In the previous TensorFlow Linear Model Tutorial, 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. 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.estimator 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.
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.estimator API:
And that's it! Let's go through a simple example.
To try the code for this tutorial:
Install TensorFlow if you haven't already.
Download the tutorial code.
Install the pandas data analysis library. tf.estimator doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
# Ubuntu/Linux 64-bit $ sudo apt-get install python-pip python-dev # Mac OS X $ sudo easy_install pip $ sudo easy_install --upgrade six
pip to install pandas:
$ sudo pip install pandas
If you have trouble installing pandas, consult the instructions on the pandas site.
Execute the tutorial code with the following command to train the linear model described in this tutorial:
$ python wide_n_deep_tutorial.py --model_type=wide_n_deep
Read on to find out how this code builds its linear model.
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.
import tensorflow as tf gender = tf.feature_column.categorical_column_with_vocabulary_list( "gender", ["Female", "Male"]) education = tf.feature_column.categorical_column_with_vocabulary_list( "education", [ "Bachelors", "HS-grad", "11th", "Masters", "9th", "Some-college", "Assoc-acdm", "Assoc-voc", "7th-8th", "Doctorate", "Prof-school", "5th-6th", "10th", "1st-4th", "Preschool", "12th" ]) marital_status = tf.feature_column.categorical_column_with_vocabulary_list( "marital_status", [ "Married-civ-spouse", "Divorced", "Married-spouse-absent", "Never-married", "Separated", "Married-AF-spouse", "Widowed" ]) relationship = tf.feature_column.categorical_column_with_vocabulary_list( "relationship", [ "Husband", "Not-in-family", "Wife", "Own-child", "Unmarried", "Other-relative" ]) workclass = tf.feature_column.categorical_column_with_vocabulary_list( "workclass", [ "Self-emp-not-inc", "Private", "State-gov", "Federal-gov", "Local-gov", "?", "Self-emp-inc", "Without-pay", "Never-worked" ]) # To show an example of hashing: occupation = tf.feature_column.categorical_column_with_hash_bucket( "occupation", hash_bucket_size=1000) native_country = tf.feature_column.categorical_column_with_hash_bucket( "native_country", hash_bucket_size=1000) # Continuous base columns. age = tf.feature_column.numeric_column("age") education_num = tf.feature_column.numeric_column("education_num") capital_gain = tf.feature_column.numeric_column("capital_gain") capital_loss = tf.feature_column.numeric_column("capital_loss") hours_per_week = tf.feature_column.numeric_column("hours_per_week") # Transformations. age_buckets = tf.feature_column.bucketized_column( age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
The wide model is a linear model with a wide set of sparse and crossed feature columns:
base_columns = [ gender, native_country, education, occupation, workclass, relationship, age_buckets, ] crossed_columns = [ tf.feature_column.crossed_column( ["education", "occupation"], hash_bucket_size=1000), tf.feature_column.crossed_column( [age_buckets, "education", "occupation"], hash_bucket_size=1000), tf.feature_column.crossed_column( ["native_country", "occupation"], hash_bucket_size=1000) ]
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 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, or Word Embedding on Wikipedia.
Another way to represent categorical columns to feed into a neural network is via a multi-hot representation. This is often appropriate for categorical columns with only a few possible values. E.g. for the gender column,
"Male" can be represented as
[1, 0] and
[0, 1]. This is a fixed representation, whereas embeddings are more flexible and calculated at training time.
We'll configure the embeddings for the categorical columns using
embedding_column, and concatenate them with the continuous columns. We also use
indicator_column to create multi-hot representation of some categorical columns.
deep_columns = [ tf.feature_column.indicator_column(workclass), tf.feature_column.indicator_column(education), tf.feature_column.indicator_column(gender), tf.feature_column.indicator_column(relationship), # To show an example of embedding tf.feature_column.embedding_column(native_country, dimension=8), tf.feature_column.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 \(\log_2(n)\) or \(k\sqrtn\), 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.
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
import tempfile model_dir = tempfile.mkdtemp() m = tf.estimator.DNNLinearCombinedClassifier( model_dir=model_dir, linear_feature_columns=crossed_columns, dnn_feature_columns=deep_columns, dnn_hidden_units=[100, 50])
Before we train the model, let's read in the Census dataset as we did in the TensorFlow Linear Model tutorial. The code for input data processing is provided here again for your convenience:
import pandas as pd import urllib # Define the column names for the data sets. CSV_COLUMNS = [ "age", "workclass", "fnlwgt", "education", "education_num", "marital_status", "occupation", "relationship", "race", "gender", "capital_gain", "capital_loss", "hours_per_week", "native_country", "income_bracket" ] def maybe_download(train_data, test_data): """Maybe downloads training data and returns train and test file names.""" if train_data: train_file_name = train_data else: train_file = tempfile.NamedTemporaryFile(delete=False) urllib.request.urlretrieve( "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", train_file.name) # pylint: disable=line-too-long train_file_name = train_file.name train_file.close() print("Training data is downloaded to %s" % train_file_name) if test_data: test_file_name = test_data else: test_file = tempfile.NamedTemporaryFile(delete=False) urllib.request.urlretrieve( "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test", test_file.name) # pylint: disable=line-too-long test_file_name = test_file.name test_file.close() print("Test data is downloaded to %s"% test_file_name) return train_file_name, test_file_name def input_fn(data_file, num_epochs, shuffle): """Input builder function.""" df_data = pd.read_csv( tf.gfile.Open(data_file), names=CSV_COLUMNS, skipinitialspace=True, engine="python", skiprows=1) # remove NaN elements df_data = df_data.dropna(how="any", axis=0) labels = df_data["income_bracket"].apply(lambda x: ">50K" in x).astype(int) return tf.estimator.inputs.pandas_input_fn( x=df_data, y=labels, batch_size=100, num_epochs=num_epochs, shuffle=shuffle, num_threads=5)
After reading in the data, you can train and evaluate the model:
# set num_epochs to None to get infinite stream of data. m.train( input_fn=input_fn(train_file_name, num_epochs=None, shuffle=True), steps=train_steps) # set steps to None to run evaluation until all data consumed. results = m.evaluate( input_fn=input_fn(test_file_name, num_epochs=1, shuffle=False), steps=None) print("model directory = %s" % model_dir) 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.
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 for more ideas about how to apply Wide & Deep Learning in real-world large-scale machine learning problems.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.