tf.random_normal_initializer
Initializer that generates tensors with a normal distribution.
tf.random_normal_initializer(
mean=0.0, stddev=0.05, seed=None
)
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.
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.random_normal_initializer(mean=1., stddev=2.))
v1
<tf.Variable ... shape=(3,) ... numpy=array([...], dtype=float32)>
v2
<tf.Variable ... shape=(3, 3) ... numpy=
make_variables(4, tf.random_uniform_initializer(minval=-1., maxval=1.))
(<tf.Variable...shape=(4,) dtype=float32...>, <tf.Variable...shape=(4, 4) ...
| Args |
|---|
mean | a python scalar or a scalar tensor. Mean of the random values to generate. |
stddev | a python scalar or a scalar tensor. Standard deviation of the random values to generate. |
seed | A Python integer. Used to create random seeds. See tf.random.set_seed for behavior. |
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=tf.dtypes.float32
)
Returns a tensor object initialized as specified by the initializer.
| Args |
|---|
shape | Shape of the tensor. |
dtype | Optional dtype of the tensor. Only floating point types are supported. |
| Raises |
|---|
ValueError | If the dtype is not floating point |