def __init__(self, which_set, center=False, multi_target=False): """ :param which_set: one of ['train','test'] :param center: data is in range [0,256], center=True subtracts 127.5. :param multi_target: load extra information as additional labels. """ assert which_set in ['train', 'test'] X = FoveatedNORB.load(which_set) # put things in pylearn2's DenseDesignMatrix format X = numpy.cast['float32'](X) #this is uint8 y = NORBSmall.load(which_set, 'cat') if multi_target: y_extra = NORBSmall.load(which_set, 'info') y = numpy.hstack((y[:, numpy.newaxis], y_extra)) if center: X -= 127.5 view_converter = retina.RetinaCodingViewConverter((96, 96, 2), (8, 4, 2, 2)) super(FoveatedNORB, self).__init__(X=X, y=y, view_converter=view_converter)
def __init__(self, which_set, center=False, scale=False, start=None, stop=None, one_hot=False, restrict_instances=None, preprocessor=None): """ :param which_set: one of ['train','test'] :param center: data is in range [0,256], center=True subtracts 127.5. :param multi_target: load extra information as additional labels. """ if which_set not in ['train', 'test']: raise ValueError("Unrecognized which_set value: " + which_set) X = FoveatedNORB.load(which_set) # put things in pylearn2's DenseDesignMatrix format X = numpy.cast['float32'](X) #this is uint8 y = NORBSmall.load(which_set, 'cat') y_extra = NORBSmall.load(which_set, 'info') assert y_extra.shape[0] == y.shape[0] instance = y_extra[:, 0] assert instance.min() >= 0 assert instance.max() <= 9 self.instance = instance if center: X -= 127.5 if scale: X /= 127.5 else: if scale: X /= 255. view_converter = retina.RetinaCodingViewConverter((96, 96, 2), (8, 4, 2, 2)) super(FoveatedNORB, self).__init__(X=X, y=y, view_converter=view_converter, preprocessor=preprocessor) if one_hot: self.convert_to_one_hot() if restrict_instances is not None: assert start is None assert stop is None self.restrict_instances(restrict_instances) self.restrict(start, stop) self.y = self.y.astype('float32')
def __init__(self, which_set, center=False, scale=False, start=None, stop=None, one_hot=False, restrict_instances=None, preprocessor=None): self.args = locals() if which_set not in ['train', 'test']: raise ValueError("Unrecognized which_set value: " + which_set) X = FoveatedNORB.load(which_set) X = numpy.cast['float32'](X) # this is uint8 y = NORBSmall.load(which_set, 'cat') y_extra = NORBSmall.load(which_set, 'info') assert y_extra.shape[0] == y.shape[0] instance = y_extra[:, 0] assert instance.min() >= 0 assert instance.max() <= 9 self.instance = instance if center: X -= 127.5 if scale: X /= 127.5 else: if scale: X /= 255. view_converter = retina.RetinaCodingViewConverter((96, 96, 2), (8, 4, 2, 2)) super(FoveatedNORB, self).__init__(X=X, y=y, view_converter=view_converter, preprocessor=preprocessor) if one_hot: self.convert_to_one_hot() if restrict_instances is not None: assert start is None assert stop is None self.restrict_instances(restrict_instances) self.restrict(start, stop) self.y = self.y.astype('float32')