rebase + small changes

n3fit/src/n3fit/backends/keras_backend/MetaModel.py

@@ -166,7 +166,7 @@ def perform_fit(self, x=None, y=None, epochs=1, **kwargs):
             y = self.target_tensors
 
         # Avoids Tensorflow overhead that happens at every epoch, by putting multiple steps in an epoch
-        steps_per_epoch = self.determine_steps_per_epoch(epochs)
+        steps_per_epoch = self._determine_steps_per_epoch(epochs)
 
         for k, v in x_params.items():
             x_params[k] = tf.repeat(v, steps_per_epoch, axis=0)
@@ -178,20 +178,19 @@ def perform_fit(self, x=None, y=None, epochs=1, **kwargs):
         loss_dict = history.history
         return loss_dict
 
-    def determine_steps_per_epoch(self, epochs):
-        num_replicas = self.output_shape[0][0]
-        # in this case we're most likely running on the CPU and this is not worth it
-        if num_replicas == 1:
+    def _determine_steps_per_epoch(self, epochs):
+        """Determine how many step to run in every epoch.
+        When running a single replica (CPU) or when the number of epochs is < 100 default to 1.
+        Otherwise run 100 step per epoch.
+
+        If the number of epochs requested is not divisible by 100 there will be a number
+        of extra training epochs being run equal to max_epochs % 100 in the worst case.
+
+        """
+        num_replicas = self.output_shape[0]
+        if num_replicas == 1 or epochs < 100:
             return 1
 
-        # On the GPU, run with
-        for divisor in [10, 100]:
-            if epochs % divisor != 0:
-                steps_per_epoch = divisor // 10
-                log.warning(
-                    f"Epochs {epochs} not divisible by {divisor}, using {steps_per_epoch} steps per epoch"
-                )
-                return steps_per_epoch
         return 100
 
     def predict(self, x=None, **kwargs):