visualizing 2nd convolutional layer

Question

So via tf.summary I visualized the first convolutional layer in my model, of shape [5,5,3,32], as a set of individual images, one per filter. so this layer has a filter of 5x5 dimensions, of depth 3, and there are 32 of them. Im viewing these filters as 5x5 color(RGB) images.

Im wondering how to generalize this to a second convolutional layer, and third and on...
shape of the second convolutional layer is [5,5,32,64].
my questions is how would I transform that tensor into individual 5x5x3 images?

with the first conv layer of shape [5,5,3,32] I visualize it by transposing first tf.transpose(W_conv1,(3,0,1,2)), and then having 32 5x5x3 images.

doing a tf.transpose(W_conv2,(3,0,1,2)) would produce a shape [64,5,5,32]. how would I then use those "32 color channels"? (Im know its not that simple :) ).

Answer 1

Visualization of higher level filters is usually done indirectly. To visualize a particular filter, you look for images that the filter would respond the most to. For that you perform gradient ascent in the space of images (instead of changing the parameters of the network like when you train the network, you change the input image).

You will understand it easier if you play with the following Keras code: https://github.com/keras-team/keras/blob/master/examples/conv_filter_visualization.py