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diff --git a/tensorflow/docs_src/api_guides/python/contrib.metrics.md b/tensorflow/docs_src/api_guides/python/contrib.metrics.md deleted file mode 100644 index de6346ca80..0000000000 --- a/tensorflow/docs_src/api_guides/python/contrib.metrics.md +++ /dev/null @@ -1,133 +0,0 @@ -# Metrics (contrib) -[TOC] - -##Ops for evaluation metrics and summary statistics. - -### API - -This module provides functions for computing streaming metrics: metrics computed -on dynamically valued `Tensors`. Each metric declaration returns a -"value_tensor", an idempotent operation that returns the current value of the -metric, and an "update_op", an operation that accumulates the information -from the current value of the `Tensors` being measured as well as returns the -value of the "value_tensor". - -To use any of these metrics, one need only declare the metric, call `update_op` -repeatedly to accumulate data over the desired number of `Tensor` values (often -each one is a single batch) and finally evaluate the value_tensor. For example, -to use the `streaming_mean`: - -```python -value = ... -mean_value, update_op = tf.contrib.metrics.streaming_mean(values) -sess.run(tf.local_variables_initializer()) - -for i in range(number_of_batches): - print('Mean after batch %d: %f' % (i, update_op.eval()) -print('Final Mean: %f' % mean_value.eval()) -``` - -Each metric function adds nodes to the graph that hold the state necessary to -compute the value of the metric as well as a set of operations that actually -perform the computation. Every metric evaluation is composed of three steps - -* Initialization: initializing the metric state. -* Aggregation: updating the values of the metric state. -* Finalization: computing the final metric value. - -In the above example, calling streaming_mean creates a pair of state variables -that will contain (1) the running sum and (2) the count of the number of samples -in the sum. Because the streaming metrics use local variables, -the Initialization stage is performed by running the op returned -by `tf.local_variables_initializer()`. It sets the sum and count variables to -zero. - -Next, Aggregation is performed by examining the current state of `values` -and incrementing the state variables appropriately. This step is executed by -running the `update_op` returned by the metric. - -Finally, finalization is performed by evaluating the "value_tensor" - -In practice, we commonly want to evaluate across many batches and multiple -metrics. To do so, we need only run the metric computation operations multiple -times: - -```python -labels = ... -predictions = ... -accuracy, update_op_acc = tf.contrib.metrics.streaming_accuracy( - labels, predictions) -error, update_op_error = tf.contrib.metrics.streaming_mean_absolute_error( - labels, predictions) - -sess.run(tf.local_variables_initializer()) -for batch in range(num_batches): - sess.run([update_op_acc, update_op_error]) - -accuracy, error = sess.run([accuracy, error]) -``` - -Note that when evaluating the same metric multiple times on different inputs, -one must specify the scope of each metric to avoid accumulating the results -together: - -```python -labels = ... -predictions0 = ... -predictions1 = ... - -accuracy0 = tf.contrib.metrics.accuracy(labels, predictions0, name='preds0') -accuracy1 = tf.contrib.metrics.accuracy(labels, predictions1, name='preds1') -``` - -Certain metrics, such as streaming_mean or streaming_accuracy, can be weighted -via a `weights` argument. The `weights` tensor must be the same size as the -labels and predictions tensors and results in a weighted average of the metric. - -## Metric `Ops` - -* `tf.contrib.metrics.streaming_accuracy` -* `tf.contrib.metrics.streaming_mean` -* `tf.contrib.metrics.streaming_recall` -* `tf.contrib.metrics.streaming_recall_at_thresholds` -* `tf.contrib.metrics.streaming_precision` -* `tf.contrib.metrics.streaming_precision_at_thresholds` -* `tf.contrib.metrics.streaming_auc` -* `tf.contrib.metrics.streaming_recall_at_k` -* `tf.contrib.metrics.streaming_mean_absolute_error` -* `tf.contrib.metrics.streaming_mean_iou` -* `tf.contrib.metrics.streaming_mean_relative_error` -* `tf.contrib.metrics.streaming_mean_squared_error` -* `tf.contrib.metrics.streaming_mean_tensor` -* `tf.contrib.metrics.streaming_root_mean_squared_error` -* `tf.contrib.metrics.streaming_covariance` -* `tf.contrib.metrics.streaming_pearson_correlation` -* `tf.contrib.metrics.streaming_mean_cosine_distance` -* `tf.contrib.metrics.streaming_percentage_less` -* `tf.contrib.metrics.streaming_sensitivity_at_specificity` -* `tf.contrib.metrics.streaming_sparse_average_precision_at_k` -* `tf.contrib.metrics.streaming_sparse_precision_at_k` -* `tf.contrib.metrics.streaming_sparse_precision_at_top_k` -* `tf.contrib.metrics.streaming_sparse_recall_at_k` -* `tf.contrib.metrics.streaming_specificity_at_sensitivity` -* `tf.contrib.metrics.streaming_concat` -* `tf.contrib.metrics.streaming_false_negatives` -* `tf.contrib.metrics.streaming_false_negatives_at_thresholds` -* `tf.contrib.metrics.streaming_false_positives` -* `tf.contrib.metrics.streaming_false_positives_at_thresholds` -* `tf.contrib.metrics.streaming_true_negatives` -* `tf.contrib.metrics.streaming_true_negatives_at_thresholds` -* `tf.contrib.metrics.streaming_true_positives` -* `tf.contrib.metrics.streaming_true_positives_at_thresholds` -* `tf.contrib.metrics.auc_using_histogram` -* `tf.contrib.metrics.accuracy` -* `tf.contrib.metrics.aggregate_metrics` -* `tf.contrib.metrics.aggregate_metric_map` -* `tf.contrib.metrics.confusion_matrix` - -## Set `Ops` - -* `tf.contrib.metrics.set_difference` -* `tf.contrib.metrics.set_intersection` -* `tf.contrib.metrics.set_size` -* `tf.contrib.metrics.set_union` |