pattern_based.py 文件源码

def _set_inverse_parameters(self, patterns=None):
        self.trainable_layers = [(l, self.inverse_map[l])
                                 for l in L.get_all_layers(self.output_layer)
                                 if type(l) in [L.Conv2DLayer, L.DenseLayer]]
        if patterns is not None:
            if type(patterns) is list:
                patterns = patterns[0]
            for i,layer in enumerate(self.trainable_layers):
                param = layer[0].W.get_value()
                pattern = patterns['A'][i]
                if pattern.ndim == 4:
                    if layer[0].flip_filters:
                        param = param[:,:,::-1,::-1]
                    pattern = param*pattern
                    pattern = pattern.transpose(1,0,2,3)

                elif pattern.ndim == 2:
                    pattern = param*pattern
                    pattern = pattern.T
                layer[1].W.set_value(pattern)
        else:
            print("Patterns not given, explanation is random.")