tf.random.uniform | TensorFlow v2.16.1 (original) (raw)

tf.random.uniform

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Outputs random values from a uniform distribution.

View aliases

Compat aliases for migration

tf.compat.v1.random.uniform, tf.compat.v1.random_uniform

tf.random.uniform(
    shape,
    minval=0,
    maxval=None,
    dtype=tf.dtypes.float32,
    seed=None,
    name=None
)

Used in the notebooks

Used in the guide	Used in the tutorials
Validating correctness & numerical equivalence tf.data: Build TensorFlow input pipelines Introduction to graphs and tf.function Logistic regression for binary classification with Core APIs Quickstart for the TensorFlow Core APIs	Customization basics: tensors and operations Parameter server training with ParameterServerStrategy Learned data compression DeepDream pix2pix: Image-to-image translation with a conditional GAN

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.

For floats, the default range is [0, 1). For ints, at least maxval must be specified explicitly.

In the integer case, the random integers are slightly biased unlessmaxval - minval is an exact power of two. The bias is small for values ofmaxval - minval significantly smaller than the range of the output (either2**32 or 2**64).

Examples:

tf.random.uniform(shape=[2]) <tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)> tf.random.uniform(shape=[], minval=-1., maxval=0.) <tf.Tensor: shape=(), dtype=float32, numpy=-...> tf.random.uniform(shape=[], minval=5, maxval=10, dtype=tf.int64) <tf.Tensor: shape=(), dtype=int64, numpy=...>

The seed argument produces a deterministic sequence of tensors across multiple calls. To repeat that sequence, use tf.random.set_seed:

tf.random.set_seed(5) tf.random.uniform(shape=[], maxval=3, dtype=tf.int32, seed=10) <tf.Tensor: shape=(), dtype=int32, numpy=2> tf.random.uniform(shape=[], maxval=3, dtype=tf.int32, seed=10) <tf.Tensor: shape=(), dtype=int32, numpy=0> tf.random.set_seed(5) tf.random.uniform(shape=[], maxval=3, dtype=tf.int32, seed=10) <tf.Tensor: shape=(), dtype=int32, numpy=2> tf.random.uniform(shape=[], maxval=3, dtype=tf.int32, seed=10) <tf.Tensor: shape=(), dtype=int32, numpy=0>

Without tf.random.set_seed but with a seed argument is specified, small changes to function graphs or previously executed operations will change the returned value. See tf.random.set_seed for details.

Args
shape	A 1-D integer Tensor or Python array. The shape of the output tensor.
minval	A Tensor or Python value of type dtype, broadcastable withshape (for integer types, broadcasting is not supported, so it needs to be a scalar). The lower bound on the range of random values to generate (inclusive). Defaults to 0.
maxval	A Tensor or Python value of type dtype, broadcastable withshape (for integer types, broadcasting is not supported, so it needs to be a scalar). The upper bound on the range of random values to generate (exclusive). Defaults to 1 if dtype is floating point.
dtype	The type of the output: float16, bfloat16, float32, float64,int32, or int64. Defaults to float32.
seed	A Python integer. Used in combination with tf.random.set_seed to create a reproducible sequence of tensors across multiple calls.
name	A name for the operation (optional).

Returns
A tensor of the specified shape filled with random uniform values.

Raises
ValueError	If dtype is integral and maxval is not specified.