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Adds a sparse fully connected layer. The weight matrix is masked.

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 normalizer_fn is 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 None, 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. [batch_size, depth], [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 biases. If normalizer_fn is provided then biases_initializer and biases_regularizer are ignored and biases are 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.
trainable If True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
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.