Rename programmers_guide/ to guide/ in tf-models.

Author: lamberta

official/boosted_trees/README.md

@@ -9,7 +9,7 @@ We use Gradient Boosted Trees algorithm to distinguish the two classes.
 The code sample uses the high level `tf.estimator.Estimator` and `tf.data.Dataset`.  These APIs are great for fast iteration and quickly adapting models to your own datasets without major code overhauls.  It allows you to move from single-worker training to distributed training, and makes it easy to export model binaries for prediction.  Here, for further simplicity and faster execution, we use a utility function `tf.contrib.estimator.boosted_trees_classifier_train_in_memory`.  This utility function is especially effective when the input is provided as in-memory data sets like numpy arrays.
 
 An input function for the `Estimator` typically uses `tf.data.Dataset` API, which can handle various data control like streaming, batching, transform and shuffling. However `boosted_trees_classifier_train_in_memory()` utility function requires that the entire data is provided as a single batch (i.e. without using `batch()` API). Thus in this practice, simply `Dataset.from_tensors()` is used to convert numpy arrays into structured tensors, and `Dataset.zip()` is used to put features and label together.
-For further references of `Dataset`, [Read more here](https://www.tensorflow.org/programmers_guide/datasets).
+For further references of `Dataset`, [Read more here](https://www.tensorflow.org/guide/datasets).
 
 ## Running the code
 First make sure you've [added the models folder to your Python path](/official/#running-the-models); otherwise you may encounter an error like `ImportError: No module named official.boosted_trees`.
@@ -53,13 +53,13 @@ tensorboard --logdir=/tmp/higgs_model  # set logdir as --model_dir set during tr
 ```
 
 ## Inference with SavedModel
-You can export the model into Tensorflow [SavedModel](https://www.tensorflow.org/programmers_guide/saved_model) format by using the argument `--export_dir`:
+You can export the model into Tensorflow [SavedModel](https://www.tensorflow.org/guide/saved_model) format by using the argument `--export_dir`:
 
 ```
 python train_higgs.py --export_dir /tmp/higgs_boosted_trees_saved_model
 ```
 
-After the model finishes training, use [`saved_model_cli`](https://www.tensorflow.org/programmers_guide/saved_model#cli_to_inspect_and_execute_savedmodel) to inspect and execute the SavedModel.
+After the model finishes training, use [`saved_model_cli`](https://www.tensorflow.org/guide/saved_model#cli_to_inspect_and_execute_savedmodel) to inspect and execute the SavedModel.
 
 Try the following commands to inspect the SavedModel:
 

official/mnist/README.md

@@ -32,7 +32,7 @@ python mnist_test.py --benchmarks=.
 
 ## Exporting the model
 
-You can export the model into Tensorflow [SavedModel](https://www.tensorflow.org/programmers_guide/saved_model) format by using the argument `--export_dir`:
+You can export the model into Tensorflow [SavedModel](https://www.tensorflow.org/guide/saved_model) format by using the argument `--export_dir`:
 
 ```
 python mnist.py --export_dir /tmp/mnist_saved_model
@@ -41,7 +41,7 @@ python mnist.py --export_dir /tmp/mnist_saved_model
 The SavedModel will be saved in a timestamped directory under `/tmp/mnist_saved_model/` (e.g. `/tmp/mnist_saved_model/1513630966/`).
 
 **Getting predictions with SavedModel**
-Use [`saved_model_cli`](https://www.tensorflow.org/programmers_guide/saved_model#cli_to_inspect_and_execute_savedmodel) to inspect and execute the SavedModel.
+Use [`saved_model_cli`](https://www.tensorflow.org/guide/saved_model#cli_to_inspect_and_execute_savedmodel) to inspect and execute the SavedModel.
 
 ```
 saved_model_cli run --dir /tmp/mnist_saved_model/TIMESTAMP --tag_set serve --signature_def classify --inputs image=examples.npy

official/transformer/README.md

@@ -214,7 +214,7 @@ big | 28.9
    demonstration purposes only, but will be optimized in the coming weeks.
 
 ## Export trained model
-To export the model as a Tensorflow [SavedModel](https://www.tensorflow.org/programmers_guide/saved_model) format, use the argument `--export_dir` when running `transformer_main.py`. A folder will be created in the directory with the name as the timestamp (e.g. $EXPORT_DIR/1526427396).
+To export the model as a Tensorflow [SavedModel](https://www.tensorflow.org/guide/saved_model) format, use the argument `--export_dir` when running `transformer_main.py`. A folder will be created in the directory with the name as the timestamp (e.g. $EXPORT_DIR/1526427396).
 
 ```
 EXPORT_DIR=$HOME/transformer/saved_model
@@ -366,4 +366,4 @@ The [newstest2014 files](test_data) are extracted from the [NMT Seq2Seq tutorial
 
 Example: Consider a training a dataset with 100 examples that is divided into 20 batches with 5 examples per batch. A single training step trains the model on one batch. After 20 training steps, the model will have trained on every batch in the dataset, or one epoch.
 
-**Subtoken**: Words are referred to as tokens, and parts of words are referred to as 'subtokens'. For example, the word 'inclined' may be split into `['incline', 'd_']`. The '\_' indicates the end of the token. The subtoken vocabulary list is guaranteed to contain the alphabet (including numbers and special characters), so all words can be tokenized.
+**Subtoken**: Words are referred to as tokens, and parts of words are referred to as 'subtokens'. For example, the word 'inclined' may be split into `['incline', 'd_']`. The '\_' indicates the end of the token. The subtoken vocabulary list is guaranteed to contain the alphabet (including numbers and special characters), so all words can be tokenized.

official/wide_deep/README.md

@@ -10,7 +10,7 @@ For the purposes of this example code, the Census Income Data Set was chosen to
 
 The code sample in this directory uses the high level `tf.estimator.Estimator` API. This API is great for fast iteration and quickly adapting models to your own datasets without major code overhauls. It allows you to move from single-worker training to distributed training, and it makes it easy to export model binaries for prediction.
 
-The input function for the `Estimator` uses `tf.contrib.data.TextLineDataset`, which creates a `Dataset` object. The `Dataset` API makes it easy to apply transformations (map, batch, shuffle, etc.) to the data. [Read more here](https://www.tensorflow.org/programmers_guide/datasets).
+The input function for the `Estimator` uses `tf.contrib.data.TextLineDataset`, which creates a `Dataset` object. The `Dataset` API makes it easy to apply transformations (map, batch, shuffle, etc.) to the data. [Read more here](https://www.tensorflow.org/guide/datasets).
 
 The `Estimator` and `Dataset` APIs are both highly encouraged for fast development and efficient training.
 
@@ -48,13 +48,13 @@ tensorboard --logdir=/tmp/census_model
 ```
 
 ## Inference with SavedModel
-You can export the model into Tensorflow [SavedModel](https://www.tensorflow.org/programmers_guide/saved_model) format by using the argument `--export_dir`:
+You can export the model into Tensorflow [SavedModel](https://www.tensorflow.org/guide/saved_model) format by using the argument `--export_dir`:
 
 ```
 python wide_deep.py --export_dir /tmp/wide_deep_saved_model
 ```
 
-After the model finishes training, use [`saved_model_cli`](https://www.tensorflow.org/programmers_guide/saved_model#cli_to_inspect_and_execute_savedmodel) to inspect and execute the SavedModel.
+After the model finishes training, use [`saved_model_cli`](https://www.tensorflow.org/guide/saved_model#cli_to_inspect_and_execute_savedmodel) to inspect and execute the SavedModel.
 
 Try the following commands to inspect the SavedModel:
 

research/astronet/README.md

@@ -207,7 +207,7 @@ the second deepest transits).
 
 To train a model to identify exoplanets, you will need to provide TensorFlow
 with training data in
-[TFRecord](https://www.tensorflow.org/programmers_guide/datasets) format. The
+[TFRecord](https://www.tensorflow.org/guide/datasets) format. The
 TFRecord format consists of a set of sharded files containing serialized
 `tf.Example` [protocol buffers](https://developers.google.com/protocol-buffers/).
 
@@ -343,7 +343,7 @@ bazel-bin/astronet/train \
   --model_dir=${MODEL_DIR}
 ```
 
-Optionally, you can also run a [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard)
+Optionally, you can also run a [TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard)
 server in a separate process for real-time
 monitoring of training progress and evaluation metrics.
 

research/seq2species/README.md

@@ -117,8 +117,8 @@ python seq2species/run_training.py --train_files ${TFRECORD}
 --logdir $HOME/seq2species
 ```
 This will output [TensorBoard
-summaries](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), [TensorFlow
-checkpoints](https://www.tensorflow.org/programmers_guide/variables#checkpoint_files), Seq2LabelModelInfo and
+summaries](https://www.tensorflow.org/guide/summaries_and_tensorboard), [TensorFlow
+checkpoints](https://www.tensorflow.org/guide/variables#checkpoint_files), Seq2LabelModelInfo and
 Seq2LabelExperimentMeasures metadata to the logdir `$HOME/seq2species`.
 
 ### Preprocessed Seq2Species Data

research/tcn/README.md

@@ -92,7 +92,7 @@ it to the TFRecord format expected by this library.
 ## Data Pipelines
 
 We use the [tf.data.Dataset
-API](https://www.tensorflow.org/programmers_guide/datasets) to construct input
+API](https://www.tensorflow.org/guide/datasets) to construct input
 pipelines that feed training, evaluation, and visualization. These pipelines are
 defined in `data_providers.py`.
 

samples/core/get_started/basic_classification.ipynb

@@ -129,7 +129,7 @@
       "source": [
         "In this guide, we will train a neural network model to classify images of clothing, like sneakers and shirts. It's fine if you don't understand all the details, this is a fast-paced overview of a complete TensorFlow program with the details explained as we go.\n",
         "\n",
-        "This guide uses [tf.keras](https://www.tensorflow.org/programmers_guide/keras), a high-level API to build and train models in TensorFlow."
+        "This guide uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow."
       ]
     },
     {

samples/core/get_started/basic_regression.ipynb

@@ -120,7 +120,7 @@
         "\n",
         "This notebook builds a model to predict the median price of homes in a Boston suburb during the mid-1970s. To do this, we'll provide the model with some data points about the suburb, such as the crime rate and the local property tax rate.\n",
         "\n",
-        "This example uses the `tf.keras` API, see [this guide](https://www.tensorflow.org/programmers_guide/keras) for details."
+        "This example uses the `tf.keras` API, see [this guide](https://www.tensorflow.org/guide/keras) for details."
       ]
     },
     {

samples/core/get_started/basic_text_classification.ipynb

@@ -118,7 +118,7 @@
         "\n",
         "We'll use the [IMDB dataset](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb) that contains the text of 50,000 movie reviews from the [Internet Movie Database](https://www.imdb.com/). These are split into 25,000 reviews for training and 25,000 reviews for testing. The training and testing sets are *balanced*, meaning they contain an equal number of positive and negative reviews. \n",
         "\n",
-        "This notebook uses [tf.keras](https://www.tensorflow.org/programmers_guide/keras), a high-level API to build and train models in TensorFlow."
+        "This notebook uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow."
       ]
     },
     {

samples/core/get_started/eager.ipynb

@@ -87,8 +87,8 @@
         "\n",
         "There are many [TensorFlow APIs](https://www.tensorflow.org/api_docs/python/) available, but start with these high-level TensorFlow concepts:\n",
         "\n",
-        "* Enable an [eager execution](https://www.tensorflow.org/programmers_guide/eager) development environment,\n",
-        "* Import data with the [Datasets API](https://www.tensorflow.org/programmers_guide/datasets),\n",
+        "* Enable an [eager execution](https://www.tensorflow.org/guide/eager) development environment,\n",
+        "* Import data with the [Datasets API](https://www.tensorflow.org/guide/datasets),\n",
         "* Build models and layers with TensorFlow's [Keras API](https://keras.io/getting-started/sequential-model-guide/).\n",
         "\n",
         "This tutorial is structured like many TensorFlow programs:\n",
@@ -155,9 +155,9 @@
       "source": [
         "### Configure imports and eager execution\n",
         "\n",
-        "Import the required Python modules—including TensorFlow—and enable eager execution for this program. Eager execution makes TensorFlow evaluate operations immediately, returning concrete values instead of creating a [computational graph](https://www.tensorflow.org/programmers_guide/graphs) that is executed later. If you are used to a REPL or the `python` interactive console, this feels familiar.\n",
+        "Import the required Python modules—including TensorFlow—and enable eager execution for this program. Eager execution makes TensorFlow evaluate operations immediately, returning concrete values instead of creating a [computational graph](https://www.tensorflow.org/guide/graphs) that is executed later. If you are used to a REPL or the `python` interactive console, this feels familiar.\n",
         "\n",
-        "Once eager execution is enabled, it *cannot* be disabled within the same program. See the [eager execution guide](https://www.tensorflow.org/programmers_guide/eager) for more details."
+        "Once eager execution is enabled, it *cannot* be disabled within the same program. See the [eager execution guide](https://www.tensorflow.org/guide/eager) for more details."
       ]
     },
     {
@@ -349,7 +349,7 @@
       "source": [
         "### Create a `tf.data.Dataset`\n",
         "\n",
-        "TensorFlow's [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) handles many common cases for loading data into a model. This is a high-level API for reading data and transforming it into a form used for training. See the [Datasets Quick Start guide](https://www.tensorflow.org/get_started/datasets_quickstart) for more information.\n",
+        "TensorFlow's [Dataset API](https://www.tensorflow.org/guide/datasets) handles many common cases for loading data into a model. This is a high-level API for reading data and transforming it into a form used for training. See the [Datasets Quick Start guide](https://www.tensorflow.org/get_started/datasets_quickstart) for more information.\n",
         "\n",
         "\n",
         "Since the dataset is a CSV-formatted text file, use the the [make_csv_dataset](https://www.tensorflow.org/api_docs/python/tf/contrib/data/make_csv_dataset) function to parse the data into a suitable format. Since this function generates data for training models, the default behavior is to shuffle the data (`shuffle=True, shuffle_buffer_size=10000`), and repeat the dataset forever (`num_epochs=None`). We also set the [batch_size](https://developers.google.com/machine-learning/glossary/#batch_size) parameter."
@@ -713,7 +713,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "Use the [tf.GradientTape](https://www.tensorflow.org/api_docs/python/tf/GradientTape) context to calculate the *[gradients](https://developers.google.com/machine-learning/crash-course/glossary#gradient)* used to optimize our model. For more examples of this, see the [eager execution guide](https://www.tensorflow.org/programmers_guide/eager)."
+        "Use the [tf.GradientTape](https://www.tensorflow.org/api_docs/python/tf/GradientTape) context to calculate the *[gradients](https://developers.google.com/machine-learning/crash-course/glossary#gradient)* used to optimize our model. For more examples of this, see the [eager execution guide](https://www.tensorflow.org/guide/eager)."
       ]
     },
     {
@@ -894,7 +894,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "While it's helpful to print out the model's training progress, it's often *more* helpful to see this progress. [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard) is a nice visualization tool that is packaged with TensorFlow, but we can create basic charts using the `matplotlib` module.\n",
+        "While it's helpful to print out the model's training progress, it's often *more* helpful to see this progress. [TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard) is a nice visualization tool that is packaged with TensorFlow, but we can create basic charts using the `matplotlib` module.\n",
         "\n",
         "Interpreting these charts takes some experience, but you really want to see the *loss* go down and the *accuracy* go up."
       ]
@@ -1123,7 +1123,7 @@
       "source": [
         "These predictions look good!\n",
         "\n",
-        "To dig deeper into machine learning models, take a look at the TensorFlow [Programmer's Guide](https://www.tensorflow.org/programmers_guide/) and check out the [community](https://www.tensorflow.org/community/)."
+        "To dig deeper into machine learning models, take a look at the [TensorFlow Guide](https://www.tensorflow.org/guide/) and check out the [community](https://www.tensorflow.org/community/)."
       ]
     },
     {

samples/core/get_started/overfit_and_underfit.ipynb

@@ -134,7 +134,7 @@
         "\n",
         "This notebook is based on the book [Deep Learning with Python](https://manning.com/books/deep-learning-with-python), which is a great way to continue learning more about Deep Learning and the Keras API—especially if you enjoy this style of exploring machine learning concepts with code examples. \n",
         "\n",
-        "As always, the code in this example will use the `tf.keras` API, which you can learn more about in the TensorFlow [Keras guide](https://www.tensorflow.org/programmers_guide/keras).\n",
+        "As always, the code in this example will use the `tf.keras` API, which you can learn more about in the TensorFlow [Keras guide](https://www.tensorflow.org/guide/keras).\n",
         "\n",
         "In both of the previous examples—classifying movie reviews, and predicting housing prices—we saw that the accuracy of our model on the validation data would peak after training for a number of epochs, and would then start decreasing. \n",
         "\n",