View source on GitHub |
Calculates how often predictions
matches labels
.
Inherits From: Mean
tf.contrib.eager.metrics.BinaryAccuracy(
threshold, name=None, dtype=tf.dtypes.double
)
This class is compatible with tf.keras.losses.binary_crossentropy
,
tf.compat.v1.losses.sigmoid_cross_entropy
,
tf.nn.sigmoid_cross_entropy_with_logits
.
If there is more than one label, this will become multi-label classification.
Attributes | |
---|---|
name
|
name of the accuracy object. |
threshold
|
Used for rounding off the predictions.
If the predictions are,
|
dtype
|
data type of tensor. |
variables
|
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 and type.
Args | |
---|---|
labels
|
Binary Tensor(containing 0 or 1). |
predictions
|
Tensor with probabilities or logits. |
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() .
|