Introduction

Below are few simple examples of the API to get you started with TensorFlow Learn. For more examples, please see examples.

General tips

• It's useful to re-scale dataset before passing to estimator to 0 mean and unit standard deviation. Stochastic Gradient Descent doesn't always do the right thing when variable are very different scale.

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

Linear Classifier

Simple linear classification:

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

iris = datasets.load_iris()
classifier = learn.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)

Linear Regressor

Simple linear regression:

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

boston = datasets.load_boston()
X = preprocessing.StandardScaler().fit_transform(boston.data)
regressor = learn.TensorFlowLinearRegressor()
regressor.fit(X, boston.target)
score = metrics.mean_squared_error(regressor.predict(X), boston.target)
print ("MSE: %f" % score)

Deep Neural Network

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

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

iris = datasets.load_iris()
feature_columns = learn.infer_real_valued_columns_from_input(iris.data)
classifier = learn.DNNClassifier(
    feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target, steps=100)
score = metrics.accuracy_score(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)

Custom model

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

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

iris = datasets.load_iris()

def my_model(X, y):
    """This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability."""
    layers = learn.ops.dnn(X, [10, 20, 10], keep_prob=0.5)
    return learn.models.logistic_regression(layers, y)

classifier = learn.TensorFlowEstimator(model_fn=my_model, n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(iris.target, classifier.predict(iris.data))
print("Accuracy: %f" % score)

Saving / Restoring models

Each estimator has a save method which takes folder path where all model information will be saved. For restoring you can just call learn.TensorFlowEstimator.restore(path) and it will return object of your class.

Some example code:

from tensorflow.contrib import learn

classifier = learn.TensorFlowLinearRegression()
classifier.fit(...)
classifier.save('/tmp/tf_examples/my_model_1/')

new_classifier = TensorFlowEstimator.restore('/tmp/tf_examples/my_model_2')
new_classifier.predict(...)

Summaries

To get nice visualizations and summaries you can use logdir parameter on fit. It will start writing summaries for loss and histograms for variables in your model. You can also add custom summaries in your custom model function by calling tf.summary and passing Tensors to report.

classifier = learn.TensorFlowLinearRegression()
classifier.fit(X, y, logdir='/tmp/tf_examples/my_model_1/')

Then run next command in command line:

tensorboard --logdir=/tmp/tf_examples/my_model_1

and follow reported url.

Graph visualization: Text classification RNN Graph image

Loss visualization: Text classification RNN Loss image

More examples

See examples folder for:

• Easy way to handle categorical variables - words are just an example of categorical variable.
• Text Classification - see examples for RNN, CNN on word and characters.
• Language modeling and text sequence to sequence.
• Images (CNNs) - see example for digit recognition.
• More & deeper - different examples showing DNNs and CNNs