layer_dense

Add a densely-connected NN layer to an output

Description

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.

Usage

 
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 
)

Arguments

Arguments	Description
object	What to compose the new `Layer` instance with. Typically a Sequential model or a Tensor (e.g., as returned by `layer_input()`). The return value depends on `object`. If `object` is: - missing or `NULL`, the `Layer` instance is returned. - a `Sequential` model, the model with an additional layer is returned. - a Tensor, the output tensor from `layer_instance(object)` is returned.
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.

Section

Input and Output Shapes

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).