Keras: Multi-outputs & class_weight

[bityangke]

Assume our model have two outputs :
output 1 'class' for classification
output 2 'location' for regression

Now we have the imbalance dataset(eg. binary classification, class '0': 98 percent, class '1': 2 percent), so we need set the class_weight params in model.fit() function, but for output 2 'location' regression task, we do not need class_weight.
How can we set the class_weight in this situation?
Does "class_weight = {0: ratio 1 , 1: ratio 2} is applied the classification task and ignore the regression task by default？

[bityangke]

And a second situation is that:
output 1 'class_1' for classification
output 2 'class_2' for classification

Both outputs do the classification, but we want to give each output different class_weight params.
How can we do this? (I am not sure whether this situation makes sense or not. )
Maybe Like this?:
class_weight = { 'class_1': {0: ratio 1 , 1: ratio 2} , 'class_2': {0: ratio 3 , 1: ratio 4} }

[fchollet]

You should pass a dict with string keys corresponding to the names of your
outputs. Like you describe.

On 15 December 2016 at 15:14, Ke Young [e-mail] wrote:

And a second situation is that:
output 1 'class_1' for classification
output 2 'class_2' for classification

Both outputs do the classification, but we want to give each output
different class_weight params.
How can we do this? (I am not sure whether this situation makes sense or
not. )
Maybe Like this?:
class_weight = { 'class_1': {0: ratio 1 , 1: ratio 2} , 'class_2': {0:
ratio 3 , 1: ratio 4} }

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[bityangke]

Thanks for your quick response.
If we use the one-hot code.
For the two classes classification, should the class_weight like this? :
class_weight = {10: weight 1, 11: weight 2}
10 and 11 repesent the one hot codes of the two classes

[fchollet]

No, use integers. 0 and 1.

On 15 December 2016 at 15:31, Ke Young [e-mail] wrote:

Thanks for your quick response.
If we use the one-hot code.
For the two classes classification, should the class_weight like this? :
class_weight = {10: weight 1, 11: weight 2}
10 and 11 repesent the one hot codes of the two classes

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[olix20]

@fchollet what is the right way to set weights given multi-class, one-hot encoded outputs? e.g. y = [0, 1, 1, 1, 0]

[shamoons]

Yup. Same issue

[pratiklodha95]

@olix20 did you find a solution ?

[QuickLearner171998]

For a multi output model how can I change class_weights during training ie after each step according to a batch.

[adriangb]

In 2020 / TF 2.3.0 this does not work: class_weights = {'output1': {0: 1, 1: 10}, 'output2': {0: 5, 1: 1, 2: 10}}