model_def_new.py 文件源码

def generator(hparams, z, scope_name, train, reuse):

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

        output_size = 64
        s = output_size
        s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16)

        g_bn0 = ops.batch_norm(name='g_bn0')
        g_bn1 = ops.batch_norm(name='g_bn1')
        g_bn2 = ops.batch_norm(name='g_bn2')
        g_bn3 = ops.batch_norm(name='g_bn3')

        # project `z` and reshape
        h0 = tf.reshape(ops.linear(z, hparams.gf_dim*8*s16*s16, 'g_h0_lin'), [-1, s16, s16, hparams.gf_dim * 8])
        h0 = tf.nn.relu(g_bn0(h0, train=train))

        h1 = ops.deconv2d(h0, [hparams.batch_size, s8, s8, hparams.gf_dim*4], name='g_h1')
        h1 = tf.nn.relu(g_bn1(h1, train=train))

        h2 = ops.deconv2d(h1, [hparams.batch_size, s4, s4, hparams.gf_dim*2], name='g_h2')
        h2 = tf.nn.relu(g_bn2(h2, train=train))

        h3 = ops.deconv2d(h2, [hparams.batch_size, s2, s2, hparams.gf_dim*1], name='g_h3')
        h3 = tf.nn.relu(g_bn3(h3, train=train))

        h4 = ops.deconv2d(h3, [hparams.batch_size, s, s, hparams.c_dim], name='g_h4')
        x_gen = tf.nn.tanh(h4)

    return x_gen