keras
Introduction
1. Examples
2. Optimizers
3. Objectives
4. Models
5. Activations
6. Initializations
7. Regularizers
8. Constraints
9. Callbacks
10. Datasets
11. Layers, Core Layers
12. Layers, Convolutional Layers
13. Layers, Recurrent Layers
14. Layers, Advanced Activations Layers
15. Layers, Normalization Layers
16. Layers, Embedding Layers
17. Layers, Containers
18. Preprocessing, Sequence Preprocessing
19. Preprocessing, Text Preprocessing
20. Preprocessing, Image Preprocessing
21. Utils, Visualization Utilities

keras

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 and takes the following two arguments:

y_true: True labels. Theano tensor.
y_pred: Predictions. Theano tensor of the same shape as y_true.

For a few examples of such functions, check out the objectives source.

Available objectives

mean_squared_error / mse
mean_absolute_error / mae
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).