/TensorFlow 2.4

# tf.math.confusion_matrix

Computes the confusion matrix from predictions and labels.

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.
`weights` An optional `Tensor` whose shape matches `predictions`.
`dtype` Data type of the confusion matrix.
`name` Scope name.
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`.