tf.contrib.layers.fully_connected( inputs, num_outputs, activation_fn=tf.nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=tf.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None )
Adds a fully connected layer.
fully_connected creates a variable called
weights, representing a fully
connected weight matrix, which is multiplied by the
inputs to produce a
Tensor of hidden units. If a
normalizer_fn is provided (such as
batch_norm), it is then applied. Otherwise, if
None and a
biases_initializer is provided then a
biases variable would be
created and added the hidden units. Finally, if
activation_fn is not
it is applied to the hidden units as well.
inputs: A tensor of at least rank 2 and static value for the last dimension; i.e.
[None, None, None, channels].
num_outputs: Integer or long, the number of output units in the layer.
activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation.
normalizer_fn: Normalization function to use instead of
normalizer_fnis provided then
biases_regularizerare ignored and
biasesare not created nor added. default set to None for no normalizer function
normalizer_params: Normalization function parameters.
weights_initializer: An initializer for the weights.
weights_regularizer: Optional regularizer for the weights.
biases_initializer: An initializer for the biases. If None skip biases.
biases_regularizer: Optional regularizer for the biases.
reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given.
variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collections per variable.
outputs_collections: Collection to add the outputs.
Truealso add variables to the graph collection
scope: Optional scope for variable_scope.
The tensor variable representing the result of the series of operations.
ValueError: If x has rank less than 2 or if its last dimension is not set.