Esempio n. 1
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def Classifier_y(source, target):
    if source == 'usps' or target == 'usps':
        return usps.Predictory()
    if source == 'svhn':
        return svhn2mnist.Predictor()
    if source == 'synth':
        return syn2gtrsb.Predictor()
Esempio n. 2
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def Classifier(source,
               target,
               num_classifiers_train=2,
               num_classifiers_test=1,
               init='kaiming_u',
               use_init=False):

    if source == 'usps' or target == 'usps':
        return usps.Predictor(num_classifiers_train, num_classifiers_test,
                              init, use_init)

    if source == 'svhn':
        return svhn2mnist.Predictor(num_classifiers_train,
                                    num_classifiers_test, init, use_init)

    if source == 'synth':
        return syn2gtrsb.Predictor(num_classifiers_train, num_classifiers_test,
                                   init, use_init)
Esempio n. 3
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def Classifier(source, target):
    # if source == 'usps' or target == 'usps':
    # return usps.Predictor()
    # else:
    return svhn2mnist.Predictor()
def Classifier():
    # if source == 'usps' or target == 'usps':
    #     return usps.Predictor()
    # if source == 'svhn':
    return svhn2mnist.Predictor()
Esempio n. 5
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def Classifier(source, target, prob=0.5, pixelda=False):
    if source == 'usps' or target == 'usps':
        return usps.Predictor()
    if source == 'svhn':
        return svhn2mnist.Predictor()