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I am building an GAN and when i started calling my discriminator twice, using reuse, my GAN started to diverge. I first created my discriminator as following:

def discriminator(self, x_past, x_future, gen_future):
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
 with tf.variable_scope("disc") as disc:
 gen_future = tf.concat([gen_future, x_past], 2)
 x_future = tf.concat([x_future, x_past], 2)
 x_in = tf.concat([gen_future, x_future], 0)
 conv1 = tf.layers.conv1d(inputs=x_in, filters=20, kernel_size=3, strides=1,
 padding='same', activation=tf.nn.relu)
 max_pool_1 = tf.layers.max_pooling1d(inputs=conv1, pool_size=2, strides=2, padding='same')
 conv2 = tf.layers.conv1d(inputs=max_pool_1, filters=3, kernel_size=2, strides=1,
 padding='same', activation=tf.nn.relu)
 max_pool_2 = tf.layers.max_pooling1d(inputs=conv2, pool_size=2, strides=2, padding='same')

 # Flatten and add dropout
 flat = tf.reshape(max_pool_2, (-1, 9))
 flat = tf.nn.dropout(flat, keep_prob=self.keep_prob)

 # Predictions
 logits = tf.layers.dense(flat, 2)

 y_true = logits[:self.batch_size]
 y_gen = logits[self.batch_size:]

 return y_true, y_gen

And I was calling it like this:

y_true, y_gen = self.discriminator(x_past, x_future, gen_future)

I was able to train the GAN properly. Now I need to use reuse to be able to call it without having to send real and fake data at once. I changed it to:

def discriminator(self, x_past, x_future, reuse=False):
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
 with tf.variable_scope("disc", reuse=reuse) as disc:
 x_in = tf.concat([x_future, x_past], 2)
 conv1 = tf.layers.conv1d(inputs=x_in, filters=20, kernel_size=3, strides=1,
 padding='same', activation=tf.nn.relu)
 max_pool_1 = tf.layers.max_pooling1d(inputs=conv1, pool_size=2, strides=2, padding='same')
 conv2 = tf.layers.conv1d(inputs=max_pool_1, filters=3, kernel_size=2, strides=1,
 padding='same', activation=tf.nn.relu)
 max_pool_2 = tf.layers.max_pooling1d(inputs=conv2, pool_size=2, strides=2, padding='same')

 # Flatten and add dropout
 flat = tf.reshape(max_pool_2, (-1, 9))
 flat = tf.nn.dropout(flat, keep_prob=self.keep_prob)

 # Predictions
 logits = tf.layers.dense(flat, 2)
 return logits

And calling it like this:

y_true = self.discriminator(x_past, x_future)
y_gen = self.discriminator(x_past, gen_future, reuse=True)

Now it started to diverge. Any idea why is that?