tf.keras.metrics.FalseNegatives

Calculates the number of false negatives.

If `sample_weight` is given, calculates the sum of the weights of false negatives. This metric creates one local variable, `accumulator` that is used to keep track of the number of false negatives.

If `sample_weight` is `None`, weights default to 1. Use `sample_weight` of 0 to mask values.

Usage:

````m = tf.keras.metrics.FalseNegatives()`
`_ = m.update_state([0, 1, 1, 1], [0, 1, 0, 0])`
`m.result().numpy()`
`2.0`
```
````m.reset_states()`
`_ = m.update_state([0, 1, 1, 1], [0, 1, 0, 0], sample_weight=[0, 0, 1, 0])`
`m.result().numpy()`
`1.0`
```

Usage with tf.keras API:

``````model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.FalseNegatives()])
``````

`thresholds` (Optional) Defaults to 0.5. A float value or a python list/tuple of float threshold values in [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is `true`, below is `false`). One metric value is generated for each threshold value.
`name` (Optional) string name of the metric instance.
`dtype` (Optional) data type of the metric result.

Methods

`reset_states`

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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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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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Accumulates the given confusion matrix condition statistics.

Args
`y_true` The ground truth values.
`y_pred` The predicted values.
`sample_weight` Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.

Returns
Update op.

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