Activation functions
- statinf.ml.activations.elu(x, alpha=1.0)[source]
Exponential Linear Unit activation function.
- Parameters
x (
float
ornumpy.array
) – Input value- Formula
- \[\begin{split}\mathrm{elu}(x) = \begin{cases} x, & x > 0\\ \alpha \left(e^{x} - 1\right), & x \le 0 \end{cases}\end{split}\]
- Returns
Activated value.
- Return type
float
- statinf.ml.activations.logit(x, weights, bias=0)[source]
Logistic function
- Parameters
x (numpy.array) – Input value
weights (numpy.array) – Vector of weights \(\beta\)
bias (numpy.array) – Vector of bias \(\epsilon\), defaults to 0.
- Returns
Logistic transformation: \(\mathrm{logit}(x, \beta) = \dfrac{1}{1 + e^{-x \beta}}\)
- Return type
float
- statinf.ml.activations.relu(x)[source]
Rectified Linear Unit activation function.
- Parameters
x (
float
ornumpy.array
) – Input value- Returns
Activated value: \(\mathrm{relu}(x) = \max(0, x)\)
- Return type
float
- statinf.ml.activations.sigmoid(x)[source]
Sigmoid activation function.
- Parameters
x (
float
ornumpy.array
) – Input value- Returns
Sigmoid activated value: \(sigmoid(x) = \dfrac{1}{1 + e^{-x}}\)
- Return type
float
- statinf.ml.activations.softmax(x, axis=-1)[source]
Softmax activation function.
- Parameters
x (
float
ornumpy.array
) – Input value- Returns
Activated value: \(\mathrm{softmax}(x) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}\)
- Return type
float