- 22 10月, 2018 1 次提交
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由 Xin Pan 提交于
test=develop
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- 27 6月, 2018 1 次提交
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由 qiaolongfei 提交于
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- 11 6月, 2018 1 次提交
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由 dzhwinter 提交于
* "add inplace attribute" * "register inplace attribute" * "change se-next model for memory-reuse" * "fix typo" * repick * fix merge conflict * "fix stupid error"
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- 08 5月, 2018 1 次提交
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由 Yu Yang 提交于
Do not use ctor * Reduce line of codes. * We can use virtual function for Maker now. * The implementation does not care what maker holds, it is easier to refactor later.
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- 06 5月, 2018 1 次提交
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由 dzhwinter 提交于
* "optimizer op support float64" * "fix ci" * "fix ftrl op"
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- 12 2月, 2018 1 次提交
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由 qingqing01 提交于
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- 10 2月, 2018 2 次提交
- 20 12月, 2017 1 次提交
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由 Yu Yang 提交于
* Move framework.proto to proto namespace * Fix compile * Fix compile * Fix Compile
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- 12 12月, 2017 2 次提交
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由 QI JUN 提交于
There are mainly following fixes: - take `DeviceContext` as the template parameter of math functors and OpKernel instead of `Place` - remove `eigen_device` interface in base class `DeviceContext` - remove `GetEigenDevice` interface in `ExecutionContext` and base class `DeviceContext` - remove unused `platform::EigenDeviceConverter` - rename `REGISTER_OP_GPU_KERNEL` to `REGISTER_OP_CUDA_KERNEL` - rename `USE_GPU_ONLY_OP` to `USE_CUDA_ONLY_OP`
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由 kavyasrinet 提交于
* Updating the Latex equation for Adagrad * Fixing Latex euqations for adadelta, adam and adamax
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- 21 11月, 2017 1 次提交
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由 Yu Yang 提交于
* Support many data types of several operators * SeqConv only support float/double * Revert adagrad
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- 05 11月, 2017 1 次提交
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由 kavyasrinet 提交于
* Adding the doc format for AdaDelta * Updating the documentation for Adagrad, Adam and Adamax * Updating the auc op * Fix review comments * Updating doc for Batch Norm * Updating the cast op * Updating the clip op * Fixing review comment * Fixing review comment: * Small change to restart PR_CI
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- 20 10月, 2017 1 次提交
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由 Abhinav Arora 提交于
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- 17 10月, 2017 1 次提交
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由 Yu Yang 提交于
They are public now
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- 13 10月, 2017 1 次提交
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由 Abhinav Arora 提交于
* add adam op moment1_out = beta1 * moment1 + (1 − beta1) * grad moment2_out = beta2 * moment2 + (1 − beta2) * grad * grad moment1_hat = moment1_out / (1 - beta1^t) moment2_hat = moment2_out / (1 - beta2^t) param_out = param - learning_rate * moment1_hat / (sqrt(moment2_hat) + epsilon) * fix moment 2 * Adding the Adam optimization operator * Adding more tests for Adam op
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