TensorFlow Hub is a library for reusable machine learning modules.
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))
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.
