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from keras.src import backend
from keras.src.api_export import keras_export
@keras_export("keras.random.normal")
def normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
"""Draw random samples from a normal (Gaussian) distribution.
Args:
shape: The shape of the random values to generate.
mean: Float, defaults to 0. Mean of the random values to generate.
stddev: Float, defaults to 1. Standard deviation of the random values
to generate.
dtype: Optional dtype of the tensor. Only floating point types are
supported. If not specified, `keras.config.floatx()` is used,
which defaults to `float32` unless you configured it otherwise (via
`keras.config.set_floatx(float_dtype)`).
seed: Optional Python integer or instance of
`keras.random.SeedGenerator`.
By default, the `seed` argument is `None`, and an internal global
`keras.random.SeedGenerator` is used. The `seed` argument can be
used to ensure deterministic (repeatable) random number generation.
Note that passing an integer as the `seed` value will produce the
same random values for each call. To generate different random
values for repeated calls, an instance of
`keras.random.SeedGenerator` must be provided as the `seed` value.
Remark concerning the JAX backend: When tracing functions with the
JAX backend the global `keras.random.SeedGenerator` is not
supported. Therefore, during tracing the default value `seed=None`
will produce an error, and a `seed` argument must be provided.
"""
return backend.random.normal(
shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed
)
@keras_export("keras.random.categorical")
def categorical(logits, num_samples, dtype="int32", seed=None):
"""Draws samples from a categorical distribution.
This function takes as input `logits`, a 2-D input tensor with shape
(batch_size, num_classes). Each row of the input represents a categorical
distribution, with each column index containing the log-probability for a
given class.
The function will output a 2-D tensor with shape (batch_size, num_samples),
where each row contains samples from the corresponding row in `logits`.
Each column index contains an independent samples drawn from the input
distribution.
Args:
logits: 2-D Tensor with shape (batch_size, num_classes). Each row
should define a categorical distribution with the unnormalized
log-probabilities for all classes.
num_samples: Int, the number of independent samples to draw for each
row of the input. This will be the second dimension of the output
tensor's shape.
dtype: Optional dtype of the output tensor.
seed: Optional Python integer or instance of
`keras.random.SeedGenerator`.
By default, the `seed` argument is `None`, and an internal global
`keras.random.SeedGenerator` is used. The `seed` argument can be
used to ensure deterministic (repeatable) random number generation.
Note that passing an integer as the `seed` value will produce the
same random values for each call. To generate different random
values for repeated calls, an instance of
`keras.random.SeedGenerator` must be provided as the `seed` value.
Remark concerning the JAX backend: When tracing functions with the
JAX backend the global `keras.random.SeedGenerator` is not
supported. Therefore, during tracing the default value seed=None
will produce an error, and a `seed` argument must be provided.
Returns:
A 2-D tensor with (batch_size, num_samples).
"""
logits_shape = list(backend.convert_to_tensor(logits).shape)
if len(logits_shape) != 2:
raise ValueError(
"`logits` should be a 2-D tensor with shape "
f"[batch_size, num_classes]. Received: logits={logits}"
)
return backend.random.categorical(
logits, num_samples, dtype=dtype, seed=seed
)
@keras_export("keras.random.uniform")
def uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
"""Draw samples from a uniform distribution.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range,
while the upper bound `maxval` is excluded.
`dtype` must be a floating point type, the default range is `[0, 1)`.
Args:
shape: The shape of the random values to generate.
minval: Float, defaults to 0. Lower bound of the range of
random values to generate (inclusive).
maxval: Float, defaults to 1. Upper bound of the range of
random values to generate (exclusive).
dtype: Optional dtype of the tensor. Only floating point types are
supported. If not specified, `keras.config.floatx()` is used,
which defaults to `float32` unless you configured it otherwise (via
`keras.config.set_floatx(float_dtype)`)
seed: Optional Python integer or instance of
`keras.random.SeedGenerator`.
By default, the `seed` argument is `None`, and an internal global
`keras.random.SeedGenerator` is used. The `seed` argument can be
used to ensure deterministic (repeatable) random number generation.
Note that passing an integer as the `seed` value will produce the
same random values for each call. To generate different random
values for repeated calls, an instance of
`keras.random.SeedGenerator` must be provided as the `seed` value.
Remark concerning the JAX backend: When tracing functions with the
JAX backend the global `keras.random.SeedGenerator` is not
supported. Therefore, during tracing the default value seed=None
will produce an error, and a `seed` argument must be provided.
"""
if dtype and not backend.is_float_dtype(dtype):
raise ValueError(
"`keras.random.uniform` requires a floating point `dtype`. "
f"Received: dtype={dtype} "
)
return backend.random.uniform(
shape, minval=minval, maxval=maxval, dtype=dtype, seed=seed
)
@keras_export("keras.random.randint")
def randint(shape, minval, maxval, dtype="int32", seed=None):
"""Draw random integers from a uniform distribution.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range,
while the upper bound `maxval` is excluded.
`dtype` must be an integer type.
Args:
shape: The shape of the random values to generate.
minval: Float, defaults to 0. Lower bound of the range of
random values to generate (inclusive).
maxval: Float, defaults to 1. Upper bound of the range of
random values to generate (exclusive).
dtype: Optional dtype of the tensor. Only integer types are
supported. If not specified, `keras.config.floatx()` is used,
which defaults to `float32` unless you configured it otherwise (via
`keras.config.set_floatx(float_dtype)`)
seed: Optional Python integer or instance of
`keras.random.SeedGenerator`.
By default, the `seed` argument is `None`, and an internal global
`keras.random.SeedGenerator` is used. The `seed` argument can be
used to ensure deterministic (repeatable) random number generation.
Note that passing an integer as the `seed` value will produce the
same random values for each call. To generate different random
values for repeated calls, an instance of
`keras.random.SeedGenerator` must be provided as the `seed` value.
Remark concerning the JAX backend: When tracing functions with the
JAX backend the global `keras.random.SeedGenerator` is not
supported. Therefore, during tracing the default value seed=None
will produce an error, and a `seed` argument must be provided.
"""
if dtype and not backend.is_int_dtype(dtype):
raise ValueError(
"`keras.random.randint` requires an integer `dtype`. "
f"Received: dtype={dtype} "
)
return backend.random.randint(
shape, minval=minval, maxval=maxval, dtype=dtype, seed=seed
)
@keras_export("keras.random.truncated_normal")
def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
"""Draw samples from a truncated normal distribution.
The values are drawn from a normal distribution with specified mean and
standard deviation, discarding and re-drawing any samples that are more
than two standard deviations from the mean.
Args:
shape: The shape of the random values to generate.
mean: Float, defaults to 0. Mean of the random values to generate.
stddev: Float, defaults to 1. Standard deviation of the random values
to generate.
dtype: Optional dtype of the tensor. Only floating point types are
supported. If not specified, `keras.config.floatx()` is used,
which defaults to `float32` unless you configured it otherwise (via
`keras.config.set_floatx(float_dtype)`)
seed: Optional Python integer or instance of
`keras.random.SeedGenerator`.
By default, the `seed` argument is `None`, and an internal global
`keras.random.SeedGenerator` is used. The `seed` argument can be
used to ensure deterministic (repeatable) random number generation.
Note that passing an integer as the `seed` value will produce the
same random values for each call. To generate different random
values for repeated calls, an instance of
`keras.random.SeedGenerator` must be provided as the `seed` value.
Remark concerning the JAX backend: When tracing functions with the
JAX backend the global `keras.random.SeedGenerator` is not
supported. Therefore, during tracing the default value seed=None
will produce an error, and a `seed` argument must be provided.
"""
return backend.random.truncated_normal(
shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed
)
@keras_export("keras.random.dropout")
def dropout(inputs, rate, noise_shape=None, seed=None):
return backend.random.dropout(
inputs, rate, noise_shape=noise_shape, seed=seed
)
@keras_export("keras.random.shuffle")
def shuffle(x, axis=0, seed=None):
"""Shuffle the elements of a tensor uniformly at random along an axis.
Args:
x: The tensor to be shuffled.
axis: An integer specifying the axis along which to shuffle. Defaults to
`0`.
seed: Optional Python integer or instance of
`keras.random.SeedGenerator`.
By default, the `seed` argument is `None`, and an internal global
`keras.random.SeedGenerator` is used. The `seed` argument can be
used to ensure deterministic (repeatable) random number generation.
Note that passing an integer as the `seed` value will produce the
same random values for each call. To generate different random
values for repeated calls, an instance of
`keras.random.SeedGenerator` must be provided as the `seed` value.
Remark concerning the JAX backend: When tracing functions with the
JAX backend the global `keras.random.SeedGenerator` is not
supported. Therefore, during tracing the default value seed=None
will produce an error, and a `seed` argument must be provided.
"""
return backend.random.shuffle(x, axis=axis, seed=seed)
@keras_export("keras.random.gamma")
def gamma(shape, alpha, dtype=None, seed=None):
"""Draw random samples from the Gamma distribution.
Args:
shape: The shape of the random values to generate.
alpha: Float, the parameter of the distribution.
dtype: Optional dtype of the tensor. Only floating point types are
supported. If not specified, `keras.config.floatx()` is used,
which defaults to `float32` unless you configured it otherwise (via
`keras.config.set_floatx(float_dtype)`).
seed: Optional Python integer or instance of
`keras.random.SeedGenerator`.
By default, the `seed` argument is `None`, and an internal global
`keras.random.SeedGenerator` is used. The `seed` argument can be
used to ensure deterministic (repeatable) random number generation.
Note that passing an integer as the `seed` value will produce the
same random values for each call. To generate different random
values for repeated calls, an instance of
`keras.random.SeedGenerator` must be provided as the `seed` value.
Remark concerning the JAX backend: When tracing functions with the
JAX backend the global `keras.random.SeedGenerator` is not
supported. Therefore, during tracing the default value seed=None
will produce an error, and a `seed` argument must be provided.
"""
return backend.random.gamma(shape, alpha=alpha, dtype=dtype, seed=seed)
@keras_export("keras.random.binomial")
def binomial(shape, counts, probabilities, dtype=None, seed=None):
"""Draw samples from a Binomial distribution.
The values are drawn from a Binomial distribution with
specified trial count and probability of success.
Args:
shape: The shape of the random values to generate.
counts: A number or array of numbers representing the
number of trials. It must be broadcastable with `probabilities`.
probabilities: A float or array of floats representing the
probability of success of an individual event.
It must be broadcastable with `counts`.
dtype: Optional dtype of the tensor. Only floating point types are
supported. If not specified, `keras.config.floatx()` is used,
which defaults to `float32` unless you configured it otherwise (via
`keras.config.set_floatx(float_dtype)`).
seed: Optional Python integer or instance of
`keras.random.SeedGenerator`.
By default, the `seed` argument is `None`, and an internal global
`keras.random.SeedGenerator` is used. The `seed` argument can be
used to ensure deterministic (repeatable) random number generation.
Note that passing an integer as the `seed` value will produce the
same random values for each call. To generate different random
values for repeated calls, an instance of
`keras.random.SeedGenerator` must be provided as the `seed` value.
Remark concerning the JAX backend: When tracing functions with the
JAX backend the global `keras.random.SeedGenerator` is not
supported. Therefore, during tracing the default value seed=None
will produce an error, and a `seed` argument must be provided.
"""
return backend.random.binomial(
shape,
counts=counts,
probabilities=probabilities,
dtype=dtype,
seed=seed,
)
@keras_export("keras.random.beta")
def beta(shape, alpha, beta, dtype=None, seed=None):
"""Draw samples from a Beta distribution.
The values are drawn from a Beta distribution parametrized
by alpha and beta.
Args:
shape: The shape of the random values to generate.
alpha: Float or an array of floats representing the first
parameter alpha. Must be broadcastable with `beta` and `shape`.
beta: Float or an array of floats representing the second
parameter beta. Must be broadcastable with `alpha` and `shape`.
dtype: Optional dtype of the tensor. Only floating point types are
supported. If not specified, `keras.config.floatx()` is used,
which defaults to `float32` unless you configured it otherwise (via
`keras.config.set_floatx(float_dtype)`).
seed: Optional Python integer or instance of
`keras.random.SeedGenerator`.
By default, the `seed` argument is `None`, and an internal global
`keras.random.SeedGenerator` is used. The `seed` argument can be
used to ensure deterministic (repeatable) random number generation.
Note that passing an integer as the `seed` value will produce the
same random values for each call. To generate different random
values for repeated calls, an instance of
`keras.random.SeedGenerator` must be provided as the `seed` value.
Remark concerning the JAX backend: When tracing functions with the
JAX backend the global `keras.random.SeedGenerator` is not
supported. Therefore, during tracing the default value seed=None
will produce an error, and a `seed` argument must be provided.
"""
return backend.random.beta(
shape=shape, alpha=alpha, beta=beta, dtype=dtype, seed=seed
)
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