tf_model.py 文件源码

def loss(logit_tensor, targets_pl, one_hot_labels):
    """Add L2Loss to all the trainable variables.

    Add summary for "Loss" and "Loss/avg".
    Args:
    logits: Logits from inference().
    labels: Targets Placeholder. 1-D tensor of shape [batch_size]

    Returns:
    Loss tensor of type float.
    """
    targets = tf.to_int64(targets_pl)

    # calculate the average cross entropy loss across the batch.
    if one_hot_labels:
        cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logit_tensor, targets, name='cross_entropy_per_example')
    else:
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logit_tensor, targets, name='cross_entropy_per_example_sparse')
    cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy_mean')
    tf.add_to_collection('losses', cross_entropy_mean)
    tf.summary.scalar('loss', cross_entropy_mean)
    return cross_entropy_mean