如何在培训期间批量激活图层

Question

我正在使用Keras（带有tensorflow后端）并试图在列车时间内使用“适合”功能在我的训练集上获得图层输出（实际激活）

有没有什么方法可以将最后一批用于训练的激活作为on_batch_end回调的一部分？或任何其他方式来访问图层输出？

我在下面找到了这个代码，但它在新数据上再次运行了正向传递。我正在尝试利用这样一个事实，即我的网络已经作为批量培训的一部分进行了正向传递，只是拉动当前的激活，这是可能的吗？

 def get_activations(model, model_inputs, print_shape_only=False, layer_name=None):
        print('----- activations -----')
        activations = []
        inp = model.input

        model_multi_inputs_cond = True
        if not isinstance(inp, list):
            # only one input! let's wrap it in a list.
            inp = [inp]
            model_multi_inputs_cond = False

        outputs = [layer.output for layer in model.layers if
                   layer.name == layer_name or layer_name is None]  # all layer outputs

        funcs = [K.function(inp + [K.learning_phase()], [out]) for out in outputs]  # evaluation functions

        if model_multi_inputs_cond:
            list_inputs = []
            list_inputs.extend(model_inputs)
            list_inputs.append(0.)
        else:
            list_inputs = [model_inputs, 0.]

        # Learning phase. 0 = Test mode (no dropout or batch normalization)
        # layer_outputs = [func([model_inputs, 0.])[0] for func in funcs]
        layer_outputs = [func(list_inputs)[0] for func in funcs]
        for layer_activations in layer_outputs:
            activations.append(layer_activations)
            if print_shape_only:
                print(layer_activations.shape)
            else:
                print(layer_activations)
        return activations