tf.keras.initializers.Identity

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Initializer that generates the identity matrix.

Inherits From: Initializer

tf.keras.initializers.Identity(
    gain=1.0
)

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.

Only usable for generating 2D matrices.

Examples:

def make_variable(k, initializer): 
  return tf.Variable(initializer(shape=[k, k], dtype=tf.float32)) 
make_variable(2, tf.initializers.Identity()) 
<tf.Variable ... shape=(2, 2) dtype=float32, numpy= 
array([[1., 0.], 
       [0., 1.]], dtype=float32)> 
make_variable(3, tf.initializers.Identity(gain=0.5)) 
<tf.Variable ... shape=(3, 3) dtype=float32, numpy= 
array([[0.5, 0. , 0. ], 
       [0. , 0.5, 0. ], 
       [0. , 0. , 0.5]], dtype=float32)> 

Args:

  • gain: Multiplicative factor to apply to the identity matrix.

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 floating point types are supported.

Raises:

  • ValueError: If the dtype is not floating point
  • ValueError: If the requested shape does not have exactly two axes.

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.