diff --git a/src/twomartens/masterthesis/aae/train.py b/src/twomartens/masterthesis/aae/train.py
index 8fa69f2..be94b7e 100644
--- a/src/twomartens/masterthesis/aae/train.py
+++ b/src/twomartens/masterthesis/aae/train.py
@@ -143,6 +143,7 @@ def train_mnist(folding_id: int, inlier_classes: Sequence[int], total_classes: i
             print("learning rate change!")
         
         nr_batches = len(mnist_train_x) // batch_size
+        log_frequency = 10
         for it in range(nr_batches):
             x = k.expand_dims(extract_batch(mnist_train_x, it, batch_size))
             # x discriminator
@@ -189,6 +190,14 @@ def train_mnist(folding_id: int, inlier_classes: Sequence[int], total_classes: i
                                                                               global_step=global_step_enc_dec)
             enc_dec_loss_avg(reconstruction_loss)
             encoder_loss_avg(encoder_loss)
+
+            if it % log_frequency == 0:
+                # log the losses every log frequency batches
+                summary_ops_v2.scalar('encoder_loss', encoder_loss_avg.result(), step=global_step_enc_dec)
+                summary_ops_v2.scalar('decoder_loss', decoder_loss_avg.result(), step=global_step_decoder)
+                summary_ops_v2.scalar('encoder_decoder_loss', enc_dec_loss_avg.result(), step=global_step_enc_dec)
+                summary_ops_v2.scalar('z_discriminator_loss', zd_loss_avg.result(), step=global_step_zd)
+                summary_ops_v2.scalar('x_discriminator_loss', xd_loss_avg.result(), step=global_step_xd)
             
             if it == 0:
                 directory = 'results' + str(inlier_classes[0])