Help protect the Great Barrier Reef with TensorFlow on Kaggle Join Challenge

tf.keras.metrics.Metric

TensorFlow 2 version View source on GitHub

Encapsulates metric logic and state.

Inherits From: Layer

Usage:

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

Usage with tf.keras 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.compat.v1.train.RMSPropOptimizer(0.01),
              loss=tf.keras.losses.categorical_crossentropy,
              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)
dataset = dataset.repeat()

model.fit(dataset, epochs=10, steps_per_epoch=30)

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 value 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_weights(sample_weight, values)
      values = tf.multiply(values, sample_weight)
    self.true_positives.assign_add(tf.reduce_sum(values))

  def result(self):
    return self.true_positives

Methods

add_weight

View source

Adds state variable. Only for use by subclasses.

reset_states

View source

Resets all of the metric state variables.

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

result

View source

Computes and returns the metric value tensor.

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

update_state

View source

Accumulates statistics for the metric.

Please use tf.config.experimental_run_functions_eagerly(True) to execute this function eagerly for debugging or profiling.

Args
*args

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