This layer implements the operation:
outputs = activation(inputs * kernel + bias)
Where activation is the activation function passed as the activation
argument (if not None), kernel is a weights matrix created by the layer,
and bias is a bias vector created by the layer
(only if use_bias is True).
Arguments
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 tf.compat.v1.get_variable.
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 Optimizer.
trainable
Boolean, if True also add variables to the graph collection
GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
name
String, the name of the layer. Layers with the same name will
share weights, but to avoid mistakes we require reuse=True in such cases.
_reuse
Boolean, whether to reuse the weights of a previous layer
by the same name.
Properties:
units: Python integer, dimensionality of the output space.
activation: Activation function (callable).
use_bias: Boolean, whether the layer uses a bias.
kernel_initializer: Initializer instance (or name) for the kernel matrix.
bias_initializer: Initializer instance (or name) for the bias.
kernel_regularizer: Regularizer instance for the kernel matrix (callable)
bias_regularizer: Regularizer instance for the bias (callable).
activity_regularizer: Regularizer instance for the output (callable)
kernel_constraint: Constraint function for the kernel matrix.
bias_constraint: Constraint function for the bias.
kernel: Weight matrix (TensorFlow variable or tensor).
bias: Bias vector, if applicable (TensorFlow variable or tensor).
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[]]