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Diffstat (limited to 'tensorflow/g3doc/tutorials/deep_cnn/index.md')
| -rw-r--r-- | tensorflow/g3doc/tutorials/deep_cnn/index.md | 6 |
1 files changed, 2 insertions, 4 deletions
diff --git a/tensorflow/g3doc/tutorials/deep_cnn/index.md b/tensorflow/g3doc/tutorials/deep_cnn/index.md index 1491c91bae..57722ed18a 100644 --- a/tensorflow/g3doc/tutorials/deep_cnn/index.md +++ b/tensorflow/g3doc/tutorials/deep_cnn/index.md @@ -9,8 +9,6 @@ CIFAR-10 classification is a common benchmark problem in machine learning. The problem is to classify RGB 32x32 pixel images across 10 categories: ```airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.``` - - For more details refer to the [CIFAR-10 page](http://www.cs.toronto.edu/~kriz/cifar.html) and a [Tech Report](http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) by Alex Krizhevsky. @@ -117,7 +115,7 @@ learn more about how the `Reader` class works. The images are processed as follows: * They are cropped to 24 x 24 pixels, centrally for evaluation or - [randomly](../../api_docs/python/image.md#random_crop) for training. + [randomly](../../api_docs/python/constant_op.md#random_crop) for training. * They are [approximately whitened](../../api_docs/python/image.md#per_image_whitening) to make the model insensitive to dynamic range. @@ -168,7 +166,7 @@ Here is a graph generated from TensorBoard describing the inference operation: </div> > **EXERCISE**: The output of `inference` are un-normalized logits. Try editing -the network architecture to return normalized predictions using [`tf.softmax()`] +the network architecture to return normalized predictions using [`tf.nn.softmax()`] (../../api_docs/python/nn.md#softmax). The `inputs()` and `inference()` functions provide all the components |