tf.contrib.metrics.confusion_matrix()

tf.contrib.metrics.confusion_matrix(predictions, labels, 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.

Considering a prediction array such as: [1, 2, 3] And a label array such as: [2, 2, 3]

The confusion matrix returned would be the following one:

[[0, 0, 0]
 [0, 1, 0]
 [0, 1, 0]
 [0, 0, 1]]

If weights is not None, then the confusion matrix elements are the corresponding weights elements.

Where the matrix rows represent the prediction labels and the columns represents 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.

Args:

• predictions: A 1-D array represeting the predictions for a given classification.
• labels: A 1-D represeting the real labels for the classification task.
• 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 k X k matrix represeting the confusion matrix, where k 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.