Developer Guide


Loss Forward Layer

The loss function of a neural network is the sum of loss functions for each data sample. The loss function measures and penalizes the difference between the output of the neural network and ground truth. Because the loss layer evaluates the quality of the model being trained, it must be the last layer in the neural network topology.
The loss layer takes the ground truth
and output values
of the preceding layer as input and computes the value
of the cost function

Product and Performance Information


Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804