tf.zeros_initializer

Initializer that generates tensors initialized to 0.

Used in the notebooks

Used in the guide
Making new Layers and Models via subclassing

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`

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 numeric or boolean dtypes are supported.
`**kwargs`	Additional keyword arguments.

Raises
`ValuesError`	If the dtype is not numeric or boolean.