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Draw samples from a uniform distribution.
tf.keras.random.uniform(
shape, minval=0.0, maxval=1.0, dtype=None, seed=None
)
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.
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