From 87f46ebb368929feae76b7d909944b317d7dad92 Mon Sep 17 00:00:00 2001 From: Siddharth Goyal Date: Tue, 2 Jan 2018 14:46:49 -0800 Subject: [PATCH] Add squared error layers doc (#6862) --- python/paddle/v2/fluid/layers/nn.py | 32 +++++++++++++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py index 55b35ad543..55d8bf8a8a 100644 --- a/python/paddle/v2/fluid/layers/nn.py +++ b/python/paddle/v2/fluid/layers/nn.py @@ -426,8 +426,36 @@ def cross_entropy(input, label, **kwargs): def square_error_cost(input, label, **kwargs): """ - This functions returns the squared error cost using the input and label. - The output is appending the op to do the above. + **Square error cost layer** + + 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) minus_out = helper.create_tmp_variable(dtype=input.dtype) -- GitLab