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add test-full-test-func un rbmTest.py
add matrixImage add regulariaztion add clocks control
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,106 @@ | ||
| __author__ = 'gavr' | ||
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| from newRTRBM import * | ||
| from clocks import * | ||
| from utils import * | ||
| from tictoc import tic, toc | ||
| from StringIO import StringIO | ||
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| def rbmTest(imageSize = 30, \ | ||
| NotDrawBackGround = False, \ | ||
| countIteration = 2401, \ | ||
| outputEveryIteration = 100, \ | ||
| countGibbs = 10, | ||
| learningRate = 0.01, | ||
| hiddenVaribles = 100, | ||
| secWidth = 1, | ||
| learningMode = MODE_WITHOUT_COIN, | ||
| numOutputRandom = 10, | ||
| regularization = 0, prefixName = ''): | ||
| string = StringIO() | ||
| string.write(prefixName) | ||
| string.write('IS_'+str(imageSize)) | ||
| string.write('_bg_'+str(NotDrawBackGround)) | ||
| string.write('_ci_'+str(countIteration)) | ||
| string.write('_cg_'+str(countGibbs)) | ||
| string.write('_lr_'+str(learningRate)) | ||
| string.write('_lm_'+MODE_NAMES[learningMode]) | ||
| string.write('_h_'+str(hiddenVaribles)) | ||
| string.write('_sW_'+str(secWidth)) | ||
| string.write('_r_'+str(regularization)) | ||
| setCurrentDirectory(string.getvalue()) | ||
| SetGreyAsBlack() | ||
| if NotDrawBackGround: | ||
| SetDontDrawBlackContour() | ||
| else: | ||
| SetDrawBlackContour() | ||
| SetSecWidth(secWidth) | ||
| dials = DrawDials(Tick(0, 0, 0), Tick(59, 0, 0), imageSize) | ||
| appearance = dials[0] | ||
| dataPrime = [convertImageToVector(element) for element in dials] | ||
| rbm = createSimpleRBM(hiddenVaribles, imageSize * imageSize) | ||
| m = T.matrix() | ||
| n = T.iscalar() | ||
| s = T.fscalar() | ||
| v = T.vector() | ||
| print "start create learning function", tic() | ||
| grad_func = rbm.grad_function(m, countGibbs, learningMode, learningRate, regularization) | ||
| print "learning function has been built: ", toc() | ||
| print "start contruct gibbs function" | ||
| tic() | ||
| sample = rbm.bm.generateRandomsFromBinoZeroOne( | ||
| T.reshape( | ||
| T.repeat(T.ones_like(rbm.vBias) * 0.5, numOutputRandom), | ||
| (numOutputRandom, imageSize * imageSize))) | ||
| res, updates = rbm.bm.gibbs_all(sample, rbm.W, rbm.vBias, rbm.hBias, countGibbs, MODE_WITHOUT_COIN) | ||
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| rnd_gibbs = theano.function([], T.concatenate([[sample], res]), updates=updates) | ||
|
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| res, updates = rbm.bm.gibbs_all(m, rbm.W, rbm.vBias, rbm.hBias, countGibbs, MODE_WITHOUT_COIN) | ||
| data_gibbs = theano.function([m], res, updates=updates) | ||
| print "Constructed Gibbs function: ", toc() | ||
| saveOutput = lambda x, name: \ | ||
| saveImage(\ | ||
| makeAnimImageFromMatrixImages(\ | ||
| convertProbabilityTensorToImages(appearance, x)), | ||
| name) | ||
| print "Start Learn" | ||
| tic() | ||
| tic() | ||
| for idx in range(countIteration): | ||
| res = grad_func(dataPrime) | ||
| if idx % outputEveryIteration == 0: | ||
| saveOutput(data_gibbs(dataPrime), 'data' + str(idx)) | ||
| saveOutput(rnd_gibbs(), 'random' + str(idx)) | ||
| print idx, res, toc() | ||
| tic() | ||
| print "learning time: ", toc() | ||
| saveData(rbm.save()) | ||
|
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| # rbmTest(countIteration=3201, learningMode=MODE_WITH_COIN_EXCEPT_LAST, countGibbs=10, hiddenVaribles=400, prefixName='1') | ||
|
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| countIterations = [3201] | ||
| modes = [MODE_WITHOUT_COIN, MODE_WITH_COIN, MODE_WITH_COIN_EXCEPT_LAST, MODE_WITHOUT_COIN_EXCEPT_LAST] | ||
| countGibbs = [2, 5, 10, 20, 40, 80] | ||
| hidden = [50, 100, 200, 400, 600, 900] | ||
| secWidth = [1, 2, 3] | ||
| regularization = [0, 0.001, 0.01, 0.005] | ||
| NotDrawBackGround = [False, True] | ||
|
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| for ci in countIterations: | ||
| for cg in countGibbs: | ||
| for m in modes: | ||
| for h in hidden: | ||
| for sw in secWidth: | ||
| for r in regularization: | ||
| for ndbg in NotDrawBackGround: | ||
| rbmTest(\ | ||
| countIteration=ci, \ | ||
| learningMode=m, \ | ||
| countGibbs=cg, \ | ||
| hiddenVaribles=h, \ | ||
| NotDrawBackGround=ndbg, \ | ||
| secWidth=sw, \ | ||
| regularization=r,\ | ||
| prefixName='2') | ||
|
|
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