Initializer that generates tensors with a uniform distribution.
tf.random_uniform_initializer(
    minval=-0.05, maxval=0.05, seed=None
)
Initializers allow you to pre-specify an initialization strategy, encoded in
the Initializer object, without knowing the shape and dtype of the variable
being initialized.
Examples:
def make_variables(k, initializer):
  return (tf.Variable(initializer(shape=[k], dtype=tf.float32)),
          tf.Variable(initializer(shape=[k, k], dtype=tf.float32)))
v1, v2 = make_variables(3, tf.ones_initializer())
v1
<tf.Variable ... shape=(3,) ... numpy=array([1., 1., 1.], dtype=float32)>
v2
<tf.Variable ... shape=(3, 3) ... numpy=
array([[1., 1., 1.],
       [1., 1., 1.],
       [1., 1., 1.]], dtype=float32)>
make_variables(4, tf.random_uniform_initializer(minval=-1., maxval=1.))
(<tf.Variable...shape=(4,) dtype=float32...>, <tf.Variable...shape=(4, 4) ...
Args | 
minval
 | 
A python scalar or a scalar tensor. Lower bound of the range of
random values to generate (inclusive).
 | 
maxval
 | 
A python scalar or a scalar tensor. Upper bound of the range of
random values to generate (exclusive).
 | 
seed
 | 
A Python integer. Used to create random seeds. See
tf.random.set_seed for behavior.
 | 
Methods
from_config
View source
@classmethod
from_config(
    config
)
Instantiates an initializer from a configuration dictionary.
Example:
initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)
| Args | 
config
 | 
A Python dictionary.
It will typically be the output of get_config.
 | 
| Returns | 
| 
An Initializer instance.
 | 
get_config
View source
get_config()
Returns the configuration of the initializer as a JSON-serializable dict.
| Returns | 
| 
A JSON-serializable Python dict.
 | 
__call__
View source
__call__(
    shape,
    dtype=tf.dtypes.float32,
    **kwargs
)
Returns a tensor object initialized as specified by the initializer.
| Args | 
shape
 | 
Shape of the tensor.
 | 
dtype
 | 
Optional dtype of the tensor. Only floating point and integer
types are supported.
 | 
**kwargs
 | 
Additional keyword arguments.
 | 
| Raises | 
ValueError
 | 
If the dtype is not numeric.
 |