[Applied tasks of CV] Week 1

Week1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47a9b963-e6b5-4b10-8bc7-cbc065003990",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "import torchvision\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf975433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check whether CUDA is available.\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6bc9e58e-ec6c-4870-857c-b76157e7f3d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "\n",
    "train_data = datasets.CIFAR10(root='data', train=True, transform=transform, download=True)\n",
    "test_data = datasets.CIFAR10(root='data', train=False, transform=transform, download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c0d2777e-80ee-428e-977f-8cfdb63a8f7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 32, 32]) 6\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2100x300 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CIFAR_CLASSES = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
    "\n",
    "image, label = train_data[0]\n",
    "print(image.shape, label)\n",
    "\n",
    "# Because the train_data and test_data are normalised\n",
    "# it results in weird graphical artifacts when displayed.\n",
    "# So let's load the dataset from disk one more time and\n",
    "# take a look at it without normalisation.\n",
    "visualisation_data = datasets.CIFAR10(root='data', train=True, transform=transforms.ToTensor(), download=True)\n",
    "\n",
    "to_show = 7\n",
    "size = 3\n",
    "\n",
    "plt.figure(figsize=(size * to_show, size))\n",
    "for i in range(to_show):\n",
    "    image, label = visualisation_data[i]\n",
    "\n",
    "    image = np.transpose(image, (1, 2, 0))\n",
    "    label = CIFAR_CLASSES[label]\n",
    "    \n",
    "    plt.subplot(1, to_show, i + 1)\n",
    "\n",
    "    plt.imshow(image)\n",
    "    plt.text(1, 0, label, bbox=dict(fill=True))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2cb0bd9c-34ee-44c9-95dc-5f1844d67b35",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 8\n",
    "num_workers = 0\n",
    "\n",
    "train_dl = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
    "test_dl = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=num_workers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51609a18",
   "metadata": {},
   "source": [
    "### Utility methods\n",
    "\n",
    "- **train_net**: trains network on the given train set\n",
    "- **test_net**: tests network on the given test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb1490f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_net(net: nn.Module,\n",
    "              train_dataloader: DataLoader,\n",
    "              epochs: int = 1,\n",
    "              learning_rate: float = 0.001,\n",
    "              debug: bool = True):\n",
    "    net = net.to(device)\n",
    "    net.train()\n",
    "    \n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    criterion = criterion.to(device)\n",
    "    \n",
    "    optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)\n",
    " \n",
    "    losses = list()\n",
    "    \n",
    "    iterations_counter = 0\n",
    "    running_loss = 0.0\n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        \n",
    "        one_epoch_losses = list()\n",
    "        for inputs, labels in tqdm(train_dataloader):\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "\n",
    "            logits = net(inputs)\n",
    "            loss = criterion(logits, labels)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            running_loss += loss.item()\n",
    "            \n",
    "            if iterations_counter % 2000 == 1999:\n",
    "                actual_loss = running_loss / 2000\n",
    "                losses.append(actual_loss) \n",
    "                running_loss = 0.0\n",
    "            \n",
    "            iterations_counter += 1\n",
    "    \n",
    "    if debug:\n",
    "        plt.plot(losses)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c16ca1e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_net(net: nn.Module, \n",
    "             test_dataloader: DataLoader):\n",
    "  net = net.to(device)\n",
    "  net.eval()\n",
    " \n",
    "  correct = 0\n",
    "  total = 0\n",
    "\n",
    "  for inputs, labels in tqdm(test_dataloader):\n",
    "    inputs, labels = inputs.to(device), labels.to(device)\n",
    "    \n",
    "    outputs = net(inputs)    \n",
    "    _, predicted = torch.max(outputs.data, 1)  \n",
    "    \n",
    "    total += labels.size(0)\n",
    "    correct += (predicted == labels).sum().item()\n",
    "    \n",
    "  return correct / total"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb61c792-b516-4674-a3a2-a109ae12124b",
   "metadata": {},
   "source": [
    "### Task 1: Train Dense (fully-connected) neural network on CIFAR10 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ce54ae5-17aa-4048-9c94-39a52a9eace0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DenseNet(nn.Module):\n",
    "  # CIFAR10 contains 10 classes.  \n",
    "  def __init__(self, \n",
    "               input_shape, \n",
    "               hidden, \n",
    "               num_classes = 10):\n",
    "    super().__init__()\n",
    "    \n",
    "    input_size = input_shape[0] * input_shape[1] * input_shape[2]\n",
    "\n",
    "    self.linear1 = nn.Linear(input_size, hidden)\n",
    "    self.act1 = nn.ReLU()\n",
    "\n",
    "    self.linear2 = nn.Linear(hidden, num_classes)\n",
    "\n",
    "  def forward(self, x):\n",
    "    x = x.view(x.shape[0], -1)\n",
    "    x = self.linear1(x)\n",
    "    x = self.act1(x)\n",
    "    x = self.linear2(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "18791cb3-a074-4f04-9fe7-fea612790962",
   "metadata": {},
   "outputs": [],
   "source": [
    "dense_net = DenseNet(input_shape=(3, 32, 32), hidden=512, num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2317ddcc-93ca-4f69-b799-29d07708ab8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:24<00:00, 73.55it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:08<00:00, 90.60it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:08<00:00, 91.77it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_net(dense_net, train_dl, epochs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a39f29a-6794-48b4-8577-bc5c588514fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:08<00:00, 140.43it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4401"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = test_net(dense_net, test_dl)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8f1cc72-91e1-4ff3-b3b7-f03651002589",
   "metadata": {},
   "source": [
    "## Task 2: Train Convolutional neural network (CNN) on CIFAR10 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5484b9c-3022-4751-b394-3be412643f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvNet(nn.Module):\n",
    "    def __init__(self,\n",
    "                 input_shape,\n",
    "                 hidden_layers_channels,\n",
    "                 kernels,\n",
    "                 use_pooling = True):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.__use_pooling = use_pooling\n",
    "\n",
    "        assert len(hidden_layers_channels) == len(kernels)\n",
    "\n",
    "        in_channels = input_shape[0]\n",
    "        in_height = input_shape[1]\n",
    "        in_width = input_shape[2]\n",
    "\n",
    "        self.convolutions = list()\n",
    "        for channels, kernel in zip(hidden_layers_channels, kernels):\n",
    "            conv = nn.Conv2d(in_channels, channels, kernel)\n",
    "            conv = conv.to(device)\n",
    "\n",
    "            in_channels = channels\n",
    "            in_height = self.__get_size(in_height, kernel)\n",
    "            in_width = self.__get_size(in_width, kernel)\n",
    "            \n",
    "            if self.__use_pooling:\n",
    "                # Pooling with kernel 2 and stride 2.\n",
    "                in_height = self.__get_size(in_height, 2, stride=2)\n",
    "                in_width = self.__get_size(in_width, 2, stride=2)\n",
    "\n",
    "            self.convolutions.append(conv)\n",
    "\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "\n",
    "        self.fc1 = nn.Linear(in_channels * in_height * in_width, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 10)\n",
    "\n",
    "    def __get_size(self, size, kernel_size, padding = 0, dilation = 1, stride = 1):\n",
    "        return int((size + 2 * padding - dilation * (kernel_size - 1) - 1) / stride + 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        for conv in self.convolutions:            \n",
    "            x = F.relu(conv(x))\n",
    "            if self.__use_pooling:\n",
    "                x = self.pool(x)\n",
    "\n",
    "        x = torch.flatten(x, 1) # flatten all dimensions except batch\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a884b78b-8c72-4b06-8530-037cee55143f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implementation from: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html\n",
    "# 2 conv layers with 6 and 16 channels and 5x5 kernels,\n",
    "# and 3 fully connected layers.\n",
    "# This model will be a baseline.\n",
    "conv_net = ConvNet(input_shape=(3, 32, 32),\n",
    "                   hidden_layers_channels=[6, 16],\n",
    "                   kernels=[5, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d9a7d30-bce9-492d-97d6-7187eadc0be6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:41<00:00, 61.79it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:16<00:00, 81.54it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:16<00:00, 82.21it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:13<00:00, 85.50it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:13<00:00, 84.64it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA4L0lEQVR4nO3deXhU9aH/8c+ZmWSyT0ggIQkJIYCsEoMIVdxQrI1tWuuv6q0LKGrrLRYt1irtVa+3tlztLa4Uq1ZRq7WtClqtVhaFSqusEZAlhCAJSUgIkEwWss2c3x9JRqIEEsjMyWTer+c5D83MnMxn5okzn57v93yPYZqmKQAAAIvYrA4AAABCG2UEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGAph9UBusPr9aqsrEyxsbEyDMPqOAAAoBtM01Rtba1SU1Nls3V9/CMoykhZWZnS09OtjgEAAE5CSUmJhgwZ0uX9QVFGYmNjJbW9mLi4OIvTAACA7nC73UpPT/d9j3clKMpIx9BMXFwcZQQAgCBzoikWPZ7Aunr1auXl5Sk1NVWGYWjp0qUn3GfhwoUaM2aMIiMjNWrUKL344os9fVoAANBP9fjISH19vbKzszVr1ixdccUVJ3z8okWLNG/ePD3zzDM666yztHbtWt1yyy0aMGCA8vLyTio0AADoP3pcRnJzc5Wbm9vtx7/00kv64Q9/qKuvvlqSlJWVpXXr1umhhx6ijAAAAP/PGWlqalJERESn2yIjI7V27Vq1tLQoLCzsmPs0NTX5fna73f6OCQAALOL3Rc8uvfRSPfvss9qwYYNM09T69ev17LPPqqWlRVVVVcfcZ/78+XK5XL6N03oBAOi//F5G7r33XuXm5uprX/uawsLC9J3vfEczZ85se/IuFkCZN2+eampqfFtJSYm/YwIAAIv4vYxERkbqueeeU0NDgz7//HMVFxcrMzNTsbGxGjRo0DH3cTqdvtN4OZ0XAID+LWDrjISFhflWX3v11Vf1rW9967hLwwIAgNDQ4zJSV1enwsJC38979uxRfn6+EhISlJGRoXnz5qm0tNS3lkhBQYHWrl2rKVOm6PDhw1qwYIG2bt2qF154ofdeBQAACFo9LiPr16/XtGnTfD/PnTtXkjRz5kwtXrxY5eXlKi4u9t3v8Xj029/+Vjt37lRYWJimTZumf/3rX8rMzDz19AAAIOgZpmmaVoc4EbfbLZfLpZqaGuaPAAAQJLr7/c2kDQAAYKmQLiN/+7RMd/31U23ZV2N1FAAAQlZIl5G/bynXXzfs05rdx158DQAA+F9Il5GJGQMkSRv2HrY4CQAAoSu0y8jQtjKyqfiwgmAeLwAA/VJIl5HxaXEKt9tUVdes4kMNVscBACAkhXQZcTrsGpfWdqrRxmKGagAAsEJIlxFJOrN93sjGvdXWBgEAIESFfBnpmDfCJFYAAKxBGWk/MrJjv1v1Ta0WpwEAIPSEfBkZ7IpQWnykvKb06b5qq+MAABByQr6MSFJORrwkaSNDNQAABBxlRNKZ7fNGNhZXWxsEAIAQRBnRF/NGNrL4GQAAAUcZkTQmJU5Oh03VDS0qqqq3Og4AACGFMiIp3GFT9pB4ScwbAQAg0Cgj7XKGxktiJVYAAAKNMtKOlVgBALAGZaRdx0qsBZW1cje2WJwGAIDQQRlpNzDGqYyEKJmmlM8pvgAABAxl5ChfrDfCvBEAAAKFMnKUie0rsXLRPAAAAocycpSc9kms+SXV8npZ/AwAgECgjBxl9OBYRYXbVdvYqsIDdVbHAQAgJFBGjuKwf7H4GUM1AAAEBmXkS3yTWCkjAAAEBGXkSya2r8S6gTNqAAAICMrIl+Sktx0ZKTpQr+qGZovTAADQ/1FGvmRAdLiyBkVLkjax+BkAAH5HGTmGie2n+DKJFQAA/6OMHAMrsQIAEDiUkWOYeNTiZ60er8VpAADo3ygjxzAyKUaxTocamj3aWVFrdRwAAPo1ysgx2GyGzmi/Ts1GJrECAOBXlJEudAzVsPgZAAD+RRnpwkQmsQIAEBCUkS6ckR4vw5D2HmxQVV2T1XEAAOi3KCNdcEWGaWRSjCSGagAA8CfKyHF8sd5ItbVBAADoxygjx5HDJFYAAPyOMnIcHWfUbC6tVguLnwEA4BeUkePIGhit+KgwNbZ4tb3cbXUcAAD6JcrIcdhshnLS4yVx0TwAAPyFMnICTGIFAMC/KCMnwEqsAAD4F2XkBLLT42UzpNLqI6pwN1odBwCAfocycgLRTodGD46TxNERAAD8gTLSDROHxktiEisAAP5AGekG37wRLpoHAECvo4x0Q8cZNVtL3Wpq9VicBgCA/oUy0g0ZCVFKjA5Xs8erraUsfgYAQG+ijHSDYRia2H50ZBNDNQAA9CrKSDd1zBthEisAAL2LMtJNEzPiJbVNYjVN09owAAD0I5SRbpowJF4Om6EKd5PKalj8DACA3kIZ6abIcLvGprYtfsZQDQAAvYcy0gNcpwYAgN5HGekBzqgBAKD3UUZ6oGMS62dlbjW2sPgZAAC9ocdlZPXq1crLy1NqaqoMw9DSpUtPuM/LL7+s7OxsRUVFKSUlRbNmzdLBgwdPJq+l0uIjlRznVKvX1OZ9NVbHAQCgX+hxGamvr1d2drYWLlzYrcevWbNGM2bM0E033aTPPvtMf/3rX7V27VrdcsstPQ5rNcMwWG8EAIBe5ujpDrm5ucrNze324//9738rMzNTc+bMkSQNGzZMP/zhD/XQQw/19Kn7hIkZA/Tu1v1cNA8AgF7i9zkjZ599tkpKSvT3v/9dpmmqoqJCr732mi677LIu92lqapLb7e609RVHT2Jl8TMAAE6d38vI1KlT9fLLL+vqq69WeHi4Bg8eLJfLddxhnvnz58vlcvm29PR0f8fstvFpcQq321RV16ziQw1WxwEAIOj5vYxs27ZNt99+u+677z5t2LBB7733nj7//HPdeuutXe4zb9481dTU+LaSkhJ/x+w2p8Ou8Wlti58xVAMAwKnr8ZyRnpo/f76mTp2qu+66S5I0YcIERUdH67zzztODDz6olJSUr+zjdDrldDr9He2kTcwYoI3F1dq4t1rfzRlidRwAAIKa34+MNDQ0yGbr/DR2u12SgnbORce8Ec6oAQDg1PW4jNTV1Sk/P1/5+fmSpD179ig/P1/FxcWS2oZYZsyY4Xt8Xl6e3njjDS1atEhFRUVas2aN5syZo8mTJys1NbV3XkWAndleRnbsd6u+qdXiNAAABLcel5H169crJydHOTk5kqS5c+cqJydH9913nySpvLzcV0wk6YYbbtCCBQv05JNPavz48bryyis1atQovfHGG730EgIvOS5CafGR8prSpyXVVscBACCoGWYQjJW43W65XC7V1NQoLi7O6jiSpB//aZP+9mmZfvr103TbRSOtjgMAQJ/T3e9vrk1zkjquU7OxuNrSHAAABDvKyEnqWBZ+I4ufAQBwSigjJ2lsapwiwmyqbmhRUVW91XEAAAhalJGTFGa3aUJavCRO8QUA4FRQRk5BztB4SW3XqQEAACeHMnIKzuyYN7K32togAAAEMcrIKehYibWgslbuxhaL0wAAEJwoI6dgYIxTQxOjZJpSPqf4AgBwUigjp+joU3wBAEDPUUZOUcfiZ5xRAwDAyaGMnKKOeSP5xdXyeln8DACAnqKMnKJRybGKCrertqlVuyrrrI4DAEDQoYycIofdpuwh8ZKYNwIAwMmgjPSCM4d2rDdCGQEAoKcoI71gYvtKrBs4MgIAQI9RRnpBTnrbkZGiA/U6XN9scRoAAIILZaQXDIgOV9agaEnSphKOjgAA0BOUkV4ykevUAABwUigjvcQ3iZV5IwAA9AhlpJd0HBnJL6lWq8drcRoAAIIHZaSXjEyKUazToYZmj3ZW1FodBwCAoEEZ6SU2m6Ez2q9Ts5Er+AIA0G2UkV70xSRW5o0AANBdlJFexCRWAAB6jjLSi87IiJdhSHsPNqiqrsnqOAAABAXKSC+KiwjTyKQYSQzVAADQXZSRXvbFUE21tUEAAAgSlJFelsMkVgAAeoQy0ss6joxsLq1WC4ufAQBwQpSRXpY1MFrxUWFqbPFqe7nb6jgAAPR5lJFeZhiGctLjJUkbGKoBAOCEKCN+wCRWAAC6jzLiB6zECgBA91FG/CA7PV42QyqtPqIKd6PVcQAA6NMoI34Q7XRo9OA4SRwdAQDgRCgjfjJxaLwkJrECAHAilBE/4aJ5AAB0D2XETzomsW4tdaup1WNxGgAA+i7KiJ9kJEQpMTpczR6vtpay+BkAAF2hjPiJYRia2D5Us4mhGgAAukQZ8aOOoRomsQIA0DXKiB8dPYnVNE2L0wAA0DdRRvxowhCXHDZDFe4mldWw+BkAAMdCGfGjiDC7xqa2LX7GUA0AAMdGGfEzrlMDAMDxUUb8bCKLnwEAcFyUET/rmMS6rcytxhYWPwMA4MsoI36W6opQcpxTrV5Tm/fVWB0HAIA+hzLiZ4ZhsN4IAADHQRkJAC6aBwBA1ygjAZBz1Bk1LH4GAEBnlJEAGJ8Wp3C7TQfrm1V8qMHqOAAA9CmUkQBwOuwan9a2+BlDNQAAdEYZCRAmsQIAcGyUkQDxTWLdW21tEAAA+hjKSIB0rMS6Y79b9U2tFqcBAKDvoIwESHJchNLiI+U1pU9Lqq2OAwBAn0EZCSCuUwMAwFdRRgJoYka8JCaxAgBwtB6XkdWrVysvL0+pqakyDENLly497uNvuOEGGYbxlW3cuHEnmzlodUxi3VRSzeJnAAC063EZqa+vV3Z2thYuXNitxz/22GMqLy/3bSUlJUpISNCVV17Z47DBbkxKnCLCbKpuaFFRVb3VcQAA6BMcPd0hNzdXubm53X68y+WSy+Xy/bx06VIdPnxYN954Y0+fOuiF2W2akBavtZ8f0oa9hzV8UIzVkQAAsFzA54z84Q9/0PTp0zV06NAuH9PU1CS3291p6y86JrFuYhIrAACSAlxGysrK9O677+rmm28+7uPmz5/vO6LicrmUnp4eoIT+1zGJlcXPAABoE9Ay8sILLyg+Pl6XX375cR83b9481dTU+LaSkpLABAyAjiMjBZW1cje2WJwGAADrBayMmKap5557Ttdff73Cw8OP+1in06m4uLhOW38xMMapoYlRMk0pv7ja6jgAAFguYGVk1apVKiws1E033RSop+yzuGgeAABf6HEZqaurU35+vvLz8yVJe/bsUX5+voqLiyW1DbHMmDHjK/v94Q9/0JQpUzR+/PhTS9wPsBIrAABf6PGpvevXr9e0adN8P8+dO1eSNHPmTC1evFjl5eW+YtKhpqZGr7/+uh577LFTjNs/dExizS+ultdrymYzrA0EAICFelxGLrzwwuOuHrp48eKv3OZyudTQ0NDTp+q3RiXHKircrtqmVu2qrNOowbFWRwIAwDJcm8YCDrtNZ6THS2KoBgAAyohFmMQKAEAbyohFJg6Nl8SREQAAKCMWyUlvOzJSdKBeh+ubLU4DAIB1KCMWGRAdrqxB0ZKkTSUcHQEAhC7KiIXObJ838q/CgxYnAQDAOpQRC51/2iBJ0vP/+lwf7Ky0OA0AANagjFjoWxNSdMXENHm8pm57eaM+K6uxOhIAAAFHGbGQYRj63ysm6Jzhiapv9mjW4nUqqz5idSwAAAKKMmKxcIdNi647UyOTYlThbtKsxetU29hidSwAAAKGMtIHuCLD9PyNZ2lQrFM79tfqRy9vVIvHa3UsAAACgjLSRwwZEKXnZp6lyDC7/rmrSv+1ZOtxrwEEAEB/QRnpQ04f4tKT1+TIZkh/Xl+ihR8UWh0JAAC/o4z0MRePSdYD3x4nSfq/9wu0dFOpxYkAAPAvykgfdP3ZmbrlvGGSpJ+9tlkfF7EoGgCg/6KM9FHzcsfostMHq9nj1Q9eXK/CylqrIwEA4BeUkT7KZjO04KozNDEjXu7GVt3w/DodqG2yOhYAAL2OMtKHRYTZ9cyMSRqaGKV9h4/o5hfW6Uizx+pYAAD0KspIH5cY49TiGydrQFSYPt1XozmvbpLHyym/AID+gzISBIYNjNYzMyYp3GHTsm0VevCdbVZHAgCg11BGgsSkzAQtuCpbkvT8ms/13Ed7LE4EAEDvoIwEkW9NSNU9uaMlSb98Z5v+8dl+ixMBAHDqKCNB5ofnZ+naKRkyTen2Vzcpv6Ta6kgAAJwSykiQMQxDD3x7nKaNGqTGFq9uWrxOxQcbrI4FAMBJo4wEIYfdpievmahxqXE6WN+sGxavVXVDs9WxAAA4KZSRIBXtdOi5G85SqitCRQfq9YOXNqiplTVIAADBhzISxJLjIvT8jZMV63Ro7Z5D+tlrm+VlDRIAQJChjAS5UYNjtei6M+WwGXozv0wLlhVYHQkAgB6hjPQD544cqPlXnC5JevKDQr26ttjiRAAAdB9lpJ+4clK65lw8UpL0i6VbtbrggMWJAADoHspIP/KT6SN1RU6aPF5TP3p5o7aVua2OBADACVFG+hHDMPS//2+Czs5KVF1Tq2YtXqfymiNWxwIA4LgoI/1MuMOmp647UyOSYrTf3agbn1+n2sYWq2MBANAlykg/5IoK0/M3nKWBMU7t2F+r2a9sUovHa3UsAACOiTLST6UnROm5GyYpMsyu1QUHdO/SrTJN1iABAPQ9lJF+bMKQeD3x/RzZDOnVdSX63Ye7rY4EAMBXUEb6ueljk3V/3jhJ0m/+sVNv5pdanAgAgM4oIyFg5jmZuvncYZKku/66WZ8UHbQ4EQAAX6CMhIifXzZGueMHq9nj1Q9e2qDdB+qsjgQAgCTKSMiw2Qw9cvUZysmIV82RFt3w/FpV1TVZHQsAAMpIKIkIs+vZGZOUkRClkkNHdPML63Wk2WN1LABAiKOMhJjEGKcW33iW4qPClF9SrTv+vEkeL6f8AgCsQxkJQVmDYvTMjEkKt9v0j88q9NB7O6yOBAAIYZSREHVWZoJ+e1W2JOnp1UX61+4qixMBAEIVZSSE5WWn6popGZKkn722WfVNrRYnAgCEIspIiPv5ZWOUFh+pfYePaP67262OAwAIQZSREBfjdOjh702QJP3x42KtKWS4BgAQWJQRaOqIgbrua18M19QxXAMACCDKCCRJ83LHaMiASJVWH9Gv/85wDQAgcCgjkCRFHzVc88onxfrnrgMWJwIAhArKCHzOGT5QM84eKkm6+7XNqm1ssTgRACAUUEbQyd3fGK30hEiV1TQyXAMACAjKCDqJdjr0m++1LYb2p7UlWl3AcA0AwL8oI/iKr2Ul6oZzMiVJd7++WW6GawAAfkQZwTH97BujNDQxSuU1jfrV2wzXAAD8hzKCY4oKd+jh/9d2ds2f15fow52VFicCAPRXlBF0aUpWom6cmilJuuf1Lao5wnANAKD3UUZwXD+7dLQyE6O0392oB9/eZnUcAEA/RBnBcUWG2/WbK7NlGNJfN+zTBzsYrgEA9K4el5HVq1crLy9PqampMgxDS5cuPeE+TU1N+sUvfqGhQ4fK6XQqMzNTzz333MnkhQXOykzQrKnDJEn3vLFZNQ0M1wAAek+Py0h9fb2ys7O1cOHCbu9z1VVXacWKFfrDH/6gnTt36k9/+pNGjRrV06eGhX769VEaNjBaFe4m/Q/DNQCAXuTo6Q65ubnKzc3t9uPfe+89rVq1SkVFRUpISJAkZWZm9vRpYbHIcLt+870JuvL3/9brG/fpstMH6+IxyVbHAgD0A36fM/LWW29p0qRJevjhh5WWlqbTTjtNP/3pT3XkyJEu92lqapLb7e60wXqTMhN087ltwzXz3tjCcA0AoFf4vYwUFRXpo48+0tatW7VkyRI9+uijeu211/SjH/2oy33mz58vl8vl29LT0/0dE91059dHKWtQtCprm/TA3z6zOg4AoB/wexnxer0yDEMvv/yyJk+erMsuu0wLFizQCy+80OXRkXnz5qmmpsa3lZSU+DsmuikizK7ffC9bNkN6Y1Oplm+rsDoSACDI+b2MpKSkKC0tTS6Xy3fbmDFjZJqm9u3bd8x9nE6n4uLiOm3oO84cOkC3nJclSZq3ZIuqG5otTgQACGZ+LyNTp05VWVmZ6urqfLcVFBTIZrNpyJAh/n56+MlPLjlNwwdF60Btk/77LYZrAAAnr8dlpK6uTvn5+crPz5ck7dmzR/n5+SouLpbUNsQyY8YM3+OvueYaJSYm6sYbb9S2bdu0evVq3XXXXZo1a5YiIyN751Ug4CLC7Pq/K9uGa5bml+n9z/ZbHQkAEKR6XEbWr1+vnJwc5eTkSJLmzp2rnJwc3XfffZKk8vJyXzGRpJiYGC1btkzV1dWaNGmSrr32WuXl5enxxx/vpZcAq+RkDNAPzh8uSfr5kq06XM9wDQCg5wzTNE2rQ5yI2+2Wy+VSTU0N80f6mMYWj/Ke+Ei7Kuv07exUPf79HKsjAQD6iO5+f3NtGpySiLC2a9fYDOmtT8v03laGawAAPUMZwSk7Iz1et17QNlzzX0u36BDDNQCAHqCMoFfcPn2kTkuOUVVds+57c6vVcQAAQYQygl7hdLSdXWO3GXp7c7ne3VJudSQAQJCgjKDXTBgSr//0Ddds1cG6JosTAQCCAWUEverHF4/QqORYHaxv1n1vshgaAODEKCPoVUcP17yzpVzvbGa4BgBwfJQR9LrTh7g0+8K24Zp739yqKoZrAADHQRmBX9x20UiNHhyrQ/XNunfpVgXB2noAAItQRuAX4Q6b/u/KbDlsht7dul9vM1wDAOgCZQR+Mz7NpdnTRkiS7ntzqw7UMlwDAPgqygj8ava0ERqTEqfDDS36r6VbGK4BAHwFZQR+1TZcM0EOm6F/fFahtz4tszoSAKCPoYzA78aluvTji0ZKku5/6zNV1jZanAgA0JdQRhAQP5o2XONS41Td0KJfLOHsGgDAFygjCIgwe9vZNWF2Q8u2VejNfIZrAABtKCMImDEpcZpz9HCNm+EaAABlBAF264XDNT4tTjVHWvTzJZxdAwCgjCDAjh6uWb69Uks2lVodCQBgMcoIAm704DjdMf00SdJ/v/WZKhiuAYCQRhmBJX54fpYmDHHJ3diqH7+ySRv2HmLIBgBClGEGwTeA2+2Wy+VSTU2N4uLirI6DXlJQUatvPf6Rmj1eSVJGQpQuz0nTd3PSNGxgtMXpAACnqrvf35QRWGrzvmot/tfnem/rfjU0e3y3n5Eer+/mpOlbE1KUGOO0MCEA4GRRRhBUGppbtWxbhZZsKtU/d1XJ4237s3TYDF1w2iBdnpOmS8YmKyLMbnFSAEB3UUYQtA7UNulvn5ZpaX6pNu+r8d0e43Qod/xgfTcnTVOyEmW3GRamBACcCGUE/UJhZa2WbirTkk2lKq0+4rt9cFyEvpOTqu/mpGn0YP4mAKAvooygX/F6Ta3fe1hLNpXqnc1lcje2+u4bkxKn7+ak6tvZaRrsirAwJQDgaJQR9FuNLR59uLNtwbSVOyrV4mn7EzYMaerwgbo8J03fGD9YMU6HxUkBILRRRhASqhua9c6Wci3dVKp1nx/23R4RZtPXx7bNLzl35ECF2VlSBwACjTKCkFN8sEFv5pdqyaZSFVXV+25PjA5XXnbb/JIJQ1wyDCa+AkAgUEYQskzT1OZ9NVqyqVR/+7RMB+ubffdlDYrWd89I0+U5aUpPiLIwJQD0f5QRQFKLx6uPdlVpyaZSvb9tvxpbvL77zsocoMtz0vTN01MUHxVuYUoA6J8oI8CX1Da26B+fVWjpplKt2V2ljr98p8Om6742VLdeMFyDYlntFQB6C2UEOI79NY1669NSvbGxVDv210pqm/Q64+xM/eD8LA1kCXoAOGWUEaAbTNPU6l1VemRZgfJLqiVJkWF2zTynrZQkRDN8AwAnizIC9IBpmvpw5wE9srzAtwR9dHhbKbnlvCwNoJQAQI9RRoCTYJqmVmyv1CPLC/RZmVtS2zVxbpyaqZvPzZIrKszihAAQPCgjwCkwTVPLtlXokeW7tL28rZTEOh2ade4wzTp3mFyRlBIAOBHKCNALvF5T72/br0eW7dLOiraJrnERDt18XpZunJqp2AhKCQB0hTIC9CKv19S7W/fr0eUF2lVZJ0mKjwrTLedlaeY5mVwHBwCOgTIC+IHHa+qdLeV6bHmBdh9oW3J+QFSYfnD+cM04e6iiKSUA4EMZAfzI4zX1t0/L9PiKXb7r4CREh+vWC7J0/dcyFRlutzghAFiPMgIEQKvHqzfzy/T4yl3ae7BBkjQwJly3XjBc131tqCLCKCUAQhdlBAigVo9Xb2wq1RMrd6nk0BFJ0qBYp3504XB9f3IGpQRASKKMABZo8Xj1+oZ9emJloUqr20pJcpxTs6eN0NVnpcvpoJQACB2UEcBCza1e/XVDiRauLFRZTaMkKcUVodnTRuiqSekKd9gsTggA/kcZAfqAplaP/rJ+nxauLNR+d1spSYuP1G0XjdD3zhyiMDulBED/RRkB+pDGFo9eXVus3324W5W1TZKkIQMiNeeikfruxDRKCYB+iTIC9EGNLR69/EmxFn24W1V1baUkIyFKP79stC4dN1iGYVicEAB6D2UE6MOONHv0x4/36qlVu3WwvlmSdP5pg/TAt8dp2MBoi9MBQO+gjABBoKG5VYs+3K3frypSs8ercLtNPzg/S7OnjWDhNABBr7vf3wxUAxaKCnfozq+P0j9+cr7OP22Qmj1ePflBoaYvWKX3tu5XEPx/BQA4ZZQRoA8YNjBaL9x4lp667kylxUeqtPqIbv3jBt3w/DrtaV9uHgD6K8oI0EcYhqFvjB+s5XMv0G3TRijcbtOqggO69JHV+u37O3Wk2WN1RADwC8oI0MdEhtv100tH6b07ztN5Iweq2ePVEyvbhm7e/4yhGwD9D2UE6KOyBsXoxVmT9dR1E5XqilBp9RH94KUNmrV4nT5n6AZAP0IZAfqwtqGbFC2/8wL96MLhCrMb+mDnAX39kdVawNANgH6CMgIEgahwh372jdF6747zfUM3j68s1CWPrNKybRUM3QAIapQRIIgMbx+6WXTtRKW4IrTv8BHd8uJ63fTCeu09yNANgODU4zKyevVq5eXlKTU1VYZhaOnSpcd9/IcffijDML6y7d+//2QzAyHNMAzlnp6iFXdeoP9sH7pZuaNSlzyyWguWFaixhaEbAMGlx2Wkvr5e2dnZWrhwYY/227lzp8rLy31bUlJST58awFGiwh26u33o5twRA9Xc6tXjK3bpkkdWafm2CqvjAUC3OXq6Q25urnJzc3v8RElJSYqPj+/xfgCOb/igGL1002S9u3W/fvn2NpUcOqKbX1yvi0cn6f68ccpIjLI6IgAcV8DmjJxxxhlKSUnRJZdcojVr1gTqaYGQYBiGLjs9RcvnXqBbLxguh83Qih2Vmv7IKj26nKEbAH2b38tISkqKnnrqKb3++ut6/fXXlZ6ergsvvFAbN27scp+mpia53e5OG4ATi3Y6dE/uaL13x3maOiJRza1ePbq8behmxXaGbgD0Tad01V7DMLRkyRJdfvnlPdrvggsuUEZGhl566aVj3v/f//3feuCBB75yO1ftBbrPNE29s6VcD769XfvdjZKk6WPahm7SExi6AeB/ffqqvZMnT1ZhYWGX98+bN081NTW+raSkJIDpgP7BMAx9a0KqVtx5gX54QZYcNkPLt1dq+oJVemz5LoZuAPQZlpSR/Px8paSkdHm/0+lUXFxcpw3AyYl2OjQvd4zeu+M8nTM8UU2tXj2yvEBff2S1Vu5g6AaA9Xp8Nk1dXV2noxp79uxRfn6+EhISlJGRoXnz5qm0tFQvvviiJOnRRx/VsGHDNG7cODU2NurZZ5/VypUr9f777/feqwBwQiOSYvXyzVP09uZyPfjONhUfatCsxes1fUyyrpw0RCOSYjQ0IUoOO2shAgisHpeR9evXa9q0ab6f586dK0maOXOmFi9erPLychUXF/vub25u1p133qnS0lJFRUVpwoQJWr58eaffASAwDMNQXnaqpo1O0hMrdukPH+3R8u0VWt4+uTXMbigzMVojkmJ82/BBbVtkuN3i9AD6q1OawBoo3Z0AA6BndlXU6veri7S93K3dB+rU2OI95uMMQ0qLj2wrKIM6F5UB0eEBTg0gWHT3+5syAkCS5PWaKq0+osIDddpdWafdB+pUWNm2HW5o6XK/xOhwDe84knJUUUlxRcgwjAC+AgB9DWUEQK85WNfUVkyOKihFB+pVWn2ky32iw+1tJWVQjK+sDB8Uo6GJUQpjXgoQEigjAPyuvqlVRQfqVXig1ldSCivrtPdgg1q9x/5oCbMbGpoY3ekoyoikGGUNilZUeI+nsQHowygjACzT4vFq78F6FVbWafeBel9J2X2gTg3NXa9vkhYfqaxB0Ro+6KjJs0nRGhTjZMgHCEKUEQB9jtdrqtzd2OkoSsf8lIP1zV3uFxfh0PCjzuxpKyrRyuBUZKBPo4wACCqH65tVVFXnO5qyu32OSsmhBnUx4uM7FbnjCErH0ZSsQTGKcTLkA1iNMgKgX2hs8WjvwQbfMI9vq6zXkeMsaT84LsJ3BOXoibRJsQz5AIFCGQHQr3UM+ew+aj5K2+nI9aqqa+pyv1inQ1kdJaV92GdiRryS4iICmB4IDZQRACGrpqFFu6uOKimV9dp9oE7FhxrkOcaYj9Nh0y+/M15XnZVuQVqg/6KMAMCXNLV6VHywwbeg2+4D9dpaWqNdlXWSpCvPHKL/+c54lr4Hekl3v7+Z4QUgZDgddo1MjtXI5FjfbV6vqd99WKgFywr01w37tKW0RouuO1PDBkZbmBQILZwTByCk2WyGbrtopP540xQNjAnXjv21ynviI/19S7nV0YCQQRkBAEnnjBiod+acp8mZCapratWPXt6oB/72mZpbj33xQAC9hzICAO2S4yL0yi1TdOsFwyVJz6/5XFc//e/jXoMHwKmjjADAURx2m+7JHa1nZkxSXIRDm4qr9a3H/6kPd1ZaHQ3otygjAHAMl4xN1jtzztP4tDgdbmjRjYvXacH7O495ajCAU0MZAYAupCdE6bVbz9G1UzJkmtLjKws147lPjruoGoCeo4wAwHFEhNn1q++erkevPkORYXatKTyobz7+T637/JDV0YB+gzICAN1weU6a3rptqkYkxajC3aT/ePpjPb16t4Jg3Uigz6OMAEA3jUyO1Zuzp+o7Z6TK4zX167/v0A9f2qCaIy1WRwOCGmUEAHog2unQo1efoQcvH69wu03vb6tQ3hMfaWtpjdXRgKBFGQGAHjIMQ9d9bahe+8+zNWRApIoPNeiKRf/SK58UM2wDnATKCACcpAlD4vXOj8/TxaOT1Nzq1c+XbNGdf/lUDc2tVkcDggplBABOgSsqTM/MmKS7vzFadpuhNzaV6vKFa1TYfiVgACdGGQGAU2SzGfrPC4frlZunaFCsUwUVdfr2kx/prU/LrI4GBAXKCAD0kilZiXpnzrk6OytRDc0ezfnTJt335lY1tXqsjgb0aZQRAOhFSbEReummyZo9re1iey/+e6+ueurfKjnUYHEyoO+ijABAL3PYbbrr0tF67oZJckWG6dN9NfrWEx9p5Y4Kq6MBfRJlBAD85KLRyXpnzrnKHuJSzZEWzVq8Xg+/t0OtHq/V0YA+hTICAH40ZECU/nLr2Zp59lBJ0u8+3K1rn/1ElbWNFicD+g7KCAD4mdNh1wPfGa8nvp+j6HC7PtlzSN98/CN9XHTQ6mhAn0AZAYAAyctO1Vs/PlenJcfoQG2TrnnmYy38oFBeL6u2IrQZZhCsXex2u+VyuVRTU6O4uDir4wDAKWlobtV/LdmqNzaVSpLOHTFQl4xN1sjkGI1KjlVijNPihEDv6O73N2UEACxgmqZeXVei+9/6TM2tnSe0DowJ12nJsb5t1OAYjUyOVVxEmEVpgZNDGQGAIFBQUaulm0pVUFGngopaFR9nPZIUV0R7OYnVyKQYjRocqxFJMYoKdwQwMdB9lBEACEL1Ta0qrGwrJgUVtdpZUaddFbUqrzn22TeGIaUPiPIdQek4mpI1KFpOhz3A6YHOKCMA0I/UHGnRropa3xGUnftrtauyVlV1zcd8vN1maNjAaJ2W3FZQRiXHamRyrDITo+Swc+4CAoMyAgAhoKquSQUVtdpVUaedFbUq2F+rnRW1qm1sPebjw+02DU+K6VRShifFKDLMLofdkMNmyGG3tf1rM2S3GTIMI8CvCv1Fd7+/GWgEgCA2MMapgTFOnTN8oO820zRV4W7ylZOOIZ+CijodafFoe7lb28vd3X6OjlLSqajYDTlsNjnsbfeF2Wxt/9o7HnvUffaj77MprOP3tf+uMLtNE4a4NG1UklxRTNINRZQRAOhnDMPQYFeEBrsidMFpg3y3e72mSquPaGf70ZNd7XNSPq+qV4vHq9Yu1jtp9Zpq9ZpqkiT57wrEdpuhyZkJmj42WZeMSVZGYpTfngt9C8M0AABJbUdUPO3Fo9VrqrW9oLR6TLV6ve3/tj2mxeNtf+wXt3e1zxe/0/vF7b7HmKprbNWawirtrKjtlGdUcqymj03SJWMHa0KaSzYbw0XBhjkjAICgUnywQcu2V2j5tgqt/fyQPEcdqRkU69T0MUm6ZGyyzhk+UBFhnCkUDCgjAICgVdPQog92VmrZ9gqt2nlAdU1fTMiNDLPrvJFtq9ZeNDqJFWv7MMoIAKBfaG716uOig1reftSk7Kg1V2yGdObQAZo+JlnTxyZr+KAYC5PiyygjAIB+xzRNfVbm1vLtFVq2rUKflXU+KyhrULQuGZOsS8YmKydjgOzMM7EUZQQA0O+VVR/xFZOPiw6qxfPFV1pCdLguGt02z+S8kQNZNt8ClBEAQEipbWzRqoIDWr6tQit3VMp91MJv4Q6b7+rIF49OUlJchIVJQwdlBAAQslo8Xq37/JCWb6vUsu37VXLoSKf7z0iP1yVjkzV9TLJOS45hlVk/oYwAAKC2eSYFFXVavr1C72+r0Kcl1Z3uz0iI0qTMARqaEK2hiVHKSIxSRkKUEqPDKSmniDICAMAxVLobtWJHpZZtq9BHhVVqbvUe83HR4XalJ0RpaGKUhiZGt/3vhLaikjYgUmFccPCEKCMAAJxAQ3Or1hQeVEFFrfYerFfxoQYVH2xQubtRx/t2tNsMpcZHKCMhShkdR1Tai0pGYpTiIrjGjkQZAQDgpDW2eFRafUTFBxtUfKhBew82qPhQe1k51KDGlmMfTekwICpMGYnRyjjqaEpGYttRluTYiJBZ2p6r9gIAcJIiwuwaPijmmIuomaapytqmo0pKg4oP1mvvoQaVHGpQVV2zDje06HBD9Vfmp0htZ/akD4hsKyodhSUxSsMGtg0FheLwD0dGAADoRXVNrSr50tGUjtJSevhIl1dHliSHzVBGQlsxGTYwWsMGRWtYYtu/g+Migm5CLUdGAACwQIzToTEpcRqT8tUv31aPV+U1jb6CsvdQvYoPtv3vPVX1OtLiUVFVvYqq6r+yb2SYXZkDo5XVUVTay0rWwGjFR4UH4qX5DUdGAADoA0zTVIW7SUVVddpTVa89B+rb/q1qO7pyvCMqA6LC2gtKjLIGfVFWMhOjFRlu3RWOmcAKAEA/0eLxat/hI9pTVaeio0rKnqp6lR914cBjSXVFtA33dJSV9qIyZECkHH6en0IZAQAgBDQ0t+rzqob2clKnovaSUnSgXjVHWrrcz2EzlJEY5Ssn385O0+lDXL2ajTkjAACEgKhwh8amxmls6le/7A/XN/vKyZ724Z+iA/X6/GC9Glu8KjrQ9rMkjU9z9XoZ6S7KCAAA/dSA6HCdGR2uM4cO6HS712tqv7uxrZy0z085Pc2aIiJJPR4sWr16tfLy8pSamirDMLR06dJu77tmzRo5HA6dccYZPX1aAADQS2w2Q6nxkZo6YqCu/9pQ3Zc3VlnHWFMlYHl6ukN9fb2ys7O1cOHCHu1XXV2tGTNm6OKLL+7pUwIAgH6sx8M0ubm5ys3N7fET3Xrrrbrmmmtkt9t7dDQFAAD0bwFZc/b5559XUVGR7r///m49vqmpSW63u9MGAAD6J7+XkV27dumee+7RH//4Rzkc3TsQM3/+fLlcLt+Wnp7u55QAAMAqfi0jHo9H11xzjR544AGddtpp3d5v3rx5qqmp8W0lJSV+TAkAAKzk11N7a2trtX79em3atEm33XabJMnr9co0TTkcDr3//vu66KKLvrKf0+mU0+n0ZzQAANBH+LWMxMXFacuWLZ1u+93vfqeVK1fqtdde07Bhw/z59AAAIAj0uIzU1dWpsLDQ9/OePXuUn5+vhIQEZWRkaN68eSotLdWLL74om82m8ePHd9o/KSlJERERX7kdAACEph6XkfXr12vatGm+n+fOnStJmjlzphYvXqzy8nIVFxf3XkIAANCvcaE8AADgF939/g7IOiMAAABdoYwAAABLUUYAAICl/Hpqb2/pmNbCsvAAAASPju/tE01PDYoyUltbK0ksCw8AQBCqra2Vy+Xq8v6gOJvG6/WqrKxMsbGxMgyj136v2+1Wenq6SkpKQvYsnVB/D0L99Uu8B7z+0H79Eu+BP1+/aZqqra1VamqqbLauZ4YExZERm82mIUOG+O33x8XFheQf4NFC/T0I9dcv8R7w+kP79Uu8B/56/cc7ItKBCawAAMBSlBEAAGCpkC4jTqdT999/f0hfITjU34NQf/0S7wGvP7Rfv8R70Bdef1BMYAUAAP1XSB8ZAQAA1qOMAAAAS1FGAACApSgjAADAUiFdRhYuXKjMzExFRERoypQpWrt2rdWRAmL+/Pk666yzFBsbq6SkJF1++eXauXOn1bEs9b//+78yDEN33HGH1VECprS0VNddd50SExMVGRmp008/XevXr7c6VsB4PB7de++9GjZsmCIjIzV8+HD98pe/POE1NILV6tWrlZeXp9TUVBmGoaVLl3a63zRN3XfffUpJSVFkZKSmT5+uXbt2WRPWT473HrS0tOjuu+/W6aefrujoaKWmpmrGjBkqKyuzLnAvO9HfwNFuvfVWGYahRx99NCDZQraM/PnPf9bcuXN1//33a+PGjcrOztall16qyspKq6P53apVqzR79mx9/PHHWrZsmVpaWvT1r39d9fX1VkezxLp16/T73/9eEyZMsDpKwBw+fFhTp05VWFiY3n33XW3btk2//e1vNWDAAKujBcxDDz2kRYsW6cknn9T27dv10EMP6eGHH9YTTzxhdTS/qK+vV3Z2thYuXHjM+x9++GE9/vjjeuqpp/TJJ58oOjpal156qRobGwOc1H+O9x40NDRo48aNuvfee7Vx40a98cYb2rlzp7797W9bkNQ/TvQ30GHJkiX6+OOPlZqaGqBkkswQNXnyZHP27Nm+nz0ej5mammrOnz/fwlTWqKysNCWZq1atsjpKwNXW1pojR440ly1bZl5wwQXm7bffbnWkgLj77rvNc8891+oYlvrmN79pzpo1q9NtV1xxhXnttddalChwJJlLlizx/ez1es3Bgwebv/nNb3y3VVdXm06n0/zTn/5kQUL/+/J7cCxr1641JZl79+4NTKgA6ur179u3z0xLSzO3bt1qDh061HzkkUcCkickj4w0Nzdrw4YNmj59uu82m82m6dOn69///reFyaxRU1MjSUpISLA4SeDNnj1b3/zmNzv9LYSCt956S5MmTdKVV16ppKQk5eTk6JlnnrE6VkCdc845WrFihQoKCiRJn376qT766CPl5uZanCzw9uzZo/3793f678DlcmnKlCkh+ZnYoaamRoZhKD4+3uooAeH1enX99dfrrrvu0rhx4wL63EFxobzeVlVVJY/Ho+Tk5E63Jycna8eOHRalsobX69Udd9yhqVOnavz48VbHCahXX31VGzdu1Lp166yOEnBFRUVatGiR5s6dq5///Odat26d5syZo/DwcM2cOdPqeAFxzz33yO12a/To0bLb7fJ4PPrVr36la6+91upoAbd//35JOuZnYsd9oaaxsVF33323vv/974fMxfMeeughORwOzZkzJ+DPHZJlBF+YPXu2tm7dqo8++sjqKAFVUlKi22+/XcuWLVNERITVcQLO6/Vq0qRJ+vWvfy1JysnJ0datW/XUU0+FTBn5y1/+opdfflmvvPKKxo0bp/z8fN1xxx1KTU0NmfcAx9bS0qKrrrpKpmlq0aJFVscJiA0bNuixxx7Txo0bZRhGwJ8/JIdpBg4cKLvdroqKik63V1RUaPDgwRalCrzbbrtNb7/9tj744AMNGTLE6jgBtWHDBlVWVmrixIlyOBxyOBxatWqVHn/8cTkcDnk8Hqsj+lVKSorGjh3b6bYxY8aouLjYokSBd9ddd+mee+7Rf/zHf+j000/X9ddfr5/85CeaP3++1dECruNzL9Q/E6UvisjevXu1bNmykDkq8s9//lOVlZXKyMjwfSbu3btXd955pzIzM/3+/CFZRsLDw3XmmWdqxYoVvtu8Xq9WrFihs88+28JkgWGapm677TYtWbJEK1eu1LBhw6yOFHAXX3yxtmzZovz8fN82adIkXXvttcrPz5fdbrc6ol9NnTr1K6dzFxQUaOjQoRYlCryGhgbZbJ0/Au12u7xer0WJrDNs2DANHjy402ei2+3WJ598EhKfiR06isiuXbu0fPlyJSYmWh0pYK6//npt3ry502diamqq7rrrLv3jH//w+/OH7DDN3LlzNXPmTE2aNEmTJ0/Wo48+qvr6et14441WR/O72bNn65VXXtGbb76p2NhY35iwy+VSZGSkxekCIzY29itzZKKjo5WYmBgSc2d+8pOf6JxzztGvf/1rXXXVVVq7dq2efvppPf3001ZHC5i8vDz96le/UkZGhsaNG6dNmzZpwYIFmjVrltXR/KKurk6FhYW+n/fs2aP8/HwlJCQoIyNDd9xxhx588EGNHDlSw4YN07333qvU1FRdfvnl1oXuZcd7D1JSUvS9731PGzdu1Ntvvy2Px+P7bExISFB4eLhVsXvNif4Gvly+wsLCNHjwYI0aNcr/4QJyzk4f9cQTT5gZGRlmeHi4OXnyZPPjjz+2OlJASDrm9vzzz1sdzVKhdGqvaZrm3/72N3P8+PGm0+k0R48ebT799NNWRwoot9tt3n777WZGRoYZERFhZmVlmb/4xS/MpqYmq6P5xQcffHDM/+5nzpxpmmbb6b333nuvmZycbDqdTvPiiy82d+7caW3oXna892DPnj1dfjZ+8MEHVkfvFSf6G/iyQJ7aa5hmP11uEAAABIWQnDMCAAD6DsoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALAUZQQAAFiKMgIAACz1/wGyLkrPdy6AdQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_net(conv_net, train_dl, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f3582aa6-9610-4bfa-a76c-e06901fd2df4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:08<00:00, 153.44it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.478"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_net(conv_net, test_dl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e3a8668",
   "metadata": {},
   "source": [
    "## Task 3: Measure dependence of validation quality on CIFAR10 dataset from the number of parameters in your CNN. Create at least 10 different measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5c71b3e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def try_parameters(hidden_layers_channels, \n",
    "                   kernels,\n",
    "                   epochs = 1,\n",
    "                   use_pooling = True):\n",
    "    \"\"\"Utility method to try different conv net configurations.\n",
    "    \n",
    "    Re-trains the model with the given set of parameters and\n",
    "    returns the accuracy on test set.\n",
    "    \"\"\"\n",
    "    \n",
    "    conv_net = ConvNet(input_shape=(3, 32, 32),\n",
    "                       hidden_layers_channels=hidden_layers_channels,\n",
    "                       kernels=kernels,\n",
    "                       use_pooling=use_pooling) \n",
    "    \n",
    "    train_net(conv_net, train_dl, epochs=epochs, debug=True)\n",
    "    return test_net(conv_net, test_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "540773b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:11<00:00, 87.96it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:15<00:00, 83.17it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:11<00:00, 87.80it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:13<00:00, 84.88it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:16<00:00, 81.56it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:07<00:00, 166.90it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4694"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shallow network with only 2 conv. layers and 3 default fully connected.\n",
    "# Works really good but worse than the baseline.\n",
    "try_parameters(hidden_layers_channels=[8, 16],\n",
    "               kernels=[3, 3],\n",
    "               epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f3d066c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:19<00:00, 78.65it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:19<00:00, 78.92it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:18<00:00, 79.66it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:31<00:00, 68.04it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:22<00:00, 76.21it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:19<00:00, 78.62it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:21<00:00, 76.22it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:21<00:00, 77.01it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:20<00:00, 77.91it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:37<00:00, 64.29it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:11<00:00, 110.47it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4137"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attempt to increase the depth and width of the network.\n",
    "# Unfortunately, the network declines in quality. Let's\n",
    "# try more deeper network before coming to a conclusion.\n",
    "try_parameters(hidden_layers_channels=[8, 16, 32],\n",
    "               kernels=[3, 3, 3],\n",
    "              epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9d48d8a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:55<00:00, 54.18it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:45<00:00, 59.26it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:44<00:00, 59.66it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:51<00:00, 56.10it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:51<00:00, 56.19it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:45<00:00, 59.28it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:44<00:00, 59.75it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:09<00:00, 128.81it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attempt to create a bigger network.\n",
    "# We need to disable pooling, otherwise,\n",
    "# the width and height will shrink too fast.\n",
    "# The model works quite terrible. Looking at the\n",
    "# diagram it does not look like the model is learning\n",
    "# something and it makes me think\n",
    "# that it is not worth it to make the network deeper.\n",
    "# Maybe the dataset is too small or we are not training\n",
    "# it long enough? \n",
    "try_parameters(hidden_layers_channels=[8, 16, 32, 64, 128, 256],\n",
    "               kernels=[5, 5, 5, 3, 3, 3],\n",
    "               use_pooling=False,\n",
    "               epochs=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fc3beddb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:17<00:00, 81.11it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:17<00:00, 80.47it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:22<00:00, 75.38it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:08<00:00, 150.14it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.441"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attempt to increase convolution kernel size\n",
    "# in order to learn more information/more complicated features from the image.\n",
    "# Still works worse than the very first network.\n",
    "try_parameters(hidden_layers_channels=[8, 16],\n",
    "               kernels=[5, 5],\n",
    "               epochs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9a0b53fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:37<00:00, 64.11it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:39<00:00, 62.57it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:31<00:00, 68.46it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:30<00:00, 69.27it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:27<00:00, 71.07it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:09<00:00, 133.07it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4165"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attempt to find the right balance between shallow (2 conv. layers) and deep(6 conv. layers) network.\n",
    "# We need to disable pooling, otherwise,\n",
    "# the width and height will shrink too fast.\n",
    "# The quality is still not that good as for a simpler model. The hyphotesis is that the pooling layer\n",
    "# helps to ignore some non-important noise.\n",
    "try_parameters(hidden_layers_channels=[8, 16, 32, 64],\n",
    "               kernels=[3, 3, 3, 3],\n",
    "               use_pooling=False,\n",
    "               epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1dc194b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:14<00:00, 84.01it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:12<00:00, 86.54it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:17<00:00, 80.89it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:22<00:00, 76.04it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:23<00:00, 74.60it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:07<00:00, 170.51it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5303"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Back to our shallow network. Let's try to make it wider.\n",
    "# The accuracy is better than the baseline. I think making the\n",
    "# model wider is the right direction for future improvements.\n",
    "try_parameters(hidden_layers_channels=[16, 32],\n",
    "               kernels=[3, 3],\n",
    "               epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dab034d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:19<00:00, 78.52it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:21<00:00, 76.37it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:21<00:00, 76.71it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:23<00:00, 74.94it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:18<00:00, 79.95it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:08<00:00, 147.10it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5359"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's go even wider.\n",
    "# Though the accuracy improved just a bit.\n",
    "# Maybe the convolution kernel is too small to learn imporant features?\n",
    "try_parameters(hidden_layers_channels=[32, 64],\n",
    "               kernels=[3, 3],\n",
    "               epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "db84e159",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:20<00:00, 77.18it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:40<00:00, 61.95it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:32<00:00, 67.75it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:35<00:00, 65.33it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:33<00:00, 67.10it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:10<00:00, 115.30it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5517"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Increasing the kernel size for both conv. layers.\n",
    "# It boost the quality to ~55%.\n",
    "# Seems increased kernel size helped with capturing some\n",
    "# important features.\n",
    "try_parameters(hidden_layers_channels=[32, 64],\n",
    "               kernels=[5, 5],\n",
    "               epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ffb378d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:19<00:00, 79.06it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:18<00:00, 79.55it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:19<00:00, 78.19it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:13<00:00, 84.76it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:17<00:00, 80.18it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:07<00:00, 165.21it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5672"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making our shallow network more wider.\n",
    "# Still able to improve the quality up to 56.7%.\n",
    "try_parameters(hidden_layers_channels=[64, 64],\n",
    "               kernels=[5, 5],\n",
    "               epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d332f9e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:16<00:00, 82.21it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:18<00:00, 79.60it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:16<00:00, 82.18it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:16<00:00, 81.86it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:14<00:00, 83.42it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGeCAYAAABGlgGHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA4nUlEQVR4nO3deXhU9cH28XuWZBKyTAiQfZV9jdEAVbSKQn3QprW+1rqCUn3qU6xarK+lVXxsa3m0i1t5tbYq7nZRsPpYFTdwhwCjKMgigYQQEgIkk3WSzJz3jyQjCIEMZObMZL6f6zoXTOYc5p5cMXN7fr/zOxbDMAwBAACYxGp2AAAAEN0oIwAAwFSUEQAAYCrKCAAAMBVlBAAAmIoyAgAATEUZAQAApqKMAAAAU1FGAACAqexmB+gLn8+nXbt2KSkpSRaLxew4AACgDwzDUGNjo7KysmS1HuH8hxGgFStWGN/+9reNzMxMQ5KxdOnSox7z1FNPGZMmTTLi4+ONjIwM46qrrjLq6ur6/JqVlZWGJDY2NjY2NrYI3CorK4/4OR/wmZHm5mYVFRVp7ty5uuCCC466//vvv6/Zs2frnnvuUWlpqaqqqnTttdfqmmuu0QsvvNCn10xKSpIkVVZWKjk5OdDIAADABG63W7m5uf7P8d4EXEZmzZqlWbNm9Xn/Dz/8UAUFBbr++uslSYWFhfrRj36ku+66q8//Rs/QTHJyMmUEAIAIc7QpFkGfwHrKKaeosrJSr7zyigzDUE1Njf75z3/q3HPP7fUYj8cjt9t90AYAAAamoJeRadOm6emnn9YPfvADxcbGKiMjQ06nU4sXL+71mEWLFsnpdPq33NzcYMcEAAAmCXoZ2bBhg2644QYtXLhQa9as0auvvqrt27fr2muv7fWYBQsWqKGhwb9VVlYGOyYAADBJ0C/tXbRokaZNm6abb75ZkjRp0iQlJCTo9NNP129+8xtlZmYecozD4ZDD4Qh2NAAAEAaCfmakpaXlkGuLbTabJMkwjGC/PAAACHMBl5Gmpia5XC65XC5JUnl5uVwulyoqKiR1DbHMnj3bv39paaleeOEFPfjgg9q2bZvef/99XX/99ZoyZYqysrL6510AAICIFfAwTVlZmaZPn+5/PH/+fEnSnDlztGTJElVXV/uLiSRdeeWVamxs1J/+9CfddNNNSklJ0VlnnRXQpb0AAGDgshgRMFbidrvldDrV0NDAOiMAAESIvn5+c6M8AABgKsoIAAAwFWUEAACYijICAABMFdVl5OVPd+nmf3yi9TsbzI4CAEDUiu4y8km1/rFmp97bWmd2FAAAolZUl5HJhamSpNXb95mcBACA6BXdZaRgsCSpbPs++Xxhv9wKAAADUlSXkXGZyRoUa5O7rVNbapvMjgMAQFSK6jJit1l1Ul7X2ZFVDNUAAGCKqC4jklRywFANAAAIvagvI5MLuiaxlm3fb3ISAACiU9SXkeK8FNmsFlXVt6qqvtXsOAAARJ2oLyODYu2akNV1J0GGagAACL2oLyOSVFLAeiMAAJiFMqKv1htZXc68EQAAQo0yoq/OjGyqaVRDS4fJaQAAiC6UEUlDEx06YWiCJGlNBUM1AACEEmWkW896I6u5xBcAgJCijHTrWW9kdTlnRgAACCXKSLeeMvLpzga1dXhNTgMAQPSgjHTLHzJIQxMdavf6tL6qwew4AABEDcpIN4vFoimF3TfNY6gGAICQoYwcoCS/5z41lBEAAEKFMnIA/03zduyXz2eYnAYAgOhAGTnA2MwkJcTa1NjWqU01jWbHAQAgKlBGDmC3WXVSfte8EYZqAAAIDcrI1/TMG2HxMwAAQoMy8jU9N83jzAgAAKFBGfmaE/NSZLdatKuhTTv3t5gdBwCAAY8y8jWDYu0an+2UJJUxVAMAQNBRRg5jcn7PTfMYqgEAINgoI4cxubBnEitlBACAYKOMHEZJ95mRzTVNqm9pNzkNAAADG2XkMIYkOnTCsARJ0podzBsBACCYKCO9mNK9NPwqhmoAAAgqykgvSnruU8MVNQAABBVlpBc9i599urNebR1ek9MAADBwUUZ6kZc6SMOSHOrwGvqkst7sOAAADFiUkV5YLBb/vJEyJrECABA0lJEjKClg8TMAAIIt4DKycuVKlZaWKisrSxaLRcuWLTvi/ldeeaUsFssh2/jx4481c8hM7j4zsmbHfnl9hslpAAAYmAIuI83NzSoqKtLixYv7tP99992n6upq/1ZZWanU1FR9//vfDzhsqI3JSFKiw67Gtk5t2t1odhwAAAYke6AHzJo1S7Nmzerz/k6nU06n0/942bJl2r9/v6666qpAXzrk7DarivNS9O6WOpXt2KdxWclmRwIAYMAJ+ZyRRx55RDNmzFB+fn6v+3g8Hrnd7oM2s/QM1axmvREAAIIipGVk165d+ve//62rr776iPstWrTIf0bF6XQqNzc3RAkP5S8j5ftkGMwbAQCgv4W0jDz++ONKSUnR+eeff8T9FixYoIaGBv9WWVkZmoCHcWJuiuxWi3a727Rzf6tpOQAAGKhCVkYMw9Cjjz6qK664QrGxsUfc1+FwKDk5+aDNLPGxNk3I7przUraDS3wBAOhvISsjK1as0NatW/XDH/4wVC/Zb3qWhl9VzrwRAAD6W8BlpKmpSS6XSy6XS5JUXl4ul8uliooKSV1DLLNnzz7kuEceeURTp07VhAkTji+xCSb7b5rHmREAAPpbwGWkrKxMxcXFKi4uliTNnz9fxcXFWrhwoSSpurraX0x6NDQ06Pnnn4/IsyKSdHJ+15mRLbVN2t/cbnIaAAAGloDXGTnzzDOPeFXJkiVLDvma0+lUS0tLoC8VNoYkOjR8WIK+3NOssh37NXNcutmRAAAYMLg3TR9NKWSoBgCAYKCM9FFJfs/iZ5QRAAD6E2Wkj3omsa6valBbh9fkNAAADByUkT7KTY1XerJDHV5Drsp6s+MAADBgUEb6yGKxqIRLfAEA6HeUkQBM7r7El5vmAQDQfygjAZjcfUXN2h375fVx0zwAAPoDZSQAYzKSleiwq9HTqS92u82OAwDAgEAZCYDNatFJ3UM1ZQzVAADQLygjAeqZN7KKSawAAPQLykiAJh+wEuuRlsUHAAB9QxkJUFFOimJsFtW4Pdq5v9XsOAAARDzKSIDiY22akO2UJK0qZ6gGAIDjRRk5BlN6Fj/bQRkBAOB4UUaOQc9KrCx+BgDA8aOMHIOS7itqttY2aV9zu8lpAACIbJSRYzA4IVYj0xIlcZ8aAACOF2XkGPlvmreDoRoAAI4HZeQYTS7ouWkeZ0YAADgelJFjNLn7zMj6nQ1qbfeanAYAgMhFGTlGOYPjlZEcp06fIVdlvdlxAACIWJSRY2SxWFRS0HPTPIZqAAA4VpSR49AzVMNN8wAAOHaUkePQU0bW7tivTq/P5DQAAEQmyshxGJ2RpCSHXc3tXn2xu9HsOAAARCTKyHGwWS06KZ9LfAEAOB6UkeM0pbB78TPuUwMAwDGhjBynkgPOjBiGYXIaAAAiD2XkOBXlpijGZlFto0eV+1rNjgMAQMShjBynuBibJuWkSOISXwAAjgVlpB+w+BkAAMeOMtIPJud3TWLlihoAAAJHGekHJ3dPYv1yT7P2NnlMTgMAQGShjPSDwQmxGpWeKEkq28ElvgAABIIy0k9KCnrWG2GoBgCAQFBG+snk7kmsq1j8DACAgFBG+knPTfM+r2pQS3unyWkAAIgclJF+kp0Sr0xnnDp9hlyV9WbHAQAgYlBG+onFYjlg3ghDNQAA9BVlpB9NKeAOvgAABIoy0o96zoys3bFfnV6fyWkAAIgMAZeRlStXqrS0VFlZWbJYLFq2bNlRj/F4PPrlL3+p/Px8ORwOFRQU6NFHHz2WvGFtVHqSkuLsam736ovdjWbHAQAgItgDPaC5uVlFRUWaO3euLrjggj4dc9FFF6mmpkaPPPKIRowYoerqavl8A+/Mgc1qUUn+YL29aY9Wle/ThGyn2ZEAAAh7AZeRWbNmadasWX3e/9VXX9WKFSu0bds2paZ2DWMUFBQE+rIRo6QgVW9v2qOyHfs097RCs+MAABD2gj5n5F//+pdKSkp09913Kzs7W6NGjdLPfvYztba29nqMx+OR2+0+aIsUPeuNrN6+X4ZhmJwGAIDwF/CZkUBt27ZN7733nuLi4rR06VLV1dXpxz/+sfbu3avHHnvssMcsWrRId9xxR7CjBcWkHKdibVbtafRox94WFQxNMDsSAABhLehnRnw+nywWi55++mlNmTJF5557rv74xz/q8ccf7/XsyIIFC9TQ0ODfKisrgx2z38TF2DQpp2uuCJf4AgBwdEEvI5mZmcrOzpbT+dVkzrFjx8owDO3cufOwxzgcDiUnJx+0RRIWPwMAoO+CXkamTZumXbt2qampyf+1zZs3y2q1KicnJ9gvb4rJLH4GAECfBVxGmpqa5HK55HK5JEnl5eVyuVyqqKiQ1DXEMnv2bP/+l156qYYMGaKrrrpKGzZs0MqVK3XzzTdr7ty5io+P7593EWZK8rvOjGyra1Zdk8fkNAAAhLeAy0hZWZmKi4tVXFwsSZo/f76Ki4u1cOFCSVJ1dbW/mEhSYmKili9frvr6epWUlOiyyy5TaWmp7r///n56C+HHOShGo9OTJDFUAwDA0ViMCLj+1O12y+l0qqGhIWLmj/xy6Xo9/XGFrj6tULd+e5zZcQAACLm+fn5zb5ogmVLYs94I80YAADgSykiQ9FxR89kut1raO01OAwBA+KKMBEl2SryynHHy+gy5KurNjgMAQNiijARRz9mRVQzVAADQK8pIEE0uZPEzAACOhjISRD2Ln62t2K9Or8/kNAAAhCfKSBCNSktScpxdLe1ebaiOnDsPAwAQSpSRILJaLf55I6sZqgEA4LAoI0FW0j1UU8YkVgAADosyEmSTC75a/CwCFrsFACDkKCNBNinHqVi7VXVN7dq+t8XsOAAAhB3KSJA57DYV5TglsTQ8AACHQxkJgZ5JrMwbAQDgUJSREJjCFTUAAPSKMhICJ+UNlsUildc1a0+jx+w4AACEFcpICDgHxWh0epIkac0OhmoAADgQZSREetYbWVXOUA0AAAeijIRIz3ojZZwZAQDgIJSREOkpI5/vcqvZ02lyGgAAwgdlJESyUuKVnRIvr8/Quop6s+MAABA2KCMhNLl73giLnwEA8BXKSAiVMG8EAIBDUEZCqGfeyNod9erw+kxOAwBAeKCMhNDItEQ542PU2uHVhl1us+MAABAWKCMhZLVaVJLPvBEAAA5EGQmxr26ax+JnAABIlJGQO/CKGsMwTE4DAID5KCMhNjHHqVi7VXub21Ve12x2HAAATEcZCTGH3aYTc1IkMVQDAIBEGTGF/6Z5TGIFAIAyYobJhT2TWCkjAABQRkxwUt5gWSzS9r0tqm1sMzsOAACmooyYwBkfo9HpSZKYNwIAAGXEJFO6h2pY/AwAEO0oIyZh8TMAALpQRkzSs/jZ57sa1OTpNDkNAADmoYyYJNMZr5zB8fIZ0roKzo4AAKIXZcREk7uHat7bUmdyEgAAzEMZMdGZo4dJkv7y7ja99vluk9MAAGAOyoiJvlOUpR+U5MpnSD95dp0+/HKv2ZEAAAg5yoiJLBaL7vzeBJ0zPl3tnT5d80SZ1u9sMDsWAAAhFXAZWblypUpLS5WVlSWLxaJly5Ydcf933nlHFovlkG33boYlJMlus+q+i4t1yglD1OTp1JzHVunLPU1mxwIAIGQCLiPNzc0qKirS4sWLAzpu06ZNqq6u9m9paWmBvvSAFRdj08OzT9bEbKf2Nbdr9iOrVN3QanYsAABCwh7oAbNmzdKsWbMCfqG0tDSlpKQEfFy0SIqL0ZKrJuv7D32obXXNuuKRVfrHj07R4IRYs6MBABBUIZszcuKJJyozM1MzZ87U+++/f8R9PR6P3G73QVs0GJLo0JNXT1WmM05ba5t05ZLVamZBNADAABf0MpKZmamHHnpIzz//vJ5//nnl5ubqzDPP1Nq1a3s9ZtGiRXI6nf4tNzc32DHDRnZKvJ784RQNHhSjTyrrde1Ta+Tp9JodCwCAoLEYhmEc88EWi5YuXarzzz8/oOPOOOMM5eXl6cknnzzs8x6PRx6Px//Y7XYrNzdXDQ0NSk5OPta4EcVVWa9L//KRWtq9Om9ipu6/pFg2q8XsWAAA9Jnb7ZbT6Tzq57cpl/ZOmTJFW7du7fV5h8Oh5OTkg7Zoc2Juih6+okQxNov+d321bnvxMx1HbwQAIGyZUkZcLpcyMzPNeOmIctrIobrv4mJZLdIzH1foD69vNjsSAAD9LuCraZqamg46q1FeXi6Xy6XU1FTl5eVpwYIFqqqq0hNPPCFJuvfee1VYWKjx48erra1Nf/3rX/XWW2/p9ddf7793MYCdOzFTd35voha8sF5/enurUgbF6OrTTzA7FgAA/SbgMlJWVqbp06f7H8+fP1+SNGfOHC1ZskTV1dWqqKjwP9/e3q6bbrpJVVVVGjRokCZNmqQ33njjoH8DR3bJlDztb2nX3a9u0m/+d6NSBsXqwpNzzI4FAEC/OK4JrKHS1wkwA5lhGPrtKxv1l3fLZbNa9NDlJ2vmuHSzYwEA0KuwnsCKwFksFv3i3LG68OQceX2G5j2zVh9t48Z6AIDIRxmJIBaLRf9zwUTNGNt9Y73Hy/RZFTfWAwBENspIhLHbrPrTpcWaWpiqRk+nrnxslcrrms2OBQDAMaOMRKC4GJv+MqdE47OSVdfUrsv/+rF2N7SZHQsAgGNCGYlQyXExenzuFBUOTVBVfatmP/qx6lvazY4FAEDAKCMRbGiiQ0/MnaKM5DhtrmnSVUtWq6WdG+sBACILZSTC5aYO0pM/nKKUQTFaV1Gva59aq/ZOn9mxAADoM8rIADAyPUmPXTlZg2JtWrl5j+b/3SWvL+yXjwEAQBJlZMAozhushy4/WTE2i17+tFoLubEeACBCUEYGkG+OGqZ7fnCiLBbp6Y8rdM9ybqwHAAh/lJEB5tuTsvTr706QJN3/1lY9+l65yYkAADgyysgAdPk38vWzb42SJP3q5Q1aum6nyYkAAOgdZWSAmjd9hOZOK5Qk/ewfn+qtL2pMTgQAwOFRRgYoi8WiW88bqwuKs+X1Gfqvp9ZqVfk+s2MBAHAIysgAZrVadNeFk3T2mDR5On364eOrtWGX2+xYAAAchDIywMXYrFp82UmaUpCqxrZOzX50lXbs5cZ6AIDwQRmJAnExNv31yhKNzUxWXZNHlz/ysWrd3FgPABAeKCNRIjkuRk/MnaKCIYNUua9VVzyySg0tHWbHAgCAMhJNhiU59OQPpyotyaFNNY2a+zg31gMAmI8yEmW6bqw3Vclxdq3ZsV//xY31AAAmo4xEodEZSXrsqimKj7FpxeY9WvDCeu5jAwAwDWUkSp2cP1gPXn6SbFaLnl+7U/9cwyqtAABzUEai2Jmj0zR/Ztey8Qtf/Fxba5tMTgQAiEaUkSj3X2cM17QRQ9Ta4dVPnl2ntg6v2ZEAAFGGMhLlrFaL7rnoRA1JiNXGarcWvbLR7EgAgChDGYHSkuP0h4uKJEmPf7hDr3++2+REAIBoQhmBpK75I9ec3nWX35v/+al21beanAgAEC0oI/C7+ZwxmpTjVENrh258zqVOL+uPAACCjzICv1i7VQ9cUqxEh12rtu/TA29tNTsSACAKUEZwkPwhCbrzexMkSQ+8tUUfbdtrciIAwEBHGcEhvntitr5/co58hnTjcy7ta243OxIAYACjjOCw7vjueJ0wLEG73W36v//8hOXiAQBBQxnBYQ2KteuBS4oVa7fqjY21WvLBdrMjAQAGKMoIejU+y6lfnjtWkrTolS/0WVWDyYkAAAMRZQRHNPuUfM0cl652r08/eXadmj2dZkcCAAwwlBEckcVi0e8unKRMZ5zK65q18MXPzY4EABhgKCM4qpRBsbrv4mJZLdLza3dq6bqdZkcCAAwglBH0yZTCVN1w9ihJ0q1LP1N5XbPJiQAAAwVlBH123VkjNLUwVc3tXv3k2bXydHrNjgQAGAAoI+gzm9Wiey8+UYMHxeizKrfufnWT2ZEAAAMAZQQByXTG63cXFkmSHnmvXG99UWNyIgBApAu4jKxcuVKlpaXKysqSxWLRsmXL+nzs+++/L7vdrhNPPDHQl0UYmTEuXVeeWiBJ+tk/PlWNu83cQACAiBZwGWlublZRUZEWL14c0HH19fWaPXu2zj777EBfEmFowbljND4rWfua23Xjcy55fSwXDwA4NgGXkVmzZuk3v/mNvve97wV03LXXXqtLL71Up5xySqAviTDksNv0wCXFGhRr04fb9ur/vb3V7EgAgAgVkjkjjz32mLZt26bbb7+9T/t7PB653e6DNoSfE4Yl6tffnSBJuvfNLVq9fZ/JiQAAkSjoZWTLli36+c9/rqeeekp2u71PxyxatEhOp9O/5ebmBjkljtX/OTlH3yvOltdn6IZn16m+pd3sSACACBPUMuL1enXppZfqjjvu0KhRo/p83IIFC9TQ0ODfKisrg5gSx+vX509QwZBB2tXQplue/1SGwfwRAEDfBbWMNDY2qqysTNddd53sdrvsdrt+9atf6ZNPPpHdbtdbb7112OMcDoeSk5MP2hC+Eh12PXDJSYqxWfTa5zV66uMKsyMBACJIUMtIcnKy1q9fL5fL5d+uvfZajR49Wi6XS1OnTg3myyOEJuY4dct/jJEk/frlDdpYzTwfAEDf9G0SxwGampq0detXV06Ul5fL5XIpNTVVeXl5WrBggaqqqvTEE0/IarVqwoQJBx2flpamuLi4Q76OyPfD0wr1wZd79dYXtfrJs+v0r+umaVBswD9iAIAoE/CZkbKyMhUXF6u4uFiSNH/+fBUXF2vhwoWSpOrqalVUcJo+GlksFv3uwklKS3Joa22T7vjXBrMjAQAigMWIgNmGbrdbTqdTDQ0NzB+JAB98WafL/vqxDEN64JJilRZlmR0JAGCCvn5+c28a9LtThw/VddNHSJJ+8cJ6VextMTkRACCcUUYQFDecPVIl+YPV6OnUT55bpw6vz+xIAIAwRRlBUNhtVt13SbGS4+z6pLJev399k9mRAABhijKCoMlOidfdFxZJkv68YptWbN5jciIAQDiijCCo/mNChi7/Rp4k6aa/u1Tb2GZyIgBAuKGMIOhuPW+cxmQkqa6pXfP/9ol8vrC/gAsAEEKUEQRdXIxNf7q0WHExVr23tU5/XrnN7EgAgDBCGUFIjEhL0h3fGS9J+sPrm7S2Yr/JiQAA4YIygpC5qCRX356UqU6foeufXaeG1g6zIwEAwgBlBCFjsVj02wsmKjc1Xjv3t+oXL6xXBCwADAAIMsoIQio5Lkb3X1wsu9Wi/11fredWV5odCQBgMsoIQq44b7B+ds5oSdIdL32uLTWNJicCAJiJMgJT/OfpJ+j0kUPV1uHTNU+UaVX5PrMjAQBMQhmBKaxWi/540YlKT3Zo+94WXfTnD3XdM2tVVd9qdjQAQIhRRmCaYUkOvXL96bp0ap6sFunlT6t11u/f0R+Xb1ZLe6fZ8QAAIWIxIuByBrfbLafTqYaGBiUnJ5sdB0GwYZdbv3r5c320rWu4JiM5TgvOHaPvFGXJYrGYnA4AcCz6+vlNGUHYMAxDr362W3e+slE793cN15ycP1gLvz1ORbkp5oYDAASMMoKI1dbh1SPvlWvx21vV0u6VJF14co7+7zmjlZYcZ3I6AEBfUUYQ8Wrcbbrr1S/0wtoqSVJCrE3zzhqhudMKFRdjMzkdAOBoKCMYMNZV7NcdL22Qq7JekpSXOki/OHeszhmfznwSAAhjlBEMKD6foWWuKt316heqcXskSacOH6KFpeM0JoOfCQAIR5QRDEjNnk49+M6XevjdbWrv9MlqkS6dmqf5M0crNSHW7HgAgANQRjCgVe5r0aJ/b9Qr63dLkpLj7LpxxihdcUq+YmwsnwMA4YAygqjw0ba9uuOlDdpY7ZYkDR+WoNu+PU5njk4zORkAgDKCqOH1Gfrb6kr9/vVN2tfcLkk6a0yabj1vrE4YlmhyOgCIXpQRRJ2G1g498OYWLflguzp9huxWi648tUA/OXuknPExZscDgKhDGUHU+nJPk+78341664taSdKQhFjd9K3R+sHkXNmsXAoMAKFCGUHUe2dTrX798gZ9uadZkjQ2M1m3l47TN04YYnIyAIgOlBFAUofXpyc/3KF739gsd1vXnYDPnZihBbPGKjd1kMnpAGBgo4wAB9jX3K4/Lt+kZz6ukM+QYu1W/efpJ+i/zhyuBIfd7HgAMCBRRoDD2Fjt1q9e2qAPt+2VJKUnO/STs0ZqYrZTOYPjlZoQyxLzANBPKCNALwzD0Guf1+jOVzaocl/rQc/Fx9iUMzi+exv0tT8pKwAQCMoIcBRtHV4t+WC73thQo537W1XT2Kaj/dcwKNZ2SEE5sLQMHhRDWQGAbpQRIECeTq+q69u0c3+rdu5v8f9Z2f1nzw36juTAspJ7mLMrKZQVAFGkr5/fzNwDujnsNhUMTVDB0ITDPu/p9GpXfdtBRaXrz1ZV7mtRbaNHLe1eba5p0uaapsP+GwmxtkPOqoxIS9Q3Rw1jDRQAUYsyAvSRw25T4dAEFfZSVto6vNpV3+ovKF8vLbWNHjW3e7WpplGbahoPOvbciRm67+JibvIHICpRRoB+Ehdj0wnDEnu9H05bh1dV9QcXlcp9LXr985ruuw+vo5AAiEqUESBE4mJsGj4sUcO/Vlbe+qJG1z65lkICIGrxGw8w2Vlj0vXQFScp1mbVK+t364bn1qnD6zM7FgCEDGUECAMUEgDRjDIChAkKCYBoFXAZWblypUpLS5WVlSWLxaJly5Ydcf/33ntP06ZN05AhQxQfH68xY8bonnvuOda8wIBGIQEQjQIuI83NzSoqKtLixYv7tH9CQoKuu+46rVy5Uhs3btStt96qW2+9VQ8//HDAYYFoQCEBEG2OawVWi8WipUuX6vzzzw/ouAsuuEAJCQl68skn+7Q/K7AiGvVcZdPu9bEOCYCI1NfP75D/Zlu3bp0++OADnXHGGb3u4/F45Ha7D9qAaMMZEgDRImRlJCcnRw6HQyUlJZo3b56uvvrqXvddtGiRnE6nf8vNzQ1VTCCsfL2QXP8shQTAwBOyMvLuu++qrKxMDz30kO699149++yzve67YMECNTQ0+LfKyspQxQTCzoGF5N+fUUgADDwhKyOFhYWaOHGirrnmGv30pz/Vf//3f/e6r8PhUHJy8kEbEM0oJAAGMlNmw/l8Pnk8R78dO4CvUEgADFQBl5Gmpia5XC65XC5JUnl5uVwulyoqKiR1DbHMnj3bv//ixYv10ksvacuWLdqyZYseeeQR/f73v9fll1/eP+8AiCIUEgADUcA3yisrK9P06dP9j+fPny9JmjNnjpYsWaLq6mp/MZG6zoIsWLBA5eXlstvtGj58uO666y796Ec/6of4QPTpKSTXPrnWX0juv4TLfgFEruNaZyRUWGcEONSB65DMmpBBIQEQdsJ2nREA/eOsMen68xUnM2QDIOJRRoAINn1MGoUEQMSjjAARjkICINJRRoABgEICIJJRRoABgkICIFJRRoAB5OuF5CfPUEgAhD/KCDDAHFhIXv2cQgIg/FFGgAGIQgIgklBGgAGKQgIgUlBGgAGMQgIgElBGgAGOQgIg3FFGgChAIQEQzigjQJSgkAAIV5QRIIpQSACEI8oIEGUOV0jaOykkAMxjMQzDMDvE0bjdbjmdTjU0NCg5OdnsOMCA8PYXtfrRk2vU7vUpPsam8VnJmpSTokk5Tk3KcapgSIKsVovZMQFEsL5+flNGgCj29qZa3fT3T7Svuf2Q55Li7JqY7TyooGSnxMtioaAA6BvKCIA+8foMldc16ZPKBq2vatAnO+v1+S73YYduhiTEamJOd0HJdmpSrlNpSXEmpAYQCSgjAI5Zh9enzTWN+nRnQ/dWr027G9XpO/TXRaYzThOznSrK7TqDMjHbqZRBsSakBhBuKCMA+lVbh1cbq936dGfX2ZP1Oxu0dU+TDvcbJH/IoK6C0j3EMz7bqUSHPfShAZiKMgIg6Jo8nfq8quGrglLVoB17Ww7Zz2KRRgxL1MScrwrK2MxkxcXYTEgNIFQoIwBMUd/Srk93ds8/qewqKNUNbYfsZ7daNDojSZNynBqTkawThiWocGiCspzxXMUDDBCUEQBho9bd1jX3pKpr/smnOxsOewWPJDnsVhUMSfCXk8KhPX9PVGoCc1GASEIZARC2DMNQVX2rf4Ls1tomldc1qWJfizq8vf9KShkU81VBGdpVUHoex8cy5AOEG8oIgIjT6fWpqr5V2+qaVb6nWeV1zdpW16TyPc3adZihngNlOeNU6D+bkqgThnUVluyUeNltLDYNmIEyAmBAaW33avveroJSXtesbXu6isq2Pc1qaO3o9bgYm0V5qYNUODRRww8Y+ikclqBhiQ4WcQOCiDICIGrsb27vOptS16zyuiZ/WSmva5bnCPfdSXTYVTg0QSfnD9Z/fvMEZaXEhzA1MPBRRgBEPZ/PULW7rXvIp8lfWLbtadbO/S06cA23WLtVl0/N14+nD9fQRId5oYEBhDICAEfg6fSqcl+LttQ0ackH2/Vx+T5J0qBYm354WqGuPv0EOeNjTE4JRDbKCAD0kWEYem9rnX732iZ9urNBkuSMj9G1ZwzXlacWcKUOcIwoIwAQIMMw9NrnNfrD65u0pbZJkjQsyaGfnDVCF0/OU6ydq3KAQFBGAOAYeX2GXnRV6Z43NqtyX6skKWdwvG6cMUrfK86WjRVigT6hjADAcWrv9OlvZZV64M0tqm30SJJGpCXqppmj9B8TMrgsGDgKyggA9JPWdq+e+HC7HlzxpepbutY0mZjt1E3fGqUzRg2jlAC9oIwAQD9zt3Xor++W65F3t6m53StJmlKQqpv/Y7QmF6SanA4IP5QRAAiSvU0ePbTiSz3+4Q61dy+qduboYfrZt0ZrQrbT5HRA+KCMAECQVTe06oG3tupvqyvl7V5B7byJmfrpzFEakZZocjrAfJQRAAiR7XXNuveNzXrxk10yDMlqkf7PSTm6YcZI5QweZHY8wDSUEQAIsS92u/WH1zdr+YYaSV036buse4n5tKQ4k9MBoUcZAQCTrKvYr9+/vknvb90rSYqPsemqaQX60TeHyzmIJeYRPSgjAGCy97uXmHdV1kuSkuLs/iXmExx2c8MBIdDXz++A1zZeuXKlSktLlZWVJYvFomXLlh1x/xdeeEEzZ87UsGHDlJycrFNOOUWvvfZaoC8LABFn2oihWvrjU/WX2SUanZ6kxrZO/e61TTrjd2/rsffL5en0mh0RCAsBl5Hm5mYVFRVp8eLFfdp/5cqVmjlzpl555RWtWbNG06dPV2lpqdatWxdwWACINBaLRTPHpevfN5yu+y4+UflDBqmuqV13vLRB03/3jv62ukKdXp/ZMQFTHdcwjcVi0dKlS3X++ecHdNz48eP1gx/8QAsXLuzT/gzTABgoOrw+/aNsp+5/c4t2u9skSZnOOI1KT1L24Hhlp8QrZ3C8slK6/p6eHMe9cBCx+vr5HfJBS5/Pp8bGRqWm9r5aocfjkcfj8T92u92hiAYAQRdjs+rSqXm64KRsPfXRDv2/d75UdUObqhvaDru/zWpRRnKcsgfHKyelu6R0l5aewhIfawvxuwD6V8jLyO9//3s1NTXpoosu6nWfRYsW6Y477ghhKgAIrbgYm64+/QRdMiVPayv2a1d9q6r2t2pnfWvX3+tbVV3fpk6foarux6t6+beGJMT6i0n2AWdVes6wDB4Uw/1zENZCOkzzzDPP6JprrtGLL76oGTNm9Lrf4c6M5ObmMkwDIKp4fYb2NHpUVd+inftbtau+TVX1Lary/71VTZ7Oo/478TG2w5SUOGWnDFL24HilJzlktwU8hRA4qrAbpnnuued09dVX6x//+McRi4gkORwOORyOECUDgPBks1qU4YxThjNOJ+cf+rxhGHK3dapqf9eZk54zKj2Pq+pbtafRo9YOr7bWNmlrbdNhXyfGZtGUwlTNGJuuGWPTlZvKqrEIrZCUkWeffVZz587Vc889p/POOy8ULwkAA57FYpEzPkbO+BiNyzr8/3W2dXi1u6HNX06+Xlyq69vU7vXp/a179f7WvbrjpQ0alZ6os8ema8bYNJ2YO5gJtAi6gMtIU1OTtm7d6n9cXl4ul8ul1NRU5eXlacGCBaqqqtITTzwhqWtoZs6cObrvvvs0depU7d69W5IUHx8vp5O7WwJAMMXF2FQwNEEFQxMO+7zPZ6h8b7Pe/qJWb2ys0ert+7W5pkmba5r04DtfakhCrKaPSdOMsWk6feQwFmtDUAQ8Z+Sdd97R9OnTD/n6nDlztGTJEl155ZXavn273nnnHUnSmWeeqRUrVvS6f19waS8AhEZDS4fe2VyrNzbW6p1NtWps+2pOSqzNqm8MH6IZY9N09th0ZafEm5gUkYDl4AEAx6XD69Pq7fv05sZavbmxRtv3thz0/NjMZH8xmZTtlJXhHHwNZQQA0G8Mw9CXe5r1xsYavbmxRmt27JfvgE+PYUkOnTU6TWePTdNpI4dqUCzDOaCMAACCaF9zu97ZVKs3N9ZqxeY9B11i7LBbNW3EUJ09Nk1nj0lXhjPOxKQwE2UEABAS7Z0+rSrfpzc21uiNjTXaub/1oOcnZCfr7DFdlw1PyE5mAbYoQhkBAIScYRjaXNPkH85ZV1mvAz9l0pMd/suGTx0+VHExLGU/kFFGAACmq2vy+C8bfndLnVravf7n4mKsOm3EMM0Ym6azxqYpLYnhnIGGMgIACCttHV59tG2v/+qcXQfcHNBikU7MTdHMcen61rh0DR+WyHDOAEAZAQCELcMwtKHa7S8mn+xsOOj5giGDNHNcumaOy9DJ+awCG6koIwCAiLG7oc0/AfaDrXvV7vX5n0tNiNVZY9I0Y2y6vjmKy4YjCWUEABCRmjydWrl5j5ZvqNFbX9SqobXD/5zDbtVpI4Zq5rh0nT02XcOSuKlqOKOMAAAiXqfXp9Xb92v5hhot37hblfu+umyYeSbhjzICABhQDMPQpppGvbGhRss3HDrPpHBogmaMTWOeSRihjAAABrS+zDOZOS5dp7M8vWkoIwCAqME8k/BEGQEARKWjzTMpzk3RDOaZhARlBAAQ9foyz2TmuHSVTsrSxBynSSkHLsoIAABf0zPPZPmGGn345cHzTE7KS9GcUws0a0KmYu1WE1MOHJQRAACOoGeeyauf7darn+32F5O0JIcu/0a+LpmSx/yS40QZAQCgj/Y0evTsqgo99dEO1TZ6JEmxNqu+XZSpK08t0KScFHMDRijKCAAAAWrv9Onfn1VryQfbta6i3v/1k/JSdOW0Qs2akKEYG0M4fUUZAQDgOHxSWa/HP9iulz7dpQ5v10clQziBoYwAANAPahvb9OzHlXrq4x3awxBOQCgjAAD0I4ZwAkcZAQAgSFzdQzgvHzCEk57s0GVTI3MIp63DK5vV0u9lijICAECQHWkI56pTC8NuITVPp1fb9jRrc02jttQ0df1Z26Qde5v15A+natqIof36epQRAABCpLchnJPzB3cvpBbaIZz2Tp+21TVpc02TttY0anNNkzbXNmrH3hZ5fYf/2P/1d8frilMK+jUHZQQAABP0NoRz+dR8XTI1T0MT+28Ip73Tp+17u850bK5p0paaRm2uadT2I5SOpDi7RqUnaVR6okamJfn/PizJ0e/36aGMAABgotrGNj3zcYWe/rjiuIdwOrw+7djb3HWG44AhlvK6ZnX2Vjocdo1MT9So9CSN7C4co9KTlBaE0tEbyggAAGGgZwjnsfe3y1VZ7//64YZwOr0+bd/b0n2Go2toZUt36eg5y/J1iQ67RqQl+stGT/HISI4z/Y7ElBEAAMJMb0M4Jfmp+nJPk7btaT7o5n0HSoi1aUR6kkZ2F4+u0pGkLKf5paM3lBEAAMJUzxDOUx9VqK7Jc9Bz8TE2jfTP5+g525GoLGe8rNbwLB29oYwAABDm2jt9eu3z3dpV39o91JKk7JTIKx296evntz2EmQAAwAFi7VaVFmWZHcN0rFsLAABMRRkBAACmoowAAABTUUYAAICpKCMAAMBUlBEAAGAqyggAADAVZQQAAJiKMgIAAEwVcBlZuXKlSktLlZWVJYvFomXLlh1x/+rqal166aUaNWqUrFarbrzxxmOMCgAABqKAy0hzc7OKioq0ePHiPu3v8Xg0bNgw3XrrrSoqKgo4IAAAGNgCvjfNrFmzNGvWrD7vX1BQoPvuu0+S9Oijjwb6cgAAYIALyxvleTweeTxf3VLZ7XabmAYAAARTWJaRRYsW6Y477jjk65QSAAAiR8/ntmEYR9wvLMvIggULNH/+fP/jqqoqjRs3Trm5uSamAgAAx6KxsVFOp7PX58OyjDgcDjkcDv/jxMREVVZWKikpSRaLpd9ex+12Kzc3V5WVlUpOTu63fzeSRPv3INrfv8T3gPcf3e9f4nsQzPdvGIYaGxuVlZV1xP3Csox8ndVqVU5OTtD+/eTk5Kj8ATxQtH8Pov39S3wPeP/R/f4lvgfBev9HOiPSI+Ay0tTUpK1bt/ofl5eXy+VyKTU1VXl5eVqwYIGqqqr0xBNP+PdxuVz+Y/fs2SOXy6XY2FiNGzcu0JcHAAADTMBlpKysTNOnT/c/7pnbMWfOHC1ZskTV1dWqqKg46Jji4mL/39esWaNnnnlG+fn52r59+zHGBgAAA0XAZeTMM8884qzYJUuWHPK1o82iNYvD4dDtt99+0PyUaBPt34Nof/8S3wPef3S/f4nvQTi8f4sRrk0BAABEBW6UBwAATEUZAQAApqKMAAAAU1FGAACAqaK6jCxevFgFBQWKi4vT1KlTtWrVKrMjhcSiRYs0efJkJSUlKS0tTeeff742bdpkdixT/c///I8sFotuvPFGs6OETFVVlS6//HINGTJE8fHxmjhxosrKysyOFTJer1e33XabCgsLFR8fr+HDh+vXv/512F79d7xWrlyp0tJSZWVlyWKxaNmyZQc9bxiGFi5cqMzMTMXHx2vGjBnasmWLOWGD5Ejfg46ODt1yyy2aOHGiEhISlJWVpdmzZ2vXrl3mBe5nR/sZONC1114ri8Wie++9NyTZoraM/O1vf9P8+fN1++23a+3atSoqKtI555yj2tpas6MF3YoVKzRv3jx99NFHWr58uTo6OvStb31Lzc3NZkczxerVq/XnP/9ZkyZNMjtKyOzfv1/Tpk1TTEyM/v3vf2vDhg36wx/+oMGDB5sdLWTuuusuPfjgg/rTn/6kjRs36q677tLdd9+tBx54wOxoQdHc3KyioiItXrz4sM/ffffduv/++/XQQw/p448/VkJCgs455xy1tbWFOGnwHOl70NLSorVr1+q2227T2rVr9cILL2jTpk36zne+Y0LS4Djaz0CPpUuX6qOPPjrqEu79yohSU6ZMMebNm+d/7PV6jaysLGPRokUmpjJHbW2tIclYsWKF2VFCrrGx0Rg5cqSxfPly44wzzjBuuOEGsyOFxC233GKcdtppZscw1XnnnWfMnTv3oK9dcMEFxmWXXWZSotCRZCxdutT/2OfzGRkZGcbvfvc7/9fq6+sNh8NhPPvssyYkDL6vfw8OZ9WqVYYkY8eOHaEJFUK9vf+dO3ca2dnZxmeffWbk5+cb99xzT0jyROWZkfb2dq1Zs0YzZszwf81qtWrGjBn68MMPTUxmjoaGBklSamqqyUlCb968eTrvvPMO+lmIBv/6179UUlKi73//+0pLS1NxcbH+8pe/mB0rpE499VS9+eab2rx5syTpk08+0XvvvadZs2aZnCz0ysvLtXv37oP+O3A6nZo6dWpU/k7s0dDQIIvFopSUFLOjhITP59MVV1yhm2++WePHjw/pa0fEjfL6W11dnbxer9LT0w/6enp6ur744guTUpnD5/Ppxhtv1LRp0zRhwgSz44TUc889p7Vr12r16tVmRwm5bdu26cEHH9T8+fP1i1/8QqtXr9b111+v2NhYzZkzx+x4IfHzn/9cbrdbY8aMkc1mk9fr1Z133qnLLrvM7Gght3v3bkk67O/EnueiTVtbm2655RZdcsklUXPzvLvuukt2u13XX399yF87KssIvjJv3jx99tlneu+998yOElKVlZW64YYbtHz5csXFxZkdJ+R8Pp9KSkr029/+VlLX/aM+++wzPfTQQ1FTRv7+97/r6aef1jPPPKPx48fL5XLpxhtvVFZWVtR8D3B4HR0duuiii2QYhh588EGz44TEmjVrdN9992nt2rWyWCwhf/2oHKYZOnSobDabampqDvp6TU2NMjIyTEoVetddd51efvllvf3228rJyTE7TkitWbNGtbW1Oumkk2S322W327VixQrdf//9stvt8nq9ZkcMqszMzEPumj127NhDbnI5kN188836+c9/rosvvlgTJ07UFVdcoZ/+9KdatGiR2dFCruf3XrT/TpS+KiI7duzQ8uXLo+asyLvvvqva2lrl5eX5fyfu2LFDN910kwoKCoL++lFZRmJjY3XyySfrzTff9H/N5/PpzTff1CmnnGJistAwDEPXXXedli5dqrfeekuFhYVmRwq5s88+W+vXr5fL5fJvJSUluuyyy+RyuWSz2cyOGFTTpk075HLuzZs3Kz8/36REodfS0iKr9eBfgTabTT6fz6RE5iksLFRGRsZBvxPdbrc+/vjjqPid2KOniGzZskVvvPGGhgwZYnakkLniiiv06aefHvQ7MSsrSzfffLNee+21oL9+1A7TzJ8/X3PmzFFJSYmmTJmie++9V83NzbrqqqvMjhZ08+bN0zPPPKMXX3xRSUlJ/jFhp9Op+Ph4k9OFRlJS0iFzZBISEjRkyJComDvz05/+VKeeeqp++9vf6qKLLtKqVav08MMP6+GHHzY7WsiUlpbqzjvvVF5ensaPH69169bpj3/8o+bOnWt2tKBoamrS1q1b/Y/Ly8vlcrmUmpqqvLw83XjjjfrNb36jkSNHqrCwULfddpuysrJ0/vnnmxe6nx3pe5CZmakLL7xQa9eu1csvvyyv1+v/3ZiamqrY2FizYvebo/0MfL18xcTEKCMjQ6NHjw5+uJBcsxOmHnjgASMvL8+IjY01pkyZYnz00UdmRwoJSYfdHnvsMbOjmSqaLu01DMN46aWXjAkTJhgOh8MYM2aM8fDDD5sdKaTcbrdxww03GHl5eUZcXJxxwgknGL/85S8Nj8djdrSgePvttw/73/2cOXMMw+i6vPe2224z0tPTDYfDYZx99tnGpk2bzA3dz470PSgvL+/1d+Pbb79tdvR+cbSfga8L5aW9FsMYoMsNAgCAiBCVc0YAAED4oIwAAABTUUYAAICpKCMAAMBUlBEAAGAqyggAADAVZQQAAJiKMgIAAExFGQEAAKaijAAAAFNRRgAAgKkoIwAAwFT/Hz5hCwI5gsVkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:08<00:00, 144.43it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5675"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Yay! The model became a bit more accurate.\n",
    "try_parameters(hidden_layers_channels=[64, 128],\n",
    "               kernels=[5, 5],\n",
    "               epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dbf716be",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:21<00:00, 76.37it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:17<00:00, 80.13it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:17<00:00, 80.25it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:24<00:00, 74.38it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:30<00:00, 69.18it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA77ElEQVR4nO3deXRU5eHG8edOJpmEkExISEiGJOwQ1ogiEaECilqqUWqtraKgtLZWrFqsVX51bW1p7eJW6tK6r10URK1YlCUgsjvsWyCQkIUkQPZkssz9/RESpLIkkMzNZL6fc+ZgZu7kPjMHZh7f+973GqZpmgIAALCIzeoAAAAgsFFGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWslsdoCW8Xq/y8vIUEREhwzCsjgMAAFrANE2Vl5fL5XLJZjv5+IdflJG8vDwlJSVZHQMAAJyBnJwcJSYmnvRxvygjERERkhpfTGRkpMVpAABAS5SVlSkpKan5e/xk/KKMNB2aiYyMpIwAAOBnTjfFggmsAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAIClKCMAAMBSlBEAAGApyggAALAUZQQAAFgqoMvIx5vz9bN/uLUtr8zqKAAABCy/uGpve3nvy1wt2nZQ/WLDNcTF1YABALBCQI+MXDQwVpKUsavY4iQAAASugC4j4wc0lpEN2UdUXlNncRoAAAJTQJeR5Jgu6h3TRfVeUyv3HLI6DgAAASmgy4j01UM1RRYnAQAgMFFGjh6qydhdJNM0LU4DAEDgCfgyMqZfjIKDDOUcrta+Q1VWxwEAIOAEfBkJd9h1Xq9ukjhUAwCAFQK+jEjMGwEAwEqUER2bN/LF3kOqrfdanAYAgMBCGZE0JCFS3buGqKq2Qev2H7Y6DgAAAYUyIslmM/SNAazGCgCAFSgjR100sLsk5o0AAOBrlJGjmkZGtuWXqajcY3EaAAACB2XkqO5dHRp69Mq9y3czOgIAgK9QRr6CU3wBAPA9yshXNJ3iu3x3sbxeloYHAMAXKCNfcV6vbgoPCdKhylptyy+zOg4AAAGh1WUkIyND6enpcrlcMgxD8+fPP+X2N998swzD+Npt6NChZ5q53YTYbRrTL0ZS44XzAABA+2t1GamsrFRqaqrmzp3bou2feuop5efnN99ycnIUHR2t7373u60O6wvMGwEAwLfsrX3C5MmTNXny5BZv73Q65XQ6m3+eP3++jhw5oltuuaW1u/aJpnkj6/cfUaWnXuGOVr9FAACgFXw+Z+TFF1/UpEmT1KtXr5Nu4/F4VFZWdtzNV3p3D1dydBfVNZj6Ys8hn+0XAIBA5dMykpeXp48//lg//OEPT7ndnDlzmkdUnE6nkpKSfJSwUfNqrMwbAQCg3fm0jLz66quKiorSlClTTrnd7NmzVVpa2nzLycnxTcCjLhrAvBEAAHzFZxMiTNPUSy+9pJtuukkhISGn3NbhcMjhcPgo2deN6Rcju83QvkNVyj5UpeSYLpZlAQCgs/PZyMiyZcuUmZmpH/zgB77a5RmLCA3Wub26SZKWcagGAIB21eoyUlFRIbfbLbfbLUnKysqS2+1Wdna2pMZDLNOmTfva81588UWlpaVp2LBhZ5fYR8Zzii8AAD7R6jKybt06jRw5UiNHjpQkzZo1SyNHjtRDDz0kScrPz28uJk1KS0v17rvv+sWoSJOmeSNf7DmkugavxWkAAOi8Wj1nZMKECTLNk1+35ZVXXvnafU6nU1VVVa3dlaWGuiIVEx6iQ5W12rD/iNL6xlgdCQCATolr05yEzWZo3ABO8QUAoL1RRk7h2Cm+xRYnAQCg86KMnMI3ji5+tiWvVIcqPBanAQCgc6KMnEJcRKgGJ0TKNKUVmYyOAADQHigjp9G0NPwyTvEFAKBdUEZOY/zReSPLdxef8iwiAABwZigjp3Fe724KCw5SUblH2/PLrY4DAECnQxk5DYc9SGP6Na4xwim+AAC0PcpIC1zUtN4I80YAAGhzlJEWuOjodWrW7Tuiqtp6i9MAANC5UEZaoE/3cCV2C1Ntg1er9h6yOg4AAJ0KZaQFDMNoHh1hNVYAANoWZaSFji0Nz7wRAADaEmWkhS7sH6Mgm6G9xZXKOexfVyAGAKAjo4y0UGRosM5NjpLEKb4AALQlykgrcKgGAIC2RxlphaZJrCszD6muwWtxGgAAOgfKSCsM6+lUty7BKvfUy51TYnUcAAA6BcpIKwTZDI3jUA0AAG2KMtJKLA0PAEDbooy0UtO8kU25pTpcWWtxGgAA/B9lpJV6RIYqJT5CpimtyGQ1VgAAzhZl5AwcWxqeQzUAAJwtysgZaFpvZPnuIpmmaXEaAAD8G2XkDIzq3U2hwTYdLPNo58Fyq+MAAODXKCNnIDQ4SBf0jZHEoRoAAM4WZeQMHVsankmsAACcDcrIGWqaxLpm32FV1zZYnAYAAP9FGTlD/WLD1TMqTLX1Xq3KOmR1HAAA/BZl5AwZhqGLBrIaKwAAZ4sychYu4jo1AACcNcrIWbiwf3cF2QztKapUbkm11XEAAPBLlJGz4AwL1jlJUZIYHQEA4ExRRs4Sh2oAADg7lJGz1DSJdUVmseobvBanAQDA/1BGztKIxChFdQlWeU29Nh4osToOAAB+hzJyloJshsb2bxwdWcZqrAAAtBplpA2MZ94IAABnrNVlJCMjQ+np6XK5XDIMQ/Pnzz/tczwej375y1+qV69ecjgc6t27t1566aUzydshfePovJFNB0pUUlVrcRoAAPyLvbVPqKysVGpqqmbMmKFrrrmmRc+57rrrdPDgQb344ovq37+/8vPz5fV2nsmeCc4wDezRVbsOVmhFZrGuHOGyOhIAAH6j1WVk8uTJmjx5cou3X7hwoZYtW6a9e/cqOjpaktS7d+/W7rbDu2hArHYdrFDGriLKCAAArdDuc0YWLFigUaNG6fHHH1fPnj01cOBA/fznP1d19clXLPV4PCorKzvu1tE1XcU3Y1exTNO0OA0AAP6j1SMjrbV3716tWLFCoaGhmjdvnoqLi3X77bfr0KFDevnll0/4nDlz5ujRRx9t72htanSfaDnsNhWU1Wh3YYUG9oiwOhIAAH6h3UdGvF6vDMPQm2++qdGjR+tb3/qW/vznP+vVV1896ejI7NmzVVpa2nzLyclp75hnLTQ4SGl9YyRxVg0AAK3R7mUkISFBPXv2lNPpbL5v8ODBMk1TBw4cOOFzHA6HIiMjj7v5g4sGNK03QhkBAKCl2r2MjB07Vnl5eaqoqGi+b9euXbLZbEpMTGzv3fvU+KPzRtZkHVZNXYPFaQAA8A+tLiMVFRVyu91yu92SpKysLLndbmVnZ0tqPMQybdq05u1vuOEGxcTE6JZbbtG2bduUkZGhe++9VzNmzFBYWFjbvIoOon9cVyU4Q+Wp92p11mGr4wAA4BdaXUbWrVunkSNHauTIkZKkWbNmaeTIkXrooYckSfn5+c3FRJK6du2qRYsWqaSkRKNGjdLUqVOVnp6up59+uo1eQsdhGAZX8QUAoJUM0w/OQy0rK5PT6VRpaWmHnz/y0aZ8zXxrgwbEddWiWeOtjgMAgGVa+v3NtWna2Lj+3WUzpN2FFcorOflaKgAAoBFlpI05uwQrNSlKkrR8N4dqAAA4HcpIOzg2b6TY4iQAAHR8lJF20LQ0/IrMYjV4O/yUHAAALEUZaQepiU5FhtpVWl2njQdKrI4DAECHRhlpB/Ygm8YdXY2VU3wBADg1ykg7Yb0RAABahjLSTprmjbhzSlRaVWdxGgAAOi7KSDtxRYWpf1xXeU3p8z2cVQMAwMlQRtoRh2oAADg9ykg7umjgsUmsfrDqPgAAlqCMtKO0PjEKsduUV1qjPUUVVscBAKBDooy0o7CQIKX1iZYkLWM1VgAATogy0s6YNwIAwKlRRtpZ0ym+q7MOqaauweI0AAB0PJSRdjawR1fFR4aqps6rtfsOWx0HAIAOhzLSzgzD0DdYGh4AgJOijPhA06GaDCaxAgDwNZQRHxjXv7sMQ9p5sFwFpTVWxwEAoEOhjPhAt/AQjUiMkiRl7OZQDQAAX0UZ8ZHxzBsBAOCEKCM+0jRvZEVmsRq8LA0PAEATyoiPnJMUpYhQu0qq6rQ5t9TqOAAAdBiUER+xB9k0th+HagAA+F+UER86doovZQQAgCaUER9qWvzsy5wSldXUWZwGAICOgTLiQ0nRXdS3e7gavKZWZrIAGgAAEmXE55oO1SxjNVYAACRRRnzuooHHJrGaJqf4AgBAGfGxC/rGKCTIptySau0trrQ6DgAAlqOM+FiXELtG9e4mibNqAACQKCOW4BRfAACOoYxY4KIBjWVk1d7D8tQ3WJwGAABrUUYsMDghQrERDlXXNWjdviNWxwEAwFKUEQsYhtG8ABqHagAAgY4yYpHxzeuNUEYAAIGNMmKRcf27yzCkHQXlKiyrsToOAACWoYxYJKarQ8NcTklSxm5WYwUABK5Wl5GMjAylp6fL5XLJMAzNnz//lNsvXbpUhmF87VZQUHCmmTuNpkM1L67IUnUtZ9UAAAJTq8tIZWWlUlNTNXfu3FY9b+fOncrPz2++xcXFtXbXnc6NF/RSTHiItueX6RfvbmJ5eABAQLK39gmTJ0/W5MmTW72juLg4RUVFtfp5nVm8M1R/nXqupv59tT7YmKchCZH6yYR+VscCAMCnfDZn5JxzzlFCQoIuvfRSff7556fc1uPxqKys7LhbZ5XWN0aPXDVUkvT4Jzu0ZEehxYkAAPCtdi8jCQkJeu655/Tuu+/q3XffVVJSkiZMmKANGzac9Dlz5syR0+lsviUlJbV3TEvdeEEv3ZCWLNOU7nz7S+0pqrA6EgAAPmOYZzFRwTAMzZs3T1OmTGnV88aPH6/k5GS9/vrrJ3zc4/HI4/E0/1xWVqakpCSVlpYqMjLyTON2aLX1Xk39+yqt3XdEfbuHa97MsXKGBVsdCwCAM1ZWVian03na729LTu0dPXq0MjMzT/q4w+FQZGTkcbfOLsRu01+nnieXM1R7iyt11ztfqsHLhFYAQOdnSRlxu91KSEiwYtcdWmyEQy9MGyWH3aalO4v0h092Wh0JAIB21+qzaSoqKo4b1cjKypLb7VZ0dLSSk5M1e/Zs5ebm6rXXXpMkPfnkk+rTp4+GDh2qmpoa/f3vf9fixYv13//+t+1eRScyrKdTj187Qne949Zzy/ZocEKErj6np9WxAABoN60uI+vWrdPEiRObf541a5Ykafr06XrllVeUn5+v7Ozs5sdra2t1zz33KDc3V126dNGIESP06aefHvc7cLyrz+mp7fnlem7ZHv3i35vUt3tXDU90Wh0LAIB2cVYTWH2lpRNgOpMGr6kfvLpWS3cWKcEZqgV3jFNshMPqWAAAtFiHnsCK0wuyGXrq+yPVt3u48ktrdPub61Vb77U6FgAAbY4y0oE5w4L1wrRRinDYtXbfET3ywVarIwEA0OYoIx1c/7iuevr6kTIM6a3V2Xpj1X6rIwEA0KYoI35gYkqcfnF5iiTpkQVbtXrvIYsTAQDQdigjfuK28X2VnupSvdfU7W9u0IEjVVZHAgCgTVBG/IRhGHr8OyM01BWpQ5W1+tFr61Vd22B1LAAAzhplxI+EhQTphWmjFBMeom35Zbr33xvlB2dmAwBwSpQRP9MzKkzP3nie7DZDH27K17PL9lgdCQCAs0IZ8UOj+0Tr0auHSpL+8MlOfbb9oMWJAAA4c5QRPzU1rZempiXLNKW73nErs7Dc6kgAAJwRyogfezh9qEb3jlaFp163vrZepdV1VkcCAKDVKCN+LMRu019vPFcuZ6iyiit159tfqsHLhFYAgH+hjPi57l0demHaKIUG27RsV5Ee/2SH1ZEAAGgVykgnMKynU49fmypJen7ZXr3vzrU4EQAALUcZ6SSuSnXpJxP6SZJ+8e9N2nyg1OJEAAC0DGWkE/n5ZYM0cVCsPPVe/ej1dSoq91gdCQCA06KMdCJBNkNPXT9SfWPDlV9ao5+8sV619V6rYwEAcEqUkU4mMjRYf5s2ShEOu9btP6KHF2xhyXgAQIdGGemE+sV21dPXj5RhSG+vydEbq7OtjgQAwElRRjqpiSlx+sXlKZKkRxds1aq9hyxOBADAiVFGOrHbxvdVeqpL9V5Tt7+5QQeOVFkdCQCAr6GMdGKGYejx74zQUFekDlfW6tbX1quqtt7qWAAAHIcy0smFhQTphWmjFBMeou35Zbr3X5uY0AoA6FAoIwGgZ1SYnr3xPNlthj7anK+/Lt1jdSQAAJpRRgLE6D7RevTqoZKkP/53pz7ddtDiRAAANKKMBJCpab00NS1Zpind/Q+3MgvLrY4EAABlJNA8nD5Uo3tHq8JTr1tfW6/SqjqrIwEAAhxlJMCE2G36643nyuUMVVZxpX76zpdq8DKhFQBgHcpIAOre1aEXpo1SaLBNGbuK9Mf/7rQ6EgAggFFGAtSwnk794dpUSdLzy/ZoS26pxYkAAIGKMhLA0lNdunJEgrym9MD8LfJyuAYAYAHKSIB74IohCg8JkjunRP9cl2N1HABAAKKMBLh4Z6h+dulASdLvFu7Q4cpaixMBAAINZQS6+cLeSomPUElVnR5fuMPqOACAAEMZgexBNv16yjBJ0jtrc7R+/xGLEwEAAgllBJKk83tH69rzEiVJD87fovoGr8WJAACBgjKCZrMnp8gZFqxt+WV6fdV+q+MAAAIEZQTNYro6dO/lgyRJf/7vLhWW1VicCAAQCFpdRjIyMpSeni6XyyXDMDR//vwWP/fzzz+X3W7XOeec09rdwkeuH52s1ESnyj31+s1/tlsdBwAQAFpdRiorK5Wamqq5c+e26nklJSWaNm2aLrnkktbuEj4UZDP06ynDZBjS++48rdxTbHUkAEAn1+oyMnnyZD322GP69re/3arn3Xbbbbrhhhs0ZsyY1u4SPjYiMUo3pvWS1DiZtbaeyawAgPbjkzkjL7/8svbu3auHH364Rdt7PB6VlZUdd4Nv/fyyQYoJD9Geokq9uCLL6jgAgE6s3cvI7t27df/99+uNN96Q3W5v0XPmzJkjp9PZfEtKSmrnlPhfzi7B+r9vDZYkPf3ZbuWWVFucCADQWbVrGWloaNANN9ygRx99VAMHDmzx82bPnq3S0tLmW04O10yxwjXn9tTo3tGqrmvQrz7YanUcAEAn1a5lpLy8XOvWrdMdd9whu90uu92uX/3qV9q4caPsdrsWL158wuc5HA5FRkYed4PvGUbjZNYgm6FPth7Ukh2FVkcCAHRC7VpGIiMjtXnzZrnd7ubbbbfdpkGDBsntdistLa09d482MCg+Qj8Y10eS9PCCraqpa7A4EQCgs2nZJI6vqKioUGZmZvPPWVlZcrvdio6OVnJysmbPnq3c3Fy99tprstlsGjZs2HHPj4uLU2ho6NfuR8d11yUDtMCdp+zDVfrr0j2adWnLD7kBAHA6rR4ZWbdunUaOHKmRI0dKkmbNmqWRI0fqoYcekiTl5+crOzu7bVPCUuEOux5KHyJJem7ZHmUVV1qcCADQmRimaZpWhzidsrIyOZ1OlZaWMn/EIqZpatpLa7R8d7EuGhirV285X4ZhWB0LANCBtfT7m2vToEUMw9Cvrh6mkCCbMnYVaeGWAqsjAQA6CcoIWqxP93DdNr6vJOnRD7ap0lNvcSIAQGdAGUGr3D6xv5Kiw1RQVqOnP9ttdRwAQCdAGUGrhAYH6dGrhkqSXlyRpV0Hyy1OBADwd5QRtNrFKT102ZAeqveaemD+FvnBHGgAQAdGGcEZeSh9iEKDbVqTdVjzvsy1Og4AwI9RRnBGErt10Z2XDJAk/fY/21VaXWdxIgCAv6KM4Iz9cFxf9YsNV3FFrf70351WxwEA+CnKCM5YiN2mX1/duKz/G6v2a/OBUosTAQD8EWUEZ+XC/t11VapLXlN6YP5mNXiZzAoAaB3KCM7aA1cMVleHXRsPlOqdtVyXCADQOpQRnLW4yFDdc1njlXwfX7hThyo8FicCAPgTygjaxE0X9NKQhEiVVtfpdx/vsDoOAMCPUEbQJuxBNv16SuNk1n+tP6B1+w5bnAgA4C8oI2gz5/Xqpu+NSpIkPTB/i+obvBYnAgD4A8oI2tR9k1MU1SVYOwrK9eoX+62OAwDwA5QRtKno8BDd980USdITi3bpYFmNxYkAAB0dZQRt7nujknROUpQqPPV67KPtVscBAHRwlBG0OZvN0GNThslmSB9szNPnmcVWRwIAdGCUEbSLYT2dmjamtyTpwfe3yFPfYG0gAECHRRlBu5l12UB17+rQ3qJK/X15ltVxAAAdFGUE7SYyNFgPXDFYkvTM4t3KOVxlcSIAQEdEGUG7uvocly7oG62aOq8e/WCb1XEAAB0QZQTtyjAM/frqYbLbDH26/aA+3XbQ6kgAgA6GMoJ2N6BHhH7wjT6SpEc+2KrqWiazAgCOoYzAJ+68eIBczlAdOFKtvy7NtDoOAKADoYzAJ8Iddj2UPlSS9PyyvdpbVGFxIgBAR0EZgc9cPrSHJgyKVW2DVw+9v1WmaVodCQDQAVBG4DOGYejRq4YqxG7TisxifbQ53+pIAIAOgDICn+oVE67bJ/STJP36w22q8NRbnAgAYDXKCHzutvH91Cumiw6WefTkol1WxwEAWIwyAp8LDQ7So1c1TmZ9eeU+7SgoszgRAMBKlBFYYsKgOE0eFq8Gr6m733HrfXeuymvqrI4FALCAYfrBKQ1lZWVyOp0qLS1VZGSk1XHQRvJKqnX5ExkqPzpvJCTIpm8M6K7Lh8Xr0sE91C08xOKEAICz0dLvb8oILLWvuFL/Wp+jj7cUaG9RZfP9QTZDY/rG6PJh8bp8aA/FRYRamBIAcCYoI/Arpmlqd2GFPt5coIVbC7Q9/9g8EsOQRvXqpsuHxuubw+KV2K2LhUkBAC1FGYFf21dcqYVbC7RwS4HcOSXHPTYi0anLh8Zr8rB49Y3tak1AAMBpUUbQaeSVVOuTo8Vk7b7D8n7lb+ygHhG6fFhjMUmJj5BhGNYFBQAcp6Xf360+myYjI0Pp6elyuVwyDEPz588/5fYrVqzQ2LFjFRMTo7CwMKWkpOiJJ55o7W4RwFxRYbplbB/948djtOaXk/Tbbw/XRQNjZbcZ2nmwXE9/tluTn1quiX9cqjkfb5c7p4Sl5gHAj9hb+4TKykqlpqZqxowZuuaaa067fXh4uO644w6NGDFC4eHhWrFihX784x8rPDxcP/rRj84oNAJX964O3ZCWrBvSklVaVadPtx/Uwq0FWrarSPsOVen5ZXv1/LK9SnCGNh/KGdU7WkE2RkwAoKM6q8M0hmFo3rx5mjJlSqued8011yg8PFyvv/56i7bnMA1Op9JTryU7C7VwS4GW7ChUZW1D82Pdu4bo0iGNxWRMvxgFB7G8DgD4Qku/v1s9MnK2vvzyS61cuVKPPfaYr3eNTizcYdeVI1y6coRLNXUNWr67WAu3FOjT7QdVXFGrt9dk6+012YoMtWvSkB6aPCxB3xjQXaHBQVZHB4CA57MykpiYqKKiItXX1+uRRx7RD3/4w5Nu6/F45PF4mn8uK2O5cLRcaHCQLh3SQ5cO6aG6Bq++2HNIC7cW6L9bC1RcUav3NuTqvQ25Cg8J0oSUOH1rWIIuTolTWAjFBACs4LMysnz5clVUVGjVqlW6//771b9/f11//fUn3HbOnDl69NFHfRUNnVhwkE0XDYzVRQNj9eurh2ndvsNauLVAn2wpUF5pjT7alK+PNuWrS0iQLhncQ+kjEjR+UKwcdooJAPiKJXNGHnvsMb3++uvauXPnCR8/0chIUlISc0bQZkzT1MYDpfp4S77+szlfOYermx+LcNh12dB4XZmaoHH9uzPHBADOUIedMyJJXq/3uLLxvxwOhxwOhw8TIdAYhqFzkqJ0TlKU7v9mijYdKNUHG/P00eZ85ZfW6N0NB/TuhgOK6hKsycPilT7CpbS+MZyVAwDtoNVlpKKiQpmZmc0/Z2Vlye12Kzo6WsnJyZo9e7Zyc3P12muvSZLmzp2r5ORkpaSkSGpcp+SPf/yj7rzzzjZ6CcDZMQxDqUlRSk2K0v99a7DWZx/RBxvz9J/N+Ucnv+bo7TU56t7VoSuGx+vKVJfOS+4mG8UEANpEqw/TLF26VBMnTvza/dOnT9crr7yim2++Wfv27dPSpUslSc8884yef/55ZWVlyW63q1+/frr11lv14x//WDZby4a/ObUXVqhv8Gp11mF9uClPH28pUElVXfNjCc5QXTE8QempLo1IdLLyKwCcAMvBA22orsGrFZnF+mBjnhZtPahyT33zY8nRXXTliARdOcKlwQksSQ8ATSgjQDupqWvQsl1F+nBTvj7ddlDVdccWWOsXG64rR7iUnupS/zgu4gcgsFFGAB+oqq3X4h2F+mBjnpbsLFJtvbf5scEJkbpyRILSR7iUHNPFwpQAYA3KCOBj5TV1WrTtoD7YmKflu4tV/5XLC6cmOpWe6tIVIxKU4AyzMCUA+A5lBLBQSVWtFm4p0Ieb8rVyT7G+0kt0fu9uunKES5OHxysuItS6kADQzigjQAdRVO7Rwi35+mBjvtbuP6ymf3E2Q7qgb4yuSnXpmnMTFWJncTUAnQtlBOiA8kur9dGmfH24KV/unJLm+8/r1U3P3XieYiNY7A9A50EZATq4nMNV+mBTnp5dukflNfVKcIbqhZtGaXii0+poANAmWvr9zbgwYJGk6C66fUJ/zZ85Vn1jw5VfWqNrn1up9925VkcDAJ+ijAAW6xfbVfNnjtXEQbHy1Ht11ztu/e7jHWrwdvhBSwBoE5QRoAOIDA3W36efr59M6CdJem7ZHv3w1bUqq6k7zTMBwP9RRoAOIshm6L5vpuip758jh92mJTuLNGXu59pTVGF1NABoV5QRoIO5+pye+vdtFyrBGaq9RZWaMvdzLdlZaHUsAGg3lBGgAxqe6NSCO8ZpVK9uKq+p14xX1ur5ZXvkBye/AUCrUUaADio2wqG3br1A149OkmlKcz7eoZ/9w62ar1yYDwA6A8oI0IGF2G367beH61dXD1WQzdB8d56++9wXyi+ttjoaALQZygjQwRmGoWljeuuNH6SpW5dgbc4tVfozn2v9/sNWRwOANkEZAfzEmH4xWnDHOKXER6i4wqPvv7BK/1ibbXUsADhrlBHAjyRFd9G7P7lQk4fFq67B1H3vbtYjC7aqrsFrdTQAOGOUEcDPhDvsmnvDuZp16UBJ0isr92n6S2t0pLLW4mQAcGYoI4AfstkM3XnJAD1/03kKDwnSyj2HdNXcFdpRUGZ1NABoNcoI4McuHxqv924fq6ToMOUcrtY1f12phVsKrI4FAK1CGQH83KD4CC2YOU5j+8eoqrZBt72xXk9+ukteLrQHwE9QRoBOoFt4iF69ZbRuGdtbkvTkp7t1+5sbVOmptzYYALQAZQToJOxBNj2cPlSPXztCIUE2LdxaoO88u1I5h6usjgYAp0QZATqZ60Yl6e0fXaDuXR3aUVCuq/6yQiv3FFsdCwBOijICdELn9eqmD346ViMSnTpSVaebXlyjV1fu40J7ADokygjQSSU4w/TPH4/Rt0f2VIPX1MMLtmr2e5vlqedCewA6FsoI0ImFBgfpz9el6v++lSKbIb2zNkc3/G21iso9VkcDgGaUEaCTMwxDP7qon166+XxFhNq1fv8RXfWXFdp8oNTqaAAgiTICBIwJg+L0/syx6hcbrvzSGl373Eq97861OhYAUEaAQNI3tqvmzRyri1Pi5Kn36q533PrdxzvUwAJpACxEGQECTGRosP42bZRun9BPkvTcsj269M/L9Pfle1VSxcX2APieYfrBuX5lZWVyOp0qLS1VZGSk1XGATmPBxjz98r3NKj+6UqvDbtOVI1yaekGyRiZFyTAMixMC8Gct/f6mjAABrtJTr/fdeXpj1X5tyz921d8hCZGaekGyppzTU+EOu4UJAfgrygiAVjFNU+6cEr25OlsfbMyTp94rSerqsGvKSJempvXS4AT+/QFoOcoIgDNWUlWrdzfk6s3V+7W3qLL5/vN6ddPUtGR9a3iCQoODLEwIwB9QRgCcNdM09cXeQ3pzVbY+2Vqg+qNn3UR1CdZ3z0vUDWm91Kd7uMUpAXRUlBEAbaqwrEb/XJejt9fkKLekuvn+cf2768YLknXJ4B4KDuIEPQDHUEYAtIsGr6mlOwv1xqr9WrqrSE2fIHERDn1/dLK+f36SXFFh1oYE0CG09Pu71f8bk5GRofT0dLlcLhmGofnz559y+/fee0+XXnqpYmNjFRkZqTFjxuiTTz5p7W4BdBBBNkOXDO6hl28ZrYx7J+r2Cf3UvWuICss9evqz3Rr3+8W69bV1WrqzUF4WUwPQAq0uI5WVlUpNTdXcuXNbtH1GRoYuvfRS/ec//9H69es1ceJEpaen68svv2x1WAAdS1J0F/3imylaef8leub6kbqgb7S8prRo20Hd/PJajf/jEj27dI+KK7gwH4CTO6vDNIZhaN68eZoyZUqrnjd06FB973vf00MPPdSi7TlMA/iPzMJyvbk6W/9ef0DlNY2LqQUHGZo8LEFT05I1uk80i6kBAaKl398+X8nI6/WqvLxc0dHRJ93G4/HI4zn2f1JlZWUn3RZAx9I/LkIPpw/VLy5P0Qeb8vTmqv3aeKBUCzbmacHGPA2I66qpacm65rxERYYGWx0XQAfg86nvf/zjH1VRUaHrrrvupNvMmTNHTqez+ZaUlOTDhADaQlhIkK4blaT37xinD+4Yp++fn6Sw4CDtLqzQIx9sU9pvPtN9/96kzQdKrY4KwGI+PUzz1ltv6dZbb9X777+vSZMmnXS7E42MJCUlcZgG8HNlNXWatyFXb6zar92FFc3394wK0+CECKXERyrl6J+9Y7rIzqnCgF/rcIdp3nnnHf3whz/Uv/71r1MWEUlyOBxyOBw+SgbAVyJDgzX9wt6aNqaX1u47ojdW7dfHW/KVW1Kt3JJqfbq9sHlbh92mgT0ilBIfoZSESA0++md0eIiFrwBAe/BJGXn77bc1Y8YMvfPOO7riiit8sUsAHZhhGBrdJ1qj+0SrrGaYtueVaUdBuXYUlGl7frl2FpSruq5Bm3NLtTn3+MM4cRGOr5STxlGUfrFdFWJnFAXwV60uIxUVFcrMzGz+OSsrS263W9HR0UpOTtbs2bOVm5ur1157TVLjoZnp06frqaeeUlpamgoKCiRJYWFhcjqdbfQyAPiryNBgpfWNUVrfmOb7vF5T2YermsvJjoLGsrL/UJUKyz0qLC9Sxq6i5u3tNkP947o2j6KkxEdoSEKkYiMcnLkD+IFWzxlZunSpJk6c+LX7p0+frldeeUU333yz9u3bp6VLl0qSJkyYoGXLlp10+5bg1F4AklThqdeug+Xa0VRQ8su1vaCs+RTi/xUdHqKU+AgNio/Q4KPzUQb2iOAif4CPsBw8gIBgmqbySmu0I79x9GT70T/3FlXoRAvA2gypd/dwDY6PbJ40m5oUpdgI5qkBbY0yAiCg1dQ1KLOwormcNB3yOVxZ+7Vtg4MMfefcRN0+ob+SY7pYkBbonCgjAPA/TNNUUYXnuMM8W/JKtetg42nGQTZDV6e6dPvE/uof19XitID/o4wAQAut23dYzyzO1LKjk2INQ/rW8ATdMbG/BifwmQOcKcoIALTSpgMlemZxphZtO9h836VDeuinF/fXiMQo64IBfooyAgBnaHt+meYuydRHm/PV9Ak5fmCsfnpxf43qffLragE4HmUEAM5SZmGF/ro0U++789Rw9NScC/pG686LB2hMvxjWMAFOgzICAG0k+1CVnl2WqX+vP6C6hsaPzHOTo/TTiwdowqBYSglwEpQRAGhjeSXVeiFjr95eky1PvVeSNKxnpO6YOECXDekhm41SAnwVZQQA2klheY3+vjxLb6zar6raBknSoB4Rmnlxf10xPEFBlBJAEmUEANrd4cpavbQiS6+u3KdyT+OS9H27h+v2if119TkuBQdx8T4ENsoIAPhIaXWdXl25Ty99nqWSqjpJUmK3MP1kQj9de16iHHauhYPARBkBAB+r8NTrzVX79bfle1Vc0bjsfHxkqH48vq++f36ywkIoJQgslBEAsEh1bYPeWZut55ftVUFZjSSpe9cQ3fqNvrrxgl4Kd9gtTgj4BmUEACzmqW/Qv9cf0LNL9+jAkWpJUlSXYP1gbB9Nu7C3nGHBFicE2hdlBAA6iLoGr9535+mvSzK1t7hSkhThsOvmsb11y9g+ig4PsTgh0D4oIwDQwTR4TX20OV9/Wby7+UrBXUKCNDUtWTdd0FvJMV0sTgi0LcoIAHRQXq+p/247qL8s2a0tuWWSGq8U/I0BsbphdLImDY6TndOC0QlQRgCggzNNU0t3FunllfuUsauo+f4ekQ59b1SSvjc6WT2jwixMCJwdyggA+JHsQ1V6e222/rUup/m0YJshTRwUpxvSkjVhUBwru8LvUEYAwA/V1nv1320Femt1tlbuOdR8f8+oMH3v/CR97/wk9YgMtTAh0HKUEQDwc3uLKvT2mmz9a/2B5pVdg2yGJg2O09S0XhrXvzsX50OHRhkBgE6ipq5BC7c0jpas2Xe4+f7k6C76/ugkffe8JMVGOCxMCJwYZQQAOqFdB8v11upsvbvhgMprGi/OFxxk6LKh8ZqalqwxfWNkGIyWoGOgjABAJ1Zd26APN+XprTXZ+jK7pPn+vt3DdUNasr5zbqK6sZgaLEYZAYAAsS2vTG+t2a/5X+apwtM4WhJit+lbw+I19YJeGtWrG6MlsARlBAACTKWnXgs25unN1fubF1OTpAFxXTU1LVnfPjeR6+HApygjABDANh0o0Vurs/W+O0/VdQ2SpNBgm9JHuHRDWrLOSYpitATtjjICAFBZTZ3e/zJXb67O1o6C8ub7BydEampasqaM7KmuDruFCdGZUUYAAM1M09SG7MbRkg835clT75XUeKG+9BEuDesZKVdUmBKcYeoZFabIMDsjJzhrlBEAwAmVVtXp3Q0H9NaabGUWVpxwm/CQILmiwppvPaNCv/LfYeoRGaoQOxfzw6lRRgAAp2SaptZkHdZ/tx3UgSNVyiupUV5JtQ5V1p72uYYhxUU4jisoLufxhSWqSzCjKwGOMgIAOCPVtQ3KL61uLie5JdXKK6lW3tH7ckuqVXv0MM+phAUHyXV0RKXnV0ZZXFGh6hkVpnhnqBz2IB+8Ililpd/fzFoCABwnLCRIfWO7qm9s1xM+bpqmDlXWNhaUkmrlHi0tX/25uMKj6roG7Smq1J6iypPuK/bo6Mq5yVG6OCVOo/tEU1ACECMjAIA2V1PXoILSr46s1DSPrjSNtNTUfX10JTwkSOMGdNfFKXGaOChOcVyh2K9xmAYA0GGZpqkjVXXKK6lWVnGllu8u0pKdRSoq9xy33fCeTk1MidMlKXEa3tPJVYr9DGUEAOBXvF5TW/PK9NmOg1qyo1AbD5Qe93j3rg5NGBSri1Pi9I0B3RURymqyHR1lBADg1wrLa7R0Z5GW7CjU8t3FzdfdkRqvVHx+72hdnBKni1PiTjq/BdaijAAAOo3aeq/W7jusxTsKtWRHofYWHz8ptndMl6OHc3podJ9o1kDpINqtjGRkZOgPf/iD1q9fr/z8fM2bN09Tpkw56fb5+fm65557tG7dOmVmZurOO+/Uk08+2ZpdUkYAAMfJKq5sLiarsw6pruHYV1nTJNhLUnpoQkqs4iKYBGuVdju1t7KyUqmpqZoxY4auueaa027v8XgUGxurBx54QE888URrdwcAwNf06R6uH4zrox+M66MKT71W7C7S4h2FWryjSMUVHn2y9aA+2XpQUuMk2KbDOUyC7ZjO6jCNYRinHRn5qgkTJuicc85hZAQA0C68XlNb8kqPFpNCbTrBJNiJRyfBjmMSbLvz60XPPB6PPJ5jp3eVlZVZmAYA4C9sNkMjEqM0IjFKd08a2DwJdvH2Qi3f3Thq8q/1B/Sv9QcUHGRodJ9oTRwUpytGJCjBGWZ1/IDVIcvInDlz9Oijj1odAwDg5+IiQnXdqCRdNyqpeRLsZ9sLtWRnobKKK/V55iF9nnlIf/hkp358UV/9ZEJ/hYWwAqyvdcjpxrNnz1ZpaWnzLScnx+pIAAA/F2K3aWz/7noofYiW/HyCFt8zXg9cMVjnJkfJU+/V04szdfGflmrBxjz5wYmmnUqHHBlxOBxyOBxWxwAAdGJN19/5wbg+WrilQI99tF25JdW68+0v9cYX+/VQ+hAN6+m0OmZA6JAjIwAA+IphGJo8PEGf3TNesy4dqNBgm9bsO6z0v6zQ7Pc261CF5/S/BGel1WWkoqJCbrdbbrdbkpSVlSW3263s7GxJjYdYpk2bdtxzmravqKhQUVGR3G63tm3bdvbpAQBoI6HBQbrzkgFafM8EXZXqkmlKb6/J1oQ/LtWLK7JU1/D1C/uhbbT61N6lS5dq4sSJX7t/+vTpeuWVV3TzzTdr3759Wrp06bGdGF8/p7tXr17at29fi/bJqb0AAF9bu++wHlmwVVvzGs/o7BcbrofSh2r8wFiLk/kPloMHAOAsNXhN/Wtdjv7wyU4dqqyVJE0aHKcHrhii3t3DLU7X8VFGAABoI6XVdXr6s916deU+1XtNBQcZmjGuj3568QB1dXTIc0E6BMoIAABtLLOwXL/6cLsydhVJkmIjHLrvmym6ZmRPlpk/AcoIAADtwDRNLd5RqF9/uE37DlVJklKTovRI+hCNTO5mcbqOhTICAEA78tQ36OXP9+mZz3arsrZBkvSdcxN13zcHKS6SKwVLlBEAAHyisLxGjy/cqX+vPyBJCg8J0h0XD9CMcb3lsAf20vKUEQAAfMidU6JHFmyVO6dEktQrposeuGKIJg2OO+ESF4GAMgIAgI95vabmfZmr3y3coaLyxpVbvzGgux5OH6L+cREWp/M9yggAABap8NRr7pJMvbg8S7UNXgXZDE0b00t3TxooZ1iwZbnKauqUfahKOYerlH24SjlHqpR9uFo5h6s055rhuqBvTNvujzICAIC19hVX6rGPtuvT7QclSdHhIfr5ZYP0vfOTFNQOpwLXNXiVV1LdWDQON/3ZWDyyD1eptLrupM/9/XeG63vnJ7dpHsoIAAAdRMauIv3qw23KLKyQJA11Rerh9KEa3Se6Vb/HNE0drqxVzpGvFI1Dx0Y58kqq5T3Nt3r3rg4lRYcpObqLkqO7KOnonynxEYrqEnKmL/GEKCMAAHQgdQ1evf7Ffj3x6S6V19RLktJTXZo9OUWuqLDm7WrqGnTgSPVxIxo5X/mz6TTik3HYbccVjaaykRzdRYndwhTuwxVjKSMAAHRAhyo8+tOiXXp7TbZMUwoNtumSlB4qLK9R9uEqHSzznPZ3xEeGHjeqkRwTpqRujf8dG+HoMGfvUEYAAOjAtuaV6tEF27Rm3+GvPRYeEqTkmHAldTt6OCXmWPHoGRWm0GD/WL+kpd/fXN0HAAALDHU59Y8fX6BPtxdqd2G5ErsdO5zSrUtwhxnd8AXKCAAAFjEMQ5cO6aFLh/SwOoqlbFYHAAAAgY0yAgAALEUZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAICl/OKqvaZpSpLKysosTgIAAFqq6Xu76Xv8ZPyijJSXl0uSkpKSLE4CAABaq7y8XE6n86SPG+bp6koH4PV6lZeXp4iICBmG0Wa/t6ysTElJScrJyVFkZGSb/V5/EujvQaC/fon3gNcf2K9f4j1oz9dvmqbKy8vlcrlks518ZohfjIzYbDYlJia22++PjIwMyL+AXxXo70Ggv36J94DXH9ivX+I9aK/Xf6oRkSZMYAUAAJaijAAAAEsFdBlxOBx6+OGH5XA4rI5imUB/DwL99Uu8B7z+wH79Eu9BR3j9fjGBFQAAdF4BPTICAACsRxkBAACWoowAAABLUUYAAIClArqMzJ07V71791ZoaKjS0tK0Zs0aqyP5xJw5c3T++ecrIiJCcXFxmjJlinbu3Gl1LEv97ne/k2EYuvvuu62O4jO5ubm68cYbFRMTo7CwMA0fPlzr1q2zOpbPNDQ06MEHH1SfPn0UFhamfv366de//vVpr6HhrzIyMpSeni6XyyXDMDR//vzjHjdNUw899JASEhIUFhamSZMmaffu3daEbSeneg/q6up03333afjw4QoPD5fL5dK0adOUl5dnXeA2drq/A1912223yTAMPfnkkz7JFrBl5B//+IdmzZqlhx9+WBs2bFBqaqouv/xyFRYWWh2t3S1btkwzZ87UqlWrtGjRItXV1emyyy5TZWWl1dEssXbtWj3//PMaMWKE1VF85siRIxo7dqyCg4P18ccfa9u2bfrTn/6kbt26WR3NZ37/+9/r2Wef1V/+8hdt375dv//97/X444/rmWeesTpau6isrFRqaqrmzp17wscff/xxPf3003ruuee0evVqhYeH6/LLL1dNTY2Pk7afU70HVVVV2rBhgx588EFt2LBB7733nnbu3KmrrrrKgqTt43R/B5rMmzdPq1atksvl8lEySWaAGj16tDlz5szmnxsaGkyXy2XOmTPHwlTWKCwsNCWZy5YtszqKz5WXl5sDBgwwFy1aZI4fP9686667rI7kE/fdd585btw4q2NY6oorrjBnzJhx3H3XXHONOXXqVIsS+Y4kc968ec0/e71eMz4+3vzDH/7QfF9JSYnpcDjMt99+24KE7e9/34MTWbNmjSnJ3L9/v29C+dDJXv+BAwfMnj17mlu2bDF79eplPvHEEz7JE5AjI7W1tVq/fr0mTZrUfJ/NZtOkSZP0xRdfWJjMGqWlpZKk6Ohoi5P43syZM3XFFVcc93chECxYsECjRo3Sd7/7XcXFxWnkyJH629/+ZnUsn7rwwgv12WefadeuXZKkjRs3asWKFZo8ebLFyXwvKytLBQUFx/07cDqdSktLC8jPxCalpaUyDENRUVFWR/EJr9erm266Sffee6+GDh3q0337xYXy2lpxcbEaGhrUo0eP4+7v0aOHduzYYVEqa3i9Xt19990aO3ashg0bZnUcn3rnnXe0YcMGrV271uooPrd37149++yzmjVrlv7v//5Pa9eu1Z133qmQkBBNnz7d6ng+cf/996usrEwpKSkKCgpSQ0ODfvOb32jq1KlWR/O5goICSTrhZ2LTY4GmpqZG9913n66//vqAuXje73//e9ntdt15550+33dAlhEcM3PmTG3ZskUrVqywOopP5eTk6K677tKiRYsUGhpqdRyf83q9GjVqlH77299KkkaOHKktW7boueeeC5gy8s9//lNvvvmm3nrrLQ0dOlRut1t33323XC5XwLwHOLG6ujpdd911Mk1Tzz77rNVxfGL9+vV66qmntGHDBhmG4fP9B+Rhmu7duysoKEgHDx487v6DBw8qPj7eolS+d8cdd+jDDz/UkiVLlJiYaHUcn1q/fr0KCwt17rnnym63y263a9myZXr66adlt9vV0NBgdcR2lZCQoCFDhhx33+DBg5WdnW1RIt+79957df/99+v73/++hg8frptuukk/+9nPNGfOHKuj+VzT516gfyZKx4rI/v37tWjRooAZFVm+fLkKCwuVnJzc/Jm4f/9+3XPPPerdu3e77z8gy0hISIjOO+88ffbZZ833eb1effbZZxozZoyFyXzDNE3dcccdmjdvnhYvXqw+ffpYHcnnLrnkEm3evFlut7v5NmrUKE2dOlVut1tBQUFWR2xXY8eO/drp3Lt27VKvXr0sSuR7VVVVstmO/wgMCgqS1+u1KJF1+vTpo/j4+OM+E8vKyrR69eqA+Exs0lREdu/erU8//VQxMTFWR/KZm266SZs2bTruM9Hlcunee+/VJ5980u77D9jDNLNmzdL06dM1atQojR49Wk8++aQqKyt1yy23WB2t3c2cOVNvvfWW3n//fUVERDQfE3Y6nQoLC7M4nW9ERER8bY5MeHi4YmJiAmLuzM9+9jNdeOGF+u1vf6vrrrtOa9as0QsvvKAXXnjB6mg+k56ert/85jdKTk7W0KFD9eWXX+rPf/6zZsyYYXW0dlFRUaHMzMzmn7OysuR2uxUdHa3k5GTdfffdeuyxxzRgwAD16dNHDz74oFwul6ZMmWJd6DZ2qvcgISFB1157rTZs2KAPP/xQDQ0NzZ+N0dHRCgkJsSp2mznd34H/LV/BwcGKj4/XoEGD2j+cT87Z6aCeeeYZMzk52QwJCTFHjx5trlq1yupIPiHphLeXX37Z6miWCqRTe03TND/44ANz2LBhpsPhMFNSUswXXnjB6kg+VVZWZt51111mcnKyGRoaavbt29f85S9/aXo8HqujtYslS5ac8N/99OnTTdNsPL33wQcfNHv06GE6HA7zkksuMXfu3Glt6DZ2qvcgKyvrpJ+NS5YssTp6mzjd34H/5ctTew3T7KTLDQIAAL8QkHNGAABAx0EZAQAAlqKMAAAAS1FGAACApSgjAADAUpQRAABgKcoIAACwFGUEAABYijICAAAsRRkBAACWoowAAABLUUYAAICl/h8d6/f73O7WbwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:07<00:00, 167.38it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5678"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The improvement is really small (almost insignificant) with such wide networks.\n",
    "try_parameters(hidden_layers_channels=[128, 256],\n",
    "               kernels=[5, 5],\n",
    "               epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4f4c3c85",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:20<00:00, 77.67it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:22<00:00, 75.67it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:21<00:00, 76.80it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:28<00:00, 70.94it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:22<00:00, 76.06it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:22<00:00, 75.39it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:19<00:00, 78.80it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:19<00:00, 78.48it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:21<00:00, 76.99it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:20<00:00, 77.89it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:07<00:00, 166.90it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.614"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perhaps, we can try to teach the previous model a bit longer and see if something changed?\n",
    "# It seems that we have a bit of space to continue as the model\n",
    "# was still learning something after 5 epochs.\n",
    "# Wow! The model actually improved up to 61% accuracy.\n",
    "try_parameters(hidden_layers_channels=[128, 256],\n",
    "               kernels=[5, 5],\n",
    "               epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "80018c06",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:16<00:00, 81.52it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:16<00:00, 81.23it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:22<00:00, 75.68it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:18<00:00, 79.38it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:25<00:00, 73.31it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:18<00:00, 79.84it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:18<00:00, 79.12it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:18<00:00, 79.40it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:20<00:00, 77.84it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:19<00:00, 78.69it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:15<00:00, 82.57it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:13<00:00, 84.52it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:17<00:00, 80.35it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:17<00:00, 80.23it/s]\n",
      "100%|██████████████████████████████████████████████████████████████████████████████| 6250/6250 [01:15<00:00, 83.26it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 1250/1250 [00:07<00:00, 163.70it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6318"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see if the wider network that has been trained longer works better.\n",
    "# Wow! Another 2%. The model now performs with ~63% accuracy.\n",
    "try_parameters(hidden_layers_channels=[256, 512],\n",
    "               kernels=[5, 5],\n",
    "               epochs=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1ab55c3",
   "metadata": {},
   "source": [
    "The baseline shows **47.8%** accuracy.\n",
    "The best model across all experiments shows **63.2%** accuracy.\n",
    "\n",
    "The best configuration is:\n",
    "- **2 conv** layers + 3 \"default\" (i.e. not changed over the experiment) fully connected layers\n",
    "- **256** and **512** 5x5 channels for conv. layers\n",
    "- **max pooling** with kernel size 2 and stride 2 for both layers\n",
    "\n",
    "I tried to vary 4 parameters:\n",
    "- **Width**: seems like **the best** option. Making the network wider allows to capture more features in the image which helps to increase accuracy;\n",
    "- **Depth**: making the network deeper requires bigger effort to teach first few layers, therefore it seems it should decline in quality when trained not long enough and when the dataset is small;\n",
    "- **Convolution kernel size**: helps to analyse bigger areas and pay attention to more details when extracting features;\n",
    "- **Max pooling usage**: helps to reduce features space to focus only on imporant features. It is better to use it as it seems it reduce noise that distracts the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6b48450-2ad6-45e1-8327-43b2207a49b1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}