提交 87f46ebb 编写于 作者: S Siddharth Goyal 提交者: Abhinav Arora

Add squared error layers doc (#6862)

上级 27fea24f
...@@ -426,8 +426,36 @@ def cross_entropy(input, label, **kwargs): ...@@ -426,8 +426,36 @@ def cross_entropy(input, label, **kwargs):
def square_error_cost(input, label, **kwargs): def square_error_cost(input, label, **kwargs):
""" """
This functions returns the squared error cost using the input and label. **Square error cost layer**
The output is appending the op to do the above.
This layer accepts input predictions and target label and returns the squared error cost.
For predictions, :math:`X`, and target labels, :math:`Y`, the equation is:
.. math::
Out = (X - Y)^2
In the above equation:
* :math:`X`: Input predictions, a tensor.
* :math:`Y`: Input labels, a tensor.
* :math:`Out`: Output value, same shape with :math:`X`.
Args:
input(Variable): Input tensor, has predictions.
label(Variable): Label tensor, has target labels.
Returns:
Variable: The tensor variable storing the element-wise squared error difference \
of input and label.
Examples:
.. code-block:: python
y = layers.data(name='y', shape=[1], dtype='float32')
y_predict = layers.data(name='y_predict', shape=[1], dtype='float32')
cost = layers.square_error_cost(input=y_predict, label=y)
""" """
helper = LayerHelper('square_error_cost', **kwargs) helper = LayerHelper('square_error_cost', **kwargs)
minus_out = helper.create_tmp_variable(dtype=input.dtype) minus_out = helper.create_tmp_variable(dtype=input.dtype)
......
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