1036 lines (1036 with data), 311.8 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"id": "WNXEyZ23rdz-"
},
"outputs": [],
"source": [
"# This cell is added by sphinx-gallery\n",
"# It can be customized to whatever you like\n",
"%matplotlib inline\n",
"#from google.colab import drive\n",
"#drive.mount('/content/drive')"
]
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "WYVI3RMhAdap"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "2XSuLtL8rdz_"
},
"source": [
"Quantum transfer learning {#quantum_transfer_learning}\n",
"=========================\n",
"\n",
"::: {.meta}\n",
":property=\\\"og:description\\\": Combine PyTorch and PennyLane to train a\n",
"hybrid quantum-classical image classifier using transfer learning.\n",
":property=\\\"og:image\\\":\n",
"<https://pennylane.ai/qml/_images/transfer_images.png>\n",
":::\n",
"\n",
"*Author: Andrea Mari --- Posted: 19 December 2019. Last updated: 28\n",
"January 2021.*\n",
"\n",
"In this tutorial we apply a machine learning method, known as *transfer\n",
"learning*, to an image classifier based on a hybrid classical-quantum\n",
"network.\n",
"\n",
"This example follows the general structure of the PyTorch [tutorial on\n",
"transfer\n",
"learning](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html)\n",
"by Sasank Chilamkurthy, with the crucial difference of using a quantum\n",
"circuit to perform the final classification task.\n",
"\n",
"More details on this topic can be found in the research paper \\[1\\]\n",
"([Mari et al. (2019)](https://arxiv.org/abs/1912.08278)).\n",
"\n",
"Introduction\n",
"------------\n",
"\n",
"Transfer learning is a well-established technique for training\n",
"artificial neural networks (see e.g., Ref. \\[2\\]), which is based on the\n",
"general intuition that if a pre-trained network is good at solving a\n",
"given problem, then, with just a bit of additional training, it can be\n",
"used to also solve a different but related problem.\n",
"\n",
"As discussed in Ref. \\[1\\], this idea can be formalized in terms of two\n",
"abstract networks $A$ and $B$, independently from their quantum or\n",
"classical physical nature.\n",
"\n",
"| \n",
"\n",
"{.align-center}\n",
"\n",
"| \n",
"\n",
"As sketched in the above figure, one can give the following **general\n",
"definition of the transfer learning method**:\n",
"\n",
"1. Take a network $A$ that has been pre-trained on a dataset $D_A$ and\n",
" for a given task $T_A$.\n",
"2. Remove some of the final layers. In this way, the resulting\n",
" truncated network $A'$ can be used as a feature extractor.\n",
"3. Connect a new trainable network $B$ at the end of the pre-trained\n",
" network $A'$.\n",
"4. Keep the weights of $A'$ constant, and train the final block $B$\n",
" with a new dataset $D_B$ and/or for a new task of interest $T_B$.\n",
"\n",
"When dealing with hybrid systems, depending on the physical nature\n",
"(classical or quantum) of the networks $A$ and $B$, one can have\n",
"different implementations of transfer learning as\n",
"\n",
"summarized in following table:\n",
"\n",
"| \n",
"\n",
"::: {.rst-class}\n",
"docstable\n",
":::\n",
"\n",
" -------------------------------------------------------------------------\n",
" Network A Network B Transfer learning scheme\n",
" ----------- ----------- -------------------------------------------------\n",
" Classical Classical CC - Standard classical method. See e.g., Ref.\n",
" \\[2\\].\n",
"\n",
" Classical Quantum CQ - **Hybrid model presented in this tutorial.**\n",
"\n",
" Quantum Classical QC - Model studied in Ref. \\[1\\].\n",
"\n",
" Quantum Quantum QQ - Model studied in Ref. \\[1\\].\n",
" -------------------------------------------------------------------------\n",
"\n",
"Classical-to-quantum transfer learning\n",
"--------------------------------------\n",
"\n",
"We focus on the CQ transfer learning scheme discussed in the previous\n",
"section and we give a specific example.\n",
"\n",
"1. As pre-trained network $A$ we use **ResNet18**, a deep residual\n",
" neural network introduced by Microsoft in Ref. \\[3\\], which is\n",
" pre-trained on the *ImageNet* dataset.\n",
"2. After removing its final layer we obtain $A'$, a pre-processing\n",
" block which maps any input high-resolution image into 512 abstract\n",
" features.\n",
"3. Such features are classified by a 4-qubit \\\"dressed quantum\n",
" circuit\\\" $B$, i.e., a variational quantum circuit sandwiched\n",
" between two classical layers.\n",
"4. The hybrid model is trained, keeping $A'$ constant, on the\n",
" *Hymenoptera* dataset (a small subclass of ImageNet) containing\n",
" images of *ants* and *bees*.\n",
"\n",
"A graphical representation of the full data processing pipeline is given\n",
"in the figure below.\n",
"\n",
"{.align-center}\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eT6yf0-xrd0A"
},
"source": [
"General setup\n",
"=============\n",
"\n",
"::: {.note}\n",
"::: {.title}\n",
"Note\n",
":::\n",
"\n",
"To use the PyTorch interface in PennyLane, you must first [install\n",
"PyTorch](https://pytorch.org/get-started/locally/#start-locally).\n",
":::\n",
"\n",
"In addition to *PennyLane*, we will also need some standard *PyTorch*\n",
"libraries and the plotting library *matplotlib*.\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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": 22,
"metadata": {
"id": "Wncy61Mdrd0B"
},
"outputs": [],
"source": [
"n_qubits = 8 # Number of qubits\n",
"step = 0.0006 # Learning rate\n",
"batch_size = 4 # Number of samples for each training step\n",
"num_epochs = 10 # 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": 23,
"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": 24,
"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": 25,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 26,
"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/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": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 207
},
"id": "u55iZYEOrd0D",
"outputId": "52ffb1ce-8481-4267-867f-88b10aec05d2"
},
"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": 28,
"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": 29,
"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": 30,
"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, 2)\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": 31,
"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": 32,
"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": 33,
"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": 34,
"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": 35,
"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": 36,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "rAQBdDA_rd0E",
"outputId": "38f29e4c-3039-4552-f489-cd8c13c1be0c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training started:\n",
"Phase: train Epoch: 1/10 Loss: 0.4817 Acc: 0.7816 \n",
"Phase: validation Epoch: 1/10 Loss: 0.2291 Acc: 0.9771 \n",
"Phase: train Epoch: 2/10 Loss: 0.3445 Acc: 0.8653 \n",
"Phase: validation Epoch: 2/10 Loss: 0.1507 Acc: 0.9902 \n",
"Phase: train Epoch: 3/10 Loss: 0.3523 Acc: 0.8571 \n",
"Phase: validation Epoch: 3/10 Loss: 0.1447 Acc: 0.9902 \n",
"Phase: train Epoch: 4/10 Loss: 0.2768 Acc: 0.8980 \n",
"Phase: validation Epoch: 4/10 Loss: 0.0993 Acc: 0.9869 \n",
"Phase: train Epoch: 5/10 Loss: 0.2918 Acc: 0.8918 \n",
"Phase: validation Epoch: 5/10 Loss: 0.0903 Acc: 0.9902 \n",
"Phase: train Epoch: 6/10 Loss: 0.3225 Acc: 0.8714 \n",
"Phase: validation Epoch: 6/10 Loss: 0.0957 Acc: 0.9902 \n",
"Phase: train Epoch: 7/10 Loss: 0.3559 Acc: 0.8469 \n",
"Phase: validation Epoch: 7/10 Loss: 0.1568 Acc: 0.9510 \n",
"Phase: train Epoch: 8/10 Loss: 0.3956 Acc: 0.8245 \n",
"Phase: validation Epoch: 8/10 Loss: 0.1835 Acc: 0.9314 \n",
"Phase: train Epoch: 9/10 Loss: 0.2395 Acc: 0.9122 \n",
"Phase: validation Epoch: 9/10 Loss: 0.0745 Acc: 0.9902 \n",
"Phase: train Epoch: 10/10 Loss: 0.2292 Acc: 0.9082 \n",
"Phase: validation Epoch: 10/10 Loss: 0.0693 Acc: 0.9902 \n",
"Training completed in 17m 42s\n",
"Best test loss: 0.0693 | Best test accuracy: 0.9902\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": 37,
"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": 38,
"metadata": {
"id": "-RoYcZQXrd0E",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"outputId": "37386b81-df6c-4c0f-8f60-a5c4d75546c6"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 4 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"visualize_model(model_hybrid, num_images=batch_size)\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
}