tf.keras.initializers.GlorotNormal

The Glorot normal initializer, also called Xavier normal initializer.

Inherits From: VarianceScaling, Initializer

tf.keras.initializers.GlorotNormal(
    seed=None
)

Used in the notebooks

Used in the tutorials

Draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(2 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor.

Examples:

# Standalone usage:
initializer = GlorotNormal()
values = initializer(shape=(2, 2))
 
# Usage in a Keras layer:
initializer = GlorotNormal()
layer = Dense(3, kernel_initializer=initializer)

seed A Python integer or instance of keras.backend.SeedGenerator. Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or None (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of keras.backend.SeedGenerator.

Reference:

Methods

clone

View source

clone()

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, the output of get_config().

Returns
An Initializer instance.

get_config

View source

get_config()

Returns the initializer's configuration as a JSON-serializable dict.

Returns
A JSON-serializable Python dict.

__call__

View source

__call__(
    shape, dtype=None
)

Returns a tensor object initialized as specified by the initializer.

Args
shape Shape of the tensor.
dtype Optional dtype of the tensor.