在TensorFlow中使用LSTM-CGAN生成MNIST编号

Question

受本文的启发,我正在尝试构建一个条件GAN,它将使用LSTM生成MNIST数字.我希望我使用与图像波纹相同的架构(除了本文中的鉴别器中的双向RNN ):

当我运行这个模型时,我得到了非常奇怪的结果.此图显示了我的模型在每个纪元后生成3号.看起来应该更像这样.这真的很糟糕.

我的鉴别器网络的丢失真的快速下降到接近零.然而,我的发电机网络的损失在一些固定点附近振荡(可能缓慢地发散).我真的不知道发生了什么.这是我的代码中最重要的部分(完整代码在这里):

timesteps = 28
X_dim = 28
Z_dim = 100
y_dim = 10

X = tf.placeholder(tf.float32, [None, timesteps, X_dim]) # reshaped MNIST image to 28x28
y = tf.placeholder(tf.float32, [None, y_dim]) # one-hot label
Z = tf.placeholder(tf.float32, [None, timesteps, Z_dim]) # numpy.random.uniform noise in range [-1; 1]

y_timesteps = tf.tile(tf.expand_dims(y, axis=1), [1, timesteps, 1]) # [None, timesteps, y_dim] - replicate y along axis=1

def discriminator(x, y):
    with tf.variable_scope('discriminator', reuse=tf.AUTO_REUSE) as vs:
        inputs = tf.concat([x, y], axis=2)
        D_cell = tf.contrib.rnn.LSTMCell(64)
        output, _ = tf.nn.dynamic_rnn(D_cell, inputs, dtype=tf.float32)
        last_output = output[:, -1, :]
        logit = tf.contrib.layers.fully_connected(last_output, 1, activation_fn=None)
        pred = tf.nn.sigmoid(logit)
        variables = [v for v in tf.all_variables() if v.name.startswith(vs.name)]
        return variables, pred, logit

def generator(z, y):
    with tf.variable_scope('generator', reuse=tf.AUTO_REUSE) as vs:
        inputs = tf.concat([z, y], axis=2)
        G_cell = tf.contrib.rnn.LSTMCell(64)
        output, _ = tf.nn.dynamic_rnn(G_cell, inputs, dtype=tf.float32)
        logit = tf.contrib.layers.fully_connected(output, X_dim, activation_fn=None)
        pred = tf.nn.sigmoid(logit)
        variables = [v for v in tf.all_variables() if v.name.startswith(vs.name)]
        return variables, pred

G_vars, G_sample = run_generator(Z, y_timesteps)
D_vars, D_real, D_logit_real = run_discriminator(X, y_timesteps)
_, D_fake, D_logit_fake = run_discriminator(G_sample, y_timesteps)

D_loss = -tf.reduce_mean(tf.log(D_real) + tf.log(1. - D_fake))
G_loss = -tf.reduce_mean(tf.log(D_fake))

D_solver = tf.train.AdamOptimizer().minimize(D_loss, var_list=D_vars)
G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=G_vars)

我的模型很可能出了问题.任何人都可以帮助我使发电机网络融合？

Answer 1

您可以采取一些措施来改进网络架构和训练阶段。

1. tf.nn.sigmoid(logit)从生成器和鉴别器中删除。仅返回pred.

2. 使用数值稳定函数来计算损失函数并修复损失函数：

   D_loss = -tf.reduce_mean(tf.log(D_real) + tf.log(1. - D_fake)) G_loss = -tf.reduce_mean(tf.log(D_fake))

应该：

D_loss_real = tf.nn.sigmoid_cross_entropy_with_logits(
              logits=D_real,
              labels=tf.ones_like(D_real))
D_loss_fake = tf.nn.sigmoid_cross_entropy_with_logits(
              logits=D_fake,
              labels=tf.zeros_like(D_fake))

D_loss = -tf.reduce_mean(D_loss_real + D_loss_fake)
G_loss = -tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
              logits=D_real,
              labels=tf.ones_like(D_real)))

一旦你修复了损失并使用了数值稳定的函数，事情就会变得更好。另外，根据经验，如果损失中有太多噪声，请降低学习率（训练 GAN 时，ADAM 的默认 lr 通常过高）。希望能帮助到你