layer_dense: Add a densely-connected NN layer to an output
In dfalbel/keras: R Interface to 'Keras'

Description Usage Arguments Input and Output Shapes See Also

Implements the operation: output = activation(dot(input, kernel) + bias) where activation is the element-wise activation function passed as the activation argument, kernel is a weights matrix created by the layer, and bias is a bias vector created by the layer (only applicable if use_bias is TRUE). Note: if the input to the layer has a rank greater than 2, then it is flattened prior to the initial dot product with kernel.

layer_dense(
  object,
  units,
  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
)

`object`	Model or layer object
`units`	Positive integer, dimensionality of the output space.
`activation`	Name of activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).
`use_bias`	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` weights 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: nD tensor with shape: (batch_size, ..., input_dim). The most common situation would be a 2D input with shape (batch_size, input_dim).

Output shape: nD tensor with shape: (batch_size, ..., units). For instance, for a 2D input with shape (batch_size, input_dim), the output would have shape (batch_size, unit).

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