TF Learn

TF Learn is a simplified interface for TensorFlow, to get people started on predictive analytics and data mining. The library covers a variety of needs: from linear models to Deep Learning applications like text and image understanding.

Why TensorFlow?

• TensorFlow provides a good backbone for building different shapes of machine learning applications.
• It will continue to evolve both in the distributed direction and as general pipelinining machinery.

Why TensorFlow Learn?

• To smooth the transition from the scikit-learn world of one-liner machine learning into the more open world of building different shapes of ML models. You can start by using fit/predict and slide into TensorFlow APIs as you are getting comfortable.
• To provide a set of reference models that will be easy to integrate with existing code.

Installation

Install TensorFlow, and then simply import learn via from tensorflow.contrib.learn or use tf.contrib.learn.

Optionally you can install scikit-learn and pandas for additional functionality.

Tutorials

• TF Learn Quickstart. Build, train, and evaluate a neural network with just a few lines of code.
• Linear Model. Learn the basics of building linear models.
• Logging and Monitoring. Use the Monitor API to audit training of a neural network.
• Wide and Deep Learning. Jointly train a linear model and a deep neural network.
• More coming soon.

Community

• Twitter #tensorflow.
• StackOverflow with tensorflow tag for questions and struggles.
• GitHub issues for technical discussions and feature requests.

Usage

Below are a few simple examples of the API. For more examples, please see examples.

General tips:

• It‘s useful to rescale a dataset to 0 mean and unit standard deviation before passing it to an Estimator. Stochastic Gradient Descent doesn’t always do the right thing when variable are at very different scales.

• Categorical variables should be managed before passing input to the estimator.

Linear Classifier

Simple linear classification:

import tensorflow.contrib.learn.python.learn as learn
from sklearn import datasets, metrics

iris = datasets.load_iris()
feature_columns = learn.infer_real_valued_columns_from_input(iris.data)
classifier = learn.LinearClassifier(n_classes=3, feature_columns=feature_columns)
classifier.fit(iris.data, iris.target, steps=200, batch_size=32)
iris_predictions = list(classifier.predict(iris.data, as_iterable=True))
score = metrics.accuracy_score(iris.target, iris_predictions)
print("Accuracy: %f" % score)

Linear Regressor

Simple linear regression:

import tensorflow.contrib.learn.python.learn as learn
from sklearn import datasets, metrics, preprocessing

boston = datasets.load_boston()
x = preprocessing.StandardScaler().fit_transform(boston.data)
feature_columns = learn.infer_real_valued_columns_from_input(x)
regressor = learn.LinearRegressor(feature_columns=feature_columns)
regressor.fit(x, boston.target, steps=200, batch_size=32)
boston_predictions = list(regressor.predict(x, as_iterable=True))
score = metrics.mean_squared_error(boston_predictions, boston.target)
print ("MSE: %f" % score)

Deep Neural Network

Example of 3 layer network with 10, 20 and 10 hidden units respectively:

import tensorflow.contrib.learn.python.learn as learn
from sklearn import datasets, metrics

iris = datasets.load_iris()
feature_columns = learn.infer_real_valued_columns_from_input(iris.data)
classifier = learn.DNNClassifier(hidden_units=[10, 20, 10], n_classes=3, feature_columns=feature_columns)
classifier.fit(iris.data, iris.target, steps=200, batch_size=32)
iris_predictions = list(classifier.predict(iris.data, as_iterable=True))
score = metrics.accuracy_score(iris.target, iris_predictions)
print("Accuracy: %f" % score)

Custom model

Example of how to pass a custom model to the Estimator:

from sklearn import datasets
from sklearn import metrics
import tensorflow as tf
import tensorflow.contrib.layers.python.layers as layers
import tensorflow.contrib.learn.python.learn as learn

iris = datasets.load_iris()

def my_model(features, labels):
  """DNN with three hidden layers."""
  # Convert the labels to a one-hot tensor of shape (length of features, 3) and
  # with a on-value of 1 for each one-hot vector of length 3.
  labels = tf.one_hot(labels, 3, 1, 0)

  # Create three fully connected layers respectively of size 10, 20, and 10.
  features = layers.stack(features, layers.fully_connected, [10, 20, 10])

  # Create two tensors respectively for prediction and loss.
  prediction, loss = (
      tf.contrib.learn.models.logistic_regression(features, labels)
  )

  # Create a tensor for training op.
  train_op = tf.contrib.layers.optimize_loss(
      loss, tf.contrib.framework.get_global_step(), optimizer='Adagrad',
      learning_rate=0.1)

  return {'class': tf.argmax(prediction, 1), 'prob': prediction}, loss, train_op

classifier = learn.Estimator(model_fn=my_model)
classifier.fit(iris.data, iris.target, steps=1000)

y_predicted = [
  p['class'] for p in classifier.predict(iris.data, as_iterable=True)]
score = metrics.accuracy_score(iris.target, y_predicted)
print('Accuracy: {0:f}'.format(score))

Saving / Restoring models

Each estimator supports a model_dir argument, which takes a folder path where all model information will be saved:

classifier = learn.DNNClassifier(..., model_dir="/tmp/my_model")

If you run multiple fit operations on the same Estimator, training will resume where the last operation left off, e.g.:

To restore checkpoints to a new Estimator, just pass it the same model_dir argument, e.g.:

Summaries

If you supply a model_dir argument to your Estimators, TensorFlow will write summaries for loss and histograms for variables in this directory. (You can also add custom summaries in your custom model function by calling Summary operations.)

To view the summaries in TensorBoard, run the following command, where logdir is the model_dir for your Estimator:

tensorboard --logdir=/tmp/tf_examples/my_model_1

and then load the reported URL.

Graph visualization

Loss visualization

More examples

See the examples folder for:

• An easy way to handle categorical variables (words are just an example of a categorical variable)
• Text Classification: see examples for RNN and CNN on characters
• Digit recognition using a CNN
• And much more!