tf.contrib.layers.legacy_fully_connected(x, num_output_units, activation_fn=None, weight_init=initializers.xavier_initializer(), bias_init=tf.zeros_initializer(), name=None, weight_collections=(ops.GraphKeys.WEIGHTS,), bias_collections=(ops.GraphKeys.BIASES,), output_collections=(ops.GraphKeys.ACTIVATIONS,), trainable=True, weight_regularizer=None, bias_regularizer=None)

tf.contrib.layers.legacy_fully_connected(x, num_output_units, activation_fn=None, weight_init=initializers.xavier_initializer(), bias_init=tf.zeros_initializer(), name=None, weight_collections=(ops.GraphKeys.WEIGHTS,), bias_collections=(ops.GraphKeys.BIASES,), output_collections=(ops.GraphKeys.ACTIVATIONS,), trainable=True, weight_regularizer=None, bias_regularizer=None)

Adds the parameters for a fully connected layer and returns the output.

A fully connected layer is generally defined as a matrix multiply: y = f(w * x + b) where f is given by activation_fn. If activation_fn is None, the result of y = w * x + b is returned.

If x has shape [\(\text{dim}0, \text{dim}_1, ..., \text{dim}_n\)] with more than 2 dimensions (\(n > 1\)), then we repeat the matrix multiply along the first dimensions. The result r is a tensor of shape [\(\text{dim}_0, ..., \text{dim}{n-1},\) num_output_units], where \( r_{i_0, ..., i_{n-1}, k} = \sum_{0 \leq j < \text{dim}n} x{i_0, ... i_{n-1}, j} \cdot w_{j, k}\). This is accomplished by reshaping x to 2-D [\(\text{dim}0 \cdot ... \cdot \text{dim}{n-1}, \text{dim}n\)] before the matrix multiply and afterwards reshaping it to [\(\text{dim}_0, ..., \text{dim}{n-1},\) num_output_units].

This op creates w and optionally b. Bias (b) can be disabled by setting bias_init to None.

The variable creation is compatible with tf.variable_scope and so can be reused with tf.variable_scope or tf.make_template.

Most of the details of variable creation can be controlled by specifying the initializers (weight_init and bias_init) and in which collections to place the created variables (weight_collections and bias_collections; note that the variables are always added to the VARIABLES collection). The output of the layer can be placed in custom collections using output_collections. The collections arguments default to WEIGHTS, BIASES and ACTIVATIONS, respectively.

A per layer regularization can be specified by setting weight_regularizer and bias_regularizer, which are applied to the weights and biases respectively, and whose output is added to the REGULARIZATION_LOSSES collection.

Args:

• x: The input Tensor.
• num_output_units: The size of the output.
• activation_fn: activation function, default set to None to skip it and maintain a linear activation.
• weight_init: An optional weight initialization, defaults to xavier_initializer.
• bias_init: An initializer for the bias, defaults to 0. Set to None in order to disable bias.
• name: The name for this operation is used to name operations and to find variables. If specified it must be unique for this scope, otherwise a unique name starting with "fully_connected" will be created. See tf.variable_scope for details.
• weight_collections: List of graph collections to which weights are added.
• bias_collections: List of graph collections to which biases are added.
• output_collections: List of graph collections to which outputs are added.
• trainable: If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
• weight_regularizer: A regularizer like the result of l1_regularizer or l2_regularizer. Used for weights.
• bias_regularizer: A regularizer like the result of l1_regularizer or l2_regularizer. Used for biases.

Returns:

The output of the fully connected layer.

Raises:

• ValueError: if x has rank less than 2 or if its last dimension is not set.

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