在yolov3中,损失函数的计算代码如下: importtorch.nn.functionalasFclassCriterion(object):def__init__(self,cfg,device,num_classes=80):self.cfg=cfgself.device=deviceself.num_classes=num_classes# loss weightself.loss_obj_weight=cfg['loss_obj_weight']self.loss_cls_weight=cfg['loss_cls_weight']se...
outputs=model(inputs)# 计算loss loss=criterion(outputs,targets)# 后向传播、 调节权重 optimizer.zero_grad()loss.backward()optimizer.step()# 定义每五个opoch打印一次if(epoch+1)%5==0:print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1,num_epochs,loss.item()))# 画曲线 predicted=model...
回归(Regression)就是找到一个函数 ,通过输入特征值,输出一个数值Scalar。 模型步骤 step1:模型假设,选择模型框架(线性模型) step2:模型评估,如何判断众多模型的好坏(损失函数) step3:模型优化,如何筛选最优的模型(梯度下降) Step 1:模型假设 - 线性模型 一元线性模型(单个特征) 以一个特征x为例,线性模型假设为y...
output = model(train_x) # Calc loss and backprop gradients loss = -mll(output, train_y) loss.backward() print('Iter %d/%d - Loss: %.3f lengthscale: %.3f noise: %.3f' % ( i + 1, training_iter, loss.item(), model.covar_module.base_kernel.lengthscale.item(), model.likelihood....
lgb_model = lgb.LGBMRegressor(objective='regression',num_leaves=5, learning_rate=0.05, n_estimators=720, max_bin = 55, bagging_fraction = 0.8, bagging_freq = 5, feature_fraction = 0.2319, feature_fraction_seed=9, bagging_seed=9,min_data_in_leaf =6, ...
# 需要导入模块: import torch [as 别名]# 或者: from torch importmm[as 别名]defcalculate_regression_loss(self, z, target):""" Calculating the regression loss for all pairs of nodes. :param z: Hidden vertex representations. :param target: Target vector. ...
smooth_l1_loss(rpn_pred_deltas, target_deltas) else: loss = torch.FloatTensor([0]).cuda() return loss Example #12Source File: mrcnn.py From RegRCNN with Apache License 2.0 6 votes def compute_mrcnn_regression_loss(tasks, pred, target, target_class_ids): """regression loss is a ...
简单线性回归 y = 2*x + 1 import numpy as np import torch import torch.nn as nn class LinearRegressionModel(nn.Module): def __init__(self, input_dim, output_
:return loss_term: Regression loss. :return predictions_soft: Predictions for each vertex pair. """ pos = torch.cat((self.positive_z_i, self.positive_z_j), 1) neg = torch.cat((self.negative_z_i, self.negative_z_j), 1) surr_neg_i = torch.cat((self.negative_z_i, self....
🐛 Describe the bug I've noticed a significant performance slowdown in torch 2.0 when enabling determinism. Here is a simple example using the diffusers library: def set_deterministic(mode=True): import torch import os torch.backends.cudn...