layer_conv_lstm_2d {keras} | R Documentation

## 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

### 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`

[Package keras version 2.2.4 Index]