diff --git a/src/twomartens/masterthesis/ssd.py b/src/twomartens/masterthesis/ssd.py
index f63f5c3..3b62ce9 100644
--- a/src/twomartens/masterthesis/ssd.py
+++ b/src/twomartens/masterthesis/ssd.py
@@ -359,6 +359,81 @@ def _get_observations(detections: Sequence[Sequence[np.ndarray]]) -> List[List[n
     return observations
 
 
+def train_keras(train_generator: callable,
+                steps_per_epoch_train: int,
+                val_generator: callable,
+                steps_per_epoch_val: int,
+                ssd_model: Union[SSD, DropoutSSD],
+                weights_prefix: str,
+                iteration: int,
+                initial_epoch: int,
+                nr_epochs: int,
+                lr: float,
+                tensorboard_callback: tf.keras.callbacks.TensorBoard) -> tf.keras.callbacks.History:
+    """
+    Trains the SSD on the given data set using Keras functionality.
+    
+    Args:
+        train_generator: generator of training data
+        steps_per_epoch_train: number of batches per training epoch
+        val_generator: generator of validation data
+        steps_per_epoch_val: number of batches per validation epoch
+        ssd_model: wrapper of SSD model
+        weights_prefix: prefix for weights directory
+        iteration: identifier for current training run
+        initial_epoch: the epoch to start training in
+        nr_epochs: number of epochs to train
+        lr: initial learning rate
+        tensorboard_callback: initialised TensorBoard callback
+    """
+    
+    # set up variables
+    learning_rate_var = K.variable(lr)
+    ssd_loss = keras_ssd_loss.SSDLoss()
+    
+    # compile the model
+    ssd_model.model.compile(
+        optimizer=tf.train.AdamOptimizer(learning_rate=learning_rate_var,
+                                         beta1=0.5, beta2=0.999),
+        loss=ssd_loss.compute_loss,
+        metrics=[
+            tf.keras.metrics.Precision(),
+            tf.keras.metrics.Recall(),
+            tf.keras.metrics.FalsePositives(),
+            tf.keras.metrics.CategoricalAccuracy()
+        ]
+    )
+
+    checkpoint_dir = os.path.join(weights_prefix, str(iteration), "/")
+    os.makedirs(checkpoint_dir, exist_ok=True)
+    
+    callbacks = [
+        tf.keras.callbacks.ModelCheckpoint(
+            filepath=f"{checkpoint_dir}ssd300-{{epoch:02d}}_loss-{{loss:.4f}}_val_loss-{{val_loss:.4f}}.h5",
+            monitor="val_loss",
+            verbose=1,
+            save_best_only=True,
+            save_weights_only=False
+        ),
+        tf.keras.callbacks.TerminateOnNaN(),
+        tf.keras.callbacks.EarlyStopping(patience=2, min_delta=0.001, monitor="val_loss"),
+        tensorboard_callback
+    ]
+    
+    history = ssd_model.model.fit_generator(generator=train_generator,
+                                            epochs=nr_epochs,
+                                            steps_per_epoch=steps_per_epoch_train,
+                                            validation_data=val_generator,
+                                            validation_steps=steps_per_epoch_val,
+                                            callbacks=callbacks,
+                                            initial_epoch=initial_epoch)
+    
+    ssd_model.model.save(f"{checkpoint_dir}ssd300.h5")
+    ssd_model.model.save_weights(f"{checkpoint_dir}ssd300_weights.h5")
+    
+    return history
+
+
 def train(dataset: tf.data.Dataset,
           iteration: int,
           use_dropout: bool,