layer_lambda: Wraps arbitrary expression as a layer
In dfalbel/keras: R Interface to 'Keras'

Description Usage Arguments Input shape Output shape See Also

Wraps arbitrary expression as a layer

layer_lambda(
  object,
  f,
  output_shape = NULL,
  mask = NULL,
  arguments = NULL,
  input_shape = NULL,
  batch_input_shape = NULL,
  batch_size = NULL,
  dtype = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

`object`	Model or layer object
`f`	The function to be evaluated. Takes input tensor as first argument.
`output_shape`	Expected output shape from the function (not required when using TensorFlow back-end).
`mask`	mask
`arguments`	optional named list of keyword arguments to be passed to the function.
`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.

Arbitrary. Use the keyword argument input_shape (list of integers, does not include the samples axis) when using this layer as the first layer in a model.

Arbitrary (based on tensor returned from the function)

Other core layers: layer_activation(), layer_activity_regularization(), layer_dense_features(), layer_dense(), layer_dropout(), layer_flatten(), layer_input(), layer_masking(), layer_permute(), layer_repeat_vector(), layer_reshape()