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| ``mx.symbol.LogisticRegressionOutput`` |
| ============================================================================ |
| |
| Description |
| ---------------------- |
| |
| Applies a logistic function to the input. |
| |
| The logistic function, also known as the sigmoid function, is computed as |
| :math:`\frac{1}{1+exp(-\textbf{x})}`. |
| |
| Commonly, the sigmoid is used to squash the real-valued output of a linear model |
| :math:`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. |
| |
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| .. note:: Use the LogisticRegressionOutput as the final output layer of a net. |
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| 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: |
| |
| :math:`-{(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`. |
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| Usage |
| ---------- |
| |
| .. code:: r |
| |
| mx.symbol.LogisticRegressionOutput(...) |
| |
| 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 | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``name`` | string, optional. | |
| | | | |
| | | Name of the resulting symbol. | |
| +----------------------------------------+------------------------------------------------------------+ |
| |
| Value |
| ---------- |
| |
| ``out`` The result mx.symbol |
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| Link to Source Code: http://github.com/apache/incubator-mxnet/blob/1.6.0/src/operator/regression_output.cc#L152 |
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