tf.keras.metrics.Metric

Encapsulates metric logic and state.

Inherits From: Layer, Module

tf.keras.metrics.Metric(
    name=None, dtype=None, **kwargs
)

name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.
**kwargs Additional layer keywords arguments.

Standalone usage:

m = SomeMetric(...)
for input in ...:
  m.update_state(input)
print('Final result: ', m.result().numpy())

Usage with compile() API:

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))

model.compile(optimizer=tf.keras.optimizers.RMSprop(0.01),
              loss=tf.keras.losses.CategoricalCrossentropy(),
              metrics=[tf.keras.metrics.CategoricalAccuracy()])

data = np.random.random((1000, 32))
labels = np.random.random((1000, 10))

dataset = tf.data.Dataset.from_tensor_slices((data, labels))
dataset = dataset.batch(32)

model.fit(dataset, epochs=10)

To be implemented by subclasses:

  • __init__(): All state variables should be created in this method by calling self.add_weight() like: self.var = self.add_weight(...)
  • update_state(): Has all updates to the state variables like: self.var.assign_add(...).
  • 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(tf.keras.metrics.Metric):

  def __init__(self, name='binary_true_positives', **kwargs):
    super(BinaryTruePositives, self).__init__(name=name, **kwargs)
    self.true_positives = self.add_weight(name='tp', initializer='zeros')

  def update_state(self, y_true, y_pred, sample_weight=None):
    y_true = tf.cast(y_true, tf.bool)
    y_pred = tf.cast(y_pred, tf.bool)

    values = tf.logical_and(tf.equal(y_true, True), tf.equal(y_pred, True))
    values = tf.cast(values, self.dtype)
    if sample_weight is not None:
      sample_weight = tf.cast(sample_weight, self.dtype)
      sample_weight = tf.broadcast_to(sample_weight, values.shape)
      values = tf.multiply(values, sample_weight)
    self.true_positives.assign_add(tf.reduce_sum(values))

  def result(self):
    return self.true_positives

Methods

merge_state

View source

merge_state(
    metrics
)

Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:

m1 = tf.keras.metrics.Accuracy()
_ = m1.update_state([[1], [2]], [[0], [2]])
 
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([[3], [4]], [[3], [4]])
 
m2.merge_state([m1])
m2.result().numpy()
0.75

Args
metrics an iterable of metrics. The metrics must have compatible state.

Raises
ValueError If the provided iterable does not contain metrics matching the metric's required specifications.

reset_state

View source

reset_state()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

View source

@abc.abstractmethod
result()

Computes and returns the scalar metric value tensor or a dict of scalars.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

Returns
A scalar tensor, or a dictionary of scalar tensors.

update_state

View source

@abc.abstractmethod
update_state(
    *args, **kwargs
)

Accumulates statistics for the metric.

Args
*args

**kwargs A mini-batch of inputs to the Metric.