Common functions

breze.arch.component.varprop.common.supervised_loss(target, prediction, loss, coord_axis=1, imp_weight=False, prefix='')

Return a dictionary populated with several expressions for a supervised loss and corresponding targets and predictions.

Version for variance propagation, where the prediction is not only a point but a mean with a variance.

Parameters:

target : Theano variable

Array representing the target variables. Has size d along the coordinate axis coord_axis.

prediction : Theano variable

Array representing the predictions. Has size 2 * d along the coordinate axis, where the first half corresponds to the mean and the second half to the variance of the prediction.

loss : callable or string

If a string, should index a member of breze.arch.component.loss. If a callable, has to be a of the form described in breze.arch.component.varprop.loss.

coord_axis : integer, optional [default: 1]

Axis aong which the coordinates of single sample are stored. I.e. not the sample axis or some spatial axis.

imp_weight : Theano variable, float or boolean, optional [default: False]

Importance weights for the loss. Will be multiplied to the coordinate wise loss.

prefix : string, optional [default: ‘’]

Each key in the resulting dictionary will be prefixed with prefix.

Returns:

res : dict

Dictionary containing the expressions. See example for keys.

Examples

>>> import theano.tensor as T
>>> prediction, target = T.matrix('prediction'), T.matrix('target')
>>> from breze.arch.component.varprop.loss import diag_gaussian_nll
>>> loss_dict = supervised_loss(target, prediction, diag_gaussian_nll,
...   prefix='mymodel-')
>>> sorted(loss_dict.items())  
[('mymodel-loss', ...), ('mymodel-loss_coord_wise', ...), ('mymodel-loss_sample_wise', ...), ('mymodel-prediction', prediction), ('mymodel-target', target)]

breze.arch.component.varprop.common.unsupervised_loss(output, loss, coord_axis=1, prefix='')

Return a dictionary populated with several expressions for a unsupervised loss and corresponding output.

Version for variance propagation, where the prediction is not only a point but a mean with a variance.

Parameters:

output : Theano variable

Array representing the output of the model. Has size 2 * d along the coordinate axis, where the first half corresponds to the mean and the second half to the variance of the prediction.

loss : callable or string

If a string, should index a member of breze.arch.component.loss. If a callable, has to be a of the form described in breze.arch.component.varprop.loss.

coord_axis : integer, optional [default: 1]

Axis aong which the coordinates of single sample are stored. I.e. not the sample axis or some spatial axis.

prefix : string, optional [default: ‘’]

Each key in the resulting dictionary will be prefixed with prefix.

Returns:

res : dict

Dictionary containing the expressions. See example for keys.

Examples

>>> import theano.tensor as T
>>> output = T.matrix('output')
>>> my_loss = lambda x: abs(x)
>>> loss_dict = unsupervised_loss(output, my_loss, prefix='$')
>>> sorted(loss_dict.items()) 
[('$loss', ...), ('$loss_coord_wise', ...), ('$loss_sample_wise', ...), ('$output', ...)]