TensorFlow Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models. A module is a self-contained piece of a TensorFlow graph, along with its weights and assets, that can be reused across different tasks in a process known as transfer learning. Transfer learning can:
- Train a model with a smaller dataset,
- Improve generalization, and
- Speed up training.
import tensorflow as tf import tensorflow_hub as hub with tf.Graph().as_default(): module_url = "https://tfhub.dev/google/nnlm-en-dim128-with-normalization/1" embed = hub.Module(module_url) embeddings = embed(["A long sentence.", "single-word", "http://example.com"]) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) sess.run(tf.tables_initializer()) print(sess.run(embeddings))