tf.contrib.eager.metrics.SparseAccuracy

Calculates how often predictions matches labels.

Inherits From: Mean

tf.contrib.eager.metrics.SparseAccuracy(
    name=None, dtype=tf.dtypes.double
)

This class is compatible with tf.keras.losses.sparse_categorical_crossentropy, tf.nn.sparse_softmax_cross_entropy_with_logits, tf.compat.v1.losses.sparse_softmax_cross_entropy.

Attributes
name	name of the accuracy object
dtype	data type of tensor.
variables

Methods

add_variable

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

Only for use by descendants of Metric.

aggregate

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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

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build(
    *args, **kwargs
)

Method to create variables.

Called by __call__() before call() for the first time.

Args
*args
**kwargs	The arguments to the first invocation of __call__(). build() may use the shape and/or dtype of these arguments when deciding how to create variables.

call

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call(
    labels, predictions, weights=None
)

Accumulate accuracy statistics.

labels and predictions should have the same shape except the predictions must have one additional trailing dimension equal to the number of classes(you want to predict).

Type of labels and predictions can be different.

Args
labels	Tensor of shape (batch_size, ) containing integers
predictions	Tensor with the logits or probabilities for each example.
weights	Optional weighting of each example. Defaults to 1.

Returns
The arguments, for easy chaining.

init_variables

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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

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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

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value()

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

__call__

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__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().