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tf.keras.initializers.Identity

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Initializer that generates the identity matrix.

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.

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)>

gain Multiplicative factor to apply to the identity matrix.

Methods

from_config

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

Returns
A JSON-serializable Python dict.

__call__

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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.