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Reduction operations

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Reduce an input, e.g. compute sum or mean over elements.

ReduceSum (x, axis=None)
ReduceLogSum (x, axis=None)
ReduceMean (x, axis=None)
ReduceMax (x, axis=None)
ReduceMin (x, axis=None)

Parameters

  • x: data to reduce
  • axis (default: None): if specified, perform reduction along this axis only. This value is 1-based; i.e. 1 stands for the first static axis of x.

Return value

Reduced value. For axis=1 (default), this is a scalar. If an axis is specified, that axis is reduced to have dimension 1.

Description

These functions compute aggregates (sum, mean, etc.) over all values of an input vector or tensor. Available aggregations are:

  • ReduceSum(): the sum over the elements
  • ReduceLogSum(): the sum over elements in log representations (logC = log (exp (logA) + exp (logB)))
  • ReduceMean(): the mean over the elements
  • ReduceMax(): the maximum value of the elements
  • ReduceMin(): the minimum value

By default, aggregation is done over all elements. In case of a tensor with rank>1, the optional axis parameter specifies a single axis that the reduction is performed over. For example, axis=2 applied to a [M x N]-dimensional matrix would aggregate over all columns, yielding a [M x 1] result.

Reducing over sequences

If the input is a sequence, reduction is performed separately for every sequence item. These operations do not support reduction over sequences. Instead, you can achieve this with a recurrence. For example, to sum up all elements of a sequence x, you can say:

sum = x + PastValue (0, sum, defaultHiddenActivation=0)

and for max pooling, you can use

max = Max(x, PastValue (0, max, defaultHiddenActivation=0))

Examples

Normalize a value by subtracting the mean of its elements (e.g. as part of layer normalization):

mean = ReduceMean (x)
xNorm = x - mean

Or, the cross-entropy with softmax criterion can be manually be defined using ReduceLogSum():

myCrossEntropyWithSoftmax (y/*label*/, z/*logit*/) = ReduceLogSum (z) - ReduceSum (y .* z)