tf.keras.utils.to_categorical
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Converts a class vector (integers) to binary class matrix.
tf.keras.utils.to_categorical(
x, num_classes=None
)
Used in the notebooks
E.g. for use with categorical_crossentropy.
Args |
|---|
x
|
Array-like with class values to be converted into a matrix
(integers from 0 to num_classes - 1).
|
num_classes
|
Total number of classes. If None, this would be inferred
as max(x) + 1. Defaults to None.
|
Returns |
|---|
|
A binary matrix representation of the input as a NumPy array. The class
axis is placed last.
|
Example:
a = keras.utils.to_categorical([0, 1, 2, 3], num_classes=4)
print(a)
[[1. 0. 0. 0.]
[0. 1. 0. 0.]
[0. 0. 1. 0.]
[0. 0. 0. 1.]]
b = np.array([.9, .04, .03, .03,
.3, .45, .15, .13,
.04, .01, .94, .05,
.12, .21, .5, .17],
shape=[4, 4])
loss = keras.ops.categorical_crossentropy(a, b)
print(np.around(loss, 5))
[0.10536 0.82807 0.1011 1.77196]
loss = keras.ops.categorical_crossentropy(a, a)
print(np.around(loss, 5))
[0. 0. 0. 0.]
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Last updated 2024-06-07 UTC.
[null,null,["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.utils.to_categorical\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/utils/numerical_utils.py#L37-L92) |\n\nConverts a class vector (integers) to binary class matrix. \n\n tf.keras.utils.to_categorical(\n x, num_classes=None\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Implement Differential Privacy with TensorFlow Privacy](https://www.tensorflow.org/responsible_ai/privacy/tutorials/classification_privacy) - [Assess privacy risks with the TensorFlow Privacy Report](https://www.tensorflow.org/responsible_ai/privacy/tutorials/privacy_report) - [On-Device Training with TensorFlow Lite](https://www.tensorflow.org/lite/examples/on_device_training/overview) - [Human Pose Classification with MoveNet and TensorFlow Lite](https://www.tensorflow.org/lite/tutorials/pose_classification) - [Classifying CIFAR-10 with XLA](https://www.tensorflow.org/xla/tf2xla/tutorials/autoclustering_xla) |\n\nE.g. for use with `categorical_crossentropy`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------|----------------------------------------------------------------------------------------------------|\n| `x` | Array-like with class values to be converted into a matrix (integers from 0 to `num_classes - 1`). |\n| `num_classes` | Total number of classes. If `None`, this would be inferred as `max(x) + 1`. Defaults to `None`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A binary matrix representation of the input as a NumPy array. The class axis is placed last. ||\n\n\u003cbr /\u003e\n\n#### Example:\n\n a = keras.utils.to_categorical([0, 1, 2, 3], num_classes=4)\n print(a)\n [[1. 0. 0. 0.]\n [0. 1. 0. 0.]\n [0. 0. 1. 0.]\n [0. 0. 0. 1.]]\n\n b = np.array([.9, .04, .03, .03,\n .3, .45, .15, .13,\n .04, .01, .94, .05,\n .12, .21, .5, .17],\n shape=[4, 4])\n loss = keras.ops.categorical_crossentropy(a, b)\n print(np.around(loss, 5))\n [0.10536 0.82807 0.1011 1.77196]\n\n loss = keras.ops.categorical_crossentropy(a, a)\n print(np.around(loss, 5))\n [0. 0. 0. 0.]"]]
View source on GitHub