5_CIFAR_Hybrid_QNN.ipynb

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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WjWDZUtmThn"
      },
      "source": [
        "!pip install qiskit"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install qiskit-aer"
      ],
      "metadata": {
        "id": "y29JSfwrHIs3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91GPJpHnpW9_"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import torch\n",
        "from torch.autograd import Function\n",
        "from torchvision import datasets, transforms\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "import qiskit\n",
        "from qiskit import transpile, assemble\n",
        "from qiskit.visualization import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16n-mC4GzOPu"
      },
      "source": [
        "To connect PyTorch to Qiskit, we need to define a function that converts tensors so obtained from PyTorch to a list so that the list could be fed into the Quantum Circuit in Qiskit."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LlQ-IkgXo2I7"
      },
      "source": [
        "def to_numbers(tensor_list):\n",
        "    num_list = []\n",
        "    for tensor in tensor_list:\n",
        "        num_list += [tensor.item()]\n",
        "    return num_list"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZqC396wo7Ej"
      },
      "source": [
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch.autograd import Function\n",
        "import torch.optim as optim\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "from torchvision import datasets, transforms\n",
        "from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister, execute\n",
        "from qiskit.circuit import Parameter\n",
        "from qiskit import Aer\n",
        "from tqdm import tqdm\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOOnAYGVz64U"
      },
      "source": [
        "We use a 1 qubit circuit with 1 trainable parameter to create a 'Quantum Class'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9DZ_PEBMRdS"
      },
      "source": [
        "class QuantumCircuit:\n",
        "    \"\"\"\n",
        "    This class provides a simple interface for interaction\n",
        "    with the quantum circuit\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, n_qubits, backend, shots):\n",
        "        # --- Circuit definition ---\n",
        "        self._circuit = qiskit.QuantumCircuit(n_qubits)\n",
        "\n",
        "        all_qubits = [i for i in range(n_qubits)]\n",
        "        self.theta = qiskit.circuit.Parameter('theta')\n",
        "\n",
        "        self._circuit.h(all_qubits)\n",
        "        self._circuit.barrier()\n",
        "        self._circuit.ry(self.theta, all_qubits)\n",
        "\n",
        "        self._circuit.measure_all()\n",
        "        # ---------------------------\n",
        "\n",
        "        self.backend = backend\n",
        "        self.shots = shots\n",
        "\n",
        "    def run(self, thetas):\n",
        "        t_qc = transpile(self._circuit,\n",
        "                         self.backend)\n",
        "        qobj = assemble(t_qc,\n",
        "                        shots=self.shots,\n",
        "                        parameter_binds = [{self.theta: theta} for theta in thetas])\n",
        "        job = self.backend.run(qobj)\n",
        "        result = job.result().get_counts()\n",
        "\n",
        "        counts = np.array(list(result.values()))\n",
        "        states = np.array(list(result.keys())).astype(float)\n",
        "\n",
        "        # Compute probabilities for each state\n",
        "        probabilities = counts / self.shots\n",
        "        # Get state expectation\n",
        "        expectation = np.sum(states * probabilities)\n",
        "\n",
        "        return np.array([expectation])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UIkWGi24z6El"
      },
      "source": [
        "To measure the output (in the z basis), the expectation must be calculated, so let's test the implementation below"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "-dhwKUDzMVfE",
        "outputId": "46bff565-7187-4758-8815-6f84f34f7fcd"
      },
      "source": [
        "simulator = qiskit.Aer.get_backend('qasm_simulator')\n",
        "\n",
        "circuit = QuantumCircuit(1, simulator, 100)\n",
        "print('Expected value for rotation pi {}'.format(circuit.run([np.pi])[0]))\n",
        "circuit._circuit.draw()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected value for rotation pi 0.48\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-6a3097f83179>:30: DeprecationWarning: Using a qobj for run() is deprecated as of qiskit-aer 0.9.0 and will be removed no sooner than 3 months from that release date. Transpiled circuits should now be passed directly using `backend.run(circuits, **run_options).\n",
            "  job = self.backend.run(qobj)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ┌───┐ ░ ┌───────────┐ ░ ┌─┐\n",
              "     q: ┤ H ├─░─┤ Ry(theta) ├─░─┤M├\n",
              "        └───┘ ░ └───────────┘ ░ └╥┘\n",
              "meas: 1/═════════════════════════╩═\n",
              "                                 0 "
            ],
            "text/html": [
              "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">        ┌───┐ ░ ┌───────────┐ ░ ┌─┐\n",
              "     q: ┤ H ├─░─┤ Ry(theta) ├─░─┤M├\n",
              "        └───┘ ░ └───────────┘ ░ └╥┘\n",
              "meas: 1/═════════════════════════╩═\n",
              "                                 0 </pre>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xp2b_2xyMi2l"
      },
      "source": [
        "**Creating the quantum classical hybrid class using Pytorch**\n",
        "\n",
        "The functions needed for backpropagation can be created by PyTorch (using the parameter shift rule). The forward and backward passes contain elements from the Qiskit class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-MIKaAcMiLF"
      },
      "source": [
        "class HybridFunction(Function):\n",
        "    \"\"\" Hybrid quantum - classical function definition \"\"\"\n",
        "\n",
        "    @staticmethod\n",
        "    def forward(ctx, input, quantum_circuit, shift):\n",
        "        \"\"\" Forward pass computation \"\"\"\n",
        "        ctx.shift = shift\n",
        "        ctx.quantum_circuit = quantum_circuit\n",
        "\n",
        "        expectation_z = ctx.quantum_circuit.run(input[0].tolist())\n",
        "        result = torch.tensor([expectation_z])\n",
        "        ctx.save_for_backward(input, result)\n",
        "\n",
        "        return result\n",
        "\n",
        "    @staticmethod\n",
        "    def backward(ctx, grad_output):\n",
        "        \"\"\" Backward pass computation \"\"\"\n",
        "        input, expectation_z = ctx.saved_tensors\n",
        "        input_list = np.array(input.tolist())\n",
        "\n",
        "        shift_right = input_list + np.ones(input_list.shape) * ctx.shift\n",
        "        shift_left = input_list - np.ones(input_list.shape) * ctx.shift\n",
        "\n",
        "        gradients = []\n",
        "        for i in range(len(input_list)):\n",
        "            expectation_right = ctx.quantum_circuit.run(shift_right[i])\n",
        "            expectation_left  = ctx.quantum_circuit.run(shift_left[i])\n",
        "\n",
        "            gradient = torch.tensor([expectation_right]) - torch.tensor([expectation_left])\n",
        "            gradients.append(gradient)\n",
        "        gradients = np.array([gradients]).T\n",
        "        return torch.tensor([gradients]).float() * grad_output.float(), None, None\n",
        "\n",
        "class Hybrid(nn.Module):\n",
        "    \"\"\" Hybrid quantum - classical layer definition \"\"\"\n",
        "\n",
        "    def __init__(self, backend, shots, shift):\n",
        "        super(Hybrid, self).__init__()\n",
        "        self.quantum_circuit = QuantumCircuit(1, backend, shots)\n",
        "        self.shift = shift\n",
        "\n",
        "    def forward(self, input):\n",
        "        return HybridFunction.apply(input, self.quantum_circuit, self.shift)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vP8UlcERp_Pt"
      },
      "source": [
        "## **Data loading and pre-processing**\n",
        "\n",
        "Here , we create a simple hybrid neural network to classify images of two types ; Aeroplanes and Automobiles (labelled 0 and 1) from the CIFAR-10 dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-pJk3Ym8vsp"
      },
      "source": [
        "Let's first load the training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KOCP-PVyqD71",
        "outputId": "f2c24234-5809-4a4e-a3f3-ef5eba225304"
      },
      "source": [
        "import torchvision\n",
        "transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) # transform images to tensors/vectors\n",
        "cifar_trainset = datasets.CIFAR10(root='./data1', train=True, download=True, transform=transform)\n",
        "\n",
        "labels = cifar_trainset.targets # get the labels for the data\n",
        "labels = np.array(labels)\n",
        "\n",
        "idx1 = np.where(labels == 0) # filter on aeroplanes\n",
        "idx2 = np.where(labels == 1) # filter on automobiles\n",
        "\n",
        "# Specify number of datapoints per class (i.e. there will be n pictures of automobiles and n pictures of aeroplanes in the training set)\n",
        "n=100\n",
        "\n",
        "# concatenate the data indices\n",
        "idx = np.concatenate((idx1[0][0:n],idx2[0][0:n]))\n",
        "\n",
        "# create the filtered dataset for our training set\n",
        "cifar_trainset.targets = labels[idx]\n",
        "cifar_trainset.data = cifar_trainset.data[idx]\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(cifar_trainset, batch_size=1, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data1/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170498071/170498071 [00:03<00:00, 43147236.30it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data1/cifar-10-python.tar.gz to ./data1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ot1Mk5dU9etZ"
      },
      "source": [
        "We now filter for pictures containing Aeroplanes and Automobiles (labelled 0 and 1 respectively)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "M67UWxgDqu96",
        "outputId": "ee0700bc-f210-42a9-ce62-874d02e1a34d"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "n_samples_show = 8\n",
        "\n",
        "data_iter = iter(train_loader)\n",
        "fig, axes = plt.subplots(nrows=1, ncols=n_samples_show, figsize=(10, 2))\n",
        "\n",
        "while n_samples_show > 0:\n",
        "    images, targets = data_iter.__next__()\n",
        "    images=images.squeeze()\n",
        "    #axes[n_samples_show - 1].imshow( tf.shape( tf.squeeze(images[0]) ),cmap='gray' )\n",
        "    #plt.imshow((tf.squeeze(images[0])))\n",
        "\n",
        "    #plt.imshow( tf.shape( tf.squeeze(x_train) ) )\n",
        "    #axes[n_samples_show - 1].imshow(images[0].numpy().squeeze(), cmap='gray')\n",
        "    axes[n_samples_show - 1].imshow(images[0].numpy(), 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.item()))\n",
        "\n",
        "    n_samples_show -= 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x200 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UqI3vuefYIYa"
      },
      "source": [
        "Now let's look at the testing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGbi3LCbu48T",
        "outputId": "d1cd1e06-dd5d-496c-eb0c-103b40b1235d"
      },
      "source": [
        "#Testing data\n",
        "\n",
        "transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()]) # transform images to tensors/vectors\n",
        "cifar_testset = datasets.CIFAR10(root='./data1', train=False, download=True, transform=transform)\n",
        "\n",
        "labels1 = cifar_testset.targets # get the labels for the data\n",
        "labels1 = np.array(labels1)\n",
        "\n",
        "idx1_ae = np.where(labels1 == 0) # filter on aeroplanes\n",
        "idx2_au = np.where(labels1 == 1) # filter on automobiles\n",
        "\n",
        "# Specify number of datapoints per class (i.e. there will be n pictures of automobiles and n pictures of aeroplanes in the training set)\n",
        "\n",
        "n=50\n",
        "\n",
        "# concatenate the data indices\n",
        "idxa = np.concatenate((idx1_ae[0][0:n],idx2_au[0][0:n]))\n",
        "\n",
        "# create the filtered dataset for our training set\n",
        "cifar_testset.targets = labels[idxa]\n",
        "cifar_testset.data = cifar_testset.data[idxa]\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(cifar_testset, batch_size=1, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6G4_pDuAHHT"
      },
      "source": [
        "**Creating the hybrid Neural Network**\n",
        "\n",
        "We create a typical Convolutional Neural Network with two fully-connected layers at the end. The value of the last neuron of the fully-connected layer is fed as the parameter  \n",
        "θ\n",
        "  into our quantum circuit."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 10, kernel_size=5)\n",
        "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
        "        self.dropout = nn.Dropout2d()\n",
        "        self.fc1 = nn.Linear(500, 500)\n",
        "        self.fc2 = nn.Linear(500, 1)\n",
        "        self.hybrid = Hybrid(qiskit.Aer.get_backend('qasm_simulator'), 100, np.pi / 2)\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(1, -1)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        x = self.hybrid(x)  # Asumiendo que `hybrid` es una instancia de `Hybrid`\n",
        "        x = (x + 1) / 2\n",
        "        x = torch.cat((x, 1 - x), -1)\n",
        "        return x"
      ],
      "metadata": {
        "id": "R-bcdBHxIU3Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZH95APjvtTQ"
      },
      "source": [
        "# qc = TorchCircuit.apply\n",
        "\"\"\"\n",
        "Ignore this cell\n",
        "\"\"\"\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 10, kernel_size=5)\n",
        "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
        "        self.conv2_drop = nn.Dropout2d()\n",
        "        self.h1 = nn.Linear(500, 500)\n",
        "        self.h2 = nn.Linear(500, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
        "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
        "        x = x.view(-1, 500)\n",
        "        x = F.relu(self.h1(x))\n",
        "        x = F.dropout(x, training=self.training)\n",
        "        x = self.h2(x)\n",
        "        x = qc(x)\n",
        "        x = (x + 1) / 2  # Normalise the inputs to 1 or 0\n",
        "        x = torch.cat((x, 1 - x), -1)\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NOXHpD1CYiOB"
      },
      "source": [
        "**Now let's train the network**\n",
        "\n",
        "We can specify any PyTorch optimiser (here we use the Adam optimiser), learning rate and cost/loss function in order to train over multiple epochs. In this instance, we use  a learning rate of 0.001 and the negative log-likelihood loss function.\n",
        "We then plot the training graph. We do this for 5, 10, 20, 30 epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "MDuIGqSowAdw",
        "outputId": "710228df-dafc-4e79-ed3a-62755e25efb9"
      },
      "source": [
        "model = Net()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "loss_func = nn.NLLLoss()\n",
        "\n",
        "epochs = 5\n",
        "loss_list = []\n",
        "\n",
        "model.train()\n",
        "for epoch in range(epochs):\n",
        "    total_loss = []\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        optimizer.zero_grad()\n",
        "        # Forward pass\n",
        "        output = model(data)\n",
        "        # Calculating loss\n",
        "        loss = loss_func(output, target)\n",
        "        # Backward pass\n",
        "        loss.backward()\n",
        "        # Optimize the weights\n",
        "        optimizer.step()\n",
        "\n",
        "        total_loss.append(loss.item())\n",
        "    loss_list.append(sum(total_loss)/len(total_loss))\n",
        "    print('Training [{:.0f}%]\\tLoss: {:.4f}'.format(100. * (epoch + 1) / epochs, loss_list[-1]))\n",
        "\n",
        "#Now plotting the training graph\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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-6a3097f83179>:30: DeprecationWarning: Using a qobj for run() is deprecated as of qiskit-aer 0.9.0 and will be removed no sooner than 3 months from that release date. Transpiled circuits should now be passed directly using `backend.run(circuits, **run_options).\n",
            "  job = self.backend.run(qobj)\n",
            "<ipython-input-9-625640256c98>:11: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:261.)\n",
            "  result = torch.tensor([expectation_z])\n",
            "<ipython-input-9-625640256c98>:32: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
            "  gradients = np.array([gradients]).T\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training [20%]\tLoss: -0.4977\n",
            "Training [40%]\tLoss: -0.5054\n",
            "Training [60%]\tLoss: -0.5169\n",
            "Training [80%]\tLoss: -0.5025\n",
            "Training [100%]\tLoss: -0.5095\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Neg Log Likelihood Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "id": "CkXIUStc7Z93",
        "outputId": "2d578cdb-cae2-4056-fec1-3f77f8f37d30"
      },
      "source": [
        "model = Net()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "loss_func = nn.NLLLoss()\n",
        "\n",
        "epochs = 10\n",
        "loss_list1 = []\n",
        "\n",
        "model.train()\n",
        "for epoch in range(epochs):\n",
        "    total_loss = []\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        optimizer.zero_grad()\n",
        "        # Forward pass\n",
        "        output = model(data)\n",
        "        # Calculating loss\n",
        "        loss = loss_func(output, target)\n",
        "        # Backward pass\n",
        "        loss.backward()\n",
        "        # Optimize the weights\n",
        "        optimizer.step()\n",
        "\n",
        "        total_loss.append(loss.item())\n",
        "    loss_list1.append(sum(total_loss)/len(total_loss))\n",
        "    print('Training [{:.0f}%]\\tLoss: {:.4f}'.format(\n",
        "        100. * (epoch + 1) / epochs, loss_list1[-1]))\n",
        "\n",
        "#Alongside, let's also plot the data\n",
        "plt.plot(loss_list1)\n",
        "plt.title('Hybrid NN Training Convergence')\n",
        "plt.xlabel('Training Iterations')\n",
        "plt.ylabel('Neg Log Likelihood Loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-6a3097f83179>:30: DeprecationWarning: Using a qobj for run() is deprecated as of qiskit-aer 0.9.0 and will be removed no sooner than 3 months from that release date. Transpiled circuits should now be passed directly using `backend.run(circuits, **run_options).\n",
            "  job = self.backend.run(qobj)\n",
            "<ipython-input-9-625640256c98>:32: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
            "  gradients = np.array([gradients]).T\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training [10%]\tLoss: -0.5094\n",
            "Training [20%]\tLoss: -0.5214\n",
            "Training [30%]\tLoss: -0.5118\n",
            "Training [40%]\tLoss: -0.4685\n",
            "Training [50%]\tLoss: -0.4923\n",
            "Training [60%]\tLoss: -0.5351\n",
            "Training [70%]\tLoss: -0.5828\n",
            "Training [80%]\tLoss: -0.6087\n",
            "Training [90%]\tLoss: -0.6040\n",
            "Training [100%]\tLoss: -0.6251\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Neg Log Likelihood Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "id": "rKKgfO0d7aSo",
        "outputId": "09585873-d5e7-4413-aed3-f7cee7ece2a3"
      },
      "source": [
        "model = Net()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "loss_func = nn.NLLLoss()\n",
        "\n",
        "epochs = 20\n",
        "loss_list2 = []\n",
        "\n",
        "model.train()\n",
        "for epoch in range(epochs):\n",
        "    total_loss = []\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        optimizer.zero_grad()\n",
        "        # Forward pass\n",
        "        output = model(data)\n",
        "        # Calculating loss\n",
        "        loss = loss_func(output, target)\n",
        "        # Backward pass\n",
        "        loss.backward()\n",
        "        # Optimize the weights\n",
        "        optimizer.step()\n",
        "\n",
        "        total_loss.append(loss.item())\n",
        "    loss_list2.append(sum(total_loss)/len(total_loss))\n",
        "    print('Training [{:.0f}%]\\tLoss: {:.4f}'.format(\n",
        "        100. * (epoch + 1) / epochs, loss_list2[-1]))\n",
        "\n",
        "#Alongside, let's also plot the data\n",
        "plt.plot(loss_list2)\n",
        "plt.title('Hybrid NN Training Convergence')\n",
        "plt.xlabel('Training Iterations')\n",
        "plt.ylabel('Neg Log Likelihood Loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-6a3097f83179>:30: DeprecationWarning: Using a qobj for run() is deprecated as of qiskit-aer 0.9.0 and will be removed no sooner than 3 months from that release date. Transpiled circuits should now be passed directly using `backend.run(circuits, **run_options).\n",
            "  job = self.backend.run(qobj)\n",
            "<ipython-input-9-625640256c98>:32: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
            "  gradients = np.array([gradients]).T\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training [5%]\tLoss: -0.5045\n",
            "Training [10%]\tLoss: -0.5103\n",
            "Training [15%]\tLoss: -0.5044\n",
            "Training [20%]\tLoss: -0.5082\n",
            "Training [25%]\tLoss: -0.4988\n",
            "Training [30%]\tLoss: -0.5049\n",
            "Training [35%]\tLoss: -0.5033\n",
            "Training [40%]\tLoss: -0.5254\n",
            "Training [45%]\tLoss: -0.5292\n",
            "Training [50%]\tLoss: -0.5267\n",
            "Training [55%]\tLoss: -0.5659\n",
            "Training [60%]\tLoss: -0.5298\n",
            "Training [65%]\tLoss: -0.5493\n",
            "Training [70%]\tLoss: -0.6004\n",
            "Training [75%]\tLoss: -0.5968\n",
            "Training [80%]\tLoss: -0.5886\n",
            "Training [85%]\tLoss: -0.6111\n",
            "Training [90%]\tLoss: -0.6123\n",
            "Training [95%]\tLoss: -0.6161\n",
            "Training [100%]\tLoss: -0.6147\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Neg Log Likelihood Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8g_UpmgL_t4O",
        "outputId": "4e914778-498c-46a7-f240-f1eaf6a60e75"
      },
      "source": [
        "model = Net()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "loss_func = nn.NLLLoss()\n",
        "\n",
        "epochs = 30\n",
        "loss_list = []\n",
        "\n",
        "model.train()\n",
        "for epoch in range(epochs):\n",
        "    total_loss = []\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        optimizer.zero_grad()\n",
        "        # Forward pass\n",
        "        output = model(data)\n",
        "        # Calculating loss\n",
        "        loss = loss_func(output, target)\n",
        "        # Backward pass\n",
        "        loss.backward()\n",
        "        # Optimize the weights\n",
        "        optimizer.step()\n",
        "\n",
        "        total_loss.append(loss.item())\n",
        "    loss_list.append(sum(total_loss)/len(total_loss))\n",
        "    print('Training [{:.0f}%]\\tLoss: {:.4f}'.format(\n",
        "        100. * (epoch + 1) / epochs, loss_list[-1]))\n",
        "\n",
        "#Alongside, let's also plot the data\n",
        "plt.plot(loss_list)\n",
        "plt.title('Hybrid NN Training Convergence')\n",
        "plt.xlabel('Training Iterations')\n",
        "plt.ylabel('Neg Log Likelihood Loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-6a3097f83179>:30: DeprecationWarning: Using a qobj for run() is deprecated as of qiskit-aer 0.9.0 and will be removed no sooner than 3 months from that release date. Transpiled circuits should now be passed directly using `backend.run(circuits, **run_options).\n",
            "  job = self.backend.run(qobj)\n",
            "<ipython-input-9-625640256c98>:32: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
            "  gradients = np.array([gradients]).T\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training [3%]\tLoss: -0.5179\n",
            "Training [7%]\tLoss: -0.4972\n",
            "Training [10%]\tLoss: -0.5027\n",
            "Training [13%]\tLoss: -0.5246\n",
            "Training [17%]\tLoss: -0.4920\n",
            "Training [20%]\tLoss: -0.4739\n",
            "Training [23%]\tLoss: -0.4985\n",
            "Training [27%]\tLoss: -0.5041\n",
            "Training [30%]\tLoss: -0.5251\n",
            "Training [33%]\tLoss: -0.4969\n",
            "Training [37%]\tLoss: -0.5292\n",
            "Training [40%]\tLoss: -0.5450\n",
            "Training [43%]\tLoss: -0.5566\n",
            "Training [47%]\tLoss: -0.5570\n",
            "Training [50%]\tLoss: -0.5833\n",
            "Training [53%]\tLoss: -0.5919\n",
            "Training [57%]\tLoss: -0.5938\n",
            "Training [60%]\tLoss: -0.5968\n",
            "Training [63%]\tLoss: -0.6103\n",
            "Training [67%]\tLoss: -0.6140\n",
            "Training [70%]\tLoss: -0.6291\n",
            "Training [73%]\tLoss: -0.6256\n",
            "Training [77%]\tLoss: -0.6105\n",
            "Training [80%]\tLoss: -0.6385\n",
            "Training [83%]\tLoss: -0.6336\n",
            "Training [87%]\tLoss: -0.6329\n",
            "Training [90%]\tLoss: -0.6450\n",
            "Training [93%]\tLoss: -0.6495\n",
            "Training [97%]\tLoss: -0.6374\n",
            "Training [100%]\tLoss: -0.6506\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Neg Log Likelihood Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lE9LnkFdJMPJ"
      },
      "source": [
        "Testing the network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kkBqjpvJPuV"
      },
      "source": [
        "model.eval()\n",
        "with torch.no_grad():\n",
        "\n",
        "    correct = 0\n",
        "    for batch_idx, (data, target) in enumerate(test_loader):\n",
        "        output = model(data)\n",
        "\n",
        "        pred = output.argmax(dim=1, keepdim=True)\n",
        "        correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "        loss = loss_func(output, target)\n",
        "        total_loss.append(loss.item())\n",
        "\n",
        "    print('Performance on test data:\\n\\tLoss: {:.4f}\\n\\tAccuracy: {:.1f}%'.format(\n",
        "        sum(total_loss) / len(total_loss),\n",
        "        correct / len(test_loader) * 100)\n",
        "        )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJqPvO-IcuoZ"
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DqhilM6PYlF5"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "0Qr3jD5Nx6-P",
        "outputId": "1cdda053-aad6-4146-d7cf-33941fe06ab1"
      },
      "source": [
        "plt.plot(loss_list)\n",
        "plt.title('Hybrid NN Training Convergence')\n",
        "plt.xlabel('Training Iterations')\n",
        "plt.ylabel('Neg Log Likelihood Loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Neg Log Likelihood Loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YjjYpTXQxW3B",
        "outputId": "806041af-37c0-49fc-f335-52796d87b078"
      },
      "source": [
        "model = Net()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "loss_func = nn.NLLLoss()\n",
        "\n",
        "epochs = 10\n",
        "loss_list = []\n",
        "\n",
        "model.train()\n",
        "for epoch in range(epochs):\n",
        "    total_loss = []\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        optimizer.zero_grad()\n",
        "        # Forward pass\n",
        "        output = model(data)\n",
        "        # Calculating loss\n",
        "        loss = loss_func(output, target)\n",
        "        # Backward pass\n",
        "        loss.backward()\n",
        "        # Optimize the weights\n",
        "        optimizer.step()\n",
        "\n",
        "        total_loss.append(loss.item())\n",
        "    loss_list.append(sum(total_loss)/len(total_loss))\n",
        "    print('Training [{:.0f}%]\\tLoss: {:.4f}'.format(\n",
        "        100. * (epoch + 1) / epochs, loss_list[-1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training [10%]\tLoss: -0.4869\n",
            "Training [20%]\tLoss: -0.5195\n",
            "Training [30%]\tLoss: -0.5255\n",
            "Training [40%]\tLoss: -0.5498\n",
            "Training [50%]\tLoss: -0.5054\n",
            "Training [60%]\tLoss: -0.4629\n",
            "Training [70%]\tLoss: -0.5347\n",
            "Training [80%]\tLoss: -0.5556\n",
            "Training [90%]\tLoss: -0.5504\n",
            "Training [100%]\tLoss: -0.5614\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "tvlupNtkxtEg",
        "outputId": "32bf2d26-60d7-4a9a-d22a-0b5ac50e2898"
      },
      "source": [
        "plt.plot(loss_list)\n",
        "plt.title('Hybrid NN Training Convergence')\n",
        "plt.xlabel('Training Iterations')\n",
        "plt.ylabel('Neg Log Likelihood Loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Neg Log Likelihood Loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2qkted-WyO-J",
        "outputId": "70dfe318-838e-453a-bb5b-e1ed7aa68a48"
      },
      "source": [
        "model = Net()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "loss_func = nn.NLLLoss()\n",
        "\n",
        "epochs = 20\n",
        "loss_list = []\n",
        "\n",
        "model.train()\n",
        "for epoch in range(epochs):\n",
        "    total_loss = []\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        optimizer.zero_grad()\n",
        "        # Forward pass\n",
        "        output = model(data)\n",
        "        # Calculating loss\n",
        "        loss = loss_func(output, target)\n",
        "        # Backward pass\n",
        "        loss.backward()\n",
        "        # Optimize the weights\n",
        "        optimizer.step()\n",
        "\n",
        "        total_loss.append(loss.item())\n",
        "    loss_list.append(sum(total_loss)/len(total_loss))\n",
        "    print('Training [{:.0f}%]\\tLoss: {:.4f}'.format(\n",
        "        100. * (epoch + 1) / epochs, loss_list[-1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training [5%]\tLoss: -0.5175\n",
            "Training [10%]\tLoss: -0.4997\n",
            "Training [15%]\tLoss: -0.4714\n",
            "Training [20%]\tLoss: -0.5602\n",
            "Training [25%]\tLoss: -0.5107\n",
            "Training [30%]\tLoss: -0.5055\n",
            "Training [35%]\tLoss: -0.4933\n",
            "Training [40%]\tLoss: -0.4986\n",
            "Training [45%]\tLoss: -0.5039\n",
            "Training [50%]\tLoss: -0.5123\n",
            "Training [55%]\tLoss: -0.4799\n",
            "Training [60%]\tLoss: -0.5091\n",
            "Training [65%]\tLoss: -0.4969\n",
            "Training [70%]\tLoss: -0.5018\n",
            "Training [75%]\tLoss: -0.4464\n",
            "Training [80%]\tLoss: -0.5011\n",
            "Training [85%]\tLoss: -0.4896\n",
            "Training [90%]\tLoss: -0.5136\n",
            "Training [95%]\tLoss: -0.5157\n",
            "Training [100%]\tLoss: -0.4603\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "n57Sanp9yyOZ",
        "outputId": "93111a40-ead3-49a9-b674-700df0bdce8a"
      },
      "source": [
        "plt.plot(loss_list)\n",
        "plt.title('Hybrid NN Training Convergence')\n",
        "plt.xlabel('Training Iterations')\n",
        "plt.ylabel('Neg Log Likelihood Loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Neg Log Likelihood Loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WW0tK5Mcy9Ul"
      },
      "source": [
        "#Testing the  quantum hybrid in order to comapre it with the classical one\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "\n",
        "    correct = 0\n",
        "    for batch_idx, (data, target) in enumerate(test_loader):\n",
        "        output = model(data)\n",
        "\n",
        "        pred = output.argmax(dim=1, keepdim=True)\n",
        "        correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "        loss = loss_func(output, target)\n",
        "        total_loss.append(loss.item())\n",
        "\n",
        "    print('Performance on test data:\\n\\tLoss: {:.4f}\\n\\tAccuracy: {:.1f}%'.format(\n",
        "        sum(total_loss) / len(total_loss),\n",
        "        correct / len(test_loader) * 100)\n",
        "        )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWNATteX3d10"
      },
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 10, kernel_size=5)\n",
        "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
        "        self.dropout = nn.Dropout2d()\n",
        "        self.fc1 = nn.Linear(500, 500)\n",
        "        self.fc2 = nn.Linear(500,49)\n",
        "        self.hybrid = Hybrid(qiskit.Aer.get_backend('qasm_simulator'), 100, np.pi / 2)\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(1, -1)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        x = self.hybrid(x)\n",
        "        return torch.cat((x, 1 - x), -1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NecmVf-pJChf"
      },
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
        "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
        "        self.conv2_drop = nn.Dropout2d()\n",
        "        self.fc1 = nn.Linear(320, 50)\n",
        "        self.fc2 = nn.Linear(50, 99)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
        "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
        "        x = x.view(-1, 320)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.dropout(x, training=self.training)\n",
        "        x = self.fc2(x)\n",
        "        return F.softmax(x)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}