tf.ones_initializer

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Initializer that generates tensors initialized to 1.

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.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

__call__

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__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.

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.