models.py 文件源码

def discriminator_gen_labeler(image, output_dim, config, reuse=None):
    batch_size=tf.shape(image)[0]
    with tf.variable_scope("disc_gen_labeler",reuse=reuse) as vs:
        dl_bn1 = batch_norm(name='dl_bn1')
        dl_bn2 = batch_norm(name='dl_bn2')
        dl_bn3 = batch_norm(name='dl_bn3')

        h0 = lrelu(conv2d(image, config.df_dim, name='dgl_h0_conv'))#16,32,32,64
        h1 = lrelu(dl_bn1(conv2d(h0, config.df_dim*2, name='dgl_h1_conv')))#16,16,16,128
        h2 = lrelu(dl_bn2(conv2d(h1, config.df_dim*4, name='dgl_h2_conv')))#16,16,16,248
        h3 = lrelu(dl_bn3(conv2d(h2, config.df_dim*8, name='dgl_h3_conv')))
        dim3=np.prod(h3.get_shape().as_list()[1:])
        h3_flat=tf.reshape(h3, [-1,dim3])
        D_labels_logits = linear(h3_flat, output_dim, 'dgl_h3_Label')
        D_labels = tf.nn.sigmoid(D_labels_logits)
        variables = tf.contrib.framework.get_variables(vs)
    return D_labels, D_labels_logits,variables