Copyright 2018 The TensorFlow Authors.¶

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Basic regression: Predict fuel efficiency¶

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In a regression problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a classification problem, where we aim to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is in the picture).

This notebook uses the classic Auto MPG Dataset and builds a model to predict the fuel efficiency of late-1970s and early 1980s automobiles. To do this, we'll provide the model with a description of many automobiles from that time period. This description includes attributes like: cylinders, displacement, horsepower, and weight.

This example uses the tf.keras API, see this guide for details.

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# Use seaborn for pairplot
!pip install -q seaborn

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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns


# Make numpy printouts easier to read.
np.set_printoptions(precision=3, suppress=True)

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import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing

print(tf.__version__)

The Auto MPG dataset¶

The dataset is available from the UCI Machine Learning Repository.

Get the data¶

First download and import the dataset using pandas:

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url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'
column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',
                'Acceleration', 'Model Year', 'Origin']

raw_dataset = pd.read_csv(url, names=column_names,
                          na_values='?', comment='\t',
                          sep=' ', skipinitialspace=True)

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dataset = raw_dataset.copy()
dataset.tail()

Clean the data¶

The dataset contains a few unknown values.

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dataset.isna().sum()

Drop those rows to keep this initial tutorial simple.

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dataset = dataset.dropna()

The "Origin" column is really categorical, not numeric. So convert that to a one-hot:

Note: You can set up the keras.Model to do this kind of transformation for you. That's beyond the scope of this tutorial. See the preprocessing layers or Loading CSV data tutorials for examples.

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dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})

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dataset = pd.get_dummies(dataset, prefix='', prefix_sep='')
dataset.tail()

Split the data into train and test¶

Now split the dataset into a training set and a test set.

We will use the test set in the final evaluation of our models.

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train_dataset = dataset.sample(frac=0.8, random_state=0)
test_dataset = dataset.drop(train_dataset.index)

Inspect the data¶

Have a quick look at the joint distribution of a few pairs of columns from the training set.

Looking at the top row it should be clear that the fuel efficiency (MPG) is a function of all the other parameters. Looking at the other rows it should be clear that they are each functions of eachother.

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sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde')

Also look at the overall statistics, note how each feature covers a very different range:

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train_dataset.describe().transpose()

Split features from labels¶

Separate the target value, the "label", from the features. This label is the value that you will train the model to predict.

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train_features = train_dataset.copy()
test_features = test_dataset.copy()

train_labels = train_features.pop('MPG')
test_labels = test_features.pop('MPG')

Normalization¶

In the table of statistics it's easy to see how different the ranges of each feature are.

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train_dataset.describe().transpose()[['mean', 'std']]

It is good practice to normalize features that use different scales and ranges.

One reason this is important is because the features are multiplied by the model weights. So the scale of the outputs and the scale of the gradients are affected by the scale of the inputs.

Although a model might converge without feature normalization, normalization makes training much more stable.

The Normalization layer¶

The preprocessing.Normalization layer is a clean and simple way to build that preprocessing into your model.

The first step is to create the layer:

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normalizer = preprocessing.Normalization()

Then .adapt() it to the data:

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normalizer.adapt(np.array(train_features))

This calculates the mean and variance, and stores them in the layer.

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print(normalizer.mean.numpy())

When the layer is called it returns the input data, with each feature independently normalized:

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first = np.array(train_features[:1])

with np.printoptions(precision=2, suppress=True):
  print('First example:', first)
  print()
  print('Normalized:', normalizer(first).numpy())

Linear regression¶

Before building a DNN model, start with a linear regression.

One Variable¶

Start with a single-variable linear regression, to predict MPG from Horsepower.

Training a model with tf.keras typically starts by defining the model architecture.

In this case use a keras.Sequential model. This model represents a sequence of steps. In this case there are two steps:

Normalize the input horsepower.
Apply a linear transformation ($y = mx+b$) to produce 1 output using layers.Dense.

The number of inputs can either be set by the input_shape argument, or automatically when the model is run for the first time.

First create the horsepower Normalization layer:

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horsepower = np.array(train_features['Horsepower'])

horsepower_normalizer = preprocessing.Normalization(input_shape=[1,])
horsepower_normalizer.adapt(horsepower)

Build the sequential model:

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horsepower_model = tf.keras.Sequential([
    horsepower_normalizer,
    layers.Dense(units=1)
])

horsepower_model.summary()

This model will predict MPG from Horsepower.

Run the untrained model on the first 10 horse-power values. The output won't be good, but you'll see that it has the expected shape, (10,1):

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horsepower_model.predict(horsepower[:10])

Once the model is built, configure the training procedure using the Model.compile() method. The most important arguments to compile are the loss and the optimizer since these define what will be optimized (mean_absolute_error) and how (using the optimizers.Adam).

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horsepower_model.compile(
    optimizer=tf.optimizers.Adam(learning_rate=0.1),
    loss='mean_absolute_error')

Once the training is configured, use Model.fit() to execute the training:

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%%time
history = horsepower_model.fit(
    train_features['Horsepower'], train_labels,
    epochs=100,
    # suppress logging
    verbose=0,
    # Calculate validation results on 20% of the training data
    validation_split = 0.2)

Visualize the model's training progress using the stats stored in the history object.

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hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
hist.tail()

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def plot_loss(history):
  plt.plot(history.history['loss'], label='loss')
  plt.plot(history.history['val_loss'], label='val_loss')
  plt.ylim([0, 10])
  plt.xlabel('Epoch')
  plt.ylabel('Error [MPG]')
  plt.legend()
  plt.grid(True)

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plot_loss(history)

Collect the results on the test set, for later:

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test_results = {}

test_results['horsepower_model'] = horsepower_model.evaluate(
    test_features['Horsepower'],
    test_labels, verbose=0)

SInce this is a single variable regression it's easy to look at the model's predictions as a function of the input:

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x = tf.linspace(0.0, 250, 251)
y = horsepower_model.predict(x)

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def plot_horsepower(x, y):
  plt.scatter(train_features['Horsepower'], train_labels, label='Data')
  plt.plot(x, y, color='k', label='Predictions')
  plt.xlabel('Horsepower')
  plt.ylabel('MPG')
  plt.legend()

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plot_horsepower(x,y)

Multiple inputs¶

You can use an almost identical setup to make predictions based on multiple inputs. This model still does the same $y = mx+b$ except that $m$ is a matrix and $b$ is a vector.

This time use the Normalization layer that was adapted to the whole dataset.

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linear_model = tf.keras.Sequential([
    normalizer,
    layers.Dense(units=1)
])

When you call this model on a batch of inputs, it produces units=1 outputs for each example.

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linear_model.predict(train_features[:10])

When you call the model it's weight matrices will be built. Now you can see that the kernel (the $m$ in $y=mx+b$) has a shape of (9,1).

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linear_model.layers[1].kernel

Use the same compile and fit calls as for the single input horsepower model:

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linear_model.compile(
    optimizer=tf.optimizers.Adam(learning_rate=0.1),
    loss='mean_absolute_error')

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%%time
history = linear_model.fit(
    train_features, train_labels, 
    epochs=100,
    # suppress logging
    verbose=0,
    # Calculate validation results on 20% of the training data
    validation_split = 0.2)

Using all the inputs achieves a much lower training and validation error than the horsepower model:

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plot_loss(history)

Collect the results on the test set, for later:

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test_results['linear_model'] = linear_model.evaluate(
    test_features, test_labels, verbose=0)

A DNN regression¶

The previous section implemented linear models for single and multiple inputs.

This section implements single-input and multiple-input DNN models. The code is basically the same except the model is expanded to include some "hidden" non-linear layers. The name "hidden" here just means not directly connected to the inputs or outputs.

These models will contain a few more layers than the linear model:

The normalization layer.
Two hidden, nonlinear, Dense layers using the relu nonlinearity.
A linear single-output layer.

Both will use the same training procedure so the compile method is included in the build_and_compile_model function below.

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def build_and_compile_model(norm):
  model = keras.Sequential([
      norm,
      layers.Dense(64, activation='relu'),
      layers.Dense(64, activation='relu'),
      layers.Dense(1)
  ])

  model.compile(loss='mean_absolute_error',
                optimizer=tf.keras.optimizers.Adam(0.001))
  return model

One variable¶

Start with a DNN model for a single input: "Horsepower"

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dnn_horsepower_model = build_and_compile_model(horsepower_normalizer)

This model has quite a few more trainable parameters than the linear models.

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dnn_horsepower_model.summary()

Train the model:

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%%time
history = dnn_horsepower_model.fit(
    train_features['Horsepower'], train_labels,
    validation_split=0.2,
    verbose=0, epochs=100)

This model does slightly better than the linear-horsepower model.

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plot_loss(history)

If you plot the predictions as a function of Horsepower, you'll see how this model takes advantage of the nonlinearity provided by the hidden layers:

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x = tf.linspace(0.0, 250, 251)
y = dnn_horsepower_model.predict(x)

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plot_horsepower(x, y)

Collect the results on the test set, for later:

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test_results['dnn_horsepower_model'] = dnn_horsepower_model.evaluate(
    test_features['Horsepower'], test_labels,
    verbose=0)

Full model¶

If you repeat this process using all the inputs it slightly improves the performance on the validation dataset.

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dnn_model = build_and_compile_model(normalizer)
dnn_model.summary()

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%%time
history = dnn_model.fit(
    train_features, train_labels,
    validation_split=0.2,
    verbose=0, epochs=100)

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plot_loss(history)

Collect the results on the test set:

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test_results['dnn_model'] = dnn_model.evaluate(test_features, test_labels, verbose=0)

Performance¶

Now that all the models are trained check the test-set performance and see how they did:

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pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T

These results match the validation error seen during training.

Make predictions¶

Finally, predict have a look at the errors made by the model when making predictions on the test set:

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test_predictions = dnn_model.predict(test_features).flatten()

a = plt.axes(aspect='equal')
plt.scatter(test_labels, test_predictions)
plt.xlabel('True Values [MPG]')
plt.ylabel('Predictions [MPG]')
lims = [0, 50]
plt.xlim(lims)
plt.ylim(lims)
_ = plt.plot(lims, lims)

It looks like the model predicts reasonably well.

Now take a look at the error distribution:

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error = test_predictions - test_labels
plt.hist(error, bins=25)
plt.xlabel('Prediction Error [MPG]')
_ = plt.ylabel('Count')

If you're happy with the model save it for later use:

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dnn_model.save('dnn_model')

If you reload the model, it gives identical output:

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reloaded = tf.keras.models.load_model('dnn_model')

test_results['reloaded'] = reloaded.evaluate(
    test_features, test_labels, verbose=0)

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pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T

Conclusion¶

This notebook introduced a few techniques to handle a regression problem. Here are a few more tips that may help:

Mean Squared Error (MSE) and Mean Absolute Error (MAE) are common loss functions used for regression problems. Mean Absolute Error is less sensitive to outliers. Different loss functions are used for classification problems.
Similarly, evaluation metrics used for regression differ from classification.
When numeric input data features have values with different ranges, each feature should be scaled independently to the same range.
Overfitting is a common problem for DNN models, it wasn't a problem for this tutorial. See the overfit and underfit tutorial for more help with this.