tf.compat.v1.constant_initializer | TensorFlow v2.16.1 (original) (raw)
Initializer that generates tensors with constant values.
View aliases
Compat aliases for migration
SeeMigration guide for more details.
tf.compat.v1.initializers.constant
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_initializeris compatible with eager execution and tf.function.
To migrate to a non-legacy TF2 API, please use tf.constant_initializerinstead. The dtypeargument in tf.compat.v1.constant_initializer._ init_() does not exist intf.constant_initializer._ 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 fromvalue. 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. Usingtf.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
Used in the notebooks
| Used in the guide |
|---|
| Migrating model checkpoints Use TF1.x models in TF2 workflows |
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 typedtype. 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. IfTrue, 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
from_config
@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 ofget_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. |
__call__
__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. |