![]() In other words, the visualization is showing the patches at the edge of the cloud of representations, along the (arbitrary) axes that correspond to the filter weights. Rather, it is more appropriate to think of multiple ReLU neurons as the basis vectors of some space that represents in image patches. One problem with this approach is that ReLU neurons do not necessarily have any semantic meaning by themselves. (In particular, note that the POOL5 neurons are a function of a relatively large portion of the input image!) It can be seen that some neurons are responsive to upper bodies, text, or specular highlights. The activation values and the receptive field of the particular neuron are shown in white. Maximally activating images for some POOL5 (5th pool layer) neurons of an AlexNet. One such visualization (among others) is shown in Rich feature hierarchies for accurate object detection and semantic segmentation by Ross Girshick et al.: We can then visualize the images to get an understanding of what the neuron is looking for in its receptive field. ![]() Retrieving images that maximally activate a neuronĪnother visualization technique is to take a large dataset of images, feed them through the network and keep track of which images maximally activate some neuron. The 2nd CONV layer weights are not as interpretable, but it is apparent that they are still smooth, well-formed, and absent of noisy patterns. The color/grayscale features are clustered because the AlexNet contains two separate streams of processing, and an apparent consequence of this architecture is that one stream develops high-frequency grayscale features and the other low-frequency color features. Notice that the first-layer weights are very nice and smooth, indicating nicely converged network. Check the 'Trim Elements to Story range' as well.Typical-looking filters on the first CONV layer (left), and the 2nd CONV layer (right) of a trained AlexNet. Activate the Filter and Cut Elements in 3D and change the option 'Stories to Show in 3D' to 'Limited' and set the required range.Select the proper Graphic Override Combinations.Change the Partial Display Structure to 'Core of Load-Bearing Elements Only'.For the first View do the following settings:.Find a good Camera View in 3D or save the Zoom within the 3D View.Set up the Graphic Overrides Combination and Rules.Make sure that all the elements belong to the right Home Story.Make sure that all the elements are assigned to a proper Layer.Make sure that all the Composite Structures have a 'Core'.Make sure that in the Element Default Settings under 'Categories and Properties' the 'Structural Function' and the 'Position' is defined.How to make similar GIFs? Presenting the Construction Process There is also a wide range of free applications for this purpose. Almost every image editor application can create animated GIFs. For creating the animated GIF you need an external application. In ARCHICAD you can publish Views in a GIF format but not as an animated GIF. In ARCHICAD the last step is publishing them into an image format. ![]() Tools for overriding elements in ARCHICAD:Īfter saving the 3D Views or 3D Documents (or being ready with the Layouts) a Publisher Set has to be done.
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