我正在尝试使用Keras InceptionV3（）使用Lucid Toolkit（https://github.com/tensorflow/lucid）执行功能可视化。
在我训练完毕后检查网络内层的形状时，它们具有给定的形状：
================================================================================
input_1 (InputLayer) (None, 300, 400, 3) 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 149, 199, 32) 864 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 149, 199, 32) 96 conv2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 149, 199, 32) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 147, 197, 32) 9216 activation_1[0][0]
...
相比之下，具有预训练的imageNet权重的模型没有这样的限制：
input_1 (InputLayer) (None, None, None, 3 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, None, None, 3 864 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, None, None, 3 96 conv2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, None, None, 3 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, None, None, 3 9216 activation_1[0][0]
所以，问题在于，当我想要进行可视化时，使用预训练的网络可行，但不是。
有谁知道，为什么没有对图层形状的限制，因为至少应该有每个转换层中的滤镜数量。
感谢您的帮助，
添