Trait opencv::prelude::GRULayerTraitConst

source · 
pub trait GRULayerTraitConst: LayerTraitConst {
    fn as_raw_GRULayer(&self) -> *const c_void;
}
Expand description

GRU recurrent one-layer

Accepts input sequence and computes the final hidden state for each element in the batch.

  • input[0] containing the features of the input sequence. input[0] should have shape [T, N, data_dims] where T is sequence length, N is batch size, data_dims is input size
  • output would have shape [T, N, D * hidden_size] where D = 2 if layer is bidirectional otherwise D = 1

Depends on the following attributes:

  • hidden_size - Number of neurons in the hidden layer
  • direction - RNN could be bidirectional or forward

The final hidden state @f$ h_t @f$ computes by the following formulas:

@f{eqnarray*}{ r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \ z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \ n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn})) \ h_t = (1 - z_t) \odot n_t + z_t \odot h_{(t-1)} \ @f} Where @f$x_t@f$ is current input, @f$h_{(t-1)}@f$ is previous or initial hidden state.

@f$W_{x?}@f$, @f$W_{h?}@f$ and @f$b_{?}@f$ are learned weights represented as matrices: @f$W_{x?} \in R^{N_h \times N_x}@f$, @f$W_{h?} \in R^{N_h \times N_h}@f$, @f$b_? \in R^{N_h}@f$.

@f$\odot@f$ is per-element multiply operation.

Required Methods§

Implementors§