tf.keras.losses.binary_crossentropy
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Computes the binary crossentropy loss.
tf.keras.losses.binary_crossentropy(
y_true, y_pred, from_logits=False, label_smoothing=0
)
Standalone usage:
y_true = [[0, 1], [0, 0]]
y_pred = [[0.6, 0.4], [0.4, 0.6]]
loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)
assert loss.shape == (2,)
loss.numpy()
array([0.916 , 0.714], dtype=float32)
Args |
|---|
y_true
|
Ground truth values. shape = [batch_size, d0, .. dN].
|
y_pred
|
The predicted values. shape = [batch_size, d0, .. dN].
|
from_logits
|
Whether y_pred is expected to be a logits tensor. By default,
we assume that y_pred encodes a probability distribution.
|
label_smoothing
|
Float in [0, 1]. If > 0 then smooth the labels.
|
Returns |
|---|
Binary crossentropy loss value. shape = [batch_size, d0, .. dN-1].
|
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.losses.binary_crossentropy\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/keras/losses/binary_crossentropy) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.3.0/tensorflow/python/keras/losses.py#L1570-L1605) |\n\nComputes the binary crossentropy loss.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.keras.metrics.binary_crossentropy`](/api_docs/python/tf/keras/losses/binary_crossentropy), [`tf.losses.binary_crossentropy`](/api_docs/python/tf/keras/losses/binary_crossentropy), [`tf.metrics.binary_crossentropy`](/api_docs/python/tf/keras/losses/binary_crossentropy)\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.keras.losses.binary_crossentropy`](/api_docs/python/tf/keras/losses/binary_crossentropy), [`tf.compat.v1.keras.metrics.binary_crossentropy`](/api_docs/python/tf/keras/losses/binary_crossentropy)\n\n\u003cbr /\u003e\n\n tf.keras.losses.binary_crossentropy(\n y_true, y_pred, from_logits=False, label_smoothing=0\n )\n\n#### Standalone usage:\n\n y_true = [[0, 1], [0, 0]]\n y_pred = [[0.6, 0.4], [0.4, 0.6]]\n loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)\n assert loss.shape == (2,)\n loss.numpy()\n array([0.916 , 0.714], dtype=float32)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------|-----------------------------------------------------------------------------------------------------------------------------|\n| `y_true` | Ground truth values. shape = `[batch_size, d0, .. dN]`. |\n| `y_pred` | The predicted values. shape = `[batch_size, d0, .. dN]`. |\n| `from_logits` | Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. |\n| `label_smoothing` | Float in \\[0, 1\\]. If \\\u003e `0` then smooth the labels. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| Binary crossentropy loss value. shape = `[batch_size, d0, .. dN-1]`. ||\n\n\u003cbr /\u003e"]]
TensorFlow 1 version
View source on GitHub