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Functional interface for the densely-connected layer. (deprecated)

    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

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).


  • 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 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.
  • 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 size units.


  • ValueError: if eager execution is enabled.