losses.py 文件源码

def calculate_loss(self, predictions, support_predictions, labels, **unused_params):
    """ 
    support_predictions batch_size x num_models x num_classes
    predictions = tf.reduce_mean(support_predictions, axis=1)
    """
    model_count = tf.shape(support_predictions)[1]
    vocab_size = tf.shape(support_predictions)[2]

    mean_predictions = tf.reduce_mean(support_predictions, axis=1, keep_dims=True)
    support_labels = tf.tile(tf.expand_dims(tf.cast(labels, dtype=tf.float32), axis=1), multiples=[1,model_count,1])
    support_means = tf.stop_gradient(tf.tile(mean_predictions, multiples=[1,model_count,1]))

    support_predictions = tf.reshape(support_predictions, shape=[-1,model_count*vocab_size])
    support_labels = tf.reshape(support_labels, shape=[-1,model_count*vocab_size])
    support_means = tf.reshape(support_means, shape=[-1,model_count*vocab_size])

    ce_loss_fn = CrossEntropyLoss()
    # The cross entropy between predictions and ground truth
    cross_entropy_loss = ce_loss_fn.calculate_loss(support_predictions, support_labels, **unused_params)
    # The cross entropy between predictions and mean predictions
    divergence = ce_loss_fn.calculate_loss(support_predictions, support_means, **unused_params)

    loss = cross_entropy_loss * (1.0 - FLAGS.support_loss_percent) - divergence * FLAGS.support_loss_percent
    return loss