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This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs. If use_bias is TRUE, a bias vector is created and added to the outputs. Finally, if activation is not NULL, it is applied to the outputs as well.

Usage

layer_conv_1d(
  object,
  filters,
  kernel_size,
  strides = 1L,
  padding = "valid",
  data_format = NULL,
  dilation_rate = 1L,
  groups = 1L,
  activation = NULL,
  use_bias = TRUE,
  kernel_initializer = "glorot_uniform",
  bias_initializer = "zeros",
  kernel_regularizer = NULL,
  bias_regularizer = NULL,
  activity_regularizer = NULL,
  kernel_constraint = NULL,
  bias_constraint = 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 list of 1 integer, specifying the size of the convolution window.

strides

int or list of 1 integer, specifying the stride length of the convolution. strides > 1 is incompatible with dilation_rate > 1.

padding

string, "valid", "same" or "causal"(case-insensitive). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input. When padding="same" and strides=1, the output has the same size as the input. "causal" results in causal (dilated) convolutions, e.g. output[t] does not depend ontail(input, t+1). Useful when modeling temporal data where the model should not violate the temporal order. See WaveNet: A Generative Model for Raw Audio, section2.1.

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 list of 1 integers, specifying the dilation rate to use for dilated convolution.

groups

A positive int specifying the number of groups in which the input is split along the channel axis. Each group is convolved separately with filters // groups filters. The output is the concatenation of all the groups results along the channel axis. Input channels and filters must both be divisible by groups.

activation

Activation function. If NULL, no activation is applied.

use_bias

bool, if TRUE, bias will be added to the output.

kernel_initializer

Initializer for the convolution kernel. If NULL, the default initializer ("glorot_uniform") will be used.

bias_initializer

Initializer for the bias vector. If NULL, the default initializer ("zeros") will be used.

kernel_regularizer

Optional regularizer for the convolution kernel.

bias_regularizer

Optional regularizer for the bias vector.

activity_regularizer

Optional regularizer function for the output.

kernel_constraint

Optional projection function to be applied to the kernel after being updated by an Optimizer (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training.

bias_constraint

Optional projection function to be applied to the bias after being updated by an Optimizer.

...

For forward/backward compatability.

Value

A 3D tensor representing activation(conv1d(inputs, kernel) + bias).

Input Shape

  • If data_format="channels_last": A 3D tensor with shape: (batch_shape, steps, channels)

  • If data_format="channels_first": A 3D tensor with shape: (batch_shape, channels, steps)

Output Shape

  • If data_format="channels_last": A 3D tensor with shape: (batch_shape, new_steps, filters)

  • If data_format="channels_first": A 3D tensor with shape: (batch_shape, filters, new_steps)

Raises

ValueError: when both strides > 1 and dilation_rate > 1.

Examples

# The inputs are 128-length vectors with 10 timesteps, and the
# batch size is 4.
x <- random_uniform(c(4, 10, 128))
y <- x |> layer_conv_1d(32, 3, activation='relu')
shape(y)

## shape(4, 8, 32)

See also

Other convolutional layers:
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_separable_conv_1d()
layer_separable_conv_2d()

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_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
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()