brainstorm/layers/sigmoid_ce_layer.py
def SigmoidCE(name=None):
    """Create a sigmoid layer with integrated Binomial Cross Entropy loss.

    Applies the sigmoid activation function on 'default' input and puts the
    results (per-label probabilities) in 'predictions'.

    It also takes as 'targets' a binary vector and computes the binomial
    cross-entropy loss. The resulting losses are stored in the 'loss' output.

    WARNING:
        This layer does not compute derivatives wrt the 'targets' input.
        It also does not use the deltas coming in from the 'predictions'.
    """
    return ConstructionWrapper.create(SigmoidCELayerImpl, name=name)
Create a sigmoid layer with integrated Binomial Cross Entropy loss.

    Applies the sigmoid activation function on 'default' input and puts the
    results (per-label probabilities) in 'predictions'.

    It also takes as 'targets' a binary vector and computes the binomial
    cross-entropy loss. The resulting losses are stored in the 'loss' output.

    WARNING:
        This layer does not compute derivatives wrt the 'targets' input.
        It also does not use the deltas coming in from the 'predictions'.
    
Create a sigmoid layer with integrated Binomial Cross Entropy loss.
Applies the sigmoid activation function on 'default' input and puts the
results (per-label probabilities) in 'predictions'.

It also takes as 'targets' a binary vector and computes the binomial
cross-entropy loss. The resulting losses are stored in the 'loss' output.

This layer does not compute derivatives wrt the 'targets' input.
It also does not use the deltas coming in from the 'predictions'.
Create a sigmoid layer with integrated Binomial Cross Entropy loss . Applies the sigmoid activation function on ' default ' input and puts the results ( per - label probabilities ) in ' predictions ' . It also takes as ' targets ' a binary vector and computes the binomial cross - entropy loss . The resulting losses are stored in the ' loss ' output . This layer does not compute derivatives wrt the ' targets ' input . It also does not use the deltas coming in from the ' predictions ' .
def SigmoidCE(name=None):
    return ConstructionWrapper.create(SigmoidCELayerImpl, name=name)
def SigmoidCE ( name = None ) : return ConstructionWrapper . create ( SigmoidCELayerImpl , name = name )
Create a sigmoid layer with integrated Binomial Cross Entropy loss.
Create a sigmoid layer with integrated Binomial Cross Entropy loss .
"""Create a sigmoid layer with integrated Binomial Cross Entropy loss.

    Applies the sigmoid activation function on 'default' input and puts the
    results (per-label probabilities) in 'predictions'.

    It also takes as 'targets' a binary vector and computes the binomial
    cross-entropy loss. The resulting losses are stored in the 'loss' output.

    WARNING:
        This layer does not compute derivatives wrt the 'targets' input.
        It also does not use the deltas coming in from the 'predictions'.
    """
name