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Computes the confusion matrix from predictions and labels.
tf.math.confusion_matrix(
labels,
predictions,
num_classes=None,
weights=None,
dtype=tf.dtypes.int32
,
name=None
)
The matrix columns represent the prediction labels and the rows represent the
real labels. The confusion matrix is always a 2-D array of shape [n, n]
,
where n
is the number of valid labels for a given classification task. Both
prediction and labels must be 1-D arrays of the same shape in order for this
function to work.
If num_classes
is None
, then num_classes
will be set to one plus the
maximum value in either predictions or labels. Class labels are expected to
start at 0. For example, if num_classes
is 3, then the possible labels
would be [0, 1, 2]
.
If weights
is not None
, then each prediction contributes its
corresponding weight to the total value of the confusion matrix cell.
For example:
tf.math.confusion_matrix([1, 2, 4], [2, 2, 4]) ==>
[[0 0 0 0 0]
[0 0 1 0 0]
[0 0 1 0 0]
[0 0 0 0 0]
[0 0 0 0 1]]
Note that the possible labels are assumed to be [0, 1, 2, 3, 4]
,
resulting in a 5x5 confusion matrix.
Returns | |
---|---|
A Tensor of type dtype with shape [n, n] representing the confusion
matrix, where n is the number of possible labels in the classification
task.
|
Raises | |
---|---|
ValueError
|
If both predictions and labels are not 1-D vectors and have
mismatched shapes, or if weights is not None and its shape doesn't
match predictions .
|