diff --git a/doc/fluid/design/motivation/api.md b/doc/fluid/design/motivation/api.md index e6a4638d9100d9b07c3ee6b92b530a17eae1c162..bc222564e3ec28e306ca0572b6a23104f6e9cbc5 100644 --- a/doc/fluid/design/motivation/api.md +++ b/doc/fluid/design/motivation/api.md @@ -77,8 +77,7 @@ print "The sematic-vector of testA: ", paddle.infer(fA, parameters, testA) ### Example 2. Sharing Parameters between "Models" -We use [GAN](https://github.com/PaddlePaddle/book/tree/develop/gan) in -this example. In the following example program, `d0` and `d1` +We use GAN in this example. In the following example program, `d0` and `d1` correspond to the two networks in the following figure: diff --git a/doc/fluid/design/multi_devices/operator_kernel_type.md b/doc/fluid/design/multi_devices/operator_kernel_type.md index 8c1bc8f76a337006497e5ab5e5a710f9f49261b8..5e391bd62b4f4e123a9a6f35b7adf5726f205635 100644 --- a/doc/fluid/design/multi_devices/operator_kernel_type.md +++ b/doc/fluid/design/multi_devices/operator_kernel_type.md @@ -75,7 +75,7 @@ Different layout leads to different implementation of the operator kernel. There - The inference of Layout is at run-time, not at compile-time. -- Every operator has to implement different kernels for different layouts. Let's take MKLDNN as an example. If we want to implement an MKLDNN convolution operator, we have to implement all the kernels for different layouts, which are listed [here](http://01org.github.io/mkl-dnn/structmkldnn_1_1memory.html). And we will have a special macro to register kernels for MKLDNN operators. +- Every operator has to implement different kernels for different layouts. Let's take MKLDNN as an example. If we want to implement an MKLDNN convolution operator, we have to implement all the kernels for different layouts, which are listed [here](http://intel.github.io/mkl-dnn/structmkldnn_1_1memory.html). And we will have a special macro to register kernels for MKLDNN operators. `Layout` is also defined as a enum variable: