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diff --git a/tensorflow/docs_src/api_guides/python/regression_examples.md b/tensorflow/docs_src/api_guides/python/regression_examples.md deleted file mode 100644 index d67f38f57a..0000000000 --- a/tensorflow/docs_src/api_guides/python/regression_examples.md +++ /dev/null @@ -1,232 +0,0 @@ -# Regression Examples - -This unit provides the following short examples demonstrating how -to implement regression in Estimators: - -<table> - <tr> <th>Example</th> <th>Demonstrates How To...</th></tr> - - <tr> - <td><a href="https://www.tensorflow.org/code/tensorflow/examples/get_started/regression/linear_regression.py">linear_regression.py</a></td> - <td>Use the `tf.estimator.LinearRegressor` Estimator to train a - regression model on numeric data.</td> - </tr> - - <tr> - <td><a href="https://www.tensorflow.org/code/tensorflow/examples/get_started/regression/linear_regression_categorical.py">linear_regression_categorical.py</a></td> - <td>Use the `tf.estimator.LinearRegressor` Estimator to train a - regression model on categorical data.</td> - </tr> - - <tr> - <td><a href="https://www.tensorflow.org/code/tensorflow/examples/get_started/regression/dnn_regression.py">dnn_regression.py</a></td> - <td>Use the `tf.estimator.DNNRegressor` Estimator to train a - regression model on discrete data with a deep neural network.</td> - </tr> - - <tr> - <td><a href="https://www.tensorflow.org/code/tensorflow/examples/get_started/regression/custom_regression.py">custom_regression.py</a></td> - <td>Use `tf.estimator.Estimator` to train a customized dnn - regression model.</td> - </tr> - -</table> - -The preceding examples rely on the following data set utility: - -<table> - <tr> <th>Utility</th> <th>Description</th></tr> - - <tr> - <td><a href="https://www.tensorflow.org/code/tensorflow/examples/get_started/regression/imports85.py">imports85.py</a></td> - <td>This program provides utility functions that load the - <tt>imports85</tt> data set into formats that other TensorFlow - programs (for example, <tt>linear_regression.py</tt> and - <tt>dnn_regression.py</tt>) can use.</td> - </tr> - - -</table> - - -<!-- -## Linear regression concepts - -If you are new to machine learning and want to learn about regression, -watch the following video: - -(todo:jbgordon) Video introduction goes here. ---> - -<!-- -[When MLCC becomes available externally, add links to the relevant MLCC units.] ---> - - -<a name="running"></a> -## Running the examples - -You must [install TensorFlow](../../install/index.md) prior to running these examples. -Depending on the way you've installed TensorFlow, you might also -need to activate your TensorFlow environment. Then, do the following: - -1. Clone the TensorFlow repository from github. -2. `cd` to the top of the downloaded tree. -3. Check out the branch for you current tensorflow version: `git checkout rX.X` -4. `cd tensorflow/examples/get_started/regression`. - -You can now run any of the example TensorFlow programs in the -`tensorflow/examples/get_started/regression` directory as you -would run any Python program: - -```bsh -python linear_regressor.py -``` - -During training, all three programs output the following information: - -* The name of the checkpoint directory, which is important for TensorBoard. -* The training loss after every 100 iterations, which helps you - determine whether the model is converging. - -For example, here's some possible output for the `linear_regressor.py` -program: - -``` None -INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpAObiz9/model.ckpt. -INFO:tensorflow:loss = 161.308, step = 1 -INFO:tensorflow:global_step/sec: 1557.24 -INFO:tensorflow:loss = 15.7937, step = 101 (0.065 sec) -INFO:tensorflow:global_step/sec: 1529.17 -INFO:tensorflow:loss = 12.1988, step = 201 (0.065 sec) -INFO:tensorflow:global_step/sec: 1663.86 -... -INFO:tensorflow:loss = 6.99378, step = 901 (0.058 sec) -INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmpAObiz9/model.ckpt. -INFO:tensorflow:Loss for final step: 5.12413. -``` - - -<a name="basic"></a> -## linear_regressor.py - -`linear_regressor.py` trains a model that predicts the price of a car from -two numerical features. - -<table> - <tr> - <td>Estimator</td> - <td><tt>LinearRegressor</tt>, which is a pre-made Estimator for linear - regression.</td> - </tr> - - <tr> - <td>Features</td> - <td>Numerical: <tt>body-style</tt> and <tt>make</tt>.</td> - </tr> - - <tr> - <td>Label</td> - <td>Numerical: <tt>price</tt> - </tr> - - <tr> - <td>Algorithm</td> - <td>Linear regression.</td> - </tr> -</table> - -After training the model, the program concludes by outputting predicted -car prices for two car models. - - - -<a name="categorical"></a> -## linear_regression_categorical.py - -This program illustrates ways to represent categorical features. It -also demonstrates how to train a linear model based on a mix of -categorical and numerical features. - -<table> - <tr> - <td>Estimator</td> - <td><tt>LinearRegressor</tt>, which is a pre-made Estimator for linear - regression. </td> - </tr> - - <tr> - <td>Features</td> - <td>Categorical: <tt>curb-weight</tt> and <tt>highway-mpg</tt>.<br/> - Numerical: <tt>body-style</tt> and <tt>make</tt>.</td> - </tr> - - <tr> - <td>Label</td> - <td>Numerical: <tt>price</tt>.</td> - </tr> - - <tr> - <td>Algorithm</td> - <td>Linear regression.</td> - </tr> -</table> - - -<a name="dnn"></a> -## dnn_regression.py - -Like `linear_regression_categorical.py`, the `dnn_regression.py` example -trains a model that predicts the price of a car from two features. -Unlike `linear_regression_categorical.py`, the `dnn_regression.py` example uses -a deep neural network to train the model. Both examples rely on the same -features; `dnn_regression.py` demonstrates how to treat categorical features -in a deep neural network. - -<table> - <tr> - <td>Estimator</td> - <td><tt>DNNRegressor</tt>, which is a pre-made Estimator for - regression that relies on a deep neural network. The - `hidden_units` parameter defines the topography of the network.</td> - </tr> - - <tr> - <td>Features</td> - <td>Categorical: <tt>curb-weight</tt> and <tt>highway-mpg</tt>.<br/> - Numerical: <tt>body-style</tt> and <tt>make</tt>.</td> - </tr> - - <tr> - <td>Label</td> - <td>Numerical: <tt>price</tt>.</td> - </tr> - - <tr> - <td>Algorithm</td> - <td>Regression through a deep neural network.</td> - </tr> -</table> - -After printing loss values, the program outputs the Mean Square Error -on a test set. - - -<a name="dnn"></a> -## custom_regression.py - -The `custom_regression.py` example also trains a model that predicts the price -of a car based on mixed real-valued and categorical input features, described by -feature_columns. Unlike `linear_regression_categorical.py`, and -`dnn_regression.py` this example does not use a pre-made estimator, but defines -a custom model using the base `tf.estimator.Estimator` class. The -custom model is quite similar to the model defined by `dnn_regression.py`. - -The custom model is defined by the `model_fn` argument to the constructor. The -customization is made more reusable through `params` dictionary, which is later -passed through to the `model_fn` when the `model_fn` is called. - -The `model_fn` returns an -`tf.estimator.EstimatorSpec` which is a simple structure -indicating to the `Estimator` which operations should be run to accomplish -various tasks. |