From d520e4f4f617b2cbd5ebe1bc7f97d4a442ab5d30 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Mon, 28 Aug 2017 07:54:25 -0700 Subject: [PATCH] clear references to deleted doc --- tensorflow/docs_src/get_started/estimator.md | 8 ++------ tensorflow/docs_src/get_started/index.md | 2 -- tensorflow/docs_src/get_started/input_fn.md | 2 +- 3 files changed, 3 insertions(+), 9 deletions(-) diff --git a/tensorflow/docs_src/get_started/estimator.md b/tensorflow/docs_src/get_started/estimator.md index a55454f8af3..4f3a438d17d 100644 --- a/tensorflow/docs_src/get_started/estimator.md +++ b/tensorflow/docs_src/get_started/estimator.md @@ -273,9 +273,7 @@ Then, the code creates a `DNNClassifier` model using the following arguments: containing 10, 20, and 10 neurons, respectively. * `n_classes=3`. Three target classes, representing the three Iris species. * `model_dir=/tmp/iris_model`. The directory in which TensorFlow will save - checkpoint data during model training. For more on logging and monitoring - with TensorFlow, see - @{$monitors$Logging and Monitoring Basics with tf.estimator}. + checkpoint data and TensorBoard summaries during model training. ## 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 want to instead use a TensorFlow @{tf.train.SessionRunHook$`SessionRunHook`} -to perform logging operations. See the tutorial -@{$monitors$Logging and Monitoring Basics with tf.estimator} -for more on this topic. +to perform logging operations. ## Evaluate Model Accuracy {#evaluate-accuracy} diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md index 3e700daa304..003fac1a287 100644 --- a/tensorflow/docs_src/get_started/index.md +++ b/tensorflow/docs_src/get_started/index.md @@ -24,8 +24,6 @@ To learn about the high-level API, read the following guides: API. * @{$get_started/input_fn$Building Input Functions}, which takes you into a somewhat more sophisticated use of this API. - * @{$get_started/monitors$Logging and Monitoring Basics with tf.contrib.learn}, - which explains how to audit the progress of model training. TensorBoard is a utility to visualize different aspects of machine learning. The following guides explain how to use TensorBoard: diff --git a/tensorflow/docs_src/get_started/input_fn.md b/tensorflow/docs_src/get_started/input_fn.md index 422f45c586a..7706c07b1d9 100644 --- a/tensorflow/docs_src/get_started/input_fn.md +++ b/tensorflow/docs_src/get_started/input_fn.md @@ -249,7 +249,7 @@ here](https://www.tensorflow.org/code/tensorflow/examples/tutorials/input_fn/bos ### Importing the Housing Data -To start, set up your imports (including `pandas` and `tensorflow`) and @{$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: ```python