TensorFlow 1 version | View source on GitHub |
Initializer that generates tensors initialized to 0.
Inherits From: Initializer
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.zeros_initializer())
v1
<tf.Variable ... shape=(3,) ... numpy=array([0., 0., 0.], dtype=float32)>
v2
<tf.Variable ... shape=(3, 3) ... numpy=
array([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]], 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
)
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. |
Raises | |
---|---|
ValuesError
|
If the dtype is not numeric or boolean. |