diff --git a/src/eynollah/training/train.py b/src/eynollah/training/train.py
index 4542297..39dac1d 100644
--- a/src/eynollah/training/train.py
+++ b/src/eynollah/training/train.py
@@ -158,9 +158,10 @@ def plot_confusion_matrix(cm, name="Confusion Matrix"):
 
 # plot predictions on train and test set during every epoch
 class TensorBoardPlotter(TensorBoard):
-    def __init__(self, *args, **kwargs):
+    def __init__(self, plot_freqs, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.model_call = None
+        self.plot_frequency_train, self.plot_frequency_val = plot_freqs
     def on_epoch_begin(self, epoch, logs=None):
         super().on_epoch_begin(epoch, logs=logs)
         # override the model's call(), so we don't have to invest extra cycles
@@ -183,14 +184,16 @@ class TensorBoardPlotter(TensorBoard):
     def plot(self, images, training=None, epoch=0):
         if training:
             writer = self._train_writer
+            freq = self.plot_frequency_train
             mode, step = "train", self._train_step.value()
         else:
             writer = self._val_writer
+            freq = self.plot_frequency_val
             mode, step = "test", self._val_step.value()
         # skip most samples, because TF's EncodePNG is so costly,
         # and now ends up in the middle of our pipeline, thus causing stalls
         # (cannot use max_outputs, as batch size may be too small)
-        if not tf.cast(step % 3, tf.bool):
+        if not tf.cast(step % freq, tf.bool):
             with writer.as_default():
                 # used to be family kwarg for tf.summary.image name prefix
                 family = "epoch_%03d/" % (1 + epoch)
@@ -634,7 +637,10 @@ def run(_config,
         _log.info("training on %d batches in %d epochs", train_steps, n_epochs)
         _log.info("validating on %d batches", valdn_steps)
 
-        callbacks = [TensorBoardPlotter(os.path.join(dir_output, 'logs'), profile_batch=(10, 20)),
+        callbacks = [TensorBoardPlotter((max(1, train_steps * n_batch // 1000),
+                                         max(1, valdn_steps * n_batch // 100)),
+                                        os.path.join(dir_output, 'logs'),
+                                        profile_batch=(10, 20)),
                      SaveWeightsAfterSteps(0, dir_output, _config),
         ]
         if save_interval: