Conv2DLSTMCell

class Conv2DLSTMCell(input_shape, hidden_channels, i2h_kernel, h2h_kernel, i2h_pad=(0, 0), i2h_dilate=(1, 1), h2h_dilate=(1, 1), i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', conv_layout='NCHW', activation='tanh', prefix=None, params=None)

2D Convolutional LSTM network cell.

“Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting” paper. Xingjian et al. NIPS2015

(26)\[\begin{split}\begin{array}{ll} i_t = \sigma(W_i \ast x_t + R_i \ast h_{t-1} + b_i) \\ f_t = \sigma(W_f \ast x_t + R_f \ast h_{t-1} + b_f) \\ o_t = \sigma(W_o \ast x_t + R_o \ast h_{t-1} + b_o) \\ c^\prime_t = tanh(W_c \ast x_t + R_c \ast h_{t-1} + b_c) \\ c_t = f_t \circ c_{t-1} + i_t \circ c^\prime_t \\ h_t = o_t \circ tanh(c_t) \\ \end{array}\end{split}\]

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 ‘NCHW’ the shape should be (C, H, 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, 0)

Pad for input convolution.

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

Input convolution dilate.

h2h_dilate : int or tuple of int, default (1, 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 ‘NCHW’

Layout for all convolution inputs, outputs and weights. Options are ‘NCHW’ and ‘NHWC’.

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

Type of activation function used in c^prime_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_lstm_’

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.