Ndtyjky

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?