Why do we Softmax at all?
$begingroup$
why take softmax at all at the final layer for multi classification problems? For example softmax of the vector [1, .5]
Is [.621, .379]
I mean if we just took the straight ratio, it'd give me
[.667, .333] instead
Does that really make a difference?
Is it cause the vector can have negative numbers that we softmax things? What benefit do we get from making an odder way to give a ratio/probability to certain numbers as opposed to just taking a ratio of the numbers?
neural-network classification multiclass-classification
asked 1 min ago
katiex7katiex7
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add a comment |
$begingroup$
why take softmax at all at the final layer for multi classification problems? For example softmax of the vector [1, .5]
Is [.621, .379]
I mean if we just took the straight ratio, it'd give me
[.667, .333] instead
Does that really make a difference?
Is it cause the vector can have negative numbers that we softmax things? What benefit do we get from making an odder way to give a ratio/probability to certain numbers as opposed to just taking a ratio of the numbers?
neural-network classification multiclass-classification
asked 1 min ago
katiex7katiex7
1061
New contributor
katiex7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
why take softmax at all at the final layer for multi classification problems? For example softmax of the vector [1, .5]
Is [.621, .379]
I mean if we just took the straight ratio, it'd give me
[.667, .333] instead
Does that really make a difference?
Is it cause the vector can have negative numbers that we softmax things? What benefit do we get from making an odder way to give a ratio/probability to certain numbers as opposed to just taking a ratio of the numbers?
neural-network classification multiclass-classification
asked 1 min ago
katiex7katiex7
1061
New contributor
katiex7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
why take softmax at all at the final layer for multi classification problems? For example softmax of the vector [1, .5]
Is [.621, .379]
I mean if we just took the straight ratio, it'd give me
[.667, .333] instead
Does that really make a difference?
Is it cause the vector can have negative numbers that we softmax things? What benefit do we get from making an odder way to give a ratio/probability to certain numbers as opposed to just taking a ratio of the numbers?
neural-network classification multiclass-classification
neural-network classification multiclass-classification
asked 1 min ago
katiex7katiex7
1061
New contributor
katiex7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
katiex7katiex7
1061
New contributor
katiex7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
katiex7katiex7
1061
New contributor
katiex7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
katiex7katiex7
1061
asked 1 min ago
katiex7katiex7
1061
1061
New contributor
katiex7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
katiex7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
katiex7 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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