Draws samples from a normal distribution for given parameters.
initializer_random_normal(mean = 0, stddev = 0.05, seed = NULL)An Initializer instance that can be passed to layer or variable
constructors, or called directly with a shape to return a Tensor.
A numeric scalar. Mean of the random values to generate.
A numeric scalar. Standard deviation of the random values to generate.
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().
# Standalone usage:
initializer <- initializer_random_normal(mean = 0.0, stddev = 1.0)
values <- initializer(shape = c(2, 2))
# Usage in a Keras layer:
initializer <- initializer_random_normal(mean = 0.0, stddev = 1.0)
layer <- layer_dense(units = 3, kernel_initializer = initializer)
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_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_normal()
initializer_lecun_uniform()
initializer_ones()
initializer_orthogonal()
initializer_random_uniform()
initializer_stft()
initializer_truncated_normal()
initializer_variance_scaling()
initializer_zeros()