885 lines (884 with data), 239.2 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 225,
"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": 226,
"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": 227,
"metadata": {
"id": "Wncy61Mdrd0B"
},
"outputs": [],
"source": [
"n_qubits = 8 # 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": 228,
"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": 229,
"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": 230,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 231,
"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": 232,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 166
},
"id": "u55iZYEOrd0D",
"outputId": "0fee6764-3064-47a5-8e17-e49c75330c88"
},
"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": 233,
"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": 234,
"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": 235,
"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": 236,
"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": 237,
"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": 238,
"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": 239,
"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": 240,
"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": 241,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "rAQBdDA_rd0E",
"outputId": "cb8c81cc-319b-428c-f133-e833d6ae6daf"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training started:\n",
"Phase: train Epoch: 1/1 Loss: 1.2974 Acc: 0.3844 \n",
"Phase: validation Epoch: 1/1 Loss: 1.2192 Acc: 0.4778 \n",
"Training completed in 3m 22s\n",
"Best test loss: 1.2192 | Best test accuracy: 0.4778\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": 242,
"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": 243,
"metadata": {
"id": "-RoYcZQXrd0E",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"outputId": "a04c895f-c00d-4b21-bd85-0d5d23db5533"
},
"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
}