885 lines (884 with data), 239.2 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"id": "WNXEyZ23rdz-"
},
"outputs": [],
"source": [
"# This cell is added by sphinx-gallery\n",
"# It can be customized to whatever you like\n",
"#from google.colab import drive\n",
"#drive.mount('/content/drive')"
]
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "WYVI3RMhAdap"
}
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "sANL984ird0B"
},
"outputs": [],
"source": [
"#!pip install pennylane\n",
"# Some parts of this code are based on the Python script:\n",
"# https://github.com/pytorch/tutorials/blob/master/beginner_source/transfer_learning_tutorial.py\n",
"# License: BSD\n",
"\n",
"import time\n",
"import os\n",
"import copy\n",
"\n",
"# PyTorch\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.optim import lr_scheduler\n",
"import torchvision\n",
"from torchvision import datasets, transforms\n",
"\n",
"# Pennylane\n",
"import pennylane as qml\n",
"from pennylane import numpy as np\n",
"\n",
"torch.manual_seed(42)\n",
"np.random.seed(42)\n",
"\n",
"# Plotting\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# OpenMP: number of parallel threads.\n",
"os.environ[\"OMP_NUM_THREADS\"] = \"1\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "o7eGTk_Prd0B"
},
"source": [
"Setting of the main hyper-parameters of the model\n",
"=================================================\n",
"\n",
"::: {.note}\n",
"::: {.title}\n",
"Note\n",
":::\n",
"\n",
"To reproduce the results of Ref. \\[1\\], `num_epochs` should be set to\n",
"`30` which may take a long time. We suggest to first try with\n",
"`num_epochs=1` and, if everything runs smoothly, increase it to a larger\n",
"value.\n",
":::\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "Wncy61Mdrd0B"
},
"outputs": [],
"source": [
"n_qubits = 16 # Number of qubits\n",
"step = 0.0006 # Learning rate\n",
"batch_size = 6 # Number of samples for each training step\n",
"num_epochs = 1 # Number of training epochs\n",
"q_depth = 6 # Depth of the quantum circuit (number of variational layers)\n",
"gamma_lr_scheduler = 0.1 # Learning rate reduction applied every 10 epochs.\n",
"q_delta = 0.01 # Initial spread of random quantum weights\n",
"start_time = time.time() # Start of the computation timer"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PVqjLo8Rrd0B"
},
"source": [
"We initialize a PennyLane device with a `default.qubit` backend.\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"id": "qLOa5trRrd0B"
},
"outputs": [],
"source": [
"dev = qml.device(\"default.qubit\", wires=n_qubits)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dxlK7Jjtrd0C"
},
"source": [
"We configure PyTorch to use CUDA only if available. Otherwise the CPU is\n",
"used.\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"id": "Br_YGwRDrd0C"
},
"outputs": [],
"source": [
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WFHuYd5xrd0C"
},
"source": [
"Dataset loading\n",
"===============\n",
"\n",
"::: {.note}\n",
"::: {.title}\n",
"Note\n",
":::\n",
"\n",
"The dataset containing images of *ants* and *bees* can be downloaded\n",
"[here](https://download.pytorch.org/tutorial/hymenoptera_data.zip) and\n",
"should be extracted in the subfolder `../_data/hymenoptera_data`.\n",
":::\n",
"\n",
"This is a very small dataset (roughly 250 images), too small for\n",
"training from scratch a classical or quantum model, however it is enough\n",
"when using *transfer learning* approach.\n",
"\n",
"The PyTorch packages `torchvision` and `torch.utils.data` are used for\n",
"loading the dataset and performing standard preliminary image\n",
"operations: resize, center, crop, normalize, *etc.*\n"
]
},
{
"cell_type": "code",
"source": [
"#os.system(\"rm -rf /content/data/.ipynb_checkpoints\")\n",
"#os.system(\"rm -rf /content/data/braintumor_data/.ipynb_checkpoints\")\n",
"#os.system(\"rm -rf /content/data/braintumor_data/train/.ipynb_checkpoints\")\n",
"#os.system(\"rm -rf /content/data/braintumor_data/val/.ipynb_checkpoints\")"
],
"metadata": {
"id": "DM8SDO3Wthcc"
},
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"id": "jQrNNVnUrd0C"
},
"outputs": [],
"source": [
"data_transforms = {\n",
" \"train\": transforms.Compose(\n",
" [\n",
" # transforms.RandomResizedCrop(224), # uncomment for data augmentation\n",
" # transforms.RandomHorizontalFlip(), # uncomment for data augmentation\n",
" transforms.Resize(256),\n",
" transforms.CenterCrop(224),\n",
" transforms.ToTensor(),\n",
" # Normalize input channels using mean values and standard deviations of ImageNet.\n",
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
" ]\n",
" ),\n",
" \"val\": transforms.Compose(\n",
" [\n",
" transforms.Resize(256),\n",
" transforms.CenterCrop(224),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
" ]\n",
" ),\n",
"}\n",
"\n",
"data_dir = \"/content/drive/MyDrive/Colab Notebooks/data/4_cl_2xbraintumor_data\"\n",
"image_datasets = {\n",
" x if x == \"train\" else \"validation\": datasets.ImageFolder(\n",
" os.path.join(data_dir, x), data_transforms[x]\n",
" )\n",
" for x in [\"train\", \"val\"]\n",
"}\n",
"dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"validation\"]}\n",
"class_names = image_datasets[\"train\"].classes\n",
"\n",
"# Initialize dataloader\n",
"dataloaders = {\n",
" x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)\n",
" for x in [\"train\", \"validation\"]\n",
"}\n",
"\n",
"# function to plot images\n",
"def imshow(inp, title=None):\n",
" \"\"\"Display image from tensor.\"\"\"\n",
" inp = inp.numpy().transpose((1, 2, 0))\n",
" # Inverse of the initial normalization operation.\n",
" mean = np.array([0.485, 0.456, 0.406])\n",
" std = np.array([0.229, 0.224, 0.225])\n",
" inp = std * inp + mean\n",
" inp = np.clip(inp, 0, 1)\n",
" plt.imshow(inp)\n",
" if title is not None:\n",
" plt.title(title)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "piWk71nkrd0C"
},
"source": [
"Let us show a batch of the test data, just to have an idea of the\n",
"classification problem.\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 166
},
"id": "u55iZYEOrd0D",
"outputId": "5acda58b-36c1-4ad0-edd0-ebc65feae9e5"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"# Get a batch of training data\n",
"inputs, classes = next(iter(dataloaders[\"validation\"]))\n",
"\n",
"# Make a grid from batch\n",
"out = torchvision.utils.make_grid(inputs)\n",
"\n",
"imshow(out, title=[class_names[x] for x in classes])\n",
"\n",
"dataloaders = {\n",
" x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)\n",
" for x in [\"train\", \"validation\"]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ds09ighxrd0D"
},
"source": [
"Variational quantum circuit\n",
"===========================\n",
"\n",
"We first define some quantum layers that will compose the quantum\n",
"circuit.\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"id": "OIbOa-KBrd0D"
},
"outputs": [],
"source": [
"def H_layer(nqubits):\n",
" \"\"\"Layer of single-qubit Hadamard gates.\n",
" \"\"\"\n",
" for idx in range(nqubits):\n",
" qml.Hadamard(wires=idx)\n",
"\n",
"def circuit(f=None):\n",
" qml.AmplitudeEmbedding(features=f, wires=range(8), pad_with=0., normalize=True)\n",
" \n",
"def RY_layer(w):\n",
" \n",
" \"\"\"Layer of parametrized qubit rotations around the y axis.\n",
" \"\"\"\n",
" for idx, element in enumerate(w):\n",
" qml.RY(element, wires=idx)\n",
"\n",
"def entangling_layer(nqubits):\n",
" \"\"\"Layer of CNOTs followed by another shifted layer of CNOT.\n",
" \"\"\"\n",
" # In other words it should apply something like :\n",
" # CNOT CNOT CNOT CNOT... CNOT\n",
" # CNOT CNOT CNOT... CNOT\n",
" for i in range(0, nqubits - 1, 2): # Loop over even indices: i=0,2,...N-2\n",
" qml.CNOT(wires=[i, i + 1])\n",
" for i in range(1, nqubits - 1, 2): # Loop over odd indices: i=1,3,...N-3\n",
" qml.CNOT(wires=[i, i + 1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ue__y4sQrd0D"
},
"source": [
"Now we define the quantum circuit through the PennyLane\n",
"[qnode]{.title-ref} decorator .\n",
"\n",
"The structure is that of a typical variational quantum circuit:\n",
"\n",
"- **Embedding layer:** All qubits are first initialized in a balanced\n",
" superposition of *up* and *down* states, then they are rotated\n",
" according to the input parameters (local embedding).\n",
"- **Variational layers:** A sequence of trainable rotation layers and\n",
" constant entangling layers is applied.\n",
"- **Measurement layer:** For each qubit, the local expectation value\n",
" of the $Z$ operator is measured. This produces a classical output\n",
" vector, suitable for additional post-processing.\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"id": "C-O_cK6jrd0D"
},
"outputs": [],
"source": [
"@qml.qnode(dev, interface=\"torch\")\n",
"def quantum_net(q_input_features, q_weights_flat):\n",
" \"\"\"\n",
" The variational quantum circuit.\n",
" \"\"\"\n",
"\n",
" # Reshape weights\n",
" q_weights = q_weights_flat.reshape(q_depth, n_qubits)\n",
"\n",
" # Start from state |+> , unbiased w.r.t. |0> and |1>\n",
" H_layer(n_qubits)\n",
"\n",
" # Embed features in the quantum node\n",
" circuit(q_input_features)\n",
"\n",
" # Sequence of trainable variational layers\n",
" #for k in range(q_depth):\n",
" # entangling_layer(n_qubits)\n",
" # RY_layer(q_weights[k])\n",
"\n",
" # Expectation values in the Z basis\n",
" exp_vals = [qml.expval(qml.PauliZ(position)) for position in range(n_qubits)]\n",
" return tuple(exp_vals)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7FNHREgVrd0D"
},
"source": [
"Dressed quantum circuit\n",
"=======================\n",
"\n",
"We can now define a custom `torch.nn.Module` representing a *dressed*\n",
"quantum circuit.\n",
"\n",
"This is a concatenation of:\n",
"\n",
"- A classical pre-processing layer (`nn.Linear`).\n",
"- A classical activation function (`torch.tanh`).\n",
"- A constant `np.pi/2.0` scaling.\n",
"- The previously defined quantum circuit (`quantum_net`).\n",
"- A classical post-processing layer (`nn.Linear`).\n",
"\n",
"The input of the module is a batch of vectors with 512 real parameters\n",
"(features) and the output is a batch of vectors with two real outputs\n",
"(associated with the two classes of images: *ants* and *bees*).\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"id": "C96kWCjWrd0D"
},
"outputs": [],
"source": [
"class DressedQuantumNet(nn.Module):\n",
" \"\"\"\n",
" Torch module implementing the *dressed* quantum net.\n",
" \"\"\"\n",
"\n",
" def __init__(self):\n",
" \"\"\"\n",
" Definition of the *dressed* layout.\n",
" \"\"\"\n",
"\n",
" super().__init__()\n",
" self.pre_net = nn.Linear(512, n_qubits)\n",
" self.q_params = nn.Parameter(q_delta * torch.randn(q_depth * n_qubits))\n",
" self.post_net = nn.Linear(n_qubits, 4)\n",
"\n",
" def forward(self, input_features):\n",
" \"\"\"\n",
" Defining how tensors are supposed to move through the *dressed* quantum\n",
" net.\n",
" \"\"\"\n",
"\n",
" # obtain the input features for the quantum circuit\n",
" # by reducing the feature dimension from 512 to 4\n",
" pre_out = self.pre_net(input_features)\n",
" q_in = torch.tanh(pre_out) * np.pi / 2.0\n",
"\n",
" # Apply the quantum circuit to each element of the batch and append to q_out\n",
" q_out = torch.Tensor(0, n_qubits)\n",
" q_out = q_out.to(device)\n",
" for elem in q_in:\n",
" q_out_elem = torch.hstack(quantum_net(elem, self.q_params)).float().unsqueeze(0)\n",
" q_out = torch.cat((q_out, q_out_elem))\n",
"\n",
" # return the two-dimensional prediction from the postprocessing layer\n",
" return self.post_net(q_out)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7hQlpDQdrd0D"
},
"source": [
"Hybrid classical-quantum model\n",
"==============================\n",
"\n",
"We are finally ready to build our full hybrid classical-quantum network.\n",
"We follow the *transfer learning* approach:\n",
"\n",
"1. First load the classical pre-trained network *ResNet18* from the\n",
" `torchvision.models` zoo.\n",
"2. Freeze all the weights since they should not be trained.\n",
"3. Replace the last fully connected layer with our trainable dressed\n",
" quantum circuit (`DressedQuantumNet`).\n",
"\n",
"::: {.note}\n",
"::: {.title}\n",
"Note\n",
":::\n",
"\n",
"The *ResNet18* model is automatically downloaded by PyTorch and it may\n",
"take several minutes (only the first time).\n",
":::\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"id": "MAh4FqBYrd0D"
},
"outputs": [],
"source": [
"model_hybrid = torchvision.models.resnet18(pretrained=True)\n",
"\n",
"for param in model_hybrid.parameters():\n",
" param.requires_grad = False\n",
"\n",
"\n",
"# Notice that model_hybrid.fc is the last layer of ResNet18\n",
"model_hybrid.fc = DressedQuantumNet()\n",
"\n",
"# Use CUDA or CPU according to the \"device\" object.\n",
"model_hybrid = model_hybrid.to(device)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ovX-Tkb0rd0E"
},
"source": [
"Training and results\n",
"====================\n",
"\n",
"Before training the network we need to specify the *loss* function.\n",
"\n",
"We use, as usual in classification problem, the *cross-entropy* which is\n",
"directly available within `torch.nn`.\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"id": "gkmFIK4Brd0E"
},
"outputs": [],
"source": [
"criterion = nn.CrossEntropyLoss()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qhFORuvard0E"
},
"source": [
"We also initialize the *Adam optimizer* which is called at each training\n",
"step in order to update the weights of the model.\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"id": "_K9M-VPMrd0E"
},
"outputs": [],
"source": [
"optimizer_hybrid = optim.Adam(model_hybrid.fc.parameters(), lr=step)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IGG8pksyrd0E"
},
"source": [
"We schedule to reduce the learning rate by a factor of\n",
"`gamma_lr_scheduler` every 10 epochs.\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"id": "nco3IrE6rd0E"
},
"outputs": [],
"source": [
"exp_lr_scheduler = lr_scheduler.StepLR(\n",
" optimizer_hybrid, step_size=10, gamma=gamma_lr_scheduler\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yJvpPNCRrd0E"
},
"source": [
"What follows is a training function that will be called later. This\n",
"function should return a trained model that can be used to make\n",
"predictions (classifications).\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"id": "8uO5BrOPrd0E"
},
"outputs": [],
"source": [
"def train_model(model, criterion, optimizer, scheduler, num_epochs):\n",
" since = time.time()\n",
" best_model_wts = copy.deepcopy(model.state_dict())\n",
" best_acc = 0.0\n",
" best_loss = 10000.0 # Large arbitrary number\n",
" best_acc_train = 0.0\n",
" best_loss_train = 10000.0 # Large arbitrary number\n",
" print(\"Training started:\")\n",
"\n",
" for epoch in range(num_epochs):\n",
"\n",
" # Each epoch has a training and validation phase\n",
" for phase in [\"train\", \"validation\"]:\n",
" if phase == \"train\":\n",
" # Set model to training mode\n",
" model.train()\n",
" else:\n",
" # Set model to evaluate mode\n",
" model.eval()\n",
" running_loss = 0.0\n",
" running_corrects = 0\n",
"\n",
" # Iterate over data.\n",
" n_batches = dataset_sizes[phase] // batch_size\n",
" it = 0\n",
" for inputs, labels in dataloaders[phase]:\n",
" since_batch = time.time()\n",
" batch_size_ = len(inputs)\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
" optimizer.zero_grad()\n",
"\n",
" # Track/compute gradient and make an optimization step only when training\n",
" with torch.set_grad_enabled(phase == \"train\"):\n",
" outputs = model(inputs)\n",
" _, preds = torch.max(outputs, 1)\n",
" loss = criterion(outputs, labels)\n",
" if phase == \"train\":\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" # Print iteration results\n",
" running_loss += loss.item() * batch_size_\n",
" batch_corrects = torch.sum(preds == labels.data).item()\n",
" running_corrects += batch_corrects\n",
" print(\n",
" \"Phase: {} Epoch: {}/{} Iter: {}/{} Batch time: {:.4f}\".format(\n",
" phase,\n",
" epoch + 1,\n",
" num_epochs,\n",
" it + 1,\n",
" n_batches + 1,\n",
" time.time() - since_batch,\n",
" ),\n",
" end=\"\\r\",\n",
" flush=True,\n",
" )\n",
" it += 1\n",
"\n",
" # Print epoch results\n",
" epoch_loss = running_loss / dataset_sizes[phase]\n",
" epoch_acc = running_corrects / dataset_sizes[phase]\n",
" print(\n",
" \"Phase: {} Epoch: {}/{} Loss: {:.4f} Acc: {:.4f} \".format(\n",
" \"train\" if phase == \"train\" else \"validation \",\n",
" epoch + 1,\n",
" num_epochs,\n",
" epoch_loss,\n",
" epoch_acc,\n",
" )\n",
" )\n",
"\n",
" # Check if this is the best model wrt previous epochs\n",
" if phase == \"validation\" and epoch_acc > best_acc:\n",
" best_acc = epoch_acc\n",
" best_model_wts = copy.deepcopy(model.state_dict())\n",
" if phase == \"validation\" and epoch_loss < best_loss:\n",
" best_loss = epoch_loss\n",
" if phase == \"train\" and epoch_acc > best_acc_train:\n",
" best_acc_train = epoch_acc\n",
" if phase == \"train\" and epoch_loss < best_loss_train:\n",
" best_loss_train = epoch_loss\n",
" \n",
" # Update learning rate\n",
" if phase == \"train\":\n",
" scheduler.step()\n",
"\n",
" # Print final results\n",
" model.load_state_dict(best_model_wts)\n",
" time_elapsed = time.time() - since\n",
" print(\n",
" \"Training completed in {:.0f}m {:.0f}s\".format(time_elapsed // 60, time_elapsed % 60)\n",
" )\n",
" print(\"Best test loss: {:.4f} | Best test accuracy: {:.4f}\".format(best_loss, best_acc))\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CmAl9fIQrd0E"
},
"source": [
"We are ready to perform the actual training process.\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "rAQBdDA_rd0E",
"outputId": "4fec6ffe-fa25-4fb3-c08d-8d2b9e771403"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training started:\n",
"Phase: train Epoch: 1/1 Loss: 1.2804 Acc: 0.4615 \n",
"Phase: validation Epoch: 1/1 Loss: 1.2618 Acc: 0.4113 \n",
"Training completed in 1m 4s\n",
"Best test loss: 1.2618 | Best test accuracy: 0.4113\n"
]
}
],
"source": [
"model_hybrid = train_model(\n",
" model_hybrid, criterion, optimizer_hybrid, exp_lr_scheduler, num_epochs=num_epochs\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "s4cEc4mird0E"
},
"source": [
"Visualizing the model predictions\n",
"=================================\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ARvjv_lbrd0E"
},
"source": [
"We first define a visualization function for a batch of test data.\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"id": "CtUY4xU_rd0E"
},
"outputs": [],
"source": [
"def visualize_model(model, num_images=6, fig_name=\"Predictions\"):\n",
" images_so_far = 0\n",
" _fig = plt.figure(fig_name)\n",
" model.eval()\n",
" with torch.no_grad():\n",
" for _i, (inputs, labels) in enumerate(dataloaders[\"validation\"]):\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
" outputs = model(inputs)\n",
" _, preds = torch.max(outputs, 1)\n",
" for j in range(inputs.size()[0]):\n",
" images_so_far += 1\n",
" ax = plt.subplot(num_images // 2, 2, images_so_far)\n",
" ax.axis(\"off\")\n",
" ax.set_title(\"[{}]\".format(class_names[preds[j]]))\n",
" imshow(inputs.cpu().data[j])\n",
" if images_so_far == num_images:\n",
" return"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F6I5Rk92rd0E"
},
"source": [
"Finally, we can run the previous function to see a batch of images with\n",
"the corresponding predictions.\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"id": "-RoYcZQXrd0E",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"outputId": "2870a58e-3570-4100-e39b-93b7129ac134"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 6 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"visualize_model(model_hybrid, num_images=6)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IBXZTnzjrd0E"
},
"source": [
"References\n",
"==========\n",
"\n",
"\\[1\\] Andrea Mari, Thomas R. Bromley, Josh Izaac, Maria Schuld, and\n",
"Nathan Killoran. *Transfer learning in hybrid classical-quantum neural\n",
"networks*. arXiv:1912.08278 (2019).\n",
"\n",
"\\[2\\] Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and\n",
"Andrew Y Ng. *Self-taught learning: transfer learning from unlabeled\n",
"data*. Proceedings of the 24th International Conference on Machine\n",
"Learning\\*, 759--766 (2007).\n",
"\n",
"\\[3\\] Kaiming He, Xiangyu Zhang, Shaoqing ren and Jian Sun. *Deep\n",
"residual learning for image recognition*. Proceedings of the IEEE\n",
"Conference on Computer Vision and Pattern Recognition, 770-778 (2016).\n",
"\n",
"\\[4\\] Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin,\n",
"Carsten Blank, Keri McKiernan, and Nathan Killoran. *PennyLane:\n",
"Automatic differentiation of hybrid quantum-classical computations*.\n",
"arXiv:1811.04968 (2018).\n",
"\n",
"About the author\n",
"================\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
},
"colab": {
"provenance": [],
"gpuType": "V100"
},
"accelerator": "GPU",
"gpuClass": "standard"
},
"nbformat": 4,
"nbformat_minor": 0
}