1D convolution layer (e.g. temporal convolution).

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. When using this layer as the first layer in a model, provide an input_shape argument (list of integers or NULL, e.g. (10, 128) for sequences of 10 vectors of 128-dimensional vectors, or (NULL, 128) for variable-length sequences of 128-dimensional vectors.

layer_conv_1d(object, filters, kernel_size, strides = 1L, padding = "valid",
  data_format = "channels_last", dilation_rate = 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, input_shape = NULL,
  batch_input_shape = NULL, batch_size = NULL, dtype = NULL,
  name = NULL, trainable = NULL, weights = 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 a single integer, specifying the length of the 1D convolution window.
strides

An integer or list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
padding

One of "valid", "causal" or "same" (case-insensitive). "valid" means "no padding". "same" results in padding the input such that the output has the same length as the original input. "causal" results in causal (dilated) convolutions, e.g. output[t] does not depend on input[t+1:]. Useful when modeling temporal data where the model should not violate the temporal order. See WaveNet: A GenerativeModel for Raw Audio, section 2.1.
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, length, channels) (default format for temporal data in Keras) while "channels_first" corresponds to inputs with shape (batch, channels, length).
dilation_rate

an integer or list of a single integer, 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).
use_bias

Boolean, whether the layer uses a bias vector.
kernel_initializer

Initializer for the kernel weights matrix.
bias_initializer

Initializer for the bias vector.
kernel_regularizer

Regularizer function applied to the 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 matrix.
bias_constraint

Constraint function applied to the bias vector.
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.
batch_input_shape

Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors.
batch_size

Fixed batch size for layer
dtype

The data type expected by the input, as a string (float32, float64, int32...)
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

3D tensor with shape: (batch_size, steps, input_dim)

Output shape

3D tensor with shape: (batch_size, new_steps, filters) steps value might have changed due to padding or strides.

See also

Other convolutional layers: layer_conv_2d_transpose, layer_conv_2d, layer_conv_3d_transpose, layer_conv_3d, layer_conv_lstm_2d, 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