8d9e06745e0c05fd3877bb1d5143309b342c362e,utils/resources/create_inverse_gan_models.py,,build_gan_graph,#Any#Any#Any#,214

Before Change

    disc_loss_tf = (disc_loss_real_tf + disc_loss_fake_tf) / 2.0

    gen_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=disc_fake_logits_tf,
            labels=tf.ones_like(disc_fake_logits_tf)
        )
    )

    // CREATE OPTIMIZERS
    // We only want generator variables to be trained when running the generator and not discriminator variables etc.
    trainable_variables = tf.trainable_variables()

After Change

    )

    disc_loss_fake_tf = tf.losses.sigmoid_cross_entropy(
        multi_class_labels=tf.zeros([batch_size, 1, 1, 1]), logits=disc_fake_logits_tf
    )
    disc_loss_tf = disc_loss_real_tf + disc_loss_fake_tf

Instances