GitLab

reallocation and copying inside conv_'s backward pass.  Doing this
required adding an add_to_output boolean option to the methods of
tensor_conv.

refactored interface to reduce complexity so conv and convt layers forward passes have to call setup explicit now and there is only one ()-operator

rather than the entire tensor.

fixed backward pass in cont layer to accumulate gradients this will pass the layer test now also removed compile warnings and changed some comments

* 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.

versions were calling into cuDNN, however, the cuDNN functions for doing this
are horrifically slow, well over 100x slower than they should be, which is
surprising since these functions are so trivial.

alias tensors.  Now any kind of tensors are supported.

cudnnGetConvolutionBackwardFilterAlgorithm() to pick invalid algorithms,
resulting in cuDNN not working correctly.

automatically sizes the tensor.

caused by num_computational_layers being wrong when tax layers were placed as
the first layer.  These visit functions being wrong also caused multi-GPU
support to not work on such networks.

skip layers and add_prev style layers.  In particular, now in-place layers only
overwrite the gradient information in their child layer if they are operating
in in-place mode.  Otherwise, they add their gradients to their child layers.

It should also be noted that it's safe for in-place layers to overwrite
gradients when in in-place mode since their child layers are inaccessible when
in-place layers operate in in-place mode.  This prevents any other layers from
trying to add to the child layer, thereby avoiding the potability of layer
interference.  So the bug this change fixes is that, when not in in-place mode
the child layers are still accessible but in-place layers were *still*
overwriting child gradients.

interfaces.  Also changed the default behavior when the stride isn't 1.  Now
the filters will be applied only to the "valid" part of the image.

to expose it in the final layer 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.