vgg里面的 ReLU默认的参数inplace=True 当我们调用vgg结构的时候注意 要将inplace改成 False 不然会报错 RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation…

问题 在用pytorch跑生成对抗网络的时候,出现错误Runtime Error: one of the variables needed for gradient computation has been modified by an inplace operation,特记录排坑记录. 环境 windows10 2004 python 3.7.4 pytorch 1.7.0 + cpu 解决过程 尝试一 这段错误代码看上去不难理解,意思为:计算梯度所需的某变量已被一就地操作修改.什么是就地操…

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [3, 1280, 28, 28]], which is output 0 of LeakyReluBackward1, is at version 2; expected version 1 instead. Hint: enab…

maxpooling 的 max 函数关于某变量的偏导也是分段的,关于它就是 1,不关于它就是 0: BP 是反向传播求关于参数的偏导,SGD 则是梯度更新,是优化算法: 1. 一个实例 relu = tf.nn.relu(tf.matmul(x, W) + b) C = [...] [db, dW, dx] = tf.gradient(C, [b, w, x])…

参考:https://pytorch-cn.readthedocs.io/zh/latest/package_references/torch-autograd/#detachsource 当我们再训练网络的时候可能希望保持一部分的网络参数不变,只对其中一部分的参数进行调整:或者值训练部分分支网络,并不让其梯度对主网络的梯度造成影响,这时候我们就需要使用detach()函数来切断一些分支的反向传播 1   detach()[source] 返回一个新的Variable,从当前计算图中分离下来的,…

1.torch.nn.state_dict(): 返回一个字典,保存着module的所有状态(state). parameters和persistent_buffers都会包含在字典中,字典的key就是parameter和buffer的names. 例子: import torch from torch.autograd import Variable import torch.nn as nn class Model(nn.Module): def __init__(self): super(…

声明:所有内容来自coursera,作为个人学习笔记记录在这里. Gradient Checking Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking. You are part of a team working to make mobile payments available globally, and…

Gradient Checking Welcome to this week's third programming assignment! You will be implementing gradient checking to make sure that your backpropagation implementation is correct. By completing this assignment you will: - Implement gradient checking…

It's important to note, especially if you have come to JavaScript from another language, that variables in JavaScript are not defined in a block scope, but in a function scope. This means that if a variable is defined inside a function, it's not visi…

所需文件:本地下载 Gradient Checking Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking. You are part of a team working to make mobile payments available globally, and are asked to build a d…

整理一下我个人觉得比较好的HOG博文 博文1:OpenCV HOGDescriptor: 参数与图解 http://blog.csdn.NET/raodotcong/article/details/6239431 博文2:opencv源码解析:各个参数讲解 http://www.cnblogs.com/tornadomeet/archive/2012/08/15/2640754.html 博文3:hog特征可视化:matlab 与 C++ http://blog.csdn.Net/u011285…

最近在做的项目有用到HOG+SVM这一方面的知识,参考相关论文和网上一些博文在此对HOG特征进行下总结. 参考资料: HOG的经典论文:Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. IEEE, 2005,…

Policy Gradient Algorithms 2019-10-02 17:37:47 This blog is from: https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html Abstract: In this post, we are going to look deep into policy gradient, why it works, and many new polic…

[源码解析] PyTorch 分布式(9) ----- DistributedDataParallel 之初始化 目录 [源码解析] PyTorch 分布式(9) ----- DistributedDataParallel 之初始化 0x00 摘要 0x01 综述 1.1 数据并行 1.2 DDP架构 1.2.1 分布式数据并行 1.2.2 进程 1.3 DDP 总体实现 0x02 初始化 2.1 __init__ 2.2 构建参数 2.2.1 _build_params_for_reducer…

Gradient Descent 机器学习中很多模型的参数估计都要用到优化算法,梯度下降是其中最简单也用得最多的优化算法之一.梯度下降(Gradient Descent)[3]也被称之为最快梯度(Steepest Descent),可用于寻找函数的局部最小值.梯度下降的思路为,函数值在梯度反方向下降是最快的,只要沿着函数的梯度反方向移动足够小的距离到一个新的点,那么函数值必定是非递增的,如图1所示. 梯度下降思想的数学表述如下: b=a−α∇F(a)⇒f(a)≥f(b)(1)(1)b=a−α∇F…

Gradient Descent 机器学习中很多模型的参数估计都要用到优化算法,梯度下降是其中最简单也用得最多的优化算法之一.梯度下降(Gradient Descent)[3]也被称之为最快梯度(Steepest Descent),可用于寻找函数的局部最小值.梯度下降的思路为,函数值在梯度反方向下降是最快的,只要沿着函数的梯度反方向移动足够小的距离到一个新的点,那么函数值必定是非递增的,如图1所示. 梯度下降思想的数学表述如下: \begin{equation} b=a-\alpha \nabl…

一. Batch Normalization 对于深度神经网络,训练起来有时很难拟合,可以使用更先进的优化算法,例如:SGD+momentum.RMSProp.Adam等算法.另一种策略则是高改变网络的结构,使其更加容易训练.Batch Normalization就是这个思想. 为什么要做Normalization? 神经网络学习过程本质就是为了学习数据分布,一旦训练数据与测试数据的分布不同,那么网络的泛化能力也大大降低:另外一方面,一旦每批训练数据的分布各不相同(batch梯度下降),那么网络…

# -*- coding: utf-8 -*- """requests.session~~~~~~~~~~~~~~~~ This module provides a Session object to manage and persist settings acrossrequests (cookies, auth, proxies)."""import osfrom collections import Mappingfrom datetime…

Tensorflow Welcome to the Tensorflow Tutorial! In this notebook you will learn all the basics of Tensorflow. You will implement useful functions and draw the parallel with what you did using Numpy. You will understand what Tensors and operations are,…

Tensorflow一些常用基本概念与函数(一) 1.tensorflow的基本运作 为了快速的熟悉TensorFlow编程,下面从一段简单的代码开始: import tensorflow as tf #定义‘符号’变量,也称为占位符 a = tf.placeholder("float") b = tf.placeholder("float") y = tf.mul(a, b) #构造一个op节点 sess = tf.Session()#建立会话 #运行会话,输入数…

摘要:本系列主要对tf的一些常用概念与方法进行描述.本文主要针对tensorflow的模型训练Training与测试Testing等相关函数进行讲解.为‘Tensorflow一些常用基本概念与函数’系列之四. 1.序言 本文所讲的内容主要为以下列表中相关函数.函数training()通过梯度下降法为最小化损失函数增加了相关的优化操作,在训练过程中,先实例化一个优化函数,比如 tf.train.GradientDescentOptimizer,并基于一定的学习率进行梯度优化训练: optimize…

对原文有大量修改,如有疑惑,请移步原文. 参考链接:MXNet设计和实现简介 文章翻译于:https://mxnet.incubator.apache.org/architecture/index.html 一.MXNet System Architecture系统概览 This figure shows the major modules and components of the MXNet system and their interaction. The modules are: Run…

About this Course If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "s…

# Chinese translations for R package # Copyright (C) 2005 The R Foundation # This file is distributed under the same license as the PACKAGE package. # 陈斐 <feic@normipaiva.com>, 2006. # 邓小冬 DENG Xiaodong <xd_deng@hotmail.com>, 2015. # msgid &qu…

一.计算图简介 在pytorch的官网上,可以看到一个简单的计算图示意图, 如下. import torchfrom torch.autograd import Variable x = Variable(torch.randn(1, 10)) prev_h = Variable(torch.randn(1, 20)) W_h = Variable(torch.randn(20, 20)) W_x = Variable(torch.randn(20, 10)) i2h = torch.mm(W_…

[源码解析] PyTorch 分布式(7) ----- DistributedDataParallel 之进程组 目录 [源码解析] PyTorch 分布式(7) ----- DistributedDataParallel 之进程组 0x00 摘要 0x01 回顾 1.1 基础概念 1.2 初始化进程组 0x02 概念与设计 2.1 功能 2.2 本质 0x03 使用 0x04 构建 4.1 Python 世界 4.1.1 rendezvous 4.1.2 _new_process_group_…

[源码解析] PyTorch 分布式 Autograd (5) ---- 引擎(上) 目录 [源码解析] PyTorch 分布式 Autograd (5) ---- 引擎(上) 0x00 摘要 0x01 支撑系统 1.1 引擎入口 1.2 SendRpcBackward 1.2.1 剖析 1.2.2 定义 1.2.3 构建 1.2.4 grads_ 0x02 定义 2.1 定义 2.2 单例 2.3 重要注释 2.3.1 成员变量 2.3.2 构建 2.3.3 GPU to CPU contin…

前言: CNN作为DL中最成功的模型之一,有必要对其更进一步研究它.虽然在前面的博文Stacked CNN简单介绍中有大概介绍过CNN的使用,不过那是有个前提的:CNN中的参数必须已提前学习好.而本文的主要目的是介绍CNN参数在使用bp算法时该怎么训练,毕竟CNN中有卷积层和下采样层,虽然和MLP的bp算法本质上相同,但形式上还是有些区别的,很显然在完成CNN反向传播前了解bp算法是必须的.本文的实验部分是参考斯坦福UFLDL新教程UFLDL:Exercise: Convolutional Ne…

Background C++ is one of the main development languages used by many of Google's open-source projects. As every C++ programmer knows, the language has many powerful features, but this power brings with it complexity, which in turn can make code more…

理论知识:Optimization: Stochastic Gradient Descent和Convolutional Neural Network CNN卷积神经网络推导和实现.Deep learning:五十一(CNN的反向求导及练习) Deep Learning 学习随记(八)CNN(Convolutional neural network)理解 ufldl学习笔记与编程作业:Convolutional Neural Network(卷积神经网络) [UFLDL]Exercise: Co…