udacity pytorch

Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for deep reinforcement learning" Https://github.com/ikostrikov/pytorch-a3c Pytorch ' s version of doom-net implementing some RL models in vizdoom environment. Https://github.com/akolishchak/doom-net-pytorch A3C as described in asynchronous Methods for deep reinforcement learning Https:/

Pytorch is a deep learning library developed by Facebook that aims to be the numpy for integrating GPU acceleration into the deep learning world. The author studies the Re-id field recently has many based on the Pytoch code, follows up. Because it is not easy to program remotely to a workstation, local development is using Windows (the laptop has poor support for Ubuntu), but Pytoch currently has no official support for Windows (Program version 0.4 st

# Because caffe and pytorch are not installed in the system at the same time, a conda in the system should be an isolated Python environment, which is generally unavailable.# Therefore, numpy can only be used as an intermediate medium. The following code is the Caffe network stored in numpy and converts it to pytorch.# I didn't automate the conversion of the prototxt. It's not necessary. I wrote the same

Yesterday (April 25), Facebook launched the Pytorch 0.4.0 version, which has a number of updates and changes, such as support Windows,variable and Tensor merger, etc., please see the article "Pytorch Heavy update." This article is a migration guide that describes some of the code changes you need to make when migrating from a previous version to a new version: Tensors/variables Merge Supports 0-D (scalar)

This article mainly introduced the detailed Pytorch batch training and the optimizer comparison, introduced in detail what is the Pytorch batch training and the Pytorch Optimizer optimizer, very has the practical value, needs the friend to consult under First, Pytorch batch training 1. Overview

TOP1 direct to 96 model:Pytorch framework, network model SE-RESNET50, optimization algorithm AdamPytorch:Pytorch official documentation, each module function has a GitHub source chainLinks to Tutorials http://pytorch.org/tutorials/Connection to the official website http://pytorch.org/Pytorch's GitHub home page Https://github.com/pytorch/pytorchPytorch (an elegant frame) https://www.jianshu.com/p/6b96cb2b414ePytorch[facebook] is a python-first deep lea

Pytorch Chinese document is out (http://pytorch-cn.readthedocs.io/zh/latest/). The first blog dedicated to the Pytorch, mainly to organize their own ideas. The original use of Caffe, always to compile, experienced countless pits. When beginning to contact Pytorch, decisive weeding Caffe. Learning

Pytorch currently supports the platform has Linux and OSX, on the Pytorch website each platform provides Conda, Pip, source three kinds of installation methods, but also can be based on the GPU for CUDA installation, here to ubuntu14.04 for installation learning. 1. Anaconda Installation ConfigurationThe installation process references my previous Anaconda+tensorflow+theano+keras installation blog.Due to w

CNN does not have isothermal variability in rotation, and data augmentation is proposed to solve this problem, but data augmentation requires a large capacity of the model, more iterations can be used to close the rotation and other changes in the training dataset. For the test set, it is not necessarily guaranteed to be the same. You may ask, what are the advantages of network rotation and other changes? What are the advantages of Data augmentation and what are the advantages of network rotatio

When I use Pycharm to run the (https://github.com/Joyce94/cnn-text-classification-pytorch) Pytorch program, multiple processes are opened on the Linux server, Occupy the server's large number of CPUs, run this program on WINDOWS10, the CPU and memory will be eaten up, because in train.py there is a lot of data training processing, will open multiple processes, occupy a large number of CPUs and processes.

This picture has expired, 2018.04.04 version, there is no Trainer and Evaluator class, only one Engine class left Recently I want to write a higher level of abstraction to more convenient training Pytorch network, inadvertently found that pytorch users under a ignite repo, curious to see what this is a thing. The original is Pytorch has provided a high-level abst

When I use Pycharm to run the (https://github.com/Joyce94/cnn-text-classification-pytorch) Pytorch program, multiple processes are opened on the Linux server, Occupy the server's large number of CPUs, run this program on WINDOWS10, the CPU and memory will be eaten up, because in train.py there is a lot of data training processing, will open multiple processes, occupy a large number of CPUs and processes.

Ubuntu16.04 ultra-low graphics card GTX730 configuration pytorch-gpu + cuda9.0 + cudnn tutorial, gtx730cudnnI. Preface Today, I have nothing to do with the configuration of the ultra-low-configuration graphics card GTX730. I think it may be possible to use cuda + cudnn for all the graphics cards. As a result, I checked it on the nvidia official website. It's a pity that I have a large GTX730 ^, so I can use cuda for 730. There are many blog posts abou

Directory Connections(1) Data processing(2) Build and customize the network(3) Test your pictures with a well-trained model(4) Processing of video data(5) Pytorch source code modification to increase the CONVLSTM layer(6) Understanding of gradient reverse transfer (backpropogate)(total) Pytorch encounters fascinating bug Pytorch learning and use (iii) In the prev

First, you need to familiarize yourself with how to use pytorch to implement a feed-forward neural network. To facilitate understanding, we only use a feed-forward neural network with only one hidden layer as an example: The source code and comments of a feed-forward neural network are as follows: This is relatively simple and we will not discuss it here. 1 class Neuralnet (NN. module): 2 def _ init _ (self, input_size, hidden_size, num_classes): 3 su

Install on Windows:Latest 0.4.0 Version:On the Pytorch official website https://pytorch.org/Select the corresponding version of the installation, Conda installation is relatively slow, it is recommended to choose PIP installation (although still very slow), of course, can find a good image is also excellent. Install the CPU version of the Cuda at the selected none.0.3.0 and other previous old versions:Recommended reference https://www.zhihu.com/questi

(Torch.cat (Zeros, Source_x.data), 2)After the dimension is consistent, we can write the code according to our formula:Transformation Gate layer in the formula is TTransformation_layer = f.sigmoid (Information_source)Carry gate layer in the formula is CCarry_layer = 1-transformation_layerFormula Y = H * T + x * CAllow_transformation = Torch.mul (Normal_fc, Transformation_layer)Allow_carry = Torch.mul (Information_source, Carry_layer)Information_flow = Torch.add (allow_transformation, Allow_carr

First, the preface Today there is nothing to configure a bit of ultra-low-matching graphics card GTX730, I think the graphics card may also be able to use CUDA+CUDNN, the results of the NVIDIA official website, sure enough, I GTX730 ^_^, then my 730 can also use Cuda. introduction of the online installation of Cuda+cudnn+pytorch/tensorflow/caffe blog, I wrote this is not to say how good my method, just want to tell you the best way to install CUDA+CU

God, chatter. I have found that the newer Pytorch have instance normalization.You don't have to toss yourself.-2017.5.25 Use NN. The subclass _batchnorm (defined in torch.nn.modules.batchnorm) in Module can achieve normalize of various requirements.In docs, you can see, there are 3 kinds of normalization layer, but in fact they are inherited _batchnorm this class, so we look at batchnorm2d, can be extrapolate to other ways ~ Take a look at the documen

This article mainly introduces about Pytorch + visdom CNN processing self-built image data set method, has a certain reference value, now share to everyone, have the need of friends can refer to Environment System: WIN10 Cpu:i7-6700hq gpu:gtx965m python:3.6 pytorch:0.3 Data download Source from Sasank chilamkurthy tutorial; Data: Download link. Download and then unzip to the project root directory: Data s