training_q.py 文件源码

def _setup_classification_predictions_and_loss(self):
        self.inputs, self.target = self.input_queue.dequeue()
        self.num_batch_elems = tf.size(self.target)
        self.inputs = tf.reshape(self.inputs, self.input_shape)
        self.training_end_points = self.model(is_training=True, reuse=None, inputs=self.inputs)
        training_logits, self.training_predictions = self.training_end_points['logits'], self.training_end_points[
            'predictions']

        self.validation_end_points = self.model(is_training=False, reuse=True, inputs=self.inputs)
        validation_logits, self.validation_predictions = self.validation_end_points['logits'], \
                                                         self.validation_end_points[
                                                             'predictions']
        with tf.name_scope('loss'):
            training_loss = tf.reduce_mean(
                tf.nn.sparse_softmax_cross_entropy_with_logits(
                    logits=training_logits, labels=self.target))

            self.validation_loss = tf.reduce_mean(
                tf.nn.sparse_softmax_cross_entropy_with_logits(
                    logits=validation_logits, labels=self.target))

            l2_loss = tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
            self.regularized_training_loss = training_loss + l2_loss * self.cnf.get('l2_reg', 0.0)