How to visualize data

Visualizing the data we're working with is indispensible both to check that data pipelines are set up correctly and to check the predictions of a trained model. For visualization, the Makie.jl plotting package is used which requires you to install a plotting backend . Learning tasks define how the data is visualized, allowing you to use the following functions for visualization:

showsample ( showsamples): Visualize an unprocessed sample (usually a pair of inputs and targets) or a vector of samples.
showencodedsample ( showbatch): Visualize processed model input and output x, y or a batch of xs and ys.
showprediction ( showpredictions): Compare a model output with the ground truth.

To add support for these to a learning task, you have to implement the plotting interface for a block: showblock!.

Let's look at an example using the Cat/Dog classifier from the saving and loading tutorial.


			
			
			
			
			import
			
			 
			CairoMakie
			;
			 
			
			
			CairoMakie
			.
			
			activate!
			(
			
			type
			=
			
			"
			png
			"
			)
			

			
			using
			
			 

	
			FastAI
			

			

			
			
			task
			,
			 
			model
			 
			=
			 
			

	
			loadtaskmodel
			(
			
			"
			catsdogs.jld2
			"
			)
			

			
			dir
			 
			=
			 
			
			joinpath
			(
			
			load
			(
			
			

	
			datasets
			(
			)
			[
			
			"
			dogscats
			"
			]
			)
			,
			 
			
			"
			train
			"
			)
			

			
			data
			 
			=
			 
			

	
			loadfolderdata
			(
			dir
			,
			 
			
			filterfn
			=

	
			isimagefile
			,
			 
			
			loadfn
			=
			
			(

	
			loadfile
			,
			 

	
			parentname
			)
			)


			(mapobs(loadfile, ["/home/lorenz/.julia/datadeps/fastai-dogscats/dogscats/train/cats/cat.0.jpg", "…]), mapobs(parentname, ["/home/lorenz/.julia/datadeps/fastai-dogscats/dogscats/train/cats/cat.0.jpg", "…]))

First we load a vector of unprocessed samples, a batch of training data and the corresponding model outputs:


			
			
			
			idxs
			 
			=
			 
			
			rand
			(
			
			1
			:
			

	
			numobs
			(
			data
			)
			,
			 
			9
			)
			

			
			samples
			 
			=
			 
			
			[
			
			

	
			getobs
			(
			data
			,
			 
			i
			)
			 
			for
			
			 
			i
			 
			in
			 
			idxs
			]
			

			
			
			xs
			,
			 
			ys
			 
			=
			 
			

	
			makebatch
			(
			task
			,
			 
			data
			,
			 
			idxs
			)
			

			
			ŷs
			 
			=
			
			 
			
			

	
			gpu
			(
			model
			)
			(
			

	
			gpu
			(
			xs
			)
			)
			 
			|>
			 

	
			cpu


			2×9 Matrix{Float32}:
  2.81113  -2.68682    4.27251  -4.39263  …   2.25726  -2.52003  -4.23501
 -1.4409    0.872713  -4.20728   2.1302      -1.23509   1.5162    3.21313

Then we can visualize the data with the functions listed above:


			
			
			

	
			showsamples
			(
			task
			,
			 
			samples
			)


			
			
			

	
			showbatch
			(
			task
			,
			 
			xs
			,
			 
			ys
			)


			
			
			

	
			showpredictions
			(
			task
			,
			 
			xs
			,
			 
			ŷs
			,
			 
			ys
			)