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/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.

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).
• 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 np.expand_dims(y, -1).
• 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.