anshidashen的博客

import matplotlib.pyplot as plt import torchfrom torch import nnfrom torch.autograd import Variableimport numpy as npimport visdomviz = visdom.Visdom(env='train')loss_win = viz.line(np.arange...

import torchimport numpy as npfrom torch import nnfrom torch.autograd import Variableimport visdomimport matplotlib.pyplot as pltimport randomviz = visdom.Visdom(env='train')loss_win = viz.l...

import numpy as npimport torchimport matplotlib.pyplot as pltfrom torch import nnfrom torch.nn import Linearfrom torch.autograd import Variablex_train = np.array([[3.3],[4.4],[5.5],[6.71],[6.9...

首先使用 python -m visdom.server激活visdom环境，再运行以下代码。import torchimport torch.nn as nnimport torchvision.datasets as normal_datasetsimport torchvision.transforms as transformsfrom torch.autograd impo...

RuntimeError: output with shape [1, 28, 28] doesn't match the broadcast shape [3, 28, 28]将transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])改为：transforms.Normalize([0.5],[0.5])...

下面通过三个实例来看看如何使用已经掌握的知识，搭建出一个基于PyTorch架构的简易神经网络模型。一、简易神经网络1.导入包import torch batch_n = 100hidden_layer = 100input_data = 1000output_data = 10我们先通过import torch 导入必要的包，然后定义4个整型变量，其中：batch_n是...

import matplotlib.pyplot as pltimport skimage.io as ioimport cv2from PIL import Imageimport numpy as npimport torch#使用Skimage读取图像# skimage.io imread()-----np.ndarray, (H x W x C), [0, 255],R...

#在深度学习中经常需要生成带标签的图片名称列表，xxxlist.txt文件，#编写脚本语言，实现对文件中图片生成带标签的txt文件方法import os def generate(dir,label): files = os.listdir(dir) files.sort() print("*****************") print("input =...

#Tensor的创建及常用方法from __future__ import print_functionimport torch as timport numpy as np#创建一个3*3全1的矩阵x1 = t.ones(3,3)print(x1)#5*5全0矩阵x2 = t.zeros(5,5)print(x2)#与x1同维度的0矩阵x3 = t.zeros_l...