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tf.compat.v1.truncated_normal_initializer

Initializer that generates a truncated normal distribution.

Migrate to TF2

Although it is a legacy compat.v1 API, this symbol is compatible with eager execution and tf.function.

To switch to native TF2, switch to using either tf.initializers.truncated_normal or tf.keras.initializers.TruncatedNormal (neither from compat.v1) and pass the dtype when calling the initializer. Keep in mind that the default stddev and the behavior of fixed seeds have changed.

Structural Mapping to Native TF2

Before:

initializer = tf.compat.v1.truncated_normal_initializer(
  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.initializers.truncated_normal(
  mean=mean,
  seed=seed,
  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 Default changes from 1.0 to 0.05
seed 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

Description

These values are similar to values from a random_normal_initializer except that values more than two standard deviations from the mean are discarded and re-drawn. This is the recommended initializer for neural network weights and filters.

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

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

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

Returns
A JSON-serializable Python dict.

__call__

View source

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.