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Computes the (weighted) mean of the given values.

tf.keras.metrics.Mean( name='mean', dtype=None )

For example, if values is [1, 3, 5, 7] then the mean is 4. If the weights were specified as [1, 1, 0, 0] then the mean would be 2.

This metric creates two variables, `total`

and `count`

that are used to compute the average of `values`

. This average is ultimately returned as `mean`

which is an idempotent operation that simply divides `total`

by `count`

.

If `sample_weight`

is `None`

, weights default to 1. Use `sample_weight`

of 0 to mask values.

m = tf.keras.metrics.Mean() m.update_state([1, 3, 5, 7]) print('Final result: ', m.result().numpy()) # Final result: 4.0

Usage with tf.keras API:

model = tf.keras.Model(inputs, outputs) model.add_metric(tf.keras.metrics.Mean(name='mean_1')(outputs)) model.compile('sgd', loss='mse')

Args | |
---|---|

`name` | (Optional) string name of the metric instance. |

`dtype` | (Optional) data type of the metric result. |

`reset_states`

reset_states()

Resets all of the metric state variables.

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

`result`

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`

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. |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/metrics/Mean