from pca import PCA
if __name__ == '__main__':
resman.start('junk', diary=True)
datasets = loadUpsonData('../data/upson_rovio_1/train_15_50000.pkl.gz',
'../data/upson_rovio_1/test_15_50000.pkl.gz')
#meanTrain = mean(datasets[0][0])
#stdTrain = std(datasets[0][0])
#datasets[0][0] = (datasets[0][0] - meanTrain) / stdTrain
#datasets[2][0] = (datasets[2][0] - meanTrain) / stdTrain
pca = PCA(datasets[0][0])
datasets[0][0] = pca.toZca(datasets[0][0], None, epsilon=.1)
datasets[2][0] = pca.toZca(datasets[2][0], None, epsilon=.1)
print 'done loading.'
test_rbm(
datasets=datasets,
training_epochs=45,
img_dim=15, # must match actual size of training data
n_hidden=int(sys.argv[1]),
learning_rate=float(sys.argv[2]),
output_dir=resman.rundir,
quickHack=False,
visibleModel='real',
initWfactor=.01,
pcaDims=None)
resman.stop()
print 'done loading.'
for useXY in [False, True]:
for size in sizes:
print 'useXY', useXY, ', Size:', size
thisDir = os.path.join(resman.rundir, '%s_size_%05d' % ('xy' if useXY else 'x', size))
os.mkdir(thisDir)
if useXY:
thisDataset = (sizedDatasetsXY[size], (array([]), None), testDatasetXY)
else:
thisDataset = (sizedDatasetsX[size], (array([]), None), testDatasetX)
# this automatically saves the RBM to the given directory
rbm, meanCosts = test_rbm(datasets = thisDataset,
training_epochs = 45,
img_dim = img_dim,
n_hidden = 400,
learning_rate = .002,
output_dir = thisDir,
quickHack = False,
initWfactor = .02,
imgPlotFunction = lambda xx: xx[:,0:img_dim*img_dim], # HACK: plot first slice
)
if __name__ == '__main__':
resman.start('junk', diary = True)
main()
resman.stop()