nanxiaoting的博客

简单实现lenet-5：#coding=utf-8import torchfrom torch.autograd import Variableimport torch.nn as nnimport torch.nn.functional as Fclass Net(nn.Module): def __init__(self): super(Net,self)....

#coding=utf-8import torchimport numpy as npimport torch.nn as nnfrom torchvision import datasets, transformsfrom torch.utils import dataimport osfrom PIL import Imageimport torch.nn.functiona...

结构：1.代码：# coding:utf8from models.BasicModule import BasicModulefrom torch import nnfrom torch.nn import functional as Fclass ResidualBlock(nn.Module): """ 实现子module: Residual Bloc...

1.  安装visdom：pip install visdom2.在shell下，输入： python -m visdom.server，则开启web服务。出现：在浏览器输入：http://localhost:8097   ，即跳出界面。3.在python下输入程序：记得运行import torchimport visdomvis = visdom.Visdom(en...

没加cuda()前，运行时间为55s。加了cuda（）后，运行时间为6s。#coding=utf-8import torchimport torch.nn as nnimport torch.utils.data as Dataimport torchvision # 数据库模块from torch.autograd import Variableimport time...

#coding=utf-8import torchimport torch.nn as nnfrom torch.autograd import Variablem = nn.LeakyReLU(0.1)input = Variable(torch.randn(2))print(input)print '---' * 10print(m(input))print '--' *...

自动微分pytorch所有网络核心是autograde自动求导包。autograde.Variable是包的核心类，它包装了张量，并且几乎支持所有的操作。可以调用.backward()方法，然后所有的梯度计算会自动完成。注意的是，通过.data属性来访问原始的张量。引用：{Variable 和 Function 是相互联系的, 并且它们构建了一个非循环的图, 编码了一个完整的计算历史信息. 每一个...

1.torch与numpy的比较及二者之间的转换。import torchimport numpy as npnumpy_data = np.arange(15).reshape(3,5)print 'numpy_data',numpy_datatorch_data = torch.from_numpy(numpy_data)print 'torch_data',torch_data...

#coding=utf-8import torchfrom torch import nnimport numpy as npimport matplotlib.pyplot as pltfrom torch.autograd import Variabletorch.manual_seed(1) # reproducible# Hyper ParametersTIME...

#coding=utf-8import torchimport torch.nn as nnimport torch.utils.data as Dataimport torchvision # 数据库模块from torch.autograd import Variabletorch.manual_seed(1) # reproducible# Hyper P...

torch.max用法：#coding=utf-8import torchfrom torch.autograd import Variable#返回输入tensor中所有元素的最大值a = torch.randn(1, 3)print aprint torch.max(a)print '--' * 10#按维度dim 返回最大值 torch.max)(a,0) 返回每...

以mnist为数据集：若原来没有数据集，把DOWNLOAD_MNIST=True。#coding=utf-8import torchimport torch.nn as nnimport torch.utils.data as Dataimport torchvision # 数据库模块import matplotlib.pyplot as pltfrom torch....

a = torch.Tensor(2,3)print a#表示方法a[0]:表示第0行a[:,0]:表示第0列a[0][2]:表示第0行第2个元素，等价于a[0,2]a[0,-1]:第0行，最后一个元素a[:2]:表示前两行a[:2,0:2]:前两行，第0,1列a[0:1,:2]:第0行，前两列。与a[0,:2]形状不同a[torch.LongTensor([0,1])]:...

分类：#coding=utf-8import torchimport matplotlib.pyplot as pltfrom torch.autograd import Variableimport torch.nn.functional as Fimport torch.nn as nnn_data = torch.ones(100,2)x0 = torch.normal...

pytorch 版本 0.2.0#coding=utf-8from matplotlib import pylab as pltimport numpy as npimport randomimport torch.nn as nnimport torchfrom torch.autograd import Variablenum_inputs = 2num_example...