def evaluate()

in src/run.py [0:0]

• 6 lines of code
• 3 McCabe index (conditional complexity)

def evaluate(sess, evX, evY, X, Y, gen_loss, clf_loss, accuracy, n_batch, desc, permute=False):
    metrics = []
    for xmb, ymb in iter_data(evX, evY, n_batch=n_batch, truncate=True, verbose=True):
        metrics.append(sess.run([gen_loss[0], clf_loss[0], accuracy[0]], {X: xmb, Y: ymb}))
    eval_gen_loss, eval_clf_loss, eval_accuracy = [np.mean(m) for m in zip(*metrics)]
    print(f"{desc} gen: {eval_gen_loss:.4f} clf: {eval_clf_loss:.4f} acc: {eval_accuracy:.2f}")