#版权所有2015 TensorFlow作者。版权所有。










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@@remove_squeezable_dimensions @@confusion_matrix """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import sparse_ops def remove_squeezable_dimensions( labels, predictions, expected_rank_diff=0, name=None): """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. """ with ops.name_scope(name, 'remove_squeezable_dimensions', [labels, predictions]): predictions = ops.convert_to_tensor(predictions) labels = ops.convert_to_tensor(labels) predictions_shape = predictions.get_shape() predictions_rank = predictions_shape.ndims labels_shape = labels.get_shape() labels_rank = labels_shape.ndims if (labels_rank is not None) and (predictions_rank is not None): # Use static rank. rank_diff = predictions_rank - labels_rank if rank_diff == expected_rank_diff + 1: predictions = array_ops.squeeze(predictions, [-1]) elif rank_diff == expected_rank_diff - 1: labels = array_ops.squeeze(labels, [-1]) return labels, predictions # Use dynamic rank. rank_diff = array_ops.rank(predictions) - array_ops.rank(labels) if (predictions_rank is None) or ( predictions_shape.dims[-1].is_compatible_with(1)): predictions = control_flow_ops.cond( math_ops.equal(expected_rank_diff + 1, rank_diff), lambda: array_ops.squeeze(predictions, [-1]), lambda: predictions) if (labels_rank is None) or ( labels_shape.dims[-1].is_compatible_with(1)): labels = control_flow_ops.cond( math_ops.equal(expected_rank_diff - 1, rank_diff), lambda: array_ops.squeeze(labels, [-1]), lambda: labels) return labels, predictions def confusion_matrix(labels, predictions, num_classes=None, dtype=dtypes.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, 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 the one plus the maximum value in either predictions or labels. Class labels are expected to start at 0. E.g., if `num_classes` was three, 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.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. 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 k X k matrix representing 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`. """ with ops.name_scope(name, 'confusion_matrix', (predictions, labels, num_classes, weights)) as name: labels, predictions = remove_squeezable_dimensions( ops.convert_to_tensor(labels, name='labels'), ops.convert_to_tensor( predictions, name='predictions')) predictions = math_ops.cast(predictions, dtypes.int64) labels = math_ops.cast(labels, dtypes.int64) if num_classes is None: num_classes = math_ops.maximum(math_ops.reduce_max(predictions), math_ops.reduce_max(labels)) + 1 if weights is not None: predictions.get_shape().assert_is_compatible_with(weights.get_shape()) weights = math_ops.cast(weights, dtype) shape = array_ops.stack([num_classes, num_classes]) indices = array_ops.transpose(array_ops.stack([labels, predictions])) values = (array_ops.ones_like(predictions, dtype) if weights is None else weights) cm_sparse = sparse_tensor.SparseTensor( indices=indices, values=values, dense_shape=math_ops.to_int64(shape)) zero_matrix = array_ops.zeros(math_ops.to_int32(shape), dtype) return sparse_ops.sparse_add(zero_matrix, cm_sparse)


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