3PRokW8dOkSBw4cSBcXF+bJk4d37txRSy7HjRundiQzRuvWraOlpSVbtWql03ElKiqKgwcPZo4cOQy+ba/29/V+LcWdO3c4ZMgQWltbJ7kARkVFGdT8xJ9j9+7dNDMzY44cOZg2bVrOmTNHTcZiY2NZvXr1JAPzGyLt8bp+/TqvXLmSpGS1Zs2aOgOC9+7dm2fOnFFrDb4HYWFhrFWrFs3NzZknTx41qTSGmjYy4ZrVrFkzlihRgrNnzyaZcMPWv39/uru7G02TmQ+RxPI7FRQURH9/f54/f55BQUE0NzfnH3/8QVI3+ZwzZw4LFSpkUDMQfMyff/5JCwsL9urVi6NGjWLRokVZqlQpTp8+nSR5//599urVi0WLFmW1atUMugedtsnB2rVraW5uzp49e6pjsT1+/Jj58+dnx44ddapPtdNU7tu3Ty8xf67Q0FCmTp2agwcPTnKSX7VqFQsWLMjixYszOjqac+bMobm5OYcNG2a0yWV8fDznzJlDMzMzuru708/Pj126dGHdunVpb29vNB11du/ezbZt27J58+Y6Scndu3c5dOhQpk2bVqdHq7G6c+cOT548meTCHR8fz0aNGjEwMJDx8fEGW+qVeD7pvHnz0sPDg5kzZ2bz5s3VDh6+vr7Mmzcvf//9d3bp0oW2trZGXQL2MZcuXWKPHj2Mok/Ah5w9e5bNmjWjpaUlCxcuzGbNmtHd3V0d0cRYSWL5nUjcNnLHjh3MlSuXOgMISQ4ePJjm5ubqFIBkwl3s4sWL2aBBA+7YseObxvslNBoNX758yUqVKulM4RUREcFu3bqxZMmSOqV4L1++NOip1n7//Xf+8ccfaowbNmxQp9h89OgR4+PjOXXqVJYqVYp+fn58+fIl7927x+HDh9PZ2dmgSx3u3LnDjBkzslGjRuoyjUajc8L/7bffmCZNGnUcx7Fjx9LOzo5Pnjz55vEmp+PHj7Nhw4YsVKgQy5Urx4EDBxp8T87ESYqNjQ07duzIgQMH0tnZmXXr1lXPK3fv3uXw4cOpKAoXLlyox4hTRkxMDAMDA+ng4GDwx4xMGDbI2tqa8+bNY2RkJLdt20ZFUbh8+XKSCaXJ5cqVY758+ejp6WnQN9nJxRCTys9prnTx4kW2aNGCmTNn5siRI426pFJLEksj9/5YeMuXL2evXr3Yu3dvkro/tiFDhlBRFPbs2ZOlS5emh4cHyYQeyNqe04ZCOycxmTBAeFxcHIsVK6a2f9Kue/LkCQsUKMCAgAC9xfoltPtRqFAhrlu3Ti2R1CaXXbt2ZWRkJN++fcsZM2awQIECNDc3p4eHB7Nly6Zzs2CIbt68yWLFirFu3bpJZgRKfHItX748fX191cfGVqX6MYY+iYD2d5O4NC4kJIS5c+dW21HevHmTWbNmpaIoLFu2rHoOuXXrFseMGWPQg9X/F0uXLmXPnj2ZOXNmgy9d1ho5cqQ6O9W1a9eYM2dOnSlQtR48eGC0NQHGTHuue/nyJWNjY9XxTz+WYJ45c4b+/v5GMaTV55DE0oi1bduWI0eOJPnPkAtlypShoiisUqXKB4dAmTp1Kr29vdmqVSuDLdVLPPzCihUr2Lp1a968eZPly5enn58fSepUVQUEBLBKlSoGf1HXHo83b97Qx8eHRYoU4Zo1az6aXGo0Gr5584Zr167lvn37jOako21Y7+3trZNcJj6pVqxYkc2bN//gOmP2oUH7DUXiWWfmzp3LEydOkEwYQ1R7I3rnzh26urqyY8eO6hzTvr6+Rtd+7XNdvnyZFStWZL169Qx+DE4tjUbDWrVqcciQIYyOjma2bNno7++vft+mT5+ullyKb097HLZs2cI6deqwaNGirFOnDjdv3vyvzzOmUSM+RRJLI7Zp0yb1hK9tn/fu3Ts2bdqUDg4OXLhwoZo8Jk4uE88eYWiDoJ87d44jR45kfHw8Hz9+TFdXV3Ve7+3bt1NRlCRzLDdu3Jh+fn4G2yYqMe2F+c2bN6xSpQqLFi3KNWvWJKkW79atm0H3Zv+UxMll4nHl4uPjeffuXdaoUSPJUFci5Xxo1hltpz0yodRSo9HQ19eXLVq0oEajYWRkJIsWLUpFUVi9enV9hZ7iHj16ZHQjEyxZsoRly5ZlxowZ2aVLF51ChPbt27Nbt27fVaJibDZv3kwrKytOmDCBa9asoZ+fHxVFMdhZqZKbJJZG6P0L8bx589i4cWO1Gufdu3esVasWCxUqxFWrVqnjlr1fomdoF3Tt2Ju//vor9+zZw9GjR7Nz5846w2P8+uuvVBSFzZo1Y58+fdipUydaW1sbxQDh2s9bW+0bFRXFKlWqfLDk0sLCgn5+fknGeTQmHyu5HDhwID09PY2mBPZ78alZZ168eEFPT091msPo6Gh26NCBf/31l8GOrvC9054z7t27x8uXL6uPT58+zfLlyzNfvnzqxBeRkZEcOnQoHRwcDH4Ugu+R9uYtKiqKderU4cSJE0mS4eHhzJEjxwebKnyvJLH8DsyePZsFCxakv7+/TnJZo0YNFi5cmKtXrzb4u9cLFy4wVapUHDFiBEly2LBh6rhr2rHYtPbs2cO6deuycuXKrF+/vkFP+fe+48ePs3HjxurwINrk8v2Sy1WrVjF9+vRqSbSxSpxcnj59mhMmTKC1tbXBz6jzvfm3WWfu3bvHsLAwRkVFsUiRIvT19eXNmzfZr18/5s6dO8k4q+LbWrt2LR0dHeno6Mj8+fNz7969JBOGXitdujRdXV1ZtmxZVq5cmVmzZjWadqLfg8mTJ+u079doNHzx4gVdXV154MABRkREqE0VtBYvXvzdJ/6SWBqRxB1a3rdgwQJ6eXmxffv2Osll7dq16eDgwN27d3/LUL/IuXPnmDFjRubNm1ddFhERwYkTJ9LExEQd44v8p9RVmygbajvRj/njjz9YqFAhtmzZUu0wlbjkct26deo+vZ9QG6uwsDDWrl2b9vb2NDc3N/qhNIzRp2adyZEjB6tXr87169fTzc2N2bJlo6OjoyQpeqI9z1+4cIGurq6cNGkS9+7dS29vb2bPnl2d6encuXNcvHgxu3btyrlz5/LatWv6DPuH8vbtW/7888+0trbmsGHD1OVxcXFs1aoVx4wZQycnJ3bq1Em9bkVERLB169ZcunSpwdUYJidJLI3Uli1buHHjRu7Zs0ddNm/ePDW51A4vERsby759+xpsx5aQkBCmTp2aFStWpIODA3v06KGue/78uVpyuWTJEpIJybX2T/vYUH0sthUrVrBs2bJs2rSp2oEiKiqK3t7edHNz46ZNm/71+cbo8uXLrFu3Ls+fP6/vUH5InzPrTN68eRkQEMBHjx7x0KFDUlL5DX2ot/7Ro0e5ePFi9u/fX2fbBg0aqMmlsU/PaOyePXvG6dOnM126dBw6dKi6fNCgQVQUhTVq1OCbN290lufJk0dnlq7vkSSWRqBHjx46c/IGBATQ3t6eWbJkoYeHB3v27KmumzdvHosUKUJ/f38eP35c53UMLbkMDg6mubk5R44cybi4OM6dO5cZM2bUSS5fvHjBwMBAKoqiDvBuyD5Uonzp0qUkJQnLli1juXLl2KRJE/UmIDIykr6+vt9tezZD6yj2o/m3WWdiYmJYrVo1tm7dWs9R/nje762vPW9rO075+Pgk+e00aNCAbm5uOuPhim8nceHG69evOWXKFKZLl46DBw9Wt2nevDnt7e3ZvXt3jhgxgm3btqWtre0PMaaoGYRBe/78OczMzLBt2zakS5cObdq0wYkTJ7Bz506Ym5tj+/btmDVrFqKiojB//nx06NABJiYmGDlyJFxcXFC8eHGQhKIoMDU11ffu6Hjz5g26dOmCESNGAACaNGkCABg6dCgAYPr06bC1tUW/fv1gamqKVq1awczMTN3O0Gg0GpiYmCA8PByHDh1CfHw8LC0tMXv2bOTMmRMDBgyAq6srAKB58+aIi4tDQEAATExM0KtXL5QoUQIbNmzQ816kHHNzc32H8EOrXLkybty4gYiICOTIkQMZM2ZU15mZmcHW1hZOTk4gCQBQFEVfof4wtOeMc+fOoWHDhsifPz8cHBwAAMHBwahZsyaOHTuGgwcPokKFCuo5fO3atahevTomTpyIunXrwsrKSp+78UPQXkeBf34bwcHByJIlC9q2bQtFURAUFASNRoPx48dj2bJlCAwMxOXLl3H8+HEULlwYhw8fRv78+fW5G9+GnhNb8RnCw8M5cuRIenh4sGHDhmzbtq1a+vjixQvOnj2bbm5u7NChg/qczZs3G1wJ5b9JPKDsh0ounz17xrFjxxrsWHPaUofQ0FC6uroyX758NDc3Z/Hixenp6Ulvb2/26tUrSWlk2bJlaW9vzw4dOvDt27ffVfW3MA7GNuvM9+ZTvfXLlClDZ2dnHjx4MEmNiIys8O3cv3+f5D99Ha5fv87MmTOrHRGfPXvGqVOn0s7OjgMGDFCfFxMTw9jYWKO6Hn8tSSwNWOKTSHh4OEeMGEEXFxeWLl1aZ7sXL15wzpw5zJMnD+vXr6+zzhi/zImTy/d73BmixEll6tSpOWDAAIaHh3PTpk2sUaMGy5cvz65du7JQoULs1auX2r7m7du37NixI8eOHSsXCKEXxjjrzPfk33rr37hxQx1uzMfHh05OTjx8+LBRjNf7vVmzZg1dXFzU0TxI8unTp3R3d9eZYjdxchkYGKiPUA2CJJYGKvHJQztQ9sOHDzlixAimS5cuyZf25cuXnDx5Mhs3bvxdnHhevnzJefPmUVEUDhw4UN/hfNKH5sgmE4aCsrOz47179/jrr7+yaNGibNKkCRcvXsyBAwcyX758Rj9HtjBOxjjrzPfmU731s2fPzoYNG5JMSC5tbW11khvxbWzfvp116tRhsWLF1Daw169fZ65cuZKcv7UdehRF4ZgxY/QRrt5JG0sDpG13AwCjR4/G6dOnMXbsWOTLlw9dunQBAKxatQqmpqYYOXIkAMDGxgadOnVC7969oSiKzmsYIxsbGzRq1Ajm5uYoVaqUvsP5pPj4eLi4uCAmJgaHDh1C2bJlAQBubm4AgNevX6Nr165IkyYN1q5diyFDhiBjxoxYunQpMmTIoM/QxQ8qT548WLVqFSwtLWFra6vvcH5Ib968wePHj3H27FlcuXIF69evx+LFi+Hh4YHRo0fD2toao0aNwpgxY7Bt2zZUrVpVzhd6UL16dVhaWmLatGno3LkzZs+ejSxZsuDVq1eIj4/X2dbOzg6tW7eGubk5KlWqpKeI9Ush/7+ltjA4AwcOxNKlSzF+/HhUqVIF2bJlAwA8ePAAc+fOxcqVK9G8eXMMHz5c53lM1MjY2BnTvly9ehU9e/aERqPB1KlT4ejoCFdXV/j5+WHChAnqdi9fvkRkZCSsrKzkIiHED27Pnj3w9vZGtmzZ8OzZM0yaNAlVqlRBzpw58e7dO9SuXRsZMmTA8uXL9R3qDynxNWjfvn2YNm0a7t27h86dO2PZsmXw9vaGi4sLNBoN3r17h5iYGOTPn98oCkRSiiSWBmrnzp1o27Yt1q9fjxIlSoAknj9/jtu3byNXrlxQFAWTJ0/G1KlT8b///Q/t2rXTd8gCCcllr1698ObNG5w9exZt2rTBlClTAABxcXEwM5NKAiGErrt3736wt75Go0HTpk2RJ08eBAUFAYBR10R9D3bt2oXZs2fjwIEDePr0KerWrYuwsDAoigILCwvEx8dj9erVcHd313eoeiNXOQP1/PlzODg4oHjx4jh9+jQ2bdqE5cuX49WrV6hcuTJmzJiB9u3bI3v27GjTpo2+wxX/L1euXGp1iY2NDerVq6euM7ThnoQQhsHR0RGOjo46y2JjYzF69GgcPnwYY8eOlYTyG9OWVJ4+fRqPHj2CRqNBrVq1ULVqVSiKAisrK5w9exZBQUHw9PRUnxcVFYU0adLoMXL9kxJLA/D27VukSpVKZ1lISAi8vLzg4+OD4OBg1K5dG5UqVYKlpSW6du2KLVu26BS1x8fHS+JiQK5du4YePXqAJIYNG4YyZcroOyQhhJH4448/EBwcjFWrVmHbtm0oXLiwvkP6Ia1btw5t27ZFlixZcP/+fTRo0ABLliwBAOzevRvTp0/Hw4cPMX78eLU9pTE130opkljq2dKlS3H9+nUMHjwYlpaWIAmNRgNTU1McPnwYa9euRcmSJVG5cmVkypQJUVFRqFChAiZOnIjKlSvrO3zxL65evYo+ffrgyZMnmDJlCkqWLKnvkIQQBu7KlSvo3Lkz7OzsMHbsWOTNm1ffIf1QtInhmzdvUKNGDXTo0AFly5bFpUuX0Lp1a5QrV06dyGL//v0YNWoU4uPj8ffff8tA9f9PEks9+u2339C5c2ds3boVPj4+OjNenDx5Evb29nBycgIAvHv3DtHR0WjcuDFevnyJgwcPSgmlEbh8+TKGDRuGyZMnq8dSCCH+TUREhPTW16OdO3di6dKlMDU1xYQJE2Bvbw8AOHz4MHx9fVG2bFmsX78eiqLg4MGDcHFxQfbs2fUcteGQxFJPli5divbt22Pjxo2oWbOmTlK5fv16+Pv7Y926dahQoQLevXuHX3/9FWvXrkVsbCwOHz4Mc3Nzox9S6EcRGxsLCwsLfYchhBDiM6xatQp+fn6wsbHBxYsXkT59erUk8/Dhw2jUqBHy5s2LXbt2/fDV3h8iWYkeLFq0CG3atEHFihVRs2ZNAAm9/xRFwcaNG9GwYUOMGTMGFSpUAJCQbHp6eqJq1ao4cuQIzM3NERcXJ0mlkZCkUgghDItGo/no43r16mHZsmWIiopCYGAggH/mBy9TpgyWL1+OO3fuIDw8/NsFbESkxPIbmzdvHjp37ox27dph69ataNiwIaZNmwYgoW3H2rVr8fz5c/j7+3/0NaSjjhBCCPF1Ll++jKVLl8Lf3x9OTk46pY/v3r3Dhg0b0LZtW3To0AHTp0/Xee6HOt2KBJJYfkNTp05Fnz598Ndff6FGjRqYO3cuAgMD0bx5czW5FEIIIUTKevfuHcqUKYOTJ08iZ86c+Omnn1C8eHE0atRI3SY6OhqbNm1C27Zt0blzZ3VMYvHvZBzLb6hw4cJYvnw5atSoAQBo2rQpFEXB0KFDAUBNLqVEUgghhEg55ubmaNSoEZo1awYPDw8cPnwYnTp1wubNm1GqVCl07twZVlZWaNKkCQCgWbNmsLCw0JlFTXyYlFjqQeJxrl69eoWVK1di6NChOiWXklwKIYQQKWffvn346aefsHv3bhQtWhQPHjzAb7/9hokTJ6JAgQJo3749KlWqhJw5c2LDhg3ImzfvDz2jzueSxNIAaJPLwMBAtGjRQorbhRBCiG+gf//+ePDgAebPnw8rKys0bdoUoaGhKFGiBG7evImjR49i0qRJ6Nmzp/QA/0xSFW4AbGxs1GrxTp06wdnZGb169dJ3WEIIIcR3rUSJEvjll19gYWGBDh06YN++fdi9ezfy58+PK1euYPv27ahSpYoklV9ASiwNyIsXL7B//37Url1bqsGFEEKIb6BChQo4dOgQsmTJgq1bt+rM/S2+nCSWBiouLg5mZlKgLIQQQqQEbX+HrVu3onfv3pgwYQJ8fX1lvu+vJCNsGyhJKoUQQoiUo00eixQpAo1Gg1OnTuksF/+NJJZCCCGE+GFlzpwZI0aMwJQpU3DixAl9h2P0JLEUQgghxA+tUqVKKFasGBwcHPQditGTNpZCCCGE+OFFR0fDyspK32EYPUkshRBCCCFEspCqcCGEEEIIkSwksRRCCCGEEMlCEkshhBBCCJEsJLEUQgghhBDJQhJLIYQQQgiRLCSxFEIIIYQQyUISSyGEMFDOzs6YOnWq+lhRFGzcuPGbxzFy5EgUKlToo+v37dsHRVHw4sWLz37NihUrIiAg4KviWrRoEdKlS/dVryGESF6SWAohhJF48OABatSo8VnbfioZFEKIlGCm7wCEEOJ7FhsbCwsLi2R5rSxZsiTL6wghREqREkshhPhMFStWRPfu3dG9e3fY2toiY8aMGDZsGBJPYObs7IzRo0ejdevWsLGxgb+/PwDg0KFDKFeuHFKlSgVHR0f07NkTUVFR6vMiIiJQp04dpEqVCi4uLli2bFmS93+/KvzevXto1qwZ0qdPjzRp0qBo0aI4fvw4Fi1ahKCgIISGhkJRFCiKgkWLFgEAXrx4gQ4dOiBTpkywsbFB5cqVERoaqvM+48ePR+bMmZE2bVq0b98e0dHRX/Q5PX36FM2aNUO2bNmQOnVqFChQACtWrEiyXVxc3L9+ljExMejXrx+yZcuGNGnSoESJEti3b98XxSKE+LYksRRCiC+wePFimJmZ4cSJE5g2bRp++eUXzJ8/X2eb//3vf/D09MSZM2cwbNgwXL9+HT4+PmjQoAHOnj2LVatW4dChQ+jevbv6nLZt2+Lu3bvYu3cv1q5di1mzZiEiIuKjcURGRqJChQoIDw/H5s2bERoaigEDBkCj0aBJkybo27cv8ufPjwcPHuDBgwdo0qQJAKBRo0aIiIjAtm3bcOrUKXh5eaFKlSp49uwZAGD16tUYOXIkxo0bh5MnTyJr1qyYNWvWF31G0dHRKFKkCP766y+cP38e/v7+aNWqFU6cOPFFn2X37t1x9OhRrFy5EmfPnkWjRo3g4+ODq1evflE8QohviEIIIT5LhQoVmDdvXmo0GnXZwIEDmTdvXvVxjhw56Ovrq/O89u3b09/fX2fZwYMHaWJiwrdv3/LKlSsEwBMnTqjrL126RACcMmWKugwAN2zYQJKcO3cu06ZNy6dPn34w1hEjRtDT0zPJe9rY2DA6OlpnuZubG+fOnUuSLFWqFLt27aqzvkSJEkleK7G9e/cSAJ8/f/7RbWrVqsW+ffuqjz/1Wd6+fZumpqYMDw/XeZ0qVapw8ODBJMmFCxfS1tb2o+8phPj2pI2lEEJ8gZIlS0JRFPVxqVKlMHnyZMTHx8PU1BQAULRoUZ3nhIaG4uzZszrV2ySh0Whw8+ZNhIWFwczMDEWKFFHXu7u7/2uP55CQEBQuXBjp06f/7NhDQ0MRGRmJDBky6Cx/+/Ytrl+/DgC4dOkSOnfurLO+VKlS2Lt372e/T3x8PMaNG4fVq1cjPDwcsbGxiImJQerUqXW2+7fP8ty5c4iPj0fu3Ll1nhMTE5MkfiGE4ZDEUgghklmaNGl0HkdGRqJTp07o2bNnkm2dnJwQFhb2xe+RKlWqL35OZGQksmbN+sF2isk5bM+kSZMwbdo0TJ06FQUKFECaNGkQEBCA2NjYL4rV1NQUp06dUhN2LWtr62SLVQiRvCSxFEKIL3D8+HGdx8eOHUOuXLmSJD+JeXl54eLFi8iZM+cH17u7uyMuLg6nTp1CsWLFAABXrlz513EhCxYsiPnz5+PZs2cfLLW0sLBAfHx8kjgePnwIMzMzODs7f/B18+bNi+PHj6N169Y6+/glDh8+jJ9++gktW7YEAGg0GoSFhSFfvnw62/3bZ1m4cGHEx8cjIiIC5cqV+6L3F0Loj3TeEUKIL3Dnzh306dMHV65cwYoVKzBjxgz06tXrX58zcOBAHDlyBN27d0dISAiuXr2KTZs2qZ138uTJAx8fH3Tq1AnHjx/HqVOn0KFDh38tlWzWrBmyZMkCX19fHD58GDdu3MC6detw9OhRAAm902/evImQkBA8efIEMTExqFq1KkqVKgVfX1/s2LEDt27dwpEjRzB06FCcPHkSANCrVy/8/vvvWLhwIcLCwjBixAhcuHDhiz6jXLlyYefOnThy5AguXbqETp064dGjR1/0WebOnRstWrRA69atsX79ety8eRMnTpzAzz//jL/++uuL4hFCfDuSWAohxBdo3bo13r59i+LFi6Nbt27o1auXOqTQxxQsWBD79+9HWFgYypUrh8KFC2P48OFwcHBQt1m4cCEcHBxQoUIF1K9fH/7+/rC3t//oa1pYWGDHjh2wt7dHzZo1UaBAAYwfP14tOW3QoAF8fHxQqVIlZMqUCStWrICiKNi6dSvKly8PPz8/5M6dG02bNsXt27eROXNmAECTJk0wbNgwDBgwAEWKFMHt27fRpUuXL/qMAgMD4eXlBW9vb1SsWFFNgL/0s1y4cCFat26Nvn37Ik+ePPD19UVwcDCcnJy+KB4hxLejkIkGDRNCCPFRFStWRKFChXSmWRRCCPEPKbEUQgghhBDJQhJLIYQQQgiRLKQqXAghhBBCJAspsRRCCCGEEMlCEkshhBBCCJEsJLEUQgghhBDJQhJLIYQQQgiRLCSxFEIIIYQQyUISSyGEEEIIkSwksRRCCCGEEMlCEkshhBBCCJEs/g/twsP+fn4r8gAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## 11. Save and load best performing model"],"metadata":{"id":"lnKUEPVnBQvz"}},{"cell_type":"code","source":["from pathlib import Path\n","\n","# Create model directory path\n","MODEL_PATH = Path(\"models\")\n","MODEL_PATH.mkdir(parents=True,\n","                 exist_ok=True)\n","\n","# Create model save\n","MODEL_NAME = \"03_pytorch_computer_vision_model_2.pth\"\n","MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n","\n","# Save the model state dict\n","print(f\"Saving model to: {MODEL_SAVE_PATH}\")\n","torch.save(obj=model_2.state_dict(),\n","           f=MODEL_SAVE_PATH)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ubtIyaXqV1Tb","executionInfo":{"status":"ok","timestamp":1711936580589,"user_tz":-330,"elapsed":8,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"991bb40c-315d-4d60-a952-241c29860fc9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Saving model to: models/03_pytorch_computer_vision_model_2.pth\n"]}]},{"cell_type":"code","source":["# Create new instance\n","torch.manual_seed(42)\n","\n","loaded_model_2 = FashionMNISTModelV2(input_shape=1,\n","                                     hidden_units=10,\n","                                     output_shape=len(class_names))\n","\n","# Load in the save state_dict()\n","loaded_model_2.load_state_dict(torch.load(f=MODEL_SAVE_PATH))\n","\n","# Send the model to the target device\n","loaded_model_2.to(device)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xbSNqUMjWV6l","executionInfo":{"status":"ok","timestamp":1711936608105,"user_tz":-330,"elapsed":643,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"668adb19-41c2-42c5-a4bd-2294c1e707ef"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["FashionMNISTModelV2(\n","  (conv_block_1): Sequential(\n","    (0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (1): ReLU()\n","    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (3): ReLU()\n","    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  )\n","  (conv_block_2): Sequential(\n","    (0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (1): ReLU()\n","    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (3): ReLU()\n","    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  )\n","  (classifier): Sequential(\n","    (0): Flatten(start_dim=1, end_dim=-1)\n","    (1): Linear(in_features=490, out_features=10, bias=True)\n","  )\n",")"]},"metadata":{},"execution_count":74}]},{"cell_type":"code","source":["# Evaluate loaded model\n","torch.manual_seed(42)\n","\n","loaded_model_2_results = eval_model(model=loaded_model_2,\n","                                    data_loader=test_dataloader,\n","                                    loss_fn=loss_fn,\n","                                    accuracy_fn=accuracy_fn,\n","                                    device=device)\n","\n","loaded_model_2_results"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":103,"referenced_widgets":["b625a3bcb868498eb784361b2f14cc62","a2ce8dc8e45441739175bd20579c220c","b86de6e7dd4b4a84acf149847c5a1086","dacb69bd6b1a46b0bf4272767adcdd0d","931cb57c837f49be818c2c20381912db","5f469d53360b447a98755d2a7176f94f","43658141124c476ca49d15a6a53beaf1","079f03530f764301963a11aaee1c5c39","0eccdb6103974a178d133aacea76f579","2a344e5570914525bb781f3cd1f0e761","3b340198098245ec870508b4dd34a685"]},"id":"3UGrzqzBXgeZ","executionInfo":{"status":"ok","timestamp":1711936726339,"user_tz":-330,"elapsed":1683,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"336a4401-7fc3-46e2-898f-42c8add1cab2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/313 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b625a3bcb868498eb784361b2f14cc62"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'model_name': 'FashionMNISTModelV2',\n"," 'model_acc': 88.46845047923323,\n"," 'model_loss': 0.32050246000289917}"]},"metadata":{},"execution_count":75}]},{"cell_type":"code","source":["model_2_results"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6Eff1i2cYVLK","executionInfo":{"status":"ok","timestamp":1711936742473,"user_tz":-330,"elapsed":4,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"92a025a1-9d2f-4d6e-9c85-043ea8b5437c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'model_name': 'FashionMNISTModelV2',\n"," 'model_acc': 88.46845047923323,\n"," 'model_loss': 0.32050246000289917}"]},"metadata":{},"execution_count":76}]},{"cell_type":"code","source":["# Check if model results are close to each other\n","torch.isclose(torch.tensor(model_2_results[\"model_loss\"]),\n","              torch.tensor(loaded_model_2_results[\"model_loss\"]),\n","              atol=1e-03) # this is to set the tolerance level"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Rv9jRxPUYZil","executionInfo":{"status":"ok","timestamp":1711936973057,"user_tz":-330,"elapsed":413,"user":{"displayName":"Yash Jain","userId":"07916721077419186041"}},"outputId":"5bd9d548-c53b-43cf-801a-7b6db299eb44"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor(True)"]},"metadata":{},"execution_count":79}]},{"cell_type":"code","source":[],"metadata":{"id":"ZRdTxV7_Y6kU"},"execution_count":null,"outputs":[]}]}