369afab10deee16ef572c738df083d2e74863e64,niftynet/application/toy_application.py,ToyApplication,connect_data_and_network,#ToyApplication#Any#Any#,43

Before Change

        real_logits, fake_logits, fake_features = self.net(features, noise)

        batch_size = tf.shape(real_logits)[0]
        d_loss = \
            tf.losses.sparse_softmax_cross_entropy(
                tf.ones([batch_size, 1], tf.int32), real_logits) + \
            tf.losses.sparse_softmax_cross_entropy(
                tf.zeros([batch_size, 1], tf.int32), fake_logits)
        g_loss = \
            tf.losses.sparse_softmax_cross_entropy(
                tf.ones([batch_size, 1], tf.int32), fake_logits)

After Change

        //g_loss = \
        //    tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        //        labels=tf.ones_like(fake_logits), logits=fake_logits))
        d_loss = tf.reduce_mean(real_logits - fake_logits)
        g_loss = tf.reduce_mean(fake_logits)

        with tf.name_scope("ComputeGradients"):
            d_vars = tf.get_collection(
                tf.GraphKeys.TRAINABLE_VARIABLES,

Instances