diff --git a/samplelib/SampleGeneratorFace.py b/samplelib/SampleGeneratorFace.py
index 1eef85d..4782848 100644
--- a/samplelib/SampleGeneratorFace.py
+++ b/samplelib/SampleGeneratorFace.py
@@ -49,7 +49,10 @@ class SampleGeneratorFace(SampleGeneratorBase):
         self.generators_random_seed = generators_random_seed
         
         samples = SampleLoader.load (self.sample_type, self.samples_path, sort_by_yaw_target_samples_path, person_id_mode=person_id_mode)
-        self.total_samples_count = len(samples)
+        self.samples_len = len(samples)
+        
+        if self.samples_len == 0:
+            raise ValueError('No training data provided.')
         
         ct_samples = SampleLoader.load (SampleType.FACE, random_ct_samples_path) if random_ct_samples_path is not None else None
         self.random_ct_sample_chance = 100
@@ -58,14 +61,14 @@ class SampleGeneratorFace(SampleGeneratorBase):
             self.generators_count = 1
             self.generators = [iter_utils.ThisThreadGenerator ( self.batch_func, (0, samples, ct_samples) )]
         else:
-            self.generators_count = min ( generators_count, len(samples) )
+            self.generators_count = min ( generators_count, self.samples_len )
             self.generators = [iter_utils.SubprocessGenerator ( self.batch_func, (i, samples[i::self.generators_count], ct_samples ) ) for i in range(self.generators_count) ]
 
         self.generator_counter = -1
     
     #overridable
     def get_total_sample_count(self):
-        return self.total_samples_count
+        return self.samples_len
         
     def __iter__(self):
         return self
@@ -86,9 +89,6 @@ class SampleGeneratorFace(SampleGeneratorBase):
 
         ct_samples_len = len(ct_samples) if ct_samples is not None else 0
 
-        if len(samples_idxs) == 0:
-            raise ValueError('No training data provided.')
-
         if self.sample_type == SampleType.FACE_YAW_SORTED or self.sample_type == SampleType.FACE_YAW_SORTED_AS_TARGET:
             if all ( [ samples[idx] == None for idx in samples_idxs] ):
                 raise ValueError('Not enough training data. Gather more faces!')