Network object has no attribute cose

[kadarakos]

This code runs but if I change the Regressor to Network I get the error message that the Network has no attribute cost. What am I doing wrong?

def getDataset():
    description_vectors = numpy.load('train_desps_features.npy')
    signature_vectors = numpy.load('train_signatures_features.npy')
    dataset = (description_vectors, signature_vectors)
    return dataset

def trainModel(dataset):
    input_layer = len(dataset[0][0])
    hidden1 = int(input_layer*(2.0/3))
    output_layer = len(dataset[1][0])
    e = theanets.Experiment(theanets.Regressor, layers=(input_layer, hidden1, output_layer))
    e.run(dataset, dataset)

dataset = getDataset()
trainModel(dataset)

[kadarakos]

The network also gives a very poor error rate error=93.2929

[lmjohns3]

The code you posted looks correct at first glance. I'm curious how you could get an attribute error and still get some sort of error value -- usually an attribute error should halt the program.

Could you post the output from running this code?

[kadarakos]

Sorry I phrased, it the wrong way then. I don't get the attribute error if i use the theanets.Regressor class and the program runs and I get bad results. If I use the theantets.Network i get this

File "/usr/local/lib/python2.7/dist-packages/theanets/feedforward.py", line 328, in J cost = self.cost AttributeError: 'Network' object has no attribute 'cost'

[lmjohns3]

Ah, yes. The Network class is abstract (it doesn't implement cost, among other things), so it won't work to use in an Experiment. I should probably make that more explicit. For your dataset, since you have explicit, continuous-valued output targets, you should be using Regressor.

As for the poor training error, training neural networks is something of a black art. There are a lot of different training methods (try optimize='cg' or optimize='hf', for instance), and several of the methods (but in particular SGD) have a lot of parameters that need tuning to get good training performance.

This has been a problem for several folks trying to use this library. (And, furthermore, it is a problem for people using neural networks in general!) I should write up some suggestions for training as part of the documentation. But as a general strategy, try different training methods, and also try setting the parameters for the training methods -- I've found that sampling randomly from a log-uniform distribution gets you a good idea of which parameter settings work well. There are also packages for selecting training parameters (see https://github.com/hyperopt/hyperopt for a good example).