Problema
After a neuron adds up its weighted inputs, an activation function decides its output. Without one, a neural network could only learn straight lines.
Implement activate(name, values) and apply the named function to every value:
| name | formula |
|---|---|
"sigmoid" | 1 / (1 + e^(-x)) |
"relu" | max(0, x) |
"leaky_relu" | x if x > 0, otherwise 0.01 × x |
"tanh" | (e^x - e^(-x)) / (e^x + e^(-x)) |
Input
name: One of "sigmoid", "relu", "leaky_relu" or "tanh".
values: A list of numbers.
Output
Return a list with the activation applied to each value, in the same order.
Restricciones
1 ≤ length of values ≤ 1,000
-50 ≤ each value ≤ 50
Ejemplos
Ejemplo 1
Entrada:
name = "sigmoid"
values = [0.0, 2.0, -2.0]
Salida esperada:
expected = [0.5, 0.880797, 0.119203]Entrada:
std::string name = "sigmoid";
std::vector<double> values = {0.0, 2.0, -2.0};
Salida esperada:
std::vector<double> expected = {0.5, 0.880797, 0.119203};Entrada:
string name = "sigmoid";
double[] values = new double[] {0.0, 2.0, -2.0};
Salida esperada:
double[] expected = new double[] {0.5, 0.880797, 0.119203};Explicación
sigmoid(0) = 1 / (1 + 1) = 0.5, sigmoid(2) ≈ 0.880797 and sigmoid(-2) ≈ 0.119203. Sigmoid squeezes any number into the range 0 to 1.
Ejemplo 2
Entrada:
name = "relu"
values = [-1.5, 0.0, 3.0]
Salida esperada:
expected = [0.0, 0.0, 3.0]Entrada:
std::string name = "relu";
std::vector<double> values = {-1.5, 0.0, 3.0};
Salida esperada:
std::vector<double> expected = {0.0, 0.0, 3.0};Entrada:
string name = "relu";
double[] values = new double[] {-1.5, 0.0, 3.0};
Salida esperada:
double[] expected = new double[] {0.0, 0.0, 3.0};Explicación
ReLU keeps positive values and turns negative values into 0: [0, 0, 3].
Referencias
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