class SHAInet::Filter
- SHAInet::Filter
- Reference
- Object
Defined in:
shainet/cnn/filter.crConstructors
Instance Method Summary
- #activation_function : ActivationFunction
- #bias : Float64
- #bias=(bias : Float64)
- #bias_grad : Float64
- #bias_grad=(bias_grad : Float64)
- #bias_grad_batch : Float64
- #bias_grad_batch=(bias_grad_batch : Float64)
- #bias_grad_sum : Float64
- #bias_grad_sum=(bias_grad_sum : Float64)
- #clone
- #input_surface : Array(Int32)
- #m_current : Float64
- #m_current=(m_current : Float64)
- #m_prev : Float64
- #m_prev=(m_prev : Float64)
- #neurons : Array(Array(Neuron))
- #neurons=(neurons : Array(Array(Neuron)))
- #padding : Int32
- #prev_bias : Float64
- #prev_bias=(prev_bias : Float64)
- #prev_bias_grad : Float64
- #prev_bias_grad=(prev_bias_grad : Float64)
- #prev_delta : Float64
- #prev_delta=(prev_delta : Float64)
- #prev_delta_b : Float64
- #prev_delta_b=(prev_delta_b : Float64)
- #propagate_backward(input_layer : ConvLayer | CNNLayer, batch : Bool = false)
- #propagate_forward(input_layer : ConvLayer | CNNLayer)
- #stride : Int32
- #synapses : Array(Array(Array(CnnSynapse)))
- #synapses=(synapses : Array(Array(Array(CnnSynapse))))
- #v_current : Float64
- #v_current=(v_current : Float64)
- #v_prev : Float64
- #v_prev=(v_prev : Float64)
- #window_size : Int32
Constructor Detail
def self.new(input_surface : Array(Int32), padding : Int32 = 0, window_size : Int32 = 1, stride : Int32 = 1, activation_function : ActivationFunction = SHAInet.none)
#