Keras Model Fit Class Weights

If you want to support the fit arguments sample weight and class weight you d simply do the following.

Keras model fit class weights. Note that when using the delayed build pattern no input shape specified the model gets built the first time you call fit eval or predict or the first time you call the model on some input data. Pre trained models and datasets built by google and the community. This can be useful to tell the model to pay more attention to samples from an under represented class. Optional named list mapping indices integers to a weight float value used for weighting the loss function during training only.

Supporting sample weight class weight. Feed this dictionary as a parameter of model fit. Pass it to compiled loss compiled metrics of course you could also just apply it manually if you don t. You may have noticed that our first basic example didn t make any mention of sample weighting.

Model fit x train y train dog batch size batch size epochs 3 class weight class weights validation data x test y test dog shuffle true. Hi i m confused about how to use the class weights i pasted a simple example here in the example i fit the same inputs to predict two different classes without weights the prediction for the inputs should be 50 for 2nd class and 50 for 4th class. Feed this dictionary as a parameter of model fit. Unpack sample weight from the data argument.

There are three ways to create keras models. The sequential model which is very straightforward a simple list of layers but is limited to single input single output stacks of layers as the name gives away. The class weight parameter of the fit function is a dictionary mapping class to a weight value. This can be used to balance classes without resampling or to train a model that gives more importance to a particular class.