Backends/SiaNet.Backend.MxNet/NDArrayOps.cs
/// <summary>
/// Applies a logistic function to the input.The logistic function, also known as the sigmoid function, is computed as:math:`\frac{1}{1+exp(-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... note::   Use the LogisticRegressionOutput as the final output layer of a net.By default, gradients of this loss function are scaled by factor `1/n`, where n is the number of training examples.The parameter `grad_scale` can be used to change this scale to `grad_scale/n`.Defined in G:\deeplearn\mxnet\src\operator\regression_output.cc:L111
/// </summary>
/// <param name="data">Input data to the function.</param>
/// <param name="label">Input label to the function.</param>
/// <param name="grad_scale">Scale the gradient by a float factor</param>
/// <returns>returns new symbol</returns>
Applies a logistic function to the input.The logistic function, also known as the sigmoid function, is computed as:math:`\frac{1}{1+exp(-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...
Applies a logistic function to the input . The logistic function also known as the sigmoid function is computed as : math : ` \ frac { 1 } { 1 + exp ( - 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 ...
public static NDArray LogisticRegressionOutput(NDArray data,
        NDArray label,
        float grad_scale = 1f)
        {
            return new Operator("LogisticRegressionOutput")
            .SetParam("grad_scale", grad_scale)
            .SetInput("data", data)
            .SetInput("label", label)
            .Invoke();
        }
public static NDArray LogisticRegressionOutput ( NDArray data , NDArray label , float grad_scale = 1f ) { return new Operator ( " " ) . SetParam ( " " , grad_scale ) . SetInput ( " " , data ) . SetInput ( " " , label ) . Invoke ( ) ; }
Applies a logistic function to the input.The logistic function, also known as the sigmoid function, is computed as:math:`\frac{1}{1+exp(-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... note::   Use the LogisticRegressionOutput as the final output layer of a net.By default, gradients of this loss function are scaled by factor `1/n`, where n is the number of training examples.The parameter `grad_scale` can be used to change this scale to `grad_scale/n`.Defined in G:\deeplearn\mxnet\src\operator\regression_output.cc:L111
Applies a logistic function to the input . The logistic function also known as the sigmoid function is computed as : math : ` \ frac { 1 } { 1 + exp ( - 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 ... note :: Use the LogisticRegressionOutput as the final output layer of a net . By default gradients of this loss function are scaled by factor ` 1 / n ` where n is the number of training examples . The parameter ` grad_scale ` can be used to change this scale to ` grad_scale / n ` . Defined in G : \ deeplearn \ mxnet \ src \ operator \ regression_output . cc : L111
returns new symbol
returns new symbol
data
NDArray
Input data to the function.
Input data to the function .
label
NDArray
Input label to the function.
Input label to the function .
grad_scale
float
Scale the gradient by a float factor
Scale the gradient by a float factor