901 lines (900 with data), 238.6 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"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": 4,
"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": 5,
"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": 6,
"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": 7,
"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": 8,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 9,
"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": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 166
},
"id": "u55iZYEOrd0D",
"outputId": "8677384f-ae74-4067-daea-0dbd724e7a6e"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
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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": 11,
"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",
"\n",
"def RY_layer(w):\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",
"\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": 12,
"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",
" RY_layer(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": 13,
"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": 14,
"metadata": {
"id": "MAh4FqBYrd0D",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"outputId": "ba412662-bb19-42ca-8365-6725955ed346"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
" warnings.warn(\n",
"/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
" warnings.warn(msg)\n",
"Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n",
"100%|██████████| 44.7M/44.7M [00:01<00:00, 32.6MB/s]\n"
]
}
],
"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": 15,
"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": 16,
"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": 17,
"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": 18,
"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": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "rAQBdDA_rd0E",
"outputId": "29163ecc-c736-4bb1-eef9-f2cdbe8a561a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training started:\n",
"Phase: train Epoch: 1/1 Loss: 1.2268 Acc: 0.5345 \n",
"Phase: validation Epoch: 1/1 Loss: 1.1617 Acc: 0.6109 \n",
"Training completed in 11m 8s\n",
"Best test loss: 1.1617 | Best test accuracy: 0.6109\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": 20,
"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": 21,
"metadata": {
"id": "-RoYcZQXrd0E",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"outputId": "f9e350d6-729e-4eaa-8771-1824a3277ce5"
},
"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
}