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Adds the parameters for a fully connected layer and returns the output.
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
)
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.compat.v1.variable_scope
and so
can be
reused with tf.compat.v1.variable_scope
or tf.compat.v1.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.compat.v1.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. |