/TensorFlow Python

Metrics (contrib)

Ops for evaluation metrics and summary statistics.


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:

value = ...
mean_value, update_op = tf.contrib.metrics.streaming_mean(values)

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:

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)

for batch in range(num_batches):
  sess.run([update_op_acc, update_op_error])

accuracy, mean_absolute_error = sess.run([accuracy, mean_absolute_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:

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`

Set `Ops`

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Code samples licensed under the Apache 2.0 License.