addGroup <group> <num-units> [[+|-]<group-type>]*
This creates a group with the specified name and number of units and
places it at the end of the network's group array. The
group must contain at least one unit. Following the number of units is
a list of group types.
The units in the group will be given names composed of the group name followed by a colon and the unit's number.
Type names are normally listed with no - or +
preceding them. Using a + before the name is essentially
equivalent to using nothing. Using a -, on the other hand,
causes the type to be removed if it would otherwise have been created by
default. For example, to create a HIDDEN group with no incoming bias
links, use the -BIASED type. You should think of the
- as a minus, not as a dash.
A group may have one of four main or fixed types: BIAS, INPUT, OUTPUT, or ELMAN. The default is no fixed type which corresponds to a hidden layer. Typically the user will never create a BIAS group as one is created with the network. It is possible to create a group that is both an INPUT and an OUTPUT group. This may be useful for Boltzmann machines. A projection is automatically created from the bias layer to hidden and OUTPUT groups. The links in this projection will be of type "bias" so they can be controlled separately.
If no input, output, or cost types are specified, Lens will attempt to guess reasonable values given the type of the network and the group. However, you may need to over-ride these if you want to use non-standard functions or if you want an INPUT or ELMAN group to use incoming connections as well as its clamping function.
Input functions compute the units' input values. Multiple input types may be specified and the functions will be computed in the order given. The main input functions are DOT_PRODUCT, DISTANCE, and PRODUCT. Additional functions and modifiers include SOFT_CLAMP, IN_BOLTZ, IN_INTEGR, IN_NORM, IN_NOISE, and IN_DERIV_NOISE. DOT_PRODUCT is the default for most networks and IN_BOLTZ is the default for BOLTZMANN networks.
Output functions compute the units' outputs. Like input functions, several may be specified and the order determines when they will be executed. The basic transfer functions include LINEAR, LOGISTIC, TERNARY, TANH, EXPONENTIAL, SOFT_MAX, KOHONEN, and OUT_BOLTZ. Clamping functions, which can be used with or without basic functions are HARD_CLAMP, BIAS_CLAMP, ELMAN_CLAMP, and WEAK_CLAMP. Modifiers include OUT_INTEGR, OUT_NORM, OUT_NOISE, OUT_DERIV_NOISE, OUT_CROPPED, and OUT_WINNER.
LOGISTIC is the default for most groups. BIAS groups will have BIAS_CLAMP, ELMAN groups ELMAN_CLAMP, and INPUT groups HARD_CLAMP. Continuous networks will have OUT_INTEGR added to most groups.
Cost functions are broken into two types: error functions which are based on the output units' targets and unit output cost functions, which are based on the strength of the outputs of the units.
The error functions include SUM_SQUARED, CROSS_ENTROPY, and DIVERGENCE. CROSS_ENTROPY is the default except that SOFT_MAX output groups will have DIVERGENCE.
Output cost functions include LINEAR_COST, QUADRATIC_COST, CONV_QUAD_COST, LOGISTIC_COST, COSINE_COST. There is no default.
Ordinarily all groups automatically receive bias inputs except INPUT, ELMAN, and BIAS groups. The BIASED and -BIASED types may be specified to change this default.
Output groups' criterion functions are used to determine when to stop training the network or when to proceed to the next event. Currently the only available function is STANDARD_CRIT. This is only activated by default for output groups in continuous networks.
Not all groups will allocate targetHistory and outputDerivHistory arrays for their units. USE_TARGET_HIST and USE_OUT_DERIV_HIST will force these arrays to be allocated if you wish to use them for your own purposes.
Finally, DERIV_NOISE will cause noise to be injected into the link derivatives prior to each weight update.
To create a hidden layer with a DOT_PRODUCT input combining function and LINEAR transfer function:
lens> addGroup myGroup 100 LINEAR
To create an output layer with a DOT_PRODUCT input function followed by the application of noise to the input, an EXPONENTIAL output function that is time averaged, a SUM_SQUARED error measure, and no bias projection:
lens> addGroup "group 2" 10 OUTPUT IN_NOISE EXPONENTIAL OUT_INTEGR SUM_SQUARED -BIASED