model.py 文件源码

def loss(self, img_batch, label_batch):
        """Create the network, run inference on the input batch and compute loss.

        Args:
          input_batch: batch of pre-processed images.

        Returns:
          Pixel-wise softmax loss.
        """
        raw_output = self._create_network(tf.cast(img_batch, tf.float32), keep_prob=tf.constant(0.5))
        prediction = tf.reshape(raw_output, [-1, n_classes])

        # Need to resize labels and convert using one-hot encoding.
        label_batch = self.prepare_label(label_batch, tf.stack(raw_output.get_shape()[1:3]))
        gt = tf.reshape(label_batch, [-1, n_classes])

        # Pixel-wise softmax loss.
        loss = tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=gt)
        reduced_loss = tf.reduce_mean(loss)

        return reduced_loss