1.2 KiB
1.2 KiB
Usage of objectives
An objective function (or loss function, or optimization score function) is one of the two parameters required to compile a model:
model.compile(loss='mean_squared_error', optimizer='sgd')
You can either pass the name of an existing objective, or pass a Theano symbolic function that returns a scalar for each data-point and takes the following two arguments:
- y_true: True labels. Theano tensor.
- y_pred: Predictions. Theano tensor of the same shape as y_true.
The actual optimized objective is the mean of the output array across all datapoints.
For a few examples of such functions, check out the objectives source.
Available objectives
- mean_squared_error / mse
- mean_absolute_error / mae
- mean_absolute_percentage_error / mape
- mean_squared_logarithmic_error / msle
- squared_hinge
- hinge
- binary_crossentropy: Also known as logloss.
- categorical_crossentropy: Also known as multiclass logloss. Note: using this objective requires that your labels are binary arrays of shape
(nb_samples, nb_classes).