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.
initializer_lecun_normal(seed = NULL)An Initializer instance that can be passed to layer or variable
constructors, or called directly with a shape to return a Tensor.
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_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)
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_stft()
initializer_truncated_normal()
initializer_variance_scaling()
initializer_zeros()