tf.raw_ops.Multinomial
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Draws samples from a multinomial distribution.
View aliases
Compat aliases for migration
See
Migration guide for
more details.
tf.compat.v1.raw_ops.Multinomial
tf.raw_ops.Multinomial(
logits,
num_samples,
seed=0,
seed2=0,
output_dtype=tf.dtypes.int64,
name=None
)
Args |
|---|
logits
|
A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
2-D Tensor with shape [batch_size, num_classes]. Each slice [i, :]
represents the unnormalized log probabilities for all classes.
|
num_samples
|
A Tensor of type int32.
0-D. Number of independent samples to draw for each row slice.
|
seed
|
An optional int. Defaults to 0.
If either seed or seed2 is set to be non-zero, the internal random number
generator is seeded by the given seed. Otherwise, a random seed is used.
|
seed2
|
An optional int. Defaults to 0.
A second seed to avoid seed collision.
|
output_dtype
|
An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of type output_dtype.
|
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Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.Multinomial\n\n\u003cbr /\u003e\n\nDraws samples from a multinomial distribution.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.Multinomial`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/Multinomial)\n\n\u003cbr /\u003e\n\n tf.raw_ops.Multinomial(\n logits,\n num_samples,\n seed=0,\n seed2=0,\n output_dtype=../../tf/dtypes#int64,\n name=None\n )\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `logits` | A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` represents the unnormalized log probabilities for all classes. |\n| `num_samples` | A `Tensor` of type `int32`. 0-D. Number of independent samples to draw for each row slice. |\n| `seed` | An optional `int`. Defaults to `0`. If either seed or seed2 is set to be non-zero, the internal random number generator is seeded by the given seed. Otherwise, a random seed is used. |\n| `seed2` | An optional `int`. Defaults to `0`. A second seed to avoid seed collision. |\n| `output_dtype` | An optional [`tf.DType`](../../tf/dtypes/DType) from: `tf.int32, tf.int64`. Defaults to [`tf.int64`](../../tf#int64). |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor` of type `output_dtype`. ||\n\n\u003cbr /\u003e"]]
