pytorch dataloader

init#定义数据源import gbbatch_size = 256train_iter, test_iter = gb.load_data_fashion_mnist(batch_size)#定义网络from mxnet.gluon import nnnet = nn.Sequential()net.add(nn.Flatten())net.add(nn.Dense(10))net.initialize(init.Normal(sigma=0.01))#损失函数from mxnet.gluon import loss as glossloss = gloss.SoftmaxCrossEntropyLoss()#优化算法from mxnet.gluon import Trainertrainer = Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})#训练模型num_epochs = 5gb.train_cpu(net, train_iter, test_iter, loss, num_epochs, batch

) print "Down *********" for (hostname, result) in results[' Dark '].items (): Try : print "%s >>>%s"% (hostname, result) except Exception,e:print E Sys.exit (1) if __name__ = = "__main__": #command (' remote-host ', ' command ', '/usr/bin/uptime ', 5) Command (' remote-host ', ' Copy ', ' src=/root/zabbix-2.4.6.tar.gz dest=/root/zabbix-2.4.6.tar.gz mode=0644 ', 5) Ansible V2.0 #!/usr/bin/env python #-*-coding:utf-8-*-import JSON from collections import Namedtuple fr

number is doubled. Combined with the 3rd way, redesigned the next, the idea is as follows: Scenarios that still use the two key: Key __load_{key} where__load_{key} This key is equivalent to a lock , only allow the add successful thread to update the data, and This key timeout time is relatively short, does not always occupy memcached memory . In the set-to-memcached value, add a time, the time, value, when the key on the memcached expires in the future, not the current system time. When get to

more details, refer to Manual. Running the program is a familiar selection interface: 1) Threaded ListModel This example is simple and describes how to use WorkScript to update the ListModel in a new thread. The program consists of two files: Timedisplay. qml: Import QtQuick 2.0 Rectangle {color: "white" width: 200 height: 300 ListView {anchors. fill: parent model: listModel delegate: Component {Text {text: time }}listmodel {id: ListModel} WorkerScript {id: worker source: "

Basic knowledge "Fluent Python" the 1th to 4th Chapter _ Data structure, code "python" basic data structure common use method "python coolbook" Data structure and algorithm _ multivariable Assignment "*" Two usages of "python Coolbook:collections "Data structure and algorithm _collections.deque queue yield application" Python COOLBOOK:HEAPQ "data structure and algorithm _HEAPQ heap queue algorithm container sequencing" Python coolbook:collections "Data structure and algorithm _ Container Type

) SOURCE Link: https://github.com/PAIR-code/deeplearnjs 7. Fast style migration base based on TensorFlow (GitHub 4843 stars, contributors are Logan Engstrom of MIT) SOURCE Link: Https://github.com/lengstrom/fast-style-transfer 8. PYSC2: StarCraft 2 Learning Environment (GitHub 3684 stars, contributors are DeepMind Timo Ewalds) SOURCE Link: https://github.com/deepmind/pysc2 9. Airsim:microsoft AI Research Open source Simulator based on Unreal Engine for automatic driving (GitHub 3861 star, contr

algorithm, the training process can be very unstable. But you can use some "tips" to get a more robust training process. In the following video, you can see the training evolution of the images generated by Gans. Code If you are interested in the basic implementation of Gans, you can see the link to the code: TensorFlow (HTTPS://GITHUB.COM/ERICJANG/GENADV_TUTORIAL/BLOB/MASTER/GENADV1.IPYNB) Torch and Python (pytorch) (https://github.com/devnag/

3087 stars, contributors are Google brain's Ryan Sepassi) SOURCE Link: https://github.com/tensorflow/tensor2tensor 13. Image conversion based on Pytorch implementation (GitHub 2847 stars, contributor Berkeley Jun-yan Zhu, Ph.D) Source Address: Https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix 14.Faiss: Efficient similarity for dense vectors search libraries and clustering libraries (GitHub 2629 sta

selection interface: 1) threaded listmodel This example is simple and describes how to use workscript to update the listmodel in a new thread. The program consists of two files: Timedisplay. qml: Import qtquick 2.0 rectangle {color: "white" width: 200 Height: 300 listview {anchors. fill: parent Model: listmodel delegate: component {text: Time }}listmodel {ID: listmodel} workerscript {ID: worker Source: "dataloader. JS "// declare JS processing f

Python version: Python 2.6.6Ansible version: Ansible 2.3.1.0Call Script: task_exec_v1.py#!/usr/bin/Env Python#coding:utf-8Import osimport sysimport jsonimport logging fromCollections Import Namedtuple fromansible.inventory Import Inventory fromansible.vars Import Variablemanager fromansible.parsing.dataloader Import Dataloader fromansible.executor.playbook_executor Import Playbookexecutor fromansible.plugins.callback Import Callbackbase fromansible.er

Who will become the first language of development in the AI and Big data era? This is a question that is not to be debated. If there were opportunities for Matlab, Scala, R, Java, and Python three years ago, the situation is unclear, and three years later, the trend is very clear, especially after the first two days of Facebook open source Pytorch, Python as AI The position of the time-cardinal language is basically established, the future suspense is

label for each pixel location.Given an RGB image X, the partitioning model outputs a class probability plot (the class probability map) s (x);The second item is based on an additional anti-convolution network.　　==============================================　　　　　　　　The Network Architecture:　　According to the above flowchart can be found, this article is to divide the result/GT two value diagram and the original image is multiplied, the results obtained, input into the confrontation network. The

1 2d Cross entropy (Mutil class split) Defined def cross_entropy2d (input, Target, Weight=none, size_average=true): # Input: (n, C, H, W), Target: (N, H, W) N, C, H, W = input.size () # log_p: (n, C, H, W) if Looseversion (torch.__version__) Use Data, target = Variable (data, volatile=true), Variable (target) score = Self.model (data) loss = Cross_entropy2d ( Score, Target, size_average=self.size_average)

Word embeddings: encoding lexical semantics Getting dense word embeddings Word embeddings in pytorch An example: n-gram Language Modeling Exercise: computing word embeddings: continuous bag-of-Words Word embeddings in pytorch import torchimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimtorch.manual_seed(1)word_to_ix = {"hello": 0, "world": 1}embeds = nn.Embedding(2, 5) #

ideal solution is to allow the computer to instantly check the resource footprint of the GPU and start digging automatically when no process is in use, and the monitor will issue an order for the computer to stop digging immediately when TensorFlow, Pytorch, or other tools need to start computing. The problem should be well solved, but I haven't found anything like it on the internet so I've tried to write a GPU monitor myself. It is not only suitab

The following is only a summary, referring to the many online information, only forget. main link Deeplab home: http://liangchiehchen.com/projects/DeepLab.html Official code: https://bitbucket.org/aquariusjay/ Deeplab-public-ver2 Python version Caffe implementation: HTTPS://GITHUB.COM/THELEGENDALI/DEEPLAB-CONTEXT2 model download:/HTTP liangchiehchen.com/projects/deeplab_models.html DEEPLABV2_VGG16 Pre-training model DEEPLABV2_RESNET101 pre-training model Pyt