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pybrain: how to print a network (nodes and weights)

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finally I managed to train a network from a file :) Now I want to print the nodes and the weights, especially the weights, because I want to train the network with pybrain and then implement a NN somewhere else that will use it. I need a way to print the layers, the nodes and the weight between nodes, so that I can easily replicate it. So far I see I can access the layers using n['in'] for example, and then for example I can do: dir(n['in']) [' class ', ' delattr ', ' dict ', ' doc ', ' format ', ' getattribute ', ' hash ', ' init ', ' module ', ' new ', ' reduce ', ' reduce_ex ', ' repr ', '

Prediction using Recurrent Neural Network on Time series dataset

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问题 Description Given a dataset that has 10 sequences - a sequence corresponds to a day of stock value recordings - where each constitutes 50 sample recordings of stock values that are separated by 5 minute intervals starting from the morning or 9:05 am. However, there is one extra recording (the 51th sample) that is only available in the training set which is 2 hours later, not 5 minutes, than the last recorded sample in the 50 sample recordings. That 51th sample is required to be predicted for

Neural Network training with PyBrain won't converge

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问题 I have the following code, from the PyBrain tutorial: from pybrain.datasets import SupervisedDataSet from pybrain.supervised.trainers import BackpropTrainer from pybrain.tools.shortcuts import buildNetwork from pybrain.structure.modules import TanhLayer ds = SupervisedDataSet(2, 1) ds.addSample((0,0), (0,)) ds.addSample((0,1), (1,)) ds.addSample((1,0), (1,)) ds.addSample((1,1), (0,)) net = buildNetwork(2, 3, 1, bias=True, hiddenclass=TanhLayer) trainer = BackpropTrainer(net, ds) for inp, tar

How to save and recover PyBrain training?

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问题 Is there a way to save and recover a trained Neural Network in PyBrain, so that I don't have to retrain it each time I run the script? 回答1: PyBrain's Neural Networks can be saved and loaded using either python's built in pickle/cPickle module, or by using PyBrain's XML NetworkWriter. # Using pickle from pybrain.tools.shortcuts import buildNetwork import pickle net = buildNetwork(2,4,1) fileObject = open('filename', 'w') pickle.dump(net, fileObject) fileObject.close() fileObject = open(

Meaning of an Epoch in Neural Networks Training

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问题 while I'm reading in how to build ANN in pybrain, they say: Train the network for some epochs. Usually you would set something like 5 here, trainer.trainEpochs( 1 ) I looked for what is that mean , then I conclude that we use an epoch of data to update weights, If I choose to train the data with 5 epochs as pybrain advice, the dataset will be divided into 5 subsets, and the wights will update 5 times as maximum. I'm familiar with online training where the wights are updated after each sample

pybrain: how to print a network (nodes and weights)

阅读更多关于 pybrain: how to print a network (nodes and weights)

问题 finally I managed to train a network from a file :) Now I want to print the nodes and the weights, especially the weights, because I want to train the network with pybrain and then implement a NN somewhere else that will use it. I need a way to print the layers, the nodes and the weight between nodes, so that I can easily replicate it. So far I see I can access the layers using n['in'] for example, and then for example I can do: dir(n['in']) [' class ', ' delattr ', ' dict ', ' doc ', '