brainstorm/layers/squared_error_layer.py
def SquaredError(name=None):
    """
    Create a SquaredError layer which computes half of the squared difference
    between the inputs `default` and `targets` element-wise. The factor of half
    is used to be consistent with common machine learning texts and resources.

    Produces outputs named `predictions` and `loss`. The `loss` output can be
    connected to a ``Loss`` layer for typical network training for a
    regression task.

    This layer acts similar to ``SigmoidCE`` and ``SoftmaxCE`` layers. Like
    the above layers, it does not compute the gradients w.r.t. the `targets`
    input and ignores incoming deltas w.r.t. the `predictions` output.
    """
    return ConstructionWrapper.create(SquaredErrorLayerImpl, name=name)

    Create a SquaredError layer which computes half of the squared difference
    between the inputs `default` and `targets` element-wise. The factor of half
    is used to be consistent with common machine learning texts and resources.

    Produces outputs named `predictions` and `loss`. The `loss` output can be
    connected to a ``Loss`` layer for typical network training for a
    regression task.

    This layer acts similar to ``SigmoidCE`` and ``SoftmaxCE`` layers. Like
    the above layers, it does not compute the gradients w.r.t. the `targets`
    input and ignores incoming deltas w.r.t. the `predictions` output.
    
Create a SquaredError layer which computes half of the squared difference
between the inputs `default` and `targets` element-wise. The factor of half
is used to be consistent with common machine learning texts and resources.

Produces outputs named `predictions` and `loss`.


Create a SquaredError layer which computes half of the squared difference between the inputs ` default ` and ` targets ` element - wise . The factor of half is used to be consistent with common machine learning texts and resources . Produces outputs named ` predictions ` and ` loss ` .
def SquaredError(name=None):
    return ConstructionWrapper.create(SquaredErrorLayerImpl, name=name)
def SquaredError ( name = None ) : return ConstructionWrapper . create ( SquaredErrorLayerImpl , name = name )
Create a SquaredError layer which computes half of the squared difference
between the inputs `default` and `targets` element-wise.
Create a SquaredError layer which computes half of the squared difference between the inputs ` default ` and ` targets ` element - wise .
"""
    Create a SquaredError layer which computes half of the squared difference
    between the inputs `default` and `targets` element-wise. The factor of half
    is used to be consistent with common machine learning texts and resources.

    Produces outputs named `predictions` and `loss`. The `loss` output can be
    connected to a ``Loss`` layer for typical network training for a
    regression task.

    This layer acts similar to ``SigmoidCE`` and ``SoftmaxCE`` layers. Like
    the above layers, it does not compute the gradients w.r.t. the `targets`
    input and ignores incoming deltas w.r.t. the `predictions` output.
    """
name