Commit 22c95c54 authored by Junwei Pan's avatar Junwei Pan
Browse files

Fix Typo

parent d066ee74
...@@ -12,7 +12,7 @@ ...@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
# ============================================================================== # ==============================================================================
"""A library to train Inception using multiple GPU's with synchronous updates. """A library to train Inception using multiple GPUs with synchronous updates.
""" """
from __future__ import absolute_import from __future__ import absolute_import
from __future__ import division from __future__ import division
...@@ -83,7 +83,7 @@ def _tower_loss(images, labels, num_classes, scope, reuse_variables=None): ...@@ -83,7 +83,7 @@ def _tower_loss(images, labels, num_classes, scope, reuse_variables=None):
"""Calculate the total loss on a single tower running the ImageNet model. """Calculate the total loss on a single tower running the ImageNet model.
We perform 'batch splitting'. This means that we cut up a batch across We perform 'batch splitting'. This means that we cut up a batch across
multiple GPU's. For instance, if the batch size = 32 and num_gpus = 2, multiple GPUs. For instance, if the batch size = 32 and num_gpus = 2,
then each tower will operate on an batch of 16 images. then each tower will operate on an batch of 16 images.
Args: Args:
......
...@@ -13,7 +13,7 @@ ...@@ -13,7 +13,7 @@
# limitations under the License. # limitations under the License.
# ============================================================================== # ==============================================================================
"""A binary to train CIFAR-10 using multiple GPU's with synchronous updates. """A binary to train CIFAR-10 using multiple GPUs with synchronous updates.
Accuracy: Accuracy:
cifar10_multi_gpu_train.py achieves ~86% accuracy after 100K steps (256 cifar10_multi_gpu_train.py achieves ~86% accuracy after 100K steps (256
......
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