tf.keras.metrics.Sum
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Compute the (weighted) sum of the given values.
Inherits From: Metric
tf.keras.metrics.Sum(
name='sum', dtype=None
)
For example, if values is [1, 3, 5, 7] then their sum is 16.
If sample_weight was specified as [1, 1, 0, 0] then the sum would be 4.
This metric creates one variable, total.
This is ultimately returned as the sum value.
Args |
|---|
name
|
(Optional) string name of the metric instance.
|
dtype
|
(Optional) data type of the metric result.
|
Example:
m = metrics.Sum()
m.update_state([1, 3, 5, 7])
m.result()
16.0
m = metrics.Sum()
m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
m.result()
4.0
Attributes |
|---|
dtype
|
|
variables
|
|
Methods
add_variable
View source
add_variable(
shape, initializer, dtype=None, aggregation='sum', name=None
)
add_weight
View source
add_weight(
shape=(), initializer=None, dtype=None, name=None
)
from_config
View source
@classmethod
from_config(
config
)
get_config
View source
get_config()
Return the serializable config of the metric.
reset_state
View source
reset_state()
Reset all of the metric state variables.
This function is called between epochs/steps,
when a metric is evaluated during training.
result
View source
result()
Compute the current metric value.
| Returns |
|---|
|
A scalar tensor, or a dictionary of scalar tensors.
|
stateless_reset_state
View source
stateless_reset_state()
stateless_result
View source
stateless_result(
metric_variables
)
stateless_update_state
View source
stateless_update_state(
metric_variables, *args, **kwargs
)
update_state
View source
update_state(
values, sample_weight=None
)
Accumulate statistics for the metric.
__call__
View source
__call__(
*args, **kwargs
)
Call self as a function.
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Last updated 2024-06-07 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.metrics.Sum\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/reduction_metrics.py#L45-L91) |\n\nCompute the (weighted) sum of the given values.\n\nInherits From: [`Metric`](../../../tf/keras/Metric) \n\n tf.keras.metrics.Sum(\n name='sum', dtype=None\n )\n\nFor example, if `values` is `[1, 3, 5, 7]` then their sum is 16.\nIf `sample_weight` was specified as `[1, 1, 0, 0]` then the sum would be 4.\n\nThis metric creates one variable, `total`.\nThis is ultimately returned as the sum value.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------|------------------------------------------------|\n| `name` | (Optional) string name of the metric instance. |\n| `dtype` | (Optional) data type of the metric result. |\n\n\u003cbr /\u003e\n\n#### Example:\n\n m = metrics.Sum()\n m.update_state([1, 3, 5, 7])\n m.result()\n 16.0\n\n m = metrics.Sum()\n m.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])\n m.result()\n 4.0\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Attributes ---------- ||\n|-------------|---------------|\n| `dtype` | \u003cbr /\u003e \u003cbr /\u003e |\n| `variables` | \u003cbr /\u003e \u003cbr /\u003e |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `add_variable`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L186-L202) \n\n add_variable(\n shape, initializer, dtype=None, aggregation='sum', name=None\n )\n\n### `add_weight`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L204-L208) \n\n add_weight(\n shape=(), initializer=None, dtype=None, name=None\n )\n\n### `from_config`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L226-L228) \n\n @classmethod\n from_config(\n config\n )\n\n### `get_config`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L222-L224) \n\n get_config()\n\nReturn the serializable config of the metric.\n\n### `reset_state`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/reduction_metrics.py#L87-L88) \n\n reset_state()\n\nReset all of the metric state variables.\n\nThis function is called between epochs/steps,\nwhen a metric is evaluated during training.\n\n### `result`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/reduction_metrics.py#L90-L91) \n\n result()\n\nCompute the current metric value.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A scalar tensor, or a dictionary of scalar tensors. ||\n\n\u003cbr /\u003e\n\n### `stateless_reset_state`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L164-L177) \n\n stateless_reset_state()\n\n### `stateless_result`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L148-L162) \n\n stateless_result(\n metric_variables\n )\n\n### `stateless_update_state`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L115-L138) \n\n stateless_update_state(\n metric_variables, *args, **kwargs\n )\n\n### `update_state`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/reduction_metrics.py#L81-L85) \n\n update_state(\n values, sample_weight=None\n )\n\nAccumulate statistics for the metric.\n\n### `__call__`\n\n[View source](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/metrics/metric.py#L217-L220) \n\n __call__(\n *args, **kwargs\n )\n\nCall self as a function."]]
View source on GitHub