tf.constant_initializer

View source on GitHub

Initializer that generates tensors with constant values.

Inherits From: Initializer

tf.constant_initializer(
    value=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.

tf.constant_initializer returns an object which when called returns a tensor populated with the value specified in the constructor. This value must be convertible to the requested dtype.

The argument value can be a scalar constant value, or a list of values. Scalars broadcast to whichever shape is requested from the initializer.

If value is a list, then the length of the list must be equal to the number of elements implied by the desired shape of the tensor. If the total number of elements in value is not equal to the number of elements required by the tensor shape, the initializer will raise a TypeError.

Examples:

def make_variables(k, initializer): 
  return (tf.Variable(initializer(shape=[k], dtype=tf.float32)), 
          tf.Variable(initializer(shape=[k, k], dtype=tf.float32))) 
v1, v2 = make_variables(3, tf.constant_initializer(2.)) 
v1 
<tf.Variable ... shape=(3,) ... numpy=array([2., 2., 2.], dtype=float32)> 
v2 
<tf.Variable ... shape=(3, 3) ... numpy= 
array([[2., 2., 2.], 
       [2., 2., 2.], 
       [2., 2., 2.]], dtype=float32)> 
make_variables(4, tf.random_uniform_initializer(minval=-1., maxval=1.)) 
(<tf.Variable...shape=(4,) dtype=float32...>, <tf.Variable...shape=(4, 4) ... 
value = [0, 1, 2, 3, 4, 5, 6, 7] 
init = tf.constant_initializer(value) 
# Fitting shape 
tf.Variable(init(shape=[2, 4], dtype=tf.float32)) 
<tf.Variable ... 
array([[0., 1., 2., 3.], 
       [4., 5., 6., 7.]], dtype=float32)> 
# Larger shape 
tf.Variable(init(shape=[3, 4], dtype=tf.float32)) 
Traceback (most recent call last): 
 
TypeError: ...value has 8 elements, shape is (3, 4) with 12 elements... 
# Smaller shape 
tf.Variable(init(shape=[2, 3], dtype=tf.float32)) 
Traceback (most recent call last): 
 
TypeError: ...value has 8 elements, shape is (2, 3) with 6 elements... 

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.

Methods

__call__

View source

__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

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.