py torch 图像识别_py图像识别_AttributeError:模块'torch._six‘没有特性'PY3’ - 腾讯云开发者社区

cifar10.py文件包含以下函数，用于搭建模型 def _activation_summary(x): def _variable_on_cpu(name, shape, initializer):...tf.cast(labels, tf.float16) return images, labels distorted_inputs ( ) 和inputs( ) 在cifar10_input.py

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图像识别（二） cifar10_input.py详解

tf.name_scope的用法：https://blog.csdn.net/uestc_c2_403/article/details/72328815 【TensorFlow代码笔记】Cifar10_input.py

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torch.eq、torch.ne、torch.gt、torch.lt、torch.ge、torch.le

PyTorch - torch.eq、torch.ne、torch.gt、torch.lt、torch.ge、torch.leflyfishtorch.eq、torch.ne、torch.gt、torch.lt...、torch.ge、torch.le 以上全是简写参数是input, other, out=None 逐元素比较input和other 返回是torch.BoolTensor?...import torcha=torch.tensor([[1, 2], [3, 4]])b=torch.tensor([[1, 2], [4, 3]])print(torch.eq(a,b))#equals...# tensor([[ True, True],# [False, False]])print(torch.ne(a,b))#not equal to# tensor([[False,...(torch.ge(a,b))#greater than or equal to# tensor([[ True, True],# [False, True]])print(torch.le

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安装torch_sparse、torch_cluster、torch_scatter、torch_spline

# conda create -n py39 python=3.9 # conda activate py39 pip3 install torch==1.21.1 torchvision==0.13.1...然后执行安装： wget https://data.pyg.org/whl/torch-1.12.0%2Bcu116/torch_sparse-0.6.15-cp39-cp39-linux_x86_64....whl wget https://data.pyg.org/whl/torch-1.12.0%2Bcu116/torch_scatter-2.0.9-cp39-cp39-linux_x86_64.whl...wget https://data.pyg.org/whl/torch-1.12.0%2Bcu116/torch_cluster-1.6.0-cp39-cp39-linux_x86_64.whl wget...https://data.pyg.org/whl/torch-1.12.0%2Bcu116/torch_spline_conv-1.2.1-cp39-cp39-linux_x86_64.whl pip

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torch.randn()、torch.mean()、torch.pow()、torch.matmul()

torch.randn()产生大小为指定的，正态分布的采样点，数据类型是tensortorch.mean()torch.mean(input) 输出input 各个元素的的均值，不指定任何参数就是所有元素的算术平均值...，指定参数可以计算每一行或者每一列的算术平均数例如：a=torch.randn(3) #生成一个一维的矩阵b=torch.randn(1,3) #生成一个二维的矩阵print(a)print(b)torch.mean...(4,4)print(a)c=torch.mean(a,dim=0,keepdim=True)print(c)d=torch.mean(a,dim=1,keepdim=True)print(d)结果：tensor...torch.pow()对输入的每分量求幂次运算a=torch.tensor(3)b=torch.pow(a,2)print(b)c=torch.randn(4)print(c)d=torch.pow(c...()torch.matmul 是做矩阵乘法例如：a=torch.tensor([1,2,3])b=torch.tensor([3,4,5])torch.matmul(a, b)结果：tensor(26)

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torch01：torch基础

MachineLP的Github（欢迎follow）：https://github.com/MachineLP MachineLP的博客目录：小鹏的博客目录本小节介绍torch的基础操作和流程：（1...代码部分：（0）import # coding=utf-8 import torch import torchvision import torch.nn as nn import numpy as...np import torchvision.transforms as transforms print (torch....__version__) （1）计算梯度值 # 创建tensor x = torch.tensor(1, requires_grad=True) w = torch.tensor(2, requires_grad...# 保存和加载模型 torch.save(resnet, 'model.ckpt') model = torch.load('model.ckpt') # 只保存和加载模型参数 torch.save(

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Torch卷积

1. 2D 卷积 class torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=...array} Wout=floor((Win+2×padding[1]−dilation[1]×(kernerl_size[1]−1)−1)/stride[1]+1) 2. 2D 反卷积 class torch.nn.ConvTranspose2d

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Torch函数

torch.clamp(input, min, max, out=None) → Tensor 将 input 张量中的每个元素值截断到区间 [min, max] 中。...torch.flip(input, dims) → Tensor 将 input 张量沿着列表/元组 dims 中的每一个维度依次翻转。...torch.Tensor.contiguous(memory_format=torch.contiguous_format) → Tensor 返回一个内存连续且有相同数据的 Tensor，如果原 Tensor...torch.Tensor.permute(*dims) → Tensor 根据 dims 给定的维度顺序对张量进行维度换位。...torch.Tensor.transpose(dim0, dim1) → Tensor 对 dim0 和 dim1 两个维度换位。

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py

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pytorch中torch.isnan()和torch.isfinite()

1.torch.isfinite()import torchnum = torch.tensor(1) # 数字1res = torch.isfinite(num)print(res)'''输出：tensor...(True)'''这个num必须是tensorimport torchnum = torch.tensor(float('inf')) # 正无穷大res = torch.isfinite(num)print...(res)'''输出：tensor(False)'''import torchnum = torch.tensor(float('-inf')) # 负无穷大res = torch.isfinite(num...)print(res)'''输出：tensor(False)'''import torchnum = torch.tensor(float('nan')) # 空res = torch.isfinite...(num)print(res)'''输出：tensor(False)'''2.torch.isnan()import torchres=torch.isnan(torch.tensor([1, float

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torch.no_grad

目录class torch.no_grad[source]----class torch.no_grad[source]不能进行梯度计算的上下文管理器。...例：>>> x = torch.tensor([1], requires_grad=True)>>> with torch.no_grad():......y = x * 2>>> y.requires_gradFalse>>> @torch.no_grad()... def doubler(x):...

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torch.shortage

torch.Storage是单个数据类型的一维数组。每个torch.Tensor都有一个对应的相同数据类型的存储。...class torch.FloatStorage[source]bfloat16()将这个存储变为bfloat16类型。

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torch.flatten()

先看函数参数：torch.flatten(input, start_dim=0, end_dim=-1)input: 一个 tensor，即要被“推平”的 tensor。...我们要先来看一下 tensor 中的 shape 是怎么样的：t = torch.tensor([[[1, 2, 2, 1], [3, 4, 4, 3],...4, 4, 3], [1, 2, 3, 4]], [[5, 6, 6, 5], [7, 8, 8, 7], [5, 6, 7, 8]]])torch.Size...示例代码：x = torch.flatten(t, start_dim=1)print(x, x.shape)y = torch.flatten(t, start_dim=0, end_dim=1)print...pytorch中的 torch.nn.Flatten 类和 torch.Tensor.flatten 方法其实都是基于上面的 torch.flatten 函数实现的。

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torch.split()

切分维度 import torch a = torch.arange(10).reshape(5,2) print(a.shape) aa = torch.split(a, 2) print(aa...) b,c,d = torch.split(a, 2) print(b.shape,c.shape,d.shape) print('*' * 30) e,f = torch.split(a, [1,4]...[0, 1], [2, 3]]), tensor([[4, 5], [6, 7]]), tensor([[8, 9]])) torch.Size([2, 2]) torch.Size...([2, 2]) torch.Size([1, 2]) ****************************** torch.Size([1, 2]) torch.Size([4, 2]) tensor..., unsure.shape) Result: torch.Size([1, 3, 512, 512]) torch.Size([1, 1, 512, 512]) torch.Size([1, 1, 512

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torch.clamp

torch.clamp(input, min, max, out=None) → Tensor Clamp all elements in input into the range [ min, max...(4) >>> a tensor([-1.7120, 0.1734, -0.0478, -0.0922]) >>> torch.clamp(a, min=-0.5, max=0.5) tensor([...-0.5000, 0.1734, -0.0478, -0.0922]) torch.clamp(input, *, min, out=None) → Tensor Clamps all elements...(4) >>> a tensor([-0.0299, -2.3184, 2.1593, -0.8883]) >>> torch.clamp(a, min=0.5) tensor([ 0.5000,...0.5000, 2.1593, 0.5000]) torch.clamp(input, *, max, out=None) → Tensor Clamps all elements in input

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torch.log

torch.log(input, out=None) → Tensor Returns a new tensor with the natural logarithm of the elements of...Example: >>> a = torch.randn(5) >>> a tensor([-0.7168, -0.5471, -0.8933, -1.4428, -0.1190]) >>> torch.log

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torch.as_tensor()

torch.as_tensor(data, dtype=None,device=None)->Tensor : 为data生成tensor。...import torchimport numpya = numpy.array([1, 2, 3])t = torch.as_tensor(a)

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torch.unbind

参数：tensor(Tensor) -- 输入张量dim(int) -- 删除的维度>>> x = torch.randn(3, 3)>>> xtensor([[ 0.4775, 0.0161, -0.9403...], [ 1.6109, 2.1144, 1.1833], [-0.2656, 0.7772, 0.5989]])>>> torch.unbind

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torch.unique()

torch.unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None)?...>>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long))>>> outputtensor([ 2, 3, 1])...>>> output, inverse_indices = torch.unique( torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted...outputtensor([ 1, 2, 3])>>> inverse_indicestensor([ 0, 2, 1, 2])>>> output, inverse_indices = torch.unique...( torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True)>>> outputtensor

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torch.mean()

torch.mean(input) 输出input 各个元素的的均值，不指定任何参数就是所有元素的算术平均值，指定参数可以计算每一行或者每一列的算术平均数a = torch.randn(1, 3)tensor...([[ 0.2294, -0.5481, 1.3288]])torch.mean(a)tensor(0.3367)

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图像识别（三）cifar10.py

图像识别（二） cifar10_input.py详解

torch.eq、torch.ne、torch.gt、torch.lt、torch.ge、torch.le

安装torch_sparse、torch_cluster、torch_scatter、torch_spline

torch.randn()、torch.mean()、torch.pow()、torch.matmul()

torch01：torch基础

Torch卷积

Torch函数

py

pytorch中torch.isnan()和torch.isfinite()

torch.no_grad

torch.shortage

torch.flatten()

torch.split()

torch.clamp

torch.log

torch.as_tensor()

torch.unbind

torch.unique()

torch.mean()

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