python run.py --datafile data/gridworld_8x8.npz --imsize 8 --lr 0.005 --epochs 30 --k 10 --batch_size 128 网格世界 16×16 python run.py --datafile data/gridworld_16x16.npz --imsize 16 --lr 0.008 --epochs 30 --k 20 -
fromtorch.utils.dataimportDataLoader# 设置批量大小超参数BATCH_SIZE=32# 将数据集转换为可迭代 (batches)train_dataloader=DataLoader(train_data,# dataset to turn into iterablebatch_size=BATCH_SIZE,# 每批有多少样本?shuffle=True# 每个时期都打乱数据)test_dataloader=DataLoader(test_data,batch_size=BATCH_SI...
import os from PIL import Image from torch.utils import data import numpy as np from torchvision import transforms as T class DogCat(data.Dataset): def __init__(self, root, transforms=None, train=True, test=False): ''' 目标:获取所有图片路径,并根据训练、验证、测试划分数据 ''' self.test...
# Initialize BCELoss functioncriterion = nn.BCELoss()# Create batch of latent vectors that we will use to visualize the progression of the generatorfixed_noise = torch.randn(100, nz,1,1, device=device)# print(f'Size of Latent Vector: {fixed_noise.size()}')# Establish convention for rea...
(0.5, 0.5, 0.5)), ])) # Create the dataloader dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=workers) # Decide which device we want to run on device = torch.device("cuda:0" if (torch.cuda.is_available() and ngpu > 0) else "...
DATA_PATH = Path("data") PATH = DATA_PATH /"mnist"PATH.mkdir(parents=True, exist_ok=True) URL ="https://github.com/pytorch/tutorials/raw/main/_static/"FILENAME ="mnist.pkl.gz"ifnot(PATH / FILENAME).exists(): content = requests.get(URL + FILENAME).content ...
How to develop learing system? 基于规则的算法(人工智能程序):依赖于规则,需要非常专业的背景知识来制定规则,构建知识库。如果是很复杂的目标,是很难把规则做通的,肯定会漏一些,导致算法有缺陷。规则只会越来越多,越来越庞大,直到人类无法维护。注意,基于规则的并不是学习系统。最出名的可能是SVM。
Illustration by Author. Two ways to visualize an image with shape (3,4,4) 在这些第一个size之后,以下维度会根据输入类型和任务的不同而变化。在CNN中,最常见的情况是将图像作为输入执行分类任务。所以我们将重点关注: nn.Conv2d(in_channels,out_channels,kernel_size,stride=1,padding=0,dilation=1) ...
BCELoss() # Create batch of latent vectors that we will use to visualize # the progression of the generator fixed_noise = torch.randn(64, nz, 1, 1, device=device) # Establish convention for real and fake labels during training real_label = 1. fake_label = 0. # Setup Adam optimizers...
Because there are 14 variables, it’s not possible to visualize the dataset, but you can get a rough idea of the data from the graph inFigure 2. The graph shows median house price as a function of the percentage of town zoned for industry for the 102 items in the test dataset. ...