resources/Qiskit_ML/QML_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantum Support Vector Machine for Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from qiskit import Aer\n",
    "from qiskit.utils import QuantumInstance, algorithm_globals\n",
    "from qiskit_machine_learning.datasets import ad_hoc_data\n",
    "from qiskit.circuit.library import ZFeatureMap, ZZFeatureMap\n",
    "from qiskit_machine_learning.kernels import QuantumKernel\n",
    "from qiskit_machine_learning.algorithms import QSVC\n",
    "\n",
    "# Set seed for random generators\n",
    "algorithm_globals.random_seed = 1234"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "qi = QuantumInstance(Aer.get_backend('qasm_simulator'), shots=1024,\n",
    "                                   seed_simulator=1234, seed_transpiler=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate data\n",
    "adhoc_dimension = 2\n",
    "train_features, train_labels, test_features, test_labels, adhoc_total = ad_hoc_data(\n",
    "    training_size=20,\n",
    "    test_size=5,\n",
    "    n=adhoc_dimension,\n",
    "    gap=0.3,\n",
    "    plot_data=False, one_hot=False, include_sample_total=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function for plotting data\n",
    "def plot_adhoc_dataset(train_features, train_labels, test_features, test_labels, adhoc_total):\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    plt.ylim(0, 2 * np.pi)\n",
    "    plt.xlim(0, 2 * np.pi)\n",
    "    plt.imshow(np.asmatrix(adhoc_total).T, interpolation='nearest',\n",
    "               origin='lower', cmap='RdBu', extent=[0, 2 * np.pi, 0, 2 * np.pi])\n",
    "\n",
    "    plt.scatter(train_features[np.where(train_labels[:] == 0), 0], train_features[np.where(train_labels[:] == 0), 1],\n",
    "                marker='s', facecolors='w', edgecolors='b', label=\"A train\")\n",
    "    plt.scatter(train_features[np.where(train_labels[:] == 1), 0], train_features[np.where(train_labels[:] == 1), 1],\n",
    "                marker='o', facecolors='w', edgecolors='r', label=\"B train\")\n",
    "    plt.scatter(test_features[np.where(test_labels[:] == 0), 0], test_features[np.where(test_labels[:] == 0), 1],\n",
    "                marker='s', facecolors='b', edgecolors='w', label=\"A test\")\n",
    "    plt.scatter(test_features[np.where(test_labels[:] == 1), 0], test_features[np.where(test_labels[:] == 1), 1],\n",
    "                marker='o', facecolors='r', edgecolors='w', label=\"B test\")\n",
    "\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n",
    "    plt.title(\"Ad hoc dataset for classification\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_adhoc_dataset(train_features, train_labels, test_features, test_labels, adhoc_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classical SVC score:  0.4\n"
     ]
    }
   ],
   "source": [
    "# Build a classical kernel\n",
    "svc = SVC(kernel='rbf') # radial basis function kernel; kernel choices: 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'\n",
    "svc.fit(train_features, train_labels)\n",
    "print(\"Classical SVC score: \", svc.score(test_features, test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build a quantum kernel\n",
    "z_feature_map = ZFeatureMap(feature_dimension=adhoc_dimension, reps=2)\n",
    "zz_feature_map = ZZFeatureMap(feature_dimension=adhoc_dimension, reps=2, entanglement='full')\n",
    "z_kernel = QuantumKernel(feature_map=z_feature_map, quantum_instance=qi)\n",
    "zz_kernel = QuantumKernel(feature_map=zz_feature_map, quantum_instance=qi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 490.878x216.72 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_feature_map.decompose().draw(output='mpl', scale=1.5) # P gate = phase gate, i.e. Rz gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1393.88x216.72 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zz_feature_map.decompose().draw(output=\"mpl\", scale=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum SVC with Z Feature Map score:  0.7\n"
     ]
    }
   ],
   "source": [
    "qsvc_z = QSVC(quantum_kernel=z_kernel)\n",
    "qsvc_z.fit(train_features, train_labels)\n",
    "print(\"Quantum SVC with Z Feature Map score: \", qsvc_z.score(test_features, test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum SVC with ZZ Feature Map score:  1.0\n"
     ]
    }
   ],
   "source": [
    "qsvc_zz = QSVC(quantum_kernel=zz_kernel)\n",
    "qsvc_zz.fit(train_features, train_labels)\n",
    "print(\"Quantum SVC with ZZ Feature Map score: \", qsvc_zz.score(test_features, test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qsvc_zz.predict(test_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hybrid QNNs with TorchConnector for MNIST Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional imports\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from qiskit.opflow import AerPauliExpectation\n",
    "from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap\n",
    "from qiskit_machine_learning.neural_networks import TwoLayerQNN\n",
    "from qiskit_machine_learning.connectors import TorchConnector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# declare quantum instance\n",
    "qi = QuantumInstance(Aer.get_backend(\"aer_simulator_statevector\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data loading (train & test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Dataset\n",
    "\n",
    "# Set train shuffle seed (for reproducibility)\n",
    "torch.manual_seed(1234)\n",
    "\n",
    "batch_size = 1\n",
    "n_samples = 100  # We will concentrate on the first 100 samples\n",
    "\n",
    "# Use pre-defined torchvision function to load MNIST train data\n",
    "X_train = datasets.MNIST(\n",
    "    root=\"./data\", train=True, download=True, transform=transforms.Compose([transforms.ToTensor()])\n",
    ")\n",
    "\n",
    "# Filter out labels (originally 0-9), leaving only labels 0 and 1\n",
    "idx = np.append(\n",
    "    np.where(X_train.targets == 0)[0][:n_samples], np.where(X_train.targets == 1)[0][:n_samples]\n",
    ")\n",
    "X_train.data = X_train.data[idx]\n",
    "X_train.targets = X_train.targets[idx]\n",
    "\n",
    "\n",
    "# Define torch dataloader with filtered data\n",
    "train_loader = DataLoader(X_train, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([200, 28, 28])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get dimension of train data\n",
    "X_train.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x216 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_samples_show = 6\n",
    "\n",
    "data_iter = iter(train_loader)\n",
    "fig, axes = plt.subplots(nrows=1, ncols=n_samples_show, figsize=(10, 3))\n",
    "\n",
    "while n_samples_show > 0:\n",
    "    images, targets = data_iter.__next__()\n",
    "\n",
    "    axes[n_samples_show - 1].imshow(images[0, 0].numpy().squeeze(), cmap=\"gray\")\n",
    "    axes[n_samples_show - 1].set_xticks([])\n",
    "    axes[n_samples_show - 1].set_yticks([])\n",
    "    axes[n_samples_show - 1].set_title(\"Labeled: {}\".format(targets[0].item()))\n",
    "\n",
    "    n_samples_show -= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test Dataset\n",
    "n_samples = 50\n",
    "\n",
    "# Use pre-defined torchvision function to load MNIST test data\n",
    "X_test = datasets.MNIST(\n",
    "    root=\"./data\", train=False, download=True, transform=transforms.Compose([transforms.ToTensor()])\n",
    ")\n",
    "\n",
    "# Filter out labels (originally 0-9), leaving only labels 0 and 1\n",
    "idx = np.append(\n",
    "    np.where(X_test.targets == 0)[0][:n_samples], np.where(X_test.targets == 1)[0][:n_samples]\n",
    ")\n",
    "X_test.data = X_test.data[idx]\n",
    "X_test.targets = X_test.targets[idx]\n",
    "\n",
    "# Define torch dataloader with filtered data\n",
    "test_loader = DataLoader(X_test, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Defining the QNN and hybrid model\n",
    "\n",
    "After defining our quantum neural network layer (in this case, a `TwoLayerQNN`), we can embed it into a layer in our torch `Module` by initializing a torch connector as `TorchConnector(qnn)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ComposedOp([\n",
      "  OperatorMeasurement(1.0 * ZZ),\n",
      "  CircuitStateFn(\n",
      "       ┌──────────────────────────┐»\n",
      "  q_0: ┤0                         ├»\n",
      "       │  ZZFeatureMap(x[0],x[1]) │»\n",
      "  q_1: ┤1                         ├»\n",
      "       └──────────────────────────┘»\n",
      "  «     ┌────────────────────────────────────────────────┐\n",
      "  «q_0: ┤0                                               ├\n",
      "  «     │  RealAmplitudes(θ[0],θ[1],θ[2],θ[3],θ[4],θ[5]) │\n",
      "  «q_1: ┤1                                               ├\n",
      "  «     └────────────────────────────────────────────────┘\n",
      "  )\n",
      "])\n"
     ]
    }
   ],
   "source": [
    "# Define and create QNN\n",
    "def create_qnn():\n",
    "    feature_map = ZZFeatureMap(2)\n",
    "    ansatz = RealAmplitudes(2, reps=2)\n",
    "    # REMEMBER TO SET input_gradients=True FOR ENABLING HYBRID GRADIENT BACKPROP\n",
    "    qnn = TwoLayerQNN(\n",
    "        2,\n",
    "        feature_map,\n",
    "        ansatz,\n",
    "        input_gradients=True,\n",
    "        exp_val=AerPauliExpectation(),\n",
    "        quantum_instance=qi,\n",
    "    )\n",
    "    return qnn\n",
    "\n",
    "\n",
    "qnn = create_qnn()\n",
    "print(qnn.operator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 581.178x216.72 with 1 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RealAmplitudes(2, reps=2).decompose().draw(output=\"mpl\", scale=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define torch NN module\n",
    "class Net(nn.Module):\n",
    "    def __init__(self, qnn):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 2, kernel_size=5)\n",
    "        self.conv2 = nn.Conv2d(2, 16, kernel_size=5)\n",
    "        self.dropout = nn.Dropout2d()\n",
    "        self.fc1 = nn.Linear(256, 64)\n",
    "        self.fc2 = nn.Linear(64, 2)  # 2-dimensional input to QNN\n",
    "        self.qnn = TorchConnector(qnn)  # Apply torch connector, weights chosen uniformly at random from interval [-1, 1].\n",
    "        self.fc3 = nn.Linear(1, 1)  # 1-dimensional output from QNN\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = F.max_pool2d(x, 2)\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = F.max_pool2d(x, 2)\n",
    "        x = self.dropout(x)\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        x = self.qnn(x)  # apply QNN, outputs the Pauli-Z expectation values of n qubits\n",
    "        # x = self.fc3(x)\n",
    "        return torch.cat((x, 1 - x), -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-0.4194,  1.4194]], grad_fn=<CatBackward0>)\n",
      "tensor(0)\n"
     ]
    }
   ],
   "source": [
    "hb_model = Net(qnn)\n",
    "print(hb_model(X_train.data[10].unsqueeze(0).unsqueeze(0).float()))\n",
    "print(X_train.targets[10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Training the hybrid model\n",
    "\n",
    "We will be using the `Adam` optimizer from `torch.optim` and the `NLLLoss` from `torch.nn`. The negative log likelihood loss is commonly used for classification, where the input can be thought of as the log-probabilities of each class. So the input dimension should be (batch_size, num_classes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training [10%]\tLoss: -0.8812\n",
      "Training [20%]\tLoss: -1.0658\n",
      "Training [30%]\tLoss: -1.2401\n",
      "Training [40%]\tLoss: -1.3481\n",
      "Training [50%]\tLoss: -1.3782\n",
      "Training [60%]\tLoss: -1.4079\n",
      "Training [70%]\tLoss: -1.4207\n",
      "Training [80%]\tLoss: -1.4299\n",
      "Training [90%]\tLoss: -1.4375\n",
      "Training [100%]\tLoss: -1.4658\n"
     ]
    }
   ],
   "source": [
    "# Define model, optimizer, and loss function\n",
    "hb_model = Net(qnn)\n",
    "optimizer = optim.Adam(hb_model.parameters(), lr=0.001)\n",
    "loss_func = nn.NLLLoss()  # Negative log likelihood loss\n",
    "\n",
    "# Start training\n",
    "epochs = 10  # Set number of epochs\n",
    "loss_list = []  # Store loss history\n",
    "hb_model.train()  # Set model to training mode\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    total_loss = []\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        optimizer.zero_grad(set_to_none=True)  # Initialize gradient\n",
    "        output = hb_model(data)  # Forward pass\n",
    "        loss = loss_func(output, target)  # Calculate loss\n",
    "        loss.backward()  # Backward pass\n",
    "        optimizer.step()  # Optimize weights\n",
    "        total_loss.append(loss.item())  # Store loss\n",
    "    loss_list.append(sum(total_loss) / len(total_loss))\n",
    "    print(\"Training [{:.0f}%]\\tLoss: {:.4f}\".format(100.0 * (epoch + 1) / epochs, loss_list[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot loss convergence\n",
    "plt.plot(loss_list)\n",
    "plt.title(\"Hybrid NN Training Convergence\")\n",
    "plt.xlabel(\"Training Iterations\")\n",
    "plt.ylabel(\"Neg. Log Likelihood Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(hb_model.state_dict(), \"hb_model.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Testing the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qnn_new = create_qnn()\n",
    "hb_model_trained = Net(qnn_new)\n",
    "hb_model_trained.load_state_dict(torch.load(\"hb_model.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance on test data:\n",
      "\tLoss: -1.4842\n",
      "\tAccuracy: 100.0%\n"
     ]
    }
   ],
   "source": [
    "loss_func = nn.NLLLoss()\n",
    "total_loss = []\n",
    "hb_model_trained.eval()  # set model to evaluation mode\n",
    "\n",
    "with torch.no_grad():\n",
    "\n",
    "    correct = 0\n",
    "    for batch_idx, (data, target) in enumerate(test_loader):\n",
    "        output = hb_model_trained(data)\n",
    "        if len(output.shape) == 1:\n",
    "            output = output.reshape(1, *output.shape)\n",
    "\n",
    "        pred = output.argmax(dim=1, keepdim=True)\n",
    "        correct += pred.eq(target.view_as(pred)).sum().item() # torch.eq computes element-wise equality\n",
    "\n",
    "        loss = loss_func(output, target)\n",
    "        total_loss.append(loss.item())\n",
    "\n",
    "    print(\n",
    "        \"Performance on test data:\\n\\tLoss: {:.4f}\\n\\tAccuracy: {:.1f}%\".format(\n",
    "            sum(total_loss) / len(total_loss), correct / len(test_loader) / batch_size * 100\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nketXREYBeS5brRaxWIxUKsXW1hZ37tyRxe2Kw+mN9Asdt0FnoJ0f0XEhNl/xARAnqtPpyJSY3W6XIkwKxaBgMpkYHR3l6tWrRKNRAoHAoSHlWq0mozSJRELW7wjq9bqUHuh2u1SrVQqFguzk2HtHZzabcbvdeL1eCoXCqVj0LBYLfr8fq9XK6Ogo0WiUaDSK1+vdtYb0dhQB8v9PQ2+0SWgxGY1Gzp8/j8FgoFqtEo/HyefzVKtVEokElUpFnqdhx2QyEQqFmJmZIRaL4fF4ZDr4cSkWkbKBh7IcHo+HSqWC3+8nFArRbDapVquntq7tOBDdXuIm9zSsAwPt/Ijul7GxMYLBoFykTCYTdrtdpsVCoRCtVotisUixWFSLiWJgsNlsvPHGG/zpn/4pHo+HkZGRQxf7VCrFp59+yvr6OolE4pHIT7fblZ+BVqtFKpVieXlZ1hN5PJ5drycELA0GA7VajcXFxef2t74o3G43Fy5cIBQKce3aNV599VVGR0ex2Wy71hCxwFssFrrd7q5N9EkxGAxomobNZmNkZASfz0e73WZ6elrWpDx48IB0Os3q6ioffPABa2tr+9ZtDSN2u52rV6/y5ptvEgwGmZycxOPxSCf1MMS5FI0xoiX7/PnzxONxSqUSy8vLZLPZF/TXDB4GgwGXy0UgEMDn88mI5iAz0M6PSHs5nc5d3qi40OFh6Fl4q7Va7SQPV6F4YkwmE9FolMuXLx8prSgiCML5WV9f3+X89KLruoz8iBEk+72/iPycljs+EfmJRCLyMTIysus5vZvqcdWwiYJQobMEyNRMqVTCarWyvb0NPKwXMplMUl9o2J0fcaM7NTUl6zwPk3Topfdc2u12rFYrtVoNv99PMBiUUQ3FwYhsirDfaVgHBtr5Ee3Wor1OXODibqnZbFKr1SiXy5TLZRXWVAwEohA3FosRCoWIxWKHLjbFYpGVlRWKxSJff/01m5ubpNNpSqXSoVHOer3OwsICTqeT0dFRKVTZi8vlYmZmBp/PRyKROPKG08+IVJbFYsFkMvVFZ4qoJ9I0jWw2KzvvRGeY6AQbpqi1iDY4nU7ppIbDYZxO51M7KyL96HA4OHPmjNRdEmNexN4xTHY+LoTQsNvtplgs9r2DNNDOj0hrhcNhvF6vXJhFh4aob0in06RSKTqdjrqoFX2PyWTi6tWrvP/++wSDQa5cuXJouiWRSPCzn/2MhYUFNjc3uXPnDoVC4bFqtuVymY8++ogvv/ySS5cucebMGebm5nY9Z2RkhNdff11uEoMc7haRF1ErKLrb+sH5sdlsjI+PE41GaTabxGIx2Yrd6XRoNBq02+2hSoEZjUbZeTg6Osr58+eZnZ2VqsJPg9iQPR4Pb775JteuXWNxcVE6QKJL8qBoqeJgrFYrgUCASqUyEPWBA+38iBy81WrFYrFIY4tWUVHgLKZYKxT9jticA4EA09PTMsd+lCLn5eVl0uk0hUKBarX62Pdqt9uk02nS6TRer3ff3xERkk6ns29XzSAh6m6EqKHQLOkH50ek8OFhtE082u22rDkaFqdHIGqkxNgWEQU6jmvQZDIRCATkZu31erHZbHS73b7ftE8KoZ59UADBaDTKMpN+uak4jIF2fhSK00QwGGR0dBSXy8Xc3JxMRTmdzkcWkna7LYXbEomEfJRKJTW7aB96x3fMzMxw9epVpqeniUaj0unoFwKBAK+//jrj4+Nks1lWV1epVCpsbGywsLAgxfqG0Rk6bEPtdDpyJElvilA48Ht/X9RS2e12pqamuHLlCul0mmq1qupD99BqteTU9mq1yvXr1x95jt1uJxqNomkayWSy72+UlPOjUPQBmqYxOjrKG2+8QSAQ4KWXXuLMmTPyTnfvot9sNkkmk+RyOVZWVlhdXWVlZUXOtVPsxmazMTMzw9jYGHNzc7z99tucPXtWTqXuJyKRCD/60Y9otVpsb2/z7bffUiwW+eSTT0gkErIwfRi0gHolAh5Hu92mUqnIwbHicyBUtsXr7HWAXC4XFy9exGq18uDBA1ZXV8lkMs/tbxpEms0mKysrFAoFSqUS3/ve9x55jtPpZGpqCo/Hw+bmZt/XByrnR6E4YcSiLFSDQ6GQ7K7aW9gpQs+tVkvm1kulEtVqVdUpHILJZJIjcMRjb1v/YYgoiyg8Pijq0ttZJDZu8fVREYM7xfsWCgWZ/hGOcL+nFF4UIg3T7XZpNBpSr0fUR8Hv0zEi3bnXhmJIs9frlV12it3ouk6j0ZC1tPvdYKm0l0KhODJCtdZqtXL+/HleeeUVwuEwExMTjyzCnU6HQqEgR1j8+te/5ttvvyWRSKg71X0QNYFiEOb09DQXL14kFos9kRq56BytVqvcv3+ftbU1GWHbW//g9/vl67vdbjnktzf18iQ4nU5isRh+v59wOCw38WFLeR1EqVRidXWVQqFALpdjdXVVTnIXw6zHx8eZmpqSaZmRkRHZmWSz2aTwpM/nU87PAYjOu2AwiM/nOxXSAOosKxQniN1uJxKJ4PF4uHDhAq+//rpcnPcuwu12m0wmQyqV4sGDB/zN3/wNn376qewGUuxGaJNYLBZ8Ph9nzpzh6tWr+Hw+nE7nkV5D3PGKSewffvghH3/8sXSI9t4BT09P89prrxEIBBgbG2NmZga73Y7L5XqqDUNs0O12m0gkgsViwWg00ul0lP4PD2Uebt++zcbGBpubm9y6dYt8Pk+pVCKfzwNw8eJFed6vX78u63xE15/RaMTlcsnZYcr5eRRho3A4LNXRBx11lhVPRe+sF7EgK54cIbjn8/nw+Xw4HI5HalB6w/rFYpF0Ok02m5WLvOJgRDeoaG3fOwrncYjp69VqlUqlQj6fJ5PJ0G639w3/ezwe0uk0uq5js9nw+/1Sq8dgMEin9qgbrIhe9Y7b6Pd0wnEhUlPCXr11PyL12Gg0KBQKZLNZstksuVxOpoKF85PP58nlcnS7XcrlMo1GA4PBsCtq12tfxf5YLBasVqua7aUYbhwOB7Ozs1y4cIHJycldwx4VR2d8fJz333+fyclJZmZmHolIdLtdcrkcxWJx1yTqbDbL1tbWCR31YGA2mwmHw4TDYaanp5mcnCQWi8kU1FFot9usr6+zsLAgI25bW1t0Op19dcPEIFSHwyHTl3a7ndHRUWKxGA6Hg+npaTkyRHEwmqbh8XiIRCKEQiHsdjuaptFutymXy9TrddbX17l9+zbffvst+Xye7e1tarXaLqHCZDLJ7du38Xq9jIyMMD4+jtvtfkTQU3EwRqNRjtfpHSU1yCjnR/FU2Gw2pqenuXr1KsFgUMr1K56MsbExvv/973Pp0iUZGeil2+1SKBTY2toiHo/z0Ucf8ctf/pJ2uz0U3T7PghiGOTU1xeTkJGNjY7IV96jRk3a7zdbWFrdv3yadTrOyssL29vaBWif5fJ61tTUMBoMcoWGz2ZidneXcuXNST2ZsbEw5P49B1OKEw2FCoZCMiHY6HUqlEqVSiXg8zr1797h9+7ZU9O90OrvSgalUikwmg9vt5vz58+RyOYChEox8VkTaKxQKqdleCoVIKZyWMOiLQoxlEXOzegdq7kUMIo3H4yQSCYrFIo1G41iUykVKQYx7OI3pFNFx1dvqfFREJ1Gr1aJer0ux1MO6vXpF4ETRbbvdJp/Pk06naTabxONxNjY2sFgsMg0n0i6HnQObzYbP55Pz2Fqt1qncvHuVuHu7sMRnRCj4l0olKpUK9Xpd1l/td26E6K14HCbUp9gfkXYvl8vUarV9u72EXXvt3M8o50fxVIhiQTFUVtX8HB2fz8fFixcJhUJcuXLl0OLber3O559/zs9+9jPy+TyLi4vHtnCLYZFer5dYLHbkIuBhQCjEi2LnYrFIuVx+Ig2lWq1GOp3GaDTKgbNWq5Vvv/2WX/3qV/j9ft58800uX76MxWLB4/EcqDmkaRqxWIw/+qM/IpVK8bvf/Y6vvvrqVBa6C/Vt0aF348YNvF4voVAITdOo1+usra2xtrbGgwcPyOfzUvhROTXPh2azyfLyMtVqlVwux2uvvfbIc4RTKpwj5fwoTiViEvJpmvL7onA6nczNzTE5Ocn09PShInutVov5+Xl++ctfUqvVjnVxNxgMeL1eIpEIgUDgVISyj4ve8Tj1el0WPD+J8yM6wuBhOmxjYwODwcD8/DwOh4ORkRG8Xi9jY2M4HA7sdvuhzk8kEuHGjRvkcjlyuRy3b98+lc6P0WiUa4uY6SXWGfi92vDKygpbW1tS2FDx/Gi1WsTjcQqFAiaTiXK5/MhzhPxDo9EYiPOhnB/FEyG6TYR+ykHdJ6ITI5/Py1lT/X4n8DwxGAxylpTH4yEcDhONRqUOzF5arZYUMmw2m8c6lNdisWA2m3E6nYyNjcli4P0iP2LKdbPZPLZ02yAgFnGRuhIdRE9bJyJSAkKgUrx2sVgkk8nQbDbx+XyHvoY4Z61WC6fTicPhkGm5QVf17lVy9vv9jIyMyLl24nPTO7tR/CseiudPr6CkQKz94roWnXeVSqXvaxKV86M4MpqmyRof0ZItOmf2i/yk02lu3bpFOp1mc3Nz4BfoZ8FisRCJRHC73Vy6dIm3336bCxcuyGGNvei6LmtExKDS41rgxdDUYDBIKBTi/fff54033sDtdjM5OfnI84WgYrVaJZVKDcU5FI67iLAsLy9z584dqtXqU0sLiE1a0zQ5od1kMjE/Py+1nnw+34EdSGIMQywWw+v1Mjk5ycTEhNxs8vn8QDsBYsyI1WrlO9/5Dt/97nfx+/1cv35d1sep6HL/omka+Xyeu3fvsra2xurqat9Hf5TzozgyIuIjHB6z2YzZbN63UFPXdcrlMhsbG6RSKbLZ7NBEDfZDqAyHQiGi0Shnzpzh3Llz+z5X13VqtRqZTIZsNkutVju2jU3TNJxOJ36/n9HRUa5cucJbb7114MbSbDbJ5/Oy5mVYzqHQ9imVSmQyGeLx+LGkmHRdl3OnqtUqyWSS1dVV2u32Y4dpWq1Wqa4bCATkCAyhZDzIGI1GqRs2MTHBd77zHfx+P2NjY6qhYkCo1WrE43HW19fJZrN9f6OknB/FkRHzp9xuNx6PRxY7H1TzI1ImQgxukO9Mn5XezhWn03moyJ2u6xSLRTY2Nkin0xSLxWOznTiHQs32cXodtVqNra0tstksmUym70PZz4KYCi7qGxYWFshkMmQymefi9HW7XarVKvl8Hp/P99g7ZTE7qdvtSuFGm80mRRAH+fPVK2Yo1hi3243Van3kxspisRAOh+U8O/F5El1dexHdfkLtuzdaLewmCtuPu65umBi06085P4ojI7qDotEosVhMio+JgYF7EeqrxWKRer0+cB+O48RisciIz9jY2KHFxZ1Oh5WVFT788EMymYycJXUcGAwGgsEg09PTjIyM4Ha7D22vTqVSfPrpp6yvr7O4uEi9Xj+W4+hHarUahUKBWq3GZ599xi9+8Qvy+TzLy8vPxelrt9uk02m58Ver1UOfLzZsoQoeCASAhyJ+g47JZMJut+N0OgkEAsRiMakns/f6dDqdXL58menpaTweD59++qmcdi+6vnoRESWXy4XL5cLtduN0OuXU8VarRS6XIx6Py/orxZMxiGu7cn4UR0bTNDld2uPxyA6VgxDFmKLGYZgxGo1yqvjjhif2Rn6y2eyxRn56z6Hb7ZYbwEGIFu319XVyudypj/yIAudEIsHCwoJM+T2PxV2oQefz+SN1LAnHB5CRH4vFIiM/g0xvdMbhcOB2uw9UjRfK3T6fj62tLRwOB2azmW63u68dxOv2Rn56U2ndblee94Mmliv2ZxCdHsFAOz+i+lyo3Q7yiVAoGo2GbKlOp9OkUiny+fyx1vwchVqtRiqVolqtsrS0xPb2Nul0mlKpdGpTArquS6dHFBELQbfnJSZoMpnkCIzx8fGh1lnq7Ybs7eqq1WrU63VarZa8ERAaYxaLhVQqdWgtlqZp2O12OY08GAwSCARwOp0y+tpsNkkkEiwtLbG1taUiP09Ab7fXoDHwzk+73ZbqnoN4AhQKQalUYnNzk2KxyPLyMisrKxSLxRcuw5/P5/niiy+Ix+PcuXOH+fl5EomELNQ9reRyOb799luy2Syrq6vkcjkqlcpzc/isVitnz57l5ZdfJhKJyALmYaS34FlEsrrdrqw1KxaLfPXVVywsLOB0OpmZmSEYDLK0tHRoutBgMODz+ZicnCQYDDI1NcXU1JTsWoWH0c35+Xn+/u//nmKxqIYFPwVPMjKmXxho5wfYJad9Wu9KB4HeC185oU9Hq9WSkR/xeFwdyJMitFTEY78Fq9lsksvlSCaTZLNZSqXSsR9HP9JqtSiXyxSLRTkc83k6e6IDUEQlTsOwyKdFpPSEfhj8/uZWdN0lEglWV1dl7Y7BYKBYLD72xtdsNsvfEU0avbZut9uUSiWy2SzVarXvW7T7mUFyggba+Wk2mySTSVwuF1arVYUrT4i9F/ugXPz9hAjxp1IpcrncsaaYNE3D4XDIYs+5uTmuX7+Oz+cjEAg8cr6azSaZTIZEIkEulzvV0Z5eLBYLPp+Pbrcr1xSRUj/OcyG6tILBIJFIRGr3HFY/d5rRNI1QKMT169cJBAKMj4/L7q1eyYFkMsn6+jo2m416vY7H45FaWKKucK8TZDAYGB0d5caNGwSDQUZHRzEYDHS7XVnjk06nqVQqUplY3bw9HeLzEwwGqVQqfT/yaKCdn0ajwdbWFq1WC4vFMhR3p4rTi9BFymQy5PP5Y91wxRiFYDDISy+9xDvvvCM34b00Gg3i8TgrKyskk8mhuRN2Op2MjIzIRdxut9PpdORA0+PAYDDgcrnw+/1Eo1EmJyeZnZ3FbrfjcDiO5T0GDTG37A/+4A+IRqPMzMxgNptlC3q5XJbjQRYWFtB1nXv37mE0Gmm327ukNPY6Lkajkenpad59910CgQAjIyMYjUbZ4ZXJZNje3t4V7VMZhCdH13VsNhsjIyO0Wi1Zm9XPDLTzIybNiotWeezPn16J897FZr9oj5jMLB6KwxEb7fPojrNYLLjdbrxeLz6fD6/X+0iaRZxP0TIsRmsMy2ZgMBjkUE1ReHucYXwxwkG0XTudTpmGsdlsj/2MiPMjolEHTTEfRKxWK16vF6/Xi81mk/o7oqxBdI0+iQ6PsLfD4cDn88nWefi9irfQ9ukdIXMa7Pmi6LWV0KESacV+zwAMtPOjeLF0Oh0Zfna5XORyOTnoTkjQ9+L1epmZmcHtdlMulwdC8vw0YjKZuHDhAu+99x7BYJC5ublHNtpWqyU7yzY3N9nc3By6oZHC+RTdRWIzPA7nT7SmOxwOrl+/ztWrVwkGg8zOzuLxeKTA32GICEilUmF9fZ2NjQ2ZIlUb9m4sFgs2m03OQBO1RL2ihpubmywsLLC5uSmV1NVk+KfHZrMRiUQwGAxsbW31/Q2vcn4UR6bT6VAoFGi32zidTjl3ymazybtlgaZp+Hw+ZmdnCQaDbG5uKon6E8JoNHLx4kX+5E/+hEAggMVi2df5SafTUlRRPA5SzT2NCEVyUfshGime1bEQdT4i6vbKK6/wwx/+EKfTSSQSwePxPDbCpOs6pVKJ9fV1isUiKysrrK2tye4ktWHvxmKxSDX1XucHfp8x2NjY4M6dO6RSKZLJpBwvohzJp8PhcDA6OordbufBgwfK+VGcLnpHAAiNpYPy7SaTCYfDQaPRwGw2930Y9LQhWofFWI3DhOPa7bacMJ7P54da7O24rlPRvi3qfETqRaR3HA7HgaNh9kPMWcvn85TLZer1ukzXKHYjotEOh0M6+736QZ1Oh2q1SqFQoFQqKamUIyDmOvau5b06PwaDYZf4Zm+krR9Rzo/iyIh6EIPBQLPZlK3AB+XJrVYrgUAAg8GA0+lUkZ8XiKZpzM7O8sorrxAIBLh+/fqhIzXy+TwffvghX3zxBclkknQ6/QKPtj8wm804nU5arZbUmzGZTE/sXIhak0gkwuTkJHa7nampKZkCfumllwgGg1Jx+Cjous7q6ip/+7d/SzKZZHFxkUKhIKNUit8jRrhcuHABn8/HxMQEbrdbdtO1Wi0qlQorKyv87ne/o1qtUiwWT/io+xuTyUQoFGJkZIRYLPZIZ6KmaXK9F+NXXC4X1WpV7hX9hnJ+FEdGFFvCw7vQVqslIz97w+4i1O/z+WSRp4r8vDg0TWN6epr33nuPaDTK1NTUoaMsCoUCn3zyCT//+c9lRG/YENEC0T0qUiVP4rQLx8dgMBAKhbh8+TJ+v5/Lly9z7do1HA4HwWAQv9//RMXUuq6zubnJb37zGzY3N6nVamoI5z4Im/p8Pubm5ggGg4yNjeF0OrHZbNJZrNVqbGxs8M0336iozxEwmUwEAgEmJiaIRqP7diZarVZZVC4im+J5/SghcGqdH7EAiaGbJpNJFbM9I5qmYTabsVqtWK1WGQLtzaf3ItpQa7Xa0I8fEZolIlWx33UoZPt7H73jW46C1WqVGjWhUEimWWw22yObuKh9aDabcqDnYaMCTjNiuKjNZqPVasmUlDhXh3UBid8VIxrsdjtms5loNMrIyIhMd/UK7O3n+IjUce9D0FuPpBTt98dkMmG1WjGZTHg8HgKBAIFAAIfDIa994fj0iliqPeHJeNx11/tZEjcTQlupn67ZU+n8iJY7TdNwu934/X7q9TrVapVqtdpXJ2CQEKHPUCjE9PQ0Y2NjjIyMyPbgXnRdp1AosLi4SC6XI5VKDXVtQrPZZHt7W26O+zkZLpeLWCyGy+UikUiwsbFBuVwmm82Sy+WOdN1OTEzwh3/4h4TDYS5fvsz58+fxeDz7ph0bjQb3799nfX2d5eXloUx19eJyuZicnKRarTI1NcXExIQsKK5UKgfa32az4ff7pc7J3NwcbrebWCzG7OwsDocDv9+P3++XdRN7HR8xXFMUWe9Vl+52u7KwWRyHWsd24/F4mJ6exuVy8eqrr/L2228TDAYJhUIyfZnJZIjH43KMjOJoiCG8xWKRSqWyb2S4V/7E5XIxMTGBw+Fga2tLyneI67sfOLXOj1hg7Ha7bLUWJ1AtGk+H0WjE5/MxOjpKJBKRsvwHUS6XicfjpNPpYxXtG0REK7nJZCISiTySAxfXajgcxmazEY1GiUajFItF6vU6+Xz+SNdtJBLhrbfeYmZmhtHRUSYmJvYVMoSHDtn6+jq3bt1ia2tr6GcaiTB9vV4nEokwMjIiZ0CJyPF+eDweYrEYbreb2dlZ3nzzTUKhEH6/n0gkIut6HtfN1Wq1ZDq5Wq3u2mA6nc6uCKpawx7F6XQyPj5OMBjk/PnzXLlyZZeCuYhwxuNxEomE7O5SPB4hD1CtVnc1Q+i6vquoWURJ7Xa7vDGuVqvyxqqf9oBT6fwAu6rRB2neyCBwmD17RdgqlQq5XI5cLjf0Tqe4sy+XyxQKBdLpNMlkUqapRGeQmOwdiUSYmZkhn89TLBaJx+O7Xq93CraYXWSxWBgfH8fv9+N2ux8rnCekCxKJBJlM5tSlvHoFG0VKr9lsyrD8QdewcPInJycpl8uUSiXK5fKB16/D4ZB1EKJ1XaTNRMfL4xDzpYSzm06nd23O3W6XeDwu03DD/FnqRXSUms1mRkZGGBsbk46nOMf1el0+tre3WV9fJ5VKHXpOFY/SOw9wv1mOYj6bruvY7XZcLhfNZlN+r58cHzjFzg/svtNSzs+LodlsUiqVaDQarK2tcefOHZLJJPF4vG/CnSeBSHvlcjmMRiO//e1vSSQSjI+Pc+3aNTweD263m4mJCdrtNqFQiGvXrpHNZvmrv/orFhYWdtnPYrEQiURwu92Ew2GuX79OOBxmenqaq1ev4vf75eZ7ENVqlXv37vHBBx9Qq9VOXdpLOHfxeBybzUYqlSKdTmOxWPB4PAd2WhmNRi5dukQgEKDdbsvC/oM2yl6RT4fDITtezGbzkbVOqtUq9+/fZ21tjWQyyddff00qlZI/13Wd7e1t6aSqTfshbreb8+fP4/f7uXDhAu+++y4jIyOEw2Hsdjvdbpft7W1WVlYoFAr89re/5ebNm5TLZaljpXg8ot5TtLHv1wQgtJU6nQ6jo6PMzs7i8/lIJBJSIqWfHKBT7fzsRTlAz592u02tVqNer5PL5djc3CSZTA69EJtQx4aHtSVLS0t0Oh00TeP8+fMAspAcIBwOA5BOp/nggw8eWWzERPBQKMTk5CQ3btxgcnKSUCjE2NjYkeZEtdtt4vH4I47VaUGkuUVERURwxJDXgzAYDIyNjTE2NvbCjrXRaLC9vc3S0hLr6+t88sknbGxsvLD3H1RsNhujo6NEo1HOnj3L5cuX5Xw2s9lMt9ulWCxKFef5+Xlu3bp1rPPahgER7RHNQ/s5P0ajEbvdjq7reL1eQqEQRqMRp9PZl2KpA+38iBSLEN3rnc0iQnR2ux2fzyfDnsoBenYOqznorV0Q3SkHTVweVur1OolEAoBgMHioTovZbObMmTN897vf3ZWWstvtjI6O4vF4iEajRCIR2VG038IkzkOr1SKZTJLJZNja2mJ7e/vUnpfe+U25XI7l5WXsdjuhUAiHw3FgLdSLQjhnzWaTTCZDIpEgHo+TyWTUxryD2HRFukV08ArxwtHRUaamphgfH2d0dFTqM4l1vtvtUigUWF9flwKeIi0/zDdjT4pI2xcKBQqFAtVqlUajIefh7d1XRdu7pmm4XC6Zou+nrt+Bdn663S7NZlMOpxOLe2/rqd/vZ2pqSrbcJZPJvvNAB4newaa9tQfi4m+321SrVcrlMsViUX5Y+umiP2ny+TxffPGFrMn54z/+4wOf63Q6ef/997lx48au61a09QrpAaGvIbqJetF1nXw+TzKZpFAo8Itf/IKPPvpIhv5P6ybQ6XQoFouyy/PnP/85n3/+OZcvXyYcDhMIBE70+FqtFolEglQqxfr6Ol9++SVff/011Wp16IvPBb3Oj4g6CNHIUCjE2bNn+cEPfsCZM2dwuVwy5Qi/vxFbXl7mN7/5jXT4lfTGk9NqtUilUtRqNUwmE6lUinw+j8Vikc6NQOgsnT17llKpxJdffonL5aLdbveV4OFAOz/AI5GfTqezayKzzWbD4/HQaDSU0N4xIJyf3nbbXpt2u91dkZ9+utj7hUajQTKZBB6mtQ6zj8lkYnp6munp6ad+PxEBKRQKZDIZ5ufn+fjjj0/9eRHaSqJtfHV1lXQ6jdPppFqtnvThyaaAbDZLJpNhe3ubRCIhNwnFQ3qbVkRRrd/vJxwOE41GmZycZGZmBqPRuGukglirROQnm82qURZPia7r0mkUNxSNRgNN0/a9eRJChyaTCZfLJR3XfhJPHXjnR3S7CKG9vd0VZrMZt9tNvV6X2j+Kp0Nc+KK7SLSwC+2ax02lVjxKqVRiZWUFs9ks8+THZUfRVVar1Xjw4AHz8/Pk83k2NzdPbbTnIIQjZDAYKJVKJBIJNjc3sdvtcqr6i0bUXN2/f59EIkEul5M3ccO2OdfrdTKZjBQoFKULHo9HDst866238Pv92O12JiYm8Pl8xGIxPB4PRqORZrMpJQJKpRK5XI5KpcLS0pKchaYcn6dD1OwIGYZ4PM7S0hJer3dXraKgXq+TzWYpl8tSZqbfJBoGercS7b7C+KK7QkR94GFtRDAYRNM0NV/qGWm1WlK3p9PpsLa2xuTkpJxOrZyfJyedTvPpp5+ysbHBhQsXeOWVVw4txj0quq6ztbXF559/Ti6X44svvuCzzz6jUqnINOQwIaIsjUaDRCLBwsICRqNRihIeh82flFqtxt27d/nggw+knIFobx8257RYLLK2tka1WsXv99PpdLBarUSjUdl1d/78earVqhygKVK/wvmpVqssLy9TqVRYXl7m7t27FItF7t69Szqdpl6vD51djwtx89Bqtchms9y9e5dWq8XU1BThcBiv17vruaVSibW1NRltPmwG5Ekx8LtV7ywd8f9exAdFOEeKp0ekTxqNBuVymUqlQqVSkdLliien9453dHT0mZ2S3nB/pVKRxc2bm5usrq4OrbCbaI4QhZulUol8Po/T6XzhjqA4R+12W+osVavVoZ3VJTbWSqWC1WqVrfxiWKaIKvj9/kNfp9VqyVpDUUclNl/RdKF4esR122w2KRaLZLNZfD7fI3YVzxHnotFo9J3jAwPu/LTbbYrFIu12m1Qqxfb2NvF4HLvdjtfrPbLGhuLJKZfL3L17l263y9jYGFarFYPBMLQL+NNSKBS4f/++vG7n5uao1Wo4nU5cLteR0rRCW0ko2G5tbVGtVpmfn+eLL76Qrb7DvPj3iqzVajU2NzcxGAy0220CgQCVSgWn04nb7X4u0WGxcbRaLdlll0wmWV1dJZ/PU6/Xh3o6uyi+L5VKnD9//sgOqWiwEHa9f/8+mUyGlZUVVldXZaRTrUnHR6vVIpPJyKib6EIV3WCNRoN79+7xd3/3d2SzWRYXF6nVajKl2y8MvPOTzWbJ5/OEQiHW1tbwer1ymJ1yfp4fxWKRTz75hPv373Pp0iU5OVk5P09GOp3m5s2bMn178eJFyuUy0Wj0yHVUjUaDeDxOuVxmcXGRjz/+mHQ6zebmJg8ePNg1xHGYER2KxWKRhYUF2bIraq2EMN5B4ofPgqiZqNfr3Lt3j88//5xMJsM333zD9vY2nU5naM+Pruvkcjnm5+fxeDxcu3btyGuIkAmoVCo8ePCAmzdvSsmAlZUVWefTT5vuoNNsNonH41SrVTweD/V6HdjtwH722Wf89Kc/JZPJyEGy/Rb9GWjnR4SOATm4tFwuY7fbd2k5iIVFbcrHR2/ULZPJSBE5caELEbF+utj7kXa7TblcxmAwkM/nKRQK2Gw2HA4HPp/vSM6PGMFQKBTIZrOyfTqZTJLL5VTn0B6EEGexWJQPs9ks53oBUk9mv8nr4t8nKUwW61GtVpOyA6IgVyjfDvP6JAppRfRYyJaI8wDsspGIpImRMWJsTD6fJ5fLUSqVqFar6tp/DnS7XTnnq1KpyH975U2y2azsYuy3QmfBQDs/vYhiN4BYLIbVasXtdrO2tiYni4tCXcWzIwpIW60WDx484Kc//SmffvopzWZTTqeen5/vi5biQUDXdVZWVvj5z3+Ox+PB4/Hg8/mOlIIRnRX1ep1UKsXi4qLcENT1/ij1ep14PE4+n6dUKlEqleRQzNnZWfn15OQkZrN5l1qwGOxYrVZZW1sjl8sd6T3FDVitVuPmzZvcunVLtrn32x3xSSAcQ4DFxUU+/PBDfD4fMzMzjI+P0263SSQSZLNZWq2WjGbmcjkWFhbI5/NSrVwoeatr//kg1OqbzSb37t3jr//6r/nwww8pl8uysHxxcZFqtdq3jg+cIuenUqmwuLgoFzOxeayurrK4uEg2myWdTg/13dVx0ul0KJfLaJpGsVhkdXUVk8kkL3ZR33DahmU+L3RdZ2lpic3NzV1F/Eep+emNQvRGOYc9mnAQ9Xpd2nl9fZ35+XlMJhMTExOcO3cOt9vNG2+8gdvtxul0yrlG4povFAqkUik+/vhjlpaWHvt+veel0Whw//59FhcXabfbKiK9Q+8k+3v37mEymfD5fHJWV6PRYGlpicXFRTn4tVwuk0wmuXXrltTLqlarMuKv7Pp8EFIComlgaWkJk8kksywiI9PvM+hOjfPTbrdlcVs+nyebzdJut8nn87IzqdVq9fXJGDR6O4uGuVjzuBBinYrnS2+6XGySRqORfD5PJpORBZ2iXqFer+NwOGi326TTaamflMlkjjwMVgiwik4Z1Xa9G7GOiAiQiIgJGzcajUe+FmNLelXklY7Pi0Fcu4MsYntqnJ9qtcrq6qqcIL6ysoLNZiORSLC+vi7bW9WCo1AoBMJx73Q6ZDIZut0uVquVXC7H3bt3ZcqrN+0l6h3W1tbI5/NHeh8R/el2u+TzebVB76HT6cg6n62tLer1OjabjXQ6zSeffEKn03kk7dVqteQNbz/qyCj6G+1JLhZN0/r6yhJpApE2gN8vOuLrk/xw6Lr+1PLS/W77AeALXddfedpfVvZ/Zvre/nvHKPSmHcXIBNgd8XyS9WTv779IBmnt2TvIVKzle1WCe1Psfe709P21f8rZ1/6nJvIDw6eKqlAojo/eTVQVy54cvTesCsXzQs16UCgUCoVCMVQo50ehUCgUCsVQoZwfhUKhUCgUQ4VyfhQKhUKhUAwVyvlRKBQKhUIxVCjnR6FQKBQKxVDxpK3uaWD1eRzIEDD1jL+vbP9sKPufLMr+J4ey/cmi7H+y7Gv/JxI5VCgUCoVCoRh0VNpLoVAoFArFUKGcH4VCoVAoFEOFcn4UCoVCoVAMFcr5USgUCoVCMVQo50ehUCgUCsVQoZwfhUKhUCgUQ4VyfhQKhUKhUAwVyvlRKBQKhUIxVCjnR6FQKBQKxVDx/wPVynMvLBWTlgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x216 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predicted labels\n",
    "\n",
    "n_samples_show = 6\n",
    "count = 0\n",
    "fig, axes = plt.subplots(nrows=1, ncols=n_samples_show, figsize=(10, 3))\n",
    "\n",
    "hb_model_trained.eval()\n",
    "with torch.no_grad():\n",
    "    for batch_idx, (data, target) in enumerate(test_loader):\n",
    "        if count == n_samples_show:\n",
    "            break\n",
    "        output = hb_model_trained(data[0:1])\n",
    "        if len(output.shape) == 1:\n",
    "            output = output.reshape(1, *output.shape)\n",
    "\n",
    "        pred = output.argmax(dim=1, keepdim=True)\n",
    "\n",
    "        axes[count].imshow(data[0].numpy().squeeze(), cmap=\"gray\")\n",
    "\n",
    "        axes[count].set_xticks([])\n",
    "        axes[count].set_yticks([])\n",
    "        axes[count].set_title(\"Predicted {}\".format(pred.item()))\n",
    "\n",
    "        count += 1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('mlenv')",
   "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": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "9a9ca972efd2d9d8ec49e62bd1a300aa01c9db9d34d779b2ebe2fe1e1bc10720"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}