tf.layers.dense( inputs, units, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, reuse=None )
Functional interface for the densely-connected layer.
This layer implements the operation:
outputs = activation(inputs * kernel + bias)
activation is the activation function passed as the
argument (if not
kernel is a weights matrix created by the layer,
bias is a bias vector created by the layer
inputs: Tensor input.
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a linear activation.
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer function for the weight matrix. If
None(default), weights are initialized using the default initializer used by
bias_initializer: Initializer function for the bias.
kernel_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
kernel_constraint: An optional projection function to be applied to the kernel after being updated by an
Optimizer(e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training.
bias_constraint: An optional projection function to be applied to the bias after being updated by an
trainable: Boolean, if
Truealso add variables to the graph collection
name: String, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer by the same name.
Output tensor the same shape as
inputs except the last dimension is of
ValueError: if eager execution is enabled.