tf.keras.metrics.Metric     View source on GitHub    Encapsulates metric logic and state. Inherits From: Layer, Module  View aliases  Main aliases 
tf.metrics.Metric Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.metrics.Metric  
tf.keras.metrics.Metric(
    name=None, dtype=None, **kwargs
)

 


 Args
  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 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_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 add_weight View source 
add_weight(
    name, shape=(), aggregation=tf.compat.v1.VariableAggregation.SUM,
    synchronization=tf.VariableSynchronization.ON_READ, initializer=None, dtype=None
)
 Adds state variable. Only for use by subclasses. reset_states View source 
reset_states()
 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 metric value tensor. Result computation is an idempotent operation that simply calculates the metric value using the state variables. update_state View source 
@abc.abstractmethod
update_state(
    *args, **kwargs
)
 Accumulates statistics for the metric. 
Note: This function is executed as a graph function in graph mode. This means: a) Operations on the same resource are executed in textual order. This should make it easier to do things like add the updated value of a variable to another, for example. b) You don't need to worry about collecting the update ops to execute. All update ops added to the graph by this function will be executed. As a result, code should generally work the same way with graph or eager execution.

 


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