tf.keras.metrics.sparse_categorical_crossentropy
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Computes the sparse categorical crossentropy loss.
tf.keras.metrics.sparse_categorical_crossentropy(
y_true, y_pred, from_logits=False, axis=-1, ignore_class=None
)
Standalone usage:
y_true = [1, 2]
y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
loss = tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred)
assert loss.shape == (2,)
loss.numpy()
array([0.0513, 2.303], dtype=float32)
y_true = [[[ 0, 2],
[-1, -1]],
[[ 0, 2],
[-1, -1]]]
y_pred = [[[[1.0, 0.0, 0.0], [0.0, 0.0, 1.0]],
[[0.2, 0.5, 0.3], [0.0, 1.0, 0.0]]],
[[[1.0, 0.0, 0.0], [0.0, 0.5, 0.5]],
[[0.2, 0.5, 0.3], [0.0, 1.0, 0.0]]]]
loss = tf.keras.losses.sparse_categorical_crossentropy(
y_true, y_pred, ignore_class=-1)
loss.numpy()
array([[[2.3841855e-07, 2.3841855e-07],
[0.0000000e+00, 0.0000000e+00]],
[[2.3841855e-07, 6.9314730e-01],
[0.0000000e+00, 0.0000000e+00]]], dtype=float32)
Args |
|---|
y_true
|
Ground truth values.
|
y_pred
|
The predicted values.
|
from_logits
|
Whether y_pred is expected to be a logits tensor. By
default, we assume that y_pred encodes a probability distribution.
|
axis
|
Defaults to -1. The dimension along which the entropy is
computed.
|
ignore_class
|
Optional integer. The ID of a class to be ignored during
loss computation. This is useful, for example, in segmentation
problems featuring a "void" class (commonly -1 or 255) in
segmentation maps. By default (ignore_class=None), all classes are
considered.
|
Returns |
|---|
|
Sparse categorical crossentropy loss value.
|
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.keras.metrics.sparse_categorical_crossentropy\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v2.14.0/keras/losses.py#L2403-L2460) |\n\nComputes the sparse categorical crossentropy loss.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.keras.losses.sparse_categorical_crossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/sparse_categorical_crossentropy), [`tf.losses.sparse_categorical_crossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/sparse_categorical_crossentropy), [`tf.metrics.sparse_categorical_crossentropy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/sparse_categorical_crossentropy)\n\n\u003cbr /\u003e\n\n tf.keras.metrics.sparse_categorical_crossentropy(\n y_true, y_pred, from_logits=False, axis=-1, ignore_class=None\n )\n\n#### Standalone usage:\n\n y_true = [1, 2]\n y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]\n loss = tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred)\n assert loss.shape == (2,)\n loss.numpy()\n array([0.0513, 2.303], dtype=float32)\n\n y_true = [[[ 0, 2],\n [-1, -1]],\n [[ 0, 2],\n [-1, -1]]]\n y_pred = [[[[1.0, 0.0, 0.0], [0.0, 0.0, 1.0]],\n [[0.2, 0.5, 0.3], [0.0, 1.0, 0.0]]],\n [[[1.0, 0.0, 0.0], [0.0, 0.5, 0.5]],\n [[0.2, 0.5, 0.3], [0.0, 1.0, 0.0]]]]\n loss = tf.keras.losses.sparse_categorical_crossentropy(\n y_true, y_pred, ignore_class=-1)\n loss.numpy()\n array([[[2.3841855e-07, 2.3841855e-07],\n [0.0000000e+00, 0.0000000e+00]],\n [[2.3841855e-07, 6.9314730e-01],\n [0.0000000e+00, 0.0000000e+00]]], dtype=float32)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `y_true` | Ground truth values. |\n| `y_pred` | The predicted values. |\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| `axis` | Defaults to -1. The dimension along which the entropy is computed. |\n| `ignore_class` | Optional integer. The ID of a class to be ignored during loss computation. This is useful, for example, in segmentation problems featuring a \"void\" class (commonly -1 or 255) in segmentation maps. By default (`ignore_class=None`), all classes are considered. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| Sparse categorical crossentropy loss value. ||\n\n\u003cbr /\u003e"]]
View source on GitHub