2_0_training_circuits_pytorch_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "94125d98",
   "metadata": {},
   "source": [
    "# Training Sequential Circuits with Pytorch\n",
    "\n",
    "This notebook shows the expands upon the sequential circuits structure <br>\n",
    "and optimize the circuit using PyTorch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "90b0d6aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install pennylane\n",
    "# !pip install pennylane-lightning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "92b1b381",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pennylane as qml\n",
    "from pennylane import numpy as np\n",
    "from pennylane.templates.embeddings import AmplitudeEmbedding\n",
    "from model import Conv1DLayer, PoolingLayer, unitarity_conv1d, unitarity_pool, unitarity_toffoli\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ac4daf",
   "metadata": {},
   "source": [
    "# Defining the device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c1275950",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Specify how many wires will be used\n",
    "num_wires = 12\n",
    "num_ancilla = 4\n",
    "num_work_wires = num_wires - num_ancilla\n",
    "\n",
    "#Specify what type of device you are using.\n",
    "#device_type = 'default.qubit'\n",
    "#device_type = 'default.qubit.torch'\n",
    "device_type = 'lightning.qubit'\n",
    "\n",
    "#Initialize Device\n",
    "dev = qml.device(device_type, wires=num_wires) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc1f7453",
   "metadata": {},
   "source": [
    "# Defining circuit to be trained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79552a28",
   "metadata": {},
   "outputs": [],
   "source": [
    "def circuit(num_wires, num_ancilla):\n",
    "    @qml.qnode(dev, interface='torch', diff_method ='adjoint')\n",
    "    def func(inputs, params): \n",
    "        work_wires = list(range(num_wires - num_ancilla))\n",
    "        ancilla_wires = list(range(len(work_wires),num_wires))\n",
    "\n",
    "        # Data Embedding Layer\n",
    "        AmplitudeEmbedding(inputs , wires=work_wires , normalize=True)\n",
    "\n",
    "        # Hidden Layers\n",
    "        work_wires , params = Conv1DLayer(unitarity_conv1d , 15)(work_wires , params)\n",
    "        work_wires , params = PoolingLayer(unitarity_pool , 2)(work_wires , params)\n",
    "        work_wires , params = Conv1DLayer(unitarity_conv1d , 15)(work_wires , params)\n",
    "        work_wires , params = PoolingLayer(unitarity_pool , 2)(work_wires , params)\n",
    "        print(len(params))\n",
    "\n",
    "        #Toffili Structure\n",
    "        unitarity_toffoli(work_wires,ancilla_wires)\n",
    "\n",
    "        return [qml.expval(qml.PauliZ(wire)) for wire in ancilla_wires]\n",
    "    return func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a15141c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "161\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 7704x936 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x is the encoded date sized to an array of x length, where x = 2^qubits.\n",
    "embedding_size = 2**num_work_wires\n",
    "dummy_embedding = [1] * embedding_size\n",
    "\n",
    "# Initializing parameters, this based off the unitarity, and model structure. \n",
    "params = np.random.normal(loc=0.0, scale=1.0, size = 357)\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "fig, ax = qml.draw_mpl(circuit(num_wires, num_ancilla), decimals=2)(dummy_embedding, params)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "747a1013",
   "metadata": {},
   "source": [
    "# Import training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c19f9d02",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "batch_size = 32\n",
    "embedding_size = 2**num_work_wires\n",
    "labels = [0,1,2,3]  #,4,5,6,7]\n",
    "num_labels = len(labels)\n",
    "\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Resize((16,16)),\n",
    "    transforms.Lambda(lambda x: torch.reshape(x, (-1,))),\n",
    "])\n",
    "\n",
    "reverse_transform = transforms.Compose([\n",
    "    transforms.Lambda(lambda x: torch.reshape(x, (-1,16,16))),\n",
    "    transforms.Resize((28,28)),\n",
    "])\n",
    "\n",
    "train_data = datasets.MNIST(\n",
    "    root='./dataset/minst/',\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "\n",
    "test_data = datasets.MNIST(\n",
    "    root='./dataset/minst/',\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "\n",
    "#Filter for classes \n",
    "train_idx = [i for i, y in enumerate(train_data.targets) if y in labels]\n",
    "train_data = torch.utils.data.Subset(train_data, train_idx)\n",
    "\n",
    "test_idx = [i for i, y in enumerate(test_data.targets) if y in labels]\n",
    "test_data = torch.utils.data.Subset(test_data, test_idx)\n",
    "\n",
    "# Pytorch loader\n",
    "train_loader = DataLoader(\n",
    "    dataset=train_data,\n",
    "    shuffle=True,\n",
    "    batch_size=batch_size,\n",
    "    drop_last = True\n",
    ")\n",
    "\n",
    "# Pytorch loader\n",
    "test_loader = DataLoader(\n",
    "    dataset=train_data,\n",
    "    shuffle=False,\n",
    "    batch_size=batch_size,\n",
    "    drop_last = False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f5f00e3",
   "metadata": {},
   "source": [
    "# Preparing the circuit for PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e4424f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "params_shapes = {\"params\": 357} #{parameters_name: number of params}\n",
    "qlayer = qml.qnn.TorchLayer(circuit(num_wires, num_ancilla), params_shapes,torch.nn.init.normal_) \n",
    "output = torch.nn.Softmax(dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "31389c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the model\n",
    "model = torch.nn.Sequential(qlayer,output)\n",
    "\n",
    "#Move model to GPU device (Recommend only if qubits > 15)\n",
    "#model = model.to(device=torch.device('cuda:0'))\n",
    "\n",
    "# Defining the loss function\n",
    "loss = torch.nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfc7f2b9",
   "metadata": {},
   "source": [
    "# Training our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "49ad22b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters\n",
    "epochs = 8\n",
    "batch_size = 32\n",
    "train_samples = len(train_data)\n",
    "batches = train_samples // batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fe70887",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d95c79c1f0da43c2a8e3f709be08fad9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/773 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  0 | Loss: 1.343029 \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4ffebc2e3d649fa8062c5b2aa54fd3e",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  1 | Loss: 1.323812 \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dbbfea49aca541779e998f3042336271",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  2 | Loss: 1.318101 \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de55b3846ee84342b1472cdd3eeacf01",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  3 | Loss: 1.315341 \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ce0c70a8ab3b4521ac62140130cbb92d",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  4 | Loss: 1.311854 \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b112af01fff34dc7aa0e5f8e678a5343",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  5 | Loss: 1.309731 \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1b51e0cf20d49b8ad815f53f03814ed",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  6 | Loss: 1.308450 \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd1e4c74091c43e2a241768f13b94018",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:  7 | Loss: 1.307810 \n"
     ]
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm, trange\n",
    "\n",
    "opt = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "#opt = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    \n",
    "    running_loss = 0\n",
    "    batch = 0\n",
    "    model.train()\n",
    "    \n",
    "    for batch, i in zip(train_loader,trange(batches)):\n",
    "        data = batch[0]\n",
    "        target = batch[1]      \n",
    "        #data, target = data.to('cuda:0'), target.to('cuda:0')\n",
    "                \n",
    "        opt.zero_grad()\n",
    "        pred = model(data)\n",
    "        loss_evaluated = loss(pred, target)\n",
    "        loss_evaluated.backward()\n",
    "        opt.step()\n",
    "        running_loss += loss_evaluated\n",
    "        print(running_loss.item() / (i + 1), end ='\\r')\n",
    "        \n",
    "    avg_loss = running_loss / batches\n",
    "    res = [epoch + 1,avg_loss]\n",
    "    print(\"Epoch: {:2d} | Loss: {:3f} \".format(*res))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d74415",
   "metadata": {},
   "source": [
    "# Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b48ecf03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on 24754 test images: 92.009 %\n"
     ]
    }
   ],
   "source": [
    "# since we're not training, we don't need to calculate the gradients for our outputs\n",
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for batch in test_loader:\n",
    "        data = batch[0]\n",
    "        target = batch[1]\n",
    "        #print(target)\n",
    "        # calculate outputs by running images through the network\n",
    "        predicted = model(data)\n",
    "        \n",
    "        # the class with the highest energy is what we choose as prediction\n",
    "        predicted = torch.argmax(predicted, 1)\n",
    "\n",
    "        total += target.size(0)\n",
    "        \n",
    "        correct += (predicted == target).sum().item()\n",
    "\n",
    "print('Accuracy of the network on {:2d} test images: {:.3f} %'.format(total,(100 * correct / total)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e8ca8647",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/SageMaker/qauntum-deep-learning/qauntum-deep-learning/qcnn_mnist\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "PATH = os.getcwd() + '/qcnn_mnist'\n",
    "print(PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "21347eb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03737b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.nn.Sequential(qlayer,output)\n",
    "model.load_state_dict(torch.load(PATH))\n",
    "model.eval()"
   ]
  }
 ],
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