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Learning Summary (1) Each optimizer is a class and must be instantiated before it can be used, for example: class Net(nn.Moddule): ··· net = Net() optim = torch.optim.SGD(net.parameters(), lr=lr) optim.step() (2) optimizer implements the following two steps in epoch of a neural network: Gradient zero, gradient update. optimizer = torch ...

BILSTM-CRF code Code from Named entity recognition (NER): introduction to the principle of BiLSTM-CRF + pytoch_ Tutorial code parsing Part I: Guide Package 1.torch.nn package mainly contains Modules used to build each layer, such as full connection, two-dimensional convolution, pooling, etc; The torch.nn package also contains a series of usefu ...

Official explanation: Dataloader combines dataset & sampler to provide iterable data Main parameters: 1. Dataset: this dataset must be torch.utils.data.Dataset itself or a class inherited from it The main method is  __ getitem__(self, index) used to retrieve data according to the index index 2,batch_size: how many pieces of data sh ...

Recently, I learned the MLP, CNN and RNN network models based on pytoch framework, and conducted text classification experiments using the commodity comment data obtained on GitHub. This paper introduces how to establish MLP under the pytoch framework to classify the data. The data sets are roughly as follows: 1. Import module import pandas ...

preface Using Baidu's open source paddleX tool, we can easily and quickly train a deep network model of target detection, image classification, instance segmentation and semantic segmentation using our own labeled data. This paper mainly records how to use pddleX to train a simple ppyolo for detecting cats and dogs in the whole process_ Tiny m ...

This article is a supplement to the knowledge of pytorch before training Transformer. Thank blogger Mo fan for his video course on Python https://www.youtube.com/watch?v=lAaCeiqE6CE&feature=emb_title , whose home page is: Don't bother Python It is recommended to directly watch the blogger's video tutorial to complete the knowledge suppleme ...

1. Gradually construct convolution network The basic network architecture built this time: Note: for each forward propagation operation, there will be corresponding backward propagation. The parameters of forward propagation will be stored, and these parameters will be used to calculate the gradient in the backward propagation process. 2. Conv ...

1 Introduction Our common target detection is rectangular box detection, but sometimes the box we detect is an irregular quadrilateral. The following figure is a common remote sensing image. At this time, the way we use rectangular box to calculate IOU is no longer applicable. We can abstract the problem. Is there a way to calculate two irregu ...

preface Learning objectives Learn about news topic classification and relevant data Master the implementation process of constructing news topic classifier using shallow network About news topic classification tasks: Taking the text description content of a news report as the input, the model is used to help us judge which type of n ...

  Author home page( Silicon based workshop of slow fire rock sugar): Slow fire rock sugar (Wang Wenbing) blog silicon based workshop of slow fire rock sugar _csdnblog Website of this article: https://blog.csdn.net/HiWangWenBing/article/details/120600611 catalogue Introduction deep learning model framework Chapter 1 business area analy ...