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diff --git a/tensorflow/docs_src/tutorials/images/deep_cnn.md b/tensorflow/docs_src/tutorials/images/deep_cnn.md index 42ad484bbf..00996b82e6 100644 --- a/tensorflow/docs_src/tutorials/images/deep_cnn.md +++ b/tensorflow/docs_src/tutorials/images/deep_cnn.md @@ -40,7 +40,7 @@ designing larger and more sophisticated models in TensorFlow: and `tf.nn.local_response_normalization` (Chapter 3.3 in [AlexNet paper](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)). -* @{$summaries_and_tensorboard$Visualization} +* [Visualization](../../guide/summaries_and_tensorboard.md) of network activities during training, including input images, losses and distributions of activations and gradients. * Routines for calculating the @@ -114,7 +114,7 @@ The input part of the model is built by the functions `inputs()` and `distorted_inputs()` which read images from the CIFAR-10 binary data files. These files contain fixed byte length records, so we use `tf.FixedLengthRecordReader`. -See @{$reading_data#reading-from-files$Reading Data} to +See [Reading Data](../../api_guides/python/reading_data.md#reading-from-files) to learn more about how the `Reader` class works. The images are processed as follows: @@ -131,10 +131,10 @@ artificially increase the data set size: * Randomly distort the `tf.image.random_brightness`. * Randomly distort the `tf.image.random_contrast`. -Please see the @{$python/image$Images} page for the list of +Please see the [Images](../../api_guides/python/image.md) page for the list of available distortions. We also attach an `tf.summary.image` to the images -so that we may visualize them in @{$summaries_and_tensorboard$TensorBoard}. +so that we may visualize them in [TensorBoard](../../guide/summaries_and_tensorboard.md). This is a good practice to verify that inputs are built correctly. <div style="width:50%; margin:auto; margin-bottom:10px; margin-top:20px;"> @@ -160,8 +160,8 @@ Layer Name | Description `conv2` | `tf.nn.conv2d` and `tf.nn.relu` activation. `norm2` | `tf.nn.local_response_normalization`. `pool2` | `tf.nn.max_pool`. -`local3` | @{$python/nn$fully connected layer with rectified linear activation}. -`local4` | @{$python/nn$fully connected layer with rectified linear activation}. +`local3` | [fully connected layer with rectified linear activation](../../api_guides/python/nn.md). +`local4` | [fully connected layer with rectified linear activation](../../api_guides/python/nn.md). `softmax_linear` | linear transformation to produce logits. Here is a graph generated from TensorBoard describing the inference operation: @@ -205,7 +205,7 @@ We visualize it in TensorBoard with a `tf.summary.scalar`: We train the model using standard [gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) -algorithm (see @{$python/train$Training} for other methods) +algorithm (see [Training](../../api_guides/python/train.md) for other methods) with a learning rate that `tf.train.exponential_decay` over time. @@ -265,7 +265,7 @@ in `cifar10_input.py`. `cifar10_train.py` periodically uses a `tf.train.Saver` to save all model parameters in -@{$guide/saved_model$checkpoint files} +[checkpoint files](../../guide/saved_model.md) but it does *not* evaluate the model. The checkpoint file will be used by `cifar10_eval.py` to measure the predictive performance (see [Evaluating a Model](#evaluating-a-model) below). @@ -282,7 +282,7 @@ how the model is training. We want more insight into the model during training: * Are the gradients, activations and weights reasonable? * What is the learning rate currently at? -@{$summaries_and_tensorboard$TensorBoard} provides this +[TensorBoard](../../guide/summaries_and_tensorboard.md) provides this functionality, displaying data exported periodically from `cifar10_train.py` via a `tf.summary.FileWriter`. @@ -413,7 +413,7 @@ scope indicating that they should be run on the first GPU. All variables are pinned to the CPU and accessed via `tf.get_variable` in order to share them in a multi-GPU version. -See how-to on @{$variables$Sharing Variables}. +See how-to on [Sharing Variables](../../guide/variables.md). ### Launching and Training the Model on Multiple GPU cards |