1D Convolutional Layer

conv1d_layer_type

conv1d_layer_type(
  num_filters,
  kernel_size,
  input_shape=...,
  stride=1,
  dilation=1,
  padding="valid",
  use_bias=.true.,
  activation="none",
  kernel_initialiser=...,
  bias_initialiser=...
)

The conv1d_layer_type derived type provides a 1D convolutional layer. The operation performed by this layer applies a 1D convolution over an input signal composed of several input planes.

Arguments

  • num_filters (integer): Number of output filters/channels in the convolution.

  • kernel_size (integer or integer, dimension(1)): Size of the convolving kernel.

  • input_shape (integer, dimension(:)): Shape of the input data (channels, width).

  • stride (integer or integer, dimension(1)): Stride of the convolution. Default: 1.

  • dilation (integer or integer, dimension(1)): Spacing between kernel elements. Default: 1.

  • padding (character(*)): Padding method, if any, to be applied to the input data prior to convolution. Refer to 1D padding layer for options. Default: "valid", i.e. no padding.

  • use_bias (logical): If .false., the layer will not use a bias term. Default: .true..

  • activation (class(*)): Activation function for the layer.

    • Accepts character(*) or class(base_actv_type).

    • See Activation Functions for available options.

    • Default: none_actv_type.

  • kernel_initialiser (class(*)): Initialiser for the kernel weights (see Initialisers).

    • If activation is selu_actv_type, default: lecun_normal_init_type.

    • If activation is a version of relu_actv_type, default: he_normal_init_type.

    • For all other activations, default: glorot_uniform_init_type.

  • bias_initialiser (class(*)): Initialiser for the biases (see Initialisers). Default: zeros_init_type.

Shape:

  • Input: (width, in_channels, batch_size).

  • Output: (width_out, num_filters, batch_size).

where:

\[\text{width_out} = \left\lfloor \frac{\text{width} + 2 \times \text{padding} - \text{dilation} \times (\text{kernel_size} - 1) - 1}{\text{stride}} + 1 \right\rfloor\]