layer_conv_lstm_2d: Convolutional LSTM.

In dfalbel/keras: R Interface to 'Keras'

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

See Also

Other convolutional layers: 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()

dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.