Hi,

this theme is originally from stackoverflow. Unfortunately, the restrictions there don’t allow me to ask advanced questions, so Foivos told me that it would be a good idea to continue the conversation here.

In summary, it was about a keras net, which I wanted to convert to gluon, but which turned out to be difficult. Foivos then sent me the (probably) exact translation of the network. Unfortunately I couldn’t find out if the networks behave the same, because the results of the Class Activation Mapping (CAM), which I also converted from keras to gluon, were different (for details see: https://stackoverflow.com/questions/55186629/how-to-convert-a-cnn-from-keras-to-mxnet/55604738?noredirect=1#comment98154231_55604738).

Now I would like to continue with the topic where I had to stop at stackoverflow.

To be honest, I’m not quite sure if the net is doing the same thing. After the training I created a Class Activation Mapping (CAM) for both keras and mxnet to show me the relevant regions over Europe. Unfortunately I get different results. But maybe my implementation is wrong?

keras:

```
def get_cam(airport):
fname = os.path.normpath( "model_{}.h5".format(airport) )
model = load_model(fname)
dry_weights = model.layers[-1].get_weights()[0][:, 0].reshape((20,30,256))
rain_weights = model.layers[-1].get_weights()[0][:, 1].reshape((20,30,256))
get_output = K.function([model.layers[0].input], [model.layers[-3].output, model.layers[-1].output])
cam = np.zeros((20,30), dtype=np.float32)
for depth in range(256):
for i in range(30):
for j in range(20):
cam[j, i] += rain_weights[j, i, depth]
cam /= np.max(cam)
im = Image.fromarray(np.uint8(cm.jet(cam)*255))
im = im.resize((120,80), Image.ANTIALIAS)
#heatmap = cv2.applyColorMap(np.uint8(255*cam), cv2.COLORMAP_JET)
im.save("{}_rain.png".format(airport))
```

mxnet:

```
def get_cam(model):
dry_weights = model.last_layer.weight.data()[0].reshape((256,20,30)).asnumpy()
rain_weights = model.last_layer.weight.data()[1].reshape((256,20,30)).asnumpy()
cam = np.zeros((20,30), dtype=np.float32)
for depth in range(256):
for i in range(30):
for j in range(20):
cam[j, i] += rain_weights[j, i, depth]
cam /= np.max(cam)
im = Image.fromarray(np.uint8(cm.jet(cam)*255))
im = im.resize((120,80), Image.ANTIALIAS)
#heatmap = cv2.applyColorMap(np.uint8(255*cam), cv2.COLORMAP_JET)
im.save("{}_rain.png".format(airport))
```

call it with:

```
model = YourNet()
model.load_parameters(in_file, ctx=ctx)
get_cam(model)
```

I thought, since the channels appear first, I would also have to change the order of the reshape. In any case, I also tried the original order, but with equally bad results. Well, that’s what I get in both cases:

Any ideas?