def infer()

in train.py [0:0]

• 19 lines of code
• 5 McCabe index (conditional complexity)

def infer(sess, model, hps, iterator):
    # Example of using model in inference mode. Load saved model using hps.restore_path
    # Can provide x, y from files instead of dataset iterator
    # If model is uncondtional, always pass y = np.zeros([bs], dtype=np.int32)
    if hps.direct_iterator:
        iterator = iterator.get_next()

    xs = []
    zs = []
    for it in range(hps.full_test_its):
        if hps.direct_iterator:
            # replace with x, y, attr if you're getting CelebA attributes, also modify get_data
            x, y = sess.run(iterator)
        else:
            x, y = iterator()

        z = model.encode(x, y)
        x = model.decode(y, z)
        xs.append(x)
        zs.append(z)

    x = np.concatenate(xs, axis=0)
    z = np.concatenate(zs, axis=0)
    np.save('logs/x.npy', x)
    np.save('logs/z.npy', z)
    return zs