tf.contrib.eager.metrics.Accuracy

Class Accuracy

Inherits From: Mean

Defined in tensorflow/contrib/eager/python/metrics_impl.py.

Calculates how often predictions matches labels.

Attributes:

  • name: name of the accuracy object
  • dtype: data type of the tensor

__init__

__init__(
    name=None,
    dtype=tf.double
)

Inits Accuracy class with name and dtype.

Properties

name

variables

Methods

__call__

__call__(
    *args,
    **kwargs
)

Returns op to execute to update this metric for these inputs.

Returns None if eager execution is enabled. Returns a graph-mode function if graph execution is enabled.

Args:

  • *args: * **kwargs: A mini-batch of inputs to the Metric, passed on to call().

add_variable

add_variable(
    name,
    shape=None,
    dtype=None,
    initializer=None
)

Only for use by descendants of Metric.

aggregate

aggregate(metrics)

Adds in the state from a list of metrics.

Default implementation sums all the metric variables.

Args:

  • metrics: A list of metrics with the same type as self.

Raises:

  • ValueError: If metrics contains invalid data.

build

build(
    *args,
    **kwargs
)

call

call(
    labels,
    predictions,
    weights=None
)

Accumulate accuracy statistics.

For example, if labels is [1, 2, 3, 4] and predictions is [0, 2, 3, 4] then the accuracy is 3/4 or .75. If the weights were specified as [1, 1, 0, 0] then the accuracy would be 1/2 or .5.

labels and predictions should have the same shape and type.

Args:

  • labels: Tensor with the true labels for each example. One example per element of the Tensor.
  • predictions: Tensor with the predicted label for each example.
  • weights: Optional weighting of each example. Defaults to 1.

Returns:

The arguments, for easy chaining.

init_variables

init_variables()

Initializes this Metric's variables.

Should be called after variables are created in the first execution of __call__(). If using graph execution, the return value should be run() in a session before running the op returned by __call__(). (See example above.)

Returns:

If using graph execution, this returns an op to perform the initialization. Under eager execution, the variables are reset to their initial values as a side effect and this function returns None.

result

result(write_summary=True)

Returns the result of the Metric.

Args:

  • write_summary: bool indicating whether to feed the result to the summary before returning.

Returns:

aggregated metric as float.

Raises:

  • ValueError: if the optional argument is not bool

value

value()

In graph mode returns the result Tensor while in eager the callable.