[keras model, short summary].py

def short_summary(model):
    from keras import backend as K
    for layer in model.layers:
        trainable_count = int( np.sum([K.count_params(p) for p in set(layer.trainable_weights)]))
        non_trainable_count = int( np.sum([K.count_params(p) for p in set(layer.non_trainable_weights)]))
        if trainable_count == 0 and non_trainable_count == 0:
            print '{:<10}[{:<10}]: {:<20} => {:<20}'.format(layer.name, layer.__class__.__name__, layer.input_shape,layer.output_shape)
        else:
            print '{:<10}[{:<10}]: {:<20} => {:<20}, with {} trainable + {} nontrainable'.format(layer.name, layer.__class__.__name__, layer.input_shape, layer.output_shape, trainable_count, non_trainable_count)