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",
  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)



Model or layer object


Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).


An integer or list of a single integer, specifying the length of the 1D convolution window.


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.


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.


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 function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).


Boolean, whether the layer uses a bias vector.


Initializer for the kernel weights matrix.


Initializer for the bias vector.


Regularizer function applied to the kernel weights matrix.


Regularizer function applied to the bias vector.


Regularizer function applied to the output of the layer (its "activation")..


Constraint function applied to the kernel matrix.


Constraint function applied to the bias vector.


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.


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.


Fixed batch size for layer


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


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.


Whether the layer weights will be updated during training.


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