tf.compat.v1.keras.initializers.Constant

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Initializer that generates tensors with constant values.

Inherits From: Initializer

tf.compat.v1.keras.initializers.Constant(
    value=0, dtype=tf.dtypes.float32, verify_shape=False
)

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: 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.

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.

>>> 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

__call__

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__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.

from_config

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@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

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get_config()

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

Returns:

A JSON-serializable Python dict.