tf.contrib.eager.metrics.CategoricalAccuracy

Calculates how often predictions matches labels.

Inherits From: Mean

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

This class is compatible with tf.keras.losses.categorical_crossentropy, tf.nn.softmax_cross_entropy_with_logits, tf.compat.v1.losses.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. As argmax is being done here, labels and predictions type can be different.

Args
labels	One-hot Tensor.
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().