Tag:Data set

  • Python algorithm learning hyperbolic embedding paper code implementation data set introduction

    Time:2022-6-13

    catalogue 1. objectives Python code dependency Library 2. dataset Data presentation Articles learned: Poincaré Embeddings for Learning Hierarchical Representations Primary reference code: poincare_embeddings gensim – Topic Modelling in Python – poincare.py Because some codes are difficult to run, some are difficult to read (with a very high degree of encapsulation), and some codes are even […]

  • Teach you how to implement the MNIST dataset of pytorch

    Time:2022-4-25

    catalogue summary get data network model Train function Test function Main function Full code: summary MNIST contains handwritten digits from 0 to 9, with 60000 training sets and 10000 test sets The data format is a single channel 28 * 28 gray image get data def get_data(): “” “get data” “” #Get test set train […]

  • Pytorch neural network — a tutorial on user defined data set

    Time:2021-7-5

    The first step is to import the required package import os import scipy.io as sio import numpy as np import torch import torch.nn as nn import torch.backends.cudnn as cudnn import torch.optim as optim from torch.utils.data import Dataset, DataLoader from torchvision import transforms, utils from torch.autograd import Variable Batchsize = 128 # the size of batchsize […]

  • How to import large data sets (large pictures) in Python

    Time:2021-5-11

    When using the torch.utils.data.dataset class to process image data, 1. We need to define three basic functions. The following is the basic process class our_datasets(Data.Dataset): def __init__(self,root,is_resize=False,is_transfrom=False): #This is just a reference. Write according to your own needs. self.root=root self.is_resize=is_resize self.is_transfrom=is_transfrom self.imgs_ List =… # it is suggested to save the path of the image […]

  • Pytorch: a simple Gan example (MNIST dataset)

    Time:2021-4-6

    I don’t want to talk much nonsense. Let’s go straight to the code! # -*- coding: utf-8 -*- “”” Created on Sat Oct 13 10:22:45 2018 @author: www “”” import torch from torch import nn from torch.autograd import Variable import torchvision.transforms as tfs from torch.utils.data import DataLoader, sampler from torchvision.datasets import MNIST import numpy as […]

  • Python uses MNIST data set to implement cgan and generate specified digital mode

    Time:2021-3-31

    Cgan is conditional generative adverse networks, which can generate countermeasure network conditionally. Based on the initial Gan, the corresponding information of the picture is added. Cgan is implemented by traditional convolution. import torch from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision import transforms from torch import optim import torch.nn as nn import matplotlib.pyplot […]

  • Python uses MNIST data set to realize the detailed explanation of basic Gan and dcgan

    Time:2021-3-30

    Generative adverse networks Gan includes generator and discriminator. The data includes real data, groundtruth, and “fake” data generated by network. The purpose is that the fake data generated by network can “cheat” the discriminator, so that the discriminator can’t recognize it. That is to say, the discriminator can’t distinguish the real data from fake data. […]

  • Detailed explanation on preprocessing of MNIST dataset of Python

    Time:2021-3-25

    Detailed explanation on preprocessing of MNIST dataset of Python The accuracy of MNIST is 99.7% The implementation of convolutional neural network (CNN) for MNIST has various technologies, such as data enhancement, loss, pseudo randomization, etc. Operating system: Ubuntu 18.04 Graphics card: gtx1080ti Python version: 2.7 (3.7) Network architecture CNN with four layers has the following […]

  • Using Python to classify cifar-10 data sets

    Time:2020-12-24

    The steps are as follows: 1. Using torch vision to load and preprocess cifar-10 data set 2. Define the network 3. Define loss function and optimizer 4. Train the network and update the network parameters 5. Test the network Operating environment: windows+python3.6.3+pycharm+pytorch0.3.0 import torchvision as tv import torchvision.transforms as transforms import torch as t from […]

  • Examples of calculating the mean and variance of the dataset required for Python normalization

    Time:2020-11-8

    The standardized use of Python transforms.Normalize (mean_ vals, std_ The mean variance of commonly used data sets is as follows: if ‘coco’ in args.dataset: mean_vals = [0.471, 0.448, 0.408] std_vals = [0.234, 0.239, 0.242] elif ‘imagenet’ in args.dataset: mean_vals = [0.485, 0.456, 0.406] std_vals = [0.229, 0.224, 0.225] Calculate the mean variance of image pixels […]

  • Tp5.1 framework database dataset operation example analysis

    Time:2020-9-20

    This paper describes the tp5.1 framework database dataset operation. For your reference, the details are as follows: The query result of the database is the dataset. By default, the type of the dataset is a two-dimensional array. We can configure it as a dataset class to support more object-oriented operations on the dataset. We need […]

  • Pytoch loads a single channel image by itself as an example of dataset training

    Time:2020-8-6

    There are many packaged datasets in the torch vision package, such as Minist, imagenet-12, cifar10 and cifar100. In the dataset package of torchvision, you can call it directly when you use it. The specific call format can be seen in the document (at present, it seems that only English). There are also a lot of […]