Making Machine Learning Easier With Tensorflow Probability

Machine learning is an increasingly important technology that is used in a wide range of applications. Yet, it is notoriously challenging to develop and deploy machine-learning models due to its complexity and the wide variety of algorithms used. To make machine learning easier, Google has developed TensorFlow Probability (TFP).

TFP is a library designed to simplify the development and deployment of machine-learning models. It brings together the power of TensorFlow with the flexibility of probability theory to provide users with a high-level API that allows them to quickly build and manipulate models. To illustrate how TFP can be used, let’s take a look at a simple linear regression example.

First, we import our data and define our features:

import numpy as np
import tensorflow as tf

# Define our features
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 3 + 2

Next, we construct our linear regression model using the TensorFlow Probability Layer API and define our coefficients and the outputs of our regression model:

# Construct the model
layer = tfp.layers.DenseVariational(
    1,
    posterior_mean_field,
    prior_trainable=True
)

outputs = layer(x_data)

# Define our coefficients
coefficients = layer.kernel.prior.variables

# Define our outputs
output_prediction = tfp.distributions.Normal(
    loc=tf.squeeze(outputs, axis=-1),
    scale=1)

Next, we define our optimization and compile the model:

# Define our optimization
neg_log_likelihood = -tf.reduce_mean(
    output_prediction.log_prob(y_data)+coefficients.variational_prior.log_prob(coefficients))
kl = sum(layer.losses)/100

# Compile the model
train = tf.train.AdamOptimizer(1e-2).minimize(neg_log_likelihood+kl)

Finally, we fit and run the model:

# Fit and run the model
with tf.Session() as sess:
  sess.run(tf.global_variables_initializer())
  for step in range(1000):
    _, neg_log_likelihood, kl_loss = sess.run([train, neg_log_likelihood,kl])
    
    if step % 100 == 0:
      print('Step:', step, ' Loss:', neg_log_likelihood, 'KL:', kl_loss)

By using TensorFlow Probability, we are able to quickly construct a linear regression model and fit it to our data. With its built-in optimizations and high-level API, TFP makes it much easier to develop and deploy machine-learning models.