tf.ones_initializer | TensorFlow v2.16.1 (original) (raw)
tf.ones_initializer
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Initializer that generates tensors initialized to 1.
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) ...
Methods
from_config
@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
get_config()
Returns the configuration of the initializer as a JSON-serializable dict.
| Returns |
|---|
| A JSON-serializable Python dict. |
__call__
__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 numeric or boolean dtypes are supported. |
| **kwargs | Additional keyword arguments. |
| Raises | |
|---|---|
| ValuesError | If the dtype is not numeric or boolean. |