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TensorFlow: merge one more commit from internal

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Vijay Vasudevan 2015-12-06 16:39:01 -08:00
commit f0b18a2364
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15
tensorflow/g3doc/tutorials/image_recognition/index.md
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@ -49,9 +49,9 @@ Andrej Karpathy who attempted to measure his own performance. He reached
[blog post]: http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/
This tutorial will teach you how to use [Inception-v3]. You'll learn how to
classify images into [1000 classes] in Python or C++. You'll learn how
to run the model on mobile devices. You'll also learn how to extract higher
level features from this model which may be reused for other vision tasks.
classify images into [1000 classes] in Python or C++. We'll also discuss how to
extract higher level features from this model which may be reused for other
vision tasks.
We're excited to see what the community will do with this model.
@ -90,11 +90,10 @@ will need to point `--model_dir` to the directory used.
## Usage with the C++ API
You can run the same [Inception-v3] model in
C++, though it's packaged in a slightly more compact file, because we don't need
to keep some data that's only used for training. You can download the archive
containing the GraphDef that defines the model like this (running from the root
directory of the TensorFlow repository):
You can run the same [Inception-v3] model in C++ for use in production
environments. You can download the archive containing the GraphDef that defines
the model like this (running from the root directory of the TensorFlow
repository):
```bash
wget https://storage.googleapis.com/download.tensorflow.org/models/inception_dec_2015.zip -O tensorflow/examples/label_image/data/inception_dec_2015.zip

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tensorflow/g3doc/tutorials/index.md
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@ -97,9 +97,7 @@ trained on ImageNet Challenge data and label set.
[View Tutorial](../tutorials/image_recognition/index.md)
We will be releasing code for training a state-of-the-art Inception model.
COMING SOON
We will soon be releasing code for training a state-of-the-art Inception model.
## Deep Dream Visual Hallucinations