udacity pytorch

Awesome-repositories-for-nli-and-semantic-similarityMainly record Pytorch implementations for NLI and similarity computing REPOSITORY REFERENCE Baidu/simnet SEVERAL Ntsc-community/awaresome-neural-models-for-semantic-match SEVERAL Lanwuwei/spm_toolkit:? ①decatt? ②esim? ③pwim? ④sse Neural Network Models For paraphrase identification, Semantic textual similarity, Natural

achieve, there are Google big guy Plus; Mxnet occupy a small memory, fast, very dapper, has a natural source of open-source genes, entirely by the community-driven framework; Caffe2 is a framework for industrial applications, but later, and the main Python2 (execuse me? 2017 years of the main Python2. And I can't help it. The user experience is not very friendly from the point of view of the installation deployment; Pytorch is a Facebook-oriented fra

Current Computer Configuration: Ubuntu 16.04 + GTX1080 GraphicsConfiguring a deep learning environment, using Tsinghua Source to install a Miniconda environment is a very good choice. In particular, today found Conda install-c Menpo opencv3 A command can be smoothly installed on the OPENCV, before their own time also encountered a lot of errors. Conda installation of the TensorFlow and pytorch two kinds of framework is also very convenient, for not go

CMake automatically looks for v140 (VS2015) compiler on Windows, and now only VS2013 IDE, so to modify the compilerModify the compiler name of VS2015, error prompt parameter Cmake_c_compiler and cmake_cxx_compiler parameter corresponding address cannot findThese two variables are explicitly set in CMakeLists.txt, pointing to the path of the VS2013 compiler, which can be compiledHowever, after the VS2015 compiler file name is changed back, the changes in the CMakeLists.txt are useless and will be

First spit groove, deep learning development speed is really fast, deep learning framework is gradually iterative, it is really hard for me to engage in deep learning programmer. I began three years ago to learn deep learning, these deep learning frameworks are also a change, from Keras, Theano, Caffe, Darknet, TensorFlow, and finally now to start using Pytorch. I. Variable, derivative Torch.autograd module When the default variable is defined, Requir

Mseloss loss function is called in Chinese. The formula is as follows: Here, the loss, X, and y dimensions are the same. They can be vectors or matrices, and I is a subscript. Many loss functions have two Boolean parameters: size_average and reduce. Generally, the loss function directly calculates the batch data. Therefore, the returned loss result is a vector with the dimension (batch_size. The general format is as follows: loss_fn = torch.nn.MSELoss(reduce=True, size_average=True) Note the fo

http://pytorch.org/ Conda Conda Installpytorch torchvision-c soumith cuda8.0 conda Install Pytorch torchvision cuda80-c soumith pip cuda7.5 python2.7 pip Install HTTP://DOWNLOAD.PYTORCH.ORG/WHL/CU75/TORCH-0.1.11.POST5-CP27-NONE-LINUX_X86_64.WHL pip install Torchvision python3.5 pip install http://download.pytorch.org/whl/cu75/ TORCH-0.1.11.POST5-CP35-CP35M-LINUX_X86_64.WHL pip Install torchvision python3.6Pip Install HTTP://DOWNLO

Most of the Pytorch introductory tutorials are trained and tested using the data in the torchvision. If we are our own picture data, how to do it. one, my data When I was studying, I used the fashion-mnist. This data is relatively small, my computer does not have the GPU, but also can feel all. About Fashion-mnist data, can Baidu, also can point this to understand, the data is like this look: Download Address: Https://github.com/zalandoresearch/fash

1, keyerror:class ' torch.cuda.ByteTensor ' SolveAbout this error on-line introduction is not much, only to find a solution: Bytetensor not working with f.conv2d?. Most of the operations in Pytorch are for Floattensor and doubletensor. 2, Runtimeerror:cudnn_status_bad_param SolveThe input size is incorrect, and the input size of the convolution layer is (N, C, H, W). 3, Typeerror:max () got an unexpected keyword argument ' Keepdim The reason is unc

', 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

First, Pytorch introduction 1, the descriptionPytorch is Torch in Python (Torch is a neural network using the Lua language) and TensorFlow comparison Pytorch established neural network is dynamic TensorFlow is a highly industrial of static graph TensorFlow , its underlying code is hard to read. Pytorch good so a little, if you dive into the API, you can at least

One. Installation1. Official Github:https://github.com/pytorch/pytorchInstall Optional Dependencieson linuxexport cmake_prefix_path="$ (dirname $ (which Conda))/. /"forif9-A network error has occurreddownloading and extracting Packagescertifi2018.1. -: ##################################################### | -%Magma-cuda802.2.0: |0%condahttperror:http theCONNECTION FAILED forURL //conda.anaconda.org/

Take it straight from pytorch tutorials and see. Required Packages:1. Scikit-image: Image io and morphing2. Pandas: Read in CSV fileData:FacesData form of CSV:A total of 68 face key points. image_name,part_0_x,part_0_y,part_1_x,part_1_y,part_2_x, part_67_x,part_67_y 0805personali01.jpg, 27,83,27,98, ... 84,134 1084239450_e76e00b7e7.jpg,70,236,71,257, ..., 128,312 Or as Pictured:the simplest to read a picture through a function #-*-Codi

Homepage (http://pytorch.org/) is the installation of the tutorial, but after the click did not respond, the reason is unclear, so you have to find a way to install.The installation reference is as follows: http://blog.csdn.net/amds123/article/details/69396953 Since my machine uses Anaconda2.7 internal nesting of Anaconda3.6, and I prefer to use the 3.6 version (personally feel that using 3.x is the trend, and 3.x is indeed more convenient than 2.7), and my Cuda version is 8, So I re

visdom pytorch Visualization tool When translating this article, the torch part is omitted. Project Address A flexible visualization tool that can be used to create, organize, and share real-time, rich data. Support Torch and NumPy. Overview Basic Concepts Setup Launches Visual interface summary Overview Visdom aims to promote the visualization of remote data, with a focus on supporting scientific experiments. Send visual images, pictures, and text

This article is void My next installment is the TensorFlow and Keras truth. Environment: Anaconda4.2;python3.5;windows10,64,cuda Previous hard cuda9.1 useless, we want to use the GPU must choose cuda8.0, I thought the official will be corresponding update, naive. First TensorFlow don't recognize, moreover cudnn own all do not recognize, only 8.0. Keras and TensorFlow are both Pip,pytorch and OpenCV are going to find WHL. About Keras backend and inst

Sometimes we use other tasks (such as classification) to pre-train the network, then fix the convolutional layer as an image feature extractor, and then use the current task's data to train only the fully connected layer. So pytorch how to fix the bottom only update the upper layer when training. This means that we want to calculate the gradient in reverse propagation, we only want to compute to the topmost convolution layer, for the convolution layer

Pytorch Detach and Detach_ Pytorch's Variable object has two methods, detach and Detach_ This article mainly describes the effect of these two methods and what can be done with these two methods. Detach This method is described in the official documentation. Returns a new Variable that is detached from the current diagram. The returned Variable will never need a gradient if the detach Variable volatile=true, then detach out of the volatile is also tr

When using Pytorch's RNN module, it is sometimes unavoidable to use pack_padded_sequence and pad_packed_sequence, when using two-way RNN, you must use Pack_padded_seque NCE ! Otherwise, the Pytorch is unable to obtain the length of the sequence, and it does not correctly calculate the results of the bidirectional rnn/gru/lstm. However, there is a problem when using pack_padded_sequence, that is, the length of the input mini-batch sequence must be orde

Torch.nn.utils.rnn.pack_padded_sequence ()Here pack , the understanding of compression is better. Presses a filled variable-length sequence. (When filling, there will be redundancy, so press tight)The process of the pack is: (Note the form of pack,