Further fixes and improvements.

keras/engine/training.py

@@ -666,6 +666,12 @@ def compile(self, optimizer, loss, metrics=None, loss_weights=None,
                 To specify different metrics for different outputs of a
                 multi-output model, you could also pass a dictionary,
                 such as `metrics={'output_a': 'accuracy'}`.
+            loss_weights: Optional list or dictionary specifying scalar
+                coefficients (Python floats) to weight the loss contributions
+                of different model outputs.
+                If a list, it is expected to have a 1:1 mapping
+                to the model's outputs. If a tensor, it is expected to map
+                output names (strings) to scalar coefficients.
             sample_weight_mode: if you need to do timestep-wise
                 sample weighting (2D weights), set this to `"temporal"`.
                 `None` defaults to sample-wise weights (1D).
@@ -1269,6 +1275,10 @@ class indices (integers) to
         # Returns
             A `History` instance. Its `history` attribute contains
             all information collected during training.
+
+        # Raises
+            ValueError: In case of mismatch between the provided input data
+                and what the model expects.
         """
         # validate user data
         x, y, sample_weights = self._standardize_user_data(
@@ -1375,6 +1385,9 @@ def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):
                 you can also pass a dictionary
                 mapping output names to Numpy arrays.
             batch_size: integer. Number of samples per gradient update.
+            verbose: verbosity mode, 0 or 1.
+            sample_weight: Array of weights to weight the contribution
+                of different samples to the loss and metrics.
 
         # Returns
             Scalar test loss (if the model has a single output and no metrics)
@@ -1411,7 +1424,13 @@ def predict(self, x, batch_size=32, verbose=0):
             verbose: verbosity mode, 0 or 1.
 
         # Returns
-            A Numpy array of predictions.
+            Numpy array(s) of predictions.
+
+        # Raises
+            ValueError: In case of mismatch between the provided
+                input data and the model's expectations,
+                or in case a stateful model receives a number of samples
+                that is not a multiple of the batch size.
         """
         # validate user data
         x = _standardize_input_data(x, self.input_names,
@@ -1531,6 +1550,12 @@ def test_on_batch(self, x, y, sample_weight=None):
 
     def predict_on_batch(self, x):
         """Returns predictions for a single batch of samples.
+
+        # Arguments
+            x: Input samples, as a Numpy array.
+
+        # Returns
+            Numpy array(s) of predictions.
         """
         x = _standardize_input_data(x, self.input_names,
                                     self.internal_input_shapes)

keras/layers/core.py

@@ -688,7 +688,7 @@ def get_config(self):
     def from_config(cls, config, custom_objects=None):
         globs = globals()
         if custom_objects:
-            globs = dict(globs.items() + custom_objects.items())
+            globs = dict(list(globs.items()) + list(custom_objects.items()))
         function_type = config.pop('function_type')
         if function_type == 'function':
             # Simple lookup in custom objects

keras/models.py

@@ -187,6 +187,11 @@ def convert_custom_objects(obj):
 
         # Arguments
             obj: object, dict, or list.
+
+        # Returns
+            The same structure, where occurences
+                of a custom object name have been replaced
+                with the custom object.
         """
         if isinstance(obj, list):
             deserialized = []