ModelsMNIST.py 文件源码

def compute_categorical_loss_and_accuracy(logits, targets):
    """return total loss, reg loss (subset of total), and accuracy"""
    with tf.variable_scope('loss'):
        regularization_losses = sum(
            tf.get_collection(
                tf.GraphKeys.REGULARIZATION_LOSSES
            )
        )
        loss = tf.reduce_mean(
            tf.nn.softmax_cross_entropy_with_logits(
                logits=logits, labels=targets
            ),
            axis=0,
            name='loss'
        ) + regularization_losses
        preds = tf.nn.softmax(logits, name='preds')
        correct_preds = tf.equal(
            tf.argmax(preds, 1), tf.argmax(targets, 1),
            name='correct_preds'
        )
        accuracy = tf.divide(
            tf.reduce_sum(tf.cast(correct_preds, tf.float32)),
            tf.cast(tf.shape(targets)[0], tf.float32),
            name='accuracy'
        )
    return loss, regularization_losses, accuracy