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tf.keras.metrics.Sum

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Class Sum

Computes the (weighted) sum of the given values.

Aliases:

  • Class tf.compat.v1.keras.metrics.Sum
  • Class tf.compat.v2.keras.metrics.Sum
  • Class tf.compat.v2.metrics.Sum
  • Class tf.metrics.Sum

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')

__init__

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__init__(
    name='sum',
    dtype=None
)

Creates a Sum instance.

Args:

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

__new__

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__new__(
    cls,
    *args,
    **kwargs
)

Create and return a new object. See help(type) for accurate signature.

Methods

reset_states

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reset_states()

Resets all of the metric state variables.

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

result

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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

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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.