This module will download dataset from https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz and https://dataset.bj.bcebos.com/cifar/cifar-100-python.tar.gz, parse train/test set into
This module will download dataset from https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz and https://dataset.bj.bcebos.com/cifar/cifar-100-python.tar.gz, parse train/test set into
paddle reader creators.
paddle reader creators.
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes,
with 6000 images per class. There are 50000 training images and 10000 test
with 6000 images per class. There are 50000 training images and 10000 test
@@ -192,7 +192,7 @@ class GradientClipByNorm(BaseGradientClipAttr):
...
@@ -192,7 +192,7 @@ class GradientClipByNorm(BaseGradientClipAttr):
"""
"""
Convert the input multidimensional Tensor :math:`X` to a multidimensional Tensor whose L2 norm does not exceed the given two-norm maximum ( :math:`clip\_norm` ).
Convert the input multidimensional Tensor :math:`X` to a multidimensional Tensor whose L2 norm does not exceed the given two-norm maximum ( :math:`clip\_norm` ).
The tensor is not passed through this class, but passed through the parametre of ``main_program`` in ``fluid.program_guard``.
The tensor is not passed through this class, but passed through the parameter of ``main_program`` in ``fluid.program_guard``.
This class limits the L2 norm of the input :math:`X` within :math:`clip\_norm`.
This class limits the L2 norm of the input :math:`X` within :math:`clip\_norm`.