Model loss functions
loss_mean_squared_error(y_true, y_pred)loss_mean_absolute_error(y_true, y_pred)
loss_mean_absolute_percentage_error(y_true, y_pred)
loss_mean_squared_logarithmic_error(y_true, y_pred)
loss_squared_hinge(y_true, y_pred)
loss_hinge(y_true, y_pred)
loss_categorical_hinge(y_true, y_pred)
loss_logcosh(y_true, y_pred)
loss_categorical_crossentropy(y_true, y_pred)
loss_sparse_categorical_crossentropy(y_true, y_pred)
loss_binary_crossentropy(y_true, y_pred)
loss_kullback_leibler_divergence(y_true, y_pred)
loss_poisson(y_true, y_pred)
loss_cosine_proximity(y_true, y_pred)
True labels (Tensor)
Predictions (Tensor of the same shape as y_true)
When using the categorical_crossentropy loss, 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
function to_categorical():
categorical_labels <- to_categorical(int_labels, num_classes = NULL)
Loss functions are to be supplied in the loss parameter of the
compile() function.
Loss functions can be specified either using the name of a built in loss function (e.g. 'loss = binary_crossentropy'), a reference to a built in loss function (e.g. 'loss = loss_binary_crossentropy()') or by passing an artitrary function that returns a scalar for each data-point and takes the following two arguments:
y_true True labels (Tensor)
y_pred Predictions (Tensor of the same shape as y_true)
The actual optimized objective is the mean of the output array across all datapoints.