diff options
Diffstat (limited to 'tensorflow/docs_src/tutorials/estimators/cnn.md')
| -rw-r--r-- | tensorflow/docs_src/tutorials/estimators/cnn.md | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/tensorflow/docs_src/tutorials/estimators/cnn.md b/tensorflow/docs_src/tutorials/estimators/cnn.md index 100f501cc2..2fd69f50a0 100644 --- a/tensorflow/docs_src/tutorials/estimators/cnn.md +++ b/tensorflow/docs_src/tutorials/estimators/cnn.md @@ -190,7 +190,7 @@ def cnn_model_fn(features, labels, mode): The following sections (with headings corresponding to each code block above) dive deeper into the `tf.layers` code used to create each layer, as well as how to calculate loss, configure the training op, and generate predictions. If -you're already experienced with CNNs and @{$custom_estimators$TensorFlow `Estimator`s}, +you're already experienced with CNNs and [TensorFlow `Estimator`s](../../guide/custom_estimators.md), and find the above code intuitive, you may want to skim these sections or just skip ahead to ["Training and Evaluating the CNN MNIST Classifier"](#train_eval_mnist). @@ -501,8 +501,8 @@ if mode == tf.estimator.ModeKeys.TRAIN: ``` > Note: For a more in-depth look at configuring training ops for Estimator model -> functions, see @{$custom_estimators#defining-the-training-op-for-the-model$"Defining the training op for the model"} -> in the @{$custom_estimators$"Creating Estimations in tf.estimator"} tutorial. +> functions, see ["Defining the training op for the model"](../../guide/custom_estimators.md#defining-the-training-op-for-the-model) +> in the ["Creating Estimations in tf.estimator"](../../guide/custom_estimators.md) tutorial. ### Add evaluation metrics @@ -567,7 +567,7 @@ be saved (here, we specify the temp directory `/tmp/mnist_convnet_model`, but feel free to change to another directory of your choice). > Note: For an in-depth walkthrough of the TensorFlow `Estimator` API, see the -> tutorial @{$custom_estimators$"Creating Estimators in tf.estimator."} +> tutorial ["Creating Estimators in tf.estimator."](../../guide/custom_estimators.md) ### Set Up a Logging Hook {#set_up_a_logging_hook} @@ -593,8 +593,8 @@ operation earlier when we generated the probabilities in `cnn_model_fn`. > Note: If you don't explicitly assign a name to an operation via the `name` > argument, TensorFlow will assign a default name. A couple easy ways to > discover the names applied to operations are to visualize your graph on -> @{$graph_viz$TensorBoard}) or to enable the -> @{$guide/debugger$TensorFlow Debugger (tfdbg)}. +> [TensorBoard](../../guide/graph_viz.md)) or to enable the +> [TensorFlow Debugger (tfdbg)](../../guide/debugger.md). Next, we create the `LoggingTensorHook`, passing `tensors_to_log` to the `tensors` argument. We set `every_n_iter=50`, which specifies that probabilities @@ -686,9 +686,9 @@ Here, we've achieved an accuracy of 97.3% on our test data set. To learn more about TensorFlow Estimators and CNNs in TensorFlow, see the following resources: -* @{$custom_estimators$Creating Estimators in tf.estimator} +* [Creating Estimators in tf.estimator](../../guide/custom_estimators.md) provides an introduction to the TensorFlow Estimator API. It walks through configuring an Estimator, writing a model function, calculating loss, and defining a training op. -* @{$deep_cnn} walks through how to build a MNIST CNN classification model +* [Advanced Convolutional Neural Networks](../../tutorials/images/deep_cnn.md) walks through how to build a MNIST CNN classification model *without estimators* using lower-level TensorFlow operations. |