tf.zeros_initializer

Initializer that generates tensors initialized to 0.

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

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@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

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get_config()

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

Returns
A JSON-serializable Python dict.

__call__

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__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.