Missed TensorFlow Dev Summit? Check out the video playlist.

# tf.keras.metrics.Sum

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.compile('sgd', loss='mse')
``````

#### Args:

• `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.

Update op.