TensorFlow 1 version | View source on GitHub |
Computes the crossentropy metric between the labels and predictions.
tf.keras.metrics.BinaryCrossentropy(
name='binary_crossentropy', dtype=None, from_logits=False, label_smoothing=0
)
This is the crossentropy metric class to be used when there are only two label classes (0 and 1).
Usage:
m = tf.keras.metrics.BinaryCrossentropy()
m.update_state([1., 0., 1., 0.], [1., 1., 1., 0.])
# EPSILON = 1e-7, y = y_true, y` = y_pred, Y_MAX = 0.9999999
# y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON)
# y` = [Y_MAX, Y_MAX, Y_MAX, EPSILON]
# Metric = -(y log(y` + EPSILON) + (1 - y) log(1 - y` + EPSILON))
# = [-log(Y_MAX + EPSILON), -log(1 - Y_MAX + EPSILON),
# -log(Y_MAX + EPSILON), -log(1)]
# = [(0 + 15.33) / 2, (0 + 0) / 2]
# Reduced metric = 7.665 / 2
print('Final result: ', m.result().numpy()) # Final result: 3.833
Usage with tf.keras API:
model = tf.keras.Model(inputs, outputs)
model.compile(
'sgd',
loss='mse',
metrics=[tf.keras.metrics.BinaryCrossentropy()])
Args | |
---|---|
name
|
(Optional) string name of the metric instance. |
dtype
|
(Optional) data type of the metric result. |
from_logits
|
(Optional )Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution. |
label_smoothing
|
(Optional) Float in [0, 1]. When > 0, label values are
smoothed, meaning the confidence on label values are relaxed.
e.g. label_smoothing=0.2 means that we will use a value of 0.1 for
label 0 and 0.9 for label 1 "
|
Methods
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 metric statistics.
y_true
and y_pred
should have the same shape.
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. |