Conv1DGRUCell

class Conv1DGRUCell(input_shape, hidden_channels, i2h_kernel, h2h_kernel, i2h_pad=(0, ), i2h_dilate=(1, ), h2h_dilate=(1, ), i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', conv_layout='NCW', activation='tanh', prefix=None, params=None)

1D Convolutional Gated Rectified Unit (GRU) network cell.

(28)\[\begin{split}\begin{array}{ll} r_t = \sigma(W_r \ast x_t + R_r \ast h_{t-1} + b_r) \\ z_t = \sigma(W_z \ast x_t + R_z \ast h_{t-1} + b_z) \\ n_t = tanh(W_i \ast x_t + b_i + r_t \circ (R_n \ast h_{t-1} + b_n)) \\ h^\prime_t = (1 - z_t) \circ n_t + z_t \circ h \\ \end{array}\end{split}\]

Parameters:

input_shape : tuple of int

Input tensor shape at each time step for each sample, excluding dimension of the batch size and sequence length. Must be consistent with conv_layout. For example, for layout ‘NCW’ the shape should be (C, W).

hidden_channels : int

Number of output channels.

i2h_kernel : int or tuple of int

Input convolution kernel sizes.

h2h_kernel : int or tuple of int

Recurrent convolution kernel sizes. Only odd-numbered sizes are supported.

i2h_pad : int or tuple of int, default (0,)

Pad for input convolution.

i2h_dilate : int or tuple of int, default (1,)

Input convolution dilate.

h2h_dilate : int or tuple of int, default (1,)

Recurrent convolution dilate.

i2h_weight_initializer : str or Initializer

Initializer for the input weights matrix, used for the input convolutions.

h2h_weight_initializer : str or Initializer

Initializer for the recurrent weights matrix, used for the input convolutions.

i2h_bias_initializer : str or Initializer, default zeros

Initializer for the input convolution bias vectors.

h2h_bias_initializer : str or Initializer, default zeros

Initializer for the recurrent convolution bias vectors.

conv_layout : str, default ‘NCW’

Layout for all convolution inputs, outputs and weights. Options are ‘NCW’ and ‘NWC’.

activation : str or gluon.Block, default ‘tanh’

Type of activation function used in n_t. If argument type is string, it’s equivalent to nn.Activation(act_type=str). See Activation() for available choices. Alternatively, other activation blocks such as nn.LeakyReLU can be used.

prefix : str, default 'conv_gru_’

Prefix for name of layers (and name of weight if params is None).

params : RNNParams, default None

Container for weight sharing between cells. Created if None.