Calculates how often
name: name of the accuracy object
dtype: data type of the tensor
__init__( name=None, dtype=tf.double )
Inits Accuracy class with name and dtype.
__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.
**kwargs: A mini-batch of inputs to the Metric, passed on to
add_variable( name, shape=None, dtype=None, initializer=None )
Only for use by descendants of Metric.
Adds in the state from a list of metrics.
Default implementation sums all the metric variables.
metrics: A list of metrics with the same type as
ValueError: If metrics contains invalid data.
build( *args, **kwargs )
call( labels, predictions, weights=None )
Accumulate accuracy statistics.
For example, if labels is [1, 2, 3, 4] and predictions is [0, 2, 3, 4] then the accuracy is 3/4 or .75. If the weights were specified as [1, 1, 0, 0] then the accuracy would be 1/2 or .5.
predictions should have the same shape and type.
labels: Tensor with the true labels for each example. One example per element of the Tensor.
predictions: Tensor with the predicted label for each example.
weights: Optional weighting of each example. Defaults to 1.
The arguments, for easy chaining.
Initializes this Metric's variables.
Should be called after variables are created in the first execution
__call__(). If using graph execution, the return value should be
run() in a session before running the op returned by
(See example above.)
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.
In graph mode returns the result Tensor while in eager the callable.