Initializer that generates tensors with constant values.

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.

tf.constant_initializer returns an object which when called returns a tensor populated with the value specified in the constructor. This value must be convertible to the requested dtype.

The argument value can be a scalar constant value, or a list of values. Scalars broadcast to whichever shape is requested from the initializer.

If value is a list, then the length of the list must be equal to the number of elements implied by the desired shape of the tensor. If the total number of elements in value is not equal to the number of elements required by the tensor shape, the initializer will raise a TypeError.


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.constant_initializer(2.))
<tf.Variable ... shape=(3,) ... numpy=array([2., 2., 2.], dtype=float32)>
<tf.Variable ... shape=(3, 3) ... numpy=
array([[2., 2., 2.],
       [2., 2., 2.],
       [2., 2., 2.]], dtype=float32)>
make_variables(4, tf.random_uniform_initializer(minval=-1., maxval=1.))
(<tf.Variable...shape=(4,) dtype=float32...>, <tf.Variable...shape=(4, 4) ...
value = [0, 1, 2, 3, 4, 5, 6, 7]
init = tf.constant_initializer(value)
# Fitting shape
tf.Variable(init(shape=[2, 4], dtype=tf.float32))
<tf.Variable ...
array([[0., 1., 2., 3.],
       [4., 5., 6., 7.]], dtype=float32)>
# Larger shape
tf.Variable(init(shape=[3, 4], dtype=tf.float32))
Traceback (most recent call last):

TypeError: ...value has 8 elements, shape is (3, 4) with 12 elements...
# Smaller shape
tf.Variable(init(shape=[2, 3], dtype=tf.float32))
Traceback (most recent call last):

TypeError: ...value has 8 elements, shape is (2, 3) with 6 elements...

value A Python scalar, list or tuple of values, or a N-dimensional numpy array. All elements of the initialized variable will be set to the corresponding value in the value argument.
support_partition If true, the initizer supports passing partition offset and partition shape arguments to variable creators. This is particularly useful when initializing sharded variables where each variable shard is initialized to a slice of constant initializer.

TypeError If the input value is not one of the expected types.



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Instantiates an initializer from a configuration dictionary.


initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)

config A Python dictionary. It will typically be the output of get_config.

An Initializer instance.


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Returns the configuration of the initializer as a JSON-serializable dict.

A JSON-serializable Python dict.


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Returns a tensor object initialized as specified by the initializer.

shape Shape of the tensor.
dtype Optional dtype of the tensor. If not provided the dtype of the tensor created will be the type of the inital value.
**kwargs Additional keyword arguments.

TypeError If the initializer cannot create a tensor of the requested dtype.