diff options
| author |  Martin Wicke <martin.wicke@gmail.com> | 2016-06-28 21:29:08 -0700 |
|---|---|---|
| committer |  GitHub <noreply@github.com> | 2016-06-28 21:29:08 -0700 |
| commit | 896086b24f0431e18af378a0d71d0494c221b356 (patch) | |
| tree | 463019c4f8950247b9c1940ac1499de16d1386eb | |
| parent | 5d468f05d92abaa3f9d4a18518087067a3e78805 (diff) | |
| parent | 990bc2c2a3d11522c21c1c54376611f28f5c55ed (diff) | |
Merge pull request #3096 from martinwicke/docs-update
Wide and Deep docs update
| -rw-r--r-- | tensorflow/g3doc/images/wide_n_deep.svg | 1540 | ||||
| -rw-r--r-- | tensorflow/g3doc/tutorials/index.md | 121 | ||||
| -rw-r--r-- | tensorflow/g3doc/tutorials/leftnav_files | 14 | ||||
| -rw-r--r-- | tensorflow/g3doc/tutorials/linear/overview.md | 237 | ||||
| -rw-r--r-- | tensorflow/g3doc/tutorials/tflearn/index.md | 251 | ||||
| -rw-r--r-- | tensorflow/g3doc/tutorials/wide/index.md | 482 | ||||
| -rw-r--r-- | tensorflow/g3doc/tutorials/wide_and_deep/index.md | 275 |
7 files changed, 2873 insertions, 47 deletions
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You'll learn about a classic problem, handwritten digit classification (MNIST), and get a @@ -10,33 +11,75 @@ gentle introduction to multiclass classification. [View Tutorial](../tutorials/mnist/beginners/index.md) -## Deep MNIST for Experts +### Deep MNIST for Experts If you're already familiar with other deep learning software packages, and are -already familiar with MNIST, this tutorial will give you a very brief primer on -TensorFlow. +already familiar with MNIST, this tutorial will give you a very brief primer +on TensorFlow. [View Tutorial](../tutorials/mnist/pros/index.md) - -## TensorFlow Mechanics 101 +### TensorFlow Mechanics 101 This is a technical tutorial, where we walk you through the details of using -TensorFlow infrastructure to train models at scale. We again use MNIST as the +TensorFlow infrastructure to train models at scale. We use MNIST as the example. [View Tutorial](../tutorials/mnist/tf/index.md) +### MNIST Data Download + +Details about downloading the MNIST handwritten digits data set. Exciting +stuff. + +[View Tutorial](../tutorials/mnist/download/index.md) + + +## Easy ML with tf.contrib.learn + +### tf.contrib.learn Quickstart + +A quick introduction to tf.contrib.learn, a high-level API for TensorFlow. +Build, train, and evaluate a neural network with just a few lines of +code. + +[View Tutorial](../tutorials/tflearn/index.md) + +### Overview of Linear Models with tf.contrib.learn + +An overview of tf.contrib.learn's rich set of tools for working with linear +models in TensorFlow. + +[View Tutorial](../tutorials/linear/overview.md) + +### Linear Model Tutorial + +This tutorial walks you through the code for building a linear model using +tf.contrib.learn. + +[View Tutorial](../tutorials/wide/index.md) + +### Wide and Deep Learning Tutorial + +This tutorial shows you how to use tf.contrib.learn to jointly train a linear +model and a deep neural net to harness the advantages of each type of model. + +[View Tutorial](../tutorials/wide_and_deep/index.md) + ## TensorFlow Serving +### TensorFlow Serving + An introduction to TensorFlow Serving, a flexible, high-performance system for serving machine learning models, designed for production environments. [View Tutorial](../tutorials/tfserve/index.md) -## Convolutional Neural Networks +## Image Processing + +### Convolutional Neural Networks An introduction to convolutional neural networks using the CIFAR-10 data set. Convolutional neural nets are particularly tailored to images, since they @@ -45,8 +88,25 @@ representations of visual content. [View Tutorial](../tutorials/deep_cnn/index.md) +### Image Recognition + +How to run object recognition using a convolutional neural network +trained on ImageNet Challenge data and label set. + +[View Tutorial](../tutorials/image_recognition/index.md) + +### Deep Dream Visual Hallucinations + +Building on the Inception recognition model, we will release a TensorFlow +version of the [Deep Dream](https://github.com/google/deepdream) neural network +visual hallucination software. + +[View Tutorial](https://www.tensorflow.org/code/tensorflow/examples/tutorials/deepdream/deepdream.ipynb) + + +## Language and Sequence Processing -## Vector Representations of Words +### Vector Representations of Words This tutorial motivates why it is useful to learn to represent words as vectors (called *word embeddings*). It introduces the word2vec model as an efficient @@ -56,16 +116,14 @@ embeddings). [View Tutorial](../tutorials/word2vec/index.md) - -## Recurrent Neural Networks +### Recurrent Neural Networks An introduction to RNNs, wherein we train an LSTM network to predict the next word in an English sentence. (A task sometimes called language modeling.) [View Tutorial](../tutorials/recurrent/index.md) - -## Sequence-to-Sequence Models +### Sequence-to-Sequence Models A follow on to the RNN tutorial, where we assemble a sequence-to-sequence model for machine translation. You will learn to build your own English-to-French @@ -73,8 +131,7 @@ translator, entirely machine learned, end-to-end. [View Tutorial](../tutorials/seq2seq/index.md) - -## SyntaxNet: Neural Models of Syntax +### SyntaxNet: Neural Models of Syntax An introduction to SyntaxNet, a Natural Language Processing framework for TensorFlow. @@ -82,44 +139,18 @@ TensorFlow. [View Tutorial](../tutorials/syntaxnet/index.md) -## Mandelbrot Set +## Non-ML Applications + +### Mandelbrot Set TensorFlow can be used for computation that has nothing to do with machine learning. Here's a naive implementation of Mandelbrot set visualization. [View Tutorial](../tutorials/mandelbrot/index.md) - -## Partial Differential Equations +### Partial Differential Equations As another example of non-machine learning computation, we offer an example of a naive PDE simulation of raindrops landing on a pond. [View Tutorial](../tutorials/pdes/index.md) - - -## MNIST Data Download - -Details about downloading the MNIST handwritten digits data set. Exciting -stuff. - -[View Tutorial](../tutorials/mnist/download/index.md) - - -## Image Recognition - -How to run object recognition using a convolutional neural network -trained on ImageNet Challenge data and label set. - -[View Tutorial](../tutorials/image_recognition/index.md) - -We will soon be releasing code for training a state-of-the-art Inception model. - - -## Deep Dream Visual Hallucinations - -Building on the Inception recognition model, we will release a TensorFlow -version of the [Deep Dream](https://github.com/google/deepdream) neural network -visual hallucination software. - -[View Tutorial](https://www.tensorflow.org/code/tensorflow/examples/tutorials/deepdream/deepdream.ipynb) diff --git a/tensorflow/g3doc/tutorials/leftnav_files b/tensorflow/g3doc/tutorials/leftnav_files index c35a936995..09cd084b49 100644 --- a/tensorflow/g3doc/tutorials/leftnav_files +++ b/tensorflow/g3doc/tutorials/leftnav_files @@ -1,13 +1,23 @@ +### Basic Neural Networks mnist/beginners/index.md mnist/pros/index.md mnist/tf/index.md +mnist/download/index.md +### Easy ML with tf.contrib.learn +tflearn/index.md +linear/overview.md +wide/index.md +wide_and_deep/index.md +### TensorFlow Serving tfserve/index.md +### Image Processing deep_cnn/index.md +image_recognition/index.md +### Language and Sequence Processing word2vec/index.md recurrent/index.md seq2seq/index.md syntaxnet/index.md +### Non-ML Applications mandelbrot/index.md pdes/index.md -mnist/download/index.md -image_recognition/index.md
\ No newline at end of file diff --git a/tensorflow/g3doc/tutorials/linear/overview.md b/tensorflow/g3doc/tutorials/linear/overview.md new file mode 100644 index 0000000000..8614011290 --- /dev/null +++ b/tensorflow/g3doc/tutorials/linear/overview.md @@ -0,0 +1,237 @@ +# Large-scale Linear Models with TensorFlow + +The tf.learn API provides (among other things) a rich set of tools for working +with linear models in TensorFlow. This document provides an overview of those +tools. It explains: + + * what a linear model is. + * why you might want to use a linear model. + * how tf.learn makes it easy to build linear models in TensorFlow. + * how you can use tf.learn to combine linear models with + deep learning to get the advantages of both. + +Read this overview to decide whether the tf.learn linear model tools might be +useful to you. Then do the [Linear Models tutorial](wide/) to +give it a try. This overview uses code samples from the tutorial, but the +tutorial walks through the code in greater detail. + +To understand this overview it will help to have some familiarity +with basic machine learning concepts, and also with +[tf.learn](../tflearn/). + +[TOC] + +## What is a linear model? + +A *linear model* uses a single weighted sum of features to make a prediction. +For example, if you have [data](https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names) +on age, years of education, and weekly hours of +work for a population, you can learn weights for each of those numbers so that +their weighted sum estimates a person's salary. You can also use linear models +for classification. + +Some linear models transform the weighted sum into a more convenient form. For +example, *logistic regression* plugs the weighted sum into the logistic +function to turn the output into a value between 0 and 1. But you still just +have one weight for each input feature. + +## Why would you want to use a linear model? + +Why would you want to use so simple a model when recent research has +demonstrated the power of more complex neural networks with many layers? + +Linear models: + + * train quickly, compared to deep neural nets. + * can work well on very large feature sets. + * can be trained with algorithms that don't require a lot of fiddling + with learning rates, etc. + * can be interpreted and debugged more easily than neural nets. + You can examine the weights assigned to each feature to figure out what's + having the biggest impact on a prediction. + * provide an excellent starting point for learning about machine learning. + * are widely used in industry. + +## How does tf.learn help you build linear models? + +You can build a linear model from scratch in TensorFlow without the help of a +special API. But tf.learn provides some tools that make it easier to build +effective large-scale linear models. + +### Feature columns and transformations + +Much of the work of designing a linear model consists of transforming raw data +into suitable input features. tf.learn uses the `FeatureColumn` abstraction to +enable these transformations. + +A `FeatureColumn` represents a single feature in your data. A `FeatureColumn` +may represent a quantity like 'height', or it may represent a category like +'eye_color' where the value is drawn from a set of discrete possibilities like {'blue', 'brown', 'green'}. + +In the case of both *continuous features* like 'height' and *categorical +features* like 'eye_color', a single value in the data might get transformed +into a sequence of numbers before it is input into the model. The +`FeatureColumn` abstraction lets you manipulate the feature as a single +semantic unit in spite of this fact. You can specify transformations and +select features to include without dealing with specific indices in the +tensors you feed into the model. + +#### Sparse columns + +Categorical features in linear models are typically translated into a sparse +vector in which each possible value has a corresponding index or id. For +example, if there are only three possible eye colors you can represent +'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would +become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called +"sparse" because they may be very long, with many zeros, when the set of +possible values is very large (such as all English words). + +While you don't need to use sparse columns to use tf.learn linear models, one +of the strengths of linear models is their ability to deal with large sparse +vectors. Sparse features are a primary use case for the tf.learn linear model +tools. + +##### Encoding sparse columns + +`FeatureColumn` handles the conversion of categorical values into vectors +automatically, with code like this: + +```python +eye_color = tf.contrib.layers.sparse_column_with_keys( + column_name="eye_color", keys=["blue", "brown", "green"]) +``` + +where `eye_color` is the name of a column in your source data. + +You can also generate `FeatureColumn`s for categorical features for which you +don't know all possible values. For this case you would use +`sparse_column_with_hash_bucket()`, which uses a hash function to assign +indices to feature values. + +```python +education = tf.contrib.layers.sparse_column_with_hash_bucket(\ + "education", hash_bucket_size=1000) +``` + +##### Feature Crosses + +Because linear models assign independent weights to separate features, they +can't learn the relative importance of specific combinations of feature +values. If you have a feature 'favorite_sport' and a feature 'home_city' and +you're trying to predict whether a person likes to wear red, your linear model +won't be able to learn that baseball fans from St. Louis especially like to +wear red. + +You can get around this limitation by creating a new feature +'favorite_sport_x_home_city'. The value of this feature for a given person is +just the concatenation of the values of the two source features: +'baseball_x_stlouis', for example. This sort of combination feature is called +a *feature cross*. + +The `crossed_column()` method makes it easy to set up feature crosses: + +```python +sport = tf.contrib.layers.sparse_column_with_hash_bucket(\ + "sport", hash_bucket_size=1000) +city = tf.contrib.layers.sparse_column_with_hash_bucket(\ + "city", hash_bucket_size=1000) +sport_x_city = tf.contrib.layers.crossed_column( + [sport, city], hash_bucket_size=int(1e4)) +``` + +#### Continuous columns + +You can specify a continuous feature like so: + +```python +age = tf.contrib.layers.real_valued_column("age") +``` + +Although, as a single real number, a continuous feature can often be input +directly into the model, tf.learn offers useful transformations for this sort +of column as well. + +##### Bucketization + +*Bucketization* turns a continuous column into a categorical column. This +transformation lets you use continuous features in feature crosses, or learn +cases where specific value ranges have particular importance. + +Bucketization divides the range of possible values into subranges called +buckets: + +```python +age_buckets = tf.contrib.layers.bucketized_column( + age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]) +``` + +The bucket into which a value falls becomes the categorical label for +that value. + +#### Input function + +`FeatureColumn`s provide a specification for the input data for your model, +indicating how to represent and transform the data. But they do not provide +the data itself. You provide the data through an input function. + +The input function must return a dictionary of tensors. Each key corresponds +to the name of a `FeatureColumn`. Each key's value is a tensor containing the +values of that feature for all data instances. See `input_fn` in the [linear +models tutorial code]( +https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py?l=160) +for an example of an input function. + +The input function is passed to the `fit()` and `evaluate()` calls that +initiate training and testing, as described in the next section. + +### Linear estimators + +tf.learn's estimator classes provide a unified training and evaluation harness +for regression and classification models. They take care of the details of the +training and evaluation loops and allow the user to focus on model inputs and +architecture. + +To build a linear estimator, you can use either the +`tf.contrib.learn.LinearClassifier` estimator or the +`tf.contrib.learn.LinearRegressor` estimator, for classification and +regression respectively. + +As with all tf.learn estimators, to run the estimator you just: + + 1. Instantiate the estimator class. For the two linear estimator classes, + you pass a list of `FeatureColumn`s to the constructor. + 2. Call the estimator's `fit()` method to train it. + 3. Call the estimator's `evaluate()` method to see how it does. + +For example: + +```python +e = tf.contrib.learn.LinearClassifier(feature_columns=[ + native_country, education, occupation, workclass, marital_status, + race, age_buckets, education_x_occupation, age_buckets_x_race_x_occupation], + model_dir=YOUR_MODEL_DIRECTORY) +e.fit(input_fn=input_fn_train, steps=200) +# Evaluate for one step (one pass through the test data). +results = e.evaluate(input_fn=input_fn_test, steps=1) + +# Print the stats for the evaluation. +for key in sorted(results): + print "%s: %s" % (key, results[key]) +``` + +### Wide and deep learning + +The tf.learn API also provides an estimator class that lets you jointly train +a linear model and a deep neural network. This novel approach combines the +ability of linear models to "memorize" key features with the generalization +ability of neural nets. Use `tf.contrib.learn.DNNLinearCombinedClassifier` to +create this sort of "wide and deep" model: + +```python +e = tf.contrib.learn.DNNLinearCombinedClassifier( + model_dir=YOUR_MODEL_DIR, + linear_feature_columns=wide_columns, + dnn_feature_columns=deep_columns, + dnn_hidden_units=[100, 50]) +``` +For more information, see the [Wide and Deep Learning tutorial](../wide_and_deep/). diff --git a/tensorflow/g3doc/tutorials/tflearn/index.md b/tensorflow/g3doc/tutorials/tflearn/index.md new file mode 100644 index 0000000000..0a228baf08 --- /dev/null +++ b/tensorflow/g3doc/tutorials/tflearn/index.md @@ -0,0 +1,251 @@ +## tf.contrib.learn Quickstart + +TensorFlow’s high-level machine learning API (tf.contrib.learn) makes it easy +to configure, train, and evaluate a variety of machine learning models. In +this quickstart tutorial, you’ll use tf.contrib.learn to construct a [neural +network](https://en.wikipedia.org/wiki/Artificial_neural_network) classifier +and train it on [Fisher’s Iris data +set](https://en.wikipedia.org/wiki/Iris_flower_data_set) to predict flower +species based on sepal/petal geometry. You’ll perform the following five +steps: + +1. Load CSVs containing Iris training/test data into a TensorFlow `Dataset` +2. Construct a [neural network classifier]( +../../api_docs/python/contrib.learn.html#DNNClassifier) +3. Fit the model using the training data +4. Evaluate the accuracy of the model +5. Classify new samples + +## Get Started + +Remember to [install TensorFlow on your +machine](../../get_started/os_setup.html#download-and-setup) before getting +started with this tutorial. + +Here is the full code for our neural network: + +```python +import tensorflow as tf +import numpy as np + +# Data sets +IRIS_TRAINING = "iris_training.csv" +IRIS_TEST = "iris_test.csv" + +# Load datasets. +training_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TRAINING, target_dtype=np.int) +test_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TEST, target_dtype=np.int) + +x_train, x_test, y_train, y_test = training_set.data, test_set.data, \ + training_set.target, test_set.target + +# Build 3 layer DNN with 10, 20, 10 units respectively. +classifier = tf.contrib.learn.DNNClassifier(hidden_units=[10, 20, 10], n_classes=3) + +# Fit model. +classifier.fit(x=x_train, y=y_train, steps=200) + +# Evaluate accuracy. +accuracy_score = classifier.evaluate(x=x_test, y=y_test)["accuracy"] +print('Accuracy: {0:f}'.format(accuracy_score)) + +# Classify two new flower samples. +new_samples = np.array( + [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float) +y = classifier.predict(new_samples) +print ('Predictions: {}'.format(str(y))) +``` + +The following sections walk through the code in detail. + +## Load the Iris CSV data to TensorFlow + +The [Iris data set](https://en.wikipedia.org/wiki/Iris_flower_data_set) +contains 150 rows of data, comprising 50 samples from each of three related +Iris species: *Iris setosa*, *Iris virginica*, and *Iris versicolor*. Each row +contains the following data for each flower sample: [sepal](https://en.wikipedia.org/wiki/Sepal) +length, sepal width, [petal](https://en.wikipedia.org/wiki/Petal) length, petal width, +and flower species. Flower species are represented as integers, with 0 denoting *Iris +setosa*, 1 denoting *Iris versicolor*, and 2 denoting *Iris virginica*. + +Sepal Length | Sepal Width | Petal Length | Petal Width | Species +:----------- | :---------- | :----------- | :---------- | :------ +5.1 | 3.5 | 1.4 | 0.2 | 0 +4.9 | 3.0 | 1.4 | 0.2 | 0 +4.7 | 3.2 | 1.3 | 0.2 | 0 +… | … | … | … | … +7.0 | 3.2 | 4.7 | 1.4 | 1 +6.4 | 3.2 | 4.5 | 1.5 | 1 +6.9 | 3.1 | 4.9 | 1.5 | 1 +… | … | … | … | … +6.5 | 3.0 | 5.2 | 2.0 | 2 +6.2 | 3.4 | 5.4 | 2.3 | 2 +5.9 | 3.0 | 5.1 | 1.8 | 2 + +<!-- TODO: The rest of this section presumes that CSVs will live in same +directory as tutorial examples; if not, update links and code --> For this +tutorial, the Iris data has been randomized and split into two separate CSVs: +a training set of 120 samples +([iris_training.csv](http://download.tensorflow.org/data/iris_training.csv)). +and a test set of 30 samples +([iris_test.csv](http://download.tensorflow.org/data/iris_test.csv)). + +To get started, first import TensorFlow and numpy: + +```python +import tensorflow as tf +import numpy as np +``` + +Next, load the training and test sets into `Dataset`s using the [`load_csv()`] +(https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/datasets/base.py) method in `learn.datasets.base`. The +`load_csv()` method has two required arguments: + +* `filename`, which takes the filepath to the CSV file, and +* `target_dtype`, which takes the [`numpy` datatype](http://docs.scipy.org/doc/numpy/user/basics.types.html) of the dataset's target value. + +Here, the target (the value you're training the model to predict) is flower +species, which is an integer from 0–2, so the appropriate `numpy` +datatype is `np.int`: + +```python +# Data sets +IRIS_TRAINING = "iris_training.csv" +IRIS_TEST = "iris_test.csv" + +# Load datasets. +training_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TRAINING, target_dtype=np.int) +test_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TEST, target_dtype=np.int) +``` + +Next, assign variables to the feature data and target values: `x_train` for +training-set feature data, `x_test` for test-set feature data, `y_train` for +training-set target values, and `y_test` for test-set target values. `Dataset`s +in tf.contrib.learn are [named tuples](https://docs.python.org/2/library/collections.h +tml#collections.namedtuple), and you can access feature data and target values +via the `data` and `target` fields, respectively: + +```python +x_train, x_test, y_train, y_test = training_set.data, test_set.data, \ + training_set.target, test_set.target +``` + +Later on, in "Fit the DNNClassifier to the Iris Training Data," you'll use +`x_train` and `y_train` to train your model, and in "Evaluate Model +Accuracy", you'll use `x_test` and `y_test`. But first, you'll construct your +model in the next section. + +## Construct a Deep Neural Network Classifier + +tf.contrib.learn offers a variety of predefined models, called [`Estimator`s +](../../api_docs/python/contrib.learn.html#estimators), which you can use "out +of the box" to run training and evaluation operations on your data. Here, +you'll configure a Deep Neural Network Classifier model to fit the Iris data. +Using tf.contrib.learn, you can instantiate your +[`DNNClassifier`](../../api_docs/python/contrib.learn.html#DNNClassifier) with +just one line of code: + +```python +# Build 3 layer DNN with 10, 20, 10 units respectively. +classifier = tf.contrib.learn.DNNClassifier(hidden_units=[10, 20, 10], n_classes=3) +``` + +The code above creates a `DNNClassifier` model with three [hidden layers](http://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw), +containing 10, 20, and 10 neurons, respectively (`hidden_units=[10, 20, 10]`), and three target +classes (`n_classes=3`). + + +## Fit the DNNClassifier to the Iris Training Data + +Now that you've configured your DNN `classifier` model, you can fit it to the Iris training data +using the [`fit`](../../api_docs/python/contrib.learn.html#BaseEstimator.fit) +method. Pass as arguments your feature data (`x_train`), target values +(`y_train`), and the number of steps to train (here, 200): + +```python +# Fit model +classifier.fit(x=x_train, y=y_train, steps=200) +``` + +<!-- Style the below (up to the next section) as an aside (note?) --> + +<!-- Pretty sure the following is correct, but maybe a SWE could verify? --> +The state of the model is preserved in the `classifier`, which means you can train iteratively if +you like. For example, the above is equivalent to the following: + +```python +classifier.fit(x=x_train, y=y_train, steps=100) +classifier.fit(x=x_train, y=y_train, steps=100) +``` + +<!-- TODO: When tutorial exists for monitoring, link to it here --> +However, if you're looking to track the model while it trains, you'll likely +want to instead use a TensorFlow [`monitor`](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn/monitors.py) +to perform logging operations. + +## Evaluate Model Accuracy + +You've fit your `DNNClassifier` model on the Iris training data; now, you can +check its accuracy on the Iris test data using the [`evaluate` +](../../api_docs/python/contrib.learn.html#BaseEstimator.evaluate) method. +Like `fit`, `evaluate` takes feature data and target values as +arguments, and returns a `dict` with the evaluation results. The following +code passes the Iris test data—`x_test` and `y_test`—to `evaluate` +and prints the `accuracy` from the results: + +```python +accuracy_score = classifier.evaluate(x=x_test, y=y_test)["accuracy"] +print('Accuracy: {0:f}'.format(accuracy_score)) +``` + +Run the full script, and check the accuracy results. You should get: + +``` +Accuracy: 0.933333 +``` + +Not bad for a relatively small data set! + +## Classify New Samples + +Use the estimator's `predict()` method to classify new samples. For example, +say you have these two new flower samples: + +Sepal Length | Sepal Width | Petal Length | Petal Width +:----------- | :---------- | :----------- | :---------- +6.4 | 3.2 | 4.5 | 1.5 +5.8 | 3.1 | 5.0 | 1.7 + +You can predict their species with the following code: + +```python +# Classify two new flower samples. +new_samples = np.array( + [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float) +y = classifier.predict(new_samples) +print ('Predictions: {}'.format(str(y))) +``` + +The `predict()` method returns an array of predictions, one for each sample: + +```python +Prediction: [1 2] +``` + +The model thus predicts that the first sample is *Iris versicolor*, and the +second sample is *Iris virginica*. + +## Additional Resources + +* For further reference materials on tf.contrib.learn, see the official +[API docs](../../api_docs/python/contrib.learn.md). + +<!-- David, will the below be live when this tutorial is released? --> +* To learn more about using tf.contrib.learn to create linear models, see +[Large-scale Linear Models with TensorFlow](../linear/). + +* To experiment with neural network modeling and visualization in the browser, +check out [Deep Playground](http://playground.tensorflow.org/). + +* For more advanced tutorials on neural networks, see [Convolutional Neural +Networks](../deep_cnn/) and [Recurrent Neural Networks](../recurrent/). diff --git a/tensorflow/g3doc/tutorials/wide/index.md b/tensorflow/g3doc/tutorials/wide/index.md new file mode 100644 index 0000000000..5dd409f4e4 --- /dev/null +++ b/tensorflow/g3doc/tutorials/wide/index.md @@ -0,0 +1,482 @@ +# TensorFlow Linear Model Tutorial + +In this tutorial, we will use the TF.Learn API in TensorFlow to solve a binary +classification problem: Given census data about a person such as age, gender, +education and occupation (the features), we will try to predict whether or not +the person earns more than 50,000 dollars a year (the target label). We will +train a **logistic regression** model, and given an individual's information our +model will output a number between 0 and 1, which can be interpreted as the +probability that the individual has an annual income of over 50,000 dollars. + +## Setup + +To try the code for this tutorial: + +1. [Install TensorFlow](../../get_started/os_setup.md) if you haven't +already. + +2. Download [the tutorial code]( +https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py). + +3. Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas: + 1. Get `pip`: + + ```shell + # Ubuntu/Linux 64-bit + $ sudo apt-get install python-pip python-dev + + # Mac OS X + $ sudo easy_install pip + $ sudo easy_install --upgrade six + ``` + + 2. Use `pip` to install pandas: + + ```shell + $ sudo pip install pandas + ``` + + If you have trouble installing pandas, consult the [instructions] +(http://pandas.pydata.org/pandas-docs/stable/install.html) on the pandas site. + +4. Execute the tutorial code with the following command to train the linear +model described in this tutorial: + + ```shell + $ python wide_n_deep_tutorial.py --model_type=wide + ``` + +Read on to find out how this code builds its linear model. + +## Reading The Census Data + +The dataset we'll be using is the [Census Income Dataset] +(https://archive.ics.uci.edu/ml/datasets/Census+Income). You can download the +[training data] +(https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data) and +[test data] +(https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test) +manually or use code like this: + +```python +import tempfile +import urllib +train_file = tempfile.NamedTemporaryFile() +test_file = tempfile.NamedTemporaryFile() +urllib.urlretrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", train_file.name) +urllib.urlretrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test", test_file.name) +``` + +Once the CSV files are downloaded, let's read them into [Pandas] +(http://pandas.pydata.org/) dataframes. + +```python +import pandas as pd +COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num", + "marital_status", "occupation", "relationship", "race", "gender", + "capital_gain", "capital_loss", "hours_per_week", "native_country", + "income_bracket"] +df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True) +df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True, skiprows=1) +``` + +Since the task is a binary classification problem, we'll construct a label +column named "label" whose value is 1 if the income is over 50K, and 0 +otherwise. + +```python +LABEL_COLUMN = "label" +df_train[LABEL_COLUMN] = (df_train["income_bracket"].apply(lambda x: ">50K" in x)).astype(int) +df_test[LABEL_COLUMN] = (df_test["income_bracket"].apply(lambda x: ">50K" in x)).astype(int) +``` + +Next, let's take a look at the dataframe and see which columns we can use to +predict the target label. The columns can be grouped into two types—categorical +and continuous columns: + +* A column is called **categorical** if its value can only be one of the + categories in a finite set. For example, the native country of a person + (U.S., India, Japan, etc.) or the education level (high school, college, + etc.) are categorical columns. +* A column is called **continuous** if its value can be any numerical value in + a continuous range. For example, the capital gain of a person (e.g. $14,084) + is a continuous column. + +```python +CATEGORICAL_COLUMNS = ["workclass", "education", "marital_status", "occupation", + "relationship", "race", "gender", "native_country"] +CONTINUOUS_COLUMNS = ["age", "education_num", "capital_gain", "capital_loss", "hours_per_week"] +``` + +Here's a list of columns available in the Census Income dataset: + +| Column Name | Type | Description | {.sortable} +| -------------- | ----------- | --------------------------------- | +| age | Continuous | The age of the individual | +| workclass | Categorical | The type of employer the | +: : : individual has (government, : +: : : military, private, etc.). : +| fnlwgt | Continuous | The number of people the census | +: : : takers believe that observation : +: : : represents (sample weight). This : +: : : variable will not be used. : +| education | Categorical | The highest level of education | +: : : achieved for that individual. : +| education_num | Continuous | The highest level of education in | +: : : numerical form. : +| marital_status | Categorical | Marital status of the individual. | +| occupation | Categorical | The occupation of the individual. | +| relationship | Categorical | Wife, Own-child, Husband, | +: : : Not-in-family, Other-relative, : +: : : Unmarried. : +| race | Categorical | White, Asian-Pac-Islander, | +: : : Amer-Indian-Eskimo, Other, Black. : +| gender | Categorical | Female, Male. | +| capital_gain | Continuous | Capital gains recorded. | +| capital_loss | Continuous | Capital Losses recorded. | +| hours_per_week | Continuous | Hours worked per week. | +| native_country | Categorical | Country of origin of the | +: : : individual. : +| income | Categorical | ">50K" or "<=50K", meaning | +: : : whether the person makes more : +: : : than \$50,000 annually. : + +## Converting Data into Tensors + +When building a TF.Learn model, the input data is specified by means of an Input +Builder function. This builder function will not be called until it is later +passed to TF.Learn methods such as `fit` and `evaluate`. The purpose of this +function is to construct the input data, which is represented in the form of +[Tensors] +(https://www.tensorflow.org/versions/r0.9/api_docs/python/framework.html#Tensor) +or [SparseTensors] +(https://www.tensorflow.org/versions/r0.9/api_docs/python/sparse_ops.html#SparseTensor). +In more detail, the Input Builder function returns the following as a pair: + +1. `feature_cols`: A dict from feature column names to `Tensors` or + `SparseTensors`. +2. `label`: A `Tensor` containing the label column. + +The keys of the `feature_cols` will be used to when construct columns in the +next section. Because we want to call the `fit` and `evaluate` methods with +different data, we define two different input builder functions, +`train_input_fn` and `test_input_fn` which are identical except that they pass +different data to `input_fn`. Note that `input_fn` will be called while +constructing the TensorFlow graph, not while running the graph. What it is +returning is a representation of the input data as the fundamental unit of +TensorFlow computations, a `Tensor` (or `SparseTensor`). + +Our model represents the input data as *constant* tensors, meaning that the +tensor represents a constant value, in this case the values of a particular +column of `df_train` or `df_test`. This is the simplest way to pass data into +TensorFlow. Another more advanced way to represent input data would be to +construct an [Input Reader] +(https://www.tensorflow.org/versions/r0.9/api_docs/python/io_ops.html#inputs-and-readers) +that represents a file or other data source, and iterates through the file as +TensorFlow runs the graph. Each continuous column in the train or test dataframe +will be converted into a `Tensor`, which in general is a good format to +represent dense data. For cateogorical data, we must represent the data as a +`SparseTensor`. This data format is good for representing sparse data. + +```python +import tensorflow as tf + +def input_fn(df): + # Creates a dictionary mapping from each continuous feature column name (k) to + # the values of that column stored in a constant Tensor. + continuous_cols = {k: tf.constant(df[k].values) + for k in CONTINUOUS_COLUMNS} + # Creates a dictionary mapping from each categorical feature column name (k) + # to the values of that column stored in a tf.SparseTensor. + categorical_cols = {k: tf.SparseTensor( + indices=[[i, 0] for i in range(df[k].size)], + values=df[k].values, + shape=[df[k].size, 1]) + for k in CATEGORICAL_COLUMNS} + # Merges the two dictionaries into one. + feature_cols = dict(continuous_cols.items() + categorical_cols.items()) + # Converts the label column into a constant Tensor. + label = tf.constant(df[LABEL_COLUMN].values) + # Returns the feature columns and the label. + return feature_cols, label + +def train_input_fn(): + return input_fn(df_train) + +def eval_input_fn(): + return input_fn(df_test) +``` + +## Selecting and Engineering Features for the Model + +Selecting and crafting the right set of feature columns is key to learning an +effective model. A **feature column** can be either one of the raw columns in +the original dataframe (let's call them **base feature columns**), or any new +columns created based on some transformations defined over one or multiple base +columns (let's call them **derived feature columns**). Basically, "feature +column" is an abstract concept of any raw or derived variable that can be used +to predict the target label. + +### Base Categorical Feature Columns + +To define a feature column for a categorical feature, we can create a +`SparseColumn` using the TF.Learn API. If you know the set of all possible +feature values of a column and there are only a few of them, you can use +`sparse_column_with_keys`. Each key in the list will get assigned an +auto-incremental ID starting from 0. For example, for the `gender` column we can +assign the feature string "female" to an integer ID of 0 and "male" to 1 by +doing: + +```python +gender = tf.contrib.layers.sparse_column_with_keys( + column_name="gender", keys=["female", "male"]) +``` + +What if we don't know the set of possible values in advance? Not a problem. We +can use `sparse_column_with_hash_bucket` instead: + +```python +education = tf.contrib.layers.sparse_column_with_hash_bucket("education", hash_bucket_size=1000) +``` + +What will happen is that each possible value in the feature column `education` +will be hashed to an integer ID as we encounter them in training. See an example +illustration below: + +ID | Feature +--- | ------------- +... | +9 | `"Bachelors"` +... | +103 | `"Doctorate"` +... | +375 | `"Masters"` +... | + +No matter which way we choose to define a `SparseColumn`, each feature string +will be mapped into an integer ID by looking up a fixed mapping or by hashing. +Note that hashing collisions are possible, but may not significantly impact the +model quality. Under the hood, the `LinearModel` class is responsible for +managing the mapping and creating `tf.Variable` to store the model parameters +(also known as model weights) for each feature ID. The model parameters will be +learned through the model training process we'll go through later. + +We'll do the similar trick to define the other categorical features: + +```python +race = tf.contrib.layers.sparse_column_with_keys(column_name="race", keys=[ + "Amer-Indian-Eskimo", "Asian-Pac-Islander", "Black", "Other", "White"]) +marital_status = tf.contrib.layers.sparse_column_with_hash_bucket("marital_status", hash_bucket_size=100) +relationship = tf.contrib.layers.sparse_column_with_hash_bucket("relationship", hash_bucket_size=100) +workclass = tf.contrib.layers.sparse_column_with_hash_bucket("workclass", hash_bucket_size=100) +occupation = tf.contrib.layers.sparse_column_with_hash_bucket("occupation", hash_bucket_size=1000) +native_country = tf.contrib.layers.sparse_column_with_hash_bucket("native_country", hash_bucket_size=1000) +``` + +### Base Continuous Feature Columns + +Similarly, we can define a `RealValuedColumn` for each continuous feature column +that we want to use in the model: + +```python +age = tf.contrib.layers.real_valued_column("age") +education_num = tf.contrib.layers.real_valued_column("education_num") +capital_gain = tf.contrib.layers.real_valued_column("capital_gain") +capital_loss = tf.contrib.layers.real_valued_column("capital_loss") +hours_per_week = tf.contrib.layers.real_valued_column("hours_per_week") +``` + +### Making Continuous Features Categorical through Bucketization + +Sometimes the relationship between a continuous feature and the label is not +linear. As an hypothetical example, a person's income may grow with age in the +early stage of one's career, then the growth may slow at some point, and finally +the income decreases after retirement. In this scenario, using the raw `age` as +a real-valued feature column might not be a good choice because the model can +only learn one of the three cases: + +1. Income always increases at some rate as age grows (positive correlation), +1. Income always decreases at some rate as age grows (negative correlation), or +1. Income stays the same no matter at what age (no correlation) + +If we want to learn the fine-grained correlation between income and each age +group seperately, we can leverage **bucketization**. Bucketization is a process +of dividing the entire range of a continuous feature into a set of consecutive +bins/buckets, and then converting the original numerical feature into a bucket +ID (as a categorical feature) depending on which bucket that value falls into. +So, we can define a `bucketized_column` over `age` as: + +```python +age_buckets = tf.contrib.layers.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]) +``` + +where the `boundaries` is a list of bucket boundaries. In this case, there are +10 boundaries, resulting in 11 age group buckets (from age 17 and below, 18-24, +25-29, ..., to 65 and over). + +### Intersecting Multiple Columns with CrossedColumn + +Using each base feature column separately may not be enough to explain the data. +For example, the correlation between education and the label (earning > 50,000 +dollars) may be different for different occupations. Therefore, if we only learn +a single model weight for `education="Bachelors"` and `education="Masters"`, we +won't be able to capture every single education-occupation combination (e.g. +distinguishing between `education="Bachelors" AND occupation="Exec-managerial"` +and `education="Bachelors" AND occupation="Craft-repair"`). To learn the +differences between different feature combinations, we can add **crossed feature +columns** to the model. + +```python +education_x_occupation = tf.contrib.layers.crossed_column([education, occupation], hash_bucket_size=int(1e4)) +``` + +We can also create a `CrossedColumn` over more than two columns. Each +constituent column can be either a base feature column that is categorical +(`SparseColumn`), a bucketized real-valued feature column (`BucketizedColumn`), +or even another `CrossColumn`. Here's an example: + +```python +age_buckets_x_race_x_occupation = tf.contrib.layers.crossed_column( + [age_buckets, race, occupation], hash_bucket_size=int(1e6)) +``` + +## Defining The Logistic Regression Model + +After processing the input data and defining all the feature columns, we're now +ready to put them all together and build a Logistic Regression model. In the +previous section we've seen several types of base and derived feature columns, +including: + +* `SparseColumn` +* `RealValuedColumn` +* `BucketizedColumn` +* `CrossedColumn` + +All of these are subclasses of the abstract `FeatureColumn` class, and can be +added to the `feature_columns` field of a model: + +```python +model_dir = tempfile.mkdtemp() +m = tf.contrib.learn.LinearClassifier(feature_columns=[ + gender, native_country, education, occupation, workclass, marital_status, race, + age_buckets, education_x_occupation, age_buckets_x_race_x_occupation], + model_dir=model_dir) +``` + +The model also automatically learns a bias term, which controls the prediction +one would make without observing any features (see the section "How Logistic +Regression Works" for more explanations). The learned model files will be stored +in `model_dir`. + +## Training and Evaluating Our Model + +After adding all the features to the model, now let's look at how to actually +train the model. Training a model is just a one-liner using the TF.Learn API: + +```python +m.fit(input_fn=train_input_fn, steps=200) +``` + +After the model is trained, we can evaluate how good our model is at predicting +the labels of the holdout data: + +```python +results = m.evaluate(input_fn=eval_input_fn, steps=1) +for key in sorted(results): + print "%s: %s" % (key, results[key]) +``` + +The first line of the output should be something like `accuracy: 0.83557522`, +which means the accuracy is 83.6%. Feel free to try more features and +transformations and see if you can do even better! + +If you'd like to see a working end-to-end example, you can download our [example +code] +(https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py) +and set the `model_type` flag to `wide`. + +## Adding Regularization to Prevent Overfitting + +Regularization is a technique used to avoid **overfitting**. Overfitting happens +when your model does well on the data it is trained on, but worse on test data +that the model has not seen before, such as live traffic. Overfitting generally +occurs when a model is excessively complex, such as having too many parameters +relative to the number of observed training data. Regularization allows for you +to control your model's complexity and makes the model more generalizable to +unseen data. + +In the Linear Model library, you can add L1 and L2 regularizations to the model +as: + +``` +m = tf.contrib.learn.LinearClassifier(feature_columns=[ + gender, native_country, education, occupation, workclass, marital_status, race, + age_buckets, education_x_occupation, age_buckets_x_race_x_occupation], + optimizer=tf.train.FtrlOptimizer( + learning_rate=0.1, + l1_regularization_strength=1.0, + l2_regularization_strength=1.0), + model_dir=model_dir) +``` + +One important difference between L1 and L2 regularization is that L1 +regularization tends to make model weights stay at zero, creating sparser +models, whereas L2 regularization also tries to make the model weights closer to +zero but not necessarily zero. Therefore, if you increase the strength of L1 +regularization, you will have a smaller model size because many of the model +weights will be zero. This is often desirable when the feature space is very +large but sparse, and when there are resource constraints that prevent you from +serving a model that is too large. + +In practice, you should try various combinations of L1, L2 regularization +strengths and find the best parameters that best control overfitting and give +you a desirable model size. + +## How Logistic Regression Works + +Finally, let's take a minute to talk about what the Logistic Regression model +actually looks like in case you're not already familiar with it. We'll denote +the label as $$Y$$, and the set of observed features as a feature vector +$$\mathbf{x}=[x_1, x_2, ..., x_d]$$. We define $$Y=1$$ if an individual earned > +50,000 dollars and $$Y=0$$ otherwise. In Logistic Regression, the probability of +the label being positive ($$Y=1$$) given the features $$\mathbf{x}$$ is given +as: + +$$ P(Y=1|\mathbf{x}) = \frac{1}{1+\exp(-(\mathbf{w}^T\mathbf{x}+b))}$$ + +where $$\mathbf{w}=[w_1, w_2, ..., w_d]$$ are the model weights for the features +$$\mathbf{x}=[x_1, x_2, ..., x_d]$$. $$b$$ is a constant that is often called +the **bias** of the model. The equation consists of two parts—A linear model and +a logistic function: + +* **Linear Model**: First, we can see that $$\mathbf{w}^T\mathbf{x}+b = b + + w_1x_1 + ... +w_dx_d$$ is a linear model where the output is a linear + function of the input features $$\mathbf{x}$$. The bias $$b$$ is the + prediction one would make without observing any features. The model weight + $$w_i$$ reflects how the feature $$x_i$$ is correlated with the positive + label. If $$x_i$$ is positively correlated with the positive label, the + weight $$w_i$$ increases, and the probability $$P(Y=1|\mathbf{x})$$ will be + closer to 1. On the other hand, if $$x_i$$ is negatively correlated with the + positive label, then the weight $$w_i$$ decreases and the probability + $$P(Y=1|\mathbf{x})$$ will be closer to 0. + +* **Logistic Function**: Second, we can see that there's a logistic function + (also known as the sigmoid function) $$S(t) = 1/(1+\exp(-t))$$ being applied + to the linear model. The logistic function is used to convert the output of + the linear model $$\mathbf{w}^T\mathbf{x}+b$$ from any real number into the + range of $$[0, 1]$$, which can be interpreted as a probability. + +Model training is an optimization problem: The goal is to find a set of model +weights (i.e. model parameters) to minimize a **loss function** defined over the +training data, such as logistic loss for Logistic Regression models. The loss +function measures the discrepancy between the ground-truth label and the model's +prediction. If the prediction is very close to the ground-truth label, the loss +value will be low; if the prediction is very far from the label, then the loss +value would be high. + +## Learn Deeper + +If you're interested in learning more, check out our [Wide & Deep Learning +Tutorial](../wide_and_deep/) where we'll show you how to combine +the strengths of linear models and deep neural networks by jointly training them +using the TF.Learn API. diff --git a/tensorflow/g3doc/tutorials/wide_and_deep/index.md b/tensorflow/g3doc/tutorials/wide_and_deep/index.md new file mode 100644 index 0000000000..910e91e1d0 --- /dev/null +++ b/tensorflow/g3doc/tutorials/wide_and_deep/index.md @@ -0,0 +1,275 @@ +# TensorFlow Wide & Deep Learning Tutorial + +In the previous [TensorFlow Linear Model Tutorial](../wide/), +we trained a logistic regression model to predict the probability that the +individual has an annual income of over 50,000 dollars using the [Census Income +Dataset](https://archive.ics.uci.edu/ml/datasets/Census+Income). TensorFlow is +great for training deep neural networks too, and you might be thinking which one +you should choose—Well, why not both? Would it be possible to combine the +strengths of both in one model? + +In this tutorial, we'll introduce how to use the TF.Learn API to jointly train a +wide linear model and a deep feed-forward neural network. This approach combines +the strengths of memorization and generalization. It's useful for generic +large-scale regression and classification problems with sparse input features +(e.g., categorical features with a large number of possible feature values). If +you're interested in learning more about how Wide & Deep Learning works, please +check out our [research paper](http://arxiv.org/abs/1606.07792). + +![Wide & Deep Spectrum of Models] +(../../images/wide_n_deep.svg "Wide & Deep") + +The figure above shows a comparison of a wide model (logistic regression with +sparse features and transformations), a deep model (feed-forward neural network +with an embedding layer and several hidden layers), and a Wide & Deep model +(joint training of both). At a high level, there are only 3 steps to configure a +wide, deep, or Wide & Deep model using the TF.Learn API: + +1. Select features for the wide part: Choose the sparse base columns and + crossed columns you want to use. +1. Select features for the deep part: Choose the continuous columns, the + embedding dimension for each categorical column, and the hidden layer sizes. +1. Put them all together in a Wide & Deep model + (`DNNLinearCombinedClassifier`). + +And that's it! Let's go through a simple example. + +## Setup + +To try the code for this tutorial: + +1. [Install TensorFlow](../../get_started/os_setup.md) if you haven't +already. + +2. Download [the tutorial code]( +https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py). + +3. Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas: + 1. Get `pip`: + + ```shell + # Ubuntu/Linux 64-bit + $ sudo apt-get install python-pip python-dev + + # Mac OS X + $ sudo easy_install pip + $ sudo easy_install --upgrade six + ``` + + 2. Use `pip` to install pandas: + + ```shell + $ sudo pip install pandas + ``` + + If you have trouble installing pandas, consult the [instructions] +(http://pandas.pydata.org/pandas-docs/stable/install.html) on the pandas site. + +4. Execute the tutorial code with the following command to train the linear +model described in this tutorial: + + ```shell + $ python wide_n_deep_tutorial.py --model_type=wide_n_deep + ``` + +Read on to find out how this code builds its linear model. + + +## Define Base Feature Columns + +First, let's define the base categorical and continuous feature columns that +we'll use. These base columns will be the building blocks used by both the wide +part and the deep part of the model. + +```python +import tensorflow as tf + +# Categorical base columns. +gender = tf.contrib.layers.sparse_column_with_keys(column_name="gender", keys=["female", "male"]) +race = tf.contrib.layers.sparse_column_with_keys(column_name="race", keys=[ + "Amer-Indian-Eskimo", "Asian-Pac-Islander", "Black", "Other", "White"]) +education = tf.contrib.layers.sparse_column_with_hash_bucket("education", hash_bucket_size=1000) +marital_status = tf.contrib.layers.sparse_column_with_hash_bucket("marital_status", hash_bucket_size=100) +relationship = tf.contrib.layers.sparse_column_with_hash_bucket("relationship", hash_bucket_size=100) +workclass = tf.contrib.layers.sparse_column_with_hash_bucket("workclass", hash_bucket_size=100) +occupation = tf.contrib.layers.sparse_column_with_hash_bucket("occupation", hash_bucket_size=1000) +native_country = tf.contrib.layers.sparse_column_with_hash_bucket("native_country", hash_bucket_size=1000) + +# Continuous base columns. +age = tf.contrib.layers.real_valued_column("age") +age_buckets = tf.contrib.layers.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]) +education_num = tf.contrib.layers.real_valued_column("education_num") +capital_gain = tf.contrib.layers.real_valued_column("capital_gain") +capital_loss = tf.contrib.layers.real_valued_column("capital_loss") +hours_per_week = tf.contrib.layers.real_valued_column("hours_per_week") +``` + +## The Wide Model: Linear Model with Crossed Feature Columns + +The wide model is a linear model with a wide set of sparse and crossed feature +columns: + +```python +wide_columns = [ + gender, native_country, education, occupation, workclass, marital_status, relationship, age_buckets, + tf.contrib.layers.crossed_column([education, occupation], hash_bucket_size=int(1e4)), + tf.contrib.layers.crossed_column([native_country, occupation], hash_bucket_size=int(1e4)), + tf.contrib.layers.crossed_column([age_buckets, race, occupation], hash_bucket_size=int(1e6))] +``` + +Wide models with crossed feature columns can memorize sparse interactions +between features effectively. That being said, one limitation of crossed feature +columns is that they do not generalize to feature combinations that have not +appeared in the training data. Let's add a deep model with embeddings to fix +that. + +## The Deep Model: Neural Network with Embeddings + +The deep model is a feed-forward neural network, as shown in the previous +figure. Each of the sparse, high-dimensional categorical features are first +converted into a low-dimensional and dense real-valued vector, often referred to +as an embedding vector. These low-dimensional dense embedding vectors are +concatenated with the continuous features, and then fed into the hidden layers +of a neural network in the forward pass. The embedding values are initialized +randomly, and are trained along with all other model parameters to minimize the +training loss. If you're interested in learning more about embeddings, check out +the TensorFlow tutorial on [Vector Representations of Words] +(https://www.tensorflow.org/versions/r0.9/tutorials/word2vec/index.html), or +[Word Embedding](https://en.wikipedia.org/wiki/Word_embedding) on Wikipedia. + +We'll configure the embeddings for the categorical columns using +`embedding_column`, and concatenate them with the continuous columns: + +```python +deep_columns = [ + tf.contrib.layers.embedding_column(workclass, dimension=8), + tf.contrib.layers.embedding_column(education, dimension=8), + tf.contrib.layers.embedding_column(marital_status, dimension=8), + tf.contrib.layers.embedding_column(gender, dimension=8), + tf.contrib.layers.embedding_column(relationship, dimension=8), + tf.contrib.layers.embedding_column(race, dimension=8), + tf.contrib.layers.embedding_column(native_country, dimension=8), + tf.contrib.layers.embedding_column(occupation, dimension=8), + age, education_num, capital_gain, capital_loss, hours_per_week] +``` + +The higher the `dimension` of the embedding is, the more degrees of freedom the +model will have to learn the representations of the features. For simplicity, we +set the dimension to 8 for all feature columns here. Empirically, a more +informed decision for the number of dimensions is to start with a value on the +order of $$k\log_2(n)$$ or $$k\sqrt[4]n$$, where $$n$$ is the number of unique +features in a feature column and $$k$$ is a small constant (usually smaller than +10). + +Through dense embeddings, deep models can generalize better and make predictions +on feature pairs that were previously unseen in the training data. However, it +is difficult to learn effective low-dimensional representations for feature +columns when the underlying interaction matrix between two feature columns is +sparse and high-rank. In such cases, the interaction between most feature pairs +should be zero except a few, but dense embeddings will lead to nonzero +predictions for all feature pairs, and thus can over-generalize. On the other +hand, linear models with crossed features can memorize these “exception rules” +effectively with fewer model parameters. + +Now, let's see how to jointly train wide and deep models and allow them to +complement each other’s strengths and weaknesses. + +## Combining Wide and Deep Models into One + +The wide models and deep models are combined by summing up their final output +log odds as the prediction, then feeding the prediction to a logistic loss +function. All the graph definition and variable allocations have already been +handled for you under the hood, so you simply need to create a +`DNNLinearCombinedClassifier`: + +```python +import tempfile +model_dir = tempfile.mkdtemp() +m = tf.contrib.learn.DNNLinearCombinedClassifier( + model_dir=model_dir, + linear_feature_columns=wide_columns, + dnn_feature_columns=deep_columns, + dnn_hidden_units=[100, 50]) +``` + +## Training and Evaluating The Model + +Before we train the model, let's read in the Census dataset as we did in the +[TensorFlow Linear Model tutorial](../wide/). The code for +input data processing is provided here again for your convenience: + +```python +import pandas as pd +import urllib + +# Define the column names for the data sets. +COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num", + "marital_status", "occupation", "relationship", "race", "gender", + "capital_gain", "capital_loss", "hours_per_week", "native_country", "income_bracket"] +LABEL_COLUMN = 'label' +CATEGORICAL_COLUMNS = ["workclass", "education", "marital_status", "occupation", + "relationship", "race", "gender", "native_country"] +CONTINUOUS_COLUMNS = ["age", "education_num", "capital_gain", "capital_loss", + "hours_per_week"] + +# Download the training and test data to temporary files. +# Alternatively, you can download them yourself and change train_file and +# test_file to your own paths. +train_file = tempfile.NamedTemporaryFile() +test_file = tempfile.NamedTemporaryFile() +urllib.urlretrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", train_file.name) +urllib.urlretrieve("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test", test_file.name) + +# Read the training and test data sets into Pandas dataframe. +df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True) +df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True, skiprows=1) +df_train[LABEL_COLUMN] = (df_train['income_bracket'].apply(lambda x: '>50K' in x)).astype(int) +df_test[LABEL_COLUMN] = (df_test['income_bracket'].apply(lambda x: '>50K' in x)).astype(int) + +def input_fn(df): + # Creates a dictionary mapping from each continuous feature column name (k) to + # the values of that column stored in a constant Tensor. + continuous_cols = {k: tf.constant(df[k].values) + for k in CONTINUOUS_COLUMNS} + # Creates a dictionary mapping from each categorical feature column name (k) + # to the values of that column stored in a tf.SparseTensor. + categorical_cols = {k: tf.SparseTensor( + indices=[[i, 0] for i in range(df[k].size)], + values=df[k].values, + shape=[df[k].size, 1]) + for k in CATEGORICAL_COLUMNS} + # Merges the two dictionaries into one. + feature_cols = dict(continuous_cols.items() + categorical_cols.items()) + # Converts the label column into a constant Tensor. + label = tf.constant(df[LABEL_COLUMN].values) + # Returns the feature columns and the label. + return feature_cols, label + +def train_input_fn(): + return input_fn(df_train) + +def eval_input_fn(): + return input_fn(df_test) +``` + +After reading in the data, you can train and evaluate the model: + +```python +m.fit(input_fn=train_input_fn, steps=200) +results = m.evaluate(input_fn=eval_input_fn, steps=1) +for key in sorted(results): + print "%s: %s" % (key, results[key]) +``` + +The first line of the output should be something like `accuracy: 0.84429705`. We +can see that the accuracy was improved from about 83.6% using a wide-only linear +model to about 84.4% using a Wide & Deep model. If you'd like to see a working +end-to-end example, you can download our [example code] +(https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/wide_n_deep_tutorial.py). + +Note that this tutorial is just a quick example on a small dataset to get you +familiar with the API. Wide & Deep Learning will be even more powerful if you +try it on a large dataset with many sparse feature columns that have a large +number of possible feature values. Again, feel free to take a look at our +[research paper](http://arxiv.org/abs/1606.07792) for more ideas about how to +apply Wide & Deep Learning in real-world large-scale maching learning problems. |
