Special Topics: Basic Group Types
The fixed type is the primary type of the units in a group. The
main effect of the fixed type is that it determines the default input,
output, and cost functions of the group. The process of determining
default functions can be fairly complicated as it depends on network
type and other group types. So if you don't want to figure it all out,
you may want to just check the types of your groups with the groupType command to make sure types
are assigned as you expect and then explicitly specify them as
Note that there is no HIDDEN type. A hidden group simply has no fixed
type. The default input type for a hidden group is DOT_PRODUCT. The
default output type is LOGISTIC. However, if the network is a BOLTZMANN
net, the default input type is IN_BOLTZ and the default output type is
OUT_BOLTZ. Hidden groups will automatically receive an incoming link
from the bias unit. This can be prevented with the -BIASED type, as
- A bias group is one that always maintains an output of 1.0 (or
whatever the initOutput is set to).
Ordinarily, a bias group will be created automatically with the
network and there is probably no need for more than one, so you may
never need to use this type. BIAS cannot be combined with any other
fixed type. A BIAS group by default is itself unbiased, has no
input type, and has a BIAS_CLAMP output type. The BIAS_CLAMP function
simply sets the output to the group's initOutput.
- The main implication of the INPUT type is that externalInputs
will be assigned to units in the group when an example event is loaded.
Any group which is expecting external inputs should be
of type INPUT. If the externalInput is NaN, the input group
should act like any other group, computing the inputs based on
incoming connections and computing the output with ordinary output
However, it is usually the case that input groups will always have
external inputs. Therefore, to save processing time there is no
default input type for an INPUT group and there are no bias inputs
created by default. If no basic output type is specified and no
clamping output type is specified and there is not a SOFT_CLAMP
input type, a HARD_CLAMP output function will be prepended to the
output types of an INPUT group. If a SOFT_CLAMP input type is
specified, the default output type is LOGISTIC.
- The main implication of the OUTPUT type is that targets
will be assigned to units in the group when an example event is
loaded. These groups are treated the same as hidden groups. However,
OUTPUT groups will also be assigned a default error function.
Groups with KOHONEN or OUT_BOLTZ output functions will have no default
error procedure. LINEAR groups will have SUM_SQUARED error. SOFT_MAX
groups will have DIVERGENCE error. All other groups will have
as their default error function. OUTPUT groups in CONTINUOUS networks
have a STANDARD_CRIT criterion function by default.
The OUTPUT type may be combined with the INPUT or ELMAN types
(although the latter doesn't really make much sense). If a group
has multiple types, the default functions will be assigned as if it
were just an OUTPUT group.
- The ELMAN type can be used to identify context groups in simple
recurrent networks. Like BIAS and INPUT groups, ELMAN groups have no
default bias inputs and no default input function. The default output
function is ELMAN_CLAMP. Before an ELMAN_CLAMPED group is functional,
you must use elmanConnect to
assign a source group to it.
Group Criterion Functions
Criterion functions are used to determine when the outputs of an OUTPUT
group are good enough to either stop training or to continue to the next
event in the example. They can also be used when testing the network to
determine the number of examples on which it is correct. When training,
the groupCriterion defaults to the network's trainGroupCrit,
but can be overridden by the group's trainGroupCrit. When testing,
the testGroupCrit is used. There are separate values for training
and testing because you may want to, for example, use the criterion when
testing but not when training.
- This criterion is true if the absolute difference between the output
and target of each unit is less than the groupCriterion. Units
with NaN targets are ignored.
- This criterion is true if the unit with the largest target is also
the most active unit and the difference between the output and target
than the groupCriterion. Units with NaN targets
Ordinarily, the inputHistory and outputHistory arrays are
allocated for every unit but the targetHistory is only allocated
for groups with error functions and the outputDerivHistory is only
allocated for groups with some cost type. However, when writing your own
code, it may be useful to have the latter two arrays allocated anyway, to
be used for your own purposes. These flags will force them to be built.
Also, to save time the history arrays will only be filled in when
necessary during training or testing. You can use these flags to force
the arrays to be used.
- Forces the inputHistory to be written when the unit inputs
- Forces the outputHistory to be written when the unit outputs
- Forces the targetHistory to be allocated and written when the
cost is computed.
- Forces the outputDerivHistory to be allocated and written
when the unit output derivatives are computed.
Most noise types are classified as either input or output types. However,
two forms of noise don't fit into the standard pipelines.
- This injects noise on the derivatives of incoming links to the group
just before each weight update. There is currently no way to specify
exactly which type of links are to be affected.
- This injects noise to the external inputs when they are loaded from
the example event. This differs from INPUT_NOISE in that input noise
changes every tick but this remains constant across the event.
- If this is on, the units in the group will receive incoming links
from the bias unit when they are created. This only has an effect
when the group is created. This defaults to off for BIAS, INPUT, and
ELMAN groups and to on for HIDDEN and OUTPUT groups.
- If this is on, the outputs of units in the group will be reset to
the initOutput at the start of each example and any integrating
information reset. This defaults to on for CONTINUOUS networks and
to off for other networks.
- If this is on, units using a LOGISTIC, TANH, or GAUSSIAN output
function will train their gain parameter along with the incoming
weights. The adaptiveGainRate is the learning rate for the
gain changes. Steepest descent is always used. When the network is
reset, unit gains will be set to the group or network default.
- When the unit activations and targets are written to the activation
output file, which is opened with openNetOutputFile
command, only groups with this type will have their values written.
This type is added by default to OUTPUT groups, because those are the
groups whose activations one is usually interested in.
- This is turned on if any unit in the group is lesioned. This makes
it slower to traverse the units in the group because it must check
each unit and avoid the lesioned ones. The user can't set this group
- This is turned on if the group is frozen and thus no weight updates
will be performed on incoming links to the group. The user can't set
this group type.
Last modified: Mon Nov 20 13:31:51 EST 2000