Notebook

Copyright 2018 The TensorFlow Authors.¶

Licensed under the Apache License, Version 2.0 (the "License");

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#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
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#
# https://www.apache.org/licenses/LICENSE-2.0
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Get Started with TensorFlow Probability¶

View on TensorFlow.org

Run in Google Colab

View source on GitHub

This is a Google Colaboratory notebook file. Python programs are run directly in the browser—a great way to learn and use TensorFlow. To run the Colab notebook:

Connect to a Python runtime: At the top-right of the menu bar, select CONNECT.
Run all the notebook code cells: Select Runtime > Run all.

For more examples and guides (including details for this program), see Get Started with TensorFlow.

Import the TensorFlow and TensorFlow Probability modules into your program. Enable eager execution to display the output as we go:

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import tensorflow as tf
import tensorflow_probability as tfp

tf.enable_eager_execution()

print(tf.__version__)

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# Pretend to load synthetic data set.
features = tfp.distributions.Normal(loc=0., scale=1.).sample(int(100e3))
labels = tfp.distributions.Bernoulli(logits=1.618 * features).sample()

# Specify model.
model = tfp.glm.Bernoulli()

# Fit model given data.
coeffs, linear_response, is_converged, num_iter = tfp.glm.fit(
    model_matrix=features[:, tf.newaxis],
    response=tf.cast(labels, dtype=tf.float32),
    model=model)

print(coeffs)  # ==> coeffs is approximately [1.618] (We're golden!)