pytorch keras

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)

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 learn and use (vi) Multiple networ

Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced. http://blog.csdn.net/zzlyw/article/details/78769012 Preface This article refers to the Pytorch official website of the tutorial, divided into five basic modules to introduce Pytorch. In order to avoid the article too long, these five modules are introduced in five blog post respectively. Part1:

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

), Pytorch, and Advanced library Keras, and so on, generally support CUDA/CUDNN, first choose one to learn it; Moreover, has n card, for example my gtx960m (game this, but does not play the game), the key is the supporting drive driver to upgrade unceasingly, for example follows ubuntu18.04, the latest upgrade to the 390.XX version; N Card Driver driver version aaa.xx (such as 390.xx), it is eq

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

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 (ii) Recently, ju

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:/

Keras Introduction?? Keras is an open-source, high-level neural network API written by pure Python that can be based on TensorFlow, Theano, Mxnet, and CNTK. Keras is born to support rapid experimentation and can quickly turn your idea into a result. The Python version for Keras is: Python 2.7-3.6.??

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

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

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

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.

', linewidths=0.05) Plt.scatter (x, y, color='Green', linewidths=0.2) Plt.axis ([-4, 4,-4, 4]) plt.show ()Set the environment variables Pycharm run line.py:Add the display variable to the list of environment variables with a value of localhost:11.0 (specific value, obtained via the Echo $DISPLAY in the Linux shell after setting the Xshell X11 forwarding rule)Click the Run button to see the effect of drawing in Windows:4. Setting Pycharm using Emacs mode edit: File->settings->keymap->emacs5. Buil