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authorGravatar Patrick Nguyen <drpng@google.com>2017-04-17 20:41:44 -0800
committerGravatar TensorFlower Gardener <gardener@tensorflow.org>2017-04-17 22:15:14 -0700
commit69a4cf80a129af3fe46b6ff9c509be442d5a06f9 (patch)
tree518cff65134008841e91018be821e843500dabbd /tensorflow/examples/tutorials
parentcca1b71352d246fc292d6e6b9cda63810c659c83 (diff)
Merge changes from github.
Change: 153426348
Diffstat (limited to 'tensorflow/examples/tutorials')
-rw-r--r--tensorflow/examples/tutorials/mnist/mnist_with_summaries.py4
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
index 75ea0b9c67..698c97ca1d 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
@@ -14,7 +14,7 @@
# ==============================================================================
"""A simple MNIST classifier which displays summaries in TensorBoard.
- This is an unimpressive MNIST model, but it is a good example of using
+This is an unimpressive MNIST model, but it is a good example of using
tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of
naming summary tags so that they are grouped meaningfully in TensorBoard.
@@ -78,7 +78,7 @@ def train():
def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
"""Reusable code for making a simple neural net layer.
- It does a matrix multiply, bias add, and then uses relu to nonlinearize.
+ It does a matrix multiply, bias add, and then uses ReLU to nonlinearize.
It also sets up name scoping so that the resultant graph is easy to read,
and adds a number of summary ops.
"""