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, trainable=True, name=None, reuse=None)

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, trainable=True, name=None, reuse=None)

Functional interface for the densely-connected layer.

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).

Note: if the inputs tensor has a rank greater than 2, then it is flattened prior to the initial matrix multiply by w.

Arguments:

• 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.
• 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.
• 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.

Returns:

Output tensor.

Defined in tensorflow/python/layers/core.py.