cost_pos = tf.multiply( y[:,0],tf.reduce_sum(tf.squared_difference(predictions,y),1))
cost_neg = tf.multiply(tf.subtract(1.0,y[:,0]),tf.squared_difference(predictions[:,0],y[:,0]))
cost = tf.reduce_mean(tf.add(cost_pos,cost_neg))

def AP(y,y_hat):

    y_scores = y_hat[:,0]
    y_hat_bbox = y_hat[:,1:]
    y_bbox = y[:,1:]
    y_true = np.zeros(y_scores.shape[0])

    for i in range(y_true.shape[0]):
        y_true[i] = iou(get_corners(y_hat_bbox[i]), get_corners(y_bbox[i]), True)

    AP = average_precision_score(y_true = y_true, y_score = y_scores)
    return AP