mxnet.gluon.contrib.rnn.Conv3DLSTMCell¶

class mxnet.gluon.contrib.rnn.Conv3DLSTMCell(input_shape, hidden_channels, i2h_kernel, h2h_kernel, i2h_pad=(0, 0, 0), i2h_dilate=(1, 1, 1), h2h_dilate=(1, 1, 1), i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', conv_layout='NCDHW', activation='tanh', prefix=None, params=None)[source]¶

3D Convolutional LSTM network cell.

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

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

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 ‘NCDHW’ the shape should be (C, D, 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, 0)) – Pad for input convolution.
i2h_dilate (int or tuple of int, default (1, 1, 1)) – Input convolution dilate.
h2h_dilate (int or tuple of int, default (1, 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 'NCDHW') – Layout for all convolution inputs, outputs and weights. Options are ‘NCDHW’ and ‘NDHWC’.
activation (str or 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.

__init__(input_shape, hidden_channels, i2h_kernel, h2h_kernel, i2h_pad=(0, 0, 0), i2h_dilate=(1, 1, 1), h2h_dilate=(1, 1, 1), i2h_weight_initializer=None, h2h_weight_initializer=None, i2h_bias_initializer='zeros', h2h_bias_initializer='zeros', conv_layout='NCDHW', activation='tanh', prefix=None, params=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(input_shape, hidden_channels, …)	Initialize self.
`apply`(fn)	Applies `fn` recursively to every child block as well as self.
`begin_state`([batch_size, func])	Initial state for this cell.
`cast`(dtype)	Cast this Block to use another data type.
`collect_params`([select])	Returns a `ParameterDict` containing this `Block` and all of its children’s Parameters(default), also can returns the select `ParameterDict` which match some given regular expressions.
`export`(path[, epoch])	Export HybridBlock to json format that can be loaded by SymbolBlock.imports, mxnet.mod.Module or the C++ interface.
`forward`(inputs, states)	Unrolls the recurrent cell for one time step.
`hybrid_forward`(F, inputs, states, …)	Overrides to construct symbolic graph for this Block.
`hybridize`([active])	Activates or deactivates `HybridBlock` s recursively.
`infer_shape`(*args)	Infers shape of Parameters from inputs.
`infer_type`(*args)	Infers data type of Parameters from inputs.
`initialize`([init, ctx, verbose, force_reinit])	Initializes `Parameter` s of this `Block` and its children.
`load_parameters`(filename[, ctx, …])	Load parameters from file previously saved by save_parameters.
`load_params`(filename[, ctx, allow_missing, …])	[Deprecated] Please use load_parameters.
`name_scope`()	Returns a name space object managing a child `Block` and parameter names.
`register_child`(block[, name])	Registers block as a child of self.
`register_forward_hook`(hook)	Registers a forward hook on the block.
`register_forward_pre_hook`(hook)	Registers a forward pre-hook on the block.
`reset`()	Reset before re-using the cell for another graph.
`save_parameters`(filename)	Save parameters to file.
`save_params`(filename)	[Deprecated] Please use save_parameters.
`state_info`([batch_size])	shape and layout information of states
`summary`(*inputs)	Print the summary of the model’s output and parameters.
`unroll`(length, inputs[, begin_state, …])	Unrolls an RNN cell across time steps.

Attributes

`name`	Name of this `Block`, without ‘_’ in the end.
`params`	Returns this `Block`’s parameter dictionary (does not include its children’s parameters).
`prefix`	Prefix of this `Block`.