net_model.py 文件源码

def loss(logits, label_batch):
    """
    Add L2Loss to all the trainable variables.
    Add summary for "Loss" and "Loss/avg".
    Args:
        logits      -> logits from inference()
        label_batch -> 1D tensor of [batch_size]
    Rtns:
        total_loss  -> float tensor
    """
    # Calculate the average cross entropy loss across the batch.
    label_batch        = tf.cast(label_batch,tf.int64)
    cross_entropy      = tf.nn.sparse_softmax_cross_entropy_with_logits(logits,
                                label_batch,name='cross_entropy_per_example')
    cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
    tf.add_to_collection('losses',cross_entropy_mean)

    # The total loss is defined as the cross entropy loss plus all of the weight
    # decay terms (L2 loss).
    return tf.add_n(tf.get_collection('losses'), name='total_loss')