net.py 文件源码

def loss(logits, labels):
    """Calculates the loss from the logits and the labels.
    Args:
        logits: input tensor, float - [batch_size, NUM_CLASSES].
        labels: Labels tensor, int32 - [batch_size].
    Returns:
        loss: Loss tensor of type float.
    """
    # Convert from sparse integer labels in the range [0, NUM_CLASSES)
    # to 1-hot dense float vectors (that is we will have batch_size vectors,
    # each with NUM_CLASSES values, all of which are 0.0 except there will
    # be a 1.0 in the entry corresponding to the label).
    batch_size = tf.size(labels)
    labels = tf.expand_dims(labels, 1)
    indices = tf.expand_dims(tf.range(0, batch_size), 1)
    concated = tf.concat([indices, labels], 1)
    onehot_labels = tf.sparse_to_dense(concated,
                                       tf.shape(logits), 1.0, 0.0)


    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,
                                                            labels=onehot_labels,
                                                            name='xentropy')
    loss = tf.reduce_mean(cross_entropy, name='loss')
    tf.summary.scalar('summary/loss', loss)
    return loss