import torch import torch.nn as nn import torch.nn.functional as F import torchvision #This contains popular datasets, model architectures, and common image transformations for computer vision. import torchvision.transforms as transforms ******************** BATCH_SIZE = 32 transform = transforms.Compose([transforms.ToTensor()]) ## download and load training dataset trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,shuffle=True, num_workers=2) ## download and load testing dataset ## **** WRITE CODE TO DOWNLOAD AND LOAD TESTING DATA ***
import torch import torch.nn as nn import torch.nn.functional as F import torchvision #This contains popular datasets, model architectures, and common image transformations for computer vision. import torchvision.transforms as transforms ******************** BATCH_SIZE = 32 transform = transforms.Compose([transforms.ToTensor()]) ## download and load training dataset trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,shuffle=True, num_workers=2) ## download and load testing dataset ## **** WRITE CODE TO DOWNLOAD AND LOAD TESTING DATA ***
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
Related questions
Question
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision #This contains popular datasets, model architectures, and common image transformations for computer vision.
import torchvision.transforms as transforms
********************
BATCH_SIZE = 32
transform = transforms.Compose([transforms.ToTensor()])
## download and load training dataset
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,shuffle=True, num_workers=2)
## download and load testing dataset
## **** WRITE CODE TO DOWNLOAD AND LOAD TESTING DATA ***
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