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Computes the confusion matrix from predictions and labels.
tf.compat.v1.confusion_matrix(
labels, predictions, num_classes=None, dtype=tf.dtypes.int32, name=None,
weights=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.
Args | |
---|---|
labels
|
1-D Tensor of real labels for the classification task.
|
predictions
|
1-D Tensor of predictions for a given classification.
|
num_classes
|
The possible number of labels the classification task can have. If this value is not provided, it will be calculated using both predictions and labels array. |
dtype
|
Data type of the confusion matrix. |
name
|
Scope name. |
weights
|
An optional Tensor whose shape matches predictions .
|
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 .
|