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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).

See also

Other rnn layers:
layer_bidirectional()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_gru()
layer_lstm()
layer_rnn()
layer_simple_rnn()
layer_time_distributed()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()

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_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_feature_space()
layer_flatten()
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_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
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_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
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()