885 lines (884 with data), 249.9 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 60,
"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": 61,
"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": 62,
"metadata": {
"id": "Wncy61Mdrd0B"
},
"outputs": [],
"source": [
"n_qubits = 24 # 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": 63,
"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": 64,
"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": 65,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 66,
"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": 67,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 166
},
"id": "u55iZYEOrd0D",
"outputId": "a2b05a68-bbc0-40c4-efdf-884d20c5962e"
},
"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": 68,
"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": 69,
"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": 70,
"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": 71,
"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": 72,
"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": 73,
"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": 74,
"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": 75,
"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": 76,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "rAQBdDA_rd0E",
"outputId": "21b7c824-6704-4333-e1ee-a8ab62ee7f8c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training started:\n",
"Phase: train Epoch: 1/1 Loss: 1.2557 Acc: 0.3824 \n",
"Phase: validation Epoch: 1/1 Loss: 1.1536 Acc: 0.4727 \n",
"Training completed in 43m 24s\n",
"Best test loss: 1.1536 | Best test accuracy: 0.4727\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": 77,
"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": 78,
"metadata": {
"id": "-RoYcZQXrd0E",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"outputId": "cf67533b-0940-4367-f894-d888252ec3ff"
},
"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"
},
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