def multinomial(logits, num_samples, seed=None, name=None):
"""Draws samples from a multinomial distribution.
Example:
```python
# samples has shape [1, 5], where each value is either 0 or 1 with equal
# probability.
samples = tf.multinomial(tf.log([[10., 10.]]), 5)
```
Args:
logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice
`[i, :]` represents the unnormalized log-probabilities for all classes.
num_samples: 0-D. Number of independent samples to draw for each row slice.
seed: A Python integer. Used to create a random seed for the distribution.
See
@{tf.set_random_seed}
for behavior.
name: Optional name for the operation.
Returns:
The drawn samples of shape `[batch_size, num_samples]`.
"""
with ops.name_scope(name, "multinomial", [logits]):
logits = ops.convert_to_tensor(logits, name="logits")
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops.multinomial(
logits, num_samples, seed=seed1, seed2=seed2)
def multinomial(logits, num_samples, seed=None, name=None, output_dtype=None):
"""Draws samples from a multinomial distribution.
Example:
```python
# samples has shape [1, 5], where each value is either 0 or 1 with equal
# probability.
samples = tf.multinomial(tf.log([[10., 10.]]), 5)
```
Args:
logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice
`[i, :]` represents the unnormalized log-probabilities for all classes.
num_samples: 0-D. Number of independent samples to draw for each row slice.
seed: A Python integer. Used to create a random seed for the distribution.
See
@{tf.set_random_seed}
for behavior.
name: Optional name for the operation.
output_dtype: integer type to use for the output. Defaults to int64.
Returns:
The drawn samples of shape `[batch_size, num_samples]`.
"""
with ops.name_scope(name, "multinomial", [logits]):
logits = ops.convert_to_tensor(logits, name="logits")
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops.multinomial(logits,
num_samples,
seed=seed1,
seed2=seed2,
output_dtype=output_dtype)
def multinomial(logits, num_samples, seed=None, name=None):
"""Draws samples from a multinomial distribution.
Example:
samples = tf.multinomial(tf.log([[0.5, 0.5]]), 10)
# samples has shape [1, 10], where each value is either 0 or 1.
samples = tf.multinomial([[1, -1, -1]], 10)
# samples is equivalent to tf.zeros([1, 10], dtype=tf.int64).
Args:
logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice
`[i, :]` represents the unnormalized log probabilities for all classes.
num_samples: 0-D. Number of independent samples to draw for each row slice.
seed: A Python integer. Used to create a random seed for the distribution.
See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: Optional name for the operation.
Returns:
The drawn samples of shape `[batch_size, num_samples]`.
"""
with ops.op_scope([logits], name, "multinomial"):
logits = ops.convert_to_tensor(logits, name="logits")
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops.multinomial(logits, num_samples, seed=seed1,
seed2=seed2)
def multinomial(logits, num_samples, seed=None, name=None):
"""Draws samples from a multinomial distribution.
Example:
samples = tf.multinomial(tf.log([[0.5, 0.5]]), 10)
# samples has shape [1, 10], where each value is either 0 or 1.
samples = tf.multinomial([[1, -1, -1]], 10)
# samples is equivalent to tf.zeros([1, 10], dtype=tf.int64).
Args:
logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice
`[i, :]` represents the unnormalized log probabilities for all classes.
num_samples: 0-D. Number of independent samples to draw for each row slice.
seed: A Python integer. Used to create a random seed for the distribution.
See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: Optional name for the operation.
Returns:
The drawn samples of shape `[batch_size, num_samples]`.
"""
with ops.op_scope([logits], name, "multinomial"):
logits = ops.convert_to_tensor(logits, name="logits")
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops.multinomial(logits,
num_samples,
seed=seed1,
seed2=seed2)
def multinomial_categorical_impl(logits, num_samples, dtype, seed):
"""Implementation for random.categorical (v1) and random.categorical (v2)."""
logits = ops.convert_to_tensor(logits, name="logits")
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops.multinomial(logits,
num_samples,
seed=seed1,
seed2=seed2,
output_dtype=dtype)
def multinomial_categorical_impl(logits, num_samples, dtype, seed):
"""Implementation for random.categorical (v1) and random.categorical (v2)."""
logits = ops.convert_to_tensor(logits, name="logits")
dtype = dtypes.as_dtype(dtype) if dtype else dtypes.int64
accepted_dtypes = (dtypes.int32, dtypes.int64)
if dtype not in accepted_dtypes:
raise ValueError(
f"Argument `dtype` got invalid value {dtype}. Accepted dtypes are "
f"{accepted_dtypes}.")
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops.multinomial(logits,
num_samples,
seed=seed1,
seed2=seed2,
output_dtype=dtype)
def multinomial_categorical_impl(logits, num_samples, dtype, seed):
"""Implementation for random.multinomial (v1) and random.categorical (v2)."""
logits = ops.convert_to_tensor(logits, name="logits")
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops.multinomial(
logits, num_samples, seed=seed1, seed2=seed2, output_dtype=dtype)