layer_conv_lstm_1d: 1D Convolutional LSTM.
In rstudio/keras: R Interface to 'Keras'

layer_conv_lstm_1d

R Documentation

1D Convolutional LSTM.

Description

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

Usage

layer_conv_lstm_1d(
  object,
  filters,
  kernel_size,
  strides = 1L,
  padding = "valid",
  data_format = NULL,
  dilation_rate = 1L,
  activation = "tanh",
  recurrent_activation = "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,
  dropout = 0,
  recurrent_dropout = 0,
  seed = NULL,
  return_sequences = FALSE,
  return_state = FALSE,
  go_backwards = FALSE,
  stateful = FALSE,
  ...,
  unroll = NULL
)

Arguments

`object`	Object to compose the layer with. A tensor, array, or sequential model.
`filters`	int, the dimension of the output space (the number of filters in the convolution).
`kernel_size`	int or tuple/list of 1 integer, specifying the size of the convolution window.
`strides`	int or tuple/list of 1 integer, 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 1 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.
`...`	For forward/backward compatability.
`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.

Value

The return value depends on the value provided for the first argument. If object is:

a keras_model_sequential(), then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.
a keras_input(), then the output tensor from calling layer(input) is returned.
NULL or missing, then a Layer instance is returned.

Call Arguments

inputs: A 4D tensor.
initial_state: List of initial state tensors to be passed to the first call of the cell.
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.

Input Shape

If data_format="channels_first": 4D tensor with shape: ⁠(samples, time, channels, rows)⁠
If data_format="channels_last": 4D tensor with shape: ⁠(samples, time, rows, channels)⁠

Output Shape

If return_state: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each 3D tensor with shape: ⁠(samples, filters, new_rows)⁠ if data_format='channels_first' or shape: ⁠(samples, new_rows, filters)⁠ if data_format='channels_last'. rows values might have changed due to padding.
If return_sequences: 4D tensor with shape: ⁠(samples, timesteps, filters, new_rows)⁠ if data_format='channels_first' or shape: ⁠(samples, timesteps, new_rows, filters)⁠ if data_format='channels_last'.
Else, 3D tensor with shape: ⁠(samples, filters, new_rows)⁠ if data_format='channels_first' or shape: ⁠(samples, new_rows, filters)⁠ if data_format='channels_last'.

References

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

Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_auto_contrast()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_equalization()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_num_bounding_boxes()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_mix_up()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_rand_augment()
layer_random_brightness()
layer_random_color_degeneration()
layer_random_color_jitter()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_grayscale()
layer_random_hue()
layer_random_posterization()
layer_random_rotation()
layer_random_saturation()
layer_random_sharpness()
layer_random_shear()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_solarization()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_stft_spectrogram()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()