83f1d50ae82d314437042ddfa1567f6958be41e3,scripts/speaker_embedding.py,,tune,#Any#Any#Any#Any#Any#,249

Before Change

    // TODO dichotomic search
    WEIGHTS_H5 = weights_dir + "/{epoch:04d}.h5"
    epochs = 0
    while True:
        weights_h5 = WEIGHTS_H5.format(epoch=epochs)
        if not os.path.isfile(weights_h5):
            break
        epochs += 1

    LINE = "{epoch:04d} {eer:.6f}\n"
    PATH = output_dir + "/eer.{dataset}.txt"
    with open(PATH.format(dataset=dataset), "w") as fp:
        

After Change

    except Exception as e:
        pass

    nb_epoch = config["training"]["nb_epoch"]
    WEIGHTS_H5 = weights_dir + "/{epoch:04d}.h5"

    LINE = "{epoch:04d} {eer:.6f}\n"
    PATH = output_dir + "/eer.txt"
    with open(PATH.format(dataset=dataset), "w") as fp:

        for epoch in range(nb_epoch):

            // load model for this epoch
            weights_h5 = WEIGHTS_H5.format(epoch=epoch)

            if not os.path.isfile(weights_h5):
                continue

            sequence_embedding = SequenceEmbedding.from_disk(
                architecture_yml, weights_h5)

            // pairwise euclidean distances between embeddings
            batch_size = config["testing"]["batch_size"]
            x = sequence_embedding.transform(X, batch_size=batch_size, verbose=0)
            distances = pdist(x, metric="euclidean")
            PATH = output_dir + "/plot.{epoch:04d}"
            eer = plot_det_curve(y_true, -distances, PATH.format(epoch=epoch))

            msg = "Epoch //{epoch:04d} | EER = {eer:.2f}%"
            print(msg.format(epoch=epoch, eer=100 * eer))

            fp.write(LINE.format(epoch=epoch, eer=eer))

            // save distribution plots after each epoch
            space = config["network"]["space"]
            xlim = (0, 2 if space == "sphere" else np.sqrt(2.))
            plot_distributions(y_true, distances, PATH.format(epoch=epoch),
                               xlim=xlim, ymax=3, nbins=100)

Instances