[GitHub] feevos commented on issue #10006: How to visualise filters of MXNet model?

[GitBox <gi...@apache.org>]

feevos commented on issue #10006: How to visualise filters of MXNet model?
URL: https://github.com/apache/incubator-mxnet/issues/10006#issuecomment-375232470
 
 
   Hi this solution applies to the gluon framework. I am not sure if you want to visualize the weights or the output of the convolution for some input (the latter is what I usually do). 
   
   Assuming you have a model, in the ```forward``` call you can return more than the last layer and visualize them: 
   
   ```Python
   %pylab inline
   
   import mxnet as mx
   from mxnet import gluon, nd
   
   class tNet(gluon.nn.HybridBlock):
       
       def __init__(self,**kwards):
           gluon.nn.HybridBlock.__init__(self,**kwards)
           
           with self.name_scope():
               
               self.layer1 = gluon.nn.Conv2D(12,kernel_size=3,padding=1)
               self.layer2 = gluon.nn.Conv2D(12,kernel_size=3,padding=1)
               
       def hybrid_forward(self,F,_input):
           
           out1 = self.layer1(_input)
           out2 = self.layer2(out1)
           
           return out1, out2
   ```
   
   ```Python
   mynet = tNet()
   mynet.initialize(mx.init.Xavier())
   xx = nd.random_uniform(shape=[4,12,128,128])
   out1, out2 = mynet(xx)
   imshow(out1[0,0].asnumpy())
   ```
   this produces the following image: 
   ![image](https://user-images.githubusercontent.com/14290519/37761966-1781ea28-2df6-11e8-936d-2bd0726b1441.png)
   
   
   Now say you want to visualize the weights only of a layer, without the convolution. For example you can obtain the weights of self.layer2 or self.layer1 like this: 
   
   ```Python
   layer2_weight = mynet.layer2.weight.data() # This is an NDArray object
   ```
   
   ```Python
   mynet.layer2.weight.data().shape 
   # prints (12,12,3,3) for this particular example. 
   ```
   so you can create histograms etc. Hope this helps. 

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