cost/loss function being a multi-well function in neural networks
$begingroup$
In a 0 or 1 binary classification problem using neural networks, given the activation function is taken as sigmoid, if one takes the cost/loss function as sum of square of differences, the loss function should be a multi-well function, since each of 0 and 1 tags contributes to a 'potential well'/loss function local minimum. But are the local minima all global minima? If not, how to initialize weights to not get them trapped in a local minimum in gradient descent computation?
neural-network loss-function cost-function weight-initialization
asked 2 mins ago
feynmanfeynman
1578
$endgroup$
add a comment |
$begingroup$
In a 0 or 1 binary classification problem using neural networks, given the activation function is taken as sigmoid, if one takes the cost/loss function as sum of square of differences, the loss function should be a multi-well function, since each of 0 and 1 tags contributes to a 'potential well'/loss function local minimum. But are the local minima all global minima? If not, how to initialize weights to not get them trapped in a local minimum in gradient descent computation?
neural-network loss-function cost-function weight-initialization
asked 2 mins ago
feynmanfeynman
1578
$endgroup$
add a comment |
$begingroup$
In a 0 or 1 binary classification problem using neural networks, given the activation function is taken as sigmoid, if one takes the cost/loss function as sum of square of differences, the loss function should be a multi-well function, since each of 0 and 1 tags contributes to a 'potential well'/loss function local minimum. But are the local minima all global minima? If not, how to initialize weights to not get them trapped in a local minimum in gradient descent computation?
neural-network loss-function cost-function weight-initialization
asked 2 mins ago
feynmanfeynman
1578
$endgroup$
In a 0 or 1 binary classification problem using neural networks, given the activation function is taken as sigmoid, if one takes the cost/loss function as sum of square of differences, the loss function should be a multi-well function, since each of 0 and 1 tags contributes to a 'potential well'/loss function local minimum. But are the local minima all global minima? If not, how to initialize weights to not get them trapped in a local minimum in gradient descent computation?
neural-network loss-function cost-function weight-initialization
neural-network loss-function cost-function weight-initialization
asked 2 mins ago
feynmanfeynman
1578
asked 2 mins ago
feynmanfeynman
1578
asked 2 mins ago
feynmanfeynman
1578
asked 2 mins ago
feynmanfeynman
1578
asked 2 mins ago
feynmanfeynman
1578
1578
add a comment |
add a comment |
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