Copying weights using layer type on Nolearn NeuralNet

gistfile1.py

    def load_weights_from_by_layers(self, source):
        self._output_layer = self.initialize_layers()

        if isinstance(source, str):
            source = np.load(source)

        sourcelayers = {'Conv2DLayer':[],'DenseLayer':[]}
        for l in source.get_all_layers():
            if 'Conv2DLayer' in str(type(l)):
                sourcelayers['Conv2DLayer'].append(l)
            if 'DenseLayer' in str(type(l)):
                sourcelayers['DenseLayer'].append(l)

        selflayers = {'Conv2DLayer':[],'DenseLayer':[]}
        for l in self.get_all_layers():
            if 'Conv2DLayer' in str(type(l)):
                selflayers['Conv2DLayer'].append(l)
            if 'DenseLayer' in str(type(l)):
                selflayers['DenseLayer'].append(l)

        lenConv = min(len(sourcelayers['Conv2DLayer']),len(selflayers['Conv2DLayer']))
        if(lenConv > len(selflayers['Conv2DLayer'])):
            selflayers['Conv2DLayer'].pop(lenConv- len(selflayers['Conv2DLayer']))
        elif(lenConv > len(sourcelayers['Conv2DLayer'])):
            sourcelayers['Conv2DLayer'].pop(lenConv - len(sourcelayers['Conv2DLayer']))
        
        for l1, l2 in zip(sourcelayers['Conv2DLayer'], selflayers['Conv2DLayer']):
            w1 = l1.W.get_value()
            w2 = l2.W.get_value()
            if w1.shape != w2.shape:
                continue
            l2.W.set_value(w1)

        lenDense = min(len(sourcelayers['DenseLayer']),len(selflayers['DenseLayer']))
        if(lenDense > len(selflayers['DenseLayer'])):
            selflayers['DenseLayer'].pop(lenDense- len(selflayers['DenseLayer']))
        elif(lenDense > len(sourcelayers['DenseLayer'])):
            sourcelayers['DenseLayer'].pop(lenDense - len(sourcelayers['DenseLayer']))
        
        for l1, l2 in zip(sourcelayers['DenseLayer'], selflayers['DenseLayer']):
            w1 = l1.W.get_value()
            w2 = l2.W.get_value()
            if w1.shape != w2.shape:
                continue
            l2.W.set_value(w1)