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