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pytorch – SyntaxBug

Pytorch model use, modification, saving and loading

This article introduces the use, modification, saving and loading of Pytorch models. Taking torchvision in image processing as an example, PyTorch provides more pre-trained models through the torchvision.models module. Article directory Use and modification of network models VGG16 model use VGG16 model modification Saving and reading network models Save the model Loading the network model […]

Pytorch installation and configuration in pycharm and jupyter-CPU detailed version

1. Create a virtual environment conda create -n virtual environment name python=3.6 It prompts me that I have already created this virtual environment, select y. After creation, the following screen is displayed Check again whether the creation is successful Enter the following command conda env list From the picture below, you can see that it […]

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 […]

pytorch tensor operations

Table of Contents Creation and basic operations of tensor Initializing a Tensor–Creation method Attributes of a Tensor–Attributes of a tensor Operations on Tensors — Operations on Tensors Official Documentation Creation and basic operations of tensor Initializing a Tensor–creation method Directly from data — Create directly with data Create a tensor directly from data, and the […]

Use pytorch to build ResNet18 network to train CIFAR100 data set

Notes 1. When extracting specific classes for training, you can use the following methods: class_indices = list(range(10, 20)) + list(range(50, 60)) + list(range(80,90)) #Define the category number to be extracted #Extract training and test samples based on category numbers The labels of the #CIFAR-100 data set start from 0, so the category number [10, 19] […]

Understanding LSTM in pytorch

LSTM in pytroch LSTM function in pytorch Detailed explanation of LSTM parameters in pytorch The following are the parameters input_size input node dimension hidden_size number of hidden nodes num_layers number of layers, The shape requirement for input x is x: [seq_length, batch_size, input_size]. To understand these three parameters, you can refer to using “animations” and […]

Solve the problems encountered by UniAD when running under high versions of CUDA and pytorch

UniADhttps://github.com/OpenDriveLab/UniAD is oriented to driving planning set perception (target detection and tracking) and mapping (not mapping the environment like SLAM, but real-time panoramic segmentation of roads and isolation zones in images) It is a unified large model that integrates multi-task modules such as trajectory planning and occupancy prediction. The installation instructions on the official website […]

An overview of NLP syntax analysis: from theory to practical interpretation of PyTorch

This article comprehensively explores the theory and practice of syntactic analysis in natural language processing (NLP). From the definitions of syntax and grammar to various syntactic theories and methods, the article analyzes the multiple dimensions of syntactic analysis in detail. Finally, through practical demonstrations of PyTorch, we show how to apply these theories to specific […]

error_of_linear_regression_pytorch

《Dive-into-DL-Pytorch》-Simple implementation of linear regression (linear regression pytorch) I started learning deep learning on November 3rd. I don’t understand a lot of things. I tried my best to give my own understanding of the code in the book and analyze it. There are two places in the book that will cause errors when running directly. […]