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Posted to commits@mxnet.apache.org by jx...@apache.org on 2017/07/30 01:44:48 UTC
[incubator-mxnet] branch master updated: Update README.md (#7248)
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jxie pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new bcdde36 Update README.md (#7248)
bcdde36 is described below
commit bcdde36cff9c475fd8187b2dfd27ff1b4014f60e
Author: Kai Li <11...@qq.com>
AuthorDate: Sun Jul 30 09:44:46 2017 +0800
Update README.md (#7248)
---
example/image-classification/README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/example/image-classification/README.md b/example/image-classification/README.md
index 25050f6..a008b23 100644
--- a/example/image-classification/README.md
+++ b/example/image-classification/README.md
@@ -39,7 +39,7 @@ commonly used options are listed as following:
| Argument | Comments |
| ----------------------------- | ---------------------------------------- |
-| `network` | The network to train, which is defined in [symbol/](https://github.com/dmlc/mxnet/tree/master/example/image-classification/symbol). Some networks may accept additional arguments, such as `--num-layers` is used to specify the number of layers in ResNet. |
+| `network` | The network to train, which is defined in [symbol/](https://github.com/dmlc/mxnet/tree/master/example/image-classification/symbols). Some networks may accept additional arguments, such as `--num-layers` is used to specify the number of layers in ResNet. |
| `data-train`, `data-val` | The data for training and validation. It can be either a filename or a directory. For the latter, all files in the directory will be used. But if `--benchmark 1` is used, then there two arguments will be ignored. |
| `gpus` | The list of GPUs to use, such as `0` or `0,3,4,7`. If an empty string `''` is given, then we will use CPU. |
| `batch-size` | The batch size for SGD training. It specifies the number of examples used for each SGD iteration. If we use *k* GPUs, then each GPU will compute *batch_size/k* examples in each time. |
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