Base Metric class
Metric class
keras.metrics.Metric(dtype=None, name=None)
Encapsulates metric logic and state.
Arguments
- 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