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
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.

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_attention()`

,
`layer_dense_features()`

,
`layer_dropout()`

,
`layer_flatten()`

,
`layer_input()`

,
`layer_lambda()`

,
`layer_masking()`

,
`layer_permute()`

,
`layer_repeat_vector()`

,
`layer_reshape()`