Initializer that generates a truncated normal distribution.

The values generated are similar to values from a RandomNormal initializer, except that values more than two standard deviations from the mean are discarded and re-drawn.

Usage

initializer_truncated_normal(mean = 0, stddev = 0.05, seed = NULL)

Arguments

mean

A numeric scalar. Mean of the random values to generate.

stddev

A numeric scalar. Standard deviation of the random values to generate.

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_truncated_normal(mean = 0, stddev = 1)
values <- initializer(shape = c(2, 2))

# Usage in a Keras layer:
initializer <- initializer_truncated_normal(mean = 0, stddev = 1)
layer <- layer_dense(units = 3, kernel_initializer = initializer)

See also

• https://keras.io/api/layers/initializers#truncatednormal-class

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

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