902 lines (901 with data), 244.3 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"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": 5,
"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": 6,
"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": 7,
"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": 8,
"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": 9,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 10,
"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": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 166
},
"id": "u55iZYEOrd0D",
"outputId": "f9432bb5-cf19-4c9c-8079-c6d18999cf55"
},
"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": 12,
"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": 13,
"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",
" 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": 14,
"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": 15,
"metadata": {
"id": "MAh4FqBYrd0D",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"outputId": "24235f95-8f5c-4242-fdfe-777b1520e6bd"
},
"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 [00:00<00:00, 185MB/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": 16,
"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": 17,
"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": 18,
"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": 19,
"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": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "rAQBdDA_rd0E",
"outputId": "51ce46fb-bf95-4bd0-a22c-58faf41a7c80"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training started:\n",
"Phase: train Epoch: 1/1 Loss: 1.3340 Acc: 0.3631 \n",
"Phase: validation Epoch: 1/1 Loss: 1.2654 Acc: 0.4539 \n",
"Training completed in 14m 0s\n",
"Best test loss: 1.2654 | Best test accuracy: 0.4539\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": 21,
"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": 22,
"metadata": {
"id": "-RoYcZQXrd0E",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"outputId": "d7886b91-056f-449d-f5e9-28a2d600a49d"
},
"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
}