pybrain

I have a question in mind which relates to the usage of pybrain to do regression of a time series. I plan to use the LSTM layer in pybrain to train and predict a time series. I found an example code here in the link below Request for example: Recurrent neural network for predicting next value in a sequence In the example above, the network is able to predict a sequence after its being trained. But the issue is, network takes in all the sequential data by feeding it in one go to the input layer. For example, if the training data has 10 features each, the 10 features will be simultaneously fed

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 for example we have a sequence of numbers in the range [1,7]. First run (So first example): 1 2 4 6 2 3

I am using Python 2.7 and trying to get PyBrain to work. But I get this error even though scipy is installed - Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python2.7/site-packages/PyBrain-0.3.1- py2.7.egg/pybrain/__init__.py", line 1, in <module> from pybrain.structure.__init__ import * File "/usr/local/lib/python2.7/site-packages/PyBrain-0.3.1-py2.7.egg/pybrain/structure/__init__.py", line 1, in <module> from pybrain.structure.connections.__init__ import * File "/usr/local/lib/python2.7/site-packages/PyBrain-0.3.1-py2.7.egg/pybrain/structure

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 in ds: print [net.activate(inp), tar] errors = trainer.trainUntilConvergence() for inp, tar in ds:

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 the testing set where the first 50 samples are also given. I am using the pybrain recurrent neural

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? solartic 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('filename','r') net = pickle.load(fileObject) Note cPickle is implemented in C, and therefore should be

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 data or feature vector, My question is how to be sure that 5 epochs will be enough to build a model