tf.compat.v1.constant_initializer

Initializer that generates tensors with constant values.

tf.compat.v1.constant_initializer(
    value=0,
    dtype=tf.dtypes.float32,
    verify_shape=False
)

Migrate to TF2

Although it is a legacy API endpoint, tf.compat.v1.constant_initializer is compatible with eager execution and tf.function.

To migrate to a non-legacy TF2 API, please use tf.constant_initializer instead. The dtype argument in tf.compat.v1.constantinitializer.init_() does not exist in tf.constantinitializer.init_(). However, you can specify the dtype in __call__() in both cases.

In the compat.v1 symbol, if verify_shape is set to True, an exception is raised when initializing a variable with a different shape from value. If set to False, value is reshaped to initialize the variable if necessary. An exception would only be raised when the number of elements are different.

The verify_shape argument is not supported in TF2. Using tf.constant_initializer is equivalent to setting verify_shape to False.

Structural Mapping to TF2

Before:

value = [0, 1, 2, 3, 4, 5, 6, 7]
initializer = tf.compat.v1.constant_initializer(
    value=value,
    dtype=tf.float32,
    verify_shape=False)
variable = tf.Variable(initializer(shape=[2, 4]))

After:

value = [0, 1, 2, 3, 4, 5, 6, 7]
initializer = tf.constant_initializer(value=value)
tf.Variable(initializer(shape=[2, 4], dtype=tf.float32))

How to Map Arguments

TF1 Arg Name TF2 Arg Name Note
value value In constructor
dtype dtype In __call__() method
verify_shape Not Supported Equivalent to set to False
partition_info - (__call__ arg in TF1) Not supported

Before & After Usage Example

Before:

value = [1., 2., 3., 4.]
initializer = tf.compat.v1.constant_initializer(
    value=value, dtype=tf.float32, verify_shape=True)
tf.Variable(initializer(shape=[2, 2])).numpy()
Traceback (most recent call last):

TypeError: Expected Tensor's shape: (2, 2), got (4,).
initializer = tf.compat.v1.constant_initializer(
    value=value, dtype=tf.float32, verify_shape=False)
tf.Variable(initializer(shape=[2, 2])).numpy()
array([[1., 2.],
       [3., 4.]], dtype=float32)

After:

value = [1., 2., 3., 4.]
initializer = tf.constant_initializer(value=value)
tf.Variable(initializer(shape=[2, 2], dtype=tf.float32)).numpy()
array([[1., 2.],
       [3., 4.]], dtype=float32)

Description

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.

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 Default data type, used if no dtype argument is provided when calling the initializer.
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.

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

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. 

value = [0, 1, 2, 3, 4, 5, 6, 7]
init = tf.compat.v1.constant_initializer(value)
# 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())
[[0. 1. 2. 3.]
 [4. 5. 6. 7.]]
# Larger shape
with tf.compat.v1.Session():
  y = tf.compat.v1.get_variable('y', shape=[3, 4], initializer=init)
  y.initializer.run()
  print(y.eval())
[[0.  1.  2.  3.]
 [4.  5.  6.  7.]
 [7.  7.  7.  7.]]
# Smaller shape
with tf.compat.v1.Session():
  z = tf.compat.v1.get_variable('z', shape=[2, 3], initializer=init)
Traceback (most recent call last):

ValueError: Too many elements provided. Needed at most 6, but received 8
# Shape verification
init_verify = tf.compat.v1.constant_initializer(value, verify_shape=True)
with tf.compat.v1.Session():
 u = tf.compat.v1.get_variable('u', shape=[3, 4],
                               initializer=init_verify)
Traceback (most recent call last):

TypeError: Expected Tensor's shape: (3, 4), got (8,).

Methods

from_config

View source

@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

View source

get_config()

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

Returns
A JSON-serializable Python dict.

__call__

View source

__call__(
    shape, dtype=None, partition_info=None, verify_shape=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 use the initializer dtype.
partition_info Optional information about the possible partitioning of a tensor.