提交 46ae1c93 编写于 作者: Q qiaolongfei

add doc for softmax

上级 b77c886e
......@@ -1258,6 +1258,45 @@ def sequence_softmax(input, param_attr=None, bias_attr=None, use_cudnn=True):
def softmax(input, param_attr=None, bias_attr=None, use_cudnn=True, name=None):
"""
The input of the softmax layer is a 2-D tensor with shape N x K (N is the
batch_size, K is the dimension of input feature). The output tensor has the
same shape as the input tensor.
For each row of the input tensor, the softmax operator squashes the
K-dimensional vector of arbitrary real values to a K-dimensional vector of real
values in the range [0, 1] that add up to 1.
It computes the exponential of the given dimension and the sum of exponential
values of all the other dimensions in the K-dimensional vector input.
Then the ratio of the exponential of the given dimension and the sum of
exponential values of all the other dimensions is the output of the softmax
operator.
For each row :math:`i` and each column :math:`j` in Input(X), we have:
.. math::
Out[i, j] = \\frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}
Args:
input (Variable): The input variable.
bias_attr (ParamAttr): attributes for bias
param_attr (ParamAttr): attributes for parameter
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn \
library is installed.
Returns:
Variable: output of softmax
Examples:
.. code-block:: python
fc = fluid.layers.fc(input=x, size=10)
softmax = fluid.layers.softmax(input=fc)
"""
helper = LayerHelper('softmax', **locals())
dtype = helper.input_dtype()
softmax_out = helper.create_tmp_variable(dtype)
......
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