885 lines (884 with data), 241.2 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 41,
"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": 42,
"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": 43,
"metadata": {
"id": "Wncy61Mdrd0B"
},
"outputs": [],
"source": [
"n_qubits = 20 # 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": 44,
"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": 45,
"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": 46,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 47,
"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": 48,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 166
},
"id": "u55iZYEOrd0D",
"outputId": "2cde9816-2fe9-4e53-c84c-f7a5faee8c8f"
},
"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": 49,
"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": 50,
"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": 51,
"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": 52,
"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": 53,
"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": 54,
"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": 55,
"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": 56,
"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": 57,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "rAQBdDA_rd0E",
"outputId": "262b1578-fe30-43e0-dd69-456f9285f7e6"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training started:\n",
"Phase: train Epoch: 1/1 Loss: 1.2666 Acc: 0.4138 \n",
"Phase: validation Epoch: 1/1 Loss: 1.2495 Acc: 0.4795 \n",
"Training completed in 2m 18s\n",
"Best test loss: 1.2495 | Best test accuracy: 0.4795\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": 58,
"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": 59,
"metadata": {
"id": "-RoYcZQXrd0E",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"outputId": "35f83325-80ff-42d9-f1e1-a993b07932d6"
},
"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": {
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},
"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
}