tf.compat.v1.layers.Dense

Densely-connected layer class.

Inherits From: Dense, Layer, Layer, Module

tf.compat.v1.layers.Dense(
    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, **kwargs
)

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).

Attributes
graph
scope_name