GitLab

* feature_addition : Mean squared loss layer for multiple output (#404)

* Added loss_mean_squared_multioutput layer to support multiple outputs.
* Also added a corresponding test case to test a single variable regression
  with multiple outputs.

* Added error checks on truth argument

Added assert statements to check that truth argument in
compute_loss_value_and_gradient() method contains matrices
of correct dimension relative to the output tensor's size.
Also the requirements on argument truth to the abstract
documentation.

Added mean squared loss layer "loss_mean_squared" to
DNN as requested in https://github.com/davisking/dlib/issues/152
Also added test case of a simple linear regression with one variable
that uses this layer.

training_label_type instead of a single label_type.  This way, the label
type used for training can be distinct from the type output by the network.
This change breaks backwards compatibility with the previous API.

attendant objects.  Also fixed a minor bug in the loss layer.

rather than a compile time constant.  This also removes it from the input layer
interface since the DNN core infers its value at runtime, meaning users that
define their own input layers don't need to specify it anymore.

interface.

- Made layer() recurse into repeat objects so that the index given to layer()
  does what you would expect.
- Added an operator<< for network objects that prints the network architecture.

objects.  Then made the relevant parts of the code use these functions.

than adding them.  This way, the gradient buffer can be used as scratch space
during the loss computation.

that contain a different number of samples than their input tensors.