tf.constant_initializer

Class constant_initializer

Inherits From: Initializer

Aliases:

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

Defined in tensorflow/python/ops/init_ops.py.

See the guide: Variables > Sharing Variables

Initializer that generates tensors with constant values.

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.
• dtype: The data type.
• verify_shape: Boolean that enables verification of the shape of value. If True, the initializer will throw an error if the shape of value is not compatible with the shape of the initialized tensor.

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.constant_initializer(value)

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

* <b>`ValueError`</b>: Too many elements provided. Needed at most 6, but received 8

  >>> print('shape verification:')
  >>> init_verify = tf.constant_initializer(value, verify_shape=True)
  >>> with tf.Session():
  >>>   x = tf.get_variable('x', shape=[3, 4], initializer=init_verify)

* <b>`TypeError`</b>: Expected Tensor's shape: (3, 4), got (8,).

Methods

__init__

__init__(
    value=0,
    dtype=tf.float32,
    verify_shape=False
)

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

__call__

__call__(
    shape,
    dtype=None,
    partition_info=None,
    verify_shape=None
)

Call self as a function.

from_config

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

get_config()

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

Returns:

A JSON-serializable Python dict.