def fronzen_layers(self,  prog):
        print "start logging"
        for _idx, param in enumerate(prog.global_block().iter_parameters()):
            _trainable = True
            if param.persistable and _idx < 100:
                _trainable = False
                param.stop_gradient = _trainable
                #param.trainable = _trainable
            print ("trainable", _idx, _trainable)
            print ("param", _idx, param)

        print "end logging"

        cnn_base = ResNet50(weights='imagenet', include_top=False)
        cnn_out = GlobalAveragePooling2D()(cnn_base.output)
        resnet = Model(input=cnn_base.input, output=cnn_out)

        print "layers number is ", len(resnet.layers)

        for layer in resnet.layers[:100]:
            layer.trainable = False
        for layer in resnet.layers[100:]:
            layer.trainable = True