tf.random.stateless

TensorFlow 1 version

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Outputs random values from a uniform distribution.

View aliases

Compat aliases for migration

See Migration guide for more details.

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
tf.data: Build TensorFlow input pipelines Introduction to graphs and tf.functions Better performance with tf.function Customize what happens in Model.fit Save and load Keras models	Transformer model for language understanding Customization basics: tensors and operations DeepDream Pix2Pix Data augmentation

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 unless maxval - minval is an exact power of two. The bias is small for values of maxval - minval significantly smaller than the range of the output (either 2**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 with `shape` (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 with `shape` (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`, `float32`, `float64`, `int32`, or `int64`.
`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.

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tf.random / stateless_uniform

View aliases

Used in the notebooks

Examples:

Args

Returns

Raises