tf.keras.metrics.Sum

TensorFlow 2 version View source on GitHub

Computes the (weighted) sum of the given values.

tf.keras.metrics.Sum(
    name='sum', dtype=None
)

For example, if values is [1, 3, 5, 7] then the sum is 16. If the weights were specified as [1, 1, 0, 0] then the sum would be 4.

This metric creates one variable, total, that is used to compute the sum of values. This is ultimately returned as sum.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Usage:

m = tf.keras.metrics.Sum()
m.update_state([1, 3, 5, 7])
print('Final result: ', m.result().numpy())  # Final result: 16.0

Usage with tf.keras API:

model = tf.keras.Model(inputs, outputs)
model.add_metric(tf.keras.metrics.Sum(name='sum_1')(outputs))
model.compile('sgd', loss='mse')

name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.

Methods

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

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

update_state(
    values, sample_weight=None
)

Accumulates statistics for computing the reduction metric.

For example, if values is [1, 3, 5, 7] and reduction=SUM_OVER_BATCH_SIZE, then the value of result() is 4. If the sample_weight is specified as [1, 1, 0, 0] then value of result() would be 2.

Args
values Per-example value.
sample_weight Optional weighting of each example. Defaults to 1.

Returns
Update op.