keras: layer_conv_lstm_2d – R documentation

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keras

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,
  go_backwards = FALSE,
  stateful = FALSE,
  dropout = 0,
  recurrent_dropout = 0,
  batch_size = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL,
  input_shape = NULL
)

Arguments

`object`	Model or layer object
`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.
`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