neuralnetwork.py 文件源码

def loss(logits, labels):

    # input: logits: Logits tensor, float - [batch_size, 256, 256, NUM_CLASSES].
    # intput: labels: Labels tensor, int32 - [batch_size, 256, 256].
    # output: loss: Loss tensor of type float.

    labels = tf.to_int64(labels)
    print_tensor_shape( logits, 'logits shape before')
    print_tensor_shape( labels, 'labels shape before')

# reshape to match args required for the cross entropy function
    logits_re = tf.reshape( logits, [-1, 2] )
    labels_re = tf.reshape( labels, [-1] )
    print_tensor_shape( logits, 'logits shape after')
    print_tensor_shape( labels, 'labels shape after')

# call cross entropy with logits
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
         logits, labels, name='cross_entropy')

    loss = tf.reduce_mean(cross_entropy, name='1cnn_cross_entropy_mean')
    return loss