tf.compat.v1.zeros_initializer | TensorFlow v2.16.1 (original) (raw)
Initializer that generates tensors initialized to 0.
View aliases
Compat aliases for migration
SeeMigration guide for more details.
tf.compat.v1.initializers.zeros
tf.compat.v1.zeros_initializer(
dtype=tf.dtypes.float32
)
Migrate to TF2
tf.compat.v1.zeros_initializer is compatible with eager execution and tf.function.
To migrate to TF2, please use tf.zeros_initializer instead. The dtypeargument in tf.compat.v1.zeros_initializer._ init_() does not exist intf.zeros_initializer._ init_(). However, you can specify the dtype in__call__() in both cases.
Structural Mapping to TF2
Before:
initializer = tf.compat.v1.zeros_initializer(dtype=tf.float32)
variable = tf.Variable(initializer(shape=[3, 3]))
After:
initializer = tf.zeros_initializer()
variable = tf.Variable(initializer(shape=[3, 3], dtype=tf.float32))
How to Map Arguments
| TF1 Arg Name | TF2 Arg Name | Note |
|---|---|---|
| dtype | dtype | In __call__() method |
| partition_info | - | (__call__ arg in TF1) Not supported |
Before & After Usage Example
Before:
initializer = tf.compat.v1.zeros_initializer(dtype=tf.float32)
tf.Variable(initializer(shape=[3])).numpy()
array([0., 0., 0.], dtype=float32)
tf.Variable(initializer(shape=[3, 3])).numpy()
array([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]], dtype=float32)
initializer = tf.compat.v1.zeros_initializer()
tf.Variable(initializer(shape=[3], dtype=tf.float32)).numpy()
array([0., 0., 0.], dtype=float32)
tf.Variable(initializer(shape=[3, 3], dtype=tf.float32)).numpy()
array([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]], dtype=float32)
After:
initializer = tf.zeros_initializer()
tf.Variable(initializer(shape=[3], dtype=tf.float32)).numpy()
array([0., 0., 0.], dtype=float32)
tf.Variable(initializer(shape=[3, 3], dtype=tf.float32)).numpy()
array([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]], dtype=float32)
Description
Used in the notebooks
| Used in the guide |
|---|
| Migrating model checkpoints |
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
)
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. |