提交 d5a6c81d 编写于 作者: W wangmeng28

Update docs for factorization machine layer

上级 6a0cfd94
...@@ -36,8 +36,7 @@ namespace paddle { ...@@ -36,8 +36,7 @@ namespace paddle {
* *
* The detailed calculation for forward and backward can be found at this paper: * The detailed calculation for forward and backward can be found at this paper:
* *
* Rendle, Steffen. Factorization machines. IEEE 10th International * Factorization machines.
* Conference on Data Mining (ICDM). IEEE, 2010.
* *
* The config file api is factorization_machine. * The config file api is factorization_machine.
*/ */
...@@ -59,7 +58,7 @@ private: ...@@ -59,7 +58,7 @@ private:
// The result of input matrix * latent vector matrix that will be used in // The result of input matrix * latent vector matrix that will be used in
// both forward and backward step // both forward and backward step
MatrixPtr inputMulFactor_; MatrixPtr inputMulFactor_;
// Temporary calculation result store // Store temporary calculation result
MatrixPtr tmpOut_; MatrixPtr tmpOut_;
MatrixPtr tmpSum_; MatrixPtr tmpSum_;
// Negative identity matrix // Negative identity matrix
......
...@@ -7307,8 +7307,7 @@ def factorization_machine(input, ...@@ -7307,8 +7307,7 @@ def factorization_machine(input,
each latent vector is k. each latent vector is k.
For details of Factorization Machine, please refer to the paper: For details of Factorization Machine, please refer to the paper:
Rendle, Steffen. Factorization machines. IEEE 10th International Factorization machines.
Conference on Data Mining (ICDM). IEEE, 2010.
.. code-block:: python .. code-block:: python
factor_machine = factorization_machine(input=input_layer, factor_size=10) factor_machine = factorization_machine(input=input_layer, factor_size=10)
......
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