Small docstring precision
@@ -685,6 +685,8 @@ class Model(Container):
See [losses](/losses).
If the model has multiple outputs, you can use a different loss
on each output by passing a dictionary or a list of losses.
+ The loss value that will be minimized by the model
+ will then be the sum of all individual losses.
metrics: list of metrics to be evaluated by the model
during training and testing.
Typically you will use `metrics=['accuracy']`.
@@ -694,6 +696,9 @@ class Model(Container):
loss_weights: Optional list or dictionary specifying scalar
coefficients (Python floats) to weight the loss contributions
of different model outputs.
+ The loss value that will be minimized by the model
+ will then be the *weighted sum* of all individual losses,
+ weighted by the `loss_weights` coefficients.
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.
