Keras
Keras 3 API documentation
Layers API
Recurrent layers
ConvLSTM2D layer

ConvLSTM2D layer

source

ConvLSTM2D class 

keras.layers.ConvLSTM2D(
    filters,
    kernel_size,
    strides=1,
    padding="valid",
    data_format=None,
    dilation_rate=1,
    activation="tanh",
    recurrent_activation="sigmoid",
    use_bias=True,
    kernel_initializer="glorot_uniform",
    recurrent_initializer="orthogonal",
    bias_initializer="zeros",
    unit_forget_bias=True,
    kernel_regularizer=None,
    recurrent_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    recurrent_constraint=None,
    bias_constraint=None,
    dropout=0.0,
    recurrent_dropout=0.0,
    seed=None,
    return_sequences=False,
    return_state=False,
    go_backwards=False,
    stateful=False,
    **kwargs
)

2D Convolutional LSTM.

Similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.

Arguments

  • filters: int, the dimension of the output space (the number of filters in the convolution).
  • kernel_size: int or tuple/list of 2 integers, specifying the size of the convolution window.
  • strides: int or tuple/list of 2 integers, specifying the stride length of the convolution. strides > 1 is incompatible with dilation_rate > 1.
  • padding: string, "valid" or "same" (case-insensitive). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
  • data_format: string, either "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, steps, features) while "channels_first" corresponds to inputs with shape (batch, features, steps). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".
  • dilation_rate: int or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution.
  • activation: Activation function to use. By default hyperbolic tangent activation function is applied (tanh(x)).
  • recurrent_activation: Activation function to use for the recurrent step.
  • use_bias: Boolean, whether the layer uses a bias vector.
  • kernel_initializer: Initializer for the kernel weights matrix, used for the linear transformation of the inputs.
  • recurrent_initializer: Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state.
  • bias_initializer: Initializer for the bias vector.
  • unit_forget_bias: Boolean. If True, add 1 to the bias of the forget gate at initialization. Use in combination with bias_initializer="zeros". This is recommended in Jozefowicz et al., 2015
  • kernel_regularizer: Regularizer function applied to the kernel weights matrix.
  • recurrent_regularizer: Regularizer function applied to the recurrent_kernel weights matrix.
  • bias_regularizer: Regularizer function applied to the bias vector.
  • activity_regularizer: Regularizer function applied to.
  • kernel_constraint: Constraint function applied to the kernel weights matrix.
  • recurrent_constraint: Constraint function applied to the recurrent_kernel weights matrix.
  • bias_constraint: Constraint function applied to the bias vector.
  • dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
  • recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
  • seed: Random seed for dropout.
  • return_sequences: Boolean. Whether to return the last output in the output sequence, or the full sequence. Default: False.
  • return_state: Boolean. Whether to return the last state in addition to the output. Default: False.
  • go_backwards: Boolean (default: False). If True, process the input sequence backwards and return the reversed sequence.
  • stateful: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
  • unroll: Boolean (default: False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.

Call arguments

  • inputs: A 5D tensor.
  • mask: Binary tensor of shape (samples, timesteps) indicating whether a given timestep should be masked.
  • training: Python boolean indicating whether the layer should behave in training mode or in inference mode. This is only relevant if dropout or recurrent_dropout are set.
  • initial_state: List of initial state tensors to be passed to the first call of the cell.

Input shape

  • If data_format='channels_first': 5D tensor with shape: (samples, time, channels, rows, cols)
  • If data_format='channels_last': 5D tensor with shape: (samples, time, rows, cols, channels)

Output shape

  • If return_state: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each 4D tensor with shape: (samples, filters, new_rows, new_cols) if data_format='channels_first' or shape: (samples, new_rows, new_cols, filters) if data_format='channels_last'. rows and cols values might have changed due to padding.
  • If return_sequences: 5D tensor with shape: (samples, timesteps, filters, new_rows, new_cols) if data_format=‘channels_first’ or shape: (samples, timesteps, new_rows, new_cols, filters) if data_format='channels_last'.
  • Else, 4D tensor with shape: (samples, filters, new_rows, new_cols) if data_format='channels_first' or shape: (samples, new_rows, new_cols, filters) if data_format='channels_last'.

References

  • Shi et al., 2015 (the current implementation does not include the feedback loop on the cells output).