Table of Contents Reasons why training loss does not decrease 1. The learning rate is too large or too small 2. Data preprocessing issues 3. Model complexity issue 4. Data set size issue 5. Parameter initialization problem Example: Application scenario of why training loss does not decrease in image classification tasks 1. Data preprocessing issues […]
Tag: loss
Linear regression predicts Boston housing prices & the reason for loss is NAN & draws a scatter plot to find the relationship between features and labels
Boston house price csv file Link: https://pan.baidu.com/s/1uz6oKs7IeEzHdJkfrpiayg?pwd=vufb Extraction code: vufb Code %matplotlib inline import random import torch import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch Get the data set from CSV # Load data, the first line is a useless line, skip it directly boston = pd.read_csv(‘../data/boston_house_prices.csv’,skiprows=[0]) # There […]
Pytorch loss function, backpropagation and optimizer, Sequential use
Pytorch_Sequential usage, loss function, backpropagation and optimizer Article directory nn.Sequential Build a small practice Loss function and backpropagation optimizer nn.Sequential nn.Sequential is an ordered container. The modules used to build neural networks are added to the nn.Sequential() container in the order in which is passed into the constructor. import torch.nn as nn from collections import […]
Calculation process of multi-scale structural similarity L1 loss
Multi-scale structural similarity L1 loss SSIM Structural Similarity Index (SSIM) is an image quality measurement method used to evaluate the similarity between two images. SSIM is widely used for image quality assessment, performance evaluation of compression algorithms, image enhancement and restoration, etc. In application: The human eye perceives similarity between two images mainly based on […]
Pytorch draws train loss and val acc curves
import os importsys import json import torch import torch.nn as nn import torch.optim as optim from torchvision import transforms, datasets from tqdm import tqdm from model import resnet34,resnet101 import matplotlib.pyplot as plt # from csv import readerxon import numpy as np from osgeo import gdal from torchvision.transforms import functional as F # from torch.utils.tensorboard import […]
pytorch-loss function-difference between classification and regression
The difference between torch.nn library and torch.nn.functional library torch.nn Library: This library provides many predefined layers, such as fully connected layers (Linear), convolutional layers (Conv2d), etc., as well as some loss functions (such as MSELoss, CrossEntropyLoss, etc.). These layers are all classes, and they all inherit from nn.Module, so they can be easily integrated into […]
yolov7 improvement using QFocalLoss
The three major components of deep learning: data, model, and loss. A good Loss helps make it easier for the model to learn the required features, but deep learning has become more intense, and the improvement of Loss to a mature task is getting smaller and smaller. Even so, it does not prevent us from […]
Redis cluster distributed lock master node downtime lock loss problem
Redis series directory redis series – distributed lock redis series – cache penetration, cache breakdown, cache avalanche Redis series – Why is Redis so fast? redis series – data persistence (RDB and AOF) Redis series – consistent hash algorithm Redis series – high availability (master-slave, sentinel, cluster) redis series – things and optimistic locks Redis […]
JS decimal operation precision loss problem
In your work, do you often encounter the calculation of some data indicators, such as percentage conversion, how many decimal places to keep, etc.? Then the calculation will be inaccurate and the data precision will be lost. Through this sharing, the problem of data accuracy loss can be easily solved with the help of third-party […]