Tensorflow: Difference between revisions
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Below I've embedded a neural network classifier rendered in a [http://playground.tensorflow.org Tensorflow playground]. There are a variety of knobs, buttons, numbers, and pictures on the interface; I will explain what they all mean as we go along. For now though, let's define our primary goal throughout this tutorial... | |||
Our primary task is to train neural nets to classify items into 1 of 2 categories. Here we represent those categories as either an orange dot or blue dot. You can think of these dots as CASE and CTRL participants in an Alzheimer's Disease (AD) study. For the heck of it, say blue dots represent CASE and orange dots represent CTRL. | |||
In this first example, let's say... | |||
* dim-1 (x-axis): '''''braak score''''' | |||
* dim-2 (y-axis): '''''age''''' | |||
Notice the dots form clusters. If you were asked to draw a line on this plane, to separate these two clusters, it could be easily done. Our brain's neural nets have already solved the the spatial problem. Now let's see if an artificial neural net can solve the same problem. | |||
Go ahead and click the blue ''start'' button below; let it run for about 20 seconds, then click pause. | |||
{{#widget:Tensorflow1}} | |||
How did that single neuron do on the task? | |||
[[File: Tensorflow Tutorial img1.png]] | |||
Revision as of 01:53, 20 January 2018
Below I've embedded a neural network classifier rendered in a Tensorflow playground. There are a variety of knobs, buttons, numbers, and pictures on the interface; I will explain what they all mean as we go along. For now though, let's define our primary goal throughout this tutorial...
Our primary task is to train neural nets to classify items into 1 of 2 categories. Here we represent those categories as either an orange dot or blue dot. You can think of these dots as CASE and CTRL participants in an Alzheimer's Disease (AD) study. For the heck of it, say blue dots represent CASE and orange dots represent CTRL.
In this first example, let's say...
- dim-1 (x-axis): braak score
- dim-2 (y-axis): age
Notice the dots form clusters. If you were asked to draw a line on this plane, to separate these two clusters, it could be easily done. Our brain's neural nets have already solved the the spatial problem. Now let's see if an artificial neural net can solve the same problem.
Go ahead and click the blue start button below; let it run for about 20 seconds, then click pause.
{{#widget:Tensorflow1}}
How did that single neuron do on the task?
