keras (version 2.7.0)

layer_conv_lstm_2d: Convolutional LSTM.

Description

It is similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.

Usage

layer_conv_lstm_2d(
  object,
  filters,
  kernel_size,
  strides = c(1L, 1L),
  padding = "valid",
  data_format = NULL,
  dilation_rate = c(1L, 1L),
  activation = "tanh",
  recurrent_activation = "hard_sigmoid",
  use_bias = TRUE,
  kernel_initializer = "glorot_uniform",
  recurrent_initializer = "orthogonal",
  bias_initializer = "zeros",
  unit_forget_bias = TRUE,
  kernel_regularizer = NULL,
  recurrent_regularizer = NULL,
  bias_regularizer = NULL,
  activity_regularizer = NULL,
  kernel_constraint = NULL,
  recurrent_constraint = NULL,
  bias_constraint = NULL,
  return_sequences = FALSE,
  return_state = FALSE,
  go_backwards = FALSE,
  stateful = FALSE,
  dropout = 0,
  recurrent_dropout = 0,
  batch_size = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL,
  input_shape = NULL
)

Arguments

object

What to call the new Layer instance with. Typically a keras Model, another Layer, or a tf.Tensor/KerasTensor. If object is missing, the Layer instance is returned, otherwise, layer(object) is returned.

filters

Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).

kernel_size

An integer or list of n integers, specifying the dimensions of the convolution window.

strides

An integer or list of n integers, specifying the strides of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.

padding

One of "valid" or "same" (case-insensitive).

data_format

A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, time, ..., channels) while channels_first corresponds to inputs with shape (batch, time, channels, ...). 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

An integer or list of n integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any strides value != 1.

activation

Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = 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.

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 the output of the layer (its "activation")..

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.

return_sequences

Boolean. Whether to return the last output in the output sequence, or the full sequence.

return_state

Boolean. Whether to return the last state in addition to the output.

go_backwards

Boolean (default FALSE). If TRUE, rocess the input sequence backwards.

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.

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.

batch_size

Fixed batch size for layer

name

An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.

trainable

Whether the layer weights will be updated during training.

weights

Initial weights for layer.

input_shape

Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.

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)

References

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting The current implementation does not include the feedback loop on the cells output

See Also

Other convolutional layers: layer_conv_1d_transpose(), layer_conv_1d(), layer_conv_2d_transpose(), layer_conv_2d(), layer_conv_3d_transpose(), layer_conv_3d(), layer_cropping_1d(), layer_cropping_2d(), layer_cropping_3d(), layer_depthwise_conv_2d(), layer_separable_conv_1d(), layer_separable_conv_2d(), layer_upsampling_1d(), layer_upsampling_2d(), layer_upsampling_3d(), layer_zero_padding_1d(), layer_zero_padding_2d(), layer_zero_padding_3d()