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"""
.. _multi_objective:
Multi-objective Optimization with Optuna
========================================
This tutorial showcases Optuna's multi-objective optimization feature by
optimizing the validation accuracy of Fashion MNIST dataset and the FLOPS of the model implemented in PyTorch.
We use `fvcore <https://github.com/facebookresearch/fvcore>`__ to measure FLOPS.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from fvcore.nn import FlopCountAnalysis
import optuna
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
DIR = ".."
BATCHSIZE = 128
N_TRAIN_EXAMPLES = BATCHSIZE * 30
N_VALID_EXAMPLES = BATCHSIZE * 10
def define_model(trial):
n_layers = trial.suggest_int("n_layers", 1, 3)
layers = []
in_features = 28 * 28
for i in range(n_layers):
out_features = trial.suggest_int("n_units_l{}".format(i), 4, 128)
layers.append(nn.Linear(in_features, out_features))
layers.append(nn.ReLU())
p = trial.suggest_float("dropout_{}".format(i), 0.2, 0.5)
layers.append(nn.Dropout(p))
in_features = out_features
layers.append(nn.Linear(in_features, 10))
layers.append(nn.LogSoftmax(dim=1))
return nn.Sequential(*layers)
# Defines training and evaluation.
def train_model(model, optimizer, train_loader):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.view(-1, 28 * 28).to(DEVICE), target.to(DEVICE)
optimizer.zero_grad()
F.nll_loss(model(data), target).backward()
optimizer.step()
def eval_model(model, valid_loader):
model.eval()
correct = 0
with torch.no_grad():
for batch_idx, (data, target) in enumerate(valid_loader):
data, target = data.view(-1, 28 * 28).to(DEVICE), target.to(DEVICE)
pred = model(data).argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
accuracy = correct / N_VALID_EXAMPLES
flops = FlopCountAnalysis(model, inputs=(torch.randn(1, 28 * 28).to(DEVICE),)).total()
return flops, accuracy
###################################################################################################
# Define multi-objective objective function.
# Objectives are FLOPS and accuracy.
def objective(trial):
train_dataset = torchvision.datasets.FashionMNIST(
DIR, train=True, download=True, transform=torchvision.transforms.ToTensor()
)
train_loader = torch.utils.data.DataLoader(
torch.utils.data.Subset(train_dataset, list(range(N_TRAIN_EXAMPLES))),
batch_size=BATCHSIZE,
shuffle=True,
)
val_dataset = torchvision.datasets.FashionMNIST(
DIR, train=False, transform=torchvision.transforms.ToTensor()
)
val_loader = torch.utils.data.DataLoader(
torch.utils.data.Subset(val_dataset, list(range(N_VALID_EXAMPLES))),
batch_size=BATCHSIZE,
shuffle=True,
)
model = define_model(trial).to(DEVICE)
optimizer = torch.optim.Adam(
model.parameters(), trial.suggest_float("lr", 1e-5, 1e-1, log=True)
)
for epoch in range(10):
train_model(model, optimizer, train_loader)
flops, accuracy = eval_model(model, val_loader)
return flops, accuracy
###################################################################################################
# Run multi-objective optimization
# --------------------------------
#
# If your optimization problem is multi-objective,
# Optuna assumes that you will specify the optimization direction for each objective.
# Specifically, in this example, we want to minimize the FLOPS (we want a faster model)
# and maximize the accuracy. So we set ``directions`` to ``["minimize", "maximize"]``.
study = optuna.create_study(directions=["minimize", "maximize"])
study.optimize(objective, n_trials=30, timeout=300)
print("Number of finished trials: ", len(study.trials))
###################################################################################################
# Note that the following sections requires the installation of `Plotly <https://plotly.com/python>`__ for visualization
# and `scikit-learn <https://scikit-learn.org/stable>`__ for hyperparameter importance calculation:
#
# .. code-block:: console
#
# $ pip install plotly
# $ pip install scikit-learn
# $ pip install nbformat # Required if you are running this tutorial in Jupyter Notebook.
#
# Check trials on Pareto front visually.
optuna.visualization.plot_pareto_front(study, target_names=["FLOPS", "accuracy"])
###################################################################################################
# Fetch the list of trials on the Pareto front with :attr:`~optuna.study.Study.best_trials`.
#
# For example, the following code shows the number of trials on the Pareto front and picks the trial with the highest accuracy.
print(f"Number of trials on the Pareto front: {len(study.best_trials)}")
trial_with_highest_accuracy = max(study.best_trials, key=lambda t: t.values[1])
print("Trial with highest accuracy: ")
print(f"\tnumber: {trial_with_highest_accuracy.number}")
print(f"\tparams: {trial_with_highest_accuracy.params}")
print(f"\tvalues: {trial_with_highest_accuracy.values}")
###################################################################################################
# Learn which hyperparameters are affecting the flops most with hyperparameter importance.
optuna.visualization.plot_param_importances(
study, target=lambda t: t.values[0], target_name="flops"
)
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