o ?e)@sdZddlmZddlmZddlmZddlmZddlmZddlmZddlm Z dd lm Z dd l m Z dd l m Z dd lmZ dddZedgde jd d ejd fddZeddgde je ddd ejd d fddZd S)z#Confusion matrix related utilities.)dtypes)ops) array_ops)array_ops_stack) check_ops)cond)control_flow_ops)math_ops) deprecation)dispatch) tf_exportNc st|dgtt}|j}}|j}|durd|durd||}||dkrD|jddrDtdgn||dkrY|jddrYtdgfWdSt t }|dusz|jddrt t |d|fddfdd|dus|jddrt t |d|fddfd dfWdS1swYdS) a$Squeeze last dim if ranks differ from expected by exactly 1. In the common case where we expect shapes to match, `expected_rank_diff` defaults to 0, and we squeeze the last dimension of the larger rank if they differ by 1. But, for example, if `labels` contains class IDs and `predictions` contains 1 probability per class, we expect `predictions` to have 1 more dimension than `labels`, so `expected_rank_diff` would be 1. In this case, we'd squeeze `labels` if `rank(predictions) - rank(labels) == 0`, and `predictions` if `rank(predictions) - rank(labels) == 2`. This will use static shape if available. Otherwise, it will add graph operations, which could result in a performance hit. Args: labels: Label values, a `Tensor` whose dimensions match `predictions`. predictions: Predicted values, a `Tensor` of arbitrary dimensions. expected_rank_diff: Expected result of `rank(predictions) - rank(labels)`. name: Name of the op. Returns: Tuple of `labels` and `predictions`, possibly with last dim squeezed. remove_squeezable_dimensionsNctdgSNrrsqueeze predictionsrg/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/ops/confusion_matrix.pyQz.remove_squeezable_dimensions..cSNrrrrrrRcrrrrlabelsrrrWrcrrrrrrrrXr) r name_scopeconvert_to_tensor get_shapeZndimsdimsZis_compatible_withrrZrankrr equal) rrZexpected_rank_diffnameZpredictions_shapeZpredictions_rankZ labels_shapeZ labels_rankZ rank_diffr)rrrr sP        $r zmath.confusion_matrix)v1c Cst|d||||f}ttj|ddtj|dd\}}t|tj}t|tj}t t j |ddg|}t t j |ddg|}|durVt t |t |d }n!t|tj}t t j||d dg|}t t j||d dg|}|durtj|d d}||t||}t||g}tj||gd d }|durt||n|} tj|| t|tjdWdS1swYdS)aComputes 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: ```python 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`. confusion_matrixr)r$rz!`labels` contains negative values)messagez&`predictions` contains negative valuesNrz`labels` out of boundz`predictions` out of boundweights)Zaxis)indicesZupdatesshape)rrr r r castrZint64rZwith_dependenciesrZassert_non_negativemaximumZ reduce_maxZ assert_lessr!Zassert_is_compatible_withrstackrZ ones_likeZ scatter_nd) rr num_classesr(dtyper$Znum_classes_int64r*r)valuesrrrr&\sx:     $r&ztrain.confusion_matrixcCst||||||S)aComputes the confusion matrix from predictions and labels. The matrix columns represent the prediction labels and the rows represent the real labels. The confusion matrix is always a 2-D array of shape `[n, n]`, where `n` is the number of valid labels for a given classification task. Both prediction and labels must be 1-D arrays of the same shape in order for this function to work. If `num_classes` is `None`, then `num_classes` will be set to one plus the maximum value in either predictions or labels. Class labels are expected to start at 0. For example, if `num_classes` is 3, then the possible labels would be `[0, 1, 2]`. If `weights` is not `None`, then each prediction contributes its corresponding weight to the total value of the confusion matrix cell. For example: ```python tf.math.confusion_matrix([1, 2, 4], [2, 2, 4]) ==> [[0 0 0 0 0] [0 0 1 0 0] [0 0 1 0 0] [0 0 0 0 0] [0 0 0 0 1]] ``` Note that the possible labels are assumed to be `[0, 1, 2, 3, 4]`, resulting in a 5x5 confusion matrix. Args: labels: 1-D `Tensor` of real labels for the classification task. predictions: 1-D `Tensor` of predictions for a given classification. num_classes: The possible number of labels the classification task can have. If this value is not provided, it will be calculated using both predictions and labels array. dtype: Data type of the confusion matrix. name: Scope name. weights: An optional `Tensor` whose shape matches `predictions`. Returns: A `Tensor` of type `dtype` with shape `[n, n]` representing the confusion matrix, where `n` is the number of possible labels in the classification task. Raises: ValueError: If both predictions and labels are not 1-D vectors and have mismatched shapes, or if `weights` is not `None` and its shape doesn't match `predictions`. )r&)rrr.r/r$r(rrrconfusion_matrix_v1s ;r1)rN)__doc__Ztensorflow.python.frameworkrrZtensorflow.python.opsrrrrrr Ztensorflow.python.utilr r Z tensorflow.python.util.tf_exportr r Zadd_dispatch_supportZint32r&Zdeprecated_endpointsr1rrrrs:            > i