layer_dense {keras} | R Documentation

## 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

`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 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)`.

### See Also

Other core layers: `layer_activation`, `layer_activity_regularization`, `layer_dropout`, `layer_flatten`, `layer_input`, `layer_lambda`, `layer_masking`, `layer_permute`, `layer_repeat_vector`, `layer_reshape`

[Package keras version 2.2.4 Index]