Question

这个问题是关于在图像上使用TensorFlow的GAN的实现示例。

我已经摘录了我认为足以提供上下文的代码部分reference for full code。在下面的代码中，它定义了一个鉴别器函数，从松散的角度来看，它可以被认为是一个典型的卷积神经网络。可以看出，鉴别器操作取决于y_dim，有人可以帮助解释什么是y_dim 吗？看Args，我仍然对y_dim的定义感到困惑。

class DCGAN(object):
    def __init__(self, sess, image_size=108, is_crop=True,
                 batch_size=64, sample_size=64, output_size=64,
                 y_dim=None, z_dim=100, gf_dim=64, df_dim=64,
                 gfc_dim=1024, dfc_dim=1024, c_dim=3, dataset_name='default',
                 checkpoint_dir=None, sample_dir=None):
        """

        Args:
            sess: TensorFlow session
            batch_size: The size of batch. Should be specified before training.
            output_size: (optional) The resolution in pixels of the images. [64]
            y_dim: (optional) Dimension of dim for y. [None]
            z_dim: (optional) Dimension of dim for Z. [100]
            gf_dim: (optional) Dimension of gen filters in first conv layer. [64]
            df_dim: (optional) Dimension of discrim filters in first conv layer. [64]
            gfc_dim: (optional) Dimension of gen units for for fully connected layer. [1024]
            dfc_dim: (optional) Dimension of discrim units for fully connected layer. [1024]
            c_dim: (optional) Dimension of image color. For grayscale input, set to 1. [3]
        """
        ...

    def discriminator(self, image, y=None, reuse=False):
        if reuse:
            tf.get_variable_scope().reuse_variables()

        if not self.y_dim:
            h0 = lrelu(conv2d(image, self.df_dim, name='d_h0_conv'))
            h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim * 2, name='d_h1_conv')))
            h2 = lrelu(self.d_bn2(conv2d(h1, self.df_dim * 4, name='d_h2_conv')))
            h3 = lrelu(self.d_bn3(conv2d(h2, self.df_dim * 8, name='d_h3_conv')))
            h4 = linear(tf.reshape(h3, [self.batch_size, -1]), 1, 'd_h3_lin')

            return tf.nn.sigmoid(h4), h4
        else:
            yb = tf.reshape(y, [self.batch_size, 1, 1, self.y_dim])
            x = conv_cond_concat(image, yb)

            h0 = lrelu(conv2d(x, self.c_dim + self.y_dim, name='d_h0_conv'))
            h0 = conv_cond_concat(h0, yb)

            h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim + self.y_dim, name='d_h1_conv')))
            h1 = tf.reshape(h1, [self.batch_size, -1])
            h1 = tf.concat(1, [h1, y])

            h2 = lrelu(self.d_bn2(linear(h1, self.dfc_dim, 'd_h2_lin')))
            h2 = tf.concat(1, [h2, y])

            h3 = linear(h2, 1, 'd_h3_lin')

            return tf.nn.sigmoid(h3), h3

Answer 1

y_dim是标签的长度。

似乎在DCGAN课程中使用说“我们知道培训标签的数量＆＃39;”。如果我们这样做，我们可以用已知的尺寸定义所有这些张量。

E.g。查看使用MNIST数据集的main.py文件。看到0-9标签的y_dim设置为10？这是“没有”＆＃39;第二种选择。

希望这有帮助。

`y_dim`在以下GAN模型中表示什么？

1 个答案: