Trait opencv::prelude::RNNLayer

source ·

pub trait RNNLayer: LayerTrait + RNNLayerConst {
    fn as_raw_mut_RNNLayer(&mut self) -> *mut c_void;

    fn set_weights(
        &mut self,
        wxh: &Mat,
        bh: &Mat,
        whh: &Mat,
        who: &Mat,
        bo: &Mat
    ) -> Result<()> { ... }
    fn set_produce_hidden_output(&mut self, produce: bool) -> Result<()> { ... }
}

Expand description

Classical recurrent layer

Accepts two inputs @f$x_t@f$ and @f$h_{t-1}@f$ and compute two outputs @f$o_t@f$ and @f$h_t@f$.

input: should contain packed input @f$x_t@f$.
output: should contain output @f$o_t@f$ (and @f$h_t@f$ if setProduceHiddenOutput() is set to true).

input[0] should have shape [T, N, data_dims] where T and N is number of timestamps and number of independent samples of @f$x_t@f$ respectively.

output[0] will have shape [T, N, @f$N_o@f$], where @f$N_o@f$ is number of rows in @f$ W_{xo} @f$ matrix.

If setProduceHiddenOutput() is set to true then @p output[1] will contain a Mat with shape [T, N, @f$N_h@f$], where @f$N_h@f$ is number of rows in @f$ W_{hh} @f$ matrix.

Required Methods§

source

fn as_raw_mut_RNNLayer(&mut self) -> *mut c_void

Provided Methods§

source

fn set_weights(
    &mut self,
    wxh: &Mat,
    bh: &Mat,
    whh: &Mat,
    who: &Mat,
    bo: &Mat
) -> Result<()>

Setups learned weights.

Recurrent-layer behavior on each step is defined by current input @f$ x_t @f$, previous state @f$ h_t @f$ and learned weights as follows: @f{eqnarray*}{ h_t &= tanh&(W_{hh} h_{t-1} + W_{xh} x_t + b_h), \ o_t &= tanh&(W_{ho} h_t + b_o), @f}