tf.constant_initializer

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Class constant_initializer

Initializer that generates tensors with constant values.

Inherits From: Initializer

Aliases:

  • Class tf.compat.v2.constant_initializer
  • Class tf.compat.v2.initializers.Constant
  • Class tf.compat.v2.initializers.constant
  • Class tf.compat.v2.keras.initializers.Constant
  • Class tf.compat.v2.keras.initializers.constant
  • Class tf.initializers.Constant
  • Class tf.initializers.constant
  • Class tf.keras.initializers.Constant
  • Class tf.keras.initializers.constant

The resulting tensor is populated with values of type dtype, as specified by arguments value following the desired shape of the new tensor (see examples below).

The argument value can be a constant value, or a list of values of type dtype. If value is a list, then the length of the list must be less than or equal to the number of elements implied by the desired shape of the tensor. In the case where the total number of elements in value is less than the number of elements required by the tensor shape, the last element in value will be used to fill the remaining entries. If the total number of elements in value is greater than the number of elements required by the tensor shape, the initializer will raise a ValueError.

Args:

  • value: A Python scalar, list or tuple of values, or a N-dimensional numpy array. All elements of the initialized variable will be set to the corresponding value in the value argument.

Raises:

  • TypeError: If the input value is not one of the expected types.

Examples:

The following example can be rewritten using a numpy.ndarray instead of the value list, even reshaped, as shown in the two commented lines below the value list initialization.

  >>> import numpy as np
  >>> import tensorflow as tf

value = [0, 1, 2, 3, 4, 5, 6, 7]

value = np.array(value)

value = value.reshape([2, 4])

init = tf.compat.v1.constant_initializer(value) ```

  >>> print('fitting shape:')
  >>> with tf.compat.v1.Session():
  >>>   x = tf.compat.v1.get_variable('x', shape=[2, 4], initializer=init)
  >>>   x.initializer.run()
  >>>   print(x.eval())

fitting shape: [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.]]

  >>> print('larger shape:')
  >>> with tf.compat.v1.Session():
  >>>   x = tf.compat.v1.get_variable('x', shape=[3, 4], initializer=init)
  >>>   x.initializer.run()
  >>>   print(x.eval())

larger shape: [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 7. 7. 7. 7.]]

  >>> print('smaller shape:')
  >>> with tf.compat.v1.Session():
  >>>   x = tf.compat.v1.get_variable('x', shape=[2, 3], initializer=init)

ValueError: Too many elements provided. Needed at most 6, but received 8


<h2 id="__init__"><code>__init__</code></h2>

<a target="_blank" href="/code/stable/tensorflow/python/ops/init_ops_v2.py">View source</a>

``` python
__init__(value=0)

Initialize self. See help(type(self)) for accurate signature.

Methods

__call__

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__call__(
    shape,
    dtype=None
)

Returns a tensor object initialized as specified by the initializer.

Args:

  • shape: Shape of the tensor.
  • dtype: Optional dtype of the tensor. If not provided the dtype of the tensor created will be the type of the inital value.

Raises:

  • TypeError: If the initializer cannot create a tensor of the requested dtype.

from_config

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from_config(
    cls,
    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.

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