pybrain

AttributeError using pyBrain _splitWithPortion - object type changed?

由 夙愿已清 提交于 2019-12-03 15:44:54
问题 I'm testing out pybrain following the basic classification tutorial here and a different take on it with some more realistic data here. However I receive this error when applying trndata._convertToOneOfMany() with the error: AttributeError: 'SupervisedDataSet' object has no attribute '_convertToOneOfMany The data set is created as a classification.ClassificationDataSet object however calling splitWithProportion seems to change it supervised.SupervisedDataSet object, so being fairly new to

How to train a neural network to supervised data set using pybrain black-box optimization?

由 孤街浪徒 提交于 2019-12-03 02:46:40
问题 I have played around a bit with pybrain and understand how to generate neural networks with custom architectures and train them to supervised data sets using backpropagation algorithm. However I am confused by the optimization algorithms and the concepts of tasks, learning agents and environments. For example: How would I implement a neural network such as (1) to classify the XOR dataset using pybrain genetic algorithm (2)? (1) pybrain.tools.shortcuts.buildNetwork(2, 3, 1) (2) pybrain

PyBrain in Anaconda - ImportError: No module named 'structure'

由 匿名 (未验证) 提交于 2019-12-03 01:23:02
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I'm looking for a way to use numpy, scipy and pybrain in python. If I try to install those I get the error: Microsoft Visual C++ 10.0 is required (Unable to find vcvarsall.bat). I have installed visual studio but it still doesn't work. I have tried setting up an environment with conda including numpy and scipy and then installing pybrain in it using pip but when I try to import it I get the error: import pybrain Traceback (most recent call last): File " ", line 1, in File "C:\Users\Lucas\Anaconda3\envs\brain\lib\site-packages\pybrain\__init_

How to do supervised deepbelief training in PyBrain?

由 笑着哭i 提交于 2019-12-02 21:05:00
I have trouble getting the DeepBeliefTrainer to work on my data in PyBrain/Python. Since I can't find any examples other than unsupervised on how to use the deep learning in PyBrain, I hope that someone can give examples that would show a basic concept of usage. I have tried to initialize using: epochs = 100 layerDims = [768,100,100,1] net = buildNetwork(*layerDims) dataset = self.dataset trainer = DeepBeliefTrainer(net, dataset=dataSet) trainer.trainEpochs(epochs) I try to use a SupervisedDataset for regression, but the training just fails. Have anyone succeded with using deeplearning trainer

How to train a neural network to supervised data set using pybrain black-box optimization?

由 隐身守侯 提交于 2019-12-02 16:21:26
I have played around a bit with pybrain and understand how to generate neural networks with custom architectures and train them to supervised data sets using backpropagation algorithm. However I am confused by the optimization algorithms and the concepts of tasks, learning agents and environments. For example: How would I implement a neural network such as (1) to classify the XOR dataset using pybrain genetic algorithm (2)? (1) pybrain.tools.shortcuts.buildNetwork(2, 3, 1) (2) pybrain.optimization.GA() I finally worked it out!! Its always easy once you know how! Essentially the first arg to

How to create simple 3-layer neural network and teach it using supervised learning?

由 ↘锁芯ラ 提交于 2019-11-30 20:23:05
Based on PyBrain's tutorials I managed to knock together the following code: #!/usr/bin/env python2 # coding: utf-8 from pybrain.structure import FeedForwardNetwork, LinearLayer, SigmoidLayer, FullConnection from pybrain.datasets import SupervisedDataSet from pybrain.supervised.trainers import BackpropTrainer n = FeedForwardNetwork() inLayer = LinearLayer(2) hiddenLayer = SigmoidLayer(3) outLayer = LinearLayer(1) n.addInputModule(inLayer) n.addModule(hiddenLayer) n.addOutputModule(outLayer) in_to_hidden = FullConnection(inLayer, hiddenLayer) hidden_to_out = FullConnection(hiddenLayer, outLayer

Request for example: Recurrent neural network for predicting next value in a sequence

由 半城伤御伤魂 提交于 2019-11-30 10:32:20
问题 Can anyone give me a practicale example of a recurrent neural network in (pybrain) python in order to predict the next value of a sequence ? (I've read the pybrain documentation and there is no clear example for it I think.) I also found this question. But I fail to see how it works in a more general case. So therefore I'm asking if anyone here could work out a clear example of how to predict the next value of a sequence in pybrain, with a recurrent neural network . To give an example. Say

What are some packages that implement semi-supervised (constrained) clustering?

由 佐手、 提交于 2019-11-30 09:42:38
I want to run some experiments on semi-supervised (constrained) clustering, in particular with background knowledge provided as instance level pairwise constraints (Must-Link or Cannot-Link constraints). I would like to know if there are any good open-source packages that implement semi-supervised clustering? I tried to look at PyBrain, mlpy, scikit and orange, and I couldn't find any constrained clustering algorithms. In particular, I'm interested in constrained K-Means or constrained density based clustering algorithms (like C-DBSCAN). Packages in Matlab, Python, Java or C++ would be

activation values for all nodes in a PyBrain network

由 我们两清 提交于 2019-11-30 08:32:34
问题 I feel like this should be trivial, but I've struggled to find anything useful in the PyBrain documentation, on here, or elsewhere. The problem is this : I have a three layer (input, hidden, output) feedforward network built and trained in PyBrain. Each layer has three nodes. I want to activate the network with novel inputs and store the resultant activation values of the nodes at the hidden layer. As far as I can tell, net.activate() and net.activateOnDataset() will only return the

How to create simple 3-layer neural network and teach it using supervised learning?

由 浪尽此生 提交于 2019-11-30 05:27:15
问题 Based on PyBrain's tutorials I managed to knock together the following code: #!/usr/bin/env python2 # coding: utf-8 from pybrain.structure import FeedForwardNetwork, LinearLayer, SigmoidLayer, FullConnection from pybrain.datasets import SupervisedDataSet from pybrain.supervised.trainers import BackpropTrainer n = FeedForwardNetwork() inLayer = LinearLayer(2) hiddenLayer = SigmoidLayer(3) outLayer = LinearLayer(1) n.addInputModule(inLayer) n.addModule(hiddenLayer) n.addOutputModule(outLayer)
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