|TensorFlow 2.0 version||View source on GitHub|
Computes the confusion matrix from predictions and labels.
tf.math.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 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.
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
[0, 1, 2].
weights is not
None, then each prediction contributes its
corresponding weight to the total value of the confusion matrix cell.
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.
Tensorof real labels for the classification task.
Tensorof 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
Tensorwhose shape matches
Tensor of type
dtype with shape
[n, n] representing the confusion
n is the number of possible labels in the classification
ValueError: If both predictions and labels are not 1-D vectors and have mismatched shapes, or if
Noneand its shape doesn't match