Usage of objectives
An objective function (or loss function, or optimization score function) is one of the two parameters required to compile a model:
You can either pass the name of an existing objective, or pass a Theano/TensorFlow symbolic function that returns a scalar for each data-point and takes the following two arguments:
- y_true: True labels. Theano/TensorFlow tensor.
- y_pred: Predictions. Theano/TensorFlow 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.
- mean_squared_error / mse
- mean_absolute_error / mae
- mean_absolute_percentage_error / mape
- mean_squared_logarithmic_error / msle
- 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
- sparse_categorical_crossentropy: As above but accepts sparse labels. Note: this objective still requires that your labels have the same number of dimensions as your outputs; you may need to add a length-1 dimension to the shape of your labels, e.g with
- kullback_leibler_divergence / kld: Information gain from a predicted probability distribution Q to a true probability distribution P. Gives a measure of difference between both distributions.
- poisson: Mean of
(predictions - targets * log(predictions))
- cosine_proximity: The opposite (negative) of the mean cosine proximity between predictions and targets.
Note: when using the
categorical_crossentropy objective, your targets should be in categorical format (e.g. if you have 10 classes, the target for each sample should be a 10-dimensional vector that is all-zeros expect for a 1 at the index corresponding to the class of the sample). In order to convert integer targets into categorical targets, you can use the Keras utility
from keras.utils.np_utils import to_categorical categorical_labels = to_categorical(int_labels, nb_classes=None)