tf.confusion_matrix(labels, predictions, num_classes=None, dtype=tf.int32, name=None, weights=None)
Computes the confusion matrix from predictions and labels.
Calculate the Confusion Matrix for a pair of prediction and label 1-D int arrays.
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 is None, then
num_classes will be set to the one plus
the maximum value in either predictions or labels.
Class labels are expected to start at 0. E.g., if
three, then the possible labels would be
[0, 1, 2].
weights is not
None, then each prediction contributes its
corresponding weight to the total value of the confusion matrix cell.
tf.contrib.metrics.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
A k X k matrix representing the confusion matrix, where k is the number of possible labels in the classification task.
ValueError: If both predictions and labels are not 1-D vectors and have mismatched shapes, or if
Noneand its shape doesn't match