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clear references to deleted doc

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Mark Daoust 2017-08-28 07:54:25 -07:00 committed by Martin Wicke
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8
tensorflow/docs_src/get_started/estimator.md
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@ -273,9 +273,7 @@ Then, the code creates a `DNNClassifier` model using the following arguments:
containing 10, 20, and 10 neurons, respectively. containing 10, 20, and 10 neurons, respectively.
* `n_classes=3`. Three target classes, representing the three Iris species. * `n_classes=3`. Three target classes, representing the three Iris species.
* `model_dir=/tmp/iris_model`. The directory in which TensorFlow will save * `model_dir=/tmp/iris_model`. The directory in which TensorFlow will save
checkpoint data during model training. For more on logging and monitoring checkpoint data and TensorBoard summaries during model training.
with TensorFlow, see
@{$monitors$Logging and Monitoring Basics with tf.estimator}.
## Describe the training input pipeline {#train-input} ## Describe the training input pipeline {#train-input}
@ -315,9 +313,7 @@ classifier.train(input_fn=train_input_fn, steps=1000)
However, if you're looking to track the model while it trains, you'll likely However, if you're looking to track the model while it trains, you'll likely
want to instead use a TensorFlow @{tf.train.SessionRunHook$`SessionRunHook`} want to instead use a TensorFlow @{tf.train.SessionRunHook$`SessionRunHook`}
to perform logging operations. See the tutorial to perform logging operations.
@{$monitors$Logging and Monitoring Basics with tf.estimator}
for more on this topic.
## Evaluate Model Accuracy {#evaluate-accuracy} ## Evaluate Model Accuracy {#evaluate-accuracy}

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tensorflow/docs_src/get_started/index.md
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@ -24,8 +24,6 @@ To learn about the high-level API, read the following guides:
API. API.
* @{$get_started/input_fn$Building Input Functions}, * @{$get_started/input_fn$Building Input Functions},
which takes you into a somewhat more sophisticated use of this API. which takes you into a somewhat more sophisticated use of this API.
* @{$get_started/monitors$Logging and Monitoring Basics with tf.contrib.learn},
which explains how to audit the progress of model training.
TensorBoard is a utility to visualize different aspects of machine learning. TensorBoard is a utility to visualize different aspects of machine learning.
The following guides explain how to use TensorBoard: The following guides explain how to use TensorBoard:

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tensorflow/docs_src/get_started/input_fn.md
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@ -249,7 +249,7 @@ here](https://www.tensorflow.org/code/tensorflow/examples/tutorials/input_fn/bos
### Importing the Housing Data ### Importing the Housing Data
To start, set up your imports (including `pandas` and `tensorflow`) and @{$monitors#enabling-logging-with-tensorflow$set logging verbosity} to To start, set up your imports (including `pandas` and `tensorflow`) and set logging verbosity to
`INFO` for more detailed log output: `INFO` for more detailed log output:
```python ```python