The Glorot normal initializer, also called Xavier normal initializer.

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.

Usage

initializer_glorot_normal(seed = NULL)

Arguments

seed: An integer or instance of random_seed_generator(). Used to make the behavior of the initializer deterministic. Note that an initializer seeded with an integer or NULL (unseeded) will produce the same random values across multiple calls. To get different random values across multiple calls, use as seed an instance of random_seed_generator().

Value

An Initializer instance that can be passed to layer or variable constructors, or called directly with a shape to return a Tensor.

Examples

# Standalone usage:
initializer <- initializer_glorot_normal()
values <- initializer(shape = c(2, 2))

# Usage in a Keras layer:
initializer <- initializer_glorot_normal()
layer <- layer_dense(units = 3, kernel_initializer = initializer)

Reference

Glorot et al., 2010