Dannjs
2.3.13Deep Neural Network Library for JavaScript.
Add
( )
activation
(
name
activation
derivative
)
Add a custom activation function.
name
String
the name of the new activation function.
activation
Function
the activation function.
derivative
Function
the derivative of this activation function.
Example
Add.activation('myfunc',
(x) => {
if (x <= 0) {
return 0;
} else {
return 1;
}
},
(x) => {
return 0;
}
);
let nn = new Dann();
nn.outputActivation('myfunc');
nn.log();
loss
(
name
loss
)
Add a custom loss function.
name
String
the name of the new loss function.
loss
Function
the loss function.
Example
Add.loss('myfunc',
(predictions, target) => {
let sum = 0;
let ans = 0;
let n = target.length;
for (let i = 0; i < n; i++) {
let y = target[i];
let yHat = predictions[i];
sum += abs(y - yHat);
}
ans = sum / n;
return ans;
}
);
let nn = new Dann();
nn.setLossFunction('myfunc');
nn.log();