AE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "import torchvision.datasets as Datasets\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn.functional as F\n",
    "import torchvision.utils as vutils\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "\n",
    "# Define learning rate\n",
    "lr = 1e-4\n",
    "\n",
    "# Number of Training epochs\n",
    "nepoch = 10\n",
    "\n",
    "# Dataset location\n",
    "root = \"../data\"\n",
    "\n",
    "# Scale for the added image noise\n",
    "noise_scale = 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mps'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = (\n",
    "    \"cuda\"\n",
    "    if torch.cuda.is_available()\n",
    "    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
    ")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define our transform\n",
    "# We'll upsample the images to 32x32 as it's easier to contruct our network\n",
    "transform = transforms.Compose(\n",
    "    [transforms.Resize(32), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]\n",
    ")\n",
    "\n",
    "train_set = Datasets.MNIST(root=root, train=True, transform=transform, download=True)\n",
    "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=4)\n",
    "\n",
    "test_set = Datasets.MNIST(root=root, train=False, transform=transform, download=True)\n",
    "test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        channels,  # Number of channels in the input image\n",
    "        ch: int = 32,  # Number of channels in the first convolutional layer\n",
    "        z: int = 32,  # Dimension of the latent space\n",
    "    ):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(channels, ch, 3, stride=2, padding=1)\n",
    "        self.bn1 = nn.BatchNorm2d(ch)\n",
    "        self.conv2 = nn.Conv2d(ch, ch * 2, 3, stride=2, padding=1)\n",
    "        self.bn2 = nn.BatchNorm2d(ch * 2)\n",
    "        self.conv3 = nn.Conv2d(ch * 2, ch * 4, 3, stride=2, padding=1)\n",
    "        self.bn3 = nn.BatchNorm2d(ch * 4)\n",
    "\n",
    "        self.conv_out = nn.Conv2d(ch * 4, z, 4, stride=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.bn1(self.conv1(x)))\n",
    "        x = F.relu(self.bn2(self.conv2(x)))\n",
    "        x = F.relu(self.bn3(self.conv3(x)))\n",
    "        return self.conv_out(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Decoder(nn.Module):\n",
    "    def __init__(self, channels, ch: int = 32, z: int = 32):\n",
    "        super(Decoder, self).__init__()\n",
    "\n",
    "        self.conv1 = nn.ConvTranspose2d(z, ch * 4, 4, stride=1)\n",
    "        self.bn1 = nn.BatchNorm2d(ch * 4)\n",
    "        self.conv2 = nn.ConvTranspose2d(\n",
    "            ch * 4, ch * 2, 3, stride=2, padding=1, output_padding=1\n",
    "        )\n",
    "        self.bn2 = nn.BatchNorm2d(ch * 2)\n",
    "        self.conv3 = nn.ConvTranspose2d(\n",
    "            ch * 2, ch, 3, stride=2, padding=1, output_padding=1\n",
    "        )\n",
    "        self.bn3 = nn.BatchNorm2d(ch)\n",
    "        self.conv4 = nn.ConvTranspose2d(\n",
    "            ch, ch, 3, stride=2, padding=1, output_padding=1\n",
    "        )\n",
    "        self.bn4 = nn.BatchNorm2d(ch)\n",
    "\n",
    "        self.conv_out = nn.Conv2d(\n",
    "            ch, channels, 3, stride=1, padding=1\n",
    "        )  # padding=1 to remove the black border in the output\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.bn1(self.conv1(x)))\n",
    "        x = F.relu(self.bn2(self.conv2(x)))\n",
    "        x = F.relu(self.bn3(self.conv3(x)))\n",
    "        x = F.relu(self.bn4(self.conv4(x)))\n",
    "        return torch.tanh(self.conv_out(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AE(nn.Module):\n",
    "    def __init__(self, channel_in, ch: int = 16, z: int = 32):\n",
    "        super(AE, self).__init__()\n",
    "        self.encoder = Encoder(channel_in, ch, z)\n",
    "        self.decoder = Decoder(channel_in, ch, z)\n",
    "\n",
    "    def forward(self, x):\n",
    "        encoded = self.encoder(x)\n",
    "        x = self.decoder(encoded)\n",
    "        return x, encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 1, 32, 32])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a test image\n",
    "test_images, _ = next(iter(test_loader))\n",
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.axis(\"off\")\n",
    "out = vutils.make_grid(test_images[0:8], normalize=True)\n",
    "_ = plt.imshow(out.numpy().transpose((1, 2, 0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### De-noising Autoencoder for MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "random_sample = (\n",
    "    torch.bernoulli((1 - noise_scale) * torch.ones_like(test_images)) * 2\n",
    ") - 1\n",
    "noisy_test_img = random_sample * test_images\n",
    "\n",
    "out = vutils.make_grid(noisy_test_img[0:8], normalize=True)\n",
    "_ = plt.imshow(out.numpy().transpose((1, 2, 0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Network and Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_size = 128  # Size of the latent space\n",
    "\n",
    "# Create the model\n",
    "ae_net = AE(channel_in=1, z=latent_size).to(device)\n",
    "\n",
    "# Define the optimizer\n",
    "optimizer = optim.Adam(ae_net.parameters(), lr=lr)\n",
    "\n",
    "# Define the loss function for reconstruction\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "loss_log = []\n",
    "train_loss = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 128, 1, 1])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Network output\n",
    "recon_data, encoded = ae_net(test_images.to(device))\n",
    "\n",
    "# View the shape of the Latent space -> [batch_size, latent_size, 1, 1]\n",
    "encoded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Training: 100%|██████████| 938/938 [00:19<00:00, 48.76it/s]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Training: 100%|██████████| 938/938 [00:17<00:00, 54.35it/s]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Training: 100%|██████████| 938/938 [00:16<00:00, 55.83it/s]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Training: 100%|██████████| 938/938 [00:16<00:00, 55.94it/s]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Training: 100%|██████████| 938/938 [00:16<00:00, 55.91it/s]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Training: 100%|██████████| 938/938 [00:16<00:00, 55.62it/s]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Training: 100%|██████████| 938/938 [00:16<00:00, 56.14it/s]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Training: 100%|██████████| 938/938 [00:16<00:00, 56.50it/s]\n",
      "\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
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      "Training: 100%|██████████| 938/938 [00:16<00:00, 56.45it/s]\n",
      "\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
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      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Training: 100%|██████████| 938/938 [00:16<00:00, 56.06it/s]\n",
      "Epochs: 100%|██████████| 10/10 [02:50<00:00, 17.04s/it]\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "for epoch in tqdm(range(nepoch), desc=\"Epochs\"):\n",
    "    train_loss = 0\n",
    "    for i, data in enumerate(tqdm(train_loader, desc=\"Training\")):\n",
    "        image = data[0].to(device)\n",
    "\n",
    "        random_sample = (\n",
    "            torch.bernoulli((1 - noise_scale) * torch.ones_like(image)) * 2\n",
    "        ) - 1\n",
    "        noisy_image = random_sample * image\n",
    "\n",
    "        recon_data, _ = ae_net(noisy_image)\n",
    "        loss = criterion(recon_data, image)\n",
    "\n",
    "        loss_log.append(loss.item())\n",
    "        train_loss\n",
    "\n",
    "        ae_net.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'MSE Loss')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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391d8fLzS09NP2Hfu3Lmy2Wxui7+/fz1We2KuOTeEGwAALGN5uFm4cKGSk5M1ZcoUrVmzRn379lVSUpJyc3NPuE1ISIj279/vWvbs2VOPFZ8Yd0sBAGA9y8PNCy+8oLvvvltjx45Vz549NWvWLAUGBmrOnDkn3MZmsykyMtK1RERE1GPFJ+Z6/QJ3SwEAYBlLw01FRYUyMjKUmJjoarPb7UpMTFRaWtoJtyspKVGHDh0UHR2tG264QRs3bjxh3/LychUVFbktdcXrlwnFDgfhBgAAq1gabvLy8uRwOGqMvERERCg7O7vWbbp166Y5c+bo448/1r/+9S85nU5ddNFF+vnnn2vtn5KSotDQUNcSHR1t+ueoxsgNAADWs/yy1JlKSEjQ6NGjFRsbqyFDhuiDDz5Qq1at9M9//rPW/pMmTVJhYaFrycrKqrPavHn9AgAAlvO28uDh4eHy8vJSTk6OW3tOTo4iIyNPax8+Pj7q16+ftm/fXut6Pz8/+fn5nXOtp8NOuAEAwHKWjtz4+voqLi5Oqamprjan06nU1FQlJCSc1j4cDofWr1+vNm3a1FWZp42RGwAArGfpyI0kJScna8yYMerfv78GDBig6dOnq7S0VGPHjpUkjR49WlFRUUpJSZEkPf744xo4cKC6dOmigoICPffcc9qzZ4/uuusuKz+GJEZuAABoCCwPNyNGjNCBAwc0efJkZWdnKzY2VkuWLHFNMs7MzJTdfmyA6dChQ7r77ruVnZ2t5s2bKy4uTt9//7169uxp1UdwYeQGAADr2Qyjad3aU1RUpNDQUBUWFiokJMTUfb/+7U49+ekm3RDbVi/+rp+p+wYAoCk7k+/vRne3VEPGyA0AANYj3JjIi3ADAIDlCDcm8vplbhDhBgAA6xBuTOT1y9kk3AAAYB3CjYlcIzdNa442AAANCuHGRIzcAABgPcKNiZhzAwCA9Qg3Jqp+K3gV4QYAAMsQbkxUfSu4k3ADAIBlCDcmqg43jNwAAGAdwo2Jqp9Q7ORuKQAALEO4MVH1W8GrHIQbAACsQrgxESM3AABYj3BjIjt3SwEAYDnCjYm8vbhbCgAAqxFuTMTIDQAA1iPcmKh6zg1PKAYAwDqEGxN5EW4AALAc4cZErnDD3VIAAFiGcGMiRm4AALAe4cZEhBsAAKxHuDFR9VvBCTcAAFiHcGMiRm4AALAe4cZEhBsAAKxHuDGRN3dLAQBgOcKNiezHjdwYBBwAACxBuDFR9ciNJHFlCgAAaxBuTGQ/LtxUOZ0WVgIAQNNFuDGR28gN2QYAAEsQbkxU/VZwiZEbAACsQrgxESM3AABYj3BjIi/m3AAAYDnCjYlsNpuq8w3PugEAwBqEG5PxlGIAAKxFuDEZ4QYAAGsRbkzGm8EBALAW4cZkjNwAAGAtwo3JCDcAAFiLcGMyL/vRU8rdUgAAWINwYzKvX85olYNwAwCAFQg3JvP+ZeTGycgNAACWaBDhZubMmYqJiZG/v7/i4+OVnp5+WtstWLBANptNw4cPr9sCz4C9euSGOTcAAFjC8nCzcOFCJScna8qUKVqzZo369u2rpKQk5ebmnnS73bt368EHH9Qll1xST5WeHtfIDeEGAABLWB5uXnjhBd19990aO3asevbsqVmzZikwMFBz5sw54TYOh0O33XabHnvsMXXq1Kkeqz216tcvMHIDAIA1LA03FRUVysjIUGJioqvNbrcrMTFRaWlpJ9zu8ccfV+vWrXXnnXee8hjl5eUqKipyW+oSIzcAAFjL0nCTl5cnh8OhiIgIt/aIiAhlZ2fXus2KFSv0xhtvaPbs2ad1jJSUFIWGhrqW6Ojoc677ZOy/DN0wcgMAgDUsvyx1JoqLizVq1CjNnj1b4eHhp7XNpEmTVFhY6FqysrLqtEbv6of4cbcUAACW8Lby4OHh4fLy8lJOTo5be05OjiIjI2v037Fjh3bv3q3rrrvO1eZ0OiVJ3t7e2rJlizp37uy2jZ+fn/z8/Oqg+tpVj9w4eM4NAACWsHTkxtfXV3FxcUpNTXW1OZ1OpaamKiEhoUb/7t27a/369Vq7dq1ruf7663XppZdq7dq1dX7J6XQwcgMAgLUsHbmRpOTkZI0ZM0b9+/fXgAEDNH36dJWWlmrs2LGSpNGjRysqKkopKSny9/dXr1693LYPCwuTpBrtVuGt4AAAWMvycDNixAgdOHBAkydPVnZ2tmJjY7VkyRLXJOPMzEzZ7Y1nahAvzgQAwFo2w2ha10+KiooUGhqqwsJChYSEmL7/219fqRXb8zR9RKyG94syff8AADRFZ/L93XiGRBoJOyM3AABYinBjMm/CDQAAliLcmMxu424pAACsRLgxmTdPKAYAwFKEG5NV3y3Fu6UAALAG4cZkXozcAABgKcKNyRi5AQDAWoQbkzFyAwCAtQg3Jqt+/YKTu6UAALAE4cZkhYcrJUm780otrgQAgKaJcGOyJRuzJUnvZfxscSUAADRNhBsAAOBRzircZGVl6eefj41MpKena8KECXrttddMKwwAAOBsnFW4ufXWW7Vs2TJJUnZ2tq644gqlp6frkUce0eOPP25qgQAAAGfirMLNhg0bNGDAAEnSv//9b/Xq1Uvff/+95s2bp7lz55pZX6Nzz5DOVpcAAECTdlbhprKyUn5+fpKkL7/8Utdff70kqXv37tq/f7951TVCX2/JtboEAACatLMKN+eff75mzZqlb7/9VkuXLtVVV10lSdq3b59atmxpaoGNzd6Cw1aXAABAk3ZW4eaZZ57RP//5Tw0dOlQjR45U3759JUmffPKJ63JVU1X9hGIAAGAN77PZaOjQocrLy1NRUZGaN2/uav/DH/6gwMBA04prjKqfUAwAAKxxViM3hw8fVnl5uSvY7NmzR9OnT9eWLVvUunVrUwtsbBi5AQDAWmcVbm644Qa9/fbbkqSCggLFx8fr+eef1/Dhw/Xqq6+aWmBj0zsq1OoSAABo0s4q3KxZs0aXXHKJJOn9999XRESE9uzZo7ffflsvvfSSqQU2Nvdf1sXqEgAAaNLOKtyUlZUpODhYkvTFF1/opptukt1u18CBA7Vnzx5TC2xsgvyOTmNq0czX4koAAGiazircdOnSRR999JGysrL0+eef68orr5Qk5ebmKiQkxNQCGxv7L3NuqhxOiysBAKBpOqtwM3nyZD344IOKiYnRgAEDlJCQIOnoKE6/fv1MLbCxqb5bquhIlQzDsLgaAACanrO6FfyWW27RxRdfrP3797uecSNJl19+uW688UbTimuMyiocrp9zisoVGepvYTUAADQ9ZxVuJCkyMlKRkZGut4O3a9euyT/A79fsZzUuBgAAzsVZff06nU49/vjjCg0NVYcOHdShQweFhYXpiSeekNPZtOea9GgT7Po5K7/MwkoAAGiazmrk5pFHHtEbb7yhadOmadCgQZKkFStWaOrUqTpy5IieeuopU4tsTGzHPaH47bQ9iuvQwsJqAABoes4q3Lz11lt6/fXXXW8Dl6Q+ffooKipK9913X5MON8dzMp8YAIB6d1aXpfLz89W9e/ca7d27d1d+fv45F+UpnNwtBQBAvTurcNO3b1/NmDGjRvuMGTPUp0+fcy7KUxQdrrS6BAAAmpyzuiz17LPPatiwYfryyy9dz7hJS0tTVlaWFi9ebGqBjdm32/KsLgEAgCbnrEZuhgwZoq1bt+rGG29UQUGBCgoKdNNNN2njxo165513zK4RAADgtNkMEx+ju27dOl1wwQVyOByn7myRoqIihYaGqrCwsM5eFREz8VPXz7unDauTYwAA0JScyfc3j5kDAAAehXADAAA8CuEGAAB4lDO6W+qmm2466fqCgoJzqQUAAOCcnVG4CQ0NPeX60aNHn1NBniTE/6zfSwoAAM7SGX37vvnmm3VSxMyZM/Xcc88pOztbffv21csvv3zCN4x/8MEHevrpp7V9+3ZVVlbqvPPO05///GeNGjWqTmo7G3+5qpueXbJFV54faXUpAAA0OZbPuVm4cKGSk5M1ZcoUrVmzRn379lVSUpJyc3Nr7d+iRQs98sgjSktL0//+9z+NHTtWY8eO1eeff17PlZ9Y9c3172f8bG0hAAA0QaY+5+ZsxMfH68ILL3S9zsHpdCo6OloPPPCAJk6ceFr7uOCCCzRs2DA98cQTp+xb38+5+eahS9W+ZWCdHAcAgKai0TznpqKiQhkZGUpMTHS12e12JSYmKi0t7ZTbG4ah1NRUbdmyRYMHD67LUs/azwVlVpcAAECTYumM17y8PDkcDkVERLi1R0REaPPmzSfcrrCwUFFRUSovL5eXl5deeeUVXXHFFbX2LS8vV3l5uev3oqIic4o/TXabrV6PBwBAU9cob+cJDg7W2rVrVVJSotTUVCUnJ6tTp04aOnRojb4pKSl67LHH6r/IXxBtAACoX5aGm/DwcHl5eSknJ8etPScnR5GRJ77TyG63q0uXLpKk2NhYbdq0SSkpKbWGm0mTJik5Odn1e1FRkaKjo835AKfBbifeAABQnyydc+Pr66u4uDilpqa62pxOp1JTU5WQkHDa+3E6nW6Xno7n5+enkJAQt6WuxXds4frZ2unaAAA0PZZflkpOTtaYMWPUv39/DRgwQNOnT1dpaanGjh0rSRo9erSioqKUkpIi6ehlpv79+6tz584qLy/X4sWL9c477+jVV1+18mO4OX6azTs/7NGA48IOAACoW5aHmxEjRujAgQOaPHmysrOzFRsbqyVLlrgmGWdmZspuPzbAVFpaqvvuu08///yzAgIC1L17d/3rX//SiBEjrPoINQzt1lo/7MyXJH2+MdviagAAaFosf85NfauP59xs2l+kq1/8VpLk72PX5ieurpPjAADQVDSa59x4qh5tjp30I5VOCysBAKDpIdzUA6ezSQ2OAQBgKcJNPfho7V6rSwAAoMkg3NSD5H+vs7oEAACaDMINAADwKIQbAADgUQg3AADAoxBuAACARyHcAAAAj0K4qSM3X9DO7ffyKodFlQAA0LQQbupIgK/7qZ39zU6LKgEAoGkh3NSRsYM6uv2+NqvAmkIAAGhiCDd1pHOrILffdx4otagSAACaFsJNPdmZV6qs/DKrywAAwOMRburR7W+stLoEAAA8HuGmHu05yMgNAAB1jXADAAA8CuGmDt11cccabf9YutWCSgAAaDoIN3Xo/KiQGm0vpm6zoBIAAJoOwk0dsttsVpcAAECTQ7ipQzbCDQAA9Y5wU4cGdmpRa3tBWUU9VwIAQNNBuKlDrYP95etV8xS/m55lQTUAADQNhJs6NvHq7jXanlmyWcu3HrCgGgAAPB/hpo51jwyutX3MnPR6rgQAgKaBcFPHOrcOOnUnAABgGsJNHeOGKQAA6hfhpo7ZRLoBAKA+EW4AAIBHIdzUMUPGCdet2JZXj5UAANA0EG7q2MlewXD7GyvrsRIAAJoGwk0dCw/yU882NV+gCQAA6gbhph68d0+Cgv28rS4DAIAmgXBTD5r5eSvj0StqXVflcNZzNQAAeDbCTT3x9a79VE//cls9VwIAgGcj3FhsxrLtVpcAAIBHIdzUo/iOLawuAQAAj0e4qUf/HBVndQkAAHg8wk09Cgv0rbV9x4GSeq4EAADPRbipZ53Cm9Vou/z55RZUAgCAZyLc1LN3/zDQ6hIAAPBoDSLczJw5UzExMfL391d8fLzS09NP2Hf27Nm65JJL1Lx5czVv3lyJiYkn7d/QRIT4a+p1Pa0uAwAAj2V5uFm4cKGSk5M1ZcoUrVmzRn379lVSUpJyc3Nr7f/1119r5MiRWrZsmdLS0hQdHa0rr7xSe/furefKAQBAQ2R5uHnhhRd09913a+zYserZs6dmzZqlwMBAzZkzp9b+8+bN03333afY2Fh1795dr7/+upxOp1JTU+u5cgAA0BBZGm4qKiqUkZGhxMREV5vdbldiYqLS0tJOax9lZWWqrKxUixaN5xkytlreFJ5bdMSCSgAA8DyWhpu8vDw5HA5FRES4tUdERCg7O/u09vHXv/5Vbdu2dQtIxysvL1dRUZHb0hANeJqRJwAAzGD5ZalzMW3aNC1YsEAffvih/P39a+2TkpKi0NBQ1xIdHV3PVdZUy8ANAAAwiaXhJjw8XF5eXsrJyXFrz8nJUWRk5Em3/fvf/65p06bpiy++UJ8+fU7Yb9KkSSosLHQtWVlZptR+LuI7trS6BAAAPJal4cbX11dxcXFuk4GrJwcnJCSccLtnn31WTzzxhJYsWaL+/fuf9Bh+fn4KCQlxW6zWLTJYSyZcYnUZAAB4JG+rC0hOTtaYMWPUv39/DRgwQNOnT1dpaanGjh0rSRo9erSioqKUkpIiSXrmmWc0efJkzZ8/XzExMa65OUFBQQoKCrLsc5yp7pHWhywAADyR5eFmxIgROnDggCZPnqzs7GzFxsZqyZIlrknGmZmZstuPDTC9+uqrqqio0C233OK2nylTpmjq1Kn1WToAAGiAbIZhGFYXUZ+KiooUGhqqwsJCyy9R9Xv8Cx0qq3T9vnvaMAurAQCg4TqT7+9GfbdUY/fr90xtzSm2qBIAADwH4cZCv553k/zvtdYUAgCAByHcNCAb9hapiV0lBADAdIQbi/36gX7Tv9xmTSEAAHgIwo3F1k9Ncvv9xVTCDQAA54JwY7EgP8vvxgcAwKMQbgAAgEch3AAAAI9CuGmAnE7umAIA4GwRbhqAe4d2dvv9xle/t6gSAAAaP8JNA3DPYPdwsy6rwJpCAADwAISbBiDA18vqEgAA8BiEmwbA17vmn+HbbQcsqAQAgMaPcNNAjXoj3eoSAABolAg3DUT6I5fXaOOuKQAAzhzhpoFoHexfo+2Rj9ZbUAkAAI0b4aYBezc9y+oSAABodAg3AADAoxBuGjjDYN4NAABngnDTwM3+dqfVJQAA0KgQbhqQFs18a7Q9vXizBZUAANB4EW4akMnX9rS6BAAAGj3CTQNyQ2xb/fuPCVaXAQBAo0a4aUBsNpsGdGxhdRkAADRqhJtGoKCswuoSAABoNAg3DVBUWIDb7+MXrLWmEAAAGiHCTQPk5+P+Z1m+9YB2HiixqBoAABoXwk0D1KNNSI22y55frre+313/xQAA0MgQbhqgJ2/oVWv7lE821nMlAAA0PoSbBqh5M1+tn3ql1WUAANAoEW4aqGB/n1rby6sc9VwJAACNC+GmkSk6XGV1CQAANGiEmwZseGzbGm0XPvWlNuwttKAaAAAaB8JNA/bcb/rW2j56Tno9VwIAQONBuGnAfLzs8vOu+SfKL63Qxn2M3gAAUBvCTQOXNunyWtuHvbRChYcr67kaAAAaPsJNAxfg43XCdffPX1OPlQAA0DgQbhq4AN8Th5tvt+XJMIx6rAYAgIaPcNMIvDGm/wnXLd96oB4rAQCg4SPcNAKXdW+tG/tF1bru92+uUtI/vtG6rIL6LQoAgAaKcNMI2Gw2/WNErNIfqX1y8ZacYt0w8zuVlvOAPwAALA83M2fOVExMjPz9/RUfH6/09BM/w2Xjxo26+eabFRMTI5vNpunTp9dfoQ1A62D/k67flltST5UAANBwWRpuFi5cqOTkZE2ZMkVr1qxR3759lZSUpNzc3Fr7l5WVqVOnTpo2bZoiIyPrudqGL6+4XOVVDm3cV8hEYwBAk2UzLPwWjI+P14UXXqgZM2ZIkpxOp6Kjo/XAAw9o4sSJJ902JiZGEyZM0IQJE87omEVFRQoNDVVhYaFCQkLOtnTLxEz89LT6PTG8l0YN7FDH1QAAUD/O5PvbspGbiooKZWRkKDEx8VgxdrsSExOVlpZm2nHKy8tVVFTktjQFs77eYXUJAABYwrJwk5eXJ4fDoYiICLf2iIgIZWdnm3aclJQUhYaGupbo6GjT9t2Q7S04rE//t9/qMgAAqHeWTyiua5MmTVJhYaFrycrKsrqkczL/7nhNvrandqVcc8q+4457gvHGfYV65evtKq9y1GV5AABYzrJwEx4eLi8vL+Xk5Li15+TkmDpZ2M/PTyEhIW5LY3ZR53DdcXFH2Ww2pU267JT9H3pvnQrLKjXspRV6dskWvf7trnqoEgAA61gWbnx9fRUXF6fU1FRXm9PpVGpqqhISEqwqq1FpExpw0ndPSdJ7GT+r7+NfuH7/MfNQXZcFAIClvK08eHJyssaMGaP+/ftrwIABmj59ukpLSzV27FhJ0ujRoxUVFaWUlBRJRych//TTT66f9+7dq7Vr1yooKEhdunSx7HNYqZmflw5Xnv6lpi835cowDJVXOeV/imAEAEBjZOmt4JI0Y8YMPffcc8rOzlZsbKxeeuklxcfHS5KGDh2qmJgYzZ07V5K0e/dudezYscY+hgwZoq+//vq0jtfYbwX/tYw9h3Tzq9+f1bbz74rXRV3CTa4IAADzncn3t+Xhpr55WriRpCOVDn2+MVvjF6w9423TJl2mNqEBkqQqh1PeXh4/xxwA0Ag1iufcwDz+Pl66vm/bs9p22mebVVBWoUc+XK++j32h/YWHVVhWaXKFAADUH0ZuPEhpeZVufvV7bc4uPud9LZlwibpHetb5AQA0XozcNFHN/Ly1ZMJgU/b17spMU/YDAEB9I9x4oPfuSdAj1/Q4refgnMhbaXv00HvreOgfAKDRIdx4oAtjWujuwZ3UJjRAA2JanPV+3sv4WZf9fXmt65rY1UwAQCNCuPFwC/84UNed5WRj6eg7qsqrHJq1fIcmf7xBhYcr9c3WA+r3xFJ9vtG8d4ABAGAWJhQ3ETETP62T/XZq1Uwf3jtIoYE+dbJ/AAAkJhTjJIL9zH0o9c4DpZr8yQZT9wkAwLmw9PULqD8Tr+6u1bsPadbtF0iSnIbU9W+fmbLvj9fuU/NAX90S1069okJN2ScAAGeLy1JN2Dtpu/XoxxtN3Wf3yGB9eN8g+XjZtH5voaZ8slHTbuqjnm2b9rkGAJwbXr9wEoQbd4Zh6I/vZOiLn3Lq9Dg7n75GdrutTo8BAPBczLnBabPZbHr19jjFdWiua/u0UWSIf50cJ+WzTfpqc46+OO4Oq4KyCi1Iz9TegsN1ckwAQNPEyA3c/O2j9frXD0efThzs5y1DUkl5lanHeOrGXrp1QHv9ZlaaVu85JElKf+RytQ6um2AFAGj8GLnBWbu2z7Fn4qx/LEkbHkvSt3+51NRjPPLhBnWctNgVbCQpbcfBWvtuyS5mZAcAcEYIN3Dj513zfxLRLQK15cmrtCvlmjo77vgFaxUz8VPlFB1RQVmFHnpvnWZ8tU1J07/RoGlfqehIpYqP8LZyAMCpcVkKbgzD0PgFa9W+RaAeTOpWY/33O/J0sKRCQ7q10tOfbtKCVVn1Wt+mx69SgK+XJv7nf8o6VKZ37ohnojIANAHcLXUShBtzFR+pVJCftz5eu08TFq6t9+N/cv8g9WkX5vo9t/iIWgX5yWY7GniqHE55ezFACQCNHXNuUG+C/X1ks9l0Q2xb/ff+i9W3Xf0+xO/6Gd/pq805ej/jZ419M10DnkrV37/YIsMw9PCH69X3sS+0PbdEkuR0Hsvxx/8MAPAsjNzAVJUOp7ILj6hVsJ+e/PQn2W02lZRX6YM1e+u1jvNaB2nbL6HmqvMj1SLIV/NXZurjcYP05ne79NHafZKkB6/sqhXb8zR9RD9Fhp7+3Vrfb89ToJ+3YqPD6qJ8AMCvcFnqJAg31pny8Qa9lbZH4UG+yiupsLocN5d1b61Hr+2pvYcOKyY8UPf8K0O/v6ijbolr59bvi43Zum/eGlX9MvKze9owSVJhWSUvDwWAOkS4OQnCjbX2FhxWZIi/3k7brWeXbNH79ybIMKRrX15hdWm1urZPG/2wM18XdW6pT9btq7H+wSu76qvNuVqTWaDnbumj3/SPdq3LKylXi0BfZR0qU3TzQCY+A8A5INycBOGmYYqZ+KnVJZhi5ID26t+huT5et0/fbD3gar8tvr2eurG3JGl3Xqma+Xkr2N9b//phjzL2HNLKXfn64k+DFR7kd841TPtss2w26a9XdT/nfQFAQ0G4OQnCTcN0++srtWJ7nt6/J0Gbs4t1Va9ItQj01ZacYnVq1Uz/XbdfD763zuoyz8lDSd3kbbcp5bPNkqToFgHKyj/2gEJfb7teGxWnod1an/UxXvl6u55dskWS9N3EyxQVFnBuRQNAA0G4OQnCTcPkdBoqPFyp5s18T9rv47V7NX7BWknSp/93sYa91DAvZ5khMsRf2UVH1DsqVO/dk6ApH29UUq8IXdY9Qhl7Dmnhqkz9+cpuKq90qn3LQB2pdKj7o0tc2z+U1E3jLu0iSXrtmx1q36KZLoxprgqHU21Czzz0FB2pVDNfb3md4PKa02lox4ESdW4VxCU4AKYj3JwE4aZxO1hSrrgnv1TH8GZa9uBQSUcfPGiz2Tzm0tapNPP1UmmF47T6xnVorvsv66Kxb65ya3/rjgEa1LmlvL3sKimvUnbhYRUfqVJsdJjrGUGSVF7l0JwVu9UxvJnu+VeG2jUP0Iq/XiZJWr07XzabFNehhSTpuc83a+ayHbrr4o7627U9Tfq0AHAU4eYkCDeNX+HhSgX4eMn3V6+KyMovU9ahMvVtF6Zmft7anlusJRuyFRkaoGWbc/Xp+v2uvsH+3goL9HG7LAR31/dtW+sk6s6tmmnHgVLX72/fMUAzlm1X+q58V9vOp6/R9C+36nClQ48M6ynDMFTpMGr8zX7N4TS0LbdYXVsH1zr6M/ubnVqxPU+vjY6Tn7eXpKMjRrnF5Wd0Kz+AxodwcxKEm6Zrc3aRVu0+pMQerRUZ4i+bzaatOcX6v3d/1OCurVRe6VB20RF9vjHHtc3ro/vrrrdXW1h147fir5dq2mebteh/+/W3YT30Y1aBLukSrhEXRitjzyHNX5mph4f1UHmVU69/u1NvfrdbknTnxR318DU9lFdSrqcXb9LtAzvoN7PSJElPDO+ldVkF6h0VqnVZBfrgx6PPUVo7+QqFBdZ+afNIpUMHSyuYhwQ0UoSbkyDc4FReTt2mHQdK9MJvY2W322QYhr7fcVB/+2iDhvVuo1bBfnopdZvuuqSTnlmy2epy8StPDO+lS7qEa9fBUm3NLtb1sW1VeLhSV03/VpI06/Y4XdSlpUL8fZRbdERLN+VoeGyUAn299M9vdqpX21AldG6pT9fv1/SlW/Xq7XHqFhnsdowqh1MOw1DxkSrtPFCqnm1DtPfQYQX6eim6RaB+2lekN1bsUvKVXRt0mDIMQ2uzCtQ9MkQBvl5WlwOcFOHmJAg3MFP1fz5FR6q0cW+hDpSU64qeEQr09Xb1cToNdXp4sSQpPMhPeSXlltSKkxvStZWWH3f7frWebUL03G/6aMPeQr2Uul17C05+KfP8tiHauK9IkhQbHaYP77tIO/NK9cPOg9qWU6IHLuuilsfd8l9R5dQzSzYrr6Rc1/Zpq8u7t9bybQcU4OOlgZ1auvqVVzn0z+U7lXR+pIL9vfX6t7s0ckC0Vu7K16XdW59WiErbcVAdw5u5LuHNX5mphz9crwEdW+jff0yQ02m4XQ5M+WyTlm7M0Uf3D1KIPw+phLUINydBuIEVdueVqqzCoZ5tQzRz2XY99/kWXdEzQq+NipPNZnO9gLTjpMWubeI6NFfXiCC9m16/b15H3brq/Eit2J6nkvKqU/b927AemrFsuwrKKk/Z945BHVXhcOiDNXt1Te82im4eqDsujlGwv48Mw9APO/M1cvYPrv5/HNxJ32zL06b9R4PYJ/cP0vUzvpMkfZk8RD5eNg157mtJ0qSru+uPQzrL4TTk9ctops1mO+GLafcWHFbLZr7y92E0COYh3JwE4QYNQfGRSgXX8v+Et2QXa9XufN0S187ti6Gsokp2m02PL/pJA2JaqKzCoRv7RSnA18s1WfeGmd8pMsRP913aRUs2ZOu77XkK8vPW3DsGaEduiW6Y+V2ttbRs5quDpQ3rdRhoXD4eN0ivfL1d327L0/B+UZq/MlNhgT76YsJgfb31gLq0DlLh4Uq1CPTVzrwSlRyp0ubsYrUNC9BXm3OVseeQhse21Qu/jdWe/DLFtAx03bWXlV+mRz7aoDEJHXR5jwhJ0v7Cwwrx99HOA6XqFRXidoffyWTsOSQfL5v6tAs77c9WeLhSWfll6hV17KXA1f/9Hiwp15acYiV0annaNeDsEW5OgnCDps7hNFRR5dS8lXs0uGsrndc6SBv3FWnZ5lyl785X+xaBmrcyU8P6tFHPNiEa2KmF3k7bowmJXfXEop/01eZcqz8CmqhLzgvXt9vyarT3bBOitmEBkgx9uSlXYYE+Kiir1P9dfp6u6BGh8GBfZRce0Y2vfC9Jem3U0XlUW3NKdPdxNwwM691Gvt529W0Xql15pXroqu7qNeVzSdKt8e318DU9dPvrK7U2q0AjB7TXu+mZkqSXR/bTFT0j5OdtV3mVUxv2Fio0wEfnRQRrXVaBtuQUK8TfRwM7tdDXWw5ocNdWatHMV7vzStW8ma98vezy8bLpcKVDu/PKVHi4Um3D/BXo662IED9XcCotr9LhSsdpPcm8epStNgVlFTpQXK7zIoJrXX+8t9N2KyoswBUsrUS4OQnCDXBqFVXOk962veNAicICfFRwuFKdWwW52h96b53ySsr1+A295ONl1zfbDqhrRLDWZh7S1P/+JEn6x4i+Gta7rT5eu1cDO7WUv4+XFqRn6uLzwjX7251avD67xu3mknTXxR31+opdbm3+PnYdqXSa+MkB81zRM0JLf8o5dcfTEOjrpbJfnm8VGx2mtVkFuuvijurYqpk6hjfT6t2HtGxLrm6+oJ3CAn10//wf1b9Dc/3piq7atL9Is7/dqYMlFTq/bYjW/VwoSfriT4PV9biAs+dgqf6T8bNuT+igkiNVuuz55a5100fE6r2MLG3PLdFdF3fSnvxS3TOks4oOH7282irYTyXlVQoL8Dnlw1jPFuHmJAg3gDVW785X62B/tW8ZeMI+hmGopLzKdcnu1xNcqxxOfbkpR72iQtWueaCrT9ahMrVvEaj0XfkKDfRRfmmFysodKi6v1PDYKNlsNu3KK1WAj5eOVDr0Uuo2OQxDDyV107bckhoPOTyVpPMj3B4ZAMDdX6/qrnuHdjZ1n4SbkyDcADiR6omyx3M6DdlsUmmFQ37edjkNQ37eXjpYUq49+WVqFeSnSodT4cF+bq+nOFBcrpW7Dur++T9KkqZc11PTv9wmHy+bHhnWQ8Njo/TV5lx1iwxW+q58lVY4tHBVpjbsLTppjclXdNWC9EztKzxSNycBMMnuacNM3R/h5iQINwAaC8MwtCWnWB3Dm+lwhcPtAYUFZRV6KXW7bo6LUpvQANltUligr3YeKJHTMNSl9YnnUxSWVWrDvkJ1iwxW/ye/lHT0XWTLtxxQaKCP/jEiVsNe+lYRIf4a1ruNpnyyUdLRSyPX9G6jsooqLV6f7drfLXHt9H7Gz67fbTbp8u4R+nLTsdGtMQkd9FbaHtfvv+3fTv9efWwbeB7CTT0i3ACAu9pGrE5X9cTVNZmH5HAaujCmhWvdkUqHvvgpR5d0CT+jeRgOp6F30zN1Qfvmim4RoA17izSgYwtVOpzauK9Q0c0DFR7kJ7v96O3odptNc77bpfYtAtUyyFdBfj5yOA1d89K36tkmRAVlFdpXeES/7d9Oj13fSzlFRxQS4KOP1+5VfmmFzm8bqnkr9yg2Okwvf7Vd0tEJxJ1bBanS4dS0z3hY55k6/sW9ZiHcnAThBgBwJrblFKu0wqHY6DDtLzysQF9vhQYce5RDYVmlQgPdH+2w40CJKqqc6tHG/XvG4TRk/+Uyp4+XzfWOtOMtSM+Uj5ddCZ1bqm1YgMqrHCo5UqWwQF/ZdHRkzGazafyCH9WlVZAeuPw8GYahw5UOfbf9oO5+e7Wu7dNGnVsFaVRCB9ltNu0+WCqn09Atv7zC5L/3X6zQAB+t2p2vl7/apuH9ouTjZdcHa352TeaPCgvQ0G6t9FBSN/33f/u1cFWmbo/voLfT9igkwFsFZZV6cngvzVq+Q4G+3tpzsFQVDkMjB0RrdEKMuX8EEW5OinADAEDjcybf3yd/RS8AAEAjQ7gBAAAepUGEm5kzZyomJkb+/v6Kj49Xenr6Sfu/99576t69u/z9/dW7d28tXrz4pP0BAEDTYXm4WbhwoZKTkzVlyhStWbNGffv2VVJSknJza3/E+/fff6+RI0fqzjvv1I8//qjhw4dr+PDh2rBhQz1XDgAAGiLLJxTHx8frwgsv1IwZMyRJTqdT0dHReuCBBzRx4sQa/UeMGKHS0lItWrTI1TZw4EDFxsZq1qxZpzweE4oBAGh8Gs2E4oqKCmVkZCgxMdHVZrfblZiYqLS0tFq3SUtLc+svSUlJSSfsX15erqKiIrcFAAB4LkvDTV5enhwOhyIi3N82GhERoezs7Fq3yc7OPqP+KSkpCg0NdS3R0dHmFA8AABoky+fc1LVJkyapsLDQtWRlZVldEgAAqEPeVh48PDxcXl5eyslxf7tuTk6OIiMja90mMjLyjPr7+fnJz8/PnIIBAECDZ+nIja+vr+Li4pSamupqczqdSk1NVUJCQq3bJCQkuPWXpKVLl56wPwAAaFosHbmRpOTkZI0ZM0b9+/fXgAEDNH36dJWWlmrs2LGSpNGjRysqKkopKSmSpPHjx2vIkCF6/vnnNWzYMC1YsECrV6/Wa6+9ZuXHAAAADYTl4WbEiBE6cOCAJk+erOzsbMXGxmrJkiWuScOZmZmy248NMF100UWaP3++/va3v+nhhx/Weeedp48++ki9evWy6iMAAIAGxPLn3NQ3nnMDAEDj02iecwMAAGA2yy9L1bfqgSoe5gcAQONR/b19Ohecmly4KS4uliQe5gcAQCNUXFys0NDQk/ZpcnNunE6n9u3bp+DgYNlsNlP3XVRUpOjoaGVlZTGfxwKcf2tx/q3F+bcW57/uGYah4uJitW3b1u1Go9o0uZEbu92udu3a1ekxQkJC+B+3hTj/1uL8W4vzby3Of9061YhNNSYUAwAAj0K4AQAAHoVwYyI/Pz9NmTKFd1lZhPNvLc6/tTj/1uL8NyxNbkIxAADwbIzcAAAAj0K4AQAAHoVwAwAAPArhBgAAeBTCjUlmzpypmJgY+fv7Kz4+Xunp6VaX1OikpKTowgsvVHBwsFq3bq3hw4dry5Ytbn2OHDmicePGqWXLlgoKCtLNN9+snJwctz6ZmZkaNmyYAgMD1bp1az300EOqqqpy6/P111/rggsukJ+fn7p06aK5c+fW9cdrdKZNmyabzaYJEya42jj/dW/v3r26/fbb1bJlSwUEBKh3795avXq1a71hGJo8ebLatGmjgIAAJSYmatu2bW77yM/P12233aaQkBCFhYXpzjvvVElJiVuf//3vf7rkkkvk7++v6OhoPfvss/Xy+Royh8OhRx99VB07dlRAQIA6d+6sJ554wu1dRpz/RsLAOVuwYIHh6+trzJkzx9i4caNx9913G2FhYUZOTo7VpTUqSUlJxptvvmls2LDBWLt2rXHNNdcY7du3N0pKSlx97rnnHiM6OtpITU01Vq9ebQwcONC46KKLXOurqqqMXr16GYmJicaPP/5oLF682AgPDzcmTZrk6rNz504jMDDQSE5ONn766Sfj5ZdfNry8vIwlS5bU6+dtyNLT042YmBijT58+xvjx413tnP+6lZ+fb3To0MH4/e9/b6xcudLYuXOn8fnnnxvbt2939Zk2bZoRGhpqfPTRR8a6deuM66+/3ujYsaNx+PBhV5+rrrrK6Nu3r/HDDz8Y3377rdGlSxdj5MiRrvWFhYVGRESEcdtttxkbNmww3n33XSMgIMD45z//Wa+ft6F56qmnjJYtWxqLFi0ydu3aZbz33ntGUFCQ8eKLL7r6cP4bB8KNCQYMGGCMGzfO9bvD4TDatm1rpKSkWFhV45ebm2tIMpYvX24YhmEUFBQYPj4+xnvvvefqs2nTJkOSkZaWZhiGYSxevNiw2+1Gdna2q8+rr75qhISEGOXl5YZhGMZf/vIX4/zzz3c71ogRI4ykpKS6/kiNQnFxsXHeeecZS5cuNYYMGeIKN5z/uvfXv/7VuPjii0+43ul0GpGRkcZzzz3naisoKDD8/PyMd9991zAMw/jpp58MScaqVatcfT777DPDZrMZe/fuNQzDMF555RWjefPmrr9J9bG7detm9kdqVIYNG2bccccdbm033XSTcdtttxmGwflvTLgsdY4qKiqUkZGhxMREV5vdbldiYqLS0tIsrKzxKywslCS1aNFCkpSRkaHKykq3c929e3e1b9/eda7T0tLUu3dvRUREuPokJSWpqKhIGzdudPU5fh/Vffh7HTVu3DgNGzasxjni/Ne9Tz75RP3799dvfvMbtW7dWv369dPs2bNd63ft2qXs7Gy38xcaGqr4+Hi3v0FYWJj69+/v6pOYmCi73a6VK1e6+gwePFi+vr6uPklJSdqyZYsOHTpU1x+zwbrooouUmpqqrVu3SpLWrVunFStW6Oqrr5bE+W9MmtyLM82Wl5cnh8Ph9o+5JEVERGjz5s0WVdX4OZ1OTZgwQYMGDVKvXr0kSdnZ2fL19VVYWJhb34iICGVnZ7v61Pa3qF53sj5FRUU6fPiwAgIC6uIjNQoLFizQmjVrtGrVqhrrOP91b+fOnXr11VeVnJyshx9+WKtWrdL//d//ydfXV2PGjHGdw9rO3/Hnt3Xr1m7rvb291aJFC7c+HTt2rLGP6nXNmzevk8/X0E2cOFFFRUXq3r27vLy85HA49NRTT+m2226TJM5/I0K4QYM0btw4bdiwQStWrLC6lCYjKytL48eP19KlS+Xv7291OU2S0+lU//799fTTT0uS+vXrpw0bNmjWrFkaM2aMxdV5vn//+9+aN2+e5s+fr/PPP19r167VhAkT1LZtW85/I8NlqXMUHh4uLy+vGneM5OTkKDIy0qKqGrf7779fixYt0rJly9SuXTtXe2RkpCoqKlRQUODW//hzHRkZWevfonrdyfqEhIQ06VGDjIwM5ebm6oILLpC3t7e8vb21fPlyvfTSS/L29lZERATnv461adNGPXv2dGvr0aOHMjMzJR07hyf79yYyMlK5ublu66uqqpSfn39Gf6em6KGHHtLEiRP1u9/9Tr1799aoUaP0pz/9SSkpKZI4/40J4eYc+fr6Ki4uTqmpqa42p9Op1NRUJSQkWFhZ42MYhu6//359+OGH+uqrr2oM28bFxcnHx8ftXG/ZskWZmZmuc52QkKD169e7/eOydOlShYSEuL40EhIS3PZR3aep/70uv/xyrV+/XmvXrnUt/fv312233eb6mfNftwYNGlTj8Qdbt25Vhw4dJEkdO3ZUZGSk2/krKirSypUr3f4GBQUFysjIcPX56quv5HQ6FR8f7+rzzTffqLKy0tVn6dKl6tatW5O+JFJWVia73f1r0cvLS06nUxLnv1GxekazJ1iwYIHh5+dnzJ071/jpp5+MP/zhD0ZYWJjbHSM4tXvvvdcIDQ01vv76a2P//v2upayszNXnnnvuMdq3b2989dVXxurVq42EhAQjISHBtb76VuQrr7zSWLt2rbFkyRKjVatWtd6K/NBDDxmbNm0yZs6cya3IJ3D83VKGwfmva+np6Ya3t7fx1FNPGdu2bTPmzZtnBAYGGv/6179cfaZNm2aEhYUZH3/8sfG///3PuOGGG2q9Fblfv37GypUrjRUrVhjnnXee263IBQUFRkREhDFq1Chjw4YNxoIFC4zAwMAmfyvymDFjjKioKNet4B988IERHh5u/OUvf3H14fw3DoQbk7z88stG+/btDV9fX2PAgAHGDz/8YHVJjY6kWpc333zT1efw4cPGfffdZzRv3twIDAw0brzxRmP//v1u+9m9e7dx9dVXGwEBAUZ4eLjx5z//2aisrHTrs2zZMiM2Ntbw9fU1OnXq5HYMHPPrcMP5r3v//e9/jV69ehl+fn5G9+7djddee81tvdPpNB599FEjIiLC8PPzMy6//HJjy5Ytbn0OHjxojBw50ggKCjJCQkKMsWPHGsXFxW591q1bZ1x88cWGn5+fERUVZUybNq3OP1tDV1RUZIwfP95o37694e/vb3Tq1Ml45JFH3G7Z5vw3DjbDOO7RiwAAAI0cc24AAIBHIdwAAACPQrgBAAAehXADAAA8CuEGAAB4FMINAADwKIQbAADgUQg3AJqEmJgYTZ8+3eoyANQDwg0A0/3+97/X8OHDJUlDhw7VhAkT6u3Yc+fOVVhYWI32VatW6Q9/+EO91QHAOt5WFwAAp6OiokK+vr5nvX2rVq1MrAZAQ8bIDYA68/vf/17Lly/Xiy++KJvNJpvNpt27d0uSNmzYoKuvvlpBQUGKiIjQqFGjlJeX59p26NChuv/++zVhwgSFh4crKSlJkvTCCy+od+/eatasmaKjo3XfffeppKREkvT1119r7NixKiwsdB1v6tSpkmpelsrMzNQNN9ygoKAghYSE6Le//a1ycnJc66dOnarY2Fi98847iomJUWhoqH73u9+puLjY1ef9999X7969FRAQoJYtWyoxMVGlpaV1dDYBnC7CDYA68+KLLyohIUF333239u/fr/379ys6OloFBQW67LLL1K9fP61evVpLlixRTk6Ofvvb37pt/9Zbb8nX11ffffedZs2aJUmy2+166aWXtHHjRr311lv66quv9Je//EWSdNFFF2n69OkKCQlxHe/BBx+sUZfT6dQNN9yg/Px8LV++XEuXLtXOnTs1YsQIt347duzQRx99pEWLFmnRokVavny5pk2bJknav3+/Ro4cqTvuuEObNm3S119/rZtuukm8rg+wHpelANSZ0NBQ+fr6KjAwUJGRka72GTNmqF+/fnr66addbXPmzFF0dLS2bt2qrl27SpLOO+88Pfvss277PH7+TkxMjJ588kndc889euWVV+Tr66vQ0FDZbDa34/1aamqq1q9fr127dik6OlqS9Pbbb+v888/XqlWrdOGFF0o6GoLmzp2r4OBgSdKoUaOUmpqqp556Svv371dVVZVuuukmdejQQZLUu3fvczhbAMzCyA2Aerdu3TotW7ZMQUFBrqV79+6Sjo6WVIuLi6ux7ZdffqnLL79cUVFRCg4O1qhRo3Tw4EGVlZWd9vE3bdqk6OhoV7CRpJ49eyosLEybNm1ytcXExLiCjSS1adNGubm5kqS+ffvq8ssvV+/evfWb3/xGs2fP1qFDh07/JACoM4QbAPWupKRE1113ndauXeu2bNu2TYMHD3b1a9asmdt2u3fv1rXXXqs+ffroP//5jzIyMjRz5kxJRyccm83Hx8ftd5vNJqfTKUny8vLS0qVL9dlnn6lnz556+eWX1a1bN+3atcv0OgCcGcINgDrl6+srh8Ph1nbBBRdo48aNiomJUZcuXdyWXwea42VkZMjpdOr555/XwIED1bVrV+3bt++Ux/u1Hj16KCsrS1lZWa62n376SQUFBerZs+dpfzabzaZBgwbpscce048//ihfX199+OGHp709gLpBuAFQp2JiYrRy5Urt3r1beXl5cjqdGjdunPLz8zVy5EitWrVKO3bs0Oeff66xY8eeNJh06dJFlZWVevnll7Vz50698847ronGxx+vpKREqampysvLq/VyVWJionr37q3bbrtNa9asUXp6ukaPHq0hQ4aof//+p/W5Vq5cqaefflqrV69WZmamPvjgAx04cEA9evQ4sxMEwHSEGwB16sEHH5SXl5d69uypVq1aKTMzU23bttV3330nh8OhK6+8Ur1799aECRMUFhYmu/3E/yz17dtXL7zwgp555hn16tVL8+bNU0pKilufiy66SPfcc49GjBihVq1a1ZiQLB0dcfn444/VvHlzDR48WImJierUqZMWLlx42p8rJCRE33zzja655hp17dpVf/vb3/T888/r6quvPv2TA6BO2AzuWwQAAB6EkRsAAOBRCDcAAMCjEG4AAIBHIdwAAACPQrgBAAAehXADAAA8CuEGAAB4FMINAADwKIQbAADgUQg3AADAoxBuAACARyHcAAAAj/L/JxlNnCml+IAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss_log)\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.title(\"MSE Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "out = vutils.make_grid(recon_data[0:8], normalize=True)\n",
    "_ = plt.imshow(out.cpu().detach().numpy().transpose((1, 2, 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Noisy Input\n",
    "plt.figure(figsize=(20, 10))\n",
    "out = vutils.make_grid(noisy_image[0:8], normalize=True)\n",
    "_ = plt.imshow(out.cpu().detach().numpy().transpose((1, 2, 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reconstruction\n",
    "plt.figure(figsize=(20, 10))\n",
    "recon_data, _ = ae_net(noisy_image.to(device))\n",
    "out = vutils.make_grid(recon_data.detach().cpu()[0:8], normalize=True)\n",
    "_ = plt.imshow(out.numpy().transpose((1, 2, 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reconstruction (based on the mean of the latent space distribution)\n",
    "plt.figure(figsize=(20, 10))\n",
    "recon_data = ae_net.decoder(\n",
    "    encoded.std(0, keepdims=True) * torch.randn_like(encoded)\n",
    "    + encoded.mean(0, keepdims=True)\n",
    ")\n",
    "out = vutils.make_grid(recon_data.detach().cpu()[0:8], normalize=True)\n",
    "_ = plt.imshow(out.numpy().transpose((1, 2, 0)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}