bpr-opt-05.py

#------------------
# GRAPH EXECUTION
#------------------

# Run the session. 
session = tf.Session(config=None, graph=graph)
session.run(init)

# This has noting to do with tensorflow but gives
# us a nice progress bar for the training.
progress = tqdm(total=batches*epochs)

for _ in range(epochs):
    for _ in range(batches):

        # We want to sample one known and one unknown 
        # item for each user. 

        # First we sample 15000 uniform indices.
        idx = np.random.randint(low=0, high=len(uids), size=samples)

        # We then grab the users matching those indices.
        batch_u = uids[idx].reshape(-1, 1)

        # Then the known items for those users.
        batch_i = iids[idx].reshape(-1, 1)

        # Lastly we randomly sample one unknown item for each user.
        batch_j = np.random.randint(
                low=0, high=len(artists), size=(samples, 1), dtype='int32')

        # Feed our users, known and unknown items to
        # our tensorflow graph. 
        feed_dict = { u: batch_u, i: batch_i, j: batch_j }

        # We run the session.
        _, l, auc = session.run([step, loss, u_auc], feed_dict)

    progress.update(batches)
    progress.set_description('Loss: %.3f | AUC: %.3f' % (l, auc))

progress.close()