View source on GitHub |
Calculates the number of false positives.
tf.keras.metrics.FalsePositives( thresholds=None, name=None, dtype=None )
If sample_weight
is given, calculates the sum of the weights of false positives. This metric creates one local variable, accumulator
that is used to keep track of the number of false positives.
If sample_weight
is None
, weights default to 1. Use sample_weight
of 0 to mask values.
Args | |
---|---|
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. |
m = tf.keras.metrics.FalsePositives() m.update_state([0, 1, 0, 0], [0, 0, 1, 1]) m.result().numpy() 2.0
m.reset_states() m.update_state([0, 1, 0, 0], [0, 0, 1, 1], sample_weight=[0, 0, 1, 0]) m.result().numpy() 1.0
Usage with compile()
API:
model.compile(optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.FalsePositives()])
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( y_true, y_pred, sample_weight=None )
Accumulates the metric 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. |
© 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/r2.3/api_docs/python/tf/keras/metrics/FalsePositives