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hi my name is Chris Anderson and I will be demoing beholder for you beholder is a plug-in for tensor board that you can use to visualize things live while your network trains before I describe what's going on in the visualization itself I'll show you how you can use it in your code first there's a constructor that you need to import here and sometime before your train loop you instantiate that constructor that you just imported and then somewhere in your train loop you tell the visualizer to update there's two optional parameters by default it just visualizes TF trainable variables but you can also pass in arrays so here I'm passing in the gradients for that particular time step as well as a frame that I built which is just the images for that particular batch the input images so going back here we have a few convolutional layers and then if you fully connected layers with biases in between them you can look at either the current values where dark is low relative to that layer and white is high or you can look at variants over time steps of those values so black means that it's not changing very much and wide that it is means that it is changing at least relative to that layer you can also scale it by all sections to see which things are changing a lot relative to the rest of the entire network okay so I was also passing in the gradients too right here you can look at the current values of the gradients over time I'll slow that down to one update per second that number of updates also isn't just what you're seeing it's also how often it's being computed so if you're going to let your network train overnight or something you can set it to zero and then there's really minimal overhead so I'll describe what you're looking at we'll look at this convolutional layer here where the filters are three high three wide there's ten in channels and 20 output channels each of these sort of columns here represents a single filter and it is composed of a bunch of 3x3 blocks and every row corresponds to the same input channel so every column is an output Channel and every row is an input channel so if your network has finished draining if it's converged and you notice that a lot of the columns look really similar it means that there might be some redundancy in your network here's what the the frame option looks like this is just an image that I built myself you can pass in whatever kind of image that you want another thing that might that I haven't mentioned yet is the the recording section so if you wanted to take a look at your variables over time it's kind of hard to see how they change you can record a video here if you have ffmpeg installed it will send frames to ffmpeg in it it will build an mp4 for you which is saves as soon as you hit stop recording if you don't have ffmpeg installed then it will save pngs to disk another thing is fully connected layers so these are reshaped so that each column of values corresponds to the same output nodes they're all connected to the same output node this is an alpha notice the bias that's associated with it and each row corresponds to the same input node they're always connected to the same thin side node the start a bigger network so you can see what that looks like so this is a pre-trained vgg network with some extra stuff tacked on to the end of the network after the convolutional layers so here's the values here one of the things that's valuable about the holder I think is the questions that it brings up so when you to look at this convolutional layer and you see there's this teeny 3x3 section that is um higher in magnitude than the rest of this part of the layer makes you wonder why is that why is it that this input channel seems to be so distinctive I'm compared to the rest of the input channels which look kind of random I'm not actually showing all of the values of the vgg network here but if you want to you can and it produces a huge image give it a second okay so here's what all the parameters of vgg looks like and it is showing a minimum of one pixel per parameter and then here's the here's the untrained fully connected layer at the end if you want to download and install it you can all the instructions are here on the repo right here and let me know what you think I would love to hear what struggles you have what issues you have things that you would recommend things that you would like to see visualized differently any sort of feedback at all would be wonderful thank you
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