class
SHAInet::RecurrentLayer
- SHAInet::RecurrentLayer
- SHAInet::Layer
- Reference
- Object
Defined in:
shainet/rnn/recurrent_layer.crConstructors
Instance Method Summary
- #activate_sequence(sequence : Array(Array(GenNum)))
- #activate_step
- #backprop_sequence
- #hidden_state : Array(Float64)
- #hidden_state=(hidden_state : Array(Float64))
- #recurrent_synapses : Array(Array(Synapse))
- #recurrent_synapses=(recurrent_synapses : Array(Array(Synapse)))
- #reset_state
Instance methods inherited from class SHAInet::Layer
activation_function : Float32 | Float64 | Int32 | Int64 -> {Float64, Float64}
activation_function,
activations : Matrix(Float64)
activations,
biases : Matrix(Float64)
biases,
biases=(biases : Matrix(Float64))
biases=,
clone
clone,
input_sums : Matrix(Float64)
input_sums,
input_sums=(input_sums : Matrix(Float64))
input_sums=,
inspect
inspect,
l_size : Int32
l_size,
n_type : String
n_type,
n_type=(n_type : String)
n_type=,
neurons : Array(SHAInet::Neuron)
neurons,
neurons=(neurons : Array(SHAInet::Neuron))
neurons=,
propagate_forward_exp(prev_layer : Layer)
propagate_forward_exp,
random_seed
random_seed,
sigma_primes : Matrix(Float64)
sigma_primes,
size : Int32
size,
type_change(new_neuron_type : String)
type_change,
weights : Matrix(Float64)
weights,
weights=(weights : Matrix(Float64))
weights=
Constructor methods inherited from class SHAInet::Layer
new(n_type : String, l_size : Int32, activation_function : ActivationFunction = SHAInet.sigmoid)
new
Constructor Detail
def self.new(n_type : String, l_size : Int32, activation_function : ActivationFunction = SHAInet.sigmoid)
#