model_def_new.py 文件源码

def discriminator(hparams, x, scope_name, train, reuse):

    with tf.variable_scope(scope_name) as scope:
        if reuse:
            scope.reuse_variables()

        d_bn1 = ops.batch_norm(name='d_bn1')
        d_bn2 = ops.batch_norm(name='d_bn2')
        d_bn3 = ops.batch_norm(name='d_bn3')

        h0 = ops.lrelu(ops.conv2d(x, hparams.df_dim, name='d_h0_conv'))

        h1 = ops.conv2d(h0, hparams.df_dim*2, name='d_h1_conv')
        h1 = ops.lrelu(d_bn1(h1, train=train))

        h2 = ops.conv2d(h1, hparams.df_dim*4, name='d_h2_conv')
        h2 = ops.lrelu(d_bn2(h2, train=train))

        h3 = ops.conv2d(h2, hparams.df_dim*8, name='d_h3_conv')
        h3 = ops.lrelu(d_bn3(h3, train=train))

        h4 = ops.linear(tf.reshape(h3, [hparams.batch_size, -1]), 1, 'd_h3_lin')

        d_logit = h4
        d = tf.nn.sigmoid(d_logit)

    return d, d_logit