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authorGravatar Sergii Khomenko <sergii.khomenko@stylight.com>2018-09-01 21:19:10 +0200
committerGravatar Sergii Khomenko <sergii.khomenko@stylight.com>2018-09-01 21:19:10 +0200
commita749099117808b26dcbdece8b86f234b31ba092b (patch)
treea21498fc1941c2077b92b4873a3ba67bf872a7e8 /tensorflow/contrib/eager
parent543bc6a8b98e8d08ce9c25dcafe629e124f266eb (diff)
Fix minor typos
Diffstat (limited to 'tensorflow/contrib/eager')
-rw-r--r--tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb4
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb
index 315d7a4893..529c99b37c 100644
--- a/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb
+++ b/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb
@@ -66,7 +66,7 @@
"\n",
"[Image Source](https://commons.wikimedia.org/wiki/Surfing#/media/File:Surfing_in_Hawaii.jpg), License: Public Domain\n",
"\n",
- "Our goal is generate a caption, such as \"a surfer riding on a wave\". Here, we'll use an attention based model. This enables us to see which parts of the image the model focuses on as it generates a caption.\n",
+ "Our goal is to generate a caption, such as \"a surfer riding on a wave\". Here, we'll use an attention-based model. This enables us to see which parts of the image the model focuses on as it generates a caption.\n",
"\n",
"![Prediction](https://tensorflow.org/images/imcap_prediction.png)\n",
"\n",
@@ -128,7 +128,7 @@
"source": [
"## Download and prepare the MS-COCO dataset\n",
"\n",
- "We will use the [MS-COCO dataset](http://cocodataset.org/#home) to train our model. This dataset contains >82,000 images, each of which has been annotated with at least 5 different captions. The code code below will download and extract the dataset automatically. \n",
+ "We will use the [MS-COCO dataset](http://cocodataset.org/#home) to train our model. This dataset contains >82,000 images, each of which has been annotated with at least 5 different captions. The code below will download and extract the dataset automatically. \n",
"\n",
"**Caution: large download ahead**. We'll use the training set, it's a 13GB file."
]