chainer.functions.n_step_bigru(n_layers, dropout_ratio, hx, ws, bs, xs)[source]

Stacked Bi-directional Gated Recurrent Unit function.

This function calculates stacked Bi-directional GRU with sequences. This function gets an initial hidden state \(h_0\), an input sequence \(x\), weight matrices \(W\), and bias vectors \(b\). This function calculates hidden states \(h_t\) for each time \(t\) from input \(x_t\).

\[\begin{split}r^{f}_t &= \sigma(W^{f}_0 x_t + W^{f}_3 h_{t-1} + b^{f}_0 + b^{f}_3) \\ z^{f}_t &= \sigma(W^{f}_1 x_t + W^{f}_4 h_{t-1} + b^{f}_1 + b^{f}_4) \\ h^{f'}_t &= \tanh(W^{f}_2 x_t + b^{f}_2 + r^{f}_t \cdot (W^{f}_5 h_{t-1} + b^{f}_5)) \\ h^{f}_t &= (1 - z^{f}_t) \cdot h^{f'}_t + z^{f}_t \cdot h_{t-1} \\ r^{b}_t &= \sigma(W^{b}_0 x_t + W^{b}_3 h_{t-1} + b^{b}_0 + b^{b}_3) \\ z^{b}_t &= \sigma(W^{b}_1 x_t + W^{b}_4 h_{t-1} + b^{b}_1 + b^{b}_4) \\ h^{b'}_t &= \tanh(W^{b}_2 x_t + b^{b}_2 + r^{b}_t \cdot (W^{b}_5 h_{t-1} + b^{b}_5)) \\ h^{b}_t &= (1 - z^{b}_t) \cdot h^{b'}_t + z^{b}_t \cdot h_{t-1} \\ h_t &= [h^{f}_t; h^{b}_t] \\\end{split}\]

where \(W^{f}\) is weight matrices for forward-GRU, \(W^{b}\) is weight matrices for backward-GRU.

As the function accepts a sequence, it calculates \(h_t\) for all \(t\) with one call. Six weight matrices and six bias vectors are required for each layers. So, when \(S\) layers exists, you need to prepare \(6S\) weight matrices and \(6S\) bias vectors.

If the number of layers n_layers is greather than \(1\), input of k-th layer is hidden state h_t of k-1-th layer. Note that all input variables except first layer may have different shape from the first layer.


train and use_cudnn arguments are not supported anymore since v2. Instead, use chainer.using_config('train', train) and chainer.using_config('use_cudnn', use_cudnn) respectively. See chainer.using_config().

  • n_layers (int) – Number of layers.
  • dropout_ratio (float) – Dropout ratio.
  • hx (chainer.Variable) – Variable holding stacked hidden states. Its shape is (2S, B, N) where S is number of layers and is equal to n_layers, B is mini-batch size, and N is dimension of hidden units.
  • ws (list of list of chainer.Variable) – Weight matrices. ws[i] represents weights for i-th layer. Each ws[i] is a list containing six matrices. ws[i][j] is corresponding with W_j in the equation. Only ws[0][j] where 0 <= j < 3 is (I, N) shape as they are multiplied with input variables. All other matrices has (N, N) shape.
  • bs (list of list of chainer.Variable) – Bias vectors. bs[i] represnents biases for i-th layer. Each bs[i] is a list containing six vectors. bs[i][j] is corresponding with b_j in the equation. Shape of each matrix is (N,) where N is dimension of hidden units.
  • xs (list of chainer.Variable) – A list of Variable holding input values. Each element xs[t] holds input value for time t. Its shape is (B_t, I), where B_t is mini-batch size for time t, and I is size of input units. Note that this function supports variable length sequences. When sequneces has different lengths, sort sequences in descending order by length, and transpose the sorted sequence. transpose_sequence() transpose a list of Variable() holding sequence. So xs needs to satisfy xs[t].shape[0] >= xs[t + 1].shape[0].
  • use_bi_direction (bool) – If True, this function uses Bi-direction GRU.

This function returns a tuple containing three elements, hy and ys.

  • hy is an updated hidden states whose shape is same as hx.
  • ys is a list of Variable . Each element ys[t] holds hidden states of the last layer corresponding to an input xs[t]. Its shape is (B_t, N) where B_t is mini-batch size for time t, and N is size of hidden units. Note that B_t is the same value as xs[t].

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