Interface administrators, Administrators (Semantic MediaWiki), Curators (Semantic MediaWiki), Editors (Semantic MediaWiki), Suppressors, Administrators
7,785
edits
Input layer: Difference between revisions
no edit summary
No edit summary |
No edit summary |
||
| Line 1: | Line 1: | ||
{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
[[Input layer]] is the first [[layer]] of a [[neural network]]. It contains the [[feature vector]] of the [[input data]] ([[example]]). | [[Input layer]] is the first [[layer]] of a [[neural network]]. It contains the [[feature vector]] of the [[input data]] ([[example]]). | ||
The input layer is typically the initial stage in a neural network and consists of [[neuron]]s that process input data. Here, the number of neurons is equal to the number of [[features]] present in the input data - for instance, if an image has 28 height of pixels and 28 width of pixels, respectively, with three color channels (red, green and blue), then we would expect there to be 28 * 28 * 3 = 2352 neurons in this layer. | The input layer is typically the initial stage in a neural network and consists of [[neuron]]s that process input data. Here, the number of neurons is equal to the number of [[features]] present in the input data - for instance, if an image has 28 height of pixels and 28 width of pixels, respectively, with three color channels (red, green and blue), then we would expect there to be 28 * 28 * 3 = 2352 neurons in this layer. | ||
Each neuron in the input layer is connected to all neurons in the next [[layer]], allowing | Each neuron in the input layer is connected to all neurons in the next [[layer]], typically a [[hidden layer]], allowing the neuron to pass on input data for processing. These connections between neurons are known as [[weights]] and represent the strength of the connection. Weights are updated during [[training]] in order to minimize [[error]]s between actual [[output]] and desired output ([[label]]). | ||
==Activation Function in the Input Layer== | ==Activation Function in the Input Layer== | ||
| Line 16: | Line 16: | ||
==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||
Okay! Let's say you want your computer to learn to recognize different shapes. For this task, show the machine a variety of shapes and explain what each one is called. | |||
Okay! Let's say you want your computer to learn to recognize different shapes. | |||
The initial step is to give shapes to the computer. Think of an input layer as a big basket where shapes can be placed. From there, the computer can analyze these shapes and begin learning from them. | The initial step is to give shapes to the computer. Think of an input layer as a big basket where shapes can be placed. From there, the computer can analyze these shapes and begin learning from them. | ||
Just as you need to put your toys in a basket before playing with them, the computer | Just as you need to put your toys in a basket before playing with them, the computer needs to have the shapes in the input layer before it can begin learning about them. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] | ||