diff options
| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2016-06-28 08:24:35 -0800 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2016-06-28 09:33:01 -0700 |
| commit | ed1ab8d654020ca534bbbe2365d4a2a1517e8a2e (patch) | |
| tree | c7a7509220aa6cf7fe1859bc39af0e942c25a9d7 | |
| parent | 7dd3a2bb99eb682f13b670975f7f4e9ea5b7b7b4 (diff) | |
Publishes tutorials for tf.contrib.learn linear models and wide and deep models.
Change: 126082003
| -rw-r--r-- | tensorflow/g3doc/images/wide_n_deep.svg | 1540 | ||||
| -rw-r--r-- | tensorflow/g3doc/tutorials/wide/index.md | 482 | ||||
| -rw-r--r-- | tensorflow/g3doc/tutorials/wide_and_deep/index.md | 275 |
3 files changed, 2297 insertions, 0 deletions
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(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. |
