Row 3803

Row ID: 3803 | Dataset Entry | Axioma AXP Content Repository

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Softmax is a regular activation function, sometimes they use it as layers also but it works the same.

Basically Softmax is used when the number of output class is more than 2.

It gives the probability for each class and its summation of Probabilities is equal to 1.

You have to choose the class of the output based on the maximum probability which class got.

You can't bring Explainablity in deep learning.

Basically activation function is used to squeeze the parameters And Induce Non Linearity to the Inputs.

If you are using Binary Class then you shouldn't use Softmax activation function.

FieldValue
text Softmax is a regular activation function, sometimes they use it as layers also but it works the same. Basically Softmax is used when the number of output class is more than 2. It gives the probability for each class and its summation of Probabilities is equal to 1. You have to choose the class of the output based on the maximum probability which class got. You can't bring Explainablity in deep learning. Basically activation function is used to squeeze the parameters And Induce Non Lineari…
label r/neuralnetworks
dataType comment
communityName r/neuralnetworks
datetime 2024-03-24
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{
  "text": "Softmax is a regular activation function, sometimes they use it as layers also but it works the same.\n\nBasically Softmax is used when the number of output class is more than 2.\n\nIt gives the probability for each class and its summation of Probabilities is equal to 1.\n\nYou have to choose the class of the output  based on the maximum probability which class got.\n\nYou can't bring Explainablity  in deep learning.\n\nBasically activation function is used to squeeze the parameters And Induce Non Linearity to the Inputs.\n\nIf you are using Binary Class then you shouldn't use Softmax activation function.",
  "label": "r/neuralnetworks",
  "dataType": "comment",
  "communityName": "r/neuralnetworks",
  "datetime": "2024-03-24",
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