checking_wrong_predictions.py

def checking_wrong_predictions():

    for images, labels in validation_ds:

        predictions = model.predict(images, verbose = 0)
        
        for i in range(VALIDATION_BATCH_SIZE):

            img_A = (images[0][i].numpy()*255).astype("uint8")
            img_B = (images[1][i].numpy()*255).astype("uint8")
            label = labels[i].numpy()

            prediction = predictions[i][0]

            if (label > 0 and prediction >= 0.5) or (label < 1 and prediction < 0.5):

                fig, axs = plt.subplots(1, 2)
                axs[0].imshow(img_A)
                axs[0].axis("off")
                axs[1].imshow(img_B)
                axs[1].axis("off")

                type = "POSITIVE"
                if prediction >= 0.5:
                    type = "NEGATIVE"

                title = "Distance = {:.4f} (FALSE {})".format(prediction, type)
                fig.suptitle(title, fontsize=15)
                plt.show()

checking_wrong_predictions()