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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
|
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Methods
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
)
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
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
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.
__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().
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