This operation computes the classification error.

classification_error(output_vector, target_vector, axis = -1, topN = 1,
  name = "")

Arguments

output_vector

the output values of the network

target_vector

one-hot encoding of target values

axis

integer for axis along which the classification error is computed

topN

integer

name

string (optional) the name of the Function instance in the network

Details

It finds the index of the highest value in the output_vector and compares it to the actual ground truth label (the index of the hot bit in the target vector).

The result is a scalar (i.e., one by one matrix). This is often used as an evaluation criterion.

It cannot be used as a training criterion though since the gradient is not defined for it.

References

https://www.cntk.ai/pythondocs/cntk.metrics.html#cntk.metrics.classification_error