Improving types and simplifying return

deepchem/models/keras_model.py

@@ -273,7 +273,7 @@ def fit(self,
           variables: Optional[List[tf.Variable]] = None,
           loss: Optional[KerasLossFn] = None,
           callbacks: Union[Callable, List[Callable]] = [],
-          all_losses: Optional[list] = None) -> float:
+          all_losses: Optional[List[float]] = None) -> float:
     """Train this model on a dataset.
 
     Parameters
@@ -303,7 +303,7 @@ def fit(self,
     callbacks: function or list of functions
       one or more functions of the form f(model, step) that will be invoked after
       every step.  This can be used to perform validation, logging, etc.
-    all_losses: list, optional (default False)
+    all_losses: Optional[List[float]], optional (default False)
       If specified, all logged losses are appended into this list. Note that
       you can call `fit()` repeatedly with the same list and losses will
       continue to be appended.
@@ -326,7 +326,7 @@ def fit_generator(self,
                     variables: Optional[List[tf.Variable]] = None,
                     loss: Optional[KerasLossFn] = None,
                     callbacks: Union[Callable, List[Callable]] = [],
-                    all_losses: Optional[list] = None) -> float:
+                    all_losses: Optional[List[float]] = None) -> float:
     """Train this model on data from a generator.
 
     Parameters
@@ -352,7 +352,7 @@ def fit_generator(self,
     callbacks: function or list of functions
       one or more functions of the form f(model, step) that will be invoked after
       every step.  This can be used to perform validation, logging, etc.
-    all_losses: list, optional (default False)
+    all_losses: Optional[List[float]], optional (default False)
       If specified, all logged losses are appended into this list. Note that
       you can call `fit()` repeatedly with the same list and losses will
       continue to be appended.
@@ -367,8 +367,8 @@ def fit_generator(self,
     if checkpoint_interval > 0:
       manager = tf.train.CheckpointManager(self._checkpoint, self.model_dir,
                                            max_checkpoints_to_keep)
-    avg_losses = []
     avg_loss = 0.0
+    last_avg_loss = 0.0
     averaged_batches = 0
     train_op = None
     if loss is None:
@@ -414,7 +414,11 @@ def fit_generator(self,
         avg_loss = float(avg_loss) / averaged_batches
         logger.info(
             'Ending global_step %d: Average loss %g' % (current_step, avg_loss))
-        avg_losses.append(avg_loss)
+        if all_losses is not None:
+          all_losses.append(avg_loss)
+        # Capture the last avg_loss in case of return since we're resetting to
+        # 0 now
+        last_avg_loss = avg_loss
         avg_loss = 0.0
         averaged_batches = 0
 
@@ -433,19 +437,16 @@ def fit_generator(self,
       avg_loss = float(avg_loss) / averaged_batches
       logger.info(
           'Ending global_step %d: Average loss %g' % (current_step, avg_loss))
-      avg_losses.append(avg_loss)
+      if all_losses is not None:
+        all_losses.append(avg_loss)
+      last_avg_loss = avg_loss
 
     if checkpoint_interval > 0:
       manager.save()
 
     time2 = time.time()
     logger.info("TIMING: model fitting took %0.3f s" % (time2 - time1))
-    if all_losses is not None:
-      all_losses.extend(avg_losses)
-    if len(avg_losses) > 0:
-      return avg_losses[-1]
-    else:
-      return 0.0
+    return last_avg_loss
 
   def _create_gradient_fn(self,
                           variables: Optional[List[tf.Variable]]) -> Callable:

deepchem/models/tests/test_kerasmodel.py

@@ -78,6 +78,7 @@ def test_fit_use_all_losses():
   model.fit(dataset, nb_epoch=1000, all_losses=losses)
   # Each epoch is a single step for this model
   assert len(losses) == 100
+  assert np.count_nonzero(np.array(losses)) == 100
 
 
 def test_fit_on_batch():