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Convolutional LSTM recurrent network cell.
__init__( conv_ndims, input_shape, output_channels, kernel_shape, use_bias=True, skip_connection=False, forget_bias=1.0, initializers=None, name='conv_lstm_cell' )
conv_ndims: Convolution dimensionality (1, 2 or 3).
input_shape: Shape of the input as int tuple, excluding the batch size.
output_channels: int, number of output channels of the conv LSTM.
kernel_shape: Shape of kernel as an int tuple (of size 1, 2 or 3).
use_bias: (bool) Use bias in convolutions.
skip_connection: If set to
True, concatenate the input to the output of the conv LSTM. Default:
forget_bias: Forget bias.
name: Name of the module.
Trueand stride is different from 1 or if
input_shapeis incompatible with
Integer or TensorShape: size of outputs produced by this cell.
size(s) of state(s) used by this cell.
It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.
get_initial_state( inputs=None, batch_size=None, dtype=None )
zero_state( batch_size, dtype )
Return zero-filled state tensor(s).
batch_size: int, float, or unit Tensor representing the batch size.
dtype: the data type to use for the state.
state_size is an int or TensorShape, then the return value is a
N-D tensor of shape
[batch_size, state_size] filled with zeros.
state_size is a nested list or tuple, then the return value is
a nested list or tuple (of the same structure) of
2-D tensors with
[batch_size, s] for each s in