Example of generating embeddings for images from a saved model (tensorflow)

tf-generate-embeddings.py

# imports and other stuff


# example constants and instances of classes
data = Data()  # some data provider
model = Model()  # some model
batch_size = 16
pretrained_model_path = 'model/my_model'
log_dir = 'logs'

num_batches = data.size() / batch_size
tfconfig = tf.ConfigProto()



# a numpy array for embeddings and a list for labels
features = np.zeros(shape=(num_batches*batch_size, 128), dtype=float)
labels   = []   

# compute embeddings batch by batch
with tf.Session(config=tfconfig) as sess:
  tf.global_variables_initializer().run()
  restorer = tf.train.Saver()
  restorer.restore(sess, pretrained_model_path)

  for step in range(num_batches):
    batch_images, batch_labels = data.next()

    labels += batch_labels

    feed_dict = {model.images: batch_images}                     
    features[step*batch_size : (step+1)*batch_size, :] = \
             sess.run(model.features, feed_dict)


# write labels
metadata_path = os.path.join(log_dir, 'metadata.tsv')
with open(metadata_path, 'w') as f:
  for label in labels:
    f.write('%s\n' % label)


# write embeddings
with tf.Session(config=config) as sess:

  config = projector.ProjectorConfig()
  embedding = config.embeddings.add()
  embedding.tensor_name = 'feature_embedding'
  embedding.metadata_path = metadata_path

  embedding_var = tf.Variable(features, name='feature_embedding')
  sess.run(embedding_var.initializer)
  projector.visualize_embeddings(tf.summary.FileWriter(log_dir), config)                  

  saver = tf.train.Saver({"feature_embedding": embedding_var})
  saver.save(sess, os.path.join(log_dir, 'model_features'))