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tf.contrib.rnn.ConvLSTMCell

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Class ConvLSTMCell

Convolutional LSTM recurrent network cell.

Inherits From: RNNCell

https://arxiv.org/pdf/1506.04214v1.pdf

__init__

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__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'
)

Construct ConvLSTMCell.

Args:

  • 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: False.
  • forget_bias: Forget bias.
  • initializers: Unused.
  • name: Name of the module.

Raises:

  • ValueError: If skip_connection is True and stride is different from 1 or if input_shape is incompatible with conv_ndims.

Properties

graph

DEPRECATED FUNCTION

output_size

scope_name

state_size

Methods

get_initial_state

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get_initial_state(
    inputs=None,
    batch_size=None,
    dtype=None
)

zero_state

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zero_state(
    batch_size,
    dtype
)

Return zero-filled state tensor(s).

Args:

  • batch_size: int, float, or unit Tensor representing the batch size.
  • dtype: the data type to use for the state.

Returns:

If 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.

If 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 the shapes [batch_size, s] for each s in state_size.