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Encapsulates metric logic and state.
tf.keras.Metric( dtype=None, name=None ) Args | |
|---|---|
name | (Optional) string name of the metric instance. |
dtype | (Optional) data type of the metric result. |
Example:
m = SomeMetric(...) for input in ...: m.update_state(input) print('Final result: ', m.result()) Usage with compile() API:
model = keras.Sequential() model.add(keras.layers.Dense(64, activation='relu')) model.add(keras.layers.Dense(64, activation='relu')) model.add(keras.layers.Dense(10, activation='softmax')) model.compile(optimizer=keras.optimizers.RMSprop(0.01), loss=keras.losses.CategoricalCrossentropy(), metrics=[keras.metrics.CategoricalAccuracy()]) data = np.random.random((1000, 32)) labels = np.random.random((1000, 10)) model.fit(data, labels, epochs=10) To be implemented by subclasses:
__init__(): All state variables should be created in this method by callingself.add_variable()like:self.var = self.add_variable(...)update_state(): Has all updates to the state variables like:self.var.assign(...).result(): Computes and returns a scalar value or a dict of scalar values for the metric from the state variables.
Example subclass implementation:
class BinaryTruePositives(Metric): def __init__(self, name='binary_true_positives', **kwargs): super().__init__(name=name, **kwargs) self.true_positives = self.add_variable( shape=(), initializer='zeros', name='true_positives' ) def update_state(self, y_true, y_pred, sample_weight=None): y_true = ops.cast(y_true, "bool") y_pred = ops.cast(y_pred, "bool") values = ops.logical_and( ops.equal(y_true, True), ops.equal(y_pred, True)) values = ops.cast(values, self.dtype) if sample_weight is not None: sample_weight = ops.cast(sample_weight, self.dtype) sample_weight = ops.broadcast_to( sample_weight, ops.shape(values) ) values = ops.multiply(values, sample_weight) self.true_positives.assign(self.true_positives + ops.sum(values)) def result(self): return self.true_positives Attributes | |
|---|---|
dtype | |
variables | |
Methods
add_variable
add_variable( shape, initializer, dtype=None, aggregation='sum', name=None ) add_weight
add_weight( shape=(), initializer=None, dtype=None, name=None ) from_config
@classmethodfrom_config( config )
get_config
get_config() Return the serializable config of the metric.
reset_state
reset_state() Reset all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
result() Compute the current metric value.
| Returns | |
|---|---|
| A scalar tensor, or a dictionary of scalar tensors. |
stateless_reset_state
stateless_reset_state() stateless_result
stateless_result( metric_variables ) stateless_update_state
stateless_update_state( metric_variables, *args, **kwargs ) update_state
update_state( *args, **kwargs ) Accumulate statistics for the metric.
__call__
__call__( *args, **kwargs ) Call self as a function.