个人备案网站改企业备案,有什么电商网站做推广赚佣金的,深圳建立网站,如何设置网站布局文章目录 optuna使用1.导入相关包2.定义模型可选参数3.定义训练代码和评估代码4.定义目标函数5.运行程序6.可视化7.超参数的重要性8.查看相关信息9.可视化的一个完整示例10.lightgbm实验 optuna使用
1.导入相关包
import torch
import torch.nn as nn
import torch.nn.functi… 文章目录 optuna使用1.导入相关包2.定义模型可选参数3.定义训练代码和评估代码4.定义目标函数5.运行程序6.可视化7.超参数的重要性8.查看相关信息9.可视化的一个完整示例10.lightgbm实验 optuna使用
1.导入相关包
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from fvcore.nn import FlopCountAnalysisimport optunaDEVICE torch.device(cuda) if torch.cuda.is_available() else torch.device(cpu)
DIR ..
BATCHSIZE 128
N_TRAIN_EXAMPLES BATCHSIZE * 30 # 128 * 30个训练
N_VALID_EXAMPLES BATCHSIZE * 10 # 128 * 10个预测2.定义模型可选参数
optuna支持很多种搜索方式 1trial.suggest_categorical(‘optimizer’, [‘MomentumSGD’, ‘Adam’])表示从SGD和adam里选一个使用 2trial.suggest_int(‘num_layers’, 1, 3)从13范围内的int里选 3trial.suggest_uniform(‘dropout_rate’, 0.0, 1.0)从01内的uniform分布里选 4trial.suggest_loguniform(‘learning_rate’, 1e-5, 1e-2)从1e-51e-2的log uniform分布里选 5trial.suggest_discrete_uniform(‘drop_path_rate’, 0.0, 1.0, 0.1)从01且step为0.1的离散uniform分布里选
def define_model(trial):n_layers trial.suggest_int(n_layers, 1, 3) # 从[13]范围里面选一个layers []in_features 28 * 28for 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_featureslayers.append(nn.Linear(in_features, 10))layers.append(nn.LogSoftmax(dim1))return nn.Sequential(*layers)3.定义训练代码和评估代码
# 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 0with 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(dim1, keepdimTrue)correct pred.eq(target.view_as(pred)).sum().item()accuracy correct / N_VALID_EXAMPLESflops FlopCountAnalysis(model, inputs(torch.randn(1, 28 * 28).to(DEVICE),)).total()return flops, accuracy4.定义目标函数
def objective(trial):train_dataset torchvision.datasets.FashionMNIST(DIR, trainTrue, downloadTrue, transformtorchvision.transforms.ToTensor())train_loader torch.utils.data.DataLoader(torch.utils.data.Subset(train_dataset, list(range(N_TRAIN_EXAMPLES))),batch_sizeBATCHSIZE,shuffleTrue,)val_dataset torchvision.datasets.FashionMNIST(DIR, trainFalse, transformtorchvision.transforms.ToTensor())val_loader torch.utils.data.DataLoader(torch.utils.data.Subset(val_dataset, list(range(N_VALID_EXAMPLES))),batch_sizeBATCHSIZE,shuffleTrue,)model define_model(trial).to(DEVICE)optimizer torch.optim.Adam(model.parameters(), trial.suggest_float(lr, 1e-5, 1e-1, logTrue))for epoch in range(10):train_model(model, optimizer, train_loader)flops, accuracy eval_model(model, val_loader)return flops, accuracy5.运行程序
运行30次实验每次实验返回 flops,accuracy
study optuna.create_study(directions[minimize, maximize]) # flops 最小化 accuracy 最大化
study.optimize(objective, n_trials30, timeout300)print(Number of finished trials: , len(study.trials))6.可视化
flops, accuracy 二维图 optuna.visualization.plot_pareto_front(study, target_names[“FLOPS”, “accuracy”]) 7.超参数的重要性
对于flops optuna.visualization.plot_param_importances( study, targetlambda t: t.values[0], target_name“flops” )
对于accuracy optuna.visualization.plot_param_importances( study, targetlambda t: t.values[1], target_name“accuracy” ) 8.查看相关信息
# https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/002_multi_objective.html
# 利用pytorch mnist 识别
# 设置了一些超参数lr, layer number, feature_number等
# 然后目标是 flops 和 accurary# 最后是可视化
# 显示试验的一些结果
# optuna.visualization.plot_pareto_front(study, target_names[FLOPS, accuracy])
# 左上角是最好的# 显示重要性
# optuna.visualization.plot_param_importances(
# study, targetlambda t: t.values[0], target_nameflops
# )
# optuna.visualization.plot_param_importances(
# study, targetlambda t: t.values[1], target_nameaccuracy
# )# trials的属性
print(fNumber of trials on the Pareto front: {len(study.best_trials)})trial_with_highest_accuracy max(study.best_trials, keylambda t: t.values[1])
print(fTrial 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})9.可视化的一个完整示例
# You can use Matplotlib instead of Plotly for visualization by simply replacing optuna.visualization with
# optuna.visualization.matplotlib in the following examples.
from optuna.visualization import plot_contour
from optuna.visualization import plot_edf
from optuna.visualization import plot_intermediate_values
from optuna.visualization import plot_optimization_history
from optuna.visualization import plot_parallel_coordinate
from optuna.visualization import plot_param_importances
from optuna.visualization import plot_rank
from optuna.visualization import plot_slice
from optuna.visualization import plot_timelinedef objective(trial):train_dataset torchvision.datasets.FashionMNIST(DIR, trainTrue, downloadTrue, transformtorchvision.transforms.ToTensor())train_loader torch.utils.data.DataLoader(torch.utils.data.Subset(train_dataset, list(range(N_TRAIN_EXAMPLES))),batch_sizeBATCHSIZE,shuffleTrue,)val_dataset torchvision.datasets.FashionMNIST(DIR, trainFalse, transformtorchvision.transforms.ToTensor())val_loader torch.utils.data.DataLoader(torch.utils.data.Subset(val_dataset, list(range(N_VALID_EXAMPLES))),batch_sizeBATCHSIZE,shuffleTrue,)model define_model(trial).to(DEVICE)optimizer torch.optim.Adam(model.parameters(), trial.suggest_float(lr, 1e-5, 1e-1, logTrue))for epoch in range(10):train_model(model, optimizer, train_loader)val_accuracy eval_model(model, val_loader)trial.report(val_accuracy, epoch)if trial.should_prune():raise optuna.exceptions.TrialPruned()return val_accuracystudy optuna.create_study(directionmaximize,sampleroptuna.samplers.TPESampler(seedSEED),pruneroptuna.pruners.MedianPruner(),
)
study.optimize(objective, n_trials30, timeout300)
运行之后可视化 10.lightgbm实验 Optuna example that optimizes a classifier configuration for cancer dataset using LightGBM.In this example, we optimize the validation accuracy of cancer detection using LightGBM.
We optimize both the choice of booster model and their hyperparameters.import numpy as np
import optunaimport lightgbm as lgb
import sklearn.datasets
import sklearn.metrics
from sklearn.model_selection import train_test_split# FYI: Objective functions can take additional arguments
# (https://optuna.readthedocs.io/en/stable/faq.html#objective-func-additional-args).
def objective(trial):data, target sklearn.datasets.load_breast_cancer(return_X_yTrue)train_x, valid_x, train_y, valid_y train_test_split(data, target, test_size0.25)dtrain lgb.Dataset(train_x, labeltrain_y)param {objective: binary,metric: binary_logloss,verbosity: -1,boosting_type: gbdt,lambda_l1: trial.suggest_float(lambda_l1, 1e-8, 10.0, logTrue),lambda_l2: trial.suggest_float(lambda_l2, 1e-8, 10.0, logTrue),num_leaves: trial.suggest_int(num_leaves, 2, 256),feature_fraction: trial.suggest_float(feature_fraction, 0.4, 1.0),bagging_fraction: trial.suggest_float(bagging_fraction, 0.4, 1.0),bagging_freq: trial.suggest_int(bagging_freq, 1, 7),min_child_samples: trial.suggest_int(min_child_samples, 5, 100),}gbm lgb.train(param, dtrain)preds gbm.predict(valid_x)pred_labels np.rint(preds)accuracy sklearn.metrics.accuracy_score(valid_y, pred_labels)return accuracyif __name__ __main__:study optuna.create_study(directionmaximize)study.optimize(objective, n_trials100)print(Number of finished trials: {}.format(len(study.trials)))print(Best trial:)trial study.best_trialprint( Value: {}.format(trial.value))print( Params: )for key, value in trial.params.items():print( {}: {}.format(key, value))运行结果
https://github.com/microsoft/LightGBM/tree/master/examples
https://blog.csdn.net/yang1015661763/article/details/131364826
