# mxnet.ndarray.sparse.LinearRegressionOutput¶

mxnet.ndarray.sparse.LinearRegressionOutput(data=None, label=None, grad_scale=_Null, out=None, name=None, **kwargs)

Computes and optimizes for squared loss during backward propagation. Just outputs data during forward propagation.

If $$\hat{y}_i$$ is the predicted value of the i-th sample, and $$y_i$$ is the corresponding target value, then the squared loss estimated over $$n$$ samples is defined as

$$\text{SquaredLoss}(\textbf{Y}, \hat{\textbf{Y}} ) = \frac{1}{n} \sum_{i=0}^{n-1} \lVert \textbf{y}_i - \hat{\textbf{y}}_i \rVert_2$$

Note

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

The storage type of label can be default or csr

• LinearRegressionOutput(default, default) = default

• LinearRegressionOutput(default, csr) = default

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.

Defined in src/operator/regression_output.cc:L92

Parameters
• data (NDArray) – Input data to the function.

• label (NDArray) – Input label to the function.

• grad_scale (float, optional, default=1) – Scale the gradient by a float factor

• out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays