tf.compat.v1.keras.initializers.RandomNormal

Initializer that generates a normal distribution.

Inherits From: random_normal_initializer

tf.compat.v1.keras.initializers.RandomNormal(
    mean=0.0,
    stddev=0.05,
    seed=None,
    dtype=tf.dtypes.float32
)

Migrate to TF2

Although it is a legacy compat.v1 api, tf.compat.v1.keras.initializers.RandomNormal is compatible with eager execution and tf.function.

To switch to native TF2, switch to using tf.keras.initializers.RandomNormal (not from compat.v1) and if you need to change the default dtype use tf.keras.backend.set_floatx(float_dtype) or pass the dtype when calling the initializer, rather than passing it when constructing the initializer.

Random seed behavior: Also be aware that if you pass a seed to the TF2 initializer API it will reuse that same seed for every single initialization (unlike the TF1 initializer)

Structural Mapping to Native TF2

Before:

initializer = tf.compat.v1.keras.initializers.RandomNormal(
  mean=mean,
  stddev=stddev,
  seed=seed,
  dtype=dtype)

weight_one = tf.Variable(initializer(shape_one))
weight_two = tf.Variable(initializer(shape_two))

After:

initializer = tf.keras.initializers.RandomNormal(
  mean=mean,
  # seed=seed,  # Setting a seed in the native TF2 API
                # causes it to produce the same initializations
                # across multiple calls of the same initializer.
  stddev=stddev)

weight_one = tf.Variable(initializer(shape_one, dtype=dtype))
weight_two = tf.Variable(initializer(shape_two, dtype=dtype))

How to Map Arguments

TF1 Arg Name TF2 Arg Name Note
mean mean No change to defaults
stddev stddev No change to defaults
seed seed Different random number generation semantics (to change in a future version). If set, the TF2 version will use stateless random number generation which will produce the exact same initialization even across multiple calls of the initializer instance. the compat.v1 version will generate new initializations each time. Do not set a seed if you need different initializations each time. Instead either set a global tf seed with tf.random.set_seed if you need determinism, or initialize each weight with a separate initializer instance and a different seed.
dtype dtype The TF2 native api only takes it as a __call__ arg, not a constructor arg.
partition_info - (__call__ arg in TF1) Not supported

Example of fixed-seed behavior differences

compat.v1 Fixed seed behavior:

initializer = tf.compat.v1.keras.initializers.RandomNormal(seed=10)
a = initializer(shape=(2, 2))
b = initializer(shape=(2, 2))
tf.reduce_sum(a - b) == 0
<tf.Tensor: shape=(), dtype=bool, numpy=False>

After:

initializer = tf.keras.initializers.RandomNormal(seed=10)
a = initializer(shape=(2, 2))
b = initializer(shape=(2, 2))
tf.reduce_sum(a - b) == 0
<tf.Tensor: shape=(), dtype=bool, numpy=True>

Description

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.compat.v1.set_random_seed for behavior.
dtype Default data type, used if no dtype argument is provided when calling the initializer. Only floating point types are supported.

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=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.