# 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)`

.

## See Also

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()`