# 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`

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`大牛时代配资 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
``````

``````model = tf.keras.Model(inputs, 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.

## Methods

### `reset_states`

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``````reset_states()
``````

Resets all of the metric state variables.

### `result`

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``````result()
``````

### `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()`大牛时代配资 would be 2.

#### Args:

• `values`: Per-example value.
• `sample_weight`: Optional weighting of each example. Defaults to 1.

Update op.