layer_fixedMotif: Custom 2-D convolution wrapper for fixed IGMs or PWMs
In mPloenzke/learnMotifs: Learn Nucleotide Motifs via Convolutions

Description Usage Arguments Value Author(s)

Custom 2-D convolution wrapper for fixed motifs, such as those coming from a database.

layer_fixedMotif(object, filters, motif_maxlen, fixed.motifs,
  fixed.offsets, input_shape = NULL, strides = c(1L, 1L),
  padding = "valid", activation = "sigmoid", use_bias = TRUE,
  name = "fixedMotif_conv", trainable = FALSE)

`object`	Keras model object.
`filters`	Number of convolutional filters.
`motif_maxlen`	Maximum length of the motifs.
`fixed.motifs`	List of motifs to be used as filters.
`fixed.offsets`	Vector of offset values.
`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.
`strides`	An integer or list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.
`padding`	one of "valid" (default) or "same" (case-insensitive).
`activation`	Activation function to use. Default is the sigmoid activation.
`use_bias`	Boolean, whether the layer uses a bias vector. Default is to include a bias.
`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. Default is `'fixedMotif_conv'`.
`trainable`	Whether the layer weights will be updated during training. Default is `FALSE`.