Add a densely-connected NN layer to an output
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 |
bias_initializer |
Initializer for the bias vector. |
kernel_regularizer |
Regularizer function applied to the |
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 |
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_size |
Fixed batch size for layer |
dtype |
The data type expected by the input, as a string ( |
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()
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