Lecun normal initializer.

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.

Draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(1 / fan_in) where fan_in is the number of input units in the weight tensor.

Usage

initializer_lecun_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_lecun_normal()
values <- initializer(shape = c(2, 2))

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

Reference

• Klambauer et al., 2017

See also

Other random initializers:
initializer_glorot_normal()
initializer_glorot_uniform()
initializer_he_normal()
initializer_he_uniform()
initializer_lecun_uniform()
initializer_orthogonal()
initializer_random_normal()
initializer_random_uniform()
initializer_truncated_normal()
initializer_variance_scaling()

Other initializers:
initializer_constant()
initializer_glorot_normal()
initializer_glorot_uniform()
initializer_he_normal()
initializer_he_uniform()
initializer_identity()
initializer_lecun_uniform()
initializer_ones()
initializer_orthogonal()
initializer_random_normal()
initializer_random_uniform()
initializer_truncated_normal()
initializer_variance_scaling()
initializer_zeros()