Dear all,
any ideas on how to visualize the intermediate layers of a convolutional network? For example, say I have the following custom “network”
class MyNet(HybridBlock):
def __init__(self,**kwards):
HybridBlock.__init__(self,**kwards)
with self.name_scope():
self.conv1 = gluon.nn.Conv2D(channels=16,kernel_size=3)
self.conv2 = gluon.nn.Conv2D(channels=32,kernel_size=3)
self.conv3 = gluon.nn.Conv2D(channels=32,kernel_size=3)
def hybrid_forward(self,F,_input):
x = self.conv1(_input)
x = self.conv2(x)
x = self.conv3(x)
return x
and assume I’ve trained it with a loss function etc, so now the network is initialized and trained (the weights+bias are determined). Is there a way to visualize the self.conv2 or self.conv3 layers directly (by providing a batch of input images/data)? Or do I need to define another function that takes these layers and repeats the process of hybrid_forward
up to the layer I wish to visualize?
Thanks