# mx.nd.LogisticRegressionOutput¶

## Description¶

Applies a logistic function to the input.

The logistic function, also known as the sigmoid function, is computed as $$\frac{1}{1+exp(-\textbf{x})}$$.

Commonly, the sigmoid is used to squash the real-valued output of a linear model $$wTx+b$$ into the [0,1] range so that it can be interpreted as a probability. It is suitable for binary classification or probability prediction tasks.

Note

Use the LogisticRegressionOutput as the final output layer of a net.

The storage type of label can be default or csr

• LogisticRegressionOutput(default, default) = default

• LogisticRegressionOutput(default, csr) = default

The loss function used is the Binary Cross Entropy Loss:

$$-{(y\log(p) + (1 - y)\log(1 - p))}$$

Where y is the ground truth probability of positive outcome for a given example, and p the probability predicted by the model. By default, gradients of this loss function are scaled by factor 1/m, where m is the number of regression outputs of a training example. The parameter grad_scale can be used to change this scale to grad_scale/m.

## Arguments¶

Argument

Description

data

NDArray-or-Symbol.

Input data to the function.

label

NDArray-or-Symbol.

Input label to the function.

grad.scale

float, optional, default=1.

Scale the gradient by a float factor

## Value¶

out The result mx.ndarray